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

文献类型：期刊论文

作者Ju, Fusong3,4; Zhu, Jianwei2; Shao, Bin2; Kong, Lupeng3,4; Liu, Tie-Yan2; Zheng, Wei-Mou1,3; Bu, Dongbo3,4
刊名NATURE COMMUNICATIONS
出版日期2021-05-05
卷号12期号:1页码:9
ISSN号2041-1723
DOI10.1038/s41467-021-22869-8
英文摘要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.
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
语种英语
WOS记录号WOS:000656480900002
出版者NATURE RESEARCH
源URL[http://119.78.100.204/handle/2XEOYT63/17596]  
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 Theoret Phys, Beijing, Peoples R China
2.Microsoft Res Asia, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Key Lab Intelligent Informat Proc,Big Data Acad, 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.

入库方式： OAI收割

来源：计算技术研究所

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