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

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

Biochemical and Biophysical Research Communications 472 (2016) 217e222 Contents lists available at ScienceDirect Biochemical and Biophysical Research Communications journal homepage: /locate/ybbrc Improving residueeresidue contact prediction via low-rank and sparse decomposition of residue correlation matrix Haicang Zhang a, b, 1, Yujuan Gao c, 1, Minghua Deng c, d, e, Chao Wang a, b, Jianwei Zhu a, b, Shuai Cheng Li f, Wei-Mou Zheng g, **, Dongbo Bu a, * a Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Bejing, China b University of Chinese Academy of Sciences, Beijing, China c Center for Quantitative Biology, Peking University, Beijing, China d School of Mathematical Sciences, Peking University, Beijing, China e Center for Statistical Sciences, Peking University, Beijing, China f Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong g Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China article info Article history: Received 14 January 2016 Accepted 30 January 2016 Available online 23 February 2016 Keywords: Protein contacts prediction Correlation analysis Background correlation removal Low-rank and sparse matrix decomposition 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 matr