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Structure-Based Druggability Assessment of the Mammalian Structural Proteome with Inclusion of Light Protein Flexibility

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
Structure-Based Druggability Assessment of the Mammalian Structural Proteome with Inclusion of Light Protein Flexibility
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003741
Pubmed ID
Authors

Kathryn A. Loving, Andy Lin, Alan C. Cheng

Abstract

Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.

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Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Germany 2 3%
United Kingdom 2 3%
Japan 1 1%
Italy 1 1%
Unknown 66 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 31%
Student > Ph. D. Student 18 24%
Student > Master 9 12%
Professor 6 8%
Student > Bachelor 6 8%
Other 11 15%
Unknown 2 3%
Readers by discipline Count As %
Chemistry 24 32%
Agricultural and Biological Sciences 17 23%
Biochemistry, Genetics and Molecular Biology 13 17%
Computer Science 8 11%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Other 7 9%
Unknown 2 3%