CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

doi:10.1038/s41467-021-22869-8

	CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
	Ju, Fusong 1,2; Zhu, Jianwei 3; Shao, Bin 3; Kong, Lupeng 1,2; Liu, Tie-Yan 3; Zheng, Wei-Mou 2,4; Bu, Dongbo 1,2
	2021-05-05
发表期刊	NATURE COMMUNICATIONS
ISSN	2041-1723
卷号	12 期号:1 页码:9
摘要	Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co-evolution based on some hand-crafted features, say, a covariance matrix, calculated from multiple sequence alignment (MSA) of target protein. This indirect strategy, however, cannot fully exploit the information carried by MSA. Here, we report an end-to-end deep neural network, CopulaNet, to estimate residue co-evolution directly from MSA. The key elements of CopulaNet include: (i) an encoder to model context-specific mutation for each residue; (ii) an aggregator to model residue co-evolution, and thereafter estimate inter-residue distances. Using CASP13 (the 13th Critical Assessment of Protein Structure Prediction) target proteins as representatives, we demonstrate that CopulaNet can predict protein structure with improved accuracy and efficiency. This study represents a step toward improved end-to-end prediction of inter-residue distances and protein tertiary structures. Protein structure prediction is a challenge. A new deep learning framework, CopulaNet, is a major step forward toward end-to-end prediction of inter-residue distances and protein tertiary structures with improved accuracy and efficiency.
DOI	10.1038/s41467-021-22869-8
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFC0910405] ; National Key Research and Development Program of China[2020YFA0907000] ; National Natural Science Foundation of China[31671369] ; National Natural Science Foundation of China[31770775] ; National Natural Science Foundation of China[62072435]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000656480900002
出版者	NATURE RESEARCH
引用统计	被引频次：50[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/2XEOYT63/17597
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Jianwei; Bu, Dongbo
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Key Lab Intelligent Informat Proc,Big Data Acad, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Microsoft Res Asia, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Theoret Phys, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Ju, Fusong,Zhu, Jianwei,Shao, Bin,et al. CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction[J]. NATURE COMMUNICATIONS,2021,12(1):9.
APA	Ju, Fusong.,Zhu, Jianwei.,Shao, Bin.,Kong, Lupeng.,Liu, Tie-Yan.,...&Bu, Dongbo.(2021).CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.NATURE COMMUNICATIONS,12(1),9.
MLA	Ju, Fusong,et al."CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction".NATURE COMMUNICATIONS 12.1(2021):9.