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

doi:10.1016/j.gpb.2022.11.014

	Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms
	Huang, Bin 1,2; Kong, Lupeng 1,3; Wang, Chao 1; Ju, Fusong 4; Zhang, Qi 5; Zhu, Jianwei 4; Gong, Tiansu 1,2; Zhang, Haicang 1,2,6; Yu, Chungong 1,2,6; Zheng, Wei-Mou 7; Bu, Dongbo 1,2,6
	2023-10-01
发表期刊	GENOMICS PROTEOMICS & BIOINFORMATICS
ISSN	1672-0229
卷号	21 期号:5 页码:913-925
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.
关键词	Protein folding Protein structure prediction Deep learning Transformer Language model
DOI	10.1016/j.gpb.2022.11.014
收录类别	SCI
语种	英语
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
WOS类目	Genetics & Heredity
WOS记录号	WOS:001186312300001
出版者	ELSEVIER
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38819
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 Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Changping Lab, Beijing 102206, Peoples R China 4.Microsoft Res AI4Sci, Beijing 100080, Peoples R China 5.Huawei Noahs Ark Lab, Wuhan 430206, Peoples R China 6.Zhongke Big Data Acad, Zhengzhou 450046, Peoples R China 7.Chinese Acad Sci, Inst Theoret Phys, 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.