Prediction of protein-protein interaction with pairwise kernel support vector machine

International Journal of Molecular Sciences

Volumn 15, Issue 2, 2014, Pages 3220-3233

  Zhang, Shao Wu ^a,b   Hao, Li Yang ^a   Zhang, Ting He ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