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Pocket-space maps to identify novel binding-site conformations in proteins

Craig, Ian, Pfleger, Christopher, Gohlke, Holger, Essex, Jonathan and Spiegel, Katrin (2011) Pocket-space maps to identify novel binding-site conformations in proteins. Journal of Chemical Information and Modeling.


The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chemistry to explore a broader chemical space, and thus present additional opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics. We report a new automated approach to diverse pocket selection, PocketAnalyzerPCA, which applies principal component analysis and clustering to the output of a grid-based pocket detection algorithm. Since the approach works directly with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of atomic positional coordinates. The approach is technically straight-forward and allows simultaneous analysis of mutants, isoforms, and protein structures derived from multiple sources with different residue numbering schemes. The PocketAnalyzerPCA approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allows identification of novel computationally-derived binding-site conformations that are yet to be observed crystallographically. Subsequent alignment of experimentally-confirmed inhibitors to the crystallographic binding modes of structurally-similar ligands identifies molecules predicted to bind to these novel binding-site conformations, thus demonstrating the utility of PocketAnalyzerPCA for rationalizing and improving the understanding of the molecular basis of protein-ligand interaction and bioactivity. A Python program implementing the PocketAnalyzerPCA approach is available for download under an open-source licence (

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Keywords: Protein structure; protein dynamics; novel binding-site conformations; diverse pocket selection; pocket detection algorithm; pocket shape; Principal Component Analysis; clustering; Molecular Dynamics,;drugability
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Date Deposited: 13 Oct 2015 13:15
Last Modified: 13 Oct 2015 13:15