Compound Interaction Presentation Learning for MHC-peptide Binding Affinity Prediction

Abstract

The interaction between peptides and Major Histocompatibility Complex Class I (MHC-I) molecules plays a critical role in adaptive immune recognition. Although computational prediction algorithms have advanced over traditional experimental methods, challenges still remain. There is a scarcity of standardized datasets that provide comprehensive profiles of MHC-peptide structure. The polymorphism of MHC molecules introduces diverse binding patterns, complicating the characterization of specific amino acid interaction pairs. To address these issues, we introduce GSM, a novel deep learning model that combines a Graph Attention Neural Network with a Self-Attention Convolutional Neural Network to predict MHC-peptide binding affinities. By integrating self-attention mechanisms to capture global peptide-MHC interactions and graph-based modeling to represent local amino acid pairwise interactions, GSM provides a comprehensive understanding of binding mode. Compared to existing algorithms, GSM shows superior performance and greater stability across diverse allele datasets, as demonstrated on the benchmarks. Furthermore, by leveraging real 3D structural data and attention visualization, GSM is capacity of selecting interaction sites, offering valuable insights for vaccine design and advancing immunological research.