Attention network for predicting T-cell receptor-peptide binding can associate attention with interpretable protein structural properties.

ABSTRACT: Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide-major histocompatibility complex (TCR-pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source-target attention neural network, to predict the TCR-pMHC interaction solely from the amino acid sequen

SUBMITTER: Koyama K 

PROVIDER: S-EPMC10759225 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature