Structure-aware deep model for MHC-II peptide binding affinity prediction.

ABSTRACT: The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Expe

SUBMITTER: Yu Y 

PROVIDER: S-EPMC10826266 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature