Journal of Biosciences

, Volume 40, Issue 4, pp 809–818 | Cite as

Protein–Protein interaction site prediction in Homo sapiens and E. coli using an interaction-affinity based membership function in fuzzy SVM



Protein–protein interaction (PPI) site prediction aids to ascertain the interface residues that participate in interaction processes. Fuzzy support vector machine (F-SVM) is proposed as an effective method to solve this problem, and we have shown that the performance of the classical SVM can be enhanced with the help of an interaction-affinity based fuzzy membership function. The performances of both SVM and F-SVM on the PPI databases of the Homo sapiens and E. coli organisms are evaluated and estimated the statistical significance of the developed method over classical SVM and other fuzzy membership-based SVM methods available in the literature. Our membership function uses the residue-level interaction affinity scores for each pair of positive and negative sequence fragments. The average AUC scores in the 10-fold cross-validation experiments are measured as 79.94% and 80.48% for the Homo sapiens and E. coli organisms respectively. On the independent test datasets, AUC scores are obtained as 76.59% and 80.17% respectively for the two organisms. In almost all cases, the developed F-SVM method improves the performances obtained by the corresponding classical SVM and the other classifiers, available in the literature.


Fuzzy support vector machine interaction affinity protein–protein interaction 



This project is partially supported by the CMATER research laboratory of the Computer Science and Engineering Department, Jadavpur University, India, PURSE project and FASTTRACK grant (SR/FTP/ETA-04/2012) of DST, India.

Supplementary material

12038_2015_9564_MOESM1_ESM.pdf (423 kb)
ESM 1 (PDF 423 kb)


