Assessing the druggability of protein-protein interactions by a supervised machine-learning method.
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.
Sugaya
Nobuyoshi
N
Drug Discovery Department, Research & Development Division, PharmaDesign, Inc, Chuo-ku, Tokyo, Japan. sugaya@pharmadesign.co.jp
Ikeda
Kazuyoshi
K
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*
D004364:Pharmaceutical Preparations; D011506:Proteins
Cell. 2002 Nov 1;111(3):357-67
12419246
Nature. 2007 Dec 13;450(7172):1001-9
18075579
Nat Rev Drug Discov. 2004 Apr;3(4):301-17
15060526
BMC Pharmacol. 2007 Aug 20;7:10
17705877
Proteomics. 2006 Jan;6(2):456-61
16317777
FEBS Lett. 2002 Jun 19;521(1-3):109-14
12067737
Curr Opin Chem Biol. 2004 Aug;8(4):442-9
15288255
Melanoma Res. 1993 Feb;3(1):23-7
8471834
Nucleic Acids Res. 1997 Sep 1;25(17):3389-402
9254694
Curr Protein Pept Sci. 2008 Jun;9(3):240-7
18537679
Br J Pharmacol. 2007 Sep;152(1):21-37
17549046
Int J Biol Sci. 2007 Dec 10;4(1):15-22
18167532
Nature. 2005 Jun 2;435(7042):677-81
15902208
Curr Opin Struct Biol. 2005 Feb;15(1):31-4
15718130
J Cell Physiol. 2005 Sep;204(3):750-62
15880642
Genome Res. 2008 Feb;18(2):206-13
18083776
Nature. 2000 Jul 27;406(6794):378-82
10935628
J Med Chem. 2002 Apr 11;45(8):1543-58
11931608
Nucleic Acids Res. 2000 Jan 1;28(1):235-42
10592235
Nucleic Acids Res. 1989 Jul 25;17(14):5489-500
2762147
Pharmacol Rev. 2006 Jun;58(2):259-79
16714488
J Chem Inf Model. 2005 Jan-Feb;45(1):177-82
15667143
Nat Biotechnol. 2007 Jan;25(1):71-5
17211405
Drug Discov Today. 2007 Apr;12(7-8):304-13
17395090
Nucleic Acids Res. 2007 Jan;35(Database issue):D26-31
17148475
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4
16381900
Genome Biol. 2006;7(11):120
17147767
Curr Top Med Chem. 2007;7(10):922-7
17508923
BMC Bioinformatics. 2007 Sep 20;8:353
17883836
Nat Rev Drug Discov. 2004 Nov;3(11):965-72
15520818
Nat Biotechnol. 2007 Oct;25(10):1119-26
17921997
Proc Natl Acad Sci U S A. 2006 Feb 7;103(6):1888-93
16443686
J Med Chem. 2005 Apr 7;48(7):2518-25
15801841