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Exhaustive comparison and classification of ligand‐binding surfaces in proteins
Author(s) -
Murakami Yoichi,
Kinoshita Kengo,
Kinjo Akira R.,
Nakamura Haruki
Publication year - 2013
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.2329
Subject(s) - protein data bank (rcsb pdb) , similarity (geometry) , structural similarity , computational biology , cluster (spacecraft) , ligand (biochemistry) , surface protein , function (biology) , binding site , structural bioinformatics , protein structure , chemistry , biology , biological system , computer science , artificial intelligence , evolutionary biology , stereochemistry , genetics , biochemistry , image (mathematics) , receptor , virology , programming language
Abstract Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ∼2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.