Proteases and Cancer: Methods and Protocols (Methods in Molecular Biology, 2747) 1071635883, 9781071635889

Citation preview

Methods in Molecular Biology 2747

Salvatore Santamaria  Editor

Proteases and Cancer Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Proteases and Cancer Methods and Protocols

Edited by

Salvatore Santamaria Department of Biochemical Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK

Editor Salvatore Santamaria Department of Biochemical Sciences School of Biosciences Faculty of Health and Medical Sciences University of Surrey Guildford, Surrey, UK

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-3588-9 ISBN 978-1-0716-3589-6 (eBook) https://doi.org/10.1007/978-1-0716-3589-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A. Paper in this product is recyclable.

Preface Proteases are major players in multiple processes related to oncogenesis such as cell invasion and proliferation, neoangiogenesis, shedding, and mobilization of growth factors and cytokines. Although at the present this detrimental, dark side of proteolytic activity takes center stage in the scientific Weltanschauung, proteases can also exert immunomodulatory, benign and tumor-suppressive roles. This volume of the Methods in Molecular Biology series consists of 22 chapters covering a wide range of topics and techniques including bioinformatics analysis, biochemical assays, recombinant protein expression and purification, methods to investigate protease activity in cell-based, organoids and in vivo systems, proteomics, transcriptomics, machine learning, and novel approaches to target dysregulated protease activity in cancer. My chief aim was to provide the scientific community with a diverse and representative collection of state-of-theart methods to investigate the protean functions of proteases (no pun intended) in cancer biology. The volume is designed for a broad audience of basic and clinical researchers across different fields and disciplines (chemists, biochemists, cell and molecular biologists, bioinformaticians, and physicians) and its content was intended to be suitable for researchers at every stage of their careers. Chapter 1 introduces the reader to the world of cancer proteases by presenting a list of web-based knowledgebases and portals useful to mine data on protease function. Chapter 2 describes proteomics/N-terminomics methods to characterize and quantify the natural and neo-N-termini of proteins from complex biological samples with the final aim to identify candidate protease substrates. Detecting protease activity in biological samples has wide applications in cancer, but this has been particularly challenging in the case of metalloproteases, which lack a covalent intermediate during the proteolytic reaction. Chapter 3 describes a method to monitor and quantify matrix metalloproteinase activity using an affinity-based probe. Chapters 4–6 focus on methods for expressing and purifying recombinant proteases, an essential step to characterize protease function. Chapter 4 explains how to introduce N-glycans in recombinant proteases to map residues involved in protein-protein interactions, while Chaps. 5 and 6 describe recombinant expression and purification of two proteases with anti- and pro-cancerogenic roles, respectively. Chapters 7–9 describe a number of assays to analyze proteolytic activity in vitro. Chapter 7 describes an ELISAbased method to map collagenase binding sites on collagens. Another ELISA-based method to quantify cleavage of the proteoglycan versican by ADAMTS proteases is reported in Chap. 8, while Chap. 9 explains how to analyze cleavage fragments of the extracellular matrix protein osteopontin generated by the serine protease thrombin. Chapter 10 describes how to analyze the sheddase activity of ADAM17 by siRNA-mediated knockdown in primary human monocytes isolated from peripheral blood. Chapters 11–16 all describe methods to investigate cancer cell proliferation, migration, invasion, and extracellular matrix degradation. Chapter 11 describes the preparation of matrices that mimic the tumor microenvironment. The stiffness presented by these matrices elicits specific patterns of protease expression in the invading cell lines. Chapter 12 describes methods to study the invadopodia, protrusions that form on the advancing edge of cells and that mediate proteolytic degradation of the extracellular matrix. A number of methods to investigate the function of MT1-MMP, a major driver of cell invasion, are reported in Chap. 13. A modified

v

vi

Preface

zymography protocol to study gelatinase activity in colon cancer cells is described in Chap. 14. Chapters 15 and 16 extend these protocols from 2D to 3D cultures, while Chap. 17 describes an in vivo imaging method combining confocal and two-photon microscopy to analyze proteolytic activity and testing the effect of protease inhibitors on acute myeloid leukemia cell migration. The final Chaps. 18–22, focus on approaches to inhibit proteolytic activity in cancer. Chapter 18 explains how to set up a FRET assay for high-throughput screening of MT1-MMP inhibitors. The use of combinatorial libraries to isolate selective protease inhibitors is reported in Chaps. 19 and 20, which focus on monoclonal antibodies and tissue inhibitor of metalloproteinase variants, respectively. Chapter 21 describes the generation of variants of alpha-2 macroglobulin, a broad-spectrum protease inhibitor abundantly present in plasma, to target specific proteases. The final Chapter 22 reports a combination of mass spectrometry, artificial intelligence, and computational methods to discover and characterize protease inhibitory peptides from natural sources. I would like to thank all the authors who contributed to this volume by sharing their protocols for the benefit of the research community, and the editor-in-chief of Methods in Molecular Biology series, John Walker, for his invaluable input during the first months of preparation of the present volume. Finally, I wish to express my immense gratitude to my mentors, Prof. Hideaki Nagase and Prof. Gillian Murphy, for transmitting me their passion for proteases, the values of scientific integrity, analytic rigor, and uncompromised transparency, but most importantly, for honoring me with their friendship. London, UK

Salvatore Santamaria

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v ix

1 Web-Based Resources to Investigate Protease Function . . . . . . . . . . . . . . . . . . . . . . Salvatore Santamaria 2 N-Terminomics/TAILS of Human Tumor Biopsies and Cancer Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Afshin Derakhshani, Mitchell Bulluss, Regan Penner, and Antoine Dufour 3 A New Affinity-Based Probe to Profile MMP Active Forms . . . . . . . . . . . . . . . . . . Carole Malgorn, Franc¸ois Becher, Pierrick Bruyat, Carole Fruchart-Gaillard, Fabrice Beau, Sarah Bregant, and Laurent Devel 4 N-Glycan Insertion for Probing Protein–Protein Interactions and Epitope Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shayma Abukar, Xiaohan Zhang, Bertina Dragu ¯ naite˙, Gwladys Chabrier, and Rens de Groot 5 Expression and Purification of Recombinant ADAMTS8. . . . . . . . . . . . . . . . . . . . . Tina Burkhard, Alexander Frederick Minns, and Salvatore Santamaria 6 Expression and Purification of Active Monomeric MMP7 . . . . . . . . . . . . . . . . . . . . Kazuhiro Yamamoto, Moe Isohata, and Shouichi Higashi 7 Mapping the Binding Sites of MMPs on Types II and III Collagens Using Triple-Helical Peptide Toolkits . . . . . . . . . . . . . . . . . . . . . . . . . . . . Szymon W. Manka 8 Determination of Versikine Levels by Enzyme-Linked Immunosorbent Assay (ELISA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Frederick Minns and Salvatore Santamaria 9 Methods to Investigate Thrombin Cleavage of Osteopontin (OPN). . . . . . . . . . . Lei Zhao, Lawrence L. Leung, and John Morser 10 Using siRNA Silencing to Analyze ADAM17 in Macrophages . . . . . . . . . . . . . . . . Matthew Markham and Linda Troeberg 11 Preparation and Characterization of Collagen–Hyaluronic Acid (Col–HA) Matrices: In Vitro Mimics of the Tumor Microenvironment . . . . . . . . Sarbajeet Dutta and Shamik Sen 12 Assessment of Invadopodium Formation and Gelatin Degradation in Vitro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marguerite J. Clarke, Samantha Battagin, and Marc G. Coppolino 13 Investigation of MT1-MMP Activity in Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . Yoshifumi Itoh

1

vii

19

29

41

55

67

75

83 95 119

131

141

151

viii

14

15

16

17

18

19

20

21 22

Contents

Gelatin In Situ Zymography to Study Gelatinase Activity in Colon Cancer Cells Treated with Platelet Microparticles (PMPs) . . . . . . . . . . . Jakub Kryczka, Hassan Kassassir, Izabela Papiewska-Paja˛k, and Joanna Boncela Interrogating the Impact of Protease Activity on Tumor Progression Using 3D Spheroid Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shayin V. Gibson, Edward P. Carter, and Richard P. Grose Analysis of Matrix Metalloproteinase Activity and Inhibition in Cancer Spheroids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna M. Knapinska, Lillian Onwuha-Ekpete, Gary Drotleff, Destiny Twohill, Cedric Chai, Alexa Ernce, Isabella Grande, Michelle Rodrı´guez, Dorota Tokmina-Roszyk, Brad Larson, and Gregg B. Fields Intravital Microscopy to Study the Effect of Matrix Metalloproteinase Inhibition on Acute Myeloid Leukemia Cell Migration in the Bone Marrow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Floriane S. Tissot, Sara Gonzalez-Anton, and Cristina Lo Celso Fluorescence-Based Peptidolytic Assay for High-Throughput Screening of MMP14 Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyun Lee, Lucas Ibrahimi, and Kyu-Yeon Han Generation of Protease Inhibitory Antibodies by Functional In Vivo Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ki Baek Lee and Xin Ge Engineering Selective TIMPs Using a Counter-Selective Screening Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hannaneh Ahmadighadykolaei, Evette S. Radisky, and Maryam Raeeszadeh-Sarmazdeh Engineering New Protease Inhibitors Using α2-Macroglobulin . . . . . . . . . . . . . . . Seandean Lykke Harwood and Jan J. Enghild Design of Bioengineered Peptides/Proteases as Anti-cancer Reagents with Integrated Omics and Machine Learning Approaches . . . . . . . . . . Weimin Zuo and Hang Fai Kwok

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

167

177

189

211

229

243

257

279

295 311

Contributors SHAYMA ABUKAR • Institute of Cardiovascular Science, University College London, London, UK HANNANEH AHMADIGHADYKOLAEI • Department of Chemical and Materials Engineering, University of Nevada, Reno, NV, USA SAMANTHA BATTAGIN • Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada FABRICE BEAU • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SIMoS, Gif-sur-Yvette, France FRANC¸OIS BECHER • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SPI, Gif-sur-Yvette, France JOANNA BONCELA • Institute of Medical Biology, Polish Academy of Science, Lodz, Poland SARAH BREGANT • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SIMoS, Gif-sur-Yvette, France PIERRICK BRUYAT • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SIMoS, Gif-sur-Yvette, France MITCHELL BULLUSS • McCaig Institute for Bone and Joint Health, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada; Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada TINA BURKHARD • Department of Biochemical Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK EDWARD P. CARTER • Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK GWLADYS CHABRIER • Institute of Cardiovascular Science, University College London, London, UK CEDRIC CHAI • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA MARGUERITE J. CLARKE • Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada MARC G. COPPOLINO • Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON, Canada RENS DE GROOT • Institute of Cardiovascular Science, University College London, London, UK AFSHIN DERAKHSHANI • McCaig Institute for Bone and Joint Health, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada; Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada LAURENT DEVEL • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SIMoS, Gif-sur-Yvette, France BERTINA DRAGU¯NAITE˙ • Institute of Cardiovascular Science, University College London, London, UK

ix

x

Contributors

GARY DROTLEFF • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA; Alphazyme, Jupiter, FL, USA ANTOINE DUFOUR • McCaig Institute for Bone and Joint Health, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada; Department of Physiology and Pharmacology, Department of Biochemistry & Molecular Biology & Southern Alberta Mass Spectrometry (SAMS) Core Facility, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada SARBAJEET DUTTA • Department of Biosciences & Bioengineering, IIT Bombay, Mumbai, India JAN J. ENGHILD • Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark ALEXA ERNCE • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA GREGG B. FIELDS • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA CAROLE FRUCHART-GAILLARD • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SIMoS, Gif-sur-Yvette, France XIN GE • Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA SHAYIN V. GIBSON • Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK SARA GONZALEZ-ANTON • Department of Life Sciences, Imperial College London, London, UK; The Francis Crick Institute, London, UK ISABELLA GRANDE • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA RICHARD P. GROSE • Centre for Tumour Biology, Barts Cancer Institute, John Vane Science Centre, Queen Mary University of London, London, UK KYU-YEON HAN • Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA SEANDEAN LYKKE HARWOOD • Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark SHOUICHI HIGASHI • Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan LUCAS IBRAHIMI • Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA MOE ISOHATA • Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan YOSHIFUMI ITOH • The Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK HASSAN KASSASSIR • Institute of Medical Biology, Polish Academy of Science, Lodz, Poland ANNA M. KNAPINSKA • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA; Alphazyme, Jupiter, FL, USA JAKUB KRYCZKA • Institute of Medical Biology, Polish Academy of Science, Lodz, Poland

Contributors

xi

HANG FAI KWOK • Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR, China; School of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR, China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR, China BRAD LARSON • Agilent Technologies, Raleigh, USA HYUN LEE • Department of Pharmaceutical Sciences, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA; Biophysics Core at the Research Resources Center, University of Illinois at Chicago, Chicago, IL, USA KI BAEK LEE • Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA LAWRENCE L. LEUNG • Division of Hematology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA CRISTINA LO CELSO • Department of Life Sciences, Imperial College London, London, UK; The Francis Crick Institute, London, UK; Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London, UK CAROLE MALGORN • Universite´ Paris-Saclay, CEA, INRAE, De´partement Me´dicaments et Technologies pour la Sante´ (DMTS), SIMoS, Gif-sur-Yvette, France SZYMON W. MANKA • Institute of Prion Diseases and MRC Prion Unit at UCL, University College London, London, UK MATTHEW MARKHAM • Norwich Medical School, University of East Anglia, Norwich, UK ALEXANDER FREDERICK MINNS • Department of Biochemical Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK; Department of Biochemical Sciences, School of Biosciences, Faculty of Health and Medical Sciences, Edward Jenner Building, University of Surrey, Surrey, UK JOHN MORSER • Division of Hematology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA LILLIAN ONWUHA-EKPETE • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA IZABELA PAPIEWSKA-PAJA˛K • Institute of Medical Biology, Polish Academy of Science, Lodz, Poland REGAN PENNER • McCaig Institute for Bone and Joint Health, Snyder Institute for Chronic Diseases, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada EVETTE S. RADISKY • Mayo Clinic, Cancer Biology, Jacksonville, FL, USA MARYAM RAEESZADEH-SARMAZDEH • Department of Chemical and Materials Engineering, University of Nevada, Reno, NV, USA MICHELLE RODRI´GUEZ • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA SALVATORE SANTAMARIA • Department of Biochemical Sciences, School of Biosciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK SHAMIK SEN • Department of Biosciences & Bioengineering, IIT Bombay, Mumbai, India FLORIANE S. TISSOT • Department of Life Sciences, Imperial College London, London, UK; The Francis Crick Institute, London, UK

xii

Contributors

DOROTA TOKMINA-ROSZYK • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA LINDA TROEBERG • Norwich Medical School, University of East Anglia, Norwich, UK DESTINY TWOHILL • Institute for Human Health & Disease Intervention (I-HEALTH) and Department of Chemistry & Biochemistry, Florida Atlantic University, Jupiter, FL, USA KAZUHIRO YAMAMOTO • Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK XIAOHAN ZHANG • Institute of Cardiovascular Science, University College London, London, UK LEI ZHAO • Division of Hematology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA WEIMIN ZUO • Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR, China; School of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau SAR, China

Chapter 1 Web-Based Resources to Investigate Protease Function Salvatore Santamaria Abstract In 2001, the release of the first draft of the human genome marked the beginning of the Big Data era for biological sciences. Since then, the complexity of datasets generated by laboratories worldwide has increased exponentially. Public repositories such as the Protein Data Bank, which has exceeded the 200000 entries in 2023, have been instrumental not only to collect, organize, and distill this enormous research output but also to promote further research enterprises. The achievements of artificial intelligence programs such as AlphaFold would not have been possible without the collective efforts of countless researchers who made their work publicly available. Here, I provide a practical, but far from exhaustive, list of resources useful to investigate protease function. Key words Proteases, Cancer, Database, Metalloprotease, ADAMTS

1

Introduction 2001 is a landmark year in human history. The release of the first draft of the human genome by the International Human Genome Sequencing Consortium [1] marked the beginning of a new era characterized by massive amounts of data that started dissecting the intricacies of Nature to a level unthinkable just a decade before. This trend further expanded with the development of highthroughput -omics platforms (including proteomics, genomics, and metabolomics) and the advance in computational biology and artificial intelligence (AI). In parallel, our ability to tap into this mine of information has faced practical challenges that over the years public depositories and webtools have tried to address. Here, I provide an atlas of web resources that may assist the reader in navigating this ocean of information, with a particular focus on protease function (Table 1). This list is far from exhaustive and does not provide tutorials to navigate specific websites (scouring is in general quite intuitive). My aim is rather to flag specific resources that may be unknown to the inexperienced reader. Some of these are further discussed throughout this book. Importantly, I

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_1, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

1

2

Salvatore Santamaria

Table 1 List of webtools to study protease functions Function

Name

Website

References

Target prioritization

Open targets platform

https://www.opentargets.org/

[3]

Gene variant database

ClinVar

https://www.ncbi.nlm.nih.gov/ clinvar/

[4]

Gene variant database

COSMIC

https://cancer.sanger.ac.uk/cosmic

[5]

Gene variant database

Genomic data commons data portal

https://portal.gdc.cancer.gov/

[6]

Mouse genetics

Mutant mouse resource and research centers

https://www.mmrrc.org/

–

Mouse genetics

International mouse phenotyping consortium

https://www.mousephenotype.org/ [7]

Mouse genetics

Mouse genome informatics

https://www.informatics.jax.org/

–

Cancer mouse models

Mouse models of human cancer database

https://tumor.informatics.jax.org/ mtbwi/index.do

[8]

Protein expression database

Human protein atlas

https://www.proteinatlas.org/

[9]

Gene expression database

Genotype-Tissue expression (GTEx)

(https://gtexportal.org/home/)

[10]

Information resource database

BRENDA

https://www.brenda-enzymes.org/ index.php

[11]

Information resource database

MEROPS

https://www.ebi.ac.uk/merops/

[12]

Information resource database

Mammalian Degradome database

http://degradome.uniovi.es/ dindex.html

[19]

Information resource database

PANTHER

http://www.pantherdb.org/

[20]

Information resource database

Expasy

https://www.expasy.org/

[21]

Cleavage site specificity iceLogo

https://iomics.ugent.be/ icelogoserver/

[17]

Cleavage site specificity WebLogo

https://weblogo.threeplusone. com/

[18]

Cleavage site specificity Proteasix

http://proteasix.cs.man.ac.uk/ index.html

[26]

Prediction of glycosylation sites

https://services.healthtech.dtu.dk/ service.php?NetNGlyc-1.0

[22]

NetNGlyc - 1.0

(continued)

Mining Protease Data

Table 1 (continued) Function

Name

Website

References

Prediction of glycosylation sites

SPRINT-Gly

https://sparks-lab.org/server/ sprint-gly/

[23]

Prediction of phosphorylation sites

NetPhos 3.1

https://services.healthtech.dtu.dk/ service.php?NetPhos-3.1

[24]

Sequence alignment

EMBOSS

https://www.ebi.ac.uk/Tools/ emboss/

[25]

3D structure database

Protein data Bank

https://www.rcsb.org/

[27]

Protein structure visualization

Chimera

https://www.cgl.ucsf.edu/chimera/ [28]

Protein structure visualization

PyMOL

https://pymol.org/2/

–

Protein structure prediction

iTASSER

https://zhanggroup.org/ITASSER/

[29]

Protein structure prediction

C-I-TASSER

https://zhanggroup.org/C-ITASSER/

[31]

Protein structure prediction

AlphaFold

https://alphafold.ebi.ac.uk/

[32]

Protein structure prediction

SWISS-MODEL

https://swissmodel.expasy.org/

[33]

Protein interaction database

BioGRID

https://thebiogrid.org/

[34]

Protein interaction database

IntAct

https://www.ebi.ac.uk/intact/ home

[35]

Protein interaction database

STRING

https://string-db.org

[36]

Protein interaction database

Metascape

https://metascape.org

[2]

Protein interaction and TopFIND termini database

https://topfind.clip.msl.ubc.ca/

[37]

Matrisome database

MatrisomeDB

https://matrisomedb.org

[39]

Protein interaction database

MatrixDB

http://matrixdb.univ-lyon1.fr/

[40]

Protein interaction database

MatriNet

https://www.matrinet.org/

[41]

3

4

Salvatore Santamaria

recommend checking that the resource of interest is up to date since it has been estimated that a significant portion of them relies on knowledgebases older than 1 year [2]. Biological Function and Association with Cancer The first step is gathering as much information as possible on your protease of interest (PoI). The Open Targets Platform (https://platform. opentargets.org/) [3] is a tool that supports systematic identification and prioritization of potential therapeutic drug targets based on genome-wide association studies (GWAS) and functional genomics. It contains information about the targets, pathologies, phenotypes, and drugs and their relationships. A query of your PoI will retrieve multiple therapeutic areas. For each of them, an association score is generated (0–1), summarizing all the aggregated evidence for an association. It is possible to filter the results by disease/ phenotype, for example, by choosing “cancer or benign tumor” as a therapeutic area. Increasing the stringency of the filter provides additional levels of control. Results can be intuitively displayed as tables, graphs, or bubbles. ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) [4] is a database of clinically relevant human gene variants and their associations with diseases and other phenotypes, maintained at the National Institutes of Health (NIH). Input of the PoI gene results in a graph displaying variants according to their contribution to disease status (likely pathogenic, likely benign, or of uncertain significance) distributed across the gene as well as a list of variants arranged by location and clinical significance. Information on variation type (deletion, duplication, indel, insertion, or single nucleotide) and molecular consequence (frameshift, missense, nonsense, etc.) is also provided. The Catalogue of Somatic Mutations in Cancer (COSMIC, https://cancer.sanger.ac.uk/cosmic) [5] is a database including almost six million coding mutations associated with human cancers. The database is manually curated by experts at the Wellcome Trust/Sanger Institute in Cambridge, UK. For each entry, COSMIC provides a graphical view of mutations across the gene entry, information on drug resistance, tissue distribution, and type of mutations (Fig. 1). The COSMIC-3D functionality allows exploration of these mutations within the tridimensional protein structure. The Cancer Genome Atlas (TGA, https://www.cancer.gov/ about-nci/organization/ccg/research/structural-genomics/ tcga), established by the US National Cancer Institute, provides access to genomics, epigenomics, transcriptomics, and proteomics data for 33 different cancer types (the information stored has currently reached a staggering 2.5 petabytes).

Mining Protease Data

5

Fig. 1 Catalogue of Somatic Mutations in Cancer (COSMIC) database. (a) Entry example showing the mutation distribution across the ADAMTS1 gene. (b) Summary of different mutations. ADAMTS1, A Disintegrin-like and Metalloproteinase with thrombospondin motifs 1

The Genomic Data Commons Data Portal (https://portal.gdc. cancer.gov/) [6] contains genomic data from 74 projects, >86000 cases, and >2.7 million mutations (release 36.0, December 2022). By querying for a particular PoI, the researcher can access information on cancer distribution, cancer type, primary sites, number and type of mutations, and copy number variants, as well as number of affected cases across the different sequencing projects. The portal

6

Salvatore Santamaria

can also be used to identify potential PoIs by querying, for example, for a particular tissue, cancer type, or project. TGA has developed a number of tools to analyze these datasets (https://www.cancer. gov/about-nci/organization/ccg/research/structural-genomics/ tcga/using-tcga/tools). This wealth of information on human variants can be integrated by data on engineered and spontaneous mouse models available from repositories such as the Mutant Mouse Resource and Research Centers (MMRRC, https://www.mmrrc.org/), supported by the National Institutes of Health (NIH). Investigators can search for their PoI gene on the MMRRC’s Strain Search Form by their name or symbol. If a particular strain is available among the 50,000 mutant alleles preserved in the repository, investigators can access it either as a cryopreserved material or as live animals (the MMRRC charges fees to partially cover repository costs). The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal allows navigation through phenotyping data from 9000 mouse lines generated by the IMPC project [7]. By querying for a particular PoI (Fig. 2), the researcher gets access to significant phenotypes, associated images, human diseases associated or predicted to be associated with the PoI, histopathology data, and a list of relevant publications. The Mouse Genome Informatics (MGI; https://www.infor matics.jax.org/), the international database portal for mouse genetics, integrates data from several projects such as the Mouse Genome Database (MGD) project, the Gene Expression Database (GXD) project, and the Mouse Models of Human Cancer database (MMHCdb) project. Particularly relevant to researchers working on cancer is the MMHCdb (https://tumor.informatics.jax.org/ mtbwi/index.do) that curates mouse models of human cancers comprising spontaneous, induced, genetically defined, and patient-derived xenograft models [8]. Each entry in MGI provides information on mouse strains, association with human disease, mutations, and expression patterns. The Human Protein Atlas (https://www.proteinatlas.org/) [9] integrates various -omics data to provide protein/RNA expression patterns at cell and tissue level (Fig. 3). A similar service is provided by the Genotype-Tissue Expression (GTEx) project (https:// gtexportal.org/home/) [10] which presents gene expression data from 54 non-diseased tissues across 1000 individuals. Information Resources Once you have defined the involvement of their PoI in a particular cancer, the researchers may be interested into its classification, proteolytic mechanism, substrate repertoire, cleavage site specificity, and susceptibility to inhibitors. Excellent places to start looking into this information are the BRENDA (https://www.brenda-enzymes.org/index.php) [11] and MEROPS (https://www.ebi.ac.uk/merops/) [12] databases.

Mining Protease Data

7

Fig. 2 International Mouse Phenotyping Consortium (IMPC) database. (a) Entry summary for the ADAMTS1 gene. (b) List of phenotypes associated with ADAMTS1 null mutations

8

Salvatore Santamaria

Fig. 3 Human Protein Atlas database. Entry summary for the ADAMTS1 gene showing expression according to tissue localization

BRENDA originally developed in 1987 by the German National Research Centre for Biotechnology in Braunschweig, is now curated and hosted at the Technical University of Braunschweig. It currently lists 6500 enzymes (not only proteases) classified according to the Enzyme Commission (EC) list of enzymes. BRENDA provides information such as enzyme nomenclature (it organizes under a single EC name all the synonyms associated with a particular enzyme, some of them being widely used in the literature), enzyme–ligand interactions (substrates, inhibitors, activators, etc.), disease association, functional parameters (Km, kcat, pH optima, etc.), enzyme structures available, and posttranslational modifications as well as external links to other databases. An example of entry is shown in Fig. 4. The MEROPS database provides similar information but is structured in a hierarchical mode where each protease is assigned to a Family on the basis of amino acid similarities and homologous families are grouped together in a clan. In its 6.0 release version,

Mining Protease Data

9

Fig. 4 BRENDA database. Entry summary for ADAMTS1

MEROPS enlists 42 clans, 176 families, and 1816 distinct proteases. These relationships can be visualized by sequence alignments and phylogenetic trees. Proteases can be searched by their MEROPS identifier, name, or sequence accession number, if known. Each entry is introduced by a “PepCard” (Fig. 5a). MEROPS also provides information on protease inhibitors (“Inhibitors side” of the database). A useful feature is the “cleavage site specificity” (Fig. 5b), which provides information on the preferences for particular amino acids in the protease substrates. Knowing the cleavage site specificity of a given PoI is useful to predict cleavage sites in novel candidate substrates, designing synthetic substrates to monitor protease activity [13, 14] or peptidomimetic inhibitors [15]. Cleavage sites are listed as specificity matrices where substrate residues are aligned according to the Schechter and Berger nomenclature [16] and visualized as “logos” to represent over- or underrepresented amino acids throughout the cleavage site positions. Such logos, based on amino acid frequency in experimentally validated cleavage sites reported in the literature, can be independently generated using web services such as iceLogo (https://iomics. ugent.be/icelogoserver/) [17] or WebLogo (https://weblogo. threeplusone.com/) [18]. The Mammalian Degradome Database (http://degradome. uniovi.es/dindex.html), curated by the Lopez-Otin laboratory at the Universidad de Oviedo (Spain), covers the protease repertoire (degradome) of four mammalian genomes (Homo sapiens, Pan troglodytes, Mus musculus, and Rattus norvegicus) [19]. Proteases are classified in five different classes according to their mechanism of catalysis (aspartyl, cysteine, metallo, serine, and threonine proteases). Protease inhibitors are also annotated.

10

Salvatore Santamaria

Fig. 5 MEROPS database. (a) PepCard for ADAMTS1. (b) Cleavage site specificity for ADAMTS1

The Protein ANalysis THrough Evolutionary Relationships (PANTHER) database (http://www.pantherdb.org/) [20] classifies proteins according to family phylogenetic trees (15619 in the 17.0 release). Each entry is annotated by family and protein class, subfamily, orthologs and paralogs, function, and pathways. Expasy (https://www.expasy.org/) [21], the bioinformatics resource portal of the SIB Swiss Institute of Bioinformatics, provides links to over 160 databases and software tools, some of which will be described below.

Biochemical Parameters and Post-translational Modifications For functional studies of a PoI in vitro, it is important to know its molecular weight and extinction coefficient. These and other parameters can be computed from the primary amino acid

Mining Protease Data

11

sequence using Expasy ProtParam tool (https://web.expasy.org/ protparam/). A caveat is that molecular weights computed from the primary amino acid sequence of a PoI quite often do not match the ones observed upon SDS-PAGE, and this may be quite puzzling for researchers at the beginning of their studies. These discrepancies may be due to post-translational modifications (PTMs) such as glycosylation, phosphorylation, and ubiquitination. A number of webtools are able to predict PTMs with a relatively high degree of confidence, although I recommend verifying experimentally that such PTMs do actually take place. Glycosylation is among the most frequent PTMs. NetNGlyc (https:// services.healthtech.dtu.dk/service.php?NetNGlyc-1.0) [22] uses artificial neural networks to predict N-linked glycosylation sites with an overall accuracy of 76%, while SPRINT-Gly (https:// sparks-lab.org/server/sprint-gly/) [23] predicts both N-linked and O-linked glycans. Similarly, NetPhos (https://services. healthtech.dtu.dk/service.php?NetPhos-3.1) [24] predicts phosphorylation sites on serine, threonine, or tyrosine residues. Sequence Alignment It may be interesting to align the sequence of your PoI to one or more sequences (or even an entire proteome) to identify homologues, highlight phylogenetic relationships, or identify amino acid changes. EMBOSS (https://www.ebi.ac. uk/Tools/emboss/) [25], developed by the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBLEBI) provides a number of tools for sequence analysis ranging from pairwise sequence comparisons to multiple sequence alignments (Clustal Omega). While Needle (https://www.ebi.ac.uk/Tools/ psa/emboss_needle/) analyzes two sequences, Clustal Omega can align up to 4000 sequences (https://www.ebi.ac.uk/Tools/msa/ clustalo/). Here, as in many tools discussed in this chapter, input nucleotides or protein sequences must be provided in FASTA format. Files in a wide range of formats can be converted into FASTA using the EMBOSS Seqret tool (https://www.ebi.ac.uk/ Tools/sfc/emboss_seqret/). Alignments can be finally visualized using MView (https://www.ebi.ac.uk/Tools/msa/mview/). Prediction of Cleavage Sites PeptideCutter (https://web.expasy. org/peptide_cutter/) predicts potential proteolytic cleavage sites in a protein and as such can integrate information from logo webtools. The list of proteases that can be selected to generate the cleavage maps is quite limited and mostly includes serine proteases and cysteine proteases. This service, which is particularly useful to analyze fragmentation patterns generated, for example, by trypsin digestion before mass spectrometry analysis, is now provided by a number of websites.

12

Salvatore Santamaria

The cleavage site specificities listed in MEROPS can be used to predict the probability of cleavage by your PoI in a target protein. This service is accessible through the Proteasix tool (http:// proteasix.cs.man.ac.uk/index.html) [26] developed by the Bio-Health Informatics Group (BHIG) at the University of Manchester (UK). Once the user uploaded a list of peptides, Proteasix automatically identifies the N- and C-terminal cleavage sites and then associates the protease(s) potentially responsible for these proteolytic events by 1) matching against a library of known cleavage sites retrieved from the literature, and 2) calculating the probability of cleavage based on MEROPS specificity matrices. Unfortunately, the last update of this webtool is from 2017. Analysis of Protein Structures Knowing the 3D structure of your PoI is essential to get insights on the mechanism of proteolysis, design inhibitors, and modulators and predict the effect of mutations and post-translational modifications. The Protein Data Bank (PDB) (https://www.rcsb.org/) [27], the largest database for experimentally determined 3D structures of biomolecules, reached the threshold of 200000 entries in 2023 and is the ideal place to look for the structure of your PoI. Although the majority of these entries represent protein structures (97.9%), of these only a minor percentage is represented by proteases (PDB listed 44563 protease entries out of 200708 in 2023, i.e., 22.2%). An even smaller group represents full-length proteases. For large, multidomain proteases, the chance to find the full-length structure of a PoI is indeed quite slim. For example, all the entries for A Disintegrinlike and Metalloproteinase with Thrombospondin-like motif 1 (ADAMTS1) comprise only the metalloproteinase and disintegrin-like domain. Structure coordinates available in the PDB can be downloaded in PDB format and can be visualized using programs such as Chimera (https://www.cgl.ucsf.edu/chimera/) [28] or PyMOL (https://pymol.org/2/). An example of 3D structure available for ADAMTS1 in the PDB is shown in Fig. 6a. Coverage of proteases’ 3D structures may increase over time, thanks to the efforts of structural biologists all over the world. Unfortunately, the number of protease structures deposited in the PDB has increased only marginally over the years, and the increment has predominantly involved a small core of already well-characterized proteases (mostly representing validated pharmaceutical targets) rather than expanding the structural landscape of proteases by depositing entries lacking a previous 3D structure. In 2022, for example, out of 81708 new entries deposited in the PDB, only 6233 represented new protein sequences (i.e., 7.62%). For all these reasons, most researchers resorted to webtools able to predict the structure of their PoI.

Mining Protease Data

13

Fig. 6 Analysis of protein structures. (a) Crystal structure of ADAMTS1 (PDB 2JIH, green) covering the metalloproteinase/disintegrin-like domains (residues 253–548) superimposed with the AlphaFold model (AF-Q9UHI8-F1, orange) covering the same residues. (b) AlphaFold model of ADAMTS1 (AF-Q9UHI8-F1, orange) covering residues 258–966. The catalytic zinc ion is shown as a gray sphere. Models were visualised using PyMOL

Iterative Threading ASSEmbly Refinement (iTASSER) [29] (https://zhanggroup.org/I-TASSER/) is a structure assembly pipeline that has been ranked as one of the most accurate methods for structure prediction in the past decade [30]. iTASSER is based on template-based modeling (TBM), i.e., it generates protein structure models by using known homologous structures as templates. The server is intuitive, and researchers just need to upload their sequence of interest in FASTA format and the link to the results will be emailed to them. Since TBM works poorly for proteins that lack close homology to structures deposited in the Protein Data Bank (PDB), iTASSER developers integrated it with prediction of residue–residue contacts to generate Contact-guided (C)-I-TASSER [31]. The interface of C-I-TASSER (https:// zhanggroup.org/C-I-TASSER/) is similar to iTASSER, and this implementation is claimed to fold more than twice the number of non homologous proteins than the original I-TASSER pipeline [31]. iTASSER outperformed other pipelines in several critical assessment of methods of protein structure prediction (CASP) experiments till 2020 (CASP14) when AlphaFold, an artificial intelligence (AI) system developed by DeepMind and EMBL’s European Bioinformatics Institute (EMBL-BI), achieved an impressive median backbone accuracy of 0.96 Å r.m.s.d.95 (Cα root-mean-square deviation at 95% residue coverage), while the next best performing method had a median backbone accuracy of 2.8 Å r.m.s.d.95 [32]. The latest database release (February 2023) contains over 200 million entries (https://alphafold.ebi.ac.uk/). An example of structure predicted by AlphaFold is shown in Fig. 6b and a superimposition with an actual 3D structure in Fig. 6a. While AlphaFold is a database of AI-generated 3D

14

Salvatore Santamaria

structures, the SWISS-MODEL repository (https://swissmodel. expasy.org/repository/) [33] is a database of 3D structures automatically generated by TBM for all sequences in the UniProt. It currently contains 2291486 models. The server version is available at https://swissmodel.expasy.org/. It is necessary to point out that structures predicted by TBM or AI must be used with caution since they are not experimentally determined. Whenever possible, a comparison with experimentally determined 3D structures of homologous proteases is recommended. Analysis of Protein Interactions Multiple experimental approaches help determine how each PoI interacts with its endogenous substrates, cleavage products, inhibitors, and cofactors within the so-called protease web. The Biological General Repository for Interaction Datasets (BioGRID) (https://thebiogrid.org/) [34] contains over 1740000 interactions manually curated from both highthroughput datasets and individual focused studies, covering over 70000 publications. By querying for a particular PoI, the user can access the protease web, displayed as a list of interactors or as a protein network where the edge size represents the number of interactions while the node size the number of edges to/from the node (Fig. 7). A similar service is provided by the IntAct molecular interaction database (https://www.ebi.ac.uk/intact/home) [35], curated by the EMBL-EBI and the STRING database (https:// string-db.org) (currently covering 67592464 proteins from 14094 organisms) [36]. Biological functions between groups of genes may be identified by pathway enrichment analysis using Metascape (https:// metascape.org), a portal that extracts information from 40 databases over 10 model organisms [2]. The user provides a list of genes (max 3000) from multiple assay types, including proteomics, transcriptomics, and epigenetics, and then launches an automated analysis workflow that produces a comprehensive analysis report (advanced users can customize the single steps of the workflow). Metascape also integrates a data synchronization pipeline to maintain updated data sources. The termini-oriented protein function inferred database (TopFIND, https://topfind.clip.msl.ubc.ca/), developed by the Overall Lab at the University of British Columbia [37], is a database covering 331278 natural and neo-N- and C-termini and 35044 protease cleavage sites from 8 model organisms. Within the database, PathFINDer provides the protease web of a given PoI. A number of databases focus on extracellular matrix (ECM) molecules [38]. The Matrisome project (http://matrisome.org) is a portal maintained by the Naba Lab at the University of Chicago. It

Mining Protease Data

Fig. 7 BioGRID database. (a) Entry summary for ADAMTS1. (b) Network of ADAMTS1 interactors

15

16

Salvatore Santamaria

provides in silico prediction and annotation of all ECM genes (the matrisome) of 7 model organisms (Homo sapiens, Mus musculus, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, Schmidtea mediterranea, Coturnix japonica). Within the portal, MatrisomeDB (https://matrisomedb.org) [39] is a searchable database of all ECM genes, providing cross-references to experimental proteomics data. Other protein interaction databases focusing on ECM molecules are MatrixDB (http://matrixdb.univlyon1.fr/) [40], curated by the Ricard-Blum Lab in Lyon, and MatriNet (https://www.matrinet.org/) [41], developed by the Izzi Lab at the University of Oulu. Conclusion The list of web resources presented here should provide the reader with a guide through the mass of data made available by public repositories. In most cases, this is just the start of a scientific journey, the stimulus to formulate new hypotheses, plan new experiments, or, simply, replicate some crucial findings. Not only is this list far from being exhaustive, but new tools and resources regularly come up on the web, while old ones are dismissed. With practice, the reader will learn how to be upfront in this ever-changing world.

Acknowledgments The Santamaria Lab is supported by the British Heart Foundation (FS/IBSRF/20/25032). References 1. Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921 2. Zhou Y, Zhou B, Pache L et al (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10(1):1523 3. Ochoa D, Hercules A, Carmona M et al (2023) The next-generation open targets platform: reimagined, redesigned, rebuilt. Nucleic Acids Res 51(D1):D1353–D1359 4. Landrum MJ, Chitipiralla S, Brown GR et al (2020) ClinVar: improvements to accessing data. Nucleic Acids Res 48(D1):D835–D844 5. Tate JG, Bamford S, Jubb HC et al (2019) COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res 47(D1):D941– D947

6. Grossman RL, Heath AP, Ferretti V et al (2016) Toward a shared vision for cancer genomic data. N Engl J Med 375(12):1109–1112 7. Groza T, Gomez FL, Mashhadi HH et al (2023) The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Res 51(D1):D1038– D1045 8. Krupke DM, Begley DA, Sundberg JP et al (2017) The mouse tumor biology database: a comprehensive resource for mouse models of human cancer. Cancer Res 77(21):e67–e70 9. Uhle´n M, Fagerberg L, Hallstro¨m BM et al (2015) Proteomics. Tissue-based map of the human proteome. Science 347(6220): 1260419

Mining Protease Data 10. GTEx Consortium (2013) The GenotypeTissue Expression (GTEx) project. Nat Genet 45(6):580–585 11. Chang A, Jeske L, Ulbrich S et al (2021) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49(D1):D498–D508 12. Rawlings ND, Barrett AJ, Thomas PD et al (2018) The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res 46 (D1):D624–D632 13. Santamaria S, Buemi F, Nuti E et al (2021) Development of a fluorogenic ADAMTS-7 substrate. J Enzyme Inhib Med Chem 36(1): 2160–2169 14. Santamaria S, Nagase H (2018) Measurement of protease activities using fluorogenic substrates. Methods Mol Biol 1731:107–122 15. Cuffaro D, Ciccone L, Rossello A et al (2022) Targeting aggrecanases for osteoarthritis therapy: from zinc chelation to exosite inhibition. J Med Chem 65(20):13505–13532 16. Schechter I, Berger A (1967) On the size of the active site in proteases. I Papain. Biochem Biophys Res Commun 27(2):157–162 17. Colaert N, Helsens K, Martens L et al (2009) Improved visualization of protein consensus sequences by iceLogo. Nat Methods 6(11): 786–787 18. Crooks GE, Hon G, Chandonia JM et al (2004) WebLogo: A sequence logo generator. Genome Res 14:1188–1190 ˜ol Y, Velasco G et al 19. Pe´rez-Silva JG, Espan (2016) The Degradome database: expanding roles of mammalian proteases in life and disease. Nucleic Acids Res 44(D1):D351–D355 20. Thomas PD, Ebert D, Muruganujan A et al (2022) PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci 31(1):8–22 21. Duvaud S, Gabella C, Lisacek F et al (2021) Expasy, the Swiss Bioinformatics Resource Portal, as designed by its users. Nucleic Acids Res 49(W1):W216–W227 22. Gupta R, Brunak S (2002) Prediction of glycosylation across the human proteome and the correlation to protein function. Pac Symp Biocomput:310–322 23. Taherzadeh G, Dehzangi A, Golchin M et al (2019) SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties. Bioinformatics 35(20):4140–4146

17

24. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294(5):1351–1362 25. Madeira F, Pearce M, Tivey ARN et al (2022) Search and sequence analysis tools services from EMBL-EBI in 2022. Nucleic Acids Res 50(W1):W276–W279 26. Klein J, Eales J, Zu¨rbig P et al (2013) Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation. Proteomics 13(7):1077–1082 27. Burley SK, Bhikadiya C, Bi C et al (2023) RCSB protein data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/ machine learning. Nucleic Acids Res 51(D1): D488–D508 28. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera – a visualization system for exploratory research and analysis. Comput Chem 25(13):1605–1612 29. Yang J, Zhang Y (2015) I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 43(W1):W174– W181 30. Kryshtafovych A, Schwede T, Topf M et al (2019) Critical assessment of methods of protein structure prediction (CASP)-Round XIII. Proteins 87:1011–1020 31. Zheng W, Zhang C, Li Y et al (2021) Folding non-homology proteins by coupling deeplearning contact maps with I-TASSER assembly simulations. Cell Rep Methods 1:100014 32. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589 33. Bienert S, Waterhouse A, de Beer TA et al (2017) The SWISS-MODEL Repository-new features and functionality. Nucleic Acids Res 45 (D1):D313–D319 34. Oughtred R, Rust J, Chang C et al (2021) The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30(1): 187–200 35. Orchard S, Ammari M, Aranda B et al (2014) The MIntAct project – IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42(Database issue):D358–D363 36. Szklarczyk D, Kirsch R, Koutrouli M et al (2023) The STRING database in 2023: protein-protein association networks and

18

Salvatore Santamaria

functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51(D1):D638–D646 37. Fortelny N, Yang S, Pavlidis P et al (2015) Proteome TopFIND 3.0 with TopFINDer and PathFINDer: database and analysis tools for the association of protein termini to preand posttranslational events. Nucl. Acids Res. 43:D290–D297 38. Naba A (2023) Ten years of extracellular matrix proteomics: accomplishments, challenges, and future perspectives. Mol Cell Proteomics 22(4):100528

39. Shao X, Gomez CD, Kapoor N et al (2023) MatrisomeDB 2.0: 2023 updates to the ECM-protein knowledge database. Nucl Acids Res 51:D1519–D1530 40. Clerc O, Deniaud M, Vallet SD et al (2019) MatrixDB: integration of new data with a focus on glycosaminoglycan interactions. Nucl. Acids Res 47:D376–D381 ˜ ora VR, Pesola V et al (2022) 41. Kontio J, Son Analysis of extracellular matrix network dynamics in cancer using the MatriNet database. Matrix Biol 110:141–150

Chapter 2 N-Terminomics/TAILS of Human Tumor Biopsies and Cancer Cell Lines Afshin Derakhshani, Mitchell Bulluss, Regan Penner, and Antoine Dufour Abstract Proteases serve essential roles in numerous biological processes and signaling cascades by cleaving their substrates in a restricted manner or via degradation. It is important to determine which proteins are protease substrates and where their cleavage sites are located to characterize the impact of proteolysis on the molecular mechanisms of their substrates. N-terminomics is a branch of proteomics that enriches the N-terminal sequence of proteins. A proteome-wide collection of these sequences has been broadly applied to comprehend proteolytic cascades and for genome annotation. Terminal Amine Isotopic Labeling of Substrates (TAILS) is a combined N-terminomics and proteomics technique that has been applied for protein N-terminal characterization and quantification of natural and neo-N-termini of proteins using liquid chromatography and tandem mass spectrometry (LC–MS/MS). TAILS uses negative selection to enrich both original mature protein N-termini and neo-N-termini produced from proteolysis in a proteome labeled with isotopic tags. This approach has been applied to the investigation of protease function and substrate identification in cell culture systems, animal disease models, and, most recently, clinical samples such as blood and tumor tissues from cancer patients. Key words N-terminomics, TAILS, Proteomics, Cancer, Protease, Tumor, Mass spectrometry

1

Introduction Numerous proteomic datasets with potential diagnostic, prognostic, and therapeutic implications in human cancer have been generated by proteomic investigations [1–5]. N-terminomics is a branch of proteomics that involves the enrichment and analysis of the N-terminal sequence of proteins. A proteome-wide collection of these sequences has been extensively utilized in the research of proteolytic cascades and genome annotation. Various N-terminomic techniques have been developed during the past two decades to provide a more sensitive and descriptive analysis of proteolysis [6, 7]. Protein N-termini characterization is defined as a critical part of the functional annotation of any proteome. The localization and activity of many proteins are influenced by their

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_2, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

19

20

Afshin Derakhshani et al.

isoforms and N-terminal posttranslational modifications, such as proteolytic truncation and acetylation [8]. Several techniques for isolating protein N-terminal peptides, also referred to as N-terminomics, have been developed [7–9]. Terminal Amine Isotopic Labeling of Substrates (TAILS) is a proteomics approach that has initially developed in the laboratory of Christopher Overall, and it is used to characterize and quantify the natural and neo-Ntermini of proteins using liquid chromatography and tandem mass spectrometry (LC–MS/MS) [8]. Briefly, in TAILS, mature proteins and the neo-N-terminal of cleaved proteins are isotopically labeled. Proteins are then digested by trypsin. Negative selection is subsequently employed to exclude internal tryptic peptides, hence enhancing the detection of N-terminal peptides. Samples are analyzed using LC–MS/MS. Label-free LC–MS/MS routinely measures and identifies thousands of proteins across several samples in a single run, giving a unique opportunity to investigate changes in the proteomics profile. This can also be performed using TAILS [10]. The current protocol can be applied to various clinical tumor samples and cell lines from cancer patients, such as tumors, adjacent non-tumor tissues, blood, serum, plasma, and cancer cell lines (Fig. 1).

2

Materials Prepare all reagent solutions using high-performance liquid chromatography (HPLC) water to minimize any potential contaminations. Use protein LoBind™ Eppendorf tubes to optimize protein and peptide capture and get maximal protein recovery (see Note 1).

2.1

Reagents

1. Dithiothreitol (DTT): 1 M solution. Add 0.0154 g in 100 μL HPLC H2O. 2. 2-Iodoacetamide (IAA): 0.5 M solution. Add 0.0925 g in 1 mL HPLC H2O. 3. Light formaldehyde (CH2O): 6.5 μL CH2O + 33.5 μL HPLC H2O per sample. Prepare fresh. 4. Heavy formaldehyde (CD2O: 13C, D2 solution): 12 μL CD2O + 38 μL HPLC H2O per sample. Prepare fresh. 5. Methanol. 6. Ethanol. 7. Acetone. 8. Sodium cyanoborohydride (NaBH3CN): 1 M solution. Add 0.0628 g in 1 mL HPLC H2O. This solution needs to be prepared fresh every time. 9. Tris (hydroxymethyl)aminomethane.

TAILS of Human Tumor Biopsies

21

Fig. 1 Preparation of samples and biopsy lysis procedure. Workflow for sample preparation; lysis buffer is added to tissue biopsies or cells. Cell lysates or tissues are pulverized using homogenizing beads. Samples are centrifuged to separate from beads. Next, samples are sonicated and centrifuged before collecting the supernatant. PBMC, peripheral blood mononuclear cell

10. Sodium hydroxide. 11. Guanidine hydrochloride (GuHCl). 12. TAILS polymer (dendritic polyglycerol aldehyde polymer with high molecular weight, 100–600 kDa) [11]. 13. Amicon® filter (10 kDa cutoff). 14. Qiagen TissueLyser II (or comparable). 15. Tabletop centrifuge. 16. Probe tip sonicator (Microson ultrasonic cell disruptor). 17. 1.5 mL LoBind™ Eppendorf tubes. 18. C18 Pierce stage tips or other C18 resin amenable for desalting samples for mass spectrometry. 19. Tubes for homogenization (for performing solid tissue biopsies). 20. 2.4 mm stainless steel homogenizing beads. 21. Liquid nitrogen.

22

Afshin Derakhshani et al.

22. Bicinchoninic acid (BCA) protein quantification kit (Pierce™ BCA Protein Assay Kit or equivalent). 23. Lysis buffer (total volume 20 mL): 1% sodium dodecyl sulfate (SDS), 100 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 0.5 M ethylenediaminetetraacetic acid (EDTA), pH 8.0. Add 0.2 g SDS and 156 g ammonium bicarbonate into a 50 mL tube, followed by 2 mL HEPES from 1 M stock solution and then 1× Protease Inhibitor Cocktail Tablets (and 1× Phosphatase Inhibitor Cocktail if you plan to run phospho-proteomics), 40 μL of 10 mM EDTA stock solution, finally top up to 20 mL with HPLC water and adjust pH to 8.0. 24. Trypsin solution: 40 μg of mass spectrometry grade trypsin is required for each starting mg of sample; suspend trypsin by adding 2:1 (hydrochloric acid (HCl)/trypsin) 10 mM HCl. 25. Sterile phosphate-buffered saline (PBS) solution. 26. 1 M Tris–HCl, pH 6.8. Mix 181.65 g of Tris base with 700 ml of HPLC H2O by stirring. Next, adjust the pH to 6.8 by adding the concentrated HCl. Add HPLC H2O until final volume is 1 L. 27. Primary cancer cells or immortalized cancer cell lines (e.g., MCF-7, MDA-MB-231, THP-1, HCT-116, A549, OVCAR3. SK-OV-3, DU-145, PC-3, etc.).

3

Methods

3.1 Sample Preparation (Fig. 1)

1. Collect tissue biopsies (~1–3 cm3) and rinse three times in saline or PBS solution.

3.1.1 Tissue Homogenization

2. Freeze tissue samples in liquid nitrogen as quickly as possible after collection (see Note 2). 3. Place a small amount of biopsy (typically 0.5–1 cm3) and add 500 μL lysis buffer in bead-beating homogenizing tubes with stainless steel beads (around four to five beads per tube). 4. Using the tube adaptors, place the tubes in the homogenizer. 5. Homogenize tissue at 30 Hz for 10 min (see Note 3). 6. Spin tubes in a centrifuge at maximum speed for 1 min to reduce foam. 7. Check for visible tissue clumps (see Note 4). 8. If you are using liquid nitrogen for homogenizing, skip steps 1–7, homogenize the tissue in liquid nitrogen, and add obtained powder into the microtube, followed by 500 μL lysis buffer. Vortex gently to have a homogenized liquid. 9. Transfer cell lysates into the Eppendorf tube and follow the sonication section.

TAILS of Human Tumor Biopsies 3.1.2 Primary Cancer Cells or Immortalized Cancer Cell Line Lysis

23

1. Culture primary cancer cells or immortalized cancer cell lines until they reach 70–80% confluency. However, some cancer cells may clump or grow in clusters; therefore, you may need to resuspend cells using trypsin or select clusters for your analysis. Importantly, you will need to select the cell confluency based on the type of cancer cells you are analyzing. Some optimizations of growing conditions may be required. 2. Wash cells in sterile PBS (1× in ~5 mL; depending on the flask used for cell cultivation, the optimal amount would be the volume needed to cover the cells) (see Note 5). 3. Repeat step 2 two additional times. 4. If cells are floating, spin down and resuspend in 1x PBS. Centrifuge at 1500× g for 10 min at room temperature. 5. Repeat step 4 two additional times. 6. Depending on the flask or petri dish used for cultivating the cancer cells, add approximately 200–1000 μL lysis buffer into the flask or falcon tube. 7. For adherent cells, scrape the cells with a cell scraper. The solution usually becomes cloudy and viscous. 8. Transfer the cell lysates into Eppendorf tubes and sonicate (see Subheading 3.1.4).

3.1.3 Blood, Serum, or Plasma

1. Collect whole blood and then process the samples by centrifuging for 10 min at 2000× g at room temperature. 2. Isolate serum or plasma and then aliquot into tubes and store at -80 °C until TAILS analysis.

3.1.4 Sonication of Celland Tissue-Related Lysates

1. After transferring samples into Eppendorf tubes, sonicate samples on ice (using a probe sonicator makes direct contact with the samples) for 5–10 s using the mid-range (i.e., 5 out of 10) power setting of your sonicator. 2. Repeat step 1 twice per sample. Avoid contamination by wiping the probe with 70% ethanol between samples. 3. Centrifuge at 4 °C for 10 min at 14,000× g (cool down centrifuge before starting this step). 4. Transfer supernatants to a new Eppendorf tube. If necessary, these samples can be stored at -80 °C for several months. 5. Protein quantification. Use a BCA kit or other protein measurement assay to quantify protein concentrations. If you use plasma and serum, you will need to dilute them before measurement due to the high concentration of these fluids.

24

Afshin Derakhshani et al.

Fig. 2 A summary of N-TAILS, HYTANE, and HUNTER methods. (a) Following cell or tissue lysis, mature and neo-N-terminal proteins are isotopically labeled. Trypsin is used to digest the proteins. Negative selection using the TAILS polymer is then used to exclude internal tryptic peptides, enhancing our detection of N-terminal peptides. (b) Instead of using the TAILS polymer, HYTANE or hydrophobic tagging-assisted N-termini enrichment requires a hydrophobic tagging (hexadecanal, undecanal, or octanal) and C18 material-assisted depletion. (c) Instead of using the TAILS polymer, HUNTER is a scalable method for analyzing microscale samples using undecanal as a negative selection

3.2 Pre-enrichment TAILS and TAILS (Fig. 2) 3.2.1 Day 1: Sample Preparation and Amine Labeling

1. Transfer 500–1000 μg of each sample into a new LoBind™ Eppendorf tube and top up with lysis buffer to a final volume of 500 μL. 2. Add 500 μL 6 M GuHCl to a final volume of 1 mL. 3. Add 20 μL 1 M DTT (final concentration of 5 mM) and incubate at 37 °C for 1 h. 4. Add 30 μL 0.5 M IAA (final concentration 15 mM) and incubate at room temperature for 30 min in an environment protected from light. 5. Quench the reaction by adding 10 μL 1 M DTT and incubate at room temperature for 30 min. 6. Adjust the pH of samples to 6.5 using 1 M HCl. 7. Add 22 μL of freshly prepared light formaldehyde solution to each replicate of the condition (e.g., control samples). 8. Immediately add 22 μL of 1 M NaBH3CN to each replicate.

TAILS of Human Tumor Biopsies

25

9. Add 22 μL of freshly prepared heavy formaldehyde to each replicate of the condition (e.g., test samples). 10. Immediately add 22 μL of 1 M NaBH3CN to each replicate. 11. Incubate samples at 37 °C for 18 h. 3.2.2 Day 2: Protein Precipitation and Trypsinization

1. Optional additional labeling step: Add 11 μL of freshly prepared light formaldehyde to each replicate of the condition (e.g., control samples). 2. Immediately add 11 μL of 1 M NaBH3CN to each replicate. 3. Add 11 μL of freshly prepared heavy formaldehyde to each replicate of the condition (e.g., test samples). 4. Immediately add 11 μL of 1 M NaBH3CN to each replicate. 5. Incubate samples at 37 °C for 1 h. 6. Quench the reaction by adding 1 M Tris–HCl, pH 6.8 (final concentration of 100 mM). 7. Combine one heavy and light sample to individual 50 mL tubes and top up to 40 mL with 8:1 acetone/methanol. 8. Place samples at -80 °C for 4 h (samples may be stored at 80 °C for 5–25 days). 9. Centrifuge samples to precipitate peptides at 18,000× g for 15 min at 4 °C. 10. Discard the supernatant and replace it with 40 mL of 8:1 acetone/methanol. Centrifuge again and repeat two more times. 11. Discard the remaining 8:1 acetone/methanol and allow residual supernatant to evaporate. 12. Resolubilize peptides in 200 μL 200 mM NaOH and transfer into LoBind™ Eppendorf tubes. 13. Add 300 μL 200 mM HEPES and adjust pH to 8.5 with 1 M HCl. 14. Digest samples with 40 μg of trypsin for each starting mg of sample. Adjust pH to 8–8.5. 15. Incubate samples at 37 °C for 18 h.

3.2.3 Day 3: PreTAILS and Polymer Selection

1. Transfer 10% of each sample into a LoBind™ Eppendorf tube for pre-enrichment TAILS/shotgun. 2. Acidify this preTAILS sample to Edit > Clear outside, a popup window asking if you want to process all images will appear; select No (it must be done manually for each z/channel). The record Macro tool can be used to speed up the process for the following z: Plugins > Macros > Record. . . proceed to the ROI drawing and clearing steps describe above (they will appear in the Recorder). Once it is done, Create the macro. On the next z, draw your ROI on the background and Run the new macro.

Intravital Imaging of AML Cell Migration in the Bone Marrow

219

2. For time-lapse data: if the images shift or tilt due to the movement of the animal during the acquisition, this can be corrected using registration tools in ImageJ such as Multiview Reconstruction [19] or Fast4Reg [20] (for both, the tools’ authors provide a detailed workflow in ImageJ wiki [21, 22]). 3.7 Image Analysis: Cell Migration Tracking (2D)

1. Open the movie file with ImageJ (see Note 18). 2. Generate a z projection: Image > Stacks > Z project. . . > projection type Max intensity (see Note 19). Make sure that All time frames option is ticked. 3. If needed, adjust the brightness and contrast to clearly see your cells of interest: Image > Adjust > Brightness/Contrast. If more than one position is acquired, any modification must be applied to the whole group (especially if pixel quantification is performed during the analysis) as well as to the negative control of the experiment, whenever possible, to avoid the creation of artifacts. 4. If you have several channels, create an RGB image. First, create a composite: Image > Color > Make composite. If you want to track the cells only in one channel, you can instead split the channels (Image > color > Split channels) and chose the desired channel. On the resulting movie, convert to RGB: Image > Color > RGB color. 5. Manual cell tracking: Plugins > Tracking > Manual Tracking. Click on Add track to start a new track (Fig. 3a), on the movie you want to analyze. Make sure that the video is on the first time frame and click on the center of the cell you intend to track. The time-series will automatically move from the current time frame to the next with every click until the time-series is done. At the end of the track, within the tracking window, click End track. It is often useful to look at the whole movie before tracking the cell to avoid choosing a cell that moves out of the field of view before the end of the time-series or a cell in circulation. If the former type of cells is included, adjustments will need to be made at the track analysis stage to accommodate for shorter track lengths. If needed, a track can be deleted: in the tracking window, after Delete track n – chose the one to delete – and click on Delete track n. Once you have tracked the desired number of cells, the Overlay Dots & line option gives an overview of your work. Save the resulting table as a .csv file. 6. Edits need to be done on the .csv file in order to analyze the results using the Chemotaxis and Migration Tool. To do so, open the .csv table in Excel. Create a new empty column before the “Track n” one (depending on your Excel version, you may need to write something in the A1 cell so Excel doesn’t automatically remove the empty column). The table can be saved as Tab-delimited Text (.txt).

220

Floriane S. Tissot et al.

Fig. 3 Extraction and analysis of cell migration data from 108-min-long time-lapse IVM movies using ImageJ. (a) Overview of the Tracking plugin, (i) single time frame image displaying the MLL-AF9 AML cell that has been tracked (gray dashed circle) and its track (yellow line; white squares represent its position on each time frame). (ii) Tracking plugin window showing the steps to follow to track a cell (1–2) and have an overview of the tracks recorded (3). (b) Chemotaxis and Migration Tool plugin window displaying the steps to follow to display and analyze the migration dataset. (c) Illustration of the difference between the Euclidean and Accumulated distances

Intravital Imaging of AML Cell Migration in the Bone Marrow

221

7. Open the Chemotaxis and Migration Tool in ImageJ using the Plugins tab (Fig. 3b) (see Note 20). The Chemotaxis and Migration Tool window is divided into two sections: the upper part that displays datasets that are imported and can be analyzed and the lower part that is composed of multiple tabs to be used to import data, set up, and run the analysis. To add the dataset generated in 3.7.6: click on Import the data, chose the .txt file, and set the Number of slices in Use slice range from... to, based on your dataset (this information can be found on the table in the Slice n column; 1–36 in this example.). Click on Add dataset; the dataset will now appear in the upper section and can be selected for the different analyses. You can add the settings of the movie in the Settings tab (required if you want the scale of the graphs displayed as μm. See Note 21). Once everything is set, click on Apply settings. 8. Use the Plot feature tab to generate graphs displaying the tracks of your cells. You can also extract distance measurements in the Statistic feature tab (see Note 22). The results from this analysis showing the effect of systemic inhibition of MMPs on few MLL-AF9 AML cells from one field of view are shown in Fig. 4. We recommend tracking several cells from multiple fields of views and at least a few mice to be confident in the obtained results, as typically cell track parameters present considerable variability [17].

4

Notes 1. Several studies show that aging affects hematopoietic cells [23, 24] as well as the BM microenvironment [25, 26]. For these reasons, if you are not studying aging, it is better to consistently use mice between 2 and 4 months old. 2. We generated the AML blast as follows: Granulocyte–macrophage progenitor (GMP) cells are sorted from C57BL/6 either WT, mTmG, or PU.1 YFP mice [8] (the fluorophore used for the experiment depends on the parameter of the experiment, i.e., other fluorophores used either injected or transgenics). Sorted GMPs are transduced with pMSCV-MLL-AF9-GFPbased retrovirus [27] (note that the GFP from the retrovirus is used to sort the transfected cells but is usually too dim to be seen by microscopy). Transduced GMPs are transplanted in a sublethally irradiated mouse (two doses of 3.3Gy, at least 3 h apart). Recipient mice are expected to develop leukemia 8–16 weeks post-transplantation. GFP+ cells are harvested from the bone marrow, spleen, and blasts from fully infiltrated mouse and cryopreserved (we usually cryopreserve five million cells per vial in 1 mL of serum 10% DMSO). Each primary recipient is labeled as a separate batch.

222

Floriane S. Tissot et al.

Fig. 4 Summary of the migration analysis to study the effect of MMP inhibition on in vivo migration of MLL-AF9 AML. (a) Rose plot showing the tracks of MLL-AF9 AML cells in control (vehicle) and prinomastat condition. Quantification of cell displacement (b), Euclidean distance (c), and velocity (d) of the tracked MLL-AF AML cells (n = 8 cells per condition, one field of view per condition)

3. Prinomastat inhibits matrix metalloproteinases (MMPs) 2, 9, 13, and 14 [28]. 4. Technical drawings of the headpiece and microscope adaptor plate can be provided upon request to the corresponding author. 5. The shape and size of the screwdriver will depend on the screw chosen to secure the headpiece in Subheading 2.2, item 2. 6. We use isoflurane; therefore, the protocol is written to use this particular anesthesia but can be adapted to other types of anesthesia, for example, injectables.

Intravital Imaging of AML Cell Migration in the Bone Marrow

223

7. It is easier to have two anesthesia setups (nose mask/scavenger): one at the surgical place and one at the microscope stage. 8. Multiphoton microscopy improves depth penetration and reduces photodamage. In addition, second harmonic generation (SHG) is an important feature of two-photon microscopy, especially when working with bones. Indeed, SHG allows labelfree imaging of non-centrosymmetric structures such as type I collagen, the main component of the bone [4, 29, 30]. Hence, SHG is used to visualize bone in our experiments. To do so with our microscope setup, we use the tunable multiphoton excitation line with a wavelength of 900 nm, and SHG is detected by the non-descanned external detectors with a 400–480 nm bandpass filter. Alternative imaging setup is confocal-only, where a less precise bone signal can be identified as autofluorescence, or, as a preferable alternative, two photononly, where however dim signals can be difficult to identify and some fluorophores are not detectable. 9. AML infiltration in the bone marrow can be predicted based on the infiltration in the peripheral blood [31]. Hence, depending on if an early, intermediate, or late infiltration is needed for the experiment, tail bleed samples can be analyzed by flow cytometry (thanks to the GFP from the viral transfection, or tomato or YFP signal if AML cells are derived from mTmG or PU.1 YFP mice; see Note 2) to estimate the BM infiltration. It is better to establish the specific correlation between blood and BM infiltration with each AML batch prior to doing the IVM experiment. For example, in our hand, Computer > Macintosh HD > Application, on the Fiji/ImageJ application: hold the ^control key and right-click at the same time, and select Show Package Content. Move the plugin in the plugins folder and restart ImageJ. You can now find the Chemotaxis Tool in the Plugins tab.). 21. X/Y Calibration and Time interval (in minutes) need to be set in the Settings tab in order to display graph scales in μm instead of arbitrary unit. X/Y Calibration is the number of unit distance per pixel. All the information needed to calculate this and set the time interval can be found thanks to the metadata of the time-lapse (Image > Properties. If the movie needs to be registered, copy this parameter before the registration as the metadata will be lost in the process). 22. Two types of distances can be extracted from the dataset (Statistic feature > Track series > Distance): the Accumulated distance and the Euclidean distance. The first refers to the total distance traveled by the cell throughout the track and the second to the distance from the start point to the end point in a straight line, also known as displacement (Fig. 3c).

226

Floriane S. Tissot et al.

Acknowledgments We thank the Imperial College London Central Biomedical Services and Crick Biological Research Facilities for their support and C. Ferchaud from Quantum Optics and Laser Science group, Blackett Laboratory, Imperial College London, for the technical drawings. This work was funded by the Wellcome Investigator Award to C.L.C. 212304/Z/18/Z and CRUK Programme Foundation award to CLC C36195/A26770. CRUK PhD studentship to S.G.A. C36195/A27830. F.T. work was supported by the Wellcome Investigator Award 212304/Z/18/Z and the CRUK Programme Foundation C36195/A26770. References 1. Minsky M (1988) Memoir on inventing the confocal scanning microscope. Scanning 10: 128–138 2. Gannaway JN, Sheppard CJR (1978) Secondharmonic imaging in the scanning optical microscope. Opt Quant Electron 10:435–439 3. Elliott AD (2020) Confocal microscopy: principles and modern practices. Curr Protoc Cytom 92:e68 4. Pendleton EG, Tehrani KF, Barrow RP, Mortensen LJ (2020) Second harmonic generation characterization of collagen in whole bone. Biomed Opt Express 11:4379–4396 5. De´barre D, Supatto W, Pena A-M et al (2006) Imaging lipid bodies in cells and tissues using third-harmonic generation microscopy. Nat Methods 3:47–53 6. Lo Celso C, Fleming HE, Wu JW et al (2009) Live-animal tracking of individual haematopoietic stem/progenitor cells in their niche. Nature 457:92–96 7. Hawkins ED, Duarte D, Akinduro O et al (2016) T-cell acute leukaemia exhibits dynamic interactions with bone marrow microenvironments. Nature 538:518–522 8. Duarte D, Hawkins ED, Akinduro O et al (2018) Inhibition of endosteal vascular niche Remodeling rescues hematopoietic stem cell loss in AML. Cell Stem Cell 22:64–77.e6 9. Haltalli MLR, Watcham S, Wilson NK et al (2020) Manipulating niche composition limits damage to haematopoietic stem cells during plasmodium infection. Nat Cell Biol 22: 1399–1410 10. Zanetti C, Krause DS (2020) “Caught in the net”: the extracellular matrix of the bone marrow in normal hematopoiesis and leukemia. Exp Hematol 89:13–25

11. Muiznieks LD, Keeley FW (2013) Molecular assembly and mechanical properties of the extracellular matrix: a fibrous protein perspective. Biochim Biophys Acta Mol basis Dis 1832:866–875 12. Hynes RO (2009) The extracellular matrix: not just pretty fibrils. Science 326:1216–1219 13. Shin J-W, Spinler KR, Swift J et al (2011) Differentiation of hematopoietic stem cell modulated by actomyosin forces. Biophys J 100: 442a–443a 14. Shin J-W, Swift J, Ivanovska I et al (2013) Mechanobiology of bone marrow stem cells: from myosin-II forces to compliance of matrix and nucleus. Differentiation 86:77–86 15. Shin J-W, Mooney DJ (2016) Extracellular matrix stiffness causes systematic variations in proliferation and chemosensitivity in myeloid leukemias. Proc Natl Acad Sci U S A 113: 12126–12131 16. Verma D, Zanetti C, Godavarthy PS et al (2020) Bone marrow niche-derived extracellular matrix-degrading enzymes influence the progression of B-cell acute lymphoblastic leukemia. Leukemia 34:1540–1552 17. Pirillo C, Birch F, Tissot FS et al (2022) Metalloproteinase inhibition reduces AML growth, prevents stem cell loss, and improves chemotherapy effectiveness. Blood Adv 6:3126–3141 18. Page-McCaw A, Ewald AJ, Werb Z (2007) Matrix metalloproteinases and the regulation of tissue remodelling. Nat Rev Mol Cell Biol 8:221–233 19. Preibisch S, Saalfeld S, Schindelin J et al (2010) Software for bead-based registration of selective plane illumination microscopy data. Nat Methods 7:418–419

Intravital Imaging of AML Cell Migration in the Bone Marrow 20. Laine RF, Tosheva KL, Gustafsson N et al (2019) NanoJ: a high-performance open-source super-resolution microscopy toolbox. J Phys D Appl Phys 52:163001 21. Multiview-reconstruction. In: ImageJ Wiki. https://imagej.github.io/plugins/multiviewreconstruction. Accessed 2 Feb 2023 22. Fast4DReg. In: ImageJ Wiki. https://imagej. github.io/plugins/fast4dreg. Accessed 2 Feb 2023 23. Young K, Eudy E, Bell R et al (2021) Decline in IGF1 in the bone marrow microenvironment initiates hematopoietic stem cell aging. Cell Stem Cell 28:1473–1482.e7 24. Young K, Borikar S, Bell R et al (2016) Progressive alterations in multipotent hematopoietic progenitors underlie lymphoid cell loss in aging. J Exp Med 213:2259–2267 25. Sac¸ma M, Pospiech J, Bogeska R et al (2019) Haematopoietic stem cells in perisinusoidal niches are protected from ageing. Nat Cell Biol 21:1309–1320 26. Maryanovich M, Zahalka AH, Pierce H et al (2018) Adrenergic nerve degeneration in bone marrow drives aging of the hematopoietic stem cell niche. Nat Med 24:782–791 27. Krivtsov AV, Twomey D, Feng Z et al (2006) Transformation from committed progenitor to leukaemia stem cell initiated by MLL-AF9. Nature 442:818–822 28. PubChem Prinomastat. https://pubchem. ncbi.nlm.nih.gov/compound/466151. Accessed 23 Jan 2023

227

29. Freund I, Deutsch M (1986) Second-harmonic microscopy of biological tissue. Opt Lett 11: 94–96 30. Houle M-A, Couture C-A, Bancelin S et al (2015) Analysis of forward and backward second harmonic generation images to probe the nanoscale structure of collagen within bone and cartilage. J Biophoton 8:993–1001 31. Akinduro O, Weber TS, Ang H et al (2018) Proliferation dynamics of acute myeloid leukaemia and haematopoietic progenitors competing for bone marrow space. Nat Commun 9:519 32. Miraki-Moud F, Anjos-Afonso F, Hodby KA et al (2013) Acute myeloid leukemia does not deplete normal hematopoietic stem cells but induces cytopenias by impeding their differentiation. PNAS. https://doi.org/10.1073/ pnas.1301891110 33. Cheng H, Hao S, Liu Y et al (2015) Leukemic marrow infiltration reveals a novel role for Egr3 as a potent inhibitor of normal hematopoietic stem cell proliferation. Blood 126:1302–1313 34. Haltalli MLR, Lo Celso C (2021) Intravital imaging of bone marrow (BM) niches. In: Espe´li M, Balabanian K (eds) Bone marrow environment: methods and protocols. Springer US, New York, pp 203–222 35. Chemotaxis and migration tool | Free Software. In: ibidi. https://ibidi.com/chemo taxis-analysis/171-chemotaxis-and-migrationtool.html. Accessed 2 Feb 2023

Chapter 18 Fluorescence-Based Peptidolytic Assay for High-Throughput Screening of MMP14 Inhibitors Hyun Lee, Lucas Ibrahimi, and Kyu-Yeon Han Abstract The membrane-bound matrix metalloproteinase 14 (MMP14, also known as MT1-MMP) plays important roles in the remodeling of the extracellular matrix during various cellular processes such as cancer metastasis, angiogenesis, and wound healing through its proteolytic activity. There are no known MMP14-specific inhibitors to date, and hence identification of MMP14-specific inhibitors will be beneficial for finding potential therapeutics for various diseases, including cancer and inflammation. High-throughput screening (HTS) assays have become a common way to search for new small compounds, peptides, and natural products. Enzymatic assays are highly amenable to HTS because most enzyme activities are quantifiable with the effect of many small molecules of interest on a specific target enzyme. Here, we describe a fluorescence-based enzymatic assay that can be applied as a large-scale HTS and a follow-up enzyme kinetics assay to find MMP14-specific inhibitors. Key words Fluorescence-based enzyme assays, Enzyme kinetic assay, High-throughput screening, Matrix metalloproteinase, MMP14

1

Introduction Membrane type 1 matrix metalloproteinase 14 (MT1-MMP or MMP14) belongs to the MMP family and degrades various extracellular matrix (ECM) components, such as collagen, fibronectin, and laminin [1]. In addition, MMP14 activates pro-MMP2 to promote cell migration, which is an essential cellular event for tumor metastasis, inflammation, and angiogenesis [2]. It has been reported that tumor metastasis and corneal neovascularization were impaired in Mmp14-deficient breast cancer mice and Mmp14 total deficiency mice models, respectively [3, 4]. Therefore, MMP14specific inhibitors have the potential to be alternative therapeutics for cancer and pathological neovascularization. However, developing MMP14-specific inhibitors is difficult due to the highly conserved residues among the different MMPs [5]. Twenty-three identified MMPs can be classified into five groups depending on

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_18, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

229

230

Hyun Lee et al.

Fig. 1 MMP14-specific substrate design. (a) Structure of the catalytic domain of MMP14 with a surface representation of the Ω-loop shown in pink color (PDB/1BQQ). The two zinc ions and zinc-interacting histidine residues are shown as gray spheres and cyan sticks, respectively; two calcium ions and calcium-interacting residues are shown as orange spheres and orange sticks, respectively; residue Met264 is shown in green in the Ω-loop (magenta). (b) VEGFR1 protein sequences (residues 541–570) and MMP14 cleavage site. Residues selected for substrate synthesis are shown in purple. (c) A schematic presentation of a newly designed fluorogenic MMP14 specific substrate connected with a fluorophore (5-FAM) and a quencher (QXL™520). Ext, excitation wavelength, emi, emission wavelength. The images were modified from our previously published (ref: Hyun Lee, Isoo Youn, Robel Demissie, Tasneem M. Vaid, Chun-Tao Che, Dimitri T. Azar, Kyu-Yeon Han, Identification of small molecule inhibitors against MMP-14 via High-Throughput screening, Bioorganic & Medicinal Chemistry, Vol.85, 1 May 2023, 117289. PMID: 3709443)

their substrate specificity, such as gelatinases, stromelysins, collagenases, and matrilysins. There are two critical factors of substrate specificity. The first one is the S1′ binding pocket among six binding pockets (S1, S2, S3, S1′, S2′, and S3′) in MMPs, and the second deciding factor is a tunnel-like structure made by the so-called Ω-loop (Fig. 1a) [6, 7]. Specificity of MMPs depends on the size of the S1′ pocket and the length and flexibility of the Ω-loop [8, 9]. The Ω-loop of MMP14 is less flexible than those of MMP8 and MMP13, which contain Ω-loops of similar length. More importantly, there is a very unique residue, Met264, in the Ω-loop of MMP14. This Met264 occupies a part of the S1′ pocket and interferes with other MMP inhibitors to bind, thus providing selectivity for MMP14 [8].

Fluorescence-Based Assays for MMP14

231

Table 1 Commonly used fluorophore and quencher pairs Fluorophore

Quencher

Excitation (nm)

Emission (nm)

Abz 2-Aminobenzoyl

Dnp

320

420

EDANS 5-[(2-Aminoethyl)amino]naphthalene-1-sulfonic acid

Dabcyl

340

490

Mca 7-Methoxycoumarin-4-yl)acetyl

Dnp

325

392

Trp Tryptophan

Dnp

280

360

FAM Carboxyfluorescein

QXL

492

520

Alexa Fluor 488

QSY7

490

535

Alexa Fluor 594

QSY21

595

620

Developing enzymatic assays using novel and unique substrates can dramatically increase chances to identify MMP14-specific inhibitors. Enzymatic assays can be developed and optimized based on each enzyme activity with specific substrates, and co-factors might be needed for certain enzymes [10]. Short synthetic peptides mimicking the cleavage sites are often used to investigate activities of various proteases and MMPs. In order to detect MMP proteolytic activity with synthetic peptides, a fluorophore and quencher pair are attached on the opposite ends of the peptides containing a specific cleavage sequence, producing fluorescence signals upon cleavage. There are various fluorophores and quenchers available (Table 1). It is recommended to use fluorophores and quenchers emitting a signal with higher wavelengths in order to avoid intrinsic fluorescence signal interference from some aromatic compounds. Vascular endothelial growth factor receptor-1 (VEGFR1) was recently identified as an MMP14 substrate [11, 12], and VEGFR2mediated kinase activity was enhanced by VEGFR1 cleavage by MMP14 on the cell surface during angiogenesis [13, 14]. Here, using a catalytic domain of the MMP14 enzyme and a specific substrate from VEGFR1 (Fig. 1b, c), we describe a fluorescencebased enzymatic assay optimization process that will allow the reader to run a high-throughput screening (HTS).

232

2

Hyun Lee et al.

Materials and Equipment All solutions must be prepared with ultrapure water (prepared by purifying deionized water, 18.2 MΩ/cm at 25 °C). 1. Dimethyl sulfoxide (DMSO). 2. 384-well low-volume black plate, 384-well standard plate, DMSO-resistant 384-well PCR plate for compounds. 3. Adhesive aluminum seals. 4. Enzyme reaction buffer (10×): 500 mM Trizma base, 100 mM CaCl2, 1.5 N NaCl, 0.5% Brij-35. Dissolve weighted reagents except Brij-35 in 900 mL of ultrapure water. Add 16.6 mL of 30% Brij-35 stock. Mix and adjust to pH 7.5 with HCl. Prepare up to 1 L with ultrapure water. Store at room temperature. 5. MMP14 substrate (TDVPNGFHVS) conjugated with a 5-carboxyfluorescein (5-FAM) and QXLTM520 as fluorophore and quencher, respectively, thus generating the 5-FAM-TDVPNGFHVSLEK(QXL520)-NH2 substrate. This can be custom synthesized by various companies specialized in peptide synthesis. Prepare a 2 mM stock of the substrate stock concentration with ultrapure water, aliquot as a small volume, and store at -80 °C until use. 6. Human catalytic MMP14 (MT1-MMP) enzyme, commercially available. In order to avoid repeated freeze–thaw cycles of enzyme, store 10 μM concentration of MMP14 enzyme stock at -80 °C in small aliquots (10 μL each in 0.5 mL tubes) (see Note 1). 7. Compound libraries. There are many commercially available drug-like, lead-like, or fragment-like libraries, such as the Prestwick FDA-approved drug library consisting of 1200 compounds, 10,000 protein–protein interaction (PPI), 10,000 ChemDiv, and 50,000 Life Chemicals diverse compound libraries. Any appropriate small molecule library of interest can be tested. 8. Microplate fluorescence plate reader with plate shaking capability. Filter-based plate readers generally have higher sensitivity, but they require filters for specific wavelengths. Our substrate will generate fluorescence signal with excitation wavelength set to 490 nm and emission wavelength set to 520 nM. We used BMG Biotech Optima plate reader with appropriate filters installed.

Fluorescence-Based Assays for MMP14

3

233

Methods Pre-screen assay optimization is absolutely crucial in order to achieve high-quality and trustworthy outcomes from highthroughput screening (HTS) assays. There are various factors to be optimized, two of which are substrate and enzyme concentrations. The substrate concentration can be determined by measuring the Michaelis–Menten constant (KM). Other factors to be considered are enzyme stability, DMSO tolerance, potential co-factors if needed, and enzyme assay buffer additives such as detergents, reducing agents, and bovine serum albumin (BSA). These are widely discussed in another chapter of this series [15]. Once lead compounds with decent IC50 values typically below 10 μM are selected, an independent direct binding assay is recommended to confirm direct interaction between target enzyme and selected inhibitors. This secondary analysis step is necessary to ensure that the inhibition is not assay-specific. Available techniques to detect direct binding include surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), bio-layer interferometry (BLI), microscale thermophoresis (MST), mass photometry (MP), and thermal shift analysis (TSA). All procedures in this section must be performed at room temperature unless otherwise specified.

3.1 Determination of the Michaelis–Menten Constant (KM)

The substrate concentration corresponding the Michaelis–Menten constant (KM) is considered as a proper substrate concentration for the HTS campaign because it allows for both competitive and noncompetitive compound bindings, and typically the recommended substrate concentration is slightly lower than KM values [16, 17]. Hence, determination of the accurate KM value is critical so as to use an appropriate substrate concentration for HTS. 1. Prepare 2× (50 nM) of cat-MT1-MMP (25 nM final testing concentration) enzyme solution in enzyme assay buffer (see Note 2). 2. Prepare a series of substrate (5-FAM-TDVPNGFHVSLEK (QXL520)-NH2) concentrations at twofold (2×) the final testing concentrations (1.56, 3.125, 6.25, 12.5, 25, and 50 μM) in enzyme assay buffer (see Note 3). 3. Dispense 8 μL of 2× enzyme solution to each of 21 wells in a 384-well low-volume black plate (wells A01–C07, orange color in Fig. 2a) using a multichannel pipette. Dispense 8 μL each of enzyme assay buffer to 21 wells in the same plate (wells D01– F07, gray color in Fig. 2a) to measure the substrate background signal in the absence of enzyme. 4. Turn on the microplate reader and prepare a method to read fluorescence intensity at 490 nM excitation and 520 nM emission wavelength.

234

Hyun Lee et al.

Fig. 2 KM determination. (a) Plate layout of enzyme assays is shown. All assays are set up in triplicates. (b) Example of a dose–response curve of KM determination fitted with Eq. 1. Error bars represent standard deviations from triplicate data

5. Initiate the enzyme reaction by adding 8 μL each of the various 2× substrate concentrations to each of six wells in the plate, e.g., the 1.56 μM stock will be added to six wells in column 2 (A02–F02), the 3.13 μM stock will be added to wells in column 3 (A03–F03), and so on. 6. Shake the plate for 30 s in the microplate reader prior to measuring fluorescence intensity signals. 7. Monitor fluorescence intensity continuously for 10 min with the microplate reader at 490 nM excitation and 520 nM emission wavelength. 8. Calculate initial velocities of enzyme reaction from the initial, linear portion of each reaction. Subtract the background signals for each substrate concentration. To determine KM and the maximal velocity (Vmax) values, fit the data to Eq. 1, where y is the corrected, initial velocity and x is the concentration of substrate. An example dose–response curve for KM determination is shown in Fig. 2b. y=

V max x KM þ x

ð1Þ

Fluorescence-Based Assays for MMP14

3.2 Determination of Enzyme Concentration

235

The enzyme concentration has to be well optimized to generate the highest possible signal of the initial velocity (linear range) for at least 3 min of initial enzyme reaction time. Thorough research is necessary to find the best enzyme assay buffer components to ensure the highest enzyme activity while remaining stable at room temperature since most of HTS will be performed at room temperature. Various buffer additives (ionic or nonionic detergents, weak or strong reducing agents, ADP or ATP, BSA, and so on) need to be explored to finalize the enzyme assay buffer recipe. 1. Prepare substrate at 2× the final fixed concentration (slightly lower than KM value determined above). The determined KM value of MMP14-specific substrate (5-FAM-TDVPNGFHVSLEK(QXL520)-NH2) with cat-MMP14 is 9.6 μM. 5 μM substrate was used for screening. 2. Prepare a series of 2× the final concentrations of cat-MMP14 enzyme (0, 2.5, 5, 10, 20, 30 nM) in enzyme assay buffer. 3. Dispense 8 μL of 2× the various enzyme concentrations to each of three wells in a 384-well low-volume black plate, e.g., the 2.5 nM stock will be added to three wells in column 12 (A12, B12, C12), the 5 nM stock will be added to three wells in column 12 (A13, B13, C13), and so on. You can use the same 384-well plate (see Fig. 3a highlighted in green color). 4. Initiate enzyme reaction by adding 8 μL of 2× substrate to all wells (A11–A16, B11–B16, C11–C16), and shake the plate for 30 s in the microplate reader. 5. Monitor fluorescence intensity continuously for at least 30 min with the microplate reader at 490 nM excitation and 520 nM emission wavelength. 6. Determine the enzyme concentration that remains in the linear range up to at least 6 min. The enzyme activity curves of five tested concentrations in triplicates are shown in Fig. 3b. (see Note 4).

3.3 Reproducibility and Z′-Factor Determination Prior to HTS

Prior to the HTS campaign, it is very important to determine the signal-to-noise (S/N) ratio to establish a robust protocol that can produce high-quality outcomes. The Z′-factor can reflect the S/N ratio very well, and the acceptable Z′-factor should be at least 0.5 and preferably above 0.7 for enzymatic assay screens. If the Z′-factor is lower than 0.5, further assessment and optimization of the assay is necessary to improve the Z′-factor. A common error causing low Z′-factor is a systematic pipetting error from either multichannel pipettes or liquid handling robots during aspiration and dispense of reagents into the plate. HTS is usually performed in duplicates to validate results are reproducible and also to significantly reduce the number of false positives.

236

Hyun Lee et al.

Fig. 3 Determination of optimal enzyme concentration. (a) Plate layout for enzyme concentration determination. (b) Enzyme activity curves at increasing enzyme concentrations

1. Prepare 6.8 mL of 20 nM (1.33× of final concentration 15 nM) cat-MMP14 enzyme solution in enzyme assay buffer. 2. Prepare 5 mL of 20 μM (4× of final concentration 5 μM) substrate in the same enzyme assay buffer. 3. Dispense 30 μL of 1.33× enzyme solution into each of 192 wells located in columns 1 to 12 of a 384-well standard black plate (positive control wells) using a multichannel pipette (Fig. 4a). Dispense 30 μL of enzyme assay buffer without any enzymes into the remaining 192 wells of the plate (negative control wells). 4. Initiate enzyme reaction by adding 10 μL of 4× substrate to all wells in the plate. 5. Shake the plate for 30 s. 6. Monitor fluorescence intensity continuously for at least four to five time points at 490 nM excitation and 520 nM emission wavelength (see Note 5). 7. Calculate initial velocities of enzyme reaction from the initial, linear portion of each reaction.

Fluorescence-Based Assays for MMP14

237

Fig. 4 Determination of Z’ factor. (a) Plate layout for Z’-factor determination. (b) An example graph of % activities obtained from 192 positive and 192 negative controls. Z’-factor can be obtained from the mean values and standard deviations of positive and negative controls

8. Determine the Z′-factor [18] with Eq. 2 using the mean values (μp, μn) of the positive and negative control wells and their respective standard deviations (σ p, σ n) (Fig. 4b). Z0 - factor = 1 -

3.4 Primary HighThroughput Screening (HTS)

3 σp þ σn j μp - μ n j

ð2Þ

There are many target-specific compound libraries available from various companies, and hence selecting an appropriate compound library is an important step. The most critical aspect of the HTS is deciding the testing compound concentration since this will directly affect the resulting hit rate. Most enzyme-based HTS are performed with compound concentrations between 10 μM and 50 μM. The next important thing to consider is the plate layout because each plate must contain both positive and negative controls. An example of plate layout is shown in Fig. 5a, with 32 wells in columns 1 and 2 for negative controls and 32 wells in columns 23 and 24 for positive controls.

238

Hyun Lee et al.

Fig. 5 Example of an HTS campaign. (a) Plate layout for HTS. Each compound library plate contains 320 compounds. (b) A replicate plot with percent inhibitions (%Inh) of 1200 Prestwick FDA-approved drug library compounds tested against cat-MMP-14 enzyme in duplicates. The %Inh of replicate 1 is plotted against the %Inh of replicate 2

1. Dispense 30 μL of enzyme assay buffer into all negative control wells of columns 1 and 2 of each plate. 2. Dispense 30 μL of 1.33× (20 nM) cat-MMP14 enzyme solution into all wells of columns 3–24 of each plate. Prepare duplicate identical plates for each testing compound plate. 3. Add 0.1 μL (25 μM final concentration) each of testing compounds (10 mM stock in 100% DMSO) from the compound library plate into each of the duplicate assay plates using 0.1 μL pin tool (see Note 6). 4. Incubate enzyme and compound for 10 min to allow them to interact at room temperature. 5. Initiate enzyme reaction by adding 10 μL of 4× (20 μM) substrate solution to all wells of the assay plate. Set up a timer and make sure to have precise time interval before adding the substrate to a next plate so as not to lose detecting initial linear region of enzyme reaction.

Fluorescence-Based Assays for MMP14

239

6. Shake the plates for 30 s at 1600 rpm. 7. Monitor fluorescence intensity continuously for at least four to five time points with the microplate reader at 490 nm excitation and 520 nm emission wavelength. 8. Determine the Z′-factor for each plate using Eq. 2. Calculate the percent enzyme inhibition (%Inh) for each well with the mean values of the 32 positive (μpos) and 32 negative (μneg) control wells from the same plate (Fig. 5a). %Inh = 100  1 -

signal - μneg μpos - μneg

ð3Þ

9. Produce a replicate plot with the %Inh of duplicate wells. This replicate plot will visualize the reproducibility of the screen (Fig. 5b). 3.5 Inhibitory Activity (IC50) Determination

Percent inhibition with a cutoff value of 25–50% depending on how many compounds can be subjected to follow-up assays. Primary hit compounds with higher %Inh cutoff are either cherry-picked from compound libraries or repurchased for validation studies. Determination of IC50 values by dose–response curves is the most common next step to select the most active inhibitors. 1. Prepare 45 nM (3× of final concentration 15 nM) of cat-MMP14 enzyme solution in enzyme assay buffer. 2. Prepare 15 μM (3× of final concentration 5 μM) substrate in the same enzyme assay buffer. 3. Prepare 20 μL each of 50× of a series of compounds (e.g., 0–200 μM at twofold dilution) in 100% DMSO in a DMSOresistant 384-well PCR plate (Fig. 6a top). Dispense 47 μL each of enzyme assay buffer to wells in columns 1–12 in a standard 384-well plate (Fig. 6a middle) and add 3 μL each of 50× compounds using a 12-channel pipette followed by mixing them well. This will produce 3× compound plate (see Note 7). 4. Dispense 8 μL of enzyme assay buffer into all negative control wells (columns 1–2 shown in Fig. 6a bottom) in a low-volume black plate. 5. Dispense 8 μL of 3× cat-MMP14 enzyme solution into all compound testing wells and positive control wells (columns 3–12). 6. Add 8 μL each of 3× testing compounds and incubate enzyme and compound for 10 min to allow them to interact at room temperature.

240

Hyun Lee et al.

Fig. 6 Determination of inhibitory activity (IC50). (a) Plate layouts for four testing compounds in 50×, 3×, and final 1× assay plates as an example. (b) Dose–response curve of GM6001, a known MMP inhibitor. The determined IC50 value of GM6001 is 1.96 ± 0.36 nM. Error bars show standard deviations from triplicate data

Fluorescence-Based Assays for MMP14

241

7. Initiate the enzyme reaction by adding 8 μL of 3× substrate solution into all wells (columns 1–12). 8. Shake the plates for 30 s at 1600 rpm. 9. Monitor fluorescence intensity continuously for at least four to five time points with the microplate reader at 490 nM excitation and 520 nM emission wavelength. 10. Calculate the percent enzyme inhibition (%Inh) using Eq. 3. Fit the data to the Hill equation (Eq. 4) where yi is the % inhibition in the presence of inhibitor, Vmax is the maximum % inhibition, x is the inhibitor concentration, and n is the Hill coefficient. An example of dose–response curve for IC50 determination is shown in Fig. 6b. vi =

4

V max  x n IC 50 n þ x n

ð4Þ

Notes 1. The enzyme activity of the cat-MMP14 should be kept similar throughout the whole screening and validation processes. Freeze–thaw cycles of enzyme can significantly reduce enzyme activity, and hence aliquoting enzyme stock in a small volume at the beginning is very important. 2. The enzyme concentration should be optimized to achieve a linear progress curve for at least 3–5 min at the highest tested substrate concentration in order to determine the accurate KM value. 3. The inner filter effect may interfere with fluorescence intensity signal if the testing substrate concentration is too high and assay volume is large, and hence it is important to keep assay volume below 25 μL and use round- or flat-bottom 384-well low-volume black plate instead of V-shaped bottom plates where substrates can be distributed into the wide and shallow area so as not to get fluorescence signal interference from each other. 4. The enzyme reaction rate can vary a lot from minutes to hours depending on enzymes, and hence determination of an appropriate enzyme concentration is critical prior to a HTS campaign. 5. This step could take 6–15 min per plate depending on how fast your plate reader can read a whole 384-well plate. 6. Make sure to wash the pin tool in DMSO first, blot on filter papers to remove excess DMSO, then wash in ethanol, blot again, and allow to air-dry. Do this after each transfer so as to avoid contamination from a previous compound.

242

Hyun Lee et al.

7. It is very important to prepare 50× of a series of testing compounds using 100% DMSO for IC50 determination because some compounds may have solubility issues in aqueous buffers. A serially diluted 3× compounds will have 6% DMSO in assay buffer, yielding 2% final DMSO concentration for all testing concentrations when mixed with enzyme and substrate at the end. If the substrate stock is also prepared in DMSO, its final DMSO amount should also be taken into account. It is advised to prepare substrate stocks at a very high concentration (5–50 mM) in case DMSO is used so as to avoid too high concentration of DMSO. Most enzymes are fairly stable at 2% DMSO containing assay buffer, and it is not recommended to use over 5% DMSO. References 1. Hwang IK, Park SM, Kim SY et al (2004) A proteomic approach to identify substrates of matrix metalloproteinase-14 in human plasma. Biochim Biophys Acta 1702:79–87 2. Ries C, Egea V, Karow M et al (2007) MMP-2, MT1-MMP, and TIMP-2 are essential for the invasive capacity of human mesenchymal stem cells: differential regulation by inflammatory cytokines. Blood 109:4055–4063 3. Gonzalez-Molina J, Gramolelli S, Liao Z et al (2019) MMP14 in sarcoma: a regulator of tumor microenvironment communication in connective tissues. Cell 8(9):991 4. Holmbeck K, Bianco P, Caterina J et al (1999) MT1-MMP-deficient mice develop dwarfism, osteopenia, arthritis, and connective tissue disease due to inadequate collagen turnover. Cell 99:81–92 5. Devel L, Rogakos V, David A et al (2006) Development of selective inhibitors and substrate of matrix metalloproteinase-12. J Biol Chem 281:11152–11160 6. Gupta SP, Patil VM (2012) Specificity of binding with matrix metalloproteinases. Exp Suppl 103:35–56 7. Schechter I, Berger A (1967) On the size of the active site in proteases. I Papain. Biochem Biophys Res Commun 27:157–162 8. Gimeno A, Beltran-Debon R, Mulero M et al (2020) Understanding the variability of the S1’ pocket to improve matrix metalloproteinase inhibitor selectivity profiles. Drug Discov Today 25:38–57 9. Laronha H, Caldeira J (2020) Structure and function of human matrix metalloproteinases. Cell 9(5):1076

10. Cooney MJ (2017) Kinetic measurements for enzyme immobilization. Methods Mol Biol 504:215–232 11. Han KY, Dugas-Ford J, Lee H et al (2015) MMP14 cleavage of VEGFR1 in the cornea leads to a VEGF-trap antiangiogenic effect. Invest Ophthalmol Vis Sci 56(9):5450–5456 12. Han KY, Chang JH, Lee H et al (2016) Proangiogenic interactions of vascular endothelial MMP14 with VEGF receptor 1 in VEGFAmediated corneal angiogenesis. Invest Ophthalmol Vis Sci 57:3313–3322 13. Hiratsuka S, Minowa O, Kuno J et al (1998) Flt-1 lacking the tyrosine kinase domain is sufficient for normal development and angiogenesis in mice. PNAS 95:9349–9354 14. Gabhann FM, Popel AS (2008) Systems biology of vascular endothelial growth factors. Microcirculation 15:715–738 15. Santamaria S, Nagase H (2018) Measurement of protease activities using fluorogenic substrates. Methods Mol Biol 1731:107–122 16. Copeland RA (2005) Evaluation of enzyme inhibitors in drug discovery. A guide for medicinal chemists and pharmacologists. Methods Biochem Anal 46:1–265 17. Macarron R, Hertzberg RP (2011) Design and implementation of high throughput screening assays. Mol Biotechnol 47(3):270–285 18. Zhang JH, Chung TD, Oldenburg KR (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 4:67–73

Chapter 19 Generation of Protease Inhibitory Antibodies by Functional In Vivo Selection Ki Baek Lee and Xin Ge Abstract Targeting dysregulated protease expression and/or abnormal substrate proteolysis, highly selective inhibition of pathogenic proteases by monoclonal antibodies (mAbs) presents an attractive therapeutic approach for the treatment of diseases including cancer. Herein, we report a functional selection method for protease inhibitory mAbs by periplasmic co-expression of three recombinant proteins—a protease of interest, an antibody Fab library, and a modified β-lactamase TEM-1. We validate this approach by isolation of highly selective and potent mAbs inhibiting human matrix metalloproteinase 9 (MMP9). Key words Inhibitory antibody, Functional selection, Matrix metalloproteinase

1

Introduction As important signaling molecules, proteases precisely control a wide variety of physiological processes both in health and in diseases and thus represent one of the largest families of pharmaceutical targets [1–4]. Despite decades of intensive efforts, conventional drug discovery strategies have only achieved a limited success by targeting a small fraction of all therapeutically relevant proteases. It is because small-molecule inhibitors often lack of specificity and/or appropriate pharmacokinetic properties required for effective and safe protease-based therapy [5]. In these aspects, monoclonal antibodies (mAbs) are emerging as an attractive alternative with significant advantages such as high selectivity, long serum half-life, and clear mechanisms of action [6–8]. Since the invention of hybridoma technology, tremendous progress has been made in mAb discovery and engineering. However, routine discovery of proteaseinhibiting mAbs is still a considerable challenge in general, due to at least two obstacles: (1) the incompatibility of human antibody paratope for protease inhibition and (2) lack of functional highthroughput screening methods. To tackle the first issue, we

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_19, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

243

244

Ki Baek Lee and Xin Ge

previously reported our protocol on construction of camelidinspired convex paratope human antibody libraries [9, 10]. This chapter addresses the second challenge, aiming to develop a functional rather than binding-based selection method for protease inhibitory mAbs [11].

2

Materials

2.1 Plasmid Construction

1. Periplasmic expression plasmid pMopac16 [12] and Fab expression plasmid pHPA-His encoding a His tag at C-terminal of the heavy chain [10]. 2. DNA fragments encoding human MMP9 catalytic domain (cdMMP9) [UniProt P14780, residues 107–216 and 391–443] and β-lactamase TEM-1 [UniProt P62593]. 3. Fab library phagemids carrying long CDR-H3s pFab-pIII [9, 10]. 4. Oligonucleotides encoding peptide sequence of MMP9 substrates (Table 1). 5. Phusion High-Fidelity dNTP mix.

DNA

polymerase,

buffer,

and

6. Restriction enzymes SfiI, NheI, NsiI, SalI, and XbaI with buffers. 7. T4 DNA ligase and buffer. 8. TAE buffer: 40 mM Tris-acetate, 1.0 mM EDTA, pH 8.0. 9. TAE/agarose gel: TAE buffer, 1.0% (w/v) agarose, 1:5000 (v/v) 10% ethidium bromide. 10. DNA gel extraction kit. 11. DNA Clean & Concentration-5 kit. 12. Plasmid DNA miniprep kit.

Table 1 List of oligonucleotides Name

Oligonucleotide sequences

TEM1-1_F28

tgtcgagctagcattcaaatatgtatcc

TEM1-1_R37

aaagccaatgcggccgcttcccgaaccgccagttaat

TEM1-2_F54

aagcggccgcattggctttctgcgcaccgcgagtggtggagaactacttactct

TEM1-2_R31

gtttatatgcatttaatggtgatggtgatgg

Fab_F27

atgcctatgcatccgatatccagatga

Fab_R23

ttcttgtcgaccttggtgttgct

Functional Selection of Protease Inhibitory mAbs

2.2 Determination of Selection Windows

245

1. E. coli BL21[B F- ompT gal dcm lon hsdSB(rB-mB-) [malB+]KS 12(λ )]. 2. E. coli SS320 [F′ proAB + lacIqlacZΔM15 Tn10 (tetr)]. 3. LB/Chlor agar: LB, 15 g/L agar, 34 μg/mL chloramphenicol. 4. SOB/Chlor medium: SOB, 34 μg/mL chloramphenicol. 5. 2×YT/Chlor/IPTG agar: 2×YT, 15 g/L agar, 34 μg/mL chloramphenicol, 0.1 mM IPTG. 6. 100 mg/mL ampicillin stock (filter sterilized). 7. Autoclaved double-distilled water (DDW). 8. 0.2 mm gap electroporation cuvettes. 9. Polystyrene 96-well round-bottom microplates. 10. 150 × 15 mm petri dishes.

2.3 Selection of Protease Inhibitory Antibodies

1. 500 mL culture flasks. 2. 250 mL autoclavable polypropylene centrifuge bottles.

2.3.1 Preparation of Electrocompetent E. coli Cells 2.3.2

Initial Selection

1. pm9TEM-cd9 (described in Subheading 3.1.1). 2. Fab library plasmids pHPK-Fab carrying long CDR-H3s (described in Subheading 3.1.2). 3. 2×YT/Chlor/Amp/Kan/IPTG agar: 2×YT, 15 g/L agar, 34 μg/mL chloramphenicol, 200–500 μg/mL ampicillin (optimization described in Subheading 3.2), 50 μg/mL kanamycin, 0.1 mM IPTG. 4. LB/Kan agar: LB, 15 g/L agar, 50 μg/mL kanamycin. 5. 50 mL conical tubes. 6. 245 mm square bioassay dishes. 7. Electroporation apparatus.

2.3.3 Secondary Screening

1. 2×YT/Chlor/Amp/Kan/IPTG medium: 2×YT, 34 μg/mL chloramphenicol, 400–700 μg/mL ampicillin (optimization described in Subheading 3.2), 50 μg/mL kanamycin, 0.1 mM IPTG. 2. 96-deep well round-bottom plate, sterile, 2 mL. 3. 80% glycerol, autoclaved. 1. pHPK (described in Subheading 3.1.2), 2. Oligonucleotides (Table 1, IDT). 3. Restriction enzymes NsiI and SalI with buffer.

246

Ki Baek Lee and Xin Ge

2.4 Characterizations of Isolated Clones

4. SOB/Chlor/Kan medium: SOB, 34 μg/mL chloramphenicol, 50 μg/mL kanamycin.

2.4.1 Fab Cloning, Expression, and Purification

6. Periplasmic buffer: 200 mM Tris–HCl, pH 7.5, 20% D-sucrose, 750 μg/mL lysozyme.

5. 2×YT/Kan medium: 2×YT, 50 μg/mL kanamycin.

7. 0.45 μm filter units, 90 mm, 500 mL. 8. Ni–NTA resin (Qiagen). 9. Ultrafiltration centrifugal units, 30 kDa MWCO (Millipore). 10. Assay buffer: 50 mM Tris–HCl, pH 7.5, 150 mM NaCl, 5 mM CaCl2, 0.05% Brij-35 (w/v). 11. 12% SDS-PAGE gels.

2.4.2 Binding Affinity Measurements

1. Streptavidin, 1 mg/mL (NEB). 2. Maxisorp 96-well immunoplates (Nunc). 3. Blocking buffer: assay buffer, 2% bovine serum albumin (BSA, w/v). 4. EZ-Link Sulfo-NHS-LC-Biotin kit (Thermo Fisher Scientific). 5. Goat anti-human IgG (Fab-specific) horseradish peroxidase. 6. TMB (3,3′,5,5′-tetramethylbenzidine)-ELISA substrate solution. 7. 2 M sulfuric acid.

2.4.3 Inhibition Potency Measurements

1. Peptide M-2359: Mca-Lys-Pro-Leu-Gly-Leu-Dap(Dnp)-AlaArg-NH2 (Bachem). 2. Black flat-bottom polystyrene NBS 96-well microplate (Corning). 3. 20% dimethyl sulfoxide (DMSO).

3

Methods The following sections describe detailed protocols for E. coli periplasmically genetic selection of protease inhibitory antibodies. The overall procedure involves (1) designing and constructing the protease-reporter plasmid encoding both protease of interest (human MMP9 catalytic domain, cdMMP9, as an example in this chapter) and β-lactamase TEM-1 modified by insertion of a protease-specific cleavable peptide sequence; (2) optimizing selection conditions (e.g., ampicillin concentration) and determining the selection window; (3) transforming an antibody Fab library to E. coli electrocompetent cells carrying the constructed proteasereporter plasmid, selecting on ampicillin agar plates under predetermined conditions, and performing the secondary screening in liquid culture media; and (4) cloning discovered Fabs into expression plasmids and produce Fabs for characterization or use.

Functional Selection of Protease Inhibitory mAbs

247

Fig. 1 Functional selection design for protease inhibitory mAbs. (a) Scheme showing E. coli periplasmic co-expression of three recombinant proteins—a modified β-lactamase TEM-1 carrying a cleavable peptide insert, a protease of interest, and an antibody Fab library. The modified β-lactamase under its native promoter and protease catalytic domain under a lac promoter are cloned into a low copy number (p15A ori) plasmid of chloramphenicol resistance (CmR) and the antibody Fab library under a phoA promoter is cloned into a medium copy number (pBR322 ori) plasmid including kanamycin resistance (KanR). (b) When Fab is non-inhibitory, the protease will cleave modified β-lactamase resulting in cell death in the presence of ampicillin. In comparison, inhibitory Fab blocks proteolytic activity of target protease from cleaving modified β-lactamase, leading to cell survival in the presence of ampicillin. (Reprinted from Ref. 11) 3.1 Plasmid Construction

To select protease inhibitory Fabs, we co-express three recombinant proteins—a protease of interest, a modified β-lactamase as a reporter, and an antibody Fab library—in periplasm of E. coli. Our design is to separate Fab library plasmids from the proteasereporter plasmid (Fig. 1a). More specifically, the protease under a lac promoter and TEM-1 under its native promoter are cloned into a low copy number (p15A ori) plasmid of chloramphenicol resistance (CmR), and the antibody Fab library under a phoA promoter is cloned into a medium copy number (pBR322 ori) plasmid carrying kanamycin resistance (KanR).

3.1.1 Construction of pm9TEM-cd9 and pm9TEM-cd9E402A

Protease-reporter plasmid pm9TEM-cd9 encodes cdMMP9 and β-lactamase TEM-1 modified by insertion of MMP9-cleavable peptide sequence. Its construction is composed of three steps: step 1, clone cdMMP9 to a periplasmic expression plasmid; step 2, insert the cleavable peptide sequence into β-lactamase TEM-1 between G196 and E197 (see Note 1); and step 3, clone the modified TEM-1 to cdMMP9 expression plasmid to obtain the proteasereporter plasmid. 1. Chemically synthesize a double-stranded DNA fragment encoding cdMMP9 with codons optimized for E. coli expression [13]. 2. By PCR amplification, restriction, digestion, and DNA ligation, clone the cdMMP9 fragment into SfiI sites on pMopac16 [14] carrying CmR, pLac promoter, and pelB leader peptide to give cdMMP9 expression plasmid pMopac16-cdMMP9.

248

Ki Baek Lee and Xin Ge

3. Synthesize oligonucleotides (Table 1) encoding the MMP9cleavable peptide sequence (SGRIGFLRTA) flanked with serine–glycine linkers (GSG–peptide–SGG). 4. By overlap extension PCR, assemble modified β-lactamase TEM-1 gene carrying the cleavable peptide insert, named as fragment m9TEM. 5. Clone fragment m9TEM into NsiI/NheI sites on pMopac16cdMMP9 to give cdMMP9 reporter plasmid pm9TEM-cd9. 6. Confirm cloning results by DNA sequencing. Plasmid pm9TEM-cd9E402A, which encodes the m9TEM cassette and cdMMP9 inactive mutant E402A, is constructed similarly, as the negative control for use in Subheading 3.2. 3.1.2 Construction of pHPK-Fab Library Plasmids

1. Following standard molecular biology protocols, clone a KanR fragment into NheI/XbaI sites on pHPA-His [10] to replace AmpR with KanR, named as pHPK. 2. Digest Fab library phagemids pFab-pIII [9, 10] with NsiI/SalI and gel purify the 1.6 kb fragments encoding the Fab library. 3. Clone Fab library fragments into pHPK (kanR, pBR322ori, phoA promoter, and STII leader) with NsiI/SalI restriction sites to give pHPK-Fab library plasmids. 4. Transform constructed pHPK-Fab library into E. coli SS320 electrocompetent cells (see Note 2). 5. Extract pHPK-Fab library plasmids by miniprep (see Note 3).

3.2 Determination of Selection Windows

Beta-lactam ring hydrolysis activities of m9TEM (β-lactamase TEM-1 modified by carrying the MMP9-cleavable peptide sequence) are measured in the presence of co-expressed active or inactive cdMMP9, by culturing on 2×YT/Chlor/IPTG agar plates supplemented with different concentrations of ampicillin. 1. Individually transform pm9TEM-cd9 and pm9TEMcd9E402A into E. coli BL21 electrocompetent cells. Incubate overnight on LB/Chlor agar plates at 37 °C (see Note 4). 2. From fresh LB/Chlor agar plates, inoculate a single colony of BL21 cells harboring pm9TEM-cd9 or pm9TEM-cd9E402A to 3 mL SOB/Chlor medium. Culture at 37 °C for 12 h with shaking at 220 rpm (see Note 5). 3. Measure absorbance at 600 nm. Centrifuge 2 OD600 cells at 15000 rpm at 4 °C for 1 min and completely remove supernatant by pipetting. 4. Add 200 μL of autoclaved DDW and resuspend the cell pellets by gently pipetting.

Functional Selection of Protease Inhibitory mAbs

249

Fig. 2 Survival curves of E. coli cells harboring modified TEM-1 with co-expression of inactive cdMMP9 (black circles) or active cdMMP9 (red triangles). TEM-1 was modified by inserting a cleavage peptide sequence (SGRIGFLRTA) between Gly196 and Glu197 of TEM-1. Cells were grown on 2×YT/Chlor/IPTG agar plates supplemented with 0–800 μg/mL ampicillin at 30 °C for 16 h

5. Make tenfold serial dilutions by transferring 20 μL into 180 μL of autoclaved DDW. Repeat to achieve seven dilutions of the original resuspended cells (see Note 6). 6. Spot 10 μL of serial dilutions on 2×YT/Chlor/IPTG agar supplemented with 0–800 μg/mL ampicillin and culture at 30 °C for 16–20 h (see Note 7). 7. On the next day, determine colony numbers on titration plates and plot survival curves (typical results shown in Fig. 2). 3.3 Selection of Protease Inhibitory Antibodies

1. Transform pm9TEM-cd9 into E. coli BL21 electrocompetent cells. To grow fresh colonies, incubate overnight on LB/Chlor agar plates at 37 °C.

3.3.1 Preparation of Electrocompetent E. coli Cells

2. Pick single colony from a fresh LB/Chlor agar plate and place in 4 mL SOB/Chlor medium. Incubate at 37 °C for 12 h with shaking at 220 rpm. 3. Inoculate the 4 mL seed culture to 200 mL pre-warmed SOB. Incubate at 37 °C with shaking at 220 rpm until OD600 reaches to 0.7 (see Note 8). 4. Incubate the culture flasks on ice for 30 min. The following procedure should be conducted in a cold room or on ice with prechilled solutions and equipment. 5. Centrifuge cultured cells at 4000 rpm at 4 °C for 5 min using two autoclaved 250 mL centrifuge bottles. Decant supernatant and remove residual supernatant by pipetting.

250

Ki Baek Lee and Xin Ge

6. Add 5 mL autoclaved DDW to each centrifuge bottle and slowly rotate the bottles in their standing position at 90 rpm at 4 °C until the entire cell pellets completely resuspended. Add 100 mL autoclaved DDW to each bottle and slowly rotate to mix. Combine resuspended cells to one centrifuge bottle. 7. Centrifuge the suspended cells at 4000 rpm at 4 °C for 5 min. Decant supernatant and remove remaining traces by pipetting. 8. To fully remove culture medium, repeat steps 5–7 thrice with reduced autoclaved DDW usages of 100, 50, and 5 mL in the second, third, and fourth washes. 9. Add 1 mL autoclaved DDW and completely resuspend the cell pellets by slowly rotation at 90 rpm at 4 °C (see Note 9). 3.3.2

Initial Selection

Through the triple periplasmic co-expression (design shown in Fig. 1a), the cell can only survive in ampicillin media if the antibody clone is able to block the activity of cdMMP9 from cleaving modified β-lactamase (Fig. 1b). Non-inhibitory antibody clone results in cell death. This live or die selection facilitates the isolation of antibody clones that specifically inhibit the activity of cdMMP9. 1. Chill ~1 mL of prepared electrocompetent BL21 cells harboring the reporter plasmid pm9TEM-cd9 (described in Subheading 3.3.1), 5 μg of pHPK-Fab library plasmids (described in Subheading 3.1.2), and ten 0.2 mm gap electroporation cuvettes on ice. 2. Combine the electrocompetent cells and the library plasmids, mix by gently pipetting, and incubate on ice for 5 min (see Note 10). 3. Transfer 100 μL cell–DNA mixture to each cuvette and electroporate at 2.5 kV (see Note 11). 4. Promptly rescue electroporated cells by adding 900 μL SOB medium and gently pipetting. Wash the cuvette with 900 μL SOB medium twice. Transfer collected cells (~2.8 mL) to a 50 mL conical tube. 5. Repeat step 4 for the remaining cuvettes, collect all cells (~28 mL), and incubate at 37 °C for 1 h with shaking at 220 rpm. 6. Sample 100 μL of transformed cells for serial dilutions and incubate on LB/Kan agar plate at 37 °C overnight to determine library size (see Note 12). 7. Collect the rest of incubated cells by centrifugation at 4000 rpm at 4 °C for 10 min. Carefully decant supernatant and gently resuspend collected cells with 14 mL autoclaved DDW.

Functional Selection of Protease Inhibitory mAbs

251

Table 2 Conditions and statistics of selection for MMP9 inhibitory antibodies Cleavable peptide on TEM-1

SGRIGFLRTA

Library size

2.3 × 108

Initial selection [Amp] (μg/mL) [IPTG] (mM) Temp (°C) # of clones remaining

500 0.1 30 38

Second screening [Amp] (μg/mL) [IPTG] (mM) Temp (°C) # of clones remaining

700 0.1 30 9

8. Transfer 1 mL of resuspended cells on each 245 mm square dish of 2×YT/Chlor/Amp/Kan/IPTG agar. Fourteen square dishes are used. Spread until moisture fully absorbed. 9. Incubate at 30 °C for 16–24 h. 3.3.3 Secondary Screening

After initial selection, surviving Fab clones are individually screened by culturing in liquid medium under more stringent conditions (higher ampicillin concentrations). 1. Pick all surviving colonies from the square dishes (described in Subheading 3.3.2) using sterile pipette tips. Inoculate them into 600 μL of 2×YT/Chlor/Amp/Kan/IPTG medium in 96-deep well round-bottom plates. These plates serve as the master plates (see Note 13). 2. Incubate at 30 °C for 16 h with shaking at 220 rpm (see Note 14). 3. Transfer 200 μL incubated cells to 96-well round-bottom microplates and measure OD600. Add glycerol to the master plates (10% final concentration) for storage at -80 °C. 4. Determine screened clones (see Note 15). Table 2 shows typical results of inhibitory antibody selection.

3.4 Characterizations of Isolated Clones 3.4.1 Fab Cloning, Expression, and Purification

Fabs of isolated antibody clones are produced for biochemical characterizations. In general, ELISA and FRET inhibition assays are used to measure binding affinity and inhibition potency of isolated antibodies (see Note 16). 1. Inoculate 5 mL SOB/Chlor/Kan with surviving clones from the master plates (described in Subheading 3.3.3). Incubate overnight at 37 °C with shaking at 220 rpm.

252

Ki Baek Lee and Xin Ge

2. Extract mixture of pHPK-Fab and pm9TEM-cd9 plasmids by miniprep. By PCR with Fab_F27 and Fab_R23 (Table 1), amplify Fab fragments and then clone them into NsiI/SalI sites on the pHPK (see Note 17). 3. Transform cloned pHPK-Fab to E. coli BL21 electrocompetent cells. Incubate overnight on LB/Kan agar plates at 37 °C. 4. Extract pHPK-Fab plasmids for VH and VL DNA sequencing. 5. Seed the identified colonies to 5 mL 2×YT/Kan medium. Grow overnight at 37 °C with shaking at 220 rpm. 6. Inoculate 5 mL overnight pre-culture to 500 mL 2×YT/Kan medium. Culture overnight at 30 °C with shaking at 220 rpm. 7. Centrifuge cultured cells at 4000 rpm at 4 °C for 10 min and prepare periplasmic fractions by osmotic shock treatment [13, 14] (see Note 18). 8. By using Ni–NTA resin according to the manufacturer’s instructions, purify Fabs from periplasmic preparations. 9. Concentrate the eluant by centrifugation at 4000 rpm at 4 °C for 20 min, using ultrafiltration units (30 kDa MWCO). 10. Dialyze concentrated Fab samples with assay buffer at 4 °C overnight. 11. On the next day, measure Fab concentrations and confirm the quality of purified Fab by SDS-PAGE (typical results shown in Fig. 3). Add glycerol (20% final concentration) for storage at 80 °C.

Fig. 3 SDS-PAGE of purified Fabs stained with Coomassie blue

Functional Selection of Protease Inhibitory mAbs 3.4.2 Binding Affinity Measurements

253

1. Coat Maxisorp 96-well immunoplates with 60 μL streptavidin solution (5 μg/mL in PBS buffer) overnight at 4 °C. 2. Remove the streptavidin solution and wash with assay buffer twice. Block the wells with 200 μL blocking buffer at room temperature for 2 h with gentle shaking. 3. Remove the blocking buffer and wash with assay buffer twice. Add 50 μL biotinylated cdMMP9 (4 μg/mL in assay buffer) to each of the streptavidin-coated wells and incubate at room temperature for 1 h with gentle shaking (see Note 19). 4. Discard the biotinylated cdMMP14 solution and wash with assay buffer twice. Add 50 μL of twofold serially diluted Fabs (1–1000 nM in blocking buffer) to cdMMP9-immobilized wells and incubate at room temperature for 1 h with gentle shaking. 5. Scrap the twofold serially diluted Fab solution and wash with assay buffer twice. Add 50 μL/well goat anti-human IgG (Fab specific) HRP (1:5000 (v/v) in blocking buffer) and incubate at room temperature for 1 h with gentle shaking. 6. Decant the second antibody solution. Rinse four times with assay buffer and once with assay buffer without Brij-35. 7. Incubate with 50 μL per well of fleshly prepared TMB solution for 5–10 min and then add 25 μL 2 M H2SO4 solution to stop the reaction. 8. Read absorbance at 450 nm using a microplate reader and evaluate EC50 values (typical results shown in Fig. 4a).

3.4.3 Inhibition Potency Measurements

1. Prepare 100 μM substrate M-2350 in 20% DMSO and 1 nM cdMMP9 in assay buffer (see Note 20).

Fig. 4 Characterizations of isolated inhibitory antibodies. (a) Binding affinity toward cdMMP9 measured by ELISA. (b) Inhibition potency toward cdMMP9 measured by FRET assays

254

Ki Baek Lee and Xin Ge

2. Add 50 μL of twofold serially diluted Fabs (2–2000 nM in assay buffer) to a 96-well black assay plate. 3. Add 50 μL of 1 nM cdMMP9 solution into each well to give final concentration of 1–1000 nM Fabs and 0.5 nM cdMMP9. Incubate the mixtures for 30 min to make antibody/antigen complex. 4. Add 1 μL substrate M-2350 stock solutions into wells to start the reaction. 5. Monitor fluorescent signals (RFU) with excitation at 328 nm and emission at 393 nm for 30 min. Determine IC50 and KI values (typical results shown in Fig. 4b) (see Note 21).

4

Notes 1. This position is located at an exposed surface loop between two domain folds and opposite away from the catalytic site and has been used for the construction of cellular sensors [15]. 2. To determine library size, serially dilute small aliquots of transformed cells, inoculate on LB agar supplemented with 50 μg/ mL kanamycin, culture at 37 °C overnight, and count colony numbers on titration plates. 3. To evaluate library quality and diversity, randomly pick dozens of colonies (~20) and extract plasmids for DNA sequencing. 4. BL21 cells harboring pm9TEM-cd9E402A are used as controls. 5. We suggest using fresh transformants, as activity of cdMMP9 can decrease over time. 6. To reduce error, use a new pipette tip for each dilution. 7. In general, ampicillin concentrations of 0, 13, 25, 50, 75, 100, 150, 200, 400, and 800 μg/mL are used to determine selection window. The disparity of survival rates between co-expression of active and inactive cdMMP9 is usually more than 100-fold. Other optimizations can be considered include host cell, expression conditions (medium and IPTG or glucose concentration), co-expression of disulfide bond enzymes such as Dsb proteins [14], cleavable peptide sequences [11], and protease mutant designs [16]. 8. Due to the toxicity of cdMMP9 expression to the E. coli host cells, inoculate 4 mL of grown cells per 200 mL culture medium. It usually takes 2–3 h to reach mid-log phase. 9. To avoid loss of competency and cdMMP9 activity, we recommend using freshly prepared electrocompetent cells for periplasmic genetic selection on the same day.

Functional Selection of Protease Inhibitory mAbs

255

10. To achieve high transformation competency, avoid bubbles when mixing electrocompetent cells with the library plasmids. 11. Roughly, use 28 OD600 cells and 500 ng DNA for each 0.2 mm gap electroporation cuvette. For efficient transformations, the time of electroporation generally reads at 4.9–5.2 ms. 12. Large diversity library size (>108) is needed to isolate nanomolar range protease inhibitory antibodies. If desired library size is not achieved, repeat electrocompetent cell preparation and transformation. 13. For more stringent conditions, ampicillin concentration can be increased up to 200 μg/mL. 14. To prevent evaporation of medium during culture, cover 96-deep well round-bottom plates with aluminum foil. 15. By and large, >OD600 = 0.4 is considered as positive. 16. Other properties of isolated Fabs, such as selectivity, stability, mode of inhibition, and epitope mapping, can also be tested [17, 18]. 17. Mixture of pHPK-Fab and pm9TEM-cd9 plasmids from surviving clones is not ideal for direct use in VH and VL DNA sequencing. It is recommended to clone the Fab fragments into the plasmid pHPK for DNA sequencing and Fab production. 18. Periplasmic fraction is prepared as the following: (1) Add periplasmic buffer at a volume of 20 μL per OD600 and completely resuspend cell pellets by vortex. Incubate for 10 min at room temperature; (2) add ice-cold DDW at a volume of 20 μL per OD600 and incubate on ice for 10 min with gentle mixing; (3) centrifuge at 9000 rpm at 4 °C for 20 min and carefully collect supernatant; and (4) filtrate supernatant through 0.45 μm membrane. 19. Purified cdMMP9 was biotinylated by using EZ-Link SulfoNHS-LC kit (Thermo Fisher Scientific). 20. In accordance with target protease, specific FRET substrates are required to conduct FRET peptide inhibition assays. 21. Initial velocities are calculated to determine IC50 as the concentration that accomplishes 50% inhibition. Inhibition constants (KIs) are obtained using the following equation: KI =

IC50 : 1 þ K½Sm

256

Ki Baek Lee and Xin Ge

Acknowledgments This work was supported by Cancer Therapeutics Training Program Fellowship to K.B.L. (CPRIT RP210043) and NIH R35GM141089 to X.G. References 1. Turk B, Turk D, Turk V (2012) Protease signalling: the cutting edge. EMBO J 31:1630– 1643 2. Deu E, Verdoes M, Bogyo M (2012) New approaches for dissecting protease functions to improve probe development and drug discovery. Nat Struct Mol Biol 19:9–16 3. Lo´pez-Otı´n C, Bond JS (2008) Proteases: multifunctional enzymes in life and disease. J Biol Chem 283:30433–30437 4. Docherty AJ, Crabbe T, O’Connell JP et al (2003) Proteases as drug targets. Biochem Soc Symp 2003:147–161 5. Turk B (2006) Targeting proteases: successes, failures and future prospects. Nat Rev Drug Discov 5:785–799 6. Drag M, Salvesen GS (2010) Emerging principles in protease-based drug discovery. Nat Rev Drug Discov 9:690–701 7. Ganesan R, Eigenbrot C, Kirchhofer D (2010) Structural and mechanistic insight into how antibodies inhibit serine proteases. Biochem J 430:179–189 8. Chavarria-Smith J, Chiu CPC, Jackman JK et al (2022) Dual antibody inhibition of KLK5 and KLK7 for Netherton syndrome and atopic dermatitis. Sci Transl Med 14:eabp9159 9. Nam DH, Rodriguez C, Remacle AG et al (2016) Active-site MMP-selective antibody inhibitors discovered from convex paratope synthetic libraries. Proc Natl Acad Sci U S A 113:14970–14975 10. Nam DH, Ge X (2018) Generation of highly selective MMP antibody inhibitors. Methods Mol Biol (Clifton, NJ) 1731:307–324

11. Lopez T, Mustafa Z, Chen C et al (2019) Functional selection of protease inhibitory antibodies. Proc Natl Acad Sci U S A 116: 16314–16319 12. Hayhurst A, Happe S, Mabry R et al (2003) Isolation and expression of recombinant antibody fragments to the biological warfare pathogen Brucella melitensis. J Immunol Methods 276:185–196 13. Nam DH, Lee KB, Ge X (2018) Functional production of catalytic domains of human MMPs in Escherichia coli periplasm. Methods Mol Biol (Clifton, NJ) 1731:65–72 14. Lee KB, Nam DH, Nuhn JAM et al (2017) Direct expression of active human tissue inhibitors of metalloproteinases by periplasmic secretion in Escherichia coli. Microb Cell Factories 16:73 15. Galarneau A, Primeau M, Trudeau LE et al (2002) Beta-lactamase protein fragment complementation assays as in vivo and in vitro sensors of protein protein interactions. Nat Biotechnol 20:619–622 16. Lee KB, Dunn ZS, Lopez T et al (2020) Generation of highly selective monoclonal antibodies inhibiting a recalcitrant protease using decoy designs. Biotechnol Bioeng 117:3664– 3676 17. Nam DH, Lee KB, Kruchowy E et al (2020) Protease inhibition mechanism of camelid-like synthetic human antibodies. Biochemistry 59: 3802–3812 18. Lee KB, Dunn Z, Ge X (2019) Reducing proteolytic liability of a MMP-14 inhibitory antibody by site-saturation mutagenesis. Protein Sci 28:643–653

Chapter 20 Engineering Selective TIMPs Using a Counter-Selective Screening Strategy Hannaneh Ahmadighadykolaei, Evette S. Radisky, and Maryam Raeeszadeh-Sarmazdeh Abstract The yeast surface display platform provides a powerful approach for screening protein diversity libraries to identify binders with an enhanced affinity toward a binding partner. Here, we describe an adaptation of the approach to identify binders with enhanced specificity toward one among multiple closely related binding partners. Specifically, we describe methods for engineering selective matrix metalloproteinase (MMP) inhibitors via yeast surface display of a tissue inhibitor of metalloproteinase (TIMP) diversity library coupled with a counter-selective screening strategy. This protocol may also be employed for developing selective protein binders or inhibitors toward other targets. Key words Matrix metalloproteinase, Extracellular matrix, Tissue inhibitor of metalloproteinases, Yeast surface display, Fluorescence-activated cell sorting, Directed evolution, Rational design of proteins, Enzyme inhibitors

1

Introduction Engineering selectivity of protein–protein binders and enzyme inhibitors has a myriad of applications in biomedicine and other related biotechnology fields. Protein therapeutics can be engineered to bind more potently and selectively to their targets, to minimize undesired effects caused by nonselective binding to alternative targets. Innovative protein engineering techniques using directed evolution and yeast surface display have been used to screen libraries and isolate more selective protein binders to a specific target [1, 2]. Matrix metalloproteinases (MMPs) are a family of proteases that have long been viewed as potential therapeutic targets since their dysregulation drives pathology in several inflammations [3, 4], and diseases such as neurodegenerative and cardiovascular diseases and cancer, and yet development of selective MMP inhibitors has been challenging due to functional and

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_20, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

257

258

Hannaneh Ahmadighadykolaei et al.

Fig. 1 Yeast surface display of human TIMP1 for detecting expression and MMP binding using flow cytometry. TIMP1 is genetically fused to the N-terminus of Aga2p protein on the yeast surface. TIMP1 expression is detected by labeling c-myc epitope tag, and binding of biotinylated MMP3-catalytic domain (bMMP3cd) is measured by fluorescent-conjugated streptavidin (pink)

structural similarities among the family [5–8]. MMPs are regulated by tissue inhibitors of metalloproteinases (TIMPs), which have wide specificity for the inhibition of many MMPs [7–10]. Thus, TIMPs may offer useful scaffolds for engineering selective MMP inhibitors with potential as protein therapeutics [11–13]. Yeast surface display has previously been used as a directed evolution platform to engineer full-length human TIMP1 and the N-terminal domain of TIMP2 to develop inhibitors of specific MMPs with enhanced affinity and selectivity [5, 14, 15] (Fig. 1). We identified engineered mutations in the N-terminal and C-terminal domains of full-length TIMP1 that work synergistically to raise binding affinity toward the target MMP3 [5, 16]. These TIMP1 variants showed up to tenfold improved binding affinity toward MMP3 compared with wild-type TIMP1 (WT TIMP1), with equilibrium inhibition constant (Ki) values in the low picomolar range [5], and the detailed techniques were previously described [16]. More recently, we have described a counterselective screening strategy using the yeast surface display platform that enabled us to identify selective TIMP1 variants capable of discriminating between two of the most closely related MMPs, MMP3 and MMP10 [6]. Here, we describe in detail our protocol for counter-selective screening of a yeast surface displayed TIMP1 library to identify inhibitors with selectivity toward MMP3 in preference to MMP10. This approach and our protocol may be used for engineering TIMPs with selectivity toward other individual MMPs and might also be adapted for developing selective protein binders of other targets.

Engineering Selective TIMPs

2

259

Materials

2.1 Laboratory Equipment

1. Refrigerated centrifuge to spin 250 mL conical bottles. 2. Refrigerated microcentrifuge. 3. Refrigerated centrifuge to spin 15 mL and 50 mL conical tubes. 4. Tabletop microcentrifuge. 5. Spectrophotometer to measure optical density at 280 nm, 500 nm, and 600 nm. 6. Spectrophotometer. 7. 37 °C shaking incubator at 250 rpm. 8. 37 °C incubator with an orbital shaker at 125 rpm, 8% CO2. 9. 30 °C shaking incubator at 250 rpm. 10. Orbital shaker stationed at 4 °C, or refrigerated shaking incubator at 150 rpm. 11. Swing bucket rotor to hold 250 mL conical bottles. 12. Orbital shaker. 13. Tabletop vortex. 14. Sonicator. 15. Swing bucket rotor to hold 50 mL conical tubes. 16. Microplate reader. 17. Gene pulsar. 18. Bunsen burner and starter. 19. A vacuum system used to aspirate supernatant. 20. Flow cytometer with sorting capability. 21. Water bath set to 37 °C. 22. Synergy 2 plate reader (BioTek). 23. Stir plate stationed at 4 °C and stir bar.

2.2 Laboratory Glassware and Plasticware

1. Glass bottle (1 L). 2. Glass bottle (2 L). 3. Sterile flask (1 L). 4. Sterile flasks (150 mL). 5. Sterile microfuge tube (1.5 mL). 6. Sterile microcentrifuge tubes (1.5 mL). 7. Sterile glass culture tubes (15 mL). 8. Sterile conical tubes (15 mL). 9. Sterile conical tube (50 mL).

260

Hannaneh Ahmadighadykolaei et al.

10. Sterile conical bottles (250 mL). 11. Sterile serological pipette (50 mL). 12. Sterile serological pipette (5 mL). 13. Sterile glass culture tubes. 14. Sterile amber microcentrifuge tubes (1.5 mL). 15. Sterile culture tube to collect sorted cells. 16. Sterile toothpicks. 17. Syringe filter (0.22 μm). 18. Syringe (5 mL). 19. Disposable cuvette. 20. Sterile conical bottles (250 mL). 21. Stir plate and stir bars. 22. Gravity-flow column. 23. 18-gauge, 1-inch beveled needle. 24. SnakeSkin dialysis clips. 25. Slide-A-Lyzer dialysis cassette with 10 K molecular weight cutoff, or SnakeSkin dialysis tubing (Thermo Fisher Scientific). 26. Buoy to hold dialysis cassette or SnakeSkin tubing. 27. Disposable cuvette. 28. 50 mL Amicon ultracentrifuge filter unit or 400 mL Amicon stirred cell with 10 k MWCO membrane. 29. MWCO membrane (EMD Millipore). 30. Zeba spin desalting column (Thermo Scientific). 31. Microplate. 32. Sterile conical tubes (50 mL). 33. Electroporation apparatus such as Gene Pulser (Bio-Rad). 34. Electroporation cuvettes. 35. Pipette controller. 36. Pasteur pipette. 37. 250 mL polycarbonate, disposable, sterile Erlenmeyer flask. 38. Roller bottle (1 L). 2.3 Expression of MMP-cd

1. pET3a-pro-MMP3-cd, pET3a-pro-MMP10-cd vectors in Rosetta2 (DE3) pLysS competent Escherichia coli cells. 2. HiFi DNA assembly kit. 3. Ampicillin (amp) at 1000X stock: Dissolve 100 mg/mL of amp in MilliQ H2O and filter-sterilize. Store stock at -20 °C. 4. Chloramphenicol (cam) at 1000X stock: Dissolve 34 mg/mL of the cam in 100% ethanol and filter-sterilize. Store stock at -20 °C.

Engineering Selective TIMPs

261

5. Luria–Bertani (LB) broth + amp + cam: Add 25 g of dry LB media (10.0 g bacto tryptone, 5.0 g bacto yeast extract, 10.0 g NaCl) to a 2 L glass bottle, and dissolve in 1 L MilliQ H2O. Autoclave the broth, and add 100 μg/mL of amp and 34 μg/mL of cam once cooled to about 45 °C. Store broth at 4 °C. 6. 1 M isopropyl-ß-D-thiogalactopyranoside (IPTG): Dissolve 2.38 g of IPTG in a final volume of 10 mL MilliQ H2O. Filter-sterilize with a 0.22 μm syringe filter. Divide into 1 mL aliquots and store at -20 °C. 2.4 Extraction of Inclusion Body and Solubilization of MMP-cd

1. Expressed, pelleted MMP catalytic domain. 2. MilliQ H2O. 3. Lysozyme. 4. Triton X-100. 5. 10% (w/v) sodium deoxycholate. 6. DNase I. 7. 10 M urea: Dissolve 300.3 g of urea in 600 mL of MilliQ H2O in a 1 L flask and stir vigorously. When the solution turns to clear, add the remaining 300.3 g of urea and add MilliQ H2O to a final volume of 1 L. Avoid heating and autoclaving. Make the solution a day before use. The solution can be stored at room temperature. 8. 1 M Tris–HCl, pH 8.0: Dissolve 121.1 g of Tris–HCl base in a final volume of 1 L MilliQ H2O. For adjusting, use HCl to a final pH of 8.0. Can be stored at 4 °C. 9. 0.5 M ethylenediaminetetraacetic acid (EDTA), pH 8.0: Add 186.1 g of EDTA to 800 mL MilliQ H2O and stir vigorously. Adjust the solution to pH 8.0 (see Note 1). Add MilliQ H2O to a final volume of 1 L. Can be stored at 4 °C. 10. 1 M NaCl: Dissolve 58.44 g of NaCl in MilliQ H2O to a final volume of 1 L. 11. 1 M dithiothreitol (DTT): Dissolve 3.0 g of DTT to a final volume of 20 mL MilliQ H2O. Divide into 1 mL aliquots and store at -20 °C. 12. Lysis buffer: Contains a final concentration of 50 mM Tris– HCl (pH 8.0), 1 mM EDTA, 100 mM NaCl, 0.133 g/mL lysozyme, and 0.49% v/v Triton X-100. Bring to a final volume of 1 L with MilliQ H2O. Adjust to a final pH of 8.0. Store at 4 °C. 13. Inclusion body buffer: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 1 mM EDTA, 100 mM NaCl, 5 mM DTT, 2% v/v Triton X-100, and 0.5 M urea. Add MilliQ H2O to a final volume of 1 L. Adjust to a final pH of 8.0. Store at 4 °C.

262

Hannaneh Ahmadighadykolaei et al.

14. Solubilization buffer: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 50 mM NaCl, 10 mM DTT, and 6 M urea. Add MilliQ H2O to a final volume of 100 mL. Adjust to a final pH of 8.0. Store at 4 °C. 2.5 Purification of MMP-cd

1. Solubilized MMP-catalytic domain. 2. 20% v/v ethanol. 3. Nickel–nitrilotriacetic acid (Ni–NTA) resin. 4. 10 M urea (see Subheading 2.4). 5. 1 M Tris–HCl, pH 8.0 (see Subheading 2.4). 6. 1 M NaCl (see Subheading 2.4). 7. 5 M imidazole: Dissolve 5.1 g of imidazole in 10 mL of MilliQ H2O. 8. HT equilibration buffer: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 50 mM NaCl, and 6 M urea. Add MilliQ H2O to a final volume of 100 mL. Adjust to a final pH of 7.4. Store at 4 °C. 9. HT wash buffer: Contains a final concentration of 20 mM Tris– HCl (pH 8.0), 50 mM NaCl, 6 M urea, and 25 mM imidazole. Add MilliQ H2O to a final volume of 100 mL. Adjust to a final pH of 7.4. Store at 4 °C. 10. HT elution buffer: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 50 mM NaCl, 6 M urea, and 250 mM imidazole. Add MilliQ H2O to a final volume of 100 mL. Adjust to a final pH of 7.4. Store at 4 °C. 11. HT regeneration buffer: Contains a final concentration of 20 mM 2-(N-morpholino) ethanesulfonic acid (MES), and 0.1 M NaCl. Add MilliQ H2O to a final volume of 100 mL. Adjust to a final pH of 5.0. Store at 4 °C.

2.6 Refolding of MMP-cd

1. Purified MMP-catalytic domain. 2. 10 M urea (see Subheading 2.4). 3. 1 M Tris–HCl, pH 8.0 (see Subheading 2.4). 4. 1 M NaCl (see Subheading 2.4). 5. 1 M CaCl2: Dissolve 11.1 g of CaCl2 in MilliQ H2O to a final volume of 100 mL. 6. 1 M ZnCl2: Dissolve 2.0 g of ZnCl2 in MilliQ H2O to a final volume of 15 mL. 7. Dialysis buffer 1: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 150 mM NaCl, 10 mM CaCl2, 1 μM ZnCl2, and 4 M urea. Add MilliQ H2O to a final volume of 1 L. Adjust to a final pH of 7.4. Store at 4 °C.

Engineering Selective TIMPs

263

8. Dialysis buffer 2: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 150 mM NaCl, 10 mM CaCl2, 1 μM ZnCl2, and 2 M urea. Add MilliQ H2O to a final volume of 1 L. Adjust to a final pH of 7.4. Store at 4 °C. 9. Dialysis buffer 3: Contains a final concentration of 20 mM Tris–HCl (pH 8.0), 150 mM NaCl, 10 mM CaCl2, and 1 μM ZnCl2. Add MilliQ H2O to a final volume of 1 L. Adjust to a final pH of 7.4. Store at 4 °C. 2.7 Reconcentration, Activation, and Desalting of MMP-cd

1. Refolded MMP-catalytic domain. 2. Reconcentrated MMP catalytic domain. 3. 4-Aminophenylmercuric acetate (APMA). 4. Solution A: Contains a final concentration of 20 mM Tris–HCl (pH 8.6), 50 μM ZnCl2, and 8 M urea. 5. Solution B: Contains a final concentration of 20 mM Tris–HCl (pH 8.6), 50 μM ZnCl2, 8 M urea, and 0.5 M NaCl. 6. Captiva HiScreen™ Q Sepharose™ and SP Sepharose™ Fast Flow IEX. 7. Activated MMP-catalytic domain. 8. Sterile MilliQ water.

2.8 Biotinylation of MMP-cd

1. EZ-Link NHS-PEG4 biotinylation kit (Thermo Scientific). 2. Zeba spin desalting columns (Thermo Scientific). 3. 4′-Hydroxyazobenzene-2-carboxylic (Thermo Scientific).

acid

(HABA)

assay

4. Phosphate-buffered saline (PBS), pH 7.2: Contains a final concentration of 100 mM sodium phosphate and 150 mM sodium chloride in a final volume of 20 mL. Adjust to a final pH of 7.2. 5. HABA/avidin solution: Dissolve 10 mg of avidin into a solution containing 600 μL 10 mM HABA and 19.4 mL PBS. If a precipitate forms, the solution can be filtered. The solution can be stored for up to 2 weeks at 4 °C. 2.9 Preparation of TIMP1 Library

1. Sterilized MilliQ water. 2. 1 M sorbitol: Dissolve 18.2 g of sorbitol. Add MilliQ H2O to a final volume of 100 mL. 3. Refrigerated centrifuge to spin 50 mL conical tubes. 4. Pellet Paint® Co-Precipitant (EMD Millipore). 5. Library of human TIMP1 gene variants constructed via gene blocks [5, 6] (Fig. 2). 6. pCHA-VRC01 DNA plasmid (Addgene).

264

Hannaneh Ahmadighadykolaei et al.

Fig. 2 (a) WT TIMP1 sequence is shown, with N-terminal domain residues in gold and C-terminal domain residues in green; MMP-interacting regions are annotated with blue arrows and the name of corresponding loops in black. Residues diversified in the targeted library in each interacting loop are shown in red and underlined. (b) Positions of diversified TIMP1 loop regions are shown in red in the context of the MMP3/TIMP1 crystal structure, PDB ID: 1UEA. MMP3 is colored purple and the N- and C-terminal domains of TIMP1 are colored gold and green, respectively. (Figure adapted from Ref. [6])

7. pCHA-TIMP1 DNA plasmid. 8. Restriction enzymes: NheI, BsrGI, and BamHI. 9. Q5® High-Fidelity 2X Master Mix (New England Biolabs). 10. Forward and reverse primers with homologous 15 bp overhangs to the pCHA-VRC01 yeast display vector up- and downstream of NheI and BamHI RE sites, respectively. The forward primer must contain a BsrGI RE site, replacing that of NheI. 11. Forward and reverse primers with homologous 50 bp overhangs to the pCHA-TIMP1 yeast display vector up- and downstream of BsrGI and BamHI RE sites, respectively. 12. 10X CutSmart reaction buffer (New England Biolabs). 13. LB broth + amp: (see Subheading 2.3). Do not add chloramphenicol.

Engineering Selective TIMPs

265

14. LB + amp agar plate: Follow the protocol for LB broth + amp, and add 18 g of agar to the solution before autoclaving. Pour the agar plates under a flame to maintain sterility. Store agar plates at 4 °C. 15. Saccharomyces cerevisiae yeast strain EBY100 (MATa AGA1: GAL1-AGA1:URA3 ura3–52 trp1 leu2-Δ200 his3-Δ200 pep4: HIS3 prb11.6R can1 GAL). 16. YPD agar plates: Dissolve 10 g yeast extract, 20 g peptone, 20 g dextrose, and 15 g agar in MilliQ H2O to a final volume of 1 L. Autoclave media. Once the media has cooled enough to touch (approximately 45 °C), pour the agar into Petri dishes while under a flame to maintain sterility. Allow agar to cool and solidify, and store the YPD agar plates at 4 °C. 17. YPD media: Dissolve 10 g yeast extract, 20 g peptone, and 20 g dextrose in MilliQ H2O to a final volume of 1 L. Autoclave media. Media may appear darker after autoclaving due to dextrose caramelizing—this should not affect the media. Store media at 4 °C. 18. Ampicillin at 1000X stock: (see Subheading 2.3). 19. SD-CAA media, pH 4.5: Dissolve 20 g dextrose, 6.7 g yeast nitrogen base, 5.0 g acid casein peptone, and citrate buffer salts (14.7 g sodium citrate, 4.3 g citric acid monohydrate) in MilliQ H2O to a final volume of 1 L. Adjust to a final pH of 4.5. Filter-sterilize the solution. Store in a sterilized container at 4 °C. 20. SD-CAA agar plate, pH 6.0: Dissolve 20 g dextrose, 6.7 g yeast nitrogen base, and 5.0 g acid casein peptone in MilliQ H2O to a final volume of 100 mL. Mix well and filter-sterilize media. Dissolve phosphate buffer salts (10.2 g sodium phosphate dibasic heptahydrate, 8.6 g sodium phosphate monobasic monohydrate), 15.0 g agar, and 182 g sorbitol in MilliQ H2O to a final volume of 900 mL. Autoclave salt, agar, and sorbitol solution. Once the autoclaved solution has cooled to approximately 45 °C, add the filtered solution and mix vigorously. Pour agar plates under a flame to maintain sterility. Store the agar plates at 4 °C. 2.10 Yeast Surface Display and Cell Growth

1. Bunsen burner and starter. 2. 70% ethanol. 3. Sterile 40% glycerol. 4. Cryovials. 5. Penicillin/streptomycin (Pen/Strep) at 100X stock: Contains final concentrations of 10,000 IU/mL and 10,000 μg/mL, respectively, in a final volume of 100 mL MilliQ H2O. Filtersterilize solution. Divide into 1 mL aliquots and store at -20 °C.

266

Hannaneh Ahmadighadykolaei et al.

6. SD-CAA media, pH 4.5 + Pen/Strep: (see Subheading 2.9). The final concentration of Pen/Strep in SD-CAA media will be 1%. For instance, in 500 mL of SD-CAA media, use 5 mL of pen/step from 100X stock. 7. SG-CAA media, pH 6.0: Dissolve 20 g galactose, 6.7 g yeast nitrogen base, 5.0 g acid casein peptone, and phosphate buffer salts (5.4 g sodium phosphate dibasic, anhydrous, and 8.56 g sodium phosphate monobasic monohydrate) in 1 L MilliQ H2O. Adjust to a final pH of 6.0. Filter-sterilize the solution and store it in the sterilized container at 4 °C. 2.11 Cell Preparation for Flow Cytometry

1. 70% ethanol. 2. 10X PBS, pH 7.4: Dissolve 80 g NaCl, 2 g KCl, 27.2 g Na2HPO4 · 7H2O (dibasic heptahydrate), and 2.4 g KH2PO4 (monobasic anhydrous) in 1 L MilliQ H2O. Adjust to a final pH of 7.4. Filter-sterilize and store at room temperature. 3. Phosphate-buffered saline with albumin (PBSA), 1X PBS + 0.1% bovine serum albumin (BSA), pH 7.4: Add 50 mL of 10X PBS to a graduated cylinder. Add 0.5 g of BSA to the solution and mix well. Bring to a volume of 500 mL with MilliQ H2O. Adjust to a final pH of 7.4. Store the solution at 4 °C.

2.12

Flow Cytometry

1. Ampicillin at 1000X stock: (see Subheading 2.3). 2. SD-CAA media, pH 4.5 (see Subheading 2.9). 3. Primary labels: (a) Mouse anti-c-myc (9e10) (Sigma). (b) Biotinylated MMP3-catalytic domain. 4. Secondary labels: (a) Anti-mouse Alexa Fluor 488 (Thermo Scientific). (b) Streptavidin Alexa Fluor 647 conjugate (4E3D10H2/ E3) (Thermo Scientific).

2.13 DNA Preparation and Evaluation

1. 80% ethanol. 2. Sorted TIMP1 variant library. 3. Qiagen or Promega Miniprep kit. 4. SnapGene, or other DNA analysis software. 5. Zymoprep Yeast Plasmid Miniprep II kit (Zymo Research). 6. YPD media (see Subheading 2.9). 7. SD-CAA plate, pH 6.0 (see Subheading 2.9). 8. Ampicillin at 1000X stock (see Subheading 2.3).

Engineering Selective TIMPs

267

9. LB broth + amp (see Subheading 2.3). Do not add chloramphenicol. 10. LB + amp agar plate (see Subheading 2.9). 11. Chemically competent Escherichia coli for transformation. 12. Super Optimal broth with Catabolite repression (SOC): Often provided with chemically competent cells. To make SOC, add 2.0 g of 2% bacto tryptone, 0.5 g of 0.5% yeast extract, 0.2 mL of 5 M NaCl, 1.0 mL of 1 M MgCl2, 1.0 mL of 1 M MgSO4, and 0.36 g of dextrose to a 200 mL bottle. Bring to a final volume of 100 mL, and autoclave or filter-sterilize. 2.14 Expression of Soluble TIMP1

1. HEK FreeStyle 293-F Cells. 2. HEK FreeStyle 293-F Expression Medium. 3. DPBS. 4. Polyethyleneimine (PEI), 25 kDa.

2.15 TIMP1 Variant Binding and Inhibition

1. Colorimetric thiopeptide substrate Ac-Pro-Leu-Gly-: (2-mer(Enzo Life capto-4-methylpentanoyl)-Leu-Gly-OC2H5 Sciences). 2. Buffer A: 50 mM HEPES (pH 6.0), 10 mM CaCl2, 0.05% Brij35, and 1 mM 5,5′-dithiobis (2-nitrobenzonic acid). 3. Data analysis software. 4. TNCB buffer: 50 mM Tris–HCl (pH 7.5), 100 mM NaCl, 10 mM CaCl2, and 0.05% Brij. 5. Mca-Pro-Leu-Gly-Leu-Dpa-Ala-Arg-NH2 [where Mca is (7-methoxycoumarin-4-yl) acetyl, DPA is N-3-(2,4-dinitrophenyl)-L-2,3-diaminopropionyl] (AnaSpec). 6. Synergy 2 plate reader (BioTek).

3

Methods

3.1 MMP Expression, Purification, Refolding, Solubilization, and Biotinylation 3.1.1 pro-MMP3-cd and pro-MMP10-cd Protein Expression and Extraction

1. Grow the transformed pET3a-pro-MMP3-cd/10-cd cells in LB + amp broth at starting OD600 of 0.05–0.1 in a shaking incubator at 37 °C and 250 rpm. 2. Induce the culture when the optical density at 600 nm (OD600) reaches between 0.4 and 0.5 by 1 mM IPTG and incubate for about 3 h (see Note 2). 3. Centrifuge in 250 mL conical bottles for 5 min at 10,000× g and 4 °C and discard the supernatant. 4. Prepare 10 M fresh urea (see Note 3). 5. Resuspend the pellet in 3 mL of lysis buffer per gram of pellet, by vortexing or pipetting. Incubate overnight at 4 °C at a minimum of 150 rpm in a shaker incubator.

268

Hannaneh Ahmadighadykolaei et al.

6. Add 1.25 mL of 10% (w/v) sodium deoxycholate per liter of culture grown and incubate for 30 min at a minimum of 150 rpm at room temperature in a shaker incubator. Add 10 μL of DNase I per liter of culture grown and continue incubating in same conditions for an additional 30 min. 7. Centrifuge for 15 min at 10,000× g and 4 °C, discard the supernatant, and resuspend the pellet in 100 mL of inclusion body buffer per liter of culture grown. Sonicate in a water/ice bath in a beaker for six cycles of 15 s, output 5, and 50% pulse. Apply 15-s rest periods between cycles for cooling (see Note 4). 8. Centrifuge for 30 min at 16,000 x g and 4 °C, discard the supernatant, and resuspend each pellet in 5 mL Solubilization buffer per liter of culture grown by pipetting. 9. Incubate proteins for at least 30 min on ice to allow solubilization, and then centrifuge the mixture for 30 min at 16,000× g and 4 °C. Keep the supernatant. 3.1.2 pro-MMP3-cd and pro-MMP10-cd Protein Purification and Refolding

1. Equilibrate the Q Sepharose™ Anion exchange resin column Buffer A. 2. Add the soluble protein fraction to the column and collect the flow-through, which should be a clear supernatant. 3. Wash the resin with 15 mL of wash buffer and collect the flowthrough. 4. Elute the proteins in elution buffer (Buffer B) and collect the flow-through (see Note 5). 5. Combine and dilute the elution fraction with elution buffer to a final concentration ranging from 0.3 mg/mL to 0.6 mg/mL (A280). 6. Follow the manufacturer’s protocol and add the diluted protein to the solution to a Slide-a-Lyzer dialysis cassette with a 10 K molecular weight cutoff, Slide-a-Lyzer dialysis flask, or SnakeSkin dialysis tubing. 7. Refold pro-MMP-cd proteins with ten volumes of dialysis buffer by stepwise dialysis, stirring on a magnetic stirrer at 4 ° C, overnight twice. Subsequently, activate the purified proMMP-cd in the presence of the organomercurial compound 4-aminophenyl mercuric acetate (1 mM at 37 °C) overnight (Subheading 3.1.3). 8. Transfer the dialyzed MMP-cd proteins into new sterile tubes. If precipitate forms, centrifuge the sample for 15 min at 17,000× g at 4 °C (see Note 6). 9. Transfer the supernatant (refolded MMP-cd) into new tubes. Keep the samples at -80 °C or continue with reconcentration.

Engineering Selective TIMPs 3.1.3 pro-MMP3-cd and pro-MMP10-cd Protein Reconcentration, Activation, and Desalting

269

1. Reconcentrate the proteins by Amicon ultracentrifuge filter unit or 400 mL Amicon stirred cell with a 10 k MWCO membrane following the manufacturer’s protocol. 2. Transfer 1 mL of concentrated proteins into sterile tubes (see Note 7). Prepare 20 mM APMA stock solution under a fume hood (see Note 8). 3. Add 50 μL of 20 mM APMA per 1 mL aliquot of MMP-cd (with a concentration of 1 mg/mL) and incubate the mixture at 37 °C and 250 rpm in a shaking incubator (see Note 9). Discard APMA in the APMA waste containers. 4. Centrifuge the mixture at maximum speed for 10 min at 4 °C. Transfer supernatant (activated MMP-cd) into a sterile tube and discard the precipitate in the APMA waste container (see Note 8). 5. Recover the Zeba spin column with removing the storage buffer by centrifugation. Wash it with sterile MilliQ H2O, following the manufacturer’s protocol, and transfer the column into a new 15 mL sterile conical tube. 6. Remove the cap and gently add 0.5 mL–2 mL of the sample to the column without disturbing the resin. 7. Collect the sample by centrifugation at 1000× g for 2 min. Repeat centrifugation until all of the activated MMP-cd runs through the column (see Note 10).

3.1.4 MMP3-cd and MMP10-cd Biotinylation

1. Biotinylate MMP3-cd and MMP10-cd by following the EZ-Link NHS-PEG4 biotinylation kit protocol. 2. Add biotin base at a 1:10 (protein/biotin) molar ratio and incubate for 30 min at room temperature. 3. Purify biotinylated MMP3-cd and MMP10-cd with the Zeba spin desalting column, following the manufacturer’s protocol. 4. Test the degree of biotinylation by following the protocol of the kit HABA assay (see Note 11).

3.2 Preparation of TIMP1 Targeted Library 3.2.1 Inserting the TIMP1 Gene with Random Mutations in Interacting Loops into the pCHA Yeast Display Vector

1. Select the amino acids at the interface of the TIMP/MMP interaction loop based on the protein crystal structures (Fig. 2). The crystal structures for TIMP1/MMP3-cd include WT TIMP1/MMP3-cd (PDB ID: 1UEA), TIMP1-L34G (PDB ID: 6MAV), and TIMP1-L34G/L133P/L151C/ G154A (PDB ID: 6N9D). From five interacting loops (AB, C-connector, EF, MTL, and GH) of human TIMP1, select 17 amino acids that are highlighted. 2. Design the gene blocks with selected residues marked for random mutations (NNS degenerate codon incorporation where N = any nucleotide and S = G or C) and order them from gene synthesis companies.

270

Hannaneh Ahmadighadykolaei et al.

3. Amplify the TIMP1 gene targeted library by PCR using the constructed primers that have homologous 50 bp overhangs to the pCHA-TIMP1 yeast display vector up- and downstream of NheI and BamHI RE site using the primers that have homologous 50 bp overhangs to the pCHA-TIMP1 yeast display vector up- and downstream of BsrGI and BamHI RE sites. 4. Digest the TIMP1 gene from the pCHA-TIMP1 vector via BsrGI and BamHI restriction enzymes. 5. Run PCR products of the digested pCHA vector and TIMP1 gene variants on a 1% agarose gel and purify them from the gel by following the kit protocol. 6. Perform PCR and double digest to reach the 5 μg of cut vector and 25 μg of purified PCR product. For each electrotransformation, use 1 μg of cut vector and 5 μg of PCR product. 3.2.2 Electrotransformation of the TIMP1 Targeted Library into EBY100 Cells

1. Use the Pellet Paint® Co-Precipitant to precipitate the pCHAdigested vector. Purify the PCR product. 2. Dissolve the DNA pellet by using the sterile MilliQ H2O to a reach final concentration of 1 μg/μL of cut vector and 5 μg/μL of TIMP1 variant insert for each electroporation cuvette. 3. Centrifuge the cell cultures at 2500 x g for 3 min at 4 °C, aspirate the supernatant, and dissolve the pellet cells in 50 mL ice-cold sterile MilliQ H2O. 4. Repellet the cells by centrifugation, aspirate the supernatant, and repeat with 25 mL of ice-cold sterile MilliQ H2O. 5. Pellet the cells again and resuspend in 2 mL of 1 M ice-cold sterile sorbitol, centrifuge as in step 3, and discard the supernatant. 6. Resuspend the pellet to a final volume of 150 μL in 1 M ice-cold sterile sorbitol. Divide into 50 μL aliquots in sterile 1.5 mL microcentrifuge tubes. 7. Add 1 μL of cut vector and TIMP1 variant amplified gene with 50 bp overhangs to each 50 μL cell suspension and mix by flicking gently. Incubate on ice for 5 min without mixing. 8. Place 50 μL of the sample into a pre-cooled 2 mm electroporation cuvette and keep it on ice. 9. Run the electroporation parameters on Bio-Rad Gene Pulser: Turn on the micropulser, select “pre-set protocol,” chose “fungal” protocol, select “S. cerevisiae,” and select “cerevisiae, 2 mm.” When using the different micropulser for electrotransformation, set the parameters as 1500 V, 25 μF, 200 Ω, and 2 mm cuvette. 10. Load ice-cold cuvette into pre-set Gene Pulser and electroporate.

Engineering Selective TIMPs

271

11. Immediately mix 1 mL of YPD media (pre-warmed to 30 °C) into the cuvette by pipetting. 12. Record the time constant and electroporate the remaining cells. The time constant is normally between 4.9 and 5.1 ms (see Note 12). 13. Place the electroporated samples into a 50 mL conical tube and recover any remaining cells by rinsing each electroporation with 1 mL of YPD media. 14. Incubate the 50 mL conical tube in a shaking incubator at 30 ° C and 250 rpm for 1 h. 15. Pellet the cells by centrifugation at 2500× g for 5 min. Discard the supernatant and resuspend the cells in 10 mL of SD-CAA media (pH 4.5) + Pen/Strep. 16. Prepare several serial dilutions and plates on SD-CAA (pH 6.0) agar plates for estimating the size of the library. 17. Grow plates at 30 °C for 2–3 days. 18. Add 10 mL of resuspended cells into 140 mL of SD-CAA (pH 4.5) + Pen/Strep media in a sterile 250 mL flask and incubate in a shaker incubator at 30 °C and 250 rpm for 24–48 h. 3.3 CounterSelective Screening of TIMP1 Targeted Library for Improving Binding Selectivity

1. Inoculate TIMP1 variant yeast library with 50 mL of SD-CAA (pH 4.5) + Pen/Strep.

3.3.1 TIMP1 Variant Library Passaging and Induction

4. Inoculate the library after the final passage in a 5 mL culture of SG-CAA (pH 6.0) to a final OD600 of 1.

2. Grow the yeast culture at 30 °C and 250 rpm. 3. Inoculate the 5–10 mL of culture in fresh 100 mL SD-CAA (pH 4.5) media + Pen/Strep.

5. Induce yeast cultures by shaking at 30 °C and 250 rpm for 18–22 h (see Note 13). 6. Grow and induce the pCHA-TIMP1 transformants as control.

3.3.2 Yeast Displayed TIMP1 Variant Library Immunolabeling

1. Transfer yeast cells displaying TIMP1 variants (prepared in Subheading 3.3.1) to 1.5 mL microcentrifuge tubes with a final OD600 of 1.5. Label multiple tubes, usually 3–5. 2. Aspirate the supernatant by using a Pasteur pipette vacuum system. 3. Wash the cells by resuspending the cells in ice-cold 0.1% PBSA. Keep the cells on ice. 4. Pellet the cells and wash the supernatant for a total of two washes. 5. Aspirate the supernatant after the final wash. Keep the cells on ice.

272

Hannaneh Ahmadighadykolaei et al.

6. Resuspend the pellet in 1500 μL of PBSA. Do not label one of the tubes with any antibodies or fluorophores. This will serve as a negative control. 3.3.3 Binding of Biotinylated MMP3-cd and Unlabeled MMP10-cd to the TIMP1 Variant Displayed Protein and Strep-AF647 Labeling

1. Keep and prepare all solutions on ice. 2. Prepare stock solutions: 0.1 mg/mL of mouse anti-c-myc, 0.5 mg/mL of biotinylated MMP3-cd, and 0.5 mg/ml of unlabeled MMP10-cd in filtered PBSA buffer. 3. Incubate the cells with biotinylated MMP3-cd/unlabeled MMP10-cd (ratio 1:5) in PBSA on ice for 1 h. 4. Prepare the first immunolabeling solution by making 1:100 dilutions of anti-c-myc 9e10 and streptavidin Alexa Fluor 647 in PBSA. 5. Resuspend the washed pellet in 100 μL of the first immunolabeling solution and incubate on ice for 30 min. Aspirate the supernatant. 6. Wash the cells three times with PBSA, the same way as explained above, and aspirate the supernatant. Place the cells on ice. 7. Incubate the cells with fluorescein-conjugated goat anti-mouse secondary antibody (Alexa Fluor 488) away from light on ice for 30 min. Aspirate the supernatant and pellet the cells. 8. Wash the cells three times with PBSA, as outlined above, and resuspend the cells in 1000 μL of PBSA. 9. Keep the cells on ice and run on a cell sorter for library sorting. 10. For a sequential round of counter-selective FACS, increase the ratio of unlabeled MMP10-cd to biotinylated MMP3-cd.

3.3.4 FACS for CounterSelective Screening of the Library of TIMP1 Variants

1. Install the flow cytometer cell sorter (BD Aria II) with sorting abilities to collect data on 250,00–750,000 cell events per sample, with medium fluidics. 2. Open a scatterplot with FSC-A on the x-axis and SSC-A on the y-axis, a histogram with FITC-A on the x-axis, a histogram with APC-A on the x-axis, and a two-dimensional plot with FITC and APC as the x- and y- axis, accordingly. 3. Run the negative control sample that gently vortexed into the flow cytometer. 4. Draw a polygonal gate for sorting to collect 1–2% of the highest binding to expression (APC/FITC) ratio in the dualpositive region of the dual scatter flow cytometry plot (Fig. 3). 5. Set up the falcon tube and wash from top to bottom with PBSA to discourage the nonspecific binding of yeast cells to the collection tube. The collection area contains 1 mL SD-CAA (pH 4.5) + Pen/Strep.

Engineering Selective TIMPs

273

Fig. 3 (a) Counter-selective strategy for engineering TIMP1 variants with high binding selectivity for MMP3. TIMP1 variants were binding to biotinylated MMP3-cd in the presence of excess competitor ligand, unlabeled MMP10-cd, and the yeast displayed library of TIMP1 variants were screened. The expression of TIMP1 was verified by immunolabeling the c-myc tag using Alexa Fluor 488-conjugated anti-c-myc antibody (green sphere). In the case of MMP3 binding, the Alexa Fluor 647-conjugated streptavidin (purple sphere) was used. The TIMP1 variants which enhanced selectivity for MMP3-cd binding were isolated using FACS. Flow cytometry dual scatter plots of labeled WT TIMP1 and libraries of TIMP1. (b) Flow cytometry two-dimensional scatterplots of TIMP1 expression (y-axis) and MMP-binding (x-axis) indicated for WT TIMP1 (left panel) and the pool of TIMP1 variants selected from the library after five rounds of FACS (right panel). (Panel B adapted from Ref. [6])

6. Collect the double-positive TIMP1 variant population and start the sort. 7. Collect 1 mL or less of sample per collection tube. After collection of 1 mL of sample, place another sterile culture tube with SD-CAA in the collection area (see Note 14). 3.3.5 Recovering of Sorted TIMP1 Variant Library

1. Wash the sides of the collection tubes with an additional 0.5–1 mL SD-CAA (pH 4.5) + Pen/Strep after the sort. 2. Grow the cells in a shaker incubator overnight at 30 °C and 250 rpm.

274

Hannaneh Ahmadighadykolaei et al.

3.3.6 Testing the Sorted TIMP1 Variant Library with High Binding Selectivity for MMP3

1. Passage, induce, label, and run the yeast cells on a flow cytometer following Subheadings 3.3.1, 3.3.2 and 3.3.3 (subject first round screening by using a concentration of 25 nM biotinylated MMP3-cd in the absence of MMP10-cd). 2. Compare sorted population to population before the sort to determine if improved binding has occurred. 3. Conduct additional sorts. In one exemplar experiment, a total of five sorting rounds were completed while incrementally increasing the unlabeled MMP10-cd concentration from 100 to 1000 nM. If additional sorts are required, repeat Subheading 3.3.

3.4 Expression of Soluble WT TIMP1 and TIMP1 Variant Proteins Using HEK FreeStyle 293-F suspension Cells

1. Insert TIMP1 variant genes amplified by PCR from yeast display vector into the pTT-TIMP1 vector digested with HindIII and BamHI restriction enzymes. Follow common HiFi DNA assembly protocol. 2. Confirm the gene assembly using DNA sequencing. 3. Seed cells at 0.5 × 106 cells/mL into a final volume of 300 mL in each 1 L roller bottle (see Note 15). 4. Incubate cells in an orbital shaker incubator at 37 °C, 125 rpm, and 8% CO2 until cells reach a mid-exponential growth phase density of 1.0 × 106 cells/mL and viability of above 90%. Divide the cells approximately every 24 h. 5. Pipette a total of 300 μg of filter-sterilized TIMP1 plasmid into 30 mL of PBS and vortex vigorously for 3 s. 6. Add 0.9 mL of 1 mg/mL filter-sterilized PEI to the PBS/ TIMP1 solution and vortex vigorously for 3 s, or 0.3 mL of 1 mg/mL filter-sterilized PEI for each 100 μg of TIMP1 (see Note 16). 7. Incubate the mix at room temperature for 20 min. 8. Add the TIMP1/PEI mix to the cells, which should be at a density of 1 × 106 cells/mL. 9. Following co-transfection, incubate the cells in an orbital shaker incubator for a further 48 h at 37 °C, 125 rpm, and 8% CO2. 10. Harvest intracellular proteins by centrifuging cells at 3000× g for 5 min and store the pellet at -80 °C.

3.5 Measuring TIMP1 Variant Binding and Inhibition 3.5.1 Determine the Concentration of Active TIMP1 by Titration

1. Preincubate 200 nM of MMP3-cd with a range of sub-stoichiometric concentrations of the TIMP1 variant. 2. Assay MMP/TIMP mixtures by using a colorimetric thiopeptide substrate. 3. Dilute MMP/TIMP mixtures 40-fold into a reaction cuvette containing the thiopeptide substrate at a final concentration of 100 μm in 50 mM of Buffer A.

Engineering Selective TIMPs

275

4. Set the reaction for 10 min at 37 °C on a Varian spectrophotometer. 5. Measure the linear initial rates as an increase in the absorbance at 410 nm (∈410 = 13,600 m-1 cm-1). 6. Fit the data using linear regression analysis and extrapolate to the stoichiometric equivalence point. 3.5.2 Measuring TIMP1 Inhibition

1. Prepare the fluorogenic substrate Mca-Pro-Leu-Gly-Leu-DpaAla-Arg-NH2. 2. Mix 0.04 to 2.5 nM WT TIMP1 or TIMP1 variant and 0.24 nM MMP3-cd or 0.3 nM of MMP10-cd in TCNB buffer. 3. Incubate the mixture for 1 h at 37 °C. 4. Add the substrate that contains Mca-Pro-Leu-Gly-Leu-DpaAla-Arg-NH2 to the TIMP/MMP mixture at a final concentration of 10 μM. 5. Set up the Synergy 2 plate reader and monitor the fluorescence with 340/30 excitation and 400/30 emission filters at 37 °C every min for 120 min. 6. Determine the slope of the linear portion of the fluorescence signal as enzymatic rate. 7. Calculate Ki values by plotting the initial velocities against TIMP concentration and fitting to Morrison’s tight-binding inhibition equation by multiple regression. 1 - ð½E þ ½I þ Kiapp Þ Vt = V0

ð½E þ ½I þ Kiapp Þ2 - 4½E½I 2½E

Kiapp = Ki 1 þ

½S  Km

where Vt = enzyme velocity in the presence of inhibitor, V0 = enzyme velocity in the absence of inhibitor, [E] = enzyme concentration, [I] = inhibitor concentration, [S] = substrate concentration, Km is the Michaelis–Menten constant, and Kiapp is an apparent inhibition constant given by the equation. 8. Fit the data by using Prism 7 (GraphPad Software, Inc.) or use a similar code to calculate the inhibition constants. 9. Report inhibition constants that are average values obtained from two independent experiments, each with duplicate samples. 10. Report errors that reflect the standard deviation between the independent experiments. 11. Calculate Ki using Km values of 11.23 μM, 13.8 μM for MMP10-cd and MMP3-cd (see Eq. 2).

276

4

Hannaneh Ahmadighadykolaei et al.

Notes 1. The EDTA won’t be able to dissolve completely until the solution is at pH 8.0. Even then, it may take a long time to solubilize. Continue to stir vigorously, and leave the solution to stir overnight. 2. Can induct overnight, which may increase protein yields. 3. Be sure to make fresh 10 M urea solution and do not use more than one day’s stock. Before using, dissolve it and stir thoroughly. Avoid heating or autoclaving urea. Keep at room temperature. 4. Repeat sonication in inclusion body buffer so that more protein from lysed cell debris can be recovered, but excessive heat from sonication can harm MMP yield. 5. For analysis by SDS-PAGE, keep 50–100 μL of each sample from each protein purification step. 6. During MMP dialysis, precipitation often occurs, which can be retrieved after completing dialysis against all three buffers. After centrifugation and transferring the supernatant into sterile 1.5 mL microcentrifuge tubes, the precipitate should be dissolved in HT equilibration buffer. The resuspended protein can then be stored at -80 °C or subjected to repeat dialysis using a new cassette or SnakeSkin tubing. 7. It is possible for precipitation to occur following the concentration of MMP3-cd. If you wish to recover the precipitate, please refer to the MMP re-solubilization section for instructions. 8. APMA is a highly toxic chemical and should be handled with care under the hood with appropriate PPE. 9. The APMA incubation time for MMP activation time varies by MMP. It is at least 30 min for MMP10 and 4 h for MMP3. 10. Place the column in the centrifuge for centrifugation steps with the mark facing outward. 11. In order to determine the extent of MMP biotinylation, a HABA calculator can be used. If the biotinylation process was not successful, indicated by less than one biotin per molecule, the protein can be biotinylated again to increase the amount of biotin. 12. Set the time constants between 4.9 and 5.1 ms. Time constants outside of that range can be the result of too much DNA or salt contaminants, which can affect transformation efficiency and library size. Make sure the cell pellet is clear of any noticeable debris and contamination and introduce additional wash steps with MilliQ H2O if necessary.

Engineering Selective TIMPs

277

13. Induced cells can be stored at 4 °C for 2 weeks without significant hindrance of yeast display or loss of cell viability. 14. To recover the yeast cells, add 500 μL of SD-CAA media in the collection tube that is not pelleted. This can also be used when there are issues with yeast attaching to the collection tube or when the number of cells collected is lower. 15. Roller bottles accommodate a minimum of 150 mL to a maximum of 300 mL of suspension culture. For larger-scale transfections, use multiple bottles. 16. The ratio of 0.5 μg DNA:1.5 μg PEI was shown to be optimum for the transient transfection in HEK-293 suspension cells.

Acknowledgments M.R.-S would like to thank the funding support from NIH grants R03AG070511, R21HD109743, and P20GM103650. E.S.R. acknowledges support from NIH grants R01GM132100, R01CA258274, and R01HL157424. The cartoon images in the figures were created with BioRender.com. References 1. Boder ET, Raeeszadeh-Sarmazdeh M, Price JV (2012) Engineering antibodies by yeast display. Arch Biochem Biophys 526:99–106 2. Raeeszadeh-Sarmazdeh M, Boder ET (2022) Yeast surface display: new opportunities for a time-tested protein engineering system. Methods Mol Biol 2491:3–25 3. Stallings-Mann M, Raeeszadeh-Sarmazdeh M, Miller E et al (2020) Adenoviral delivery of matrix metalloproteinase 3 (MMP3)-directed tissue inhibitor of metalloproteinase 1 (TIMP1) variants ameliorates fibrosis established by transforming growth factor beta (TGFB) or bleomycin in mice. Am J Resp Crit Care 201 4. Radisky DC, Bissell MJ (2006) Matrix metalloproteinase-induced genomic instability. Curr Opin Genet Dev 16:45–50 5. Raeeszadeh-Sarmazdeh M, Greene KA, Sankaran B et al (2019) Directed evolution of the metalloproteinase inhibitor TIMP1 reveals that its N- and C-terminal domains cooperate in matrix metalloproteinase recognition. J Biol Chem 294:9476–9488 6. Raeeszadeh-Sarmazdeh M, Coban M, Mahajan S et al (2022) Engineering of tissue inhibitor of metalloproteinases TIMP1 for fine discrimination between closely related stromelysins

MMP3 and MMP10. J Biol Chem 298: 101654 7. Radisky ES, Raeeszadeh-Sarmazdeh M, Radisky DC (2017) Therapeutic potential of matrix metalloproteinase inhibition in breast cancer. J Cell Biochem 118:3531–3548 8. Raeeszadeh-Sarmazdeh M, Do LD, Hritz BG (2020) Metalloproteinases and their inhibitors: potential for the development of new therapeutics. Cell 9:1313 9. Jyotica Batra ESR (2014) Tissue inhibitors of metalloproteinases (TIMPs): inhibition of Zn-dependent metallopeptidases. Encyclopedia of Inorganic and Bioorganic Chemistry 10. Radisky ES, Radisky DC (2010) Matrix metalloproteinase-induced epithelial-mesenchymal transition in breast cancer. J Mammary Gland Biol Neoplasia 15:201–212 11. Lee MH, Rapti M, Murphy G (2003) Unveiling the surface epitopes that render tissue inhibitor of metalloproteinase-1 inactive against membrane type 1-matrix metalloproteinase. J Biol Chem 278:40224–40230 12. Lee MH, Verma V, Maskos K et al (2002) Engineering N-terminal domain of tissue inhibitor of metalloproteinase (TIMP)-3 to be a better inhibitor against tumour necrosis

278

Hannaneh Ahmadighadykolaei et al.

factor-alpha-converting enzyme. Biochem J 364:227–234 13. Murphy G (2011) Tissue inhibitors of metalloproteinases. Genome Biol 12:233 14. Shirian J, Arkadash V, Cohen I et al (2018) Converting a broad matrix metalloproteinase family inhibitor into a specific inhibitor of MMP-9 and MMP-14. FEBS Lett 592:1122– 1134

15. Arkadash V, Yosef G, Shirian J et al (2017) Development of high affinity and high specificity inhibitors of matrix metalloproteinase 14 through computational design and directed evolution. J Biol Chem 292:3481–3495 16. Toumaian MR, Raeeszadeh-Sarmazdeh M (2022) Engineering tissue inhibitors of metalloproteinases using yeast surface display. Methods Mol Biol 2491:361–385

Chapter 21 Engineering New Protease Inhibitors Using α2-Macroglobulin Seandean Lykke Harwood and Jan J. Enghild Abstract Protease inhibitors of the alpha-macroglobulin family (αM) have a unique mechanism that allows them to trap proteases that is dependent not on the protease’s class, but rather on its cleavage specificity. Proteases trigger a conformational change in the αM protein by cleaving within a “bait region,” resulting in the sequestering of the protease inside the αM molecule. This nonspecific inhibitory mechanism appears to have arisen early in the αM family, and the broad protease-trapping capacity that it allows may play a role in pathogen defense. Human α2-macroglobulin (A2M) is a tetrameric αM whose bait region is permissive to cleavage by most proteases, making it a broad-spectrum protease inhibitor. Recent work has demonstrated that the inhibitory capacity of A2M derives directly from its bait region sequence: modifying the bait region sequence to introduce or remove protease cleavage sites will modify A2M’s inhibition of the relevant proteases accordingly. Thus, changing the amino acid sequence of the bait region presents an effective avenue for protein engineering of new protease inhibitors if the substrate specificity of the target protease is known. The design of new A2M-based protease inhibitors with tailored inhibitory capacities has potential applications in basic research and the clinic. In this chapter, we describe the general approach and considerations for the bait region engineering of A2M. Key words α2-Macroglobulin, Bait region, Protease, Proteolysis, Protease inhibition, Mutagenesis in vitro, Protein engineering

1

Introduction Protease inhibitors of the alpha-macroglobulin protein family (αM) utilize a distinct trapping mechanism that was first identified after the study of human α2-macroglobulin (A2M) and has since been generalized to other members of the family, including A2M from other species, pregnancy zone protein (PZP), rat a1I3, and A2M-like protein 1 (A2ML1) [1–4]. As formulated by Alan Barrett in the trap hypothesis, protease inhibition is initiated when proteases cleave at a specific, preferentially accessible site known as the bait region [5]. Bait region cleavage causes a conformational

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_21, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

279

280

Seandean Lykke Harwood and Jan J. Enghild

change in A2M, resulting in an irreversible interaction between A2M and the protease. In the A2M–protease complex, the protease is unable to cleave protein-sized substrates but can still cleave smallmolecule-sized substrates. Thus, αM inhibition is not actual protease inhibition targeting the active site, but rather a sequestering of the protease that sterically prevents it from accessing larger substrates. Furthermore, the proteolytically induced conformational change of A2M reveals a binding site for the scavenger receptor LRP1, resulting in rapid clearance of A2M–protease complexes from circulation [6]. A common feature of αM proteins is a thiol ester group which spontaneously forms in their CGEQ motif (residues 972–975 in A2M) and allows them to covalently conjugate to certain targets. The native αM protein adopts a conformation where the thiol ester is protected within a hydrophobic environment, shielding it from water and other nucleophiles. During the cleavage-induced conformational change, the thiol ester becomes accessible and can be attacked by nucleophiles, leading to the formation of stable covalent bonds if attacked by primary amines and in some cases hydroxyl groups [7]. In A2M, A2ML1, and other αM protease inhibitors, the activated thiol ester is oriented toward the internal space occupied by the protease, and the major function of the thiol ester is to conjugate the trapped protease [8, 9]. However, in tetrameric αM protease inhibitors like A2M, covalent trapping is not required for the αM to retain the protease, and inhibition of many proteases is largely unaffected if covalent trapping is prevented, e.g., through the addition of a surplus of a competing nucleophile [10]. In contrast, monomeric αMs like rat a1I3 and A2ML1 can only covalently trap a protease, likely because they are unable to completely encompass a protease with a single subunit [3, 9]. The most important factor determining if a protease is inhibited by A2M is its ability to cleave the A2M bait region. The A2M bait region is a 39-residue-long sequence (positions 690–728) containing 15 of the 20 canonical amino acid residues (all except asparagine, cysteine, isoleucine, lysine, and tryptophan), is flexible and solvent-exposed, and contains little to no secondary structure [11]. This facilitates proteolysis by most proteases with simple substrate specificities, for example, where substrate recognition is primarily P1- or P1′-based. A2M also contains at least two matrix metalloproteinase (MMP) cleavage motifs (PEG’L and HAR’L) [12] and is an excellent substrate for several MMPs [13]. Interestingly, the A2M bait region is poorly conserved during evolution, with very little sequence similarity, even just within mammals. This suggests that the bait region sequence is not itself critical to A2M’s physiological function and that the bait region sequence may be adapted to the proteases requiring regulation by the animal in question during evolution.

Engineering New A2M Protease Inhibitors

281

In vitro experiments have shown that human A2M can inhibit diverse proteases with varying functions. For example, A2M inhibits human proteases involved in coagulation, inflammation, digestion, and extracellular matrix remodeling [14–16], but also pathogen-derived proteases such as Porphyromonas gingivalis gingipain R, Staphylococcus aureus GluC (a.k.a. V8), and HIV protease 1 [17–19]. A2M can even inhibit proteases from rattlesnake venom [20]. In fact, its extremely broad inhibitory repertoire makes it difficult to pinpoint the physiological role of A2M, as A2M can plausibly be involved in many physiological processes. Further complicating matters, A2M has other putative functions besides protease inhibition, including cytokine binding and chaperonelike binding to damaged proteins [21, 22]. No diseases are known to involve a deficiency of A2M and A2M knockout mice are viable and healthy [23, 24], suggesting that A2M is nonessential to development and survival but otherwise not elucidating the physiological role(s) of A2M. Although the A2M bait region appears to display its amino acid sequence in a way that makes it an excellent substrate, there is no evidence that the bait region permits cleavage beyond what would be expected from the substrate specificity of a protease. Thus, the proteases that can cleave the bait region and be inhibited by A2M can be predicted based on the amino acid sequence of the bait region and the substrate specificity of the protease. This is in contrast with the reactive center loop (RCL) of serpins, which in some cases permits cleavage beyond the classical substrate recognition of a protease (e.g., the cleavage of α1 protease inhibitor by trypsin with Met382 in the P1 position), and where serpin specificity cannot easily be predicted from the RCL sequence [25]. It follows that the inhibitory capacity of A2M can be modified by changing the bait region sequence, and as early as the 1990s, this was demonstrated by introducing cleavage sites for tobacco etch virus protease, furin, and LysC into the A2M bait region [26–28]. More recently, our group has investigated the effects of completely replacing the A2M bait region with a new sequence. We replaced the 39-residue wild-type bait region with 13 glycineglycine-serine triplets, producing a tabula rasa bait region with the same length as wild-type [29]. The resulting tabula rasa A2M assembled into a tetramer and assumed a native conformation as usual but could not be cleaved by a panel of tested proteases, including trypsin, LysC, plasmin, and a variety of MMPs and ADAMTS proteases. Using the tabula rasa bait region as a minimalistic starting point, additional bait regions were designed that incorporated a single protease cleavage site, e.g., for trypsin or for MMPs. Ultimately, we produced a tabula rasa sequence that conveyed MMP2 inhibition that was just as efficient as the wild-type bait region sequence, but more selective as it was not cleaved by non-MMPs. However, achieving this equivalent inhibition

282

Seandean Lykke Harwood and Jan J. Enghild

required optimizing both the bait region length and the position of the MMP cleavage site within the bait region. Cytonics Corporation has also developed A2M bait region variants that replace most or all of the bait region with a new sequence, some of which reportedly inhibit proteases involved in osteoarthritis with at least the same efficiency as wild-type A2M [30]. The design of new, highly specific protease inhibitors through bait region engineering of A2M could enable new proteasetargeting therapies which overcome the limitations of smallmolecule protease inhibitors, whose lack of success in clinical trials is generally attributed to their broad protease inhibition (i.e., lack of specificity) [31–33]. However, the specificity of A2M-based protease inhibitors is limited by the degree to which proteases can be distinguished based on their substrate specificities. For example, we used several sequences intended to target MMP2 for our study, several of which were derived from phage display. However, we found that all the sequences included in our study were cleaved by all tested MMPs (MMP1, MMP2, MMP3, MMP8, and MMP13), which included collagenases, a stromelysin, and a gelatinase [29]. This highlights the difficulty of distinguishing MMPs based on their cleavage of unstructured substrates, even when the MMPs have distinct preferences toward structured substrates. Similar difficulties can be imagined with other proteases; for example, urokinase plasminogen activator (uPA) and membrane-type serine protease 1 (MT-SP1) have both been proposed as therapeutic targets in cancer [34, 35], but both possess trypsin-like substrate specificity that would be difficult to distinguish from other arginine-specific proteases such as many proteases of the coagulation and fibrinolytic systems. In a follow-up to our initial tabula rasa A2M study, we investigated the introduction of disulfides into the tabula rasa bait region [36]. We found that disulfides spontaneously form between cysteines with seven or eight residues between them. If cleavage sites were included in the loops between the disulfide-participating cysteines, cleavage at these sites did not cause a conformational change in A2M until after the disulfides were reduced. These results helped to elucidate the triggering mechanism whereby bait region cleavage is sensed by A2M and leads to the conformational change alongside structural characterization of A2M and other αM proteins. It appears that the C-terminal end of the bait region threads through a hydrophobic channel in the center of each A2M subunit. Upon cleavage of the bait region, the C-terminal end retracts through the channel, allowing the channel to collapse. In addition to the mechanistic insights, the study provides new principles and proof of concept for further engineering of the bait region: residues can be included in the bait region within disulfide loops without risking that their recognition and cleavage could lead to A2M conformational change, which could allow the inclusion of longer

Engineering New A2M Protease Inhibitors

283

substrate sequences while maintaining specificity. The compatibility of A2M with disulfide loops within its bait region may also suggest that more complex structures might be included in future bait regions, for example, fragments of substrate proteins, which might be helpful to increase their protease selectivity.

2 2.1

Materials A2M Expression

1. Plasmid encoding A2M gene under the control of a CMV promoter or similar, such as pcDNA3.1(+). Plasmid DNA must be sterile, e.g., isopropanol-precipitated and resuspended in sterile water. 2. 25 kDa linear polyethylenimine (Polysciences), resuspended in sterile water to a concentration of 1 g/L, and filtered with a 0.22 μm pore size filter. 3. FreeStyle™ 293-F Cells (Thermo Fisher Scientific). 4. FreeStyle™ Scientific).

293

Expression

Medium

(Thermo

Fisher

5. Penicillin–streptomycin 10,000 U/mL. 6. Baffled plastic Erlenmeyer flasks for cell culture. 7. Incubator with orbital shaker, 37 °C, 8% CO2. 2.2

A2M Purification

¨ KTA or equivalent). 1. FPLC system (A 2. HiTrap Chelating HP column, 5 mL (Cytiva or equivalent). 3. 0.1 M sodium acetate, 0.8 M sodium chloride, pH 8 (pH adjusted with NaOH), filtered with 0.45 μm pore size filters. 4. 0.1 M sodium acetate, 0.15 M EDTA, pH 7.4 (pH adjusted with NaOH), filtered with 0.45 μm pore size filters. 5. 3 g/L ZnCl2, filtered with 0.45 μm pore size filters. 6. Water, filtered with 0.45 μm pore size filters. 7. HiTrap Q HP column, 5 mL (Cytiva or equivalent). 8. 0.05 M HEPES, pH 7.4 (pH adjusted with NaOH), filtered with 0.45 μm pore size filters. 9. 0.05 M HEPES, 1 M sodium chloride, pH 7.4 (pH adjusted with NaOH), filtered with 0.45 μm pore size filters. 10. 30 kDa MWCO spin filters (e.g., Amicon Ultra centrifugal filters). 11. HiPrep™ 16/60 Sephacryl® S-300 HR (Merck). 12. 0.05 M HEPES, 0.15 M sodium chloride, pH 7.4 (pH adjusted with NaOH), filtered with 0.45 μm pore size filters.

284

Seandean Lykke Harwood and Jan J. Enghild

2.3 Native PoreLimited PAGE

1. TBE buffer: 89 mM Tris–HCl, 89 mM boric acid, 2 mM EDTA. 2. Acrylamide/bis-acrylamide, 40% solution. 3. 10% weight/weight ammonium persulfate in water. 4. N,N,N,N-Tetramethylethylenediamine (TEMED). 5. Electrophoretic power supply. 6. A small peristaltic pump.

3

Methods

3.1 Designing A2M with Engineered Bait Regions

To design a new A2M-based protease inhibitor capable of inhibiting a target protease, a substrate sequence cleavable by the target protease must be known. The MEROPS peptidase database (https://www.ebi.ac.uk/merops/) is a helpful resource for investigating known protease specificities [37], as described in Chap. 1. Methods for determining unknown specificities include N-terminomics and phage display [38, 39]. We recommend our previously described 32-residue-long tabula rasa bait region [29] as a starting point for bait region engineering for two reasons: (i) the glycine and serine residues that comprise this bait region are not recognized by many proteases, thus minimizing undesired cleavage, and (ii) the 32-residue length improves the proportion of functional A2M that is recombinantly expressed, compared to a tabula rasa bait region with the wild-type length (39 residues). In theory, the substrate sequence can replace any part of the bait region. However, the position of the substrate sequence within the bait region can affect the inhibitory efficiency of the resulting A2M protein. For example, MMP2inhibiting tabula rasa A2M was as effective as wild-type A2M if the MMP2 cleavage site was inserted at position 703 (i.e., residue 14 in the bait region), whereas it had only 40% of the wild-type effectiveness if the cleavage site was at position 710 (residue 21 in the bait region). The optimal position is likely to depend on the target protease and may have to be determined by screening multiple positions (see Notes 1–4). The wild-type bait region is compared with some examples of tabula rasa bait regions in Fig. 1. Alternatively, substrate sequences can be placed into the wildtype bait region, although it should be noted that the resulting A2M proteins will likely retain most of wild-type A2M’s broad inhibitory capacity and will not be specific to the target protease. There is a tendency for proteases to cleave within the middle of the wild-type bait region sequence (positions Leu703-Leu720 in A2M, residues 14–31 in the bait region) [40], and this may be an advantageous location for substrate placement. As with tabula rasa bait regions, substrate location may require optimization for a given protease.

Engineering New A2M Protease Inhibitors

285

Fig. 1 Wild-type and tabula rasa bait region sequences. The wild-type and tabula rasa (TR) bait region sequences are given. The 39-aa and 32-aa TR sequences are shown, as well as a 32-aa TR sequence incorporating the S1 cleavage site for MMPs. All known MMP recognition motifs are highlighted with orange, and the P1′ residue is in bold font

3.2 Cloning of Vectors for Mammalian A2M Expression

We have synthesized a gene for recombinant A2M expression based on the mRNA sequence M11311 [41]. The gene has been inserted into the pCDNA3.1(+) plasmid using the NheI/XbaI restriction sites. Furthermore, silent mutations were used to introduce HindIII and EcoRI restriction sites flanking the bait region. This plasmid has been made freely available at GenScript with the name pCDNA_A2M_w_NotI_wo_BamHI_XhoI from order U8496FC200 (plasmid sequence available upon request) and can be purchased as it is or used as a starting point for cloning services performed by GenScript. Our recommended approach is the synthesis of nucleotides encoding the desired bait region sequence using human codon preferences and cloning of the newly synthesized bait region gene into the A2M plasmid using either HindIII/ EcoRI cloning or GenScript’s EZ-cloning service.

3.3 Expression of A2M in HEK293 FreeStyle Cells

A2M can be recombinantly expressed with a good yield (typically 4–6 mg/L conditioned medium) in HEK293 FreeStyle cells using a standard transient transfection protocol. HEK293 FreeStyle cells should be cultured according to the manufacturer’s recommendations. All materials used for cell culture must be kept sterile, and all cell culture methods should be carried out in a sterile environment (e.g., a flow bench). 1. Prior to transfection, grow enough HEK203 FreeStyle cells so that 1 × 106 cells per mL of desired transfection volume are available. On the day of transfection, split the cells in order to achieve this concentration (1 × 106 cells per mL of cell culture, i.e., 1 × 109 cells for every liter of transfected cell culture to be prepared). 2. Immediately prior to transfection, mix DNA and PEI. A total of 30 mL of FreeStyle™ 293 Expression Medium per liter of transfection volume is used for mixing. Add 1 mg of plasmid DNA per liter of transfection volume to the 30 mL of FreeStyle™ 293 Expression Medium and then mix.

286

Seandean Lykke Harwood and Jan J. Enghild

Fig. 2 The workflow for purification of recombinant A2M. (a) A schematic overview of the purification steps. (b) A normal chromatogram for the anion exchange chromatographic step using a HiTrap Q column. The absorbance at 280 nM (black) and the NaCl concentration (red) as determined from the conductivity are shown. A2M elutes as a broad peak centered around ≈65 mL or 220 mM NaCl, and all A2M-containing fractions are pooled for further purification. In this example, fractions would be pooled from 50 to 80 mL. (c) Size exclusion chromatography on a Sephacryl S-300 HR column. Tetrameric A2M elutes as a broad peak centered around 48.5 mL. However, octamer A2M eluting in the void volume and dimer A2M eluting around 55 mL may overlap with the tetramer A2M peak. Therefore, fractions must be evaluated by native PAGE and pooled carefully

3. Add PEI to the DNA-containing medium to a 4:1 weight/ weight PEI/DNA ratio, i.e., add 4 mg PEI/L of transfection volume. Incubate the PEI/DNA mixture for 10 min at room temperature (see Note 5). 4. Carefully drip the PEI/DNA mixture into the HEK293 FreeStyle cell culture (1 × 106 cells/mL) while gently swirling the cell culture. 5. Culture the transfected cells for 5 days in an incubator at 37 °C, 8% CO2, while being shaken on an orbital shaker (i.e., standard HEK293 FreeStyle culturing conditions) (see Note 6). 6. After 5 days of culturing, harvest the conditioned medium by centrifugation at 800 × g. Store the supernatant on ice until purification. Immediately before purification, filter the supernatant with a 0.45 μm pore size filter. 3.4 Purification of A2M 3.5 Zn2+ Immobilized Metal Affinity Chromatography (IMAC)

This protocol for the purification of recombinant A2M is a modified version of a protocol for purifying A2M from plasma [42, 43]. An overview of the workflow is given in Fig. 2a. 1. Install a 5 mL HiTrap Chelating HP column onto an FPLC system; all FPLC steps with this column are performed with a flow rate of 5 mL/min. 2. Wash the column with 10 mL of water, load it with 25 mL of 3 g/L ZnCl2, and then equilibrate it with 10 mL of 0.1 M sodium acetate, 0.8 M sodium chloride, pH 8. 3. Load the 0.45 μm-filtered conditioned medium onto the column. Up to 1 L of conditioned medium can be loaded per run. 4. Wash the column with 0.1 M sodium acetate, 0.8 M sodium chloride, pH 8, until the absorbance at 280 nM returns to baseline.

Engineering New A2M Protease Inhibitors

287

5. Elute A2M with 15 mL of 0.1 M sodium acetate, 0.15 M EDTA, pH 7.4, and collect all the eluate. 6. Equilibrate the column with water and then 20% ethanol for storage. 7. Dialyze the eluate against 0.05 M HEPES, pH 7.4, for at least 2 h. Dialysis can be performed overnight, in which case it should be at 4 °C. Filter the dialyzed eluate with a 0.45 μm pore size filter before further use. 3.6 Anion Exchange Chromatography

1. Install a 5 mL HiTrap Q HP column onto an FPLC system; all FPLC steps with this column are performed with a flow rate of 3 mL/min. 2. Wash the column with 20 mL of 0.05 M HEPES, 1 M sodium chloride, pH 7.4, and then with 20 mL of 0.05 M HEPES, pH 7.4. 3. Load the dialyzed and filtered eluate from Zn2+ IMAC onto the column. 4. Equilibrate the column with 15 mL of 0.05 M HEPES, pH 7.4. 5. Run a linear gradient between 0.05 M HEPES, pH 7.4, and 0 to 40% of 0.05 M HEPES, 1 M sodium chloride, over 40 min (i.e., an increase of 10 mM sodium chloride per minute). Collect fractions of 3 mL. 6. A2M will elute as a broad (≈30 mL) peak centered around 22% 0.05 M HEPES, 1 M sodium chloride (i.e., eluting around a concentration of 220 mM sodium chloride). All A2M-containing fractions are pooled (see Note 7). An example chromatogram is given in Fig. 2b. 7. Wash the column with 20 mL of 20 mL of 0.05 M HEPES, 1 M sodium chloride, pH 7.4, and then equilibrate it with water and then 20% ethanol for storage.

3.7 Size Exclusion Chromatography (SEC)

1. Concentrate the pooled A2M-containing fractions from Subheading 3.6, step 4 using 30 kDa MWCO spin filters to a volume of 2–5 mL. 2. Install a HiPrep™ 16/60 Sephacryl® S-300 HR column onto an FPLC system and equilibrate it with 0.05 M HEPES, 0.15 M sodium chloride, pH 7.4, at a flow rate of 0.5 mL/min. 3. Inject the pooled and concentrated A2M-containing anion exchange fractions onto the column and run 120 mL of 0.05 M HEPES, 0.15 M sodium chloride, pH 7.4, over the column. Collect fractions of 1 mL. Tetrameric A2M will elute as a broad (≈20 mL) peak centered around an elution volume of 48.5 mL. An example chromatogram is given in Fig. 2c.

288

Seandean Lykke Harwood and Jan J. Enghild

4. Equilibrate the column with water and then 20% ethanol for storage. 5. A2M-containing fractions can be identified by reducing SDSPAGE. Furthermore, the tetramer A2M peak centered around 48.5 mL may overlap with A2M tetramer–dimers or aggregates eluting in the void volume or A2M dimers eluting with a peak centered around 55 mL. Pore-limited native PAGE can be used to evaluate these fractions and ensure that primarily tetrameric A2M is pooled. 3.8 Characterization of New A2M Variants

Reducing SDS-PAGE and native pore-limited PAGE on control, methylamine-treated, and protease-cleaved samples can provide a great deal of information about a new engineered A2M variant. They allow a quick assessment of the A2M stock quality and the ability of a protease to cleave within the bait region. Protease inhibition assays can then be used to quantitatively determine the ability of a given A2M variant to inhibit a target protease.

3.9 PAGE-Based A2M Characterization

1. Approximately 25 μg of each A2M protein to be characterized is required, at a concentration of at least 0.25 g/L. We recommend the inclusion of wild-type A2M (either recombinantly expressed or purified from human plasma) for comparison with engineered A2M variants. 2. Each A2M protein is preferably analyzed by reducing SDSPAGE and native pore-limited PAGE either as control, after methylamine reaction of the thiol ester, or after protease cleavage. For each of these samples, use 7 μg of A2M. Prepare a master mix of each protein with a protein concentration of 0.25 g/L and distribute 28 μL for each sample (control, MA, and protease). 3. For the control sample, add 7 μL of buffer (for a final volume of 35 μL). For the MA sample, add 7 μL of 1.25 M methylamine, pH 8 (to a final methylamine concentration of 0.25 M), and incubate the sample at 37 °C for at least 45 min. 4. For the protease-cleaved sample, reaction conditions will depend on the protease of choice. A2M typically reacts with proteases with a stoichiometry between a 1:1 and 2:1 molar ratio of protease/A2M. However, the activity of a protease stock will not be 100% (e.g., commercial bovine pancreatic trypsin is typically approximately 70% active), requiring additional protease to be added. An experiment including titration of the protease to determine the necessary amount for complete A2M cleavage may be necessary. Add 7 μL of the protease at a concentration giving the desired molar ratio of protease/ A2M should be added. An example of the calculation of the protease concentration is given in Table 1 for thermolysin.

Engineering New A2M Protease Inhibitors

289

Table 1 Calculation of the protease concentration for thermolysin μg of A2M

7 6.40 × 10

5

1.09 × 10

-11

2.4

MW of A2M (g/mol) Moles of A2M Target ratio of thermolysin/A2M

2.63 × 10

-11

3.43 × 10

4

Moles of thermolysin MW of thermolysin/g/mol)

0.900

μg of thermolysin

7

μL to add

0.129

g/L thermolysin

Typically, cleavage of A2M by the protease will be completed within 5 min of incubation at 37 °C. The protease should be inhibited after this reaction time using an active site inhibitor (e.g., phenylmethylsulfonyl fluoride for some serine proteases, 1,10-phenanthroline for some metalloproteases, iodoacetamide for some cysteine proteases), especially before SDSPAGE sample preparation (as excessive A2M cleavage may occur under denaturing conditions). 5. Analyze 20 μL of each sample (containing 4 μg of A2M) by reducing SDS-PAGE. Commercial SDS-PAGE systems can be used. High-resolution SDS-PAGE can also be achieved using the discontinuous 2-amino-2-methyl-1,3-propanediol and glycine buffer system with homemade 5–15% acrylamide gradient gels [44], although these gels are not commercially available. The SDS-PAGE samples should be heated to 95 °C for 5 min to allow for autolytic thiol ester-mediated fragmentation, which allows direct confirmation of thiol ester formation. 6. Analyze each sample (12.5 μL containing 2.5 μg of A2M) by pore-limited native PAGE [45]. This method uses TBE buffer gels with a 5–15% acrylamide gradient, which must be cast in-house (see Note 8). 7. In some cases, there may be enough A2M that is not in its native, functional conformation in the purified A2M stock to be an issue. This non-native A2M can arise both from bait region cleavage of A2M by proteases originating from the HEK293F cells and due to a perturbation of the native A2M formation due to changes in the bait region sequences. Native and non-native A2M are not well-resolved by anion exchange or size exclusion chromatography. Instead, we have used a depletion method based on a fragment of the LRP1 receptor,

290

Seandean Lykke Harwood and Jan J. Enghild

which only binds to non-native A2M, to remove non-native A2M from a sample [29]. Native and non-native A2M are wellresolved by hydrophobic interaction chromatography, e.g., on a HiTrap Phenyl HP column, but a purification procedure has not yet been established. The evaluation of SDS-PAGE and native PAGE analysis of A2M can be complex, considering the potential for thiol estermediated autolysis, protease conjugation by the thiol ester, and in some cases cleavage of A2M outside of the bait region. We have published several studies including recombinant A2M variants that use these characterization methods and recommend them to introduce the interpretation of these types of data [29, 36, 46–48]. We also include an example of characterizing wild-type plasma-purified A2M in Fig. 3. In addition to gel-based characterization, a quantitative determination of the inhibitory efficiency of an A2M toward

Fig. 3 Analysis of A2M by reducing SDS-PAGE and native pore-limited PAGE. (a) Reducing SDS-PAGE analysis of A2M, either untreated, after 45 min of treatment with 250 mM methylamine (MA), or after cleave with thermolysin at a 2.2:1 molar ratio of protease/A2M. A2M migrates as a 180 kDa intact band, but due to autolysis of its thiol ester, autolytic fragments at 120 and 60 kDa are also seen. Cleavage in its bait region produces the C- and N-terminal cleavage products (which can be distinguished using this discontinuous 2-amino-2-methyl-1,3-propanediol and glycine buffer system with homemade 5–15% acrylamide gradient gels) [44], as well as thiol ester-mediated conjugation products involving the protease and one or more A2M subunits. (b) The same A2M samples as in panel A are analyzed by native pore-limited PAGE. Here, the different conformations of A2M show distinct migrations. Native A2M migrates the least distance into the gel, whereas A2M that has been collapsed by methylamine or bait region cleavage migrates further. Note that MA treatment is slow and may be incomplete after 45 min at 250 mM, pH 8, as seen here. Furthermore, excessive protease cleavage of A2M may also result in the removal of one or more of its MG8 domains, which further increases its migration distance as seen here for a small amount of cleaved A2M

Engineering New A2M Protease Inhibitors

291

its target protease can be made by incubating a constant amount of protease with a titration of A2M, and then adding a reporter substrate afterward. A reporter substrate must be found which is cleaved by the target protease; we suggest trying DQ gelatin (Invitrogen) or resorufin-labeled casein (Merck), if a good substrate for the target protease is not already established in the literature.

4

Notes 1. The differences in catalytic mechanism between the protease classes can be leveraged to design substrate sequences that distinguish between the classes. For example, many serine proteases are unable to cleave with a proline residue in the P1′ position, whereas this does not affect many cysteine proteases, and a P1′ proline residue may thus be used to possibly restrict serine proteases from cleaving a substrate. Some metalloproteases cleave at the N-terminal end of the recognized substrate sequence and would be similarly unaffected by a P1′ proline. 2. A2M inhibits up to two protease molecules per A2M tetramer. However, not all proteases are inhibited at this 2:1 ratio; for example, plasmin is only inhibited at an approximately 1:1 ratio. The protease size and cleavage speed may possibly determine the stoichiometry of its inhibition by A2M. 3. The thiol ester is not required for inhibition of some proteases and can be replaced by a disulfide [47]. This might be useful if the bait region-engineered A2M will be immobilized and used for affinity chromatography of a particular protease where covalent binding of the protease is undesirable. However, we have unpublished data suggesting that some proteases may only be inhibited by A2M through covalent conjugation; therefore, the thiol ester replacement must be tested using the target protease. 4. Incorporation of a lysine into the bait region can result in this lysine becoming conjugated by A2M’s thiol ester after bait region cleavage [29]. The consequences of this “auto-conjugation” on A2M’s inhibitory ability have not been investigated, but it can be assumed to compete with the protease for thiol ester-mediated covalent conjugation. As mentioned above, A2M does not require conjugation to inhibit some proteases, but this may depend on the protease in question. 5. Lengthening or shortening the PEI/DNA incubation time may lower transfection efficiency. The PEI/DNA ratio may require optimization, depending on the vector. The given 4:1 ratio is appropriate for the pcDNA3.1(+)-based vectors described in this protocol.

292

Seandean Lykke Harwood and Jan J. Enghild

6. Incubation time can be shortened (to a minimum of 3 days), although this will lower the yield. 7. Reducing SDS-PAGE can be used to evaluate which fractions contain A2M. 8. Note that 5–10% gels can also be used and improve the separation of native and protease-cleaved A2M conformations. Run the pore-limited PAGE gels at 100 V for 16 h. TBE buffer is used as the running buffer and may require recirculation (e.g., with a peristaltic pump) if a two-chambered gel system is used due to the long running time.

Acknowledgement This study was supported by the VELUX FONDEN (00014557), the Danish Council for Independent Research-Medical Science (DFF-4004-00471), the LEO Foundation, and the Novo Nordisk Foundation (BIO-MS) (NNF18OC0032724). References 1. Barrett AJ, Brown MA, Sayers CA (1979) The electrophoretically ‘slow’ and ‘fast’ forms of the alpha 2-macroglobulin molecule. Biochem J 181:401–418 2. Christensen U, Simonsen M, Harrit N et al (1989) Pregnancy zone protein, a proteinasebinding macroglobulin. Interactions with proteinases and methylamine. Biochemistry 28: 9324–9331 3. Enghild JJ, Salvesen G, Thogersen IB et al (1989) Proteinase binding and inhibition by the monomeric alpha-macroglobulin rat alpha 1-inhibitor-3. J Biol Chem 264:11428–11435 4. Galliano MF, Toulza E, Gallinaro H et al (2006) A novel protease inhibitor of the alpha2-macroglobulin family expressed in the human epidermis. J Biol Chem 281:5780– 5789 5. Barrett AJ, Starkey PM (1973) The interaction of alpha 2-macroglobulin with proteinases. Characteristics and specificity of the reaction, and a hypothesis concerning its molecular mechanism. Biochem J 133:709–724 6. Imber MJ, Pizzo SV (1981) Clearance and binding of two electrophoretic “fast” forms of human alpha 2-macroglobulin. J Biol Chem 256:8134–8139 7. Sottrup-Jensen L, Petersen TE, Magnusson S (1980) A thiol-ester in alpha 2-macroglobulin cleaved during proteinase complex formation. FEBS Lett 121:275–279

8. Marrero A, Duquerroy S, Trapani S et al (2012) The crystal structure of human alpha2-macroglobulin reveals a unique molecular cage. Angew Chem Int Ed Engl 51:3340– 3344 9. Nielsen NS, Zarantonello A, Harwood SL et al (2022) Cryo-EM structures of human A2ML1 elucidate the protease-inhibitory mechanism of the A2M family. Nat Commun 13:3033 10. Salvesen GS, Sayers CA, Barrett AJ (1981) Further characterization of the covalent linking reaction of alpha 2-macroglobulin. Biochem J 195:453–461 11. Gettins P, Cunningham LW (1986) Identification of 1H resonances from the bait region of human alpha 2-macroglobulin and effects of proteases and methylamine. Biochemistry 25: 5011–5017 12. Arbelaez LF, Bergmann U, Tuuttila A et al (1997) Interaction of matrix metalloproteinases-2 and -9 with pregnancy zone protein and alpha2-macroglobulin. Arch Biochem Biophys 347:62–68 13. Enghild JJ, Salvesen G, Brew K et al (1989) Interaction of human rheumatoid synovial collagenase (matrix metalloproteinase 1) and stromelysin (matrix metalloproteinase 3) with human alpha 2-macroglobulin and chicken ovostatin. Binding kinetics and identification of matrix metalloproteinase cleavage sites. J Biol Chem 264:8779–8785

Engineering New A2M Protease Inhibitors 14. Ellis V, Scully M, MacGregor I et al (1982) Inhibition of human factor Xa by various plasma protease inhibitors. Biochim Biophys Acta 701:24–31 15. Virca GD, Travis J (1984) Kinetics of association of human proteinases with human alpha 2-macroglobulin. J Biol Chem 259:8870– 8874 16. Raymond WW, Su S, Makarova A et al (2009) Alpha 2-macroglobulin capture allows detection of mast cell chymase in serum and creates a reservoir of angiotensin II-generating activity. J Immunol 182:5770–5777 17. Gron H, Pike R, Potempa J et al (1997) The potential role of alpha 2-macroglobulin in the control of cysteine proteinases (gingipains) from Porphyromonas gingivalis. J Periodontal Res 32:61–68 18. Hall PK, Nelles LP, Travis J et al (1981) Proteolytic cleavage sites on alpha 2-macroglobulin resulting in proteinase binding are different for trypsin and Staphylococcus aureus V-8 proteinase. Biochem Biophys Res Commun 100:8–16 19. Meier UC, Billich A, Mann K et al (1991) alpha 2-Macroglobulin is cleaved by HIV-1 protease in the bait region but not in the C-terminal inter-domain region. Biol Chem Hoppe Seyler 372:1051–1056 20. Baramova EN, Shannon JD, Bjarnason JB et al (1990) Interaction of hemorrhagic metalloproteinases with human alpha 2-macroglobulin. Biochemistry 29:1069–1074 21. Wyatt AR, Kumita JR, Mifsud RW et al (2014) Hypochlorite-induced structural modifications enhance the chaperone activity of human alpha2-macroglobulin. Proc Natl Acad Sci U S A 111:E2081–E2090 22. O’Connor-McCourt MD, Wakefield LM (1987) Latent transforming growth factorbeta in serum. A specific complex with alpha 2-macroglobulin. J Biol Chem 262:14090– 14099 23. Umans L, Serneels L, Overbergh L et al (1995) Targeted inactivation of the mouse alpha 2-macroglobulin gene. J Biol Chem 270: 19778–19785 24. Umans L, Serneels L, Overbergh L et al (1999) alpha2-macroglobulin- and murinoglobulin-1deficient mice. A mouse model for acute pancreatitis. Am J Pathol 155:983–993 25. Marijanovic EM, Fodor J, Riley BT et al (2019) Reactive Centre loop dynamics and serpin specificity. Sci Rep 9:3870 26. Van Rompaey L, Proost P, Van den Berghe H et al (1995) Design of a new protease inhibitor by the manipulation of the bait region of alpha

293

2-macroglobulin: inhibition of the tobacco etch virus protease by mutant alpha 2-macroglobulin. Biochem J 312(Pt 1): 191–195 27. Van Rompaey L, Ayoubi T, Van De Ven W et al (1997) Inhibition of intracellular proteolytic processing of soluble proproteins by an engineered alpha 2-macroglobulin containing a furin recognition sequence in the bait region. Biochem J 326(Pt 2):507–514 28. Ikai A, Ookata K, Shimizu M et al (1999) A recombinant bait region mutant of human alpha2-macroglobulin exhibiting an altered proteinase-inhibiting spectrum. Cytotechnology 31:53–60 29. Harwood SL, Nielsen NS, Diep K et al (2021) Development of selective protease inhibitors via engineering of the bait region of human alpha2-macroglobulin. J Biol Chem 297: 100879 30. Zhang Y, Wei X, Browning S et al (2017) Targeted designed variants of alpha-2-macroglobulin (A2M) attenuate cartilage degeneration in a rat model of osteoarthritis induced by anterior cruciate ligament transection. Arthritis Res Ther 19:175 31. Overall CM, Kleifeld O (2006) Tumour microenvironment - opinion: validating matrix metalloproteinases as drug targets and antitargets for cancer therapy. Nat Rev Cancer 6: 227–239 32. Coussens LM, Fingleton B, Matrisian LM (2002) Matrix metalloproteinase inhibitors and cancer: trials and tribulations. Science 295:2387–2392 33. Laronha H, Carpinteiro I, Portugal J et al (2020) Challenges in matrix metalloproteinases inhibition. Biomolecules 10:717 34. Uhland K (2006) Matriptase and its putative role in cancer. Cell Mol Life Sci 63:2968–2978 35. Ulisse S, Baldini E, Sorrenti S et al (2009) The urokinase plasminogen activator system: a target for anti-cancer therapy. Curr Cancer Drug Targets 9:32–71 36. Harwood SL, Diep K, Nielsen NS et al (2022) The conformational change of the protease inhibitor alpha2-macroglobulin is triggered by the retraction of the cleaved bait region from a central channel. J Biol Chem 298:102230 37. Rawlings ND, Barrett AJ, Thomas PD et al (2018) The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res 46: D624–D632

294

Seandean Lykke Harwood and Jan J. Enghild

38. Luo SY, Araya LE, Julien O (2019) Protease substrate identification using N-terminomics. ACS Chem Biol 14:2361–2371 39. Chen EI, Kridel SJ, Howard EW et al (2002) A unique substrate recognition profile for matrix metalloproteinase-2. J Biol Chem 277:4485– 4491 40. Sottrup-Jensen L, Sand O, Kristensen L et al (1989) The alpha-macroglobulin bait region. Sequence diversity and localization of cleavage sites for proteinases in five mammalian alphamacroglobulins. J Biol Chem 264:15781– 15789 41. Kan CC, Solomon E, Belt KT et al (1985) Nucleotide sequence of cDNA encoding human alpha 2-macroglobulin and assignment of the chromosomal locus. Proc Natl Acad Sci U S A 82:2282–2286 42. Harpel PC (1970) Human plasma alpha 2-macroglobulin. An inhibitor of plasma kallikrein. J Exp Med 132:329–352 43. Salvesen G, Enghild JJ (1993) Alphamacroglobulins: detection and characterization. Methods Enzymol 223:121–141

44. Bury A (1981) Analysis of protein and peptide mixtures: evaluation of three sodium dodecyl sulphate-polyacrylamide gel electrophoresis buffer systems. J Chromatogr A 213:491–500 45. Manwell C (1977) A simplified electrophoretic system for determining molecular weights of proteins. Biochem J 165:487–495 46. Harwood SL, Nielsen NS, Jensen KT et al (2020) alpha2-Macroglobulin-like protein 1 can conjugate and inhibit proteases through their hydroxyl groups, because of an enhanced reactivity of its thiol ester. J Biol Chem 295: 16732–16742 47. Harwood SL, Nielsen NS, Pedersen H et al (2020) Substituting the thiol Ester of human A2M or C3 with a Disulfide produces native proteins with altered proteolysis-induced conformational changes. Biochemistry 59:4799– 4809 48. Harwood SL, Lyngso J, Zarantonello A et al (2021) Structural investigations of human A2M identify a hollow native conformation that underlies its distinctive protease-trapping mechanism. Mol Cell Proteomics 20:100090

Chapter 22 Design of Bioengineered Peptides/Proteases as Anti-cancer Reagents with Integrated Omics and Machine Learning Approaches Weimin Zuo and Hang Fai Kwok Abstract Cancer is a heterogeneous disorder of uncontrolled growth of cells, which has proven to be a major burden worldwide. Many treatment options are available for cancer therapy, yet side effects and drug resistance remain major hurdles. Therefore, it is necessary to develop novel drugs for cancer therapy. Anti-cancer peptides (ACPs) are attractive candidates with remarkable potency, low toxicity, and high specificity advantages. However, traditional experimental identification of ACPs is time-consuming and expensive. Integrated omics combined with machine learning (ML) is considered a new powerful and cost-effective strategy to discover ACPs from natural products. In this chapter, we describe in detail experimental procedures for collecting both transcriptomic and proteomic data from venoms, followed by descriptive approaches to ML prediction. Key words Cancer, Anti-cancer peptide, Integrated omics, Machine learning

1

Introduction Cancer is a heterogeneous disorder of uncontrolled growth of cells and contains the potential of spreading, which has proven to be a significant burden for health and the economy worldwide [1– 3]. Many treatment options are available for cancer therapy, including surgery, radiotherapy, chemotherapy, immunotherapy, stem cell therapies, hormone-based therapy, and anti-angiogenic modalities [2]. However, side effects and drug resistance remain significant hurdles in cancer therapy [4]. Therefore, it is continually necessary to develop novel drugs for cancer therapy. With remarkable potency, low toxicity, and high specificity, peptide therapy has emerged as a promising treatment for multiple diseases, among which anti-cancer peptides (ACPs) are attractive for their cancerselective toxicity [5–8]. However, traditional experimental identifi-

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6_22, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

295

296

Weimin Zuo and Hang Fai Kwok

Fig. 1 Schematic representations of anti-cancer peptide discovery strategies. Combined analysis of omics, especially transcriptomics and proteomics, produces numerous peptide sequences, which have proven to provide a more economical identification of peptides than traditional experimental identification methods. Machine learning (ML) of artificial intelligence (AI) is an efficient and cost-effective method to discover bioactive peptides such as anti-cancer peptides (ACP). After obtaining millions of peptides’ sequences via analysis of omics, using ML to predict ACPs can yield peptides with higher anti-cancer probability after few rounds of selection

cation of ACPs is time-consuming and expensive. Therefore, developing convenient and economic methods for ACP identification is essential. Recently, integrated omics combined with machine learning (ML) has been considered a new powerful strategy to discover ACPs from natural products, particularly from venoms [5]. The combined analysis of omics, especially transcriptomics and proteomics, produces numerous peptide sequences, which have proven to be an economical source for peptides [9, 10]. As a subset of artificial intelligence (AI), ML predicts bioactive peptides via models after training of desired datasets [11]. Integrating omics with ML has proven to be an efficient and cost-effective method to identify potential ACP candidates (see Fig. 1) [5]. Here, we describe the experimental procedures of gathering transcriptomics data and proteomics data for ACPs from venom, followed by approaches of ML prediction.

Design of Peptides as Anti-cancer Reagents

2

297

Materials

2.1 Acquisition of Venom

1. Depending on animal species, most venoms are commercially available.

2.2 Total RNA Isolation

1. RNeasy mini kit (Qiagen). 2. 14.3 M 2-mercaptoethanol (2-ME). 3. 96–100% ethanol. 4. 70% ethanol.

2.3 Total RNA Quantification and Purity Assessment

1. RNase-free water. 2. Agilent RNA 6000 Pico kit along with: RNA Pico Chips, Electrode Cleaners, RNA 6000 Pico Dye Concentrate, RNA 6000 Pico Marker, RNA 6000 Pico Conditioning Solution, RNA 6000 Pico Gel Matrix, RNA 6000 Pico Ladder, Spin Filters, Tubes for Gel-Dye Mix, Safe-Lock Eppendorf Tubes PCR clean (DNase/RNase free) for gel-dye mix and syringe. 3. Agilent 2100 Bioanalyzer. 4. Vortex mixer.

2.4 TruSeq Stranded mRNA Library Preparation

1. TruSeq Stranded mRNA Library Prep Kit ALP (Adapter Ligation Plate), ATL (A-Tailing Mix), BBB (Bead Binding Buffer), BWB (Bead Washing Buffer), CAP (Clean Up ALP Plate), CDP (cDNA Plate), CTA (A-Tailing Control), CTE (End Repair Control), CTL (Ligation Control), DCT (Diluted Cluster Template), ELB (Elution Buffer), FPF (Fragment, Prime, Finish Mix), FSA (First Strand Synthesis Act D Mix), LIG (Ligation Mix), PCR (Polymerase Chain Reaction) plate, PDP (Pooled Dilution Plate), PMM (PCR Master Mix), PPC (PCR Primer Cocktail), RBP (RNA Bead Plate), RFP (RNA Fragmentation Plate), RPB (RNA Purification Beads), RSB (Resuspension Buffer), SMM (Second Strand Master Mix), STL (Stop Ligation Buffer) and TSP (Target Sample Plate), AMPure XP beads. 2. SuperScript II Reverse Transcriptase. 3. 10 mM Tris-HCl, pH 8.5. 4. Tween 20. 5. 80% ethanol. 6. Agencourt AMPure XP 60 ml kit. 7. Standard Sensitivity NGS Fragment Analysis Kit. 8. Advanced Analytical Fragment Analyzer.

298

Weimin Zuo and Hang Fai Kwok

2.5 mRNA Sequencing

1. Flow cell. 2. Illumina MiSeq platform. 3. FastQC program. 4. Software Trinity -v2.15.0 Nov 30, 2022 (https://github.com/ trinityrnaseq/trinityrnaseq/releases).

2.6 Protein Extraction, Purification, and Quantification

1. Protein extraction buffer: 10 mM DTT, 2% (w/v) SDS, 1% (w/v) insoluble polyvinylpolypyrrolidone, 0.2 M triethylammonium bicarbonate (TEAB), pH 8.5, protease and phosphatase inhibitors. 2. Trichloro acetic acid (TCA) solution: 10% TCA and 10 mM Dithiothreitol (DTT) in acetone. 3. Solubilization buffer: 8 M urea, 10 mM DTT, 0.1 M TEAB, pH 8.5.

2.7 Protein Alkylation and Digestion

1. Indole-3-Acetic Acid (IAA) solution: 100 mM IAA in 0.1 M TEAB, pH 8.5.

2.8 Liquid Chromatography (LC)Mass Spectrometry (MS)/MS and Protein De Novo Sequencing

1. Buffer A: LC-MS water, 2% ACN, and 0.1% formic acid.

2. Digestion buffer: 0.04 μg/μL trypsin, 0.1 M TEAB, pH 8.5, and 1.0% trifluoroacetic acid (TFA).

2. Buffer B: ACN, 2% LC-MS water, and 0.1% formic performed acid. 3. Q Exactive Orbitrap Mass Spectrometers. 4. NanoLC system. 5. Software PEAKS Studio Xpro (https://www.bioinfor.com/ peaks-studio/).

2.9 Comparison Between Transcriptome and Proteome

1. Software PEAKS Studio Xpro (https://www.bioinfor.com/ peaks-studio/).

2.10 Peptide Identification and Selection via Machine Learning

1. Anti-cancer peptide predicting web browser, such as [12], https://app.cbbio.online/acpep/home [13], https://antican cer.pythonanywhere.com/ [14], http://server.malab.cn/ ACPred-FL [15], http://codes.bio/acpred/ [16], http:// bliulab.net/PreTP-EL [17], http://118.178.58.31:9801/ [18].

2.11 Additional Materials and Equipment

1. Sterile, RNAse-free pipettes with compatible tips. 2. Disposable gloves. 3. Microcentrifuge Nanodrop Spectrophotometer.

Design of Peptides as Anti-cancer Reagents

4. Q ExactiveTM Spectrometer.

Hybrid

Quadrupole-Orbitrap

299

Mass

5. nanoLC system.

3

Methods All the procedures are conducted at room temperature unless stated specifically.

3.1 Acquisition of Venom

Venoms from different species could be purchased from animal venom suppliers. For example, in collecting secretion/venom from frogs, the animals were captured from various locations in their natural habitat and confirmed their identification by the biologist. After secretion harvesting, the frogs were released back into the wild. The skin secretions were obtained by mild transdermal electrical stimulation (2–5 V) or gently rubbing the frog’s back. The skin secretions were then washed off from the frog’s skin with deionised water, collected in a cold beaker, frozen using liquid nitrogen, and lyophilized in a freezer dryer to become stable venom powders, which would be stored at -20 °C before use.

3.2 Total RNA Isolation

Isolation RNA with good quality is of great importance to obtain premium RNA sequencing data. Several RNA isolation kits are available on the market, among which RNeasy Mini kit (Qiagen) is outstanding and widely used for high-quality RNA extraction. 1. Before the experiment, add 10 μL 2-ME to 1 mL Buffer RLT and 4 volumes of 96–100% ethanol to buffer RPE to prepare the working solutions. 2. Add 600 μL Buffer RLT to 2 ~ 5 mg sample, mix by pipetting (see Note 1) and then centrifuge the lysate at full speed for 2 min. Transfer the supernatant to a new tube. 3. Add 600 μl 70% ethanol to the supernatant and mix by pipetting. 4. Place the RNeasy spin column in a 2 mL collection tube, transfer up to 700 μL sample to the column and centrifuge at 8000 × g for 15 s (see Note 2). 5. Discard the flow-through, transfer the remaining sample to the column, and centrifuge at 8000 × g for 15 s. Discard the flowthrough. 6. Add 700 μL Buffer RW1 to the RNeasy spin column and centrifuge at 8000 × g for 15 s. Discard the flow-through. 7. Add 500 μL buffer RPE to the RNeasy spin column and centrifuge at 8000 × g for 15 s. Discard the flow-through and repeat this step.

300

Weimin Zuo and Hang Fai Kwok

8. Place the RNeasy spin column in a new 2 mL collection tube and centrifuge at full speed for 1 min. 9. Place the RNeasy spin column in a new 1.5 ml tube. Add 30–50 μL RNAse-free water to the spin column membrane and centrifuge at 8000 × g for 1 min. 10. Measure the RNA concentration by Nanodrop Spectrophotometers and store the RNA at -80 °C (see Note 3). 3.3 Total RNA Qualification and Quantification

Several RNA quantification methods are available on the market, such as microcapillary electrophoresis (MCE), ultraviolet (UV) absorbance, and fluorescence-based quantification. ND-1000 spectrophotometer (UV) and Quant-iT RiboGreen exhibit higher precise quantification in the 500 to 5 ng/μL, while the Agilent kits (MCE) can determine the integrity of RNA [19]. Therefore, total RNA qualification and quantification with Agilent RNA 6000 Pico kit are introduced below. 1. Set up the assay equipment and Bioanalyzer and replace the syringe at the chip priming station with a new reagent kit. 2. Clean the electrodes with 350 μL of fresh RNase-free water. 3. Filter 550 μL RNA 6000 Pico gel matrix into the spin filter and centrifuge at 1500 × g for 10 min to prepare the gel. 4. Add 1 μL of RNA 6000 Pico dye concentrate to a 65 μL filtered gel, vortex thoroughly and then centrifuge at 13,000 × g for 10 min to prepare the gel-dye mix. 5. Place a new RNA chip on the chip priming station, pipette 9.0 μL of the gel-dye mix at the bottom of the well marking G. Position the plunger at 1 mL and close the chip priming station for 30 s. Next, press the syringe’s plunger down to the clip, hold for 30 s and then release the plunger. Finally, pull the plunger back to the 1 mL position and pipette 9.0 μL of the gel-dye mix at the G marking well. 6. Load 9 μL RNA 6000 Pico conditioning solution into the well marking CS and pipette 5 μL RNA 6000 Pico marker into the well and each of the 11 sample wells (see Note 4). 7. Denature the sample at 70 °C for 2 min. Pipette 1 μL RNA 6000 Pico ladder into the well marking the ladder symbol. Pipette 1 μL sample into the sample well. Place the chip in the IKA vortex mixer adapter and vortex at 2400 rpm for 1 min. 8. Insert the chip in the Agilent 2100 Bioanalyzer properly and run the chip. 9. Clean the electrodes after the run with 350 μL of fresh RNasefree water.

Design of Peptides as Anti-cancer Reagents

301

Fig. 2 Workflow of TruSeq Stranded mRNA Library Preparation. The workflow explains how the TruSeq Stranded mRNA Library Prep assay works, including (1) Purification Poly-A containing mRNA with poly-T oligo attached magnetic beads; (2) Fragmentation into small pieces using divalent cations under elevated temperature; (3) Synthesis of First Strand cDNA with cleaved RNA fragment using reverse transcriptase and random primers; (4) Synthesis of Second Strand cDNA using Second Strand Marking Master Mix (SMM); (5) Adenylation at 3′Ends to prevent the fragments from ligating to each other during the adapter ligation reaction; (6) Ligation of Adapters to prepare the ds cDNA for hybridization onto a flow cell; (7) Enrichment of DNA Fragments to amplify the amount of DNA in the library; (8) Check Library Quality to achieve the highest-quality data on the sequencing platform; (9) Normalization and pooling of libraries to prepare DNA templates for cluster generation 3.4 TruSeq Stranded mRNA Library Preparation

Illumina has major sequencing advances. The following method describes how to prepare a library for mRNA sequencing using the TruSeq Stranded mRNA Library Prep Kit (see Fig. 2).

302 3.4.1

Weimin Zuo and Hang Fai Kwok mRNA Purification

1. Dilute the total RNA to a final volume of 50 μL in the RBP plate. Add 50 μL RPB to the well and mix. 2. Incubate the RBP plate at 65 °C for 5 min and then place on ice for 1 min. 3. Incubate the RBP plate at room temperature for 5 min and then centrifuge at 280 × g for 1 min. 4. Place the RBP plate on a magnetic stand and discard the supernatant after 5 min. 5. Remove the RBP plate from the magnetic stand. Add 200 μL BWB to the well and mix. 6. Place the RBP plate on a magnetic stand and discard the supernatant after 5 min. 7. Remove the RBP plate from the magnetic stand. Add 50 μL ELB to the well and mix. 8. Centrifuge the RBP plate at 280 × g for 1 min. Incubate the RBP plate at 80 °C for 2 min and then place on ice for 1 min.

3.4.2 mRNA Fragmentation

1. Add 50 μL BBB to the well and mix. Incubate at room temperature for 5 min. 2. Place the RBP plate on a magnetic stand and discard the supernatant after 5 min. 3. Remove the RBP plate from the magnetic stand. Add 200 μL BWB to the well and mix. 4. Place the RBP plate on a magnetic stand and discard the supernatant after 5 min. 5. Remove the RBP plate from the magnetic stand. Add 19.5 μL FPF to the well and mix. Centrifuge the RBP plate at 280 × g for 1 min. 6. Transfer the sample of RPB plate to the RFP plate. Place on the thermal cycler and run the Elution 2 – Frag – Prime program (preheat lid to 100 °C, 94 °C for 8 min, hold at 4 °C).

3.4.3 Synthesis of First Strand cDNA

1. Place the RFP plate on the magnetic stand and transfer 17 μL supernatant to the CDP plate after 5 min. 2. Add 50 μL SuperScript II to 450 μL FSA in one tube, mix and centrifuge at 600 × g for 5 s. 3. Add 8 μL mixture containing FSA and SuperScript II to the well of the CDP plate and mix. 4. Centrifuge the CDP plate at 280 × g for 1 min and then put on the thermal cycler to run the Synthesize first Strand program (preheat lid to 100 °C, 25 °C for 10 min, 42 °C for 15 min, 70 °C for 15 min and hold at 4 °C).

Design of Peptides as Anti-cancer Reagents 3.4.4 Synthesis of the Second Strand cDNA

303

1. Dilute CTE to 1:50 in RSB and then add 5 μL diluted CTE, 20 μL SMM to the well of the CDP plate and mix. 2. Centrifuge the CDP plate at 280 × g for 1 min and then place the CDP plate on the thermal cycler and incubate at 16 °C for 1 h. 3. Place the CDP plate on the bench to let the stand to bring to room temperature. 4. Add 90 μL AMPure XP beads to the well of CDP plate and mix. 5. Incubate at room temperature for 15 min and then centrifuge at 280 × g for 1 min. 6. Place the CDP plate on a magnetic stand and discard 135 μL supernatant after 5 min. 7. Wash the CDP plate twice with 200 μL fresh 80% ethanol (add 200 μL fresh 80% ethanol to each well, incubate the CDP plate on the magnetic stand for 30 s and then remove and discard all supernatant from each well). Remove residual ethanol and air-dry for 15 min. 8. Remove the CDP plate from the magnetic stand, add 17.5 μL RSB to the well and mix. 9. Incubate the CDP plate at room temperature for 2 min and then centrifuge at 280 × g for 1 min. 10. Place the CDP plate on a magnetic stand and transfer 15 μL supernatant to the ALP plate after 5 min.

3.4.5 Ends

Adenylation of 3′

1. Dilute CTA to 1:100 in RSB. Add 2.5 μL diluted CTA to the well of ALP plate. 2. Add 12.5 μL ATL to the well and mix. 3. Centrifuge the ALP plate at 280 × g for 1 min. 4. Incubate the ALP plate at 37 °C for 30 min and then incubate at 70 °C for 5 min. Place the ALP plate on ice for 1 min.

3.4.6 Ligation of Adapters

1. Dilute CTL 1:100 in RSB. Add 2.5 μL diluted CTL, 2.5 μL LIG, and 2.5 μL RNA adapters to the well of ALP plate and mix. 2. Centrifuge the ALP plate at 280 × g for 1 min. 3. Incubate the ALP plate at 30 °C for 10 min and then place on ice. 4. Add 5 μL STL to the well and mix, then centrifuge the ALP plate at 280 × g for 1 min. 5. Add 42 μL AMPure XP beads to the well and incubate the ALP plate at room temperature for 15 min. Centrifuge the ALP plate at 280 × g for 1 min.

304

Weimin Zuo and Hang Fai Kwok

6. Place the ALP plate on a magnetic stand and discard all supernatant after 5 min. 7. Wash the ALP plate twice with 200 μL 80% ethanol (add 200 μL fresh 80% ethanol to each well, incubate the CDP plate on the magnetic stand for 30 s and then remove and discard all supernatant from each well). Remove the residual ethanol and air-dry for 15 min. 8. Remove the ALP plate from the magnetic stand. Add 52.5 μL RSB to the well and mix. 9. Incubate the ALP plate at room temperature for 2 min and then centrifuge at 280 × g for 1 min. 10. Place the ALP plate on a magnetic stand. Then, transfer 50 μL supernatant to the CAP plate after 5 min. 11. Repeat steps 6.5 through 6.10 with the new plate using the 50 μL AMPure XP beads and 22.5 μL RSB. 12. Transfer 20 μL supernatant to the PCR plate. 3.4.7 Enrichment of DNA Fragments

1. Place the PCR plate on ice and add 5 μL PPC to the well. 2. Add 25 μL PMM to the well and mix. Then centrifuge the PCR plate at 280 × g for 1 min. 3. Place the PCR plate on the thermal cycler and run the mRNA PCR program (preheat lid to 100 °C, 98 °C for 30 s, 15 cycles of “98°C for 10 s, 60°C for 30 s, 72°C for 30 s,” 72 °C for 5 min, hold at 4 °C). 4. Centrifuge the PCR plate at 280 × g for 1 min. Add 50 μL AMPure XP beads to the Adapter tubes and mix. 5. Incubate the Adapter tubes at room temperature for 15 min and centrifuge at 280 × g for 1 min. 6. Place the Adapter tubes on the magnetic stand and discard all supernatant after 5 min. 7. Wash the Adapter tubes twice with 200 μL fresh 80% ethanol (add 200 μL fresh 80% ethanol to each well, incubate the CDP plate on the magnetic stand for 30 s and then remove and discard all supernatant from each well). Remove residual ethanol and air-dry for 15 min. 8. Remove the Adapter tubes from the magnetic stand. Add 32.5 μL RSB to the well, mix and incubate at room temperature for 2 min. 9. Centrifuge the Adapter tubes at 280 × g for 1 min and then place on a magnetic stand and transfer 30 μL supernatant to the TSP1 plate after 5 min.

Design of Peptides as Anti-cancer Reagents 3.4.8

Check Libraries

305

1. Quantify the libraries using qPCR according to the Illumina Sequencing Library qPCR Quantification Guide. 2. Use the Standard Sensitivity NGS Fragment Analysis Kit on an Advanced Analytical Fragment Analyzer to check Library Quality (dilute the DNA library 1:1 with RSB and Run 1 μL diluted DNA library). 3. Check the size and purity of the sample by electrophoresis using a 1% agarose gel. Expect the final product to be a band at ~260 bp.

3.4.9 Normalization and Pooling of Libraries

1. Transfer 10 μL library to the DCT plate. 2. Normalize with Tris-HCl 10 mM (pH 8.5) with 0.1% Tween 20 to 10 nM and mix and then centrifuge the DCT plate at 280 × g for 1 min. 3. Transfer 10 μL of normalized library to the well of the PDP plate and mix. 4. Centrifuge the PDP plate at 280 × g for 1 min, then proceed the normalized library to the Illumina MiSeq platform for cluster generation. The MiSeq System integrates cluster generation, amplification, sequencing, and data analysis into a single instrument, and cluster generation is the first step for mRNA sequencing.

3.5 mRNA Sequencing

1. Load the cDNA library into a flow cell with oligos complementary to the adapters. 2. Obtain the raw data by doing RNA sequencing on the Illumina MiSeq platform. 3. Remove the index primers and transfer the data to the program FastQC to filter low-quality reads. 4. Save the filtered data as FASTQ files. 5. De novo assemble the clean reads using the software Trinity 2.0 to obtain the transcriptome.

3.6 Protein Extraction, Purification, and Quantification

1. Transfer the sample into a 2 mL centrifuge tube, add 1 mL extraction buffer, incubate on ice for 5 min and then incubate at 80 °C for 10 min. 2. Ultrasound for 5 min on ice bath. 3. Centrifuge at 25,000 × g at 4 °C for 15 min. 4. Transfer the supernatant to a new tube and incubate at room temperature for 30 min. 5. Centrifuge at 25,000 × g at room temperature for 15 min and transfer the supernatant to a new tube. 6. Add 5 volumes of ice-cold TCA/acetone buffer to precipitate the samples for 6 h.

306

Weimin Zuo and Hang Fai Kwok

7. Centrifuge at 15,000 × g at 4 °C for 10 min and discard the supernatant. 8. Wash the pellet three times with ice-cold acetone (containing 10 mM DTT), decant the acetone and air-dry the sample for 30 min. 9. Resuspend the pellet with 300 μL solubilization buffer and vortex for 20 min. 10. Transfer the supernatant to a new tube after centrifuging at 15,000 × g at 20 °C for 15 min. 11. Determine the protein concentration by the Bicinchoninic Acid (BCA) method (see Note 5). 3.7 Protein Alkylation and Digestion

1. Transfer 12.5 μL protein sample to a new tub. Add 12.5 μL IAA solution, mix and incubate at room temperature for 30 min in the dark. 2. Add 25 μL 0.1 M TEAB and 50 μL trypsin solution and mix. 3. Incubate at 37 °C for 6 h. 4. Add 5 μL 1.0% TFA, and store the peptide sample at -80 °C.

3.8 LC-MS/MS and Protein De Novo Sequencing

Methods to obtain proteomics are illustrated on the Q ExactiveTM Hybrid Quadrupole-Orbitrap Mass Spectrometer with the nanoLC system. 1. Load the sample to the Acclaim pepMap100 C18 trap column with the LC-MS water. 2. Trap the sample to the column at 6 μL/min for 5 min, and connect the trap column to EASY-Spray column. Then, elute at the flow rate of 300 nL/min with the gradient buffer (95% buffer A and 5% buffer B for 5 min, 60% buffer A and 40% buffer B for 35 min, 5% buffer A and 95% buffer B for 45 min, 5% buffer A and 95% buffer B for 55 min, 95% buffer A and 5% buffer B for 56 min, 95% buffer A and 5% buffer B for 60 min). 3. Convert the elution to the gas-phase ions by electrospray ionization (1.8 kV spray voltage and 300 °C capillary temperature). 4. Full MS scans of peptide precursors from 300 to 1800 m/z (70 K resolution, 200 ms max injection time, and 3 × 106 ions AGC target). Perform MS2 scans in an isolation window at 2 m/z (17.5 K resolution, 110 ms max injection time, and 106 ion count target). 5. Select the top 20 precursor ions for each cycle and isolate precursors with charge state +2 to +5 for MS2 fragmentation. Next, start the MS2 spectrum from 100 m/z, set the exclusion duration to 20 s and turn on the isotope’s exclusion. 6. Gather the LC-MS/MS raw data and transfer it to the software PEAKS for de novo sequencing. Set the search parameters

Design of Peptides as Anti-cancer Reagents

307

Fig. 3 Basic workflow to predict ACPs via machine learning method. Basic workflow to predict ACPs via machine learning method including (1) Peptide data preparation for model building; (2) Features extraction from the ACPs; (3) Model construction after analysis of features; (4) Model training with ACPs to test the confidence level; (5) Model revision basing on the model training results; (6) Model validation after several revision; (7) Web server construction for public use; (8) ACPs prediction for the users

(3 ppm for precursor ion mass tolerance, 0.01 Da for fragment ion mass tolerance, employ no enzyme, use post-translational modifications). 3.9 Comparison Between Transcriptome and Proteome

1. Compare the transcriptome and proteome data using the software PEAKS (3 ppm for precursor ion mass tolerance, 0.01 Da for fragment ion mass tolerance, employ no enzyme, use posttranslational modifications, false discovery rate, ≤ 1%). 2. Reserve peptides with high confidence and accuracy.

3.10 Peptide Identification and Selection via Machine Learning

Over the past few years, more and more artificial intelligence, such as machine learning algorithms, have been introduced to build ACPs predictors. The basic workflow to predict ACPs via machine learning includes peptide data preparation, feature extraction for model construction, model training and validation, web server construction and ACPs prediction (see Fig. 3). Even though it remains a big challenge for accurate ACPs prediction, some of these predictive servers have offered a relatively outstanding prediction for ACPs [12–18]. 1. Choose a web browser from the list in Subheading 2.9. 2. Enter the query peptide sequence into the Input box or upload the sequence file in FASTA format according to the website guideline. 3. Submit the peptide sequence and save the results after prediction is finished.

308

4

Weimin Zuo and Hang Fai Kwok

Notes 1. It is crucial to choose the appropriate volume of starting material to obtain optimal purified RNA. Otherwise, the binding capacity of the RNeasy spin column would be exceeded and thus reduce the RNA yielding. 2. Do not overload the RNeasy spin column, as the overloading will significantly reduce RNA yield and purity. 3. A ratio of 260/280 around 2.0, and a ratio of 260/230 between 2.0 and 2.2 are generally indications of pure RNA. 4. Do not leave any wells empty, or the chip will not run properly. Add 5 μL of the RNA marker plus 1 μL of deionised water to each unused sample well. 5. The protein concentration can be determined using a BCA Protein Assay Kit according to the manufacturer’s guideline.

Acknowledgement This research was funded by the Science and Technology Development Fund of Macau SAR (FDCT) (file no. 0010/2021/AFJ) and the Faculty of Health Sciences (FHS) University of Macau (UM). References 1. Wild CP (2019) The global cancer burden: necessity is the mother of prevention. Nat Rev Cancer 19:123–124 2. Abbas Z, Rehman S (2018) An overview of cancer treatment modalities. Neoplasma 1: 139–157 3. Siegel RL, Miller KD, Fuchs HE et al (2021) Cancer statistics, 2021. CA Cancer J Clin 71: 7–33 4. Vasan N, Baselga J, Hyman DM (2019) A view on drug resistance in cancer. Nature 575:299– 309 5. Muttenthaler M, King GF, Adams DJ et al (2021) Trends in peptide drug discovery. Nat Rev Drug Discov 20:309–325 6. Wang L, Wang N, Zhang W et al (2022) Therapeutic peptides: current applications and future directions. Signal Transduct Target Ther 7:48 7. Mahadevappa R, Ma R, Kwok HF (2017) Venom peptides: Improving specificity in cancer therapy. Trends Cancer 3(9):611–614. https://doi.org/10.1016/j.trecan.2017. 07.004

8. Lyu P, Kwok HF (2019) High-throughput strategy accelerates the progress of marine anticancer peptide drug development. Recent Pat Anticancer Drug Discov 14(1):2–4. https:// d o i . o r g / 1 0 . 2 1 7 4 / 1574892813999181114152127 9. Himaya S, Lewis RJ (2018) Venomicsaccelerated cone snail venom peptide discovery. Int J Mol Sci 19:788 10. Ma R, Wong WS, Ge L et al (2020) In vitro and MD simulation study to explore physicochemical parameters for antibacterial peptide to become potent anticancer peptide. Mol Ther Oncolytics 16:7–19. https://doi.org/10. 1016/j.omto.2019.12.001 11. Gerdes H, Casado P, Dokal A et al (2021) Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nat Commun 12:1850 12. Huang KY, Tseng YJ, Kao HJ et al (2021) Identification of subtypes of anti-cancer peptides based on sequential features and physicochemical properties. Sci Rep 11:13594

Design of Peptides as Anti-cancer Reagents 13. Chen J, Cheong HH, Siu SW (2021) XDeepAcPEP: deep learning method for anti-cancer peptide activity prediction based on convolutional neural network and multitask learning. J Chem Inf Model 61:3789–3803 14. Ahmed S, Muhammod R, Khan ZH et al (2021) ACP-MHCNN: an accurate multiheaded deep-convolutional neural network to predict anti-cancer peptides. Sci Rep 11:23676 15. Wei L, Zhou C, Chen H et al (2018) ACPredFL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics 34: 4007–4016 16. Schaduangrat N, Nantasenamat C, Prachayasittikul V et al (2019) ACPred: a computational

309

tool for the prediction and analysis of anticancer peptides. Molecules 24:1973 17. Guo Y, Yan K, Lv H et al (2021) PreTP-EL: prediction of therapeutic peptides based on ensemble learning. Brief Bioinform 22: bbab358 18. Xu D, Wu Y, Cheng Z et al (2021) ACHP: a web server for predicting anti-cancer peptide and anti-hypertensive peptide. Int J Pept Res Ther 27:1933–1944 19. Aranda R IV, Dineen SM, Craig RL et al (2009) Comparison and evaluation of RNA quantification methods using viral, prokaryotic, and eukaryotic RNA over a 104 concentration range. Anal Biochem 387:122–127

INDEX A

B

A-172 .................................................................... 193, 205 A549 ................................................................................ 22 Accutase ....................................................... 170, 171, 175 Actin..................................................................... 141, 142, 146, 147, 161, 173 Acute myeloid leukemia (AML)......................... 212–214, 220–224 ADAM17 .................................... 119–121, 123–126, 128 ADAMTS1 ................................... 5, 7–10, 12, 13, 15, 85 ADAMTS5 ......................................................... 86, 88, 90 ADAMTS8 ................................................................55–65 Affinity-based probes (AfBPs) ........................................ 29 Agar............................................44, 49, 50, 72, 245, 246, 248–252, 254, 265, 267, 271 Alexa Fluor ........................ 132, 135, 231, 266, 272, 273 Alkylation.............................................................. 298, 306 AlphaFold ................................................ 3, 13, 45, 47, 56 α2-macroglobulin (A2M) ............................................. 279 Amino acid ............................................. 8–11, 42, 45, 46, 57, 76, 84, 95–97, 191, 195, 269, 280, 281 4-(2-Aminoethyl-benzensulfonyl fluoride hydrochloride) (AEBSF) ................. 192, 197, 207 4-Aminophenylmercuric acetate (APMA) ............. 31, 32, 37, 38, 263, 269, 276 Anesthesia ............................................213, 214, 222, 223 Angiogenesis................................. 84, 151, 167, 229, 231 Anion exchange...................................268, 286, 287, 289 Anion exchange chromatography ................................ 287 Antibiotic .................................................... 44, 49, 50, 52, 132, 133, 138, 158, 159, 164 Antibody monoclonal ....................................... 87, 99, 101, 112, 195, 206, 243, 247 polyclonal...................................................34, 64, 101, 112, 195, 206 Anti-cancer peptide (ACP) .................295, 296, 298, 307 Apheresis........................................................................ 127 Apoptosis ..................................56, 84, 85, 109–111, 114 Arp2/3 .......................................................................... 141 Artificial intelligence (AI) ...................1, 13, 14, 296, 307 Autolysis .................................................... 56, 60, 64, 290

B16.......................................................102, 107–111, 235 Benzamidine ....................................................... 45, 51, 53 β-lactamase ..........................................244, 246–248, 250 β-mercaptoethanol (β-ME) .......................................... 121 Bicinchoninic acid (BCA) ...............................22, 23, 112, 172, 306, 308 Biopsy ..................................................19–27, 97, 98, 114 Biotin ..........................................30, 31, 35, 79, 269, 276 Biotinylation ............................................... 76, 78, 79, 81, 101, 263, 267–269, 276 BL21 ........................................... 245, 248–250, 252, 254 Bone marrow (BM) ............................................ 212, 213, 217, 218, 221, 223 Bovine serum albumin (BSA)........................... 32, 34–36, 58, 59, 69, 71, 78, 79, 87–89, 99, 104, 105, 107–111, 133, 135, 143, 145, 147, 149, 169, 179, 233, 235, 246, 266 Bradford reagent ............................................................. 69 Brij .................................................... 31, 58, 86, 193, 267 BxPC-3 ........................................................ 193, 198, 207

C Cancer cell ............................................ 19–27, 56, 67, 83, 132, 133, 136–138, 141, 142, 151, 152, 154, 163, 167, 168, 173, 177, 193, 212 Carboxypeptidase .................................... 96, 97, 102, 103 Catalytic domain ........................................ 38, 67, 72, 78, 80, 230, 231, 244, 246, 247, 261, 263 C57Bl/6 ............................................................... 213, 221 CD44 ........................................67, 84, 96, 131, 132, 151 cDNA...............................................................68, 71, 121, 124–126, 301–303, 305 Cell adhesion............................45, 84, 108, 109, 151, 168 counting............................................................ 99, 179 culture .............................................23, 27, 45, 57, 59, 99, 107, 123, 144, 153, 156, 158, 160, 165, 168, 170, 178–180, 190, 193, 270, 283, 285, 286 growth .............................................. 56, 84, 108–110, 131, 151, 168, 190, 199, 200, 212, 264, 295 imaging ..........................................185, 190, 192, 201

Salvatore Santamaria (ed.), Proteases and Cancer: Methods and Protocols, Methods in Molecular Biology, vol. 2747, https://doi.org/10.1007/978-1-0716-3589-6, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024

311

PROTEASES AND CANCER: METHODS AND PROTOCOLS

312 Index

Cell (cont.) invasion .................................... 56, 84, 131–133, 137, 138, 141, 151, 155, 167, 168, 177, 185, 190 migration .................................................84, 109, 110, 141, 151, 168, 212, 219, 220, 222–224, 229 viability................................................... 127, 199, 277 Centrifugation ........................................60, 70, 122, 127, 160, 179–181, 250, 252, 269–271, 276, 286 Circular dichroism (CD) ..................................... 192, 196 Cleavage................................................ 2, 6, 9–12, 14, 27, 30, 59, 63–65, 95–115, 132, 155, 169, 177, 178, 185, 190, 191, 202, 207, 230, 231, 249, 279–282, 284, 285, 288–290 Coagulation .......................................................... 281, 282 Collagen.................................................. 75–81, 131–138, 141, 151–159, 161–166, 169, 179–186, 190, 193, 200, 207, 208, 212, 223, 229 Collagenase 1, see MMP1 Colorectal cancer (CRC) ...............................75, 167, 168 Concentration .............................................23–25, 31–33, 36, 37, 43–46, 48, 50–52, 59, 60, 62–65, 68, 70–72, 79, 86–89, 91, 96, 101, 102, 104, 106, 107, 111, 112, 115, 121, 124–126, 128, 133, 134, 147, 149, 160–162, 164, 165, 171, 172, 175, 179, 180, 186, 193, 196–199, 201, 202, 207, 213, 232–236, 238, 241, 242, 246, 248, 251, 252, 254, 255, 261–264, 266, 268–270, 274–276, 283, 285–289, 300, 306, 308 Confocal imaging ........................................ 132, 135, 139 Conformational change .............................. 279, 280, 282 Coomassie Brilliant Blue (CBB) ............................ 62, 63, 65, 69, 91, 153, 155, 158–160, 162 Cortactin............................................................... 141, 142 Cryo-SEM ..................................................................... 134 Crystal violet..........................................99, 108, 159, 163 Cytokine .........................................................84, 168, 281

D Database ............................ 2–10, 12–16, 26, 27, 95, 284 Degradomics ................................................................. 2, 9 Dextran ................................................................. 155, 224 Dialysis ................................................61, 64, 69–72, 161, 260, 262, 263, 268, 276, 287 2D ..........................................................37, 186, 190, 210 3D ................................................ 3, 4, 12–14, 42, 45–47, 56, 136–138, 157, 177–187, 190, 192, 194, 199–201, 212, 218 4′,6-Diamidino-2-phenylindole (DAPI) ............................144, 145, 147, 148, 161 Dimethylformamide (DMF) ...................... 191, 195, 196 Dimethyl sulfoxide (DMSO)............................ 31–33, 37, 43, 48, 51, 69, 113, 160, 192, 193, 198, 221, 232, 233, 238, 241, 242, 246, 253 Directed evolution ............................................... 257, 258

A disintegrin and metalloproteinase (ADAM)...................................................... 85, 119 A Disintegrin-like and Metalloproteinase with Thrombospondin-like motif (ADAMTS) ..................................... 12, 42, 55, 61, 85, 86, 97, 281 Disulfide........................................ 68, 254, 282, 283, 289 Dithiothreitol .......................................... 20, 68, 261, 298 DNA .............................................. 43, 44, 48–51, 57, 59, 64, 72, 244, 247, 248, 250, 252, 254, 255, 260, 263, 264, 266, 270, 274, 276, 277 DNA polymerase ....................................... 43, 48, 51, 244 DNase ..................................................128, 261, 268, 297 D-phenyl-L-prolyl-L-arginine chloromethyl ketone (PPACK)......................................... 99, 102 Dpn1............................................................ 43, 46, 51, 52 DU-145 ........................................................................... 22 Ductal carcinoma in situ (DCIS) ................................. 178 Dulbecco’s modified Eagle’s medium without serum (DMEM) ............................... 99, 102, 108, 110, 132–134, 138, 178, 179

E Electrophoresis ........................................... 31, 32, 34, 37, 43, 44, 46, 51, 58, 91, 153, 160, 206, 300, 305 Electroporation ................................. 120, 121, 123, 245, 250, 255, 260, 270, 271 Enzyme-linked immunosorbent assay (ELISA).................................83–91, 99, 104–107, 111, 114, 121, 125–127, 251, 253 Epidermal growth factor receptor (EGFR) .......... 85, 119 Epitope ........................................... 41–53, 112, 255, 258 Escherichia coli (E. coli) ..................................... 52, 68–70, 100, 101, 111, 245–249, 252, 254, 260, 267 Ethylenediaminetetraacetic acid (EDTA) .............. 22, 43, 53, 59, 68–71, 87, 99, 121, 123, 162, 166, 178, 179, 193, 244, 261, 276, 283, 284, 287 Exosome ........................................................................ 132 Extracellular matrix (ECM)..............................14, 16, 56, 64, 67, 75, 76, 83, 131, 141, 142, 151, 154, 168, 169, 173, 177, 212, 229, 281

F Fab .............................................. 244–248, 251–253, 255 Fetal bovine serum (FBS) .................................27, 45, 57, 64, 102, 120, 121, 123, 132, 133, 138, 142, 158, 159, 163, 164, 170, 171, 175, 176, 178, 193, 194, 198, 199 Fibronectin ..........................................131, 141, 151, 229 Ficoll-Paque PLUS ..................................... 120, 122, 127 FLAG ............................................................58, 61–63, 65 Flow cytometry ............................................ 99, 128, 223, 258, 266, 272, 273

PROTEASES Fluorescein .................................................. 169, 171, 173 Fluorescence .........................................69, 111, 114, 135, 145, 147, 149, 158, 161, 169, 173, 174, 176, 184, 189, 192, 197, 201, 207, 224, 231–236, 239, 241, 275 Fluorescent-activated cell sorting (FACS).......... 272, 273 Fluorophore ............................................... 190, 196, 212, 221, 223, 225, 230–232, 272 Focal adhesion............................................................... 154 Formaldehyde...........................20, 24, 25, 179, 181, 187 Fo¨rster resonance energy transfer (FRET)............................189, 190, 251, 253, 255 Furin ......................................................55, 193, 197, 281

G GAPDH....................................................... 121, 126, 128 Gelatin ......................................... 81, 142–149, 152–155, 157–161, 166, 169, 171, 173, 174, 176, 291 Gelation ...............................................134, 135, 137, 138 Genomics ........................................................1, 2, 4, 5, 49 Glutaraldehyde ........................................... 133, 135, 137, 143, 144, 147, 148, 158, 161 Glycan .......................................11, 41, 42, 45, 47, 52, 53 Glycosaminoglycans (GAG) ............................83, 84, 131 GM6001 ..................................... 153–157, 160, 165, 240 Granulocyte-macrophage progenitor (GMP) ............. 221 Guanidinium hydrochloride (GuHCl) ....................21, 24

H H4 ......................................................................... 194, 205 Hanks’ balanced salt solution (HBSS)................. 99, 108, 110, 120, 122 HB2 .....................................................178–180, 184, 185 HCT-116......................................................................... 22 Hematopoiesis............................................................... 212 Hemocytometer ........................................... 99, 108, 120, 122, 123, 179, 180 Heparin ........................................................ 57, 60, 61, 64 High performance liquid chromatography (HPLC)..................................... 20, 22, 26, 68–70, 102, 114, 191, 192, 196 High throughput screening (HTS) ................... 231, 233, 235, 236, 238, 241 Homogenization .......................................................21, 27 Horseradish peroxidase (HRP) ................. 32, 34, 35, 37, 58, 62, 63, 76, 78, 79, 87, 89, 90, 99, 246, 253 HT29 ........................................................... 170, 171, 173 HT1080...................................... 133, 137, 138, 153–157 Human embryonic kidney cells (HEK) ..................30–32, 35–38, 45, 57, 267 Hyaluronic acid (HA) ....................................84, 131–138 Hybridoma .................................................................... 243

AND

CANCER: METHODS

AND

PROTOCOLS Index 313

Hydrogel..............................................133, 134, 136, 178 4-(2-Hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) ...........................22, 25, 69, 71, 99, 159, 164, 169, 179, 193, 194, 200, 267, 283, 287

I Image acquisition .......................................................... 211 Image analysis............................................. 149, 156, 157, 159, 160, 162, 163, 165, 219 Imidazole ....................................................................... 262 Immobilized metal affinity chromatography (IMAC) ..................................................... 286, 287 Immunoblot, see Western blot Inclusion body.......................................70, 261, 268, 276 Indole-3-Acetic Acid (IAA).................... 20, 24, 298, 306 Inhibitory activity (IC50) ........................... 233, 238–242, 254, 255 Inner filter effect (IFE) ........................................ 207, 241 Integrin ...................................................96, 97, 115, 131, 141, 151, 183–185 Interleukin 6 receptor (IL6R) ...................................... 119 Intravital microscopy (IVM) .............................. 212–214, 217, 220, 223, 224 Invadopodium ............................................................... 144 2-Iodoacetamide ............................................................. 20 Isopropyl-D-thiogalactopyranoside (IPTG) ........................................ 68–70, 245, 248, 249, 251, 254, 261, 267

K Knockdown ....................... 121, 123–126, 128, 154, 185

L Lamellipodia ......................................................... 141, 154 L929 cells ...................................................................... 128 Leukocyte ............................ 96, 120, 122–123, 127, 171 Library ....................................................12, 76, 232, 236, 238, 244–248, 250, 251, 254, 255, 257, 258, 263, 264, 266, 269–274, 276, 297, 301–305 Linear viscoelastic region (LVE) ......................... 137, 139 Lipopolysaccharide (LPS)................................... 121, 124, 126, 127, 129 Liquid chromatography (LC)................................. 26, 30, 32, 35, 36, 112, 298, 306 Liquid chromatography and tandem mass spectrometry (LC-MS/MS) ...................... 20, 306 Liquid nitrogen ............................ 21, 113, 132, 134, 299 Luria–Bertani (LB) ...........................................44, 49, 50, 72, 245, 248–250, 252, 254, 261, 264, 265, 267 Lysozyme.............................................................. 246, 261 Lysyl oxidase (LOX) ..................................................... 131

PROTEASES AND CANCER: METHODS AND PROTOCOLS

314 Index M

Machine learning (ML) ....................................... 295–308 Macrophage......................... 99, 102, 111, 119–129, 154 Macrophage colony stimulating factor (M-CSF) .......................................... 120, 123, 128 Marimastat................................................... 193, 199, 200 Mass spectrometry (MS)...................................11, 21, 22, 26, 30–32, 36, 37, 53, 112, 192, 196, 298, 306 Matrilysin, see MMP7 Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF)................................ 112, 192, 196 Matrix metalloproteinase (MMP) .......................... 29, 30, 67, 76, 79, 85, 132, 152, 154, 155, 165, 168, 189, 190, 213, 222, 229–231, 240, 257, 258, 261, 263, 267–269, 274–276, 280, 282, 285 MCF-7 ............................................................................. 22 MDA-MB-231 ...............................................22, 132, 142 Melanoma .........................................................84, 98, 207 Melting temperature (Tm) ..................................... 51, 197 Membrane-type serine protease 1 (MT-SP1).............. 282 Metastasis..............................................29, 56, 67, 76, 84, 98, 131, 137, 142, 167, 168, 189, 229 Methyl tert-butyl ether (MTBE) ................................. 191 Michaelis-Menten constant (KM)...............233–235, 241 Minimum essential medium (MEM) .......................45, 57 MLL-AF9 ............................................................. 220–223 MMP1 ........................................75, 76, 80, 81, 154, 282 MMP2 ................................................141, 142, 152–154, 165, 166, 168, 169, 281, 282, 284 MMP3 .......................................75, 76, 80, 97, 198, 258, 264, 273, 274, 276, 282 MMP7 ................................................................ 67–72, 97 MMP8 ......................................................... 154, 230, 282 MMP9 ..................................................97, 132, 141, 142, 152, 153, 168, 169, 244, 246, 251 MMP10 ................................................................ 258, 276 MMP12 .....................................................................30–38 MMP13 ...................................... 154, 183–185, 230, 282 MMP14 ...............................................151, 189, 229–232 Monocyte....................................120, 122, 123, 127, 128 Monomer................................................................ 52, 154 Mowiol.................................................................. 179, 184 mRNA......................................................... 124, 126, 285, 297, 298, 301–305 MT1-MMP, see MMP14 Multiphoton microscopy ............................ 211, 212, 223 Mutagenesis..................................................41–43, 46, 51

N N-acyl-N-alkyl sulphonamide (NASA) ....................30, 31 N-Cyclohexyl-2-aminoethanesulfonic acid (CHES) ..........................................................76, 79 Negative selection .............................................. 20, 24, 26

Neoepitope ......................................................... 85, 87, 91 Nickel–nitrilotriacetic acid (Ni–NTA) ........................................ 246, 252, 262 Nitrocellulose ....................................................58, 59, 62, 63, 65, 103, 193, 206 N-methylpyrrolidone (NMP)....................................... 191 2-(N-morpholino)ethanesulfonic acid (MES) .............................................. 159, 163, 262 3-(N-morpholino)propanesulfonic acid (MOPS) ........................................... 195, 205, 206 N, N’-diisopropylcarbodiimide (DIPCDI) ................................................. 191, 195 N,N,N,N-tetramethylethylenediamine (TEMED) .......................................................... 284

O o-Phenylenediamine dihydrochloride (OPD) ............................................................87, 90 Osteopontin (OPN)............................55, 59, 63, 95–115 OVCAR-3........................................................................ 22

P Paraformaldehyde (PFA) .................................... 133, 135, 143–145, 147, 169 Parallel reaction monitoring (PRM) ................. 32, 36, 37 Paratope ................................................................ 243, 244 Parenchyma ................................................................... 223 PC-3................................................................................. 22 PCR............................................................. 43, 46, 48, 49, 51, 52, 121, 124, 232, 238, 247, 248, 252, 270, 274, 297, 304 Pepsin............................................................................. 157 Peptide ............................................12, 20, 24–27, 30–32, 35–38, 58, 61–63, 65, 76–78, 80, 81, 96, 101–103, 107, 112–114, 155, 157, 190, 191, 193, 195–197, 207, 231, 232, 244, 246–249, 251, 254, 255 Peripheral blood mononuclear cells (PBMCs) .............................................21, 122, 128 Periplasm ....................................................................... 247 Permeabilization.......................................... 149, 183, 186 Phagemid.............................................................. 244, 248 Phalloidin.................. 142, 144, 145, 147–149, 161, 173 Phosphate buffered saline (PBS).............................21–23, 31, 33–36, 45, 50, 57–60, 87–89, 91, 99, 104–108, 120, 122, 123, 133, 135, 137, 142–145, 147, 148, 158–163, 165, 169, 171, 172, 174, 176, 179–181, 183, 184, 195, 202, 213, 214, 216, 217, 224, 253, 263, 266, 274 Plasmid ........................................................43–45, 48–52, 57, 59, 72, 154, 155, 244–248, 250, 252, 254, 255, 263, 264, 266, 274, 283, 285 Platelet ........................................................................... 170

PROTEASES Platelet microparticles (PMPs) ...........168, 170–173, 175 Platelets.......................................127, 167, 168, 170, 175 Podosome ............................................................. 154, 168 Polyethylenimine (PEI) ....................................45, 50, 57, 59, 60, 64, 267, 274, 277, 283, 285, 286, 289 Poly-L-Lysine (PLL)...........................142, 144, 147, 148 Polyvinylidene difluoride (PVDF) membrane............... 34 Post-translational modifications (PTMs).............. 11, 307 Primer .............................................. 43, 46, 51, 121, 125, 126, 264, 270, 297, 301, 305 Prinomastat .........................................213, 214, 222, 223 Prodomain ...................................... 12, 35, 37, 38, 55, 56 proMMP2................................... 151–153, 158, 160, 165 Propeptide, see Prodomain Prostaglandin E2 (PGE2)............................ 100, 109, 111 Protease inhibitor cocktail ........................ 22, 53, 64, 193 Protein Data Bank (PDB) ........................... 3, 12, 13, 45, 46, 52, 230, 264, 269 Protein expression refolding .............................................................. 68–72 structures ................................................................. 168 Proteoglycan.............................................. 55, 83, 84, 151 Proteomics......................... 1, 4, 14, 16, 19, 20, 296, 306 Purification .............................. 44, 46, 49, 55–65, 67–72, 86, 191, 196, 246, 251, 262, 267–269, 276, 283, 286, 290, 297, 298, 301, 302, 305

Q qPCR ...................................................121, 125, 126, 305 Quencher ......................................................190, 230–232

R RAW.....................................................102, 107–109, 111 Relaxation modulus ...................................................... 137 Restriction enzyme....................................... 46, 244, 245, 264, 270, 274 Reverse transcriptase ............................................ 297, 301 Rheology .............................................................. 136, 137 RNA ................................................................................... 6 RNAse............................................................................ 297 RPMI .................................................. 120–123, 159–161, 165, 170, 193, 194, 198 RT-qPCR....................................................................... 125

S Saccharomyces cerevisiae (S. cerevisiae)................. 265, 270 Sanger sequencing.....................................................49, 50 Scanning electron microscopy (SEM) .............................................. 109, 132, 134 Secondary electron imaging (SEI) ............. 134, 135, 138 Sequence alignment ............................................... 3, 9, 11 Serpin ...................................................................... 96, 281

AND

CANCER: METHODS

AND

PROTOCOLS Index 315

Sheddase ...................................................... 119, 120, 128 Shedding............................................................... 151, 200 Short interfering RNA (siRNA)......................... 120, 121, 123–128, 154, 183 Signal peptide .................................................................. 55 Size exclusion chromatography (SEC) ............................................... 286, 287, 289 SK-OV-3.......................................................................... 22 Sodium cyanoborohydride .......................................20, 27 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)...................... 11, 32, 37, 58, 61–62, 64, 70, 91, 102, 103, 112–114, 153, 158, 160, 246, 252, 276, 288–290, 292 Solid phase binding assay..........................................76, 78 Sonication .................................................. 21, 23, 70, 276 Spectrophotometry ....................................................... 196 Spheroid.....................178–187, 190, 192, 193, 198–203 Standard curve........................86–91, 115, 125–127, 129 Streptavidin...........................................31, 32, 34, 35, 37, 76, 78, 79, 99, 104, 105, 107, 132, 136, 246, 253, 258, 266, 272, 273 Stress relaxation............................................................. 137 Stromelysin 1, see MMP3 Substrate ........................................ 6, 8, 9, 14, 20, 32, 35, 37, 55, 56, 58, 62, 63, 65, 69, 71, 76, 78, 79, 87, 89, 90, 96, 104, 106, 107, 113, 114, 119, 128, 132, 141, 151, 153, 154, 169, 178, 189, 190, 192, 195, 197–199, 201, 207, 230–236, 238, 241, 242, 244, 246, 253–255, 267, 274, 275, 280–284, 289, 291 SW480 .................................................................. 170, 171 SW620 .................................................................. 170, 171 SW1088 ................................................................ 194, 205

T Terminal Amine Isotopic Labeling of Substrates (TAILS)..........................................................19–27 Terminomics..................................................... 19–27, 284 3,3′,5,5′-Tetramethylbenzidine (TMB) ................ 78, 79, 87, 90, 246, 253 Thermolysin ......................................................... 288–290 THP-1 ............................................................................. 22 Thrombin ..................................... 95–115, 170, 171, 175 TIMP1 ........................................................ 153, 258, 263, 264, 266, 267, 269–275 TIMP2 ............................................ 69, 71, 152, 153, 258 TIMP3 .................................................................... 56, 120 Tissue culture ............................................... 99, 115, 122, 123, 127, 133, 162, 170, 201, 214 Tissue inhibitor of metalloproteases (TIMP).............................. 86, 120, 269, 274, 275 Titration............................................. 69, 71, 72, 86, 233, 249, 254, 274, 288, 291

PROTEASES AND CANCER: METHODS AND PROTOCOLS

316 Index

Transfection ................................................ 45, 50, 56, 57, 59, 60, 64, 120, 124, 126, 127, 223, 277, 285, 286, 289 Transformation........................................... 26, 44, 49, 50, 52, 72, 255, 267, 276 Transmembrane................................................... 119, 120, 151, 152 Transwell....................................................... 99, 110, 155, 156, 159, 162–163 Trichloro acetic acid (TCA)................................. 298, 305 Trifluoroacetic acid (TFA) ..............................69, 71, 191, 192, 298, 306 Triple-helical peptides (THPs) ................................75–81, 190, 195, 196 Triton ............................................... 68, 70, 99, 143, 145, 153, 158, 179, 183, 193, 261 Trizma..................................................58, 59, 86, 87, 232 Trypan blue ....................................... 127, 171, 179, 180, 193, 198, 213 Trypsin .................................................. 11, 20, 22–25, 30, 31, 35–38, 99, 108, 162, 175, 178, 179, 281, 288, 298, 306 Tumor-associated macrophage (TAM).......................... 99 Tumor microenvironment .................................. 131, 132, 167, 168, 178, 190 Tumor necrosis factor (TNF)............................. 119, 121, 125–128 Tween ................................................... 31, 33–36, 43, 58, 59, 64, 78, 87, 91, 99, 105, 195, 297, 305

U U-87 ..................................................................... 193, 205 Ultrafiltration .......................................... 69, 71, 246, 252 Urea ............................................ 261–263, 267, 276, 298 Urokinase plasminogen activator (uPA) ...................... 282

V Vascular endothelial growth factor (VEGF) ...............................................56, 168, 231 Vector...................................... 43, 51, 52, 56, 64, 68, 69, 71, 155, 260, 264, 269, 270, 274, 285, 289 Venom..................................................281, 296, 297, 299 Versican .................................................. 55, 83–86, 88, 91 Versikine ....................................................................83–91

W Western blot ............................................. 30, 32–37, 102, 103, 112, 193, 202–207

Y Yeast surface display .................................... 257, 258, 264

Z Z’-factor....................................................... 235, 237, 239 Z-stack ........................................135, 139, 201, 202, 218 Zymogen, see Prodomain Zymography ............. 152, 153, 158, 160, 168, 169, 173