BOSTON UNIVERSITY GRADUATE SCHOOL OF ARTS AND SCIENCES AND COLLEGE OF ENGINEERING Dissertation DEVELOPMENT OF STRUCTURE-BASED COMPUTATIONAL METHODS FOR PREDICTION AND DESIGN OF PROTEIN-PROTEIN INTERACTIONS by BRIAN GREGORY PIERCE B.S., Duke University, 2000 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2008 UMI Number: 3298669 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. ® UMI UMI Microform 3298669 Copyright 2008 by ProQuest LLC. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ProQuest LLC 789 E. Eisenhower Parkway PO Box 1346 Ann Arbor, Ml 48106-1346 Approved by First Reader "23 Zhiping Weng, Ph.D. Associate Professor of Biomedical Engineering Second Readei Charles DeLisi,Ph.D. Arthur G. B. Metcalf Professor of Science and Engineering ACKNOWLEDGEMENTS There are so many people that have provided help and inspiration to me as I've performed this work, far too many to recount in this text. That being said, I will now acknowledge some of the most prominent. First, I would like to thank my wife, Laura, for inspiration, moral support, listening, and understanding as I worked so diligently on this research. As I grew and became involved in science, my parents and sister have also provided invaluable support. Without question, this work could not have been performed without the guidance and expertise of my advisor, Professor Zhiping Weng. She gave me the opportunity to learn and pursue a variety of projects, providing insightful comments and criticism when necessary. I would also like to thank my thesis committee, Professor Charles DeLisi, Professor John Straub, Dr. Enoch Huang, and Professor Scott Mohr for their time, and for their useful comments and suggestions. All of the members of Zlab and the BU Bioinformatics Program have provided invaluable discussions, ideas, and feedback; in particular, Jaafar Haidar, Yong Yu, Kevin Wiehe, Weiwei Tong, Julian Mintseris, Rong Chen, and Howook Hwang. Yong Yu, Jaafar Haidar, and Bruce Miller deserve special mention for their hard work on the experiments related to the TCR, CD4, and LANA proteins. For computing support and expertise, I would like to thank Mary Ellen Fitzpatrick, and for administrative support, Caroline Lyman, Jessica Barros, and Robert Henry. iii I would also like to acknowledge the late Professor Michael Laskowski for gift of the dataset of mutation energies for the OMTKY system to my lab. Also, Marcia Osburne and Cassidy Dobson for the gift of the pelB-CD4 constructs, the late Prof. Don Wiley for the constructs for HLA-A2, and Prof. Brian Baker for the A6 TCR constructs. This work was supported by NSF grants DBI-0078194, DBI-0133834 and DBI-0116574. iv DEVELOPMENT OF STRUCTURE-BASED COMPUTATIONAL METHODS FOR PREDICTION AND DESIGN OF PROTEIN-PROTEIN INTERACTIONS (Order No. ) BRIAN GREGORY PIERCE Boston University Graduate School of Arts and Sciences and College of Engineering, 2008 Major Professor: Zhiping Weng, Associate Professor of Biomedical Engineering ABSTRACT Protein-protein interactions play a key role in the functioning of cells and pathways, and understanding these interactions on a physical and structural level can help greatly in developing therapeutics for diseases. The large amount of protein structures available presents an immense opportunity to model and predict protein interactions using computational techniques. Here we describe the development of algorithms to predict protein complex structures (referred to as protein docking) and to design proteins to improve their interaction affinities. We also present experimental results validating our protein design approach. The protein docking work we present includes the symmetric multimer docking program M- ZDOCK as well as ZRANK which rescores docking predictions using a weighted potential. Both programs have been successful when applied to docking benchmarks and in the CAPRI experiment. In addition, we have used the M-ZDOCK program to produce a tetrameric model for a disease-associated protein, the latent nuclear antigen of the Kaposi's sarcoma-associated herpesvirus. v We have also developed a protein design algorithm to improve the binding between two proteins, given their complex structure. This was applied to a T cell receptor (TCR) to enhance its binding to the Major Histocompatibility Complex and peptide. Several of the point mutations predicted by our algorithm were verified experimentally to bind several times stronger than wild type; we then combined these mutations to produce a TCR with approximately 100-fold affinity improvement. Further testing of combinations of TCR point mutations has led to striking results regarding the kinetics and cooperativity of the mutations. Finally, we have used our protein design algorithm to predict designability of protein complexes from the Protein Data Bank, and identified the complex between CD4 and HIV gpl20 as a target for future structure-based design efforts. Preliminary results for this project are given. vi TABLE OF CONTENTS List of Tables viii List of Figures x List of Abbreviations xii Chapter 1 Introduction 1 Chapter 2 M-ZDOCK: Incorporating Symmetric Searching 4 into Rigid-Body Docking Chapter 3 ZRANK: Optimal Reranking of Initial-Stage Protein 27 Docking Predictions Chapter 4 Refining Protein Docking Predictions Using ZRANK 51 and RosettaDock Chapter 5 Modeling Protein Interaction Affinity Enhancement, 81 and Application to TCR/peptide/MHC complex Chapter 6 Studying Cooperativity of TCR Mutations Through 110 Modeling and Experiments Chapter 7 Exploring the In Silico Designability of Protein 126 Complexes, and Affinity Enhancement of CD4/gpl20 Chapter 8 Summary and Future Directions 143 List of Journal Abbreviations 146 Bibliography 148 Curriculum Vitae 161 vii LIST OF TABLES 2.1 The Unbound Multimer Test Cases 14 2.2 Residues Removed from Multimeric Structures Before Determining Interface 15 Ca Atoms 2.3 M-ZDOCK Results for Quasi-Bound and Bound Test Cases 17 2.4 M-ZDOCK Results for Unbound Test Cases 18 3.1 Results from ZDOCK 2.1 and ZDOCK 2.3 Before and After ZRANK 36 3.2 Charge Terms Used for the ZRANK Candidate Electrostatics Functions 41 4.1 Results for Near-Hit Cases of the ZD3.OZR Set, Before and After Refinement 64 4.2 Number of Cases with Top-Ranked Hits Before and After Refinement 67 4.3 CAPRI Scoring Results Using ZRANK and RosettaDock 69 5.1 Binding Kinetics and Prediction Method for A6 TCR Point Mutants 90 5.2 TCR Point Mutations that Exhibited No Measurable Binding to pepMHC 91 5.3 Weighted ZAFFI Terms, Total Energy Scores, and Measured AAGs for TCR 93 Point mutations 5.4 Contribution of ZAFFI Scoring Terms to Correlation with Measured TCR 95 Point Mutations 5.5 ZAFFI Scores, Filter Scores, and Measured Binding for All TCR Point 96 Mutants 5.6 Binding Kinetics of Combinations of Point Mutants, and Cooperativity 98 of the Energetics 5.7 Binding Kinetics and Specificity of WFGMT TCR Mutant for HLA-A2 with 99 Tax Peptide and V7R Point Mutant of Tax Peptide 6.1 Binding Energy Changes of Combinations of A6 TCR Point Mutants, and 113 Cooperativity 6.2 Inter-Residue Energy Terms for Combinations of Point Mutations on the 115 TCR a Chain viii 6.3 Association Rates, Dissociation Rates, and Binding Energy Changes for 116 Combinations of Mutations from Different Chains 7.1 In Silico Designability of Nonredundant Transient Enzyme/Inhibitor and 132 Other Complexes 7.2 In Silico Designability of Nonredundant Transient Antibody/Antigen 132 Complexes LIST OF FIGURES 2.1 Diagram of Successive Rotations Through Euler Angles <j) and 0 23 2.2 The Relative Positions of the Subunits of a C3 Multimer 24 2.3 The Highest-Ranked Hits of M-ZDOCK Superposed onto the Structures 25 of the Complexes 2.4 Structural Model of the Latent Nuclear Antigen (LANA) Carboxy Terminal 26 Domain Tetramer Bound to the LBS-1 and LBS-2 DNA Binding Sites 3.1 Success Rate Versus the Number of Predictions for Reranked Predictions 47 3.2 Score Versus Interface RMSD Plots Using ZDOCK and ZRANK for 48 Several Test Cases 3.3 Success Rate for Reranking Various Numbers of ZDOCK Predictions, and 49 Success Rate comparison for Various Short-Range Electrostatics Formulations 3.4 Success Rate as a Function of the Quality of the Predictions for ZDOCK 2.3 50 and Reranked ZDOCK 2.3 4.1 Hit Success Rate and Hit and Near-hit success rate for ZDOCK 2.3 and 73 ZDOCK 3.0 with and without ZRANK 4.2 Protocol Employed for Docking and Refinement 74 4.3 Histogram of Interface RMSD Change for All Hit and Near-Hit Models After 75 Refinement 4.4 Percent of Models with RMSD Improvement for Several Search/Scoring 76 Strategies 4.5 Percent of Models with Hits After Refinement for Several Search/Scoring 77 Strategies 4.6 Refinement of Three Test Cases, with Rosetta Scores and ZRANK Scores 78 Versus Interface RMSD 4.7 Success Rates of Refinement for Predictions for Hit and Near-Hit 79 Cases for Various Numbers of RosettaDock Refinement Models 4.8 Refined Structure for Test Case 1IQD 80 x 5.1 A AG k^ and A AG k^ versus A AG for 18 Measured