Computational modeling, prediction, and design of Protein-Protein interactions

Abstract

Protein-protein interactions (PPIs) govern nearly all biological processes in human health and diseases, ranging from enzyme catalysis and inhibition, to signaling and gene regulation. Understanding the dynamics of protein interactions and the structure of protein complexes at an atomic level is key in delineating disease mechanisms, such as Huntington’s, Alzheimer’s, and cancer, and developing intervention strategies. Investigation of these structural complexes by experimental techniques is often expensive, laborious, and limited. Computational modeling provides an alternative route to elucidate structures and guide molecular engineering based on PPIs. A longstanding challenge limiting the accuracy of computational methods is the ability to predict binding-induced conformational changes during protein-protein association. In my thesis, I address this challenge by creating new tools to predict atomistic models of flexible protein complexes. First, I develop a protein docking protocol that incorporates temperature replica exchange Monte Carlo (T-REMC) and backbone flexibility to mimic induced-fit approach of protein interactions. On a benchmark of 88 protein complexes with varying degrees of flexibility, this protocol, ReplicaDock 2.0, is the first method to successfully dock 62% of complexes with conformational changes up to 2.2 Å. Building on the success of ReplicaDock2.0, I extend it to develop a novel sampling approach, namely resolution exchange. In this approach, exchanges are performed between the full-atom and the centroid configurations to improve backbone sampling and escape entrapment in non-native minima. Finally, I conclude my docking methods development work by creating a pipeline that fuses AlphaFold (a deep-learning tool for protein sequence-to-structure prediction) with aforementioned docking techniques to develop a method for improved complex structure prediction. In conjunction with development of foundational protein structure prediction tools, I equip docking tools to make contributions to human health and disease. First, to extend the functionality of MC approaches for capturing dynamics, I model the interactions between an outer membrane nutrient transporter (on bacterial cells) and a bacteriocin (Colicin B), and deduce the translocation pathway for Colicin B through the transporter. Next, I apply my knowledge of PPIs to create novel complex designs. I demonstrate a computational approach to create orthogonal interfaces with experimental validation for the PDGF signaling system. This technology has tremendous potential in regenerative medicine and therapeutic discovery as an orthogonal signaling system eliminates off-target risks (e.g., cancer) and promotes exclusivity. In sum, my work has advanced our understanding and our ability to model and design flexible protein-protein interactions