Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction.

ABSTRACT: Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. In this study, PHAT is proposed, a deep hypergraph learning framework for the prediction of peptide secondary structures and the exploration of downstream tasks. The framework includes a novel interpretable deep hypergraph multi-head attention network that uses residue-based reasoning for structure prediction. The algorithm can incorporate sequential semantic information from large-scale biological corpus and structural semantic information from multi-scale structural segmentation, leading to better accuracy and interpretability even with extremely short peptides. The interpretable models are able to highlight the reasoning of structural feature rep

SUBMITTER: Jiang Y 

PROVIDER: S-EPMC10104664 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature