GraphMHC: Neoantigen prediction model applying the graph neural network to molecular structure.

ABSTRACT: Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been primarily used for these prediction models, it is difficult to interpret the models reported thus far as accurately representing the interactions between biomolecules. In this study, we propose the GraphMHC model, which utilizes a graph neural network model applied to molecular structure to simulate the binding between MHC proteins and peptide sequences. Amino acid sequences sourced from the immune epitope database (IEDB) undergo conversion into molecular structures. Subsequently, atomic intrinsic informations and inter-atomic connections are extrac

SUBMITTER: Jeong H 

PROVIDER: S-EPMC10971776 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature