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Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model.
Wang, Jianmin; Wang, Xun; Chu, Yanyi; Li, Chunyan; Li, Xue; Meng, Xiangyu; Fang, Yitian; No, Kyoung Tai; Mao, Jiashun; Zeng, Xiangxiang.
Afiliación
  • Wang J; The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Korea.
  • Wang X; School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, P. R. China.
  • Chu Y; High Performance Computer Research Center, University of Chinese Academy of Sciences, Beijing 100190, P. R. China.
  • Li C; Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, United States.
  • Li X; School of Informatics, Yunnan Normal University, Kunming, Yunnan 650500, P. R. China.
  • Meng X; School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, P. R. China.
  • Fang Y; School of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, P. R. China.
  • No KT; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
  • Mao J; The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Korea.
  • Zeng X; School of Medical Information and Engineering, Southwest Medical University, Luzhou, Sichuan 646000, P. R. China.
J Chem Theory Comput ; 20(11): 4469-4480, 2024 Jun 11.
Article en En | MEDLINE | ID: mdl-38816696
ABSTRACT
Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Conformación Proteica / Proteínas / Simulación de Dinámica Molecular Idioma: En Revista: J Chem Theory Comput Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Conformación Proteica / Proteínas / Simulación de Dinámica Molecular Idioma: En Revista: J Chem Theory Comput Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos