Development of structure-based machine learning approaches for protein interfaces
Creators: name: Lu, Changpeng
Publisher: name: No Publisher Supplied
Year: 2024

description: Protein interfaces play vital roles as essential hubs in executing diverse protein functions within various biological processes, including proteolytic cleavage, enzymatic activities, viral attachment and entry, signal transduction, and transport. Machine learning (ML) has rapidly grown into one of the most popular and promising areas for predicting interface specificities efficiently and accurately. This dissertation aimed to use interface structures in machine learning for specific interface function prediction and designs. It focused on developing interpretable and generalizable models, with broad applications for various interface systems. The study also explored the importance of data quality, variety, and quantity for learning.To understand protease specificities, we need to quickly grasp the full substrate landscape of proteolytic cleavage and comprehend the underlying biological basis for functionally distant proteases. Herein, we introduced Protein Graph Convolutional Network (PGCN), a novel approach that leverages a physically-grounded, structure-based molecular interaction graph to capture molecular topology and interaction energies for the prediction of protease specificity. We showed PGCN’s high accuracy in predicting specificity landscapes for various variants of two model proteases, namely the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) protease. Through node and edge ablation tests, we identified key elements within the graph that contribute significantly to specificity prediction, aligning with known biochemical constraints governing protease:substrate recognition. We further employed a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving non-canonical substrates, achieving excellent agreement with experimental cleavage results. Most importantly, we found the model can accurately assess designs featuring diversity at positions not present in the training data. We expanded PGCN's use with CleavEX, a user-friendly pipeline for preprocessing deep sequencing data, enabling efficient categorization of substrates for various enzymes. It successfully prepared data for six 3C-like proteases, achieving state-of-the-art accuracies with PGCN models. We also explored PGCN's potential in predicting binding interactions, specifically between the RBD of the SARS-COV-2 spike protein and ACE2, as well as PDZ domain binding.To further explore data quality, we analyzed small molecule ligand data from the Protein Data Bank, identifying distribution and dependencies of ligand structure quality metrics. This led to composite ligand quality indicators for simplified ligand selection in drug design. Additionally, we introduced a novel all-and-subsample clustering framework to handle large-scale clustering problems, requiring human review for biological significance verification. The process can be extended to analyze various types of large datasets.Our research in this dissertation presents promising solutions for interface prediction, design, and structure quality assessment using structure-aware machine learning approaches. These findings offer possibilities for developing customized protein editors capable of selectively and irreversibly modifying specific targets, opening new avenues for future therapeutic applications.