Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure

By John A. Capra, Roman A. Laskowski, Janet M. Thornton, Mona Singh, Thomas A. Funkhouser. 2009

411 citations recorded by the source corpus.

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/).

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

I-TASSER server: new development for protein structure and function predictions (2015, methodology): “Starting from the computational structure modeling, a number of methods have been proposed to anno-tate the biological function of protein molecules, with examples including COFACTROR (8,9), COACH (10), Con-Cavity (11), FINDSITE (12), Firestar (13) and 3DLigand-Site (14), among many others.”
Role of computer-aided drug design in modern drug discovery (2015, neutral): “2009), ConCavity (Capra et al. 2009), MED-SuMo (Doppelt-Azeroual et al.”
BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions (2012, methodology): “In the output of COACH, the consensus prediction on the ligand-binding sites as well as the top five predictions from COFACTOR (19), FINDSITE (28), ConCavity (29), TMSITE and SSITE are presented.”
Using Multiple Microenvironments to Find Similar Ligand-Binding Sites: Application to Kinase Inhibitor Binding (2011, methodology): “For apo structures and uncharacterized sites, we can also use published patch-searching algorithms, such as CONCAVITY [30] and PocketPicker [2] to define sites (on-going projects).”
Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, background): “In the first approach, the ligand-binding pocket is identified by recognizing the surface cavities on the 3D structural model of the target protein (An et al., 2005; Capra et al., 2009; Hendlich et al., 1997; Laskowski, 1995).”
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf (2023, methodology): “Exploiting evolutionary data to detect functional regions in proteins and in nucleic acids is very commonly used (Capra et al. 2009; del Sol Mesa et al. 2003; Gallet et al. 2000; Landgraf et al. 2001; Lichtarge et al. 1996a; Lichtarge et al. 1996b; Lichtarge et al. 1997; Valdar 2002).”
COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking (2018, neutral): “The last one is a template-free and structurebased method ConCavity, which considers both sequence conservation and structure geometry for the binding sites prediction (5).”
PrankWeb: a web server for ligand binding site prediction and visualization (2019, background): “Fpocket (2), SiteHound (3), ConCavity (4), POCASA (5), MetaPocket 2.”
Computer-Aided Drug Design Methods (2017, background): “Other programs for binding site identification include FINDSITE (47) and ConCavity (48).”