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PocketFEATURE specifications


Unique identifier OMICS_07900
Name PocketFEATURE
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Kerwyn Casey Huang

Publication for PocketFEATURE

PocketFEATURE citations


Biological and functional relevance of CASP predictions

PMCID: 5820171
PMID: 28975675
DOI: 10.1002/prot.25396

[…] f physicochemical and structural features in a sphere volume of 7.5 Å radius. A single ligand site is often comprised of between 10 to 20 microenvironments, each centering on one of the key residues. PocketFEATURE employs a matching system that aligns similar microenvironments, or physicochemical properties, between sites or even entire proteins (instead of sequence alignments). PocketFEATURE can […]


Estimation of Maximum Recommended Therapeutic Dose Using Predicted Promiscuity and Potency

PMCID: 5161261
PMID: 27736015
DOI: 10.1111/cts.12422

[…] We employed PocketFEATURE to predict affinity between each of the 238 drugs in the Drug Dataset and the 2,291 proteins in the Human Protein Dataset (see Methods). We have previously shown that the accuracy of Poc […]


Variations in the Binding Pocket of an Inhibitor of the Bacterial Division Protein FtsZ across Genotypes and Species

PLoS Comput Biol
PMCID: 4374959
PMID: 25811761
DOI: 10.1371/journal.pcbi.1004117

[…] ectory of the wild-type SaFtsZ monomer, an average of 17 out of the 20 residues contributed to the similarity score at any given time point during the simulation, resulting in an overall shift in the PocketFEATURE score to less negative values (decreased similarity). Thus, small fluctuations in the amino acid functional centers within the pocket can perturb the local physiochemical properties calc […]


Knowledge based Fragment Binding Prediction

PLoS Comput Biol
PMCID: 3998881
PMID: 24762971
DOI: 10.1371/journal.pcbi.1003589

[…] To determine microenvironment similarity, we adopted the approach used by PocketFEATURE . PocketFEATURE first derives the background variation of microenvironment properties and uses this to calculate a Tanimoto similarity coefficient between a pair of FEATURE vectors. Simi […]


Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome

PLoS Comput Biol
PMCID: 3675009
PMID: 23754939
DOI: 10.1371/journal.pcbi.1003087

[…] tivity profiles , illustrating the utility of structural features in predicting and understanding kinase selectivity.Rather than relying upon pre-specified structural features, the recently developed Pocketfeature method decomposes a binding site into all possible “micro-environments” . Pairs of kinase binding sites with highly similar sets of micro-environments were anecdotally shown to share a c […]


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PocketFEATURE institution(s)
Department of Bioengineering, Stanford University, Stanford, CA, USA

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