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

doi:10.1038/s41467-021-22869-8

ITP OpenIR > SCI期刊论文

	Ju, Fusong ; Zhu, Jianwei ; Shao, Bin ; Kong, Lupeng ; Liu, Tie-Yan ; Zheng, Wei-Mou; Bu, Dongbo 1
	CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
Source Publication	NATURE COMMUNICATIONS
Language	英语
Keyword	CONTACTS POTENTIALS
Abstract	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.
	2021
ISSN	2041-1723
Volume	12 Issue:1 Pages:2535
Cooperation Status	国内
Subject Area	Science & Technology - Other Topics
MOST Discipline Catalogue	Multidisciplinary Sciences
DOI	10.1038/s41467-021-22869-8
Indexed By	SCIE
Citation statistics
Document Type	期刊论文
Identifier	http://ir.itp.ac.cn/handle/311006/27297
Collection	SCI期刊论文
Affiliation	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
Recommended Citation 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):2535.
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),2535.
MLA	Ju, Fusong,et al."CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction".NATURE COMMUNICATIONS 12.1(2021):2535.

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