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

Pocket-space maps to identify novel binding-site conformations in proteins
Pocket-space maps to identify novel binding-site conformations in proteins
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 straightforward 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. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby 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 license (see below).
1549-9596
2666-2679
Craig, Ian R.
47fd7541-0862-4f53-a397-55cb1577248d
Pfleger, Christopher
73bfcfc7-5a7f-4d84-89fa-215910731077
Gohlke, Holger
77ea03f8-894e-44d8-8765-18110d89128b
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Spiegel, Katrin
b1e9b7d9-579f-40c6-b9f8-1ba904fe7a0b
Craig, Ian R.
47fd7541-0862-4f53-a397-55cb1577248d
Pfleger, Christopher
73bfcfc7-5a7f-4d84-89fa-215910731077
Gohlke, Holger
77ea03f8-894e-44d8-8765-18110d89128b
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Spiegel, Katrin
b1e9b7d9-579f-40c6-b9f8-1ba904fe7a0b

Craig, Ian R., Pfleger, Christopher, Gohlke, Holger, Essex, Jonathan W. and Spiegel, Katrin (2011) Pocket-space maps to identify novel binding-site conformations in proteins. Journal of Chemical Information and Modeling, 51 (10), 2666-2679. (doi:10.1021/ci200168b).

Record type: Article

Abstract

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 straightforward 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. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby 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 license (see below).

Full text not available from this repository.

More information

Published date: 12 September 2011
Organisations: Chemistry, Faculty of Natural and Environmental Sciences, Computational Systems Chemistry

Identifiers

Local EPrints ID: 352700
URI: http://eprints.soton.ac.uk/id/eprint/352700
ISSN: 1549-9596
PURE UUID: 1b3d4c93-4dcf-40cb-b786-3cb6f7a1abb2
ORCID for Jonathan W. Essex: ORCID iD orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 20 May 2013 11:55
Last modified: 29 Oct 2019 02:06

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