IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction.

ABSTRACT: The accurate prediction of peptide-major histocompatibility complex (MHC) class I binding probabilities is a critical endeavor in immunoinformatics, with broad implications for vaccine development and immunotherapies. While recent deep neural network based approaches have showcased promise in peptide-MHC (pMHC) prediction, they have two shortcomings: (i) they rely on hand-crafted pseudo-sequence extraction, (ii) they do not generalize well to different datasets, which limits the practicality of these approaches. While existing methods rely on a 34 amino acid pseudo-sequence, our findings uncover the involvement of 147 positions in direct interactions between MHC and peptide. We further show that neural architectures can learn the intricacies of pMHC binding using even full sequences. To th

SUBMITTER: Kushwaha A 

PROVIDER: S-EPMC11540059 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature