Linked Life Data

11878989

Source:http://linkedlifedata.com/resource/pubmed/id/11878989

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2002-3-6
pubmed:abstractText
Identification of a ligand binding site on a protein is pivotal to drug discovery. To date, no reliable and computationally feasible general approach to this problem has been published. Here we present an automated efficient method for determining binding sites on proteins for potential ligands without any a priori knowledge. Our method is based upon the multiscale concept where we deal with a hierarchy of models generated using a k-means clustering algorithm for the potential ligand. This is done in a simple approach whereby a potential ligand is represented by a growing number of feature points. At each increasing level of detail, a pruning of potential binding site is performed. A nonbonding energy function is used to score the interactions between molecules at each step. The technique was successfully employed to seven protein-ligand complexes. In the current paper we show that the algorithm considerably reduces the computational effort required to solve this problem. This approach offers real opportunities for exploiting the large number of structures that will evolve from structural genomics.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0002-7863
pubmed:author
pubmed:issnType
Print
pubmed:day
13
pubmed:volume
124
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2337-44
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Identification of ligand binding sites on proteins using a multi-scale approach.
pubmed:affiliation
Department of Chemistry, Central Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, OX1 3QH, UK.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't