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

doi:10.1088/1674-1056/ad1a92

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.

Keywords: machine learning molecular simulation protein conformational space enhanced sampling

Received: 17 November 2023
Revised: 11 December 2023
Accepted manuscript online: 04 January 2024

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	87.15.ap	(Molecular dynamics simulation)
	87.14.E-	(Proteins)
	87.15.B-	(Structure of biomolecules)

Fund: 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).

Corresponding Authors: Yiming Tang, Guanghong Wei
E-mail: ymtang@fudan.edu.cn;ghwei@fudan.edu.cn

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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) Recent advances in protein conformation sampling by combining machine learning with molecular simulation 2024 Chin. Phys. B 33 030701

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

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