TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

ABSTRACT:

Motivation

T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding patterns between TCRs and peptides is critical for advancing these clinical applications. While several computational tools have been developed, they neglect the directional semantics inherent in sequence data, which are essential for accurately characterizing TCR-peptide interactions.

Results

To address this gap, we develop TPepRet, an innovative model that integrates subsequence mining with semantic integration capabilities. TPepRet combines the strengths of the Bidirectional Gated Recurrent Unit (BiGRU) network for capturing bi

SUBMITTER: Wang M 

PROVIDER: S-EPMC11784750 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature