CDS&E: Simulation- and Data-driven Peptide Antibody Design Targeting RBD and non-RBD Epitopes of SARS-CoV-2 Spike Protein

Northwestern University, Evanston IL

Investigators

Abstract

Drugs interact with proteins to disrupt bacterial and viral infections. Effective drugs are usually discovered rather than designed. Antibodies are protein complexes generated by the immune system to bind to and inactivate viruses. Peptides are short strings of amino acids that are being designed to mimic the protein binding activity of antibodies. Many aspects of protein-protein and protein-peptide interactions are not clearly understood. This project will apply an artificial intelligence approach to understand those interactions. The SARS-CoV-2 spike protein will be the model system for study. The resulting model for therapeutic peptide design will be provided to the research community on a variety of software platforms. The project will also support outreach to K-12 students regarding the SARS-CoV-2 virus and viral infections. The overall objective is to develop a hybrid machine learning-simulation (MLSim) platform that allows us to better understand the molecular interaction between peptide drugs and viral proteins. The model viral protein system will be the SARS-CoV-2 spike proteins at both the receptor-binding domain (RBD) and the non-RBD. Transfer learning techniques for existing data models for protein-peptide interactions will be implemented. Online learning techniques will allow for the timely update of the predictive models with newly available data. The multiscale simulation component aids the machine learning part by supplying high-fidelity input data and cross-validating the predictions These efforts should result in molecular-level insight into viral protein-antibody interactions. There are two key outcomes anticipated from this project. First, a simulation- and data-enabled platform that integrates a high-throughput, customizable machine learning pipeline for fast screening and filtering peptide candidates, with high-fidelity all-atom explicit-solvent molecular dynamics simulation and free energy calculations. The second is fundamental insight into viral protein-peptide interactions and how those influence the design of neutralizing peptides. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.