title: Assessing the druggability of protein-protein interactions by a supervised machine-learning method.
abstract: BACKGROUND
  Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM).
  
  
  RESULTS
  To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways.
  
  
  CONCLUSIONS
  Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.
authors.affiliation: Drug Discovery Department, Research & Development Division, PharmaDesign, Inc, Chuo-ku, Tokyo, Japan. sugaya@pharmadesign.co.jp
authors.forename: Nobuyoshi
authors.initials: N
authors.lastname: Sugaya
authors.forename: Kazuyoshi
authors.initials: K
authors.lastname: Ikeda
mesh_terms: D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins
references.citation: Cell. 2002 Nov 1;111(3):357-67
references.pmid: 12419246
references.citation: Nature. 2007 Dec 13;450(7172):1001-9
references.pmid: 18075579
references.citation: Nat Rev Drug Discov. 2004 Apr;3(4):301-17
references.pmid: 15060526
references.citation: BMC Pharmacol. 2007 Aug 20;7:10
references.pmid: 17705877
references.citation: Proteomics. 2006 Jan;6(2):456-61
references.pmid: 16317777
references.citation: FEBS Lett. 2002 Jun 19;521(1-3):109-14
references.pmid: 12067737
references.citation: Curr Opin Chem Biol. 2004 Aug;8(4):442-9
references.pmid: 15288255
references.citation: Melanoma Res. 1993 Feb;3(1):23-7
references.pmid: 8471834
references.citation: Nucleic Acids Res. 1997 Sep 1;25(17):3389-402
references.pmid: 9254694
references.citation: Curr Protein Pept Sci. 2008 Jun;9(3):240-7
references.pmid: 18537679
references.citation: Br J Pharmacol. 2007 Sep;152(1):21-37
references.pmid: 17549046
references.citation: Int J Biol Sci. 2007 Dec 10;4(1):15-22
references.pmid: 18167532
references.citation: Nature. 2005 Jun 2;435(7042):677-81
references.pmid: 15902208
references.citation: Curr Opin Struct Biol. 2005 Feb;15(1):31-4
references.pmid: 15718130
references.citation: J Cell Physiol. 2005 Sep;204(3):750-62
references.pmid: 15880642
references.citation: Genome Res. 2008 Feb;18(2):206-13
references.pmid: 18083776
references.citation: Nature. 2000 Jul 27;406(6794):378-82
references.pmid: 10935628
references.citation: J Med Chem. 2002 Apr 11;45(8):1543-58
references.pmid: 11931608
references.citation: Nucleic Acids Res. 2000 Jan 1;28(1):235-42
references.pmid: 10592235
references.citation: Nucleic Acids Res. 1989 Jul 25;17(14):5489-500
references.pmid: 2762147
references.citation: Pharmacol Rev. 2006 Jun;58(2):259-79
references.pmid: 16714488
references.citation: J Chem Inf Model. 2005 Jan-Feb;45(1):177-82
references.pmid: 15667143
references.citation: Nat Biotechnol. 2007 Jan;25(1):71-5
references.pmid: 17211405
references.citation: Drug Discov Today. 2007 Apr;12(7-8):304-13
references.pmid: 17395090
references.citation: Nucleic Acids Res. 2007 Jan;35(Database issue):D26-31
references.pmid: 17148475
references.citation: Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4
references.pmid: 16381900
references.citation: Genome Biol. 2006;7(11):120
references.pmid: 17147767
references.citation: Curr Top Med Chem. 2007;7(10):922-7
references.pmid: 17508923
references.citation: BMC Bioinformatics. 2007 Sep 20;8:353
references.pmid: 17883836
references.citation: Nat Rev Drug Discov. 2004 Nov;3(11):965-72
references.pmid: 15520818
references.citation: Nat Biotechnol. 2007 Oct;25(10):1119-26
references.pmid: 17921997
references.citation: Proc Natl Acad Sci U S A. 2006 Feb 7;103(6):1888-93
references.pmid: 16443686
references.citation: J Med Chem. 2005 Apr 7;48(7):2518-25
references.pmid: 15801841