EpiTCR-KDA: knowledge distillation model on dihedral angles for TCR-peptide prediction.

ABSTRACT:

Motivation

The prediction of the T-cell receptor (TCR) and antigen bindings is crucial for advancements in immunotherapy. However, most current TCR-peptide interaction predictors struggle to perform well on unseen data. This limitation may stem from the conventional use of TCR and/or peptide sequences as input, which may not adequately capture their structural characteristics. Therefore, incorporating the structural information of TCRs and peptides into the prediction model is necessary to improve its generalizability.

Results

We developed epiTCR-KDA (KDA stands for Knowledge Distillation model on Dihedral Angles), a new predictor of TCR-peptide binding that utilizes the dihedral angles between the residues of the peptide and the TCR as a structural descriptor. This structur

SUBMITTER: Pham MN 

PROVIDER: S-EPMC11646569 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature