Linked Life Data

16420039

Source:http://linkedlifedata.com/resource/pubmed/id/16420039

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-1-19
pubmed:abstractText
We developed a new method for the classification of chemical compounds and protein pockets and applied it to a random screening experiment for macrophage migration inhibitory factor (MIF). The principal component analysis (PCA) method was applied to the protein-compound interaction matrix, which was given by thorough docking calculations between a set of many protein pockets and chemical compounds. Each compound and protein pocket was depicted as a point in the PCA spaces of compounds and proteins, respectively. This method was applied to distinguish active compounds from negative compounds of MIF. A random screening experiment for MIF was performed, and our method revealed that the active compounds were localized in the PCA space of compounds, while the negative compounds showed a wide distribution. Furthermore, protein pockets, which bind similar compounds, were classified and were found to form a cluster in the PCA space.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0022-2623
pubmed:author
pubmed:issnType
Print
pubmed:day
26
pubmed:volume
49
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
523-33
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Classification of chemical compounds by protein-compound docking for use in designing a focused library.
pubmed:affiliation
Biological Information Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan. y-fukunishi@jbirc.aist.go.jp
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't