[Special Issue for Honor Award dedicating to Prof Kimito Funatsu]Prediction of Metabolite Activities by Repetitive Clustering of the Structural Similarity Based Networks

• Wakamatsu Nobutaka

  Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology

• Huang Ming

  Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology

• Ono Naoaki

  Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology Data Science Center, Graduate School of Science and Technology, Nara Institute of Science and Technology

• Altaf-Ul-Amin Md.

  Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology

• Kanaya Shigehiko

  Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology Data Science Center, Graduate School of Science and Technology, Nara Institute of Science and Technology

Abstract

A number of studies have investigated the relations between structures and activities of metabolites. It has been proposed that structural similarity between metabolites implies activity similarity between them. In light of this fact we propose a method for activity prediction of secondary metabolites based on association philosophy. First we determined the structural similarity scores between targeted metabolite pairs using COMPLIG algorithm. To increase the possibility of clusters rich with known metabolites we calculated structural similarity between metabolite pairs for which activities of both or at least one metabolite is known and then selected the metabolite pairs for which the similarity score is higher than a threshold (s > 0.95). The network of such metabolite pairs was then clustered using the DPClusO algorithm. Statistically significant cluster-activity pairs were then selected using the hypergeometric test. Then biological activities of unannotated metabolites were predicted from the activity of metabolites included in the statistically overrepresented clusters.

Journal

• Journal of Computer Aided Chemistry

  Journal of Computer Aided Chemistry 20 (0), 76-83, 2019

  Division of Chemical Information and Computer Sciences The Chemical Society of Japan

Keywords

• COMPLIG
• DPClusO
• graph-theory
• structure activity relationship

Details

• CRID: 1390283659835118720
• NII Article ID: 130007775607
• DOI: 10.2751/jcac.20.76
• ISSN: 13458647
• Web Site: https://www.jstage.jst.go.jp/article/jcac/20/0/20_76/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed