期刊
BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS
卷 1824, 期 12, 页码 1484-1489出版社
ELSEVIER
DOI: 10.1016/j.bbapap.2012.05.015
关键词
Correlated mutation analysis; Coevolution; Conservation; Rhodopsin; Functional site; Functional module
资金
- National Research Foundation of Korea (NRF)
- Korea Government (MEST) [2009-0086964]
- Korea Institute of Science and Technology Information Supercomputing Center
- Ministry for Health, Welfare & Family Affairs, Republic of Korea [A092006]
- Pioneer Research Center Program through the National Research Foundation of Korea
- Ministry of Education, Science and Technology [2008-2000220]
Correlated mutation analysis (CMA) has been used to investigate protein functional sites. However, CMA has suffered from low signal-to-noise ratio caused by meaningless phylogenetic signals or structural constraints. We present a new method, Structure-based Correlated Mutation Analysis (SCMA), which encodes coevolution scores into a protein structure network. A path-based network model is adapted to describe information transfer between residues, and the statistical significance is estimated by network shuffling. This model intrinsically assumes that residues in physical contact have a more reliable coevolution score than distant residues, and that coevolution in distant residues likely arises from a series of contacting and coevolving residues. In addition, coevolutionary coupling is statistically controlled to remove the structural effects. When applied to the rhodopsin structure, the SCMA method identified a much higher percentage of functional residues than the typical coevolution score (61% vs. 22%). In addition, statistically significant residues are used to construct the coevolved residue-residue subnetwork. The network has one highly connected node (retinal bound Lys296). indicating that Lys296 can induce and regulate most other coevolved residues in a variety of locations. The coevolved network consists of a few modular clusters which have distinct functional roles. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction. (C) 2012 Elsevier B.V. All rights reserved.
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