CapsNet-MHC predicts peptide-MHC class I binding based on capsule neural networks.

ABSTRACT: The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insi

SUBMITTER: Kalemati M 

PROVIDER: S-EPMC10162658 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature