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Network-based characterization of drug-protein interaction signatures with a space-efficient approach

  • Tabei, Yasuo1
  • Kotera, Masaaki2
  • Sawada, Ryusuke3
  • Yamanishi, Yoshihiro3, 4
  • 1 RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan , Tokyo (Japan)
  • 2 School of Engineering, Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan , Tokyo (Japan)
  • 3 Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Lizuka, Fukuoka, 820-8502, Japan , Fukuoka (Japan)
  • 4 PRESTO, Japan Science and Technology Agency, Saitama, 332-0012, Japan , Saitama (Japan)
Published Article
BMC Systems Biology
Springer (Biomed Central Ltd.)
Publication Date
Apr 05, 2019
Suppl 2
DOI: 10.1186/s12918-019-0691-1
Springer Nature


BackgroundCharacterization of drug-protein interaction networks with biological features has recently become challenging in recent pharmaceutical science toward a better understanding of polypharmacology.ResultsWe present a novel method for systematic analyses of the underlying features characteristic of drug-protein interaction networks, which we call “drug-protein interaction signatures” from the integration of large-scale heterogeneous data of drugs and proteins. We develop a new efficient algorithm for extracting informative drug-protein interaction signatures from the integration of large-scale heterogeneous data of drugs and proteins, which is made possible by space-efficient representations for fingerprints of drug-protein pairs and sparsity-induced classifiers.ConclusionsOur method infers a set of drug-protein interaction signatures consisting of the associations between drug chemical substructures, adverse drug reactions, protein domains, biological pathways, and pathway modules. We argue the these signatures are biologically meaningful and useful for predicting unknown drug-protein interactions and are expected to contribute to rational drug design.

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