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Singular value decomposition for the correlation of atomic fluctuations with arbitrary angle
Author(s) -
Yu Miao,
Ma Xiaomin,
Cao Huaiqing,
Chong Bin,
Lai Luhua,
Liu Zhirong
Publication year - 2018
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25586
Subject(s) - allosteric regulation , singular value decomposition , pdz domain , statistical physics , biological system , protein dynamics , chemistry , correlation coefficient , correlation , molecular dynamics , singular value , mathematics , physics , computational chemistry , algorithm , nuclear magnetic resonance , statistics , eigenvalues and eigenvectors , geometry , biology , quantum mechanics , biochemistry , enzyme
Many proteins exhibit a critical property called allostery, which enables intra‐molecular transmission of information between distal sites. Microscopically, allosteric response is closely related to correlated atomic fluctuations. Conventional correlation analysis correlates the atomic fluctuations at two sites by taking the dot product (DP) between the fluctuations, which accounts only for the parallel and antiparallel components. Here, we present a singular value decomposition (SVD) method that analyzes the correlation coefficient of fluctuation dynamics with an arbitrary angle between the correlated directions. In a model allosteric system, the second PDZ domain (PDZ2) in the human PTP1E protein, approximately one third of the strong correlations have near‐perpendicular directions, which are underestimated in the conventional method. The discrimination becomes more prominent for residue pairs with larger separation. The results of the proposed SVD method are more consistent with the experimentally determined PDZ2 dynamics than those of conventional method. In addition, the SVD method improved the prediction accuracy of the allosteric sites in a dataset of 23 known allosteric monomer proteins. The proposed method may inspire extended investigation not only into allostery, but also into protein dynamics and drug design.