TitlePredicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.
Publication TypeJournal Article
Year of Publication2009
AuthorsCapra, JA, Laskowski, RA, Thornton, JM, Singh, M, Funkhouser, TA
JournalPLoS Comput Biol
Volume5
Issue12
Paginatione1000585
Date Published2009 Dec
KeywordsAlgorithms, Apoenzymes, Area Under Curve, Binding Sites, Catalytic Domain, Computational Biology, Conserved Sequence, Enzymes, Evolution, Molecular, Holoenzymes, Models, Molecular, Protein Binding, Protein Conformation, Proteins
Abstract

Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

Alternate JournalPLoS Comput. Biol.