Linked Life Data

20058856

Source:http://linkedlifedata.com/resource/pubmed/id/20058856

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-1-25
pubmed:abstractText
Inferring the biological function of a protein from its three-dimensional structure as well as explaining why a drug may bind to various targets is of crucial importance to modern drug discovery. Here we present a generic 4833-integer vector describing druggable protein-ligand binding sites that can be applied to any protein and any binding cavity. The fingerprint registers counts of pharmacophoric triplets from the Calpha atomic coordinates of binding-site-lining residues. Starting from a customized data set of diverse protein-ligand binding site pairs, the most appropriate metric and a similarity threshold could be defined for similar binding sites. The method (FuzCav) has been used in various scenarios: (i) screening a collection of 6000 binding sites for similarity to different queries; (ii) classifying protein families (serine endopeptidases, protein kinases) by binding site diversity; (iii) discriminating adenine-binding cavities from decoys. The fingerprint generation and comparison supports ultra-high throughput (ca. 1000 measures/s), does not require prior alignment of protein binding sites, and is able to detect local similarity among subpockets. It is thus particularly well suited to the functional annotation of novel genomic structures with low sequence identity to known X-ray templates.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1549-960X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
123-35
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Alignment-free ultra-high-throughput comparison of druggable protein-ligand binding sites.
pubmed:affiliation
Structural Chemogenomics, Laboratory of Therapeutic Innovation, UMR 7200 CNRS-UdS (Universite de Strasbourg), F-67400 Illkirch, France.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't