Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA0201

Class 1 major histocompatibility complexes (MHC-I), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nucleated cells. Both self and foreign proteins are processed to peptides of 8 to 10 amino acids, loaded into MCH-1 within the endoplasmic reticulum and then presented on the cell surface. Foreign peptides presented in this fashion activate CD8+ T cells and their immunogenicity correlates with their affinity for the MHC-1 binding groove. Thus, predicting antigen binding affinity for MHC-I is a valuable tool for identifying potentially immunogenic antigens. While quite a few predictors for MHC-I binding exist, there are no currently available tools that can predict antigen/MHC-I binding affinity for antigens with explicitly labeled post-translational modifications or unusual/non-canonical amino acids (NCAAs). However, such modifications are increasingly recognized as critical mediators of peptide immunogenicity. In this work, we propose a machine learning application that quantifies the binding affinity of epitopes containing NCAAs to MHC-I and compares its performance with other commonly used regressors. Our model demonstrates robust performance, with 5-fold cross-validation yielding an R2 value of 0.477 and a root-mean-square error (RMSE) of 0.735, indicating strong predictive capability for peptides with NCAAs. This work provides a valuable tool for the computational design and optimization of peptides incorporating NCAAs, potentially accelerating the development of novel peptide-based therapeutics with enhanced properties and efficacy.