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

文献类型：期刊论文

作者Huang, Bin6,7; Kong, Lupeng5,7; Wang, Chao7; Ju, Fusong4; Zhang, Qi3; Zhu, Jianwei4; Gong, Tiansu6,7; Zhang, Haicang2,6,7; Yu, Chungong2,6,7; Zheng, Wei-Mou1
刊名GENOMICS PROTEOMICS & BIOINFORMATICS
出版日期2023-10-01
卷号21期号:5页码:913-925
关键词Protein folding Protein structure prediction Deep learning Transformer Language model
ISSN号1672-0229
DOI10.1016/j.gpb.2022.11.014
英文摘要Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer 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 cultures 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.
资助项目National Key R&D Program of China[2020YFA0907000] ; National Natural Science Foundation of China[32271297] ; National Natural Science Foundation of China[62072435] ; National Natural Science Foundation of China[31770775] ; National Natural Science Foundation of China[31671369]
WOS研究方向Genetics & Heredity
语种英语
WOS记录号WOS:001186312300001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/38818]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Haicang; Yu, Chungong; Zheng, Wei-Mou; Bu, Dongbo
作者单位1.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
2.Zhongke Big Data Acad, Zhengzhou 450046, Peoples R China
3.Huawei Noahs Ark Lab, Wuhan 430206, Peoples R China
4.Microsoft Res AI4Sci, Beijing 100080, Peoples R China
5.Changping Lab, Beijing 102206, Peoples R China
6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
7.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Bin,Kong, Lupeng,Wang, Chao,et al. Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms[J]. GENOMICS PROTEOMICS & BIOINFORMATICS,2023,21(5):913-925.
APA Huang, Bin.,Kong, Lupeng.,Wang, Chao.,Ju, Fusong.,Zhang, Qi.,...&Bu, Dongbo.(2023).Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.GENOMICS PROTEOMICS & BIOINFORMATICS,21(5),913-925.
MLA Huang, Bin,et al."Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms".GENOMICS PROTEOMICS & BIOINFORMATICS 21.5(2023):913-925.

入库方式： OAI收割

来源：计算技术研究所

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