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Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction.
Jiang, Yi; Wang, Ruheng; Feng, Jiuxin; Jin, Junru; Liang, Sirui; Li, Zhongshen; Yu, Yingying; Ma, Anjun; Su, Ran; Zou, Quan; Ma, Qin; Wei, Leyi.
Afiliación
  • Jiang Y; School of Software, Shandong University, Jinan, Shandong, 250101, China.
  • Wang R; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, 250101, China.
  • Feng J; School of Software, Shandong University, Jinan, Shandong, 250101, China.
  • Jin J; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, 250101, China.
  • Liang S; School of Software, Shandong University, Jinan, Shandong, 250101, China.
  • Li Z; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, 250101, China.
  • Yu Y; School of Software, Shandong University, Jinan, Shandong, 250101, China.
  • Ma A; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, 250101, China.
  • Su R; School of Software, Shandong University, Jinan, Shandong, 250101, China.
  • Zou Q; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, 250101, China.
  • Ma Q; School of Software, Shandong University, Jinan, Shandong, 250101, China.
  • Wei L; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, Shandong, 250101, China.
Adv Sci (Weinh) ; 10(11): e2206151, 2023 04.
Article en En | MEDLINE | ID: mdl-36794291
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Péptidos / Algoritmos / Estructura Secundaria de Proteína Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Adv Sci (Weinh) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Péptidos / Algoritmos / Estructura Secundaria de Proteína Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Adv Sci (Weinh) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania