Linked Life Data

10956211

Source:http://linkedlifedata.com/resource/pubmed/id/10956211

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
16
pubmed:dateCreated
2000-9-8
pubmed:abstractText
We present a computational procedure aimed at understanding enzyme selectivity and guiding the design of drugs with respect to selectivity. It starts from a set of 3D structures of the target proteins characterized by the program GRID. In the multivariate description proposed, the variables are organized and scaled in a different way than previously published methodologies. Then, consensus principal component analysis (CPCA) is used to analyze the GRID descriptors, allowing the straightforward identification of possible modifications in the ligand to improve its selectivity toward a chosen target. As an important new feature the computational method is able to work with more than two target proteins and with several 3D structures for each protein. Additionally, the use of a 'cutout tool' allows to focus on the important regions around the active site. The method is validated for a total number of nine structures of the three homologous serine proteases thrombin, trypsin, and factor Xa. The regions identified by the method as being important for selectivity are in excellent agreement with available experimental data and inhibitor structure-activity relationships.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
10
pubmed:volume
43
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3033-44
pubmed:dateRevised
2000-12-18
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
GRID/CPCA: a new computational tool to design selective ligands.
pubmed:affiliation
Department of Chemical Research/Structural Research, Boehringer Ingelheim Pharma KG, 88397 Biberach/Riss, Germany.
pubmed:publicationType
Journal Article