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A hybrid clustering of protein binding sites.
FEBS J. 2010 Mar; 277:1494-502
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24 May 2010
Technical Advance
DOI: 10.3410/f.3312958.2999057
This paper describes an interesting technique for comparing protein binding site structures using a hybrid sequence/structure approach to identify evolutionary and functional relationships.
The OPTICS clustering algorithm was used to analyze ~21,000 different nuclear magnetic resonance (NMR) and X-ray...Continue reading
The OPTICS clustering algorithm was used to analyze ~21,000 different nuclear magnetic resonance (NMR) and X-ray...Continue reading
This paper describes an interesting technique for comparing protein binding site structures using a hybrid sequence/structure approach to identify evolutionary and functional relationships.
The OPTICS clustering algorithm was used to analyze ~21,000 different nuclear magnetic resonance (NMR) and X-ray protein ligand binding sites. OPTICS is a fast sequence-based approach of aligning the subset of residues identified to bind a ligand using a complex silhouette coefficient scoring system. The clusters generated with this method correlated well with the Enzyme Commission (EC) numbers of the proteins within each cluster. Thus, the methodology has the potential to provide rapid evolutionary relationships based on conserved ligand binding site sequences.
The OPTICS clustering algorithm was used to analyze ~21,000 different nuclear magnetic resonance (NMR) and X-ray protein ligand binding sites. OPTICS is a fast sequence-based approach of aligning the subset of residues identified to bind a ligand using a complex silhouette coefficient scoring system. The clusters generated with this method correlated well with the Enzyme Commission (EC) numbers of the proteins within each cluster. Thus, the methodology has the potential to provide rapid evolutionary relationships based on conserved ligand binding site sequences.
Acknowledgments
I would like to thank Jaime Stark for their assistance in the preparation of this evaluation.
Disclosures
None declared
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Powers R: F1000Prime Recommendation of [Iván G et al., FEBS J 2010, 277:1494-502]. In F1000Prime, 24 May 2010; DOI: 10.3410/f.3312958.2999057. F1000Prime.com/3312958#eval2999057
F1000Prime Recommendations, Dissents and Comments for [Iván G et al., FEBS J 2010, 277:1494-502]. In F1000Prime, 25 May 2013; F1000Prime.com/3312958
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The Protein Data Bank contains the description of approximately 27 000 protein-ligand binding sites. Most of the ligands at these sites are biologically active small molecules, affecting the biological function of the protein. The classification of their binding sites may lead to relevant results in drug discovery and design. Clusters of similar binding sites were created here by a hybrid, sequence and spatial structure-based approach, using the OPTICS clustering algorithm. A dissimilarity measure was defined: a distance function on the amino acid sequences of the binding sites. All the binding sites were clustered in the Protein Data Bank according to this distance function, and it was found that the clusters characterized well the Enzyme Commission numbers of the entries. The results, carefully color coded by the Enzyme Commission numbers of the proteins, containing the 20 967 binding sites clustered, are available as html files in three parts at http://pitgroup.org/seqclust/.
DOI: 10.1111/j.1742-4658.2010.07578.x
PMID: 20148971
