DeepMHCI: an anchor position-aware deep interaction model for accurate MHC-I peptide binding affinity prediction.

ABSTRACT:

Motivation

Computationally predicting major histocompatibility complex class I (MHC-I) peptide binding affinity is an important problem in immunological bioinformatics, which is also crucial for the identification of neoantigens for personalized therapeutic cancer vaccines. Recent cutting-edge deep learning-based methods for this problem cannot achieve satisfactory performance, especially for non-9-mer peptides. This is because such methods generate the input by simply concatenating the two given sequences: a peptide and (the pseudo sequence of) an MHC class I molecule, which cannot precisely capture the anchor positions of the MHC binding motif for the peptides with variable lengths. We thus developed an anchor position-aware and high-performance deep model, DeepMHCI, with a posit

SUBMITTER: Qu W 

PROVIDER: S-EPMC10516514 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature