Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction. Issue 11 (15th February 2023)

• Record Type: Journal Article
• Authors: Jiang, Yi; Wang, Ruheng; Feng, Jiuxin; Jin, Junru; Liang, Sirui; Li, Zhongshen; Yu, Yingying; Ma, Anjun; Su, Ran; Zou, Quan; Ma, Qin; Wei, Leyi
• Is Part Of: Advanced science. Volume 10:Issue 11(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-15
• Subjects: explainable deep hypergraph learning -- hypergraph multihead attention network -- peptide secondary structure prediction; Science -- Periodicals; 505
• DOI: 10.1002/advs.202206151
• Languages: English
• ISSNs: 2198-3844

Abstract

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 representations and the classification of secondary substructures. The importance of secondary structures in peptide tertiary structure reconstruction and downstream functional analysis is further demonstrated, highlighting the versatility of our models. To facilitate the use of the model, an online server is established which is accessible via http://inner.wei‐group.net/PHAT/ . The work is expected to assist in the design of functional peptides and contribute to the advancement of structural biology research. Abstract : Accurately predicting peptide secondary structures remains a challenging task due to the lack of discriminative information in short peptides. Based on transfer learning and hypergraph algorithm, sequential semantic information can be incorporated 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.