RPEMHC: improved prediction of MHC–peptide binding affinity by a deep learning approach based on residue–residue pair encoding

Abstract

Abstract Motivation Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC–peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC–peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. Results In this work, we propose RPEMHC, a new deep learning approach based on residue–residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue–residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC–peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC–peptide interactions and can potentially facilitate the vaccine development. Availability The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.