  1. Argos P 1988 An investigation of protein subunit and domain interfaces. Protein Eng. 2 101–113CrossRefPubMedGoogle Scholar
  2. Arias AM 1989 Molecular biology of the cell. In B. Alberts, D. Bray, J. Lewis, M. Raff, K. Roberts and JD, Watson, Garland (eds), 1989 $46.95 (v+ 1187 pages) ISBN 0 8240 3695 6, 2nd edn. Elsevier Current TrendsGoogle Scholar
  3. Bandyopadhyay S, Maulik U and Wang JTL 2007 (Eds) Analysis of biological data. A Soft Computing Approach. World Scientific, Singapore Google Scholar
  4. Basu S and Plewczynski D 2010 AMS 3.0: prediction of post-translational modifications. BMC Bioinforma 11 210CrossRefGoogle Scholar
  5. Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I and Bourne P 2000 The protein data bank. Nucleic Acids Res. 28 235–242PubMedCentralCrossRefPubMedGoogle Scholar
  6. Bordner AJ and Abagyan R 2005 Statistical analysis and prediction of protein–protein interfaces. Proteins Struct. Funct. Bioinforma 60 353–366Google Scholar
  7. Caragea C, Sinapov J, Honavar V and Dobbs D 2007 Assessing the performance of macromolecular sequence classifiers. Bioinformatics and Bioengineering, BIBE 2007. Proceedings of the 7th IEEE International Conference on pp 320–326Google Scholar
  8. Chang C-C and Lin C-J 2011 LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST). 2 27Google Scholar
  9. Chatterjee P, Basu S, Kundu M, Nasipuri M and Plewczynski D 2011a PPI_SVM: prediction of protein-protein interactions using machine learning, do-main-domain affinities and frequency tables. Cell. Mol. Biol. Lett. 16 264–278CrossRefPubMedGoogle Scholar
  10. Chatterjee P, Basu S, Kundu M, Nasipuri M and Plewczynski D 2011b PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machine. J. Mol. Model. 17 2191–2201PubMedCentralCrossRefPubMedGoogle Scholar
  11. Chelliah V, Chen L, Blundell T and Lovell S 2004 Distinguishing structural and functional restraints in evolution inorder to identify interaction sites. J. Mol. Biol. 342 1487–1504CrossRefPubMedGoogle Scholar
  12. Chen Y and Wang JZ 2003 Support vector learning for fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 11 716–728CrossRefGoogle Scholar
  13. Chiang J-H and Hao P-Y 2004 Support vector learning mechanism for fuzzy rule-based modeling: a new approach. IEEE Trans. Fuzzy Syst. 12 1–12CrossRefGoogle Scholar
  14. Cortes C and Vapnik VN 1995 Support vector networks. Mach. Learn. 20 273–297Google Scholar
  15. Demšar J 2006 Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 1–30Google Scholar
  16. Huang HP and Liu YH 2002 Fuzzy support vector machine for pattern recognition and data mining. Int. J. Fuzzy Syst. 4 826–835Google Scholar
  17. Inoue T and Abe S 2001 Fuzzy support vector machines for pattern classification. Proc. IJCNN’01. 2 1449–1454Google Scholar
  18. Ishibuchi H and Yamamoto T 2005 Rule weight specification in fuzzy rule-based classification systems. IEEE Trans. Fuzzy Syst. 13 428–435CrossRefGoogle Scholar
  19. Janin J, Miller S and Chothia C 1988 Surface, subunit interfaces and interior of oligomericproteins. J. Mol. Biol. 204 155–164CrossRefPubMedGoogle Scholar
  20. Jiang X, Yi Z and Lv JC 2006 Fuzzy SVM with a new fuzzy membership function. Neural Comput. Applic. 15 268–276CrossRefGoogle Scholar
  21. Jones S and Thornton J 1995 Protein-protein interactions: a review of protein dimer structures. Prog. Biophys. Mol. Biol. 63 31–65CrossRefPubMedGoogle Scholar
  22. Jones S and Thornton JM 1996 Principles of protein-protein interactions. Proc. Natl. Acad. Sci. USA 93 13–20Google Scholar
  23. Jones S and Thornton JM 1997 Analysis of protein-protein interaction sites using surface patches. JMB. 272 121–132CrossRefGoogle Scholar
  24. Koike A and Takagi T 2004 Prediction of protein–protein interaction sites using support vector machines. Protein Eng. Des. Sel. 17 165–173CrossRefPubMedGoogle Scholar
  25. Korn A and Burnett R 1991 Distribution and complementarity of hydropathy in multi-subunit proteins. Proteins Struct. Funct. Bioinforma 9 37–55Google Scholar
  26. Krogan N, Cagney G, Yu H, Zhong G, et al. 2006 Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440 637–643CrossRefPubMedGoogle Scholar
  27. Lin C-F and Wang S-D 2002 Fuzzy support vector machines. IEEE Trans. Neural Netw. 13 464–471CrossRefPubMedGoogle Scholar
  28. Lo Conte L, Chothia C and Janin J 1999 The atomic structure of protein– protein recognition sites. J. Mol. Biol. 285 2177–2198CrossRefPubMedGoogle Scholar
  29. Maulik U, Bandyopadhyay S and Wang JT 2011a Computational intelligence and pattern analysis in biology informatics, p 20Google Scholar
  30. Maulik U, Bhattacharyya M, Mukhopadhyay A and Bandyopadhyay S 2011b Identifying the immunodeficiency gateway proteins in humans and their involvement in microrna regulation. Mol. BioSyst. 7 1842–1851CrossRefPubMedGoogle Scholar
  31. Miller S 1989 The structure of interfaces between subunits of dimeric and tetrameric proteins. Protein Eng. 3 77–83CrossRefPubMedGoogle Scholar
  32. Mukhopadhyay A, Maulik U and Bandyopadhyay S 2012 A novel biclustering approach to association rule mining for predicting HIV-1–human protein interactions. PLoS One 7 e32289Google Scholar
  33. Plewczynski D 2010 Brainstorming: weighted voting prediction of inhibitors for protein targets. J. Mol. Model 17 2133–2141Google Scholar
  34. Plewczynski D, Basu S and Saha I 2012 AMS 4.0: consensus prediction of post-translational modifications in protein sequences. Amino Acids 43 573–582PubMedCentralCrossRefPubMedGoogle Scholar
  35. Saha I, Maulik U, Bandyopadhyay S and Plewczynski D 2012 Fuzzy clustering of physicochemical and biochemical properties of amino acids. Amino Acids 43 583–594PubMedCentralCrossRefPubMedGoogle Scholar
  36. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU and Eisenberg D 2004 The database of interacting proteins: 2004 update. Nucleic Acids Res. 32 D449–D451PubMedCentralCrossRefPubMedGoogle Scholar
  37. Sengupta D, Maulik U and Bandyopadhyay S 2012 Weighted Markov chain based aggregation of biomolecule orderings. IEEE/ACM Trans. Comput. Biol. Bioinforma 9 924–933Google Scholar
  38. Šikić M, Tomić S and Vlahoviček K 2009 Prediction of protein–protein interaction sites in sequences and 3D structures by random forests. PLoS Comput. Biol. 5 e1000278PubMedCentralCrossRefPubMedGoogle Scholar
  39. Singh R, Park D, Xu J, Hosur R and Berger B 2010 Struct2Net: a web service to predict protein–protein interactions using a structure-based approach. Nucleic Acids Res. 38 W508–W515PubMedCentralCrossRefPubMedGoogle Scholar
  40. Sriwastava B, Basu S, Maulik U and Plewczynski D 2012 Prediction of E. coli protein-protein interaction sites using inter-residue distances and high-quality-index features. Information Systems Design and Intelligent Applications 2012. INDIA 837–844Google Scholar
  41. Sriwastava BK, Basu S, Maulik U and Plewczynski D 2013 PPIcons: identification of protein-protein interaction sites in selected organisms. J. Mol. Model. 9 4059–4070Google Scholar
  42. Sriwastava BK, Basu S and Maulik U 2013 Fuzzy SVM with a novel membership function for prediction of protein-protein interaction sites in Homo sapiens; In Pattern recognition and machine intelligence. Springer, Berlin Heidelberg 8251 668–673Google Scholar
  43. Tang H and Qu L-S 2008 Fuzzy support vector machine with a new fuzzy membership function for pattern classification. In Machine Learning and Cybernetics, 2008 International Conference on IEEE. Kunming 2 768–773Google Scholar
  44. Vapnik VN 1995 The nature of statistical learning theory (New York: Springer-Verlag)CrossRefGoogle Scholar
  45. Wei Y and Wu X 2012 A new fuzzy SVM based on the posterior probability weighting membership. J. Comput. 7 1385–1392CrossRefGoogle Scholar
  46. Zhou H-X and Shan Y 2001 Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins Struct. Funct. Genet. 44 336–343CrossRefPubMedGoogle Scholar

Copyright information

© Indian Academy of Sciences 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringGovernment College of Engineering and Leather TechnologyKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations