中国物理B ›› 2024, Vol. 33 ›› Issue (3): 30701-030701.doi: 10.1088/1674-1056/ad1a92

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Recent advances in protein conformation sampling by combining machine learning with molecular simulation

Yiming Tang(唐一鸣), Zhongyuan Yang(杨中元), Yifei Yao(姚逸飞), Yun Zhou(周运), Yuan Tan(谈圆),Zichao Wang(王子超), Tong Pan(潘瞳), Rui Xiong(熊瑞), Junli Sun(孙俊力), and Guanghong Wei(韦广红)   

  1. Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Shanghai 200438, China
  • 收稿日期:2023-11-17 修回日期:2023-12-11 接受日期:2024-01-04 出版日期:2024-02-22 发布日期:2024-02-29
  • 通讯作者: Yiming Tang, Guanghong Wei E-mail:ymtang@fudan.edu.cn;ghwei@fudan.edu.cn
  • 基金资助:
    Project supported by the National Key Research and Development Program of China (Grant No. 2023YFF1204402), the National Natural Science Foundation of China (Grant Nos. 12074079 and 12374208), the Natural Science Foundation of Shanghai (Grant No. 22ZR1406800), and the China Postdoctoral Science Foundation (Grant No. 2022M720815).

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

Yiming Tang(唐一鸣), Zhongyuan Yang(杨中元), Yifei Yao(姚逸飞), Yun Zhou(周运), Yuan Tan(谈圆),Zichao Wang(王子超), Tong Pan(潘瞳), Rui Xiong(熊瑞), Junli Sun(孙俊力), and Guanghong Wei(韦广红)   

  1. Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Sciences (Ministry of Education), Shanghai 200438, China
  • Received:2023-11-17 Revised:2023-12-11 Accepted:2024-01-04 Online:2024-02-22 Published:2024-02-29
  • Contact: Yiming Tang, Guanghong Wei E-mail:ymtang@fudan.edu.cn;ghwei@fudan.edu.cn
  • Supported by:
    Project supported by the National Key Research and Development Program of China (Grant No. 2023YFF1204402), the National Natural Science Foundation of China (Grant Nos. 12074079 and 12374208), the Natural Science Foundation of Shanghai (Grant No. 22ZR1406800), and the China Postdoctoral Science Foundation (Grant No. 2022M720815).

摘要: The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

关键词: machine learning, molecular simulation, protein conformational space, enhanced sampling

Abstract: The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Key words: machine learning, molecular simulation, protein conformational space, enhanced sampling

中图分类号:  (Neural networks, fuzzy logic, artificial intelligence)

  • 07.05.Mh
87.15.ap (Molecular dynamics simulation) 87.14.E- (Proteins) 87.15.B- (Structure of biomolecules)