COMPUTATIONAL METHODS TO IDENTIFY AND TARGET DRUGGABLE BINDING SITES AT PROTEIN-PROTEIN INTERACTIONS IN THE HUMAN PROTEOME David Xu Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the School of Informatics and Computing, Indiana University September 2019 Accepted by the Graduate Faculty of Indiana University, in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Doctoral Committee ______________________________________ Huanmei Wu, PhD, Co-Chair ______________________________________ Samy Meroueh, PhD, Co-Chair April 15, 2019 ______________________________________ Xiaowen Liu, PhD ______________________________________ Sarath Chandra Janga, PhD ______________________________________ Yunlong Liu, PhD ii © 2019 David Xu iii DEDICATION To my family, without whom this journey would not be possible. iv ACKNOWLEDGEMENT First and foremost, I would like to express my sincere appreciation to my advisor Dr. Samy Meroueh for his support during my graduate studies and related research over the past years. I would also like to thank him for his insight, generosity, and support throughout this experience, without whom this would not be possible. Sincere gratitude is given to my committee co-chair, Dr. Huanmei Wu, for her time and guidance through the Ph.D. process. I would also like to thank all my committee members, Dr. Xiaowen Liu, Dr. Sarath Chandra Janga, and Dr. Yunlong Liu, for their valuable comments and suggestions on my work. I would like to express my sincerest gratitude to all my colleagues through the years in the Center for Computational Biology and Bioinformatics: Dr. Liwei Li, Dr. Yubing Si, Dr. Michael Wu, Dr. Jack Chiang, Dr. Hai Lin, Bo Wang, and many others, for thoughtful discussions and valuable insights into the interdisciplinary field of bioinformatics. I would also like to thank the other members of the lab, Dr. Khuchtumur Bum-Erdene, Dr. Donghui Zhou, Dr. Degang Liu, and Mona Ghozayel, without whom much of this work would remain computational and without validation. I am grateful to collaborators at the Open Science Grid, including Rob Quick, Scott Teige, and Mats Rynge, for providing high-throughput computational resources without which much of this work would not be possible. I am indebted to Dr. Yaoqi Zhou, who guided me through the first year of my MS study. I am thankful to my undergraduate research advisor, Dr. John Cheeseman, who introduced me to the research process. Finally, I would like to express my deepest love to my family. My dearest thanks to my late father, Dr. Jigeng Xu, who passed away unexpected during my Ph.D. journey, but has forever instilled the drive for hard work and focus that made this work possible. To my mother, Li Lu, who taught me to be honest and warm, who I know will forever support me in my journey wherever it may go. To my little sister, Alice, for her love and support over the years. v David Xu COMPUTATIONAL METHODS TO IDENTIFY AND TARGET DRUGGABLE BINDING SITES AT PROTEIN-PROTEIN INTERACTIONS IN THE HUMAN PROTEOME Protein-protein interactions are fundamental in cell signaling and cancer progression. An increasing prevalent idea in cancer therapy is the development of small molecules to disrupt protein-protein interactions. Small molecules impart their action by binding to pockets on the protein surface of their physiological target. At protein-protein interactions, these pockets are often too large and tight to be disrupted by conventional design techniques. Residues that contribute a disproportionate amount of energy at these interfaces are known as hot spots. The successful disruption of protein-protein interactions with small molecules is attributed to the ability of small molecules to mimic and engage these hot spots. Here, the role of hot spots is explored in existing inhibitors and compared with the native protein ligand to explore how hot spot residues can be leveraged in protein-protein interactions. Few studies have explored the use of interface residues for the identification of hit compounds from structure-based virtual screening. The tight uPAR•uPA interaction offers a platform to test methods that leverage hot spots on both the protein receptor and ligand. A method is described that enriches for small molecules that both engage hot spots on the protein receptor uPAR and mimic hot spots on its protein ligand uPA. In addition, differences in chemical diversity in mimicking ligand hot spots is explored. In addition to uPAR•uPA, there are additional opportunities at unperturbed protein-protein interactions implicated in cancer. Projects such as TCGA, which systematically catalog the hallmarks of cancer across multiple platforms, provide opportunities to identify novel protein- protein interactions that are paramount to cancer progression. To that end, a census of cancer- specific binding sites in the human proteome are identified to provide opportunities for drug discovery at the system level. Finally, tumor genomic, protein-protein interaction, and protein structural data is integrated to create chemogenomic libraries for phenotypic screening to uncover novel GBM targets and generate starting points for the development of GBM therapeutic agents. Huanmei Wu, PhD, Co-Chair Samy Meroueh, PhD, Co-Chair vi TABLE OF CONTENTS List of Tables ................................................................................................................................. xii List of Figures ............................................................................................................................... xiii Chapter 1. Introduction .................................................................................................................... 1 1.1 Background ......................................................................................................................... 1 1.1.1 Cancer Genomics ....................................................................................................... 1 1.1.2 Protein-Protein Interactions ....................................................................................... 1 1.2 Challenges Addressed ......................................................................................................... 3 1.3 Major Contributions ............................................................................................................ 4 Chapter 2. A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein-Protein Interaction Interfaces ............................................................................................ 11 2.1 Introduction ....................................................................................................................... 11 2.2 Results ............................................................................................................................... 12 2.2.1 Protein-Protein and Protein-Compound Complexes ................................................ 12 2.2.2 Molecular Dynamics Simulations and Free Energy Calculations ............................ 18 2.2.3 Computational Alanine Scanning and Free Energy Decomposition ........................ 22 2.2.4 Bcl-xL•Bak .............................................................................................................. 22 2.2.5 MDM2•p53 .............................................................................................................. 27 2.2.6 XIAP•Smac .............................................................................................................. 30 2.2.7 IL-2•IL-2Rα ............................................................................................................. 34 2.2.8 BRD4•H4 ................................................................................................................. 38 2.2.9 Mimicking Hot Spots on the Protein Ligand ........................................................... 41 2.2.10 Effect of Native Protein Ligand and Small-Molecule Inhibitors on Receptor Dynamics ........................................................................................................... 49 2.3 Discussion ......................................................................................................................... 49 2.4 Materials and Methods ...................................................................................................... 51 2.4.1 Structural Preparation .............................................................................................. 51 2.4.2 Molecular Dynamics ................................................................................................ 52 2.4.3 Free Energy Calculations ......................................................................................... 53 2.4.4 Alanine Scanning ..................................................................................................... 54 2.4.5 Decomposition Energy............................................................................................. 54 2.4.6 Ligand Pharmacophore ............................................................................................ 56 2.4.7 Dynamic Cross-Correlation Matrix ......................................................................... 56 2.4.8 Statistical Analysis ................................................................................................... 57 vii Chapter 3. Mimicking Intermolecular Interactions of Tight Protein-Protein