title: Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.
abstract: 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://protein.ict.ac.cn/COLORS/.
authors.affiliation: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China; University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Haicang
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Center for Quantitative Biology, Peking University, Beijing, China.
authors.forename: Yujuan
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Center for Quantitative Biology, Peking University, Beijing, China; School of Mathematical Sciences, Peking University, Beijing, China; Center for Statistical Sciences, Peking University, Beijing, China.
authors.forename: Minghua
authors.initials: M
authors.lastname: Deng
authors.affiliation: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China; University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Wang
authors.affiliation: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China; University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Jianwei
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Shuai Cheng
authors.initials: SC
authors.lastname: Li
authors.affiliation: Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China. Electronic address: zheng@itp.ac.cn.
authors.forename: Wei-Mou
authors.initials: WM
authors.lastname: Zheng
authors.affiliation: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China. Electronic address: dbu@ict.ac.cn.
authors.forename: Dongbo
authors.initials: D
authors.lastname: Bu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D019143:Evolution, Molecular; D008958:Models, Molecular; D015233:Models, Statistical; D010802:Phylogeny; D025341:Principal Component Analysis; D011487:Protein Conformation; D017510:Protein Folding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods* / Q000706:statistics & numerical data; D060066:Protein Interaction Maps; D020539:Sequence Analysis, Protein
keywords: Background correlation removal; Correlation analysis; Low-rank and sparse matrix decomposition; Protein contacts prediction