Machine Learning Based Vaccine Design and HLA Based Risk Prediction for Viral Infections

We will develop and apply new methods for vaccine design and viral disease severity prediction based upon our recent developments in the prediction of the presentation of viral antigens by Class I and Class II MHC complexes. These methods will be implemented on the C3 AI Suite and other platforms, and use cloud resources for vaccine combinatorial optimization that involves considering over ~39,000 peptides to select a compact set for vaccine formulation. A benefit of our approach is that it can be targeted to any virus given its genome sequence. While our current work is based upon the development of vaccines and risk models for COVID-19, it is directly applicable to new viral strains. Given the potential escape of SARS-CoV-2 from vaccination by mutation, it is valuable to have methods to rapidly develop new vaccines and predict the severity of evolved viruses based on an individual's genotype. Our efforts are organized in two specific aims. We will develop a new computational platform for selecting epitopes that will be focused on stimulating both CD4+ and CD8+ T Cell responses to viral infection based upon the combinatorial optimization of peptide display (Aim 1). We will develop a risk model of viral disease severity based upon an individual's HLA type that determines what viral components will be displayed to their adaptive immune system (Aim 2).