DPCMHC: efficient prediction of MHC-peptide binding affinity by deep learning based on dual-padding convolution

Predicting peptide binding affinities to major histocompatibility complex class I (MHCI) is a crucial challenge in immunological bioinformatics and is essential for identifying neoantigens in personalized cancer vaccines. Current deep learning methods often struggle, especially with 10-mer and 11-mer peptides. To address this, we developed DPCMHC, an advanced deep learning model featuring an embedding module, a dual-padding convolutional module, a BiLSTM module, and an output module. This design enhances the models understanding of amino acid sequences and their lengths. DPCMHC effectively captures the information on the beginning and end of amino acids, as well as the diverse sizes of adjacent amino acids. Using concatenation, the model extracts continuous sequence information. We rigorously evaluated DPCMHC on three benchmark datasets, demonstrating its superior or comparable performance to existing state-of-the-art methods. Our validation results confirm that DPCMHC is a robust and efficient tool for predicting MHC-peptide binding affinities.