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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and com-puter science.These researchers adopt various research paradigms to attack the same structure pre-diction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribu-tion of protein structures given a target sequence and then find the most likely structure,while com-puter scientists formulate protein structure prediction as an optimization problem—finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cul-tures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Protein foldingProtein structure predictionDeep learningTransformerLanguage model

Bin Huang、Lupeng Kong、Chao Wang、Fusong Ju、Qi Zhang、Jianwei Zhu、Tiansu Gong、Haicang Zhang、Chungong Yu、Wei-Mou Zheng、Dongbo Bu

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Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China

University of Chinese Academy of Sciences,Beijing 100049,China

Changping Laboratory,Beijing 102206,China

Microsoft Research AI4Science,Beijing 100080,China

Huawei Noah's Ark Lab,Wuhan 430206,China

Zhongke Big Data Academy,Zhengzhou 450046,China

Institute of Theoretical Physics,Chinese Academy of Sciences,Beijing 100190,China

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National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China

2020YFA090700032271297620724353177077531671369

2023

10.1016/j.gpb.2022.11.014

基因组蛋白质组与生物信息学报(英文版)
中国科学院北京基因组研究所

基因组蛋白质组与生物信息学报(英文版)

CSTPCDCSCD
影响因子：0.495
ISSN：1672-0229
年,卷(期)：2023.21(5)
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