Structure-based rebuilding of coevolutionary information reveals functional modules in rhodopsin structure.
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.
Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. paintzzz@kaist.ac.kr
Keunwan
K
Park
Dongsup
D
Kim
D019143:Evolution, Molecular; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry; D012243:Rhodopsin / Q000737:chemistry*
D043562:Receptors, G-Protein-Coupled; D012243:Rhodopsin