ITP OpenIR  > 理论物理所2016年知识产出
Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix
Zhang, HC; Gao, YJ; Deng, MH; Wang, C; Zhu, JW; Li, SC; Zheng, WM; Bu, DB; Bu, DB (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.; Zheng, WM (reprint author), Chinese Acad Sci, Inst Theoret Phys, Beijing 100080, Peoples R China.
2016
发表期刊BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
卷号472期号:1页码:217-222
文章类型Article
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.
关键词Protein Contacts Prediction Correlation Analysis Background Correlation Removal Low-rank And Sparse Matrix Decomposition
学科领域Biochemistry & Molecular Biology ; Biophysics
资助者National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; European Commission [306819] ; European Commission [306819] ; National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; European Commission [306819] ; European Commission [306819]
DOIhttp://dx.doi.org/10.1016/j.bbrc.2016.01.188
关键词[WOS]MULTIPLE SEQUENCE ALIGNMENTS ; COEVOLUTION ; MUTATIONS ; PROTEINS ; EVOLUTIONARY ; INFORMATION ; COVARIANCE ; SEARCH
收录类别SCI
语种英语
资助者National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; European Commission [306819] ; European Commission [306819] ; National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Basic Research Program of China (973 Program) [2012CB316502, 2015CB910303] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; National Nature Science Foundation of China [11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; Open Project Program of State Key Laboratory of Theoretical Physics [Y4KF171CJ1] ; European Commission [306819] ; European Commission [306819]
WOS类目Biochemistry & Molecular Biology ; Biophysics
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文献类型期刊论文
条目标识符http://ir.itp.ac.cn/handle/311006/21722
专题理论物理所2016年知识产出
通讯作者Bu, DB (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.; Zheng, WM (reprint author), Chinese Acad Sci, Inst Theoret Phys, Beijing 100080, Peoples R China.
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Zhang, HC,Gao, YJ,Deng, MH,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, HC.,Gao, YJ.,Deng, MH.,Wang, C.,Zhu, JW.,...&Zheng, WM .(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, HC,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.
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