Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix

doi:10.1016/j.bbrc.2016.01.188

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix
	Zhang, Haicang 1,2; Gao, Yujuan 7; Deng, Minghua 5,6,7; Wang, Chao 1,2; Zhu, Jianwei 1,2; Li, Shuai Cheng 4; Zheng, Wei-Mou 3; Bu, Dongbo 2
刊名	BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
	2016-03-25
卷号	472 期号:1 页码:217-222
关键词	Protein contacts prediction Correlation analysis Background correlation removal Low-rank and sparse matrix decomposition
ISSN号	0006-291X
DOI	10.1016/j.bbrc.2016.01.188
英文摘要	Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates protein contact prediction by removing background correlations. An implementation of the approach called COLORS (improving COntact prediction using LOw-Rank and Sparse matrix decomposition) is available from http://proteinictac.cn/COLORS/. (C) 2016 Elsevier Inc. All rights reserved.
资助项目	National Basic Research Program of China (973 Program)[2012CB316502] ; National Basic Research Program of China (973 Program)[2015CB910303] ; National Nature Science Foundation of China[11175224] ; National Nature Science Foundation of China[11121403] ; National Nature Science Foundation of China[31270834] ; National Nature Science Foundation of China[61272318] ; National Nature Science Foundation of China[31171262] ; National Nature Science Foundation of China[31428012] ; National Nature Science Foundation of China[31471246] ; Open Project Program of State Key Laboratory of Theoretical Physics[Y4KF171CJ1] ; European Commission[306819]
WOS研究方向	Biochemistry & Molecular Biology ; Biophysics
语种	英语
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS记录号	WOS:000373248400033
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/8436]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zheng, Wei-Mou; Bu, Dongbo
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Theoret Phys, Beijing 100080, Peoples R China 4.City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China 5.Peking Univ, Ctr Stat Sci, Beijing 100871, Peoples R China 6.Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China 7.Peking Univ, Ctr Quantitat Biol, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Haicang,Gao, Yujuan,Deng, Minghua,et al. Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix[J]. BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS,2016,472(1):217-222.
APA	Zhang, Haicang.,Gao, Yujuan.,Deng, Minghua.,Wang, Chao.,Zhu, Jianwei.,...&Bu, Dongbo.(2016).Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS,472(1),217-222.
MLA	Zhang, Haicang,et al."Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix".BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 472.1(2016):217-222.