論文

査読有り
2011年2月

Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network

PLOS ONE
  • Ichigaku Takigawa
  • ,
  • Koji Tsuda
  • ,
  • Hiroshi Mamitsuka

6
2
開始ページ
e16999
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1371/journal.pone.0016999
出版者・発行元
PUBLIC LIBRARY SCIENCE

A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.

リンク情報
DOI
https://doi.org/10.1371/journal.pone.0016999
CiNii Articles
http://ci.nii.ac.jp/naid/120002949745
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/21373195
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000287657500026&DestApp=WOS_CPL
ID情報
  • DOI : 10.1371/journal.pone.0016999
  • ISSN : 1932-6203
  • CiNii Articles ID : 120002949745
  • PubMed ID : 21373195
  • Web of Science ID : WOS:000287657500026

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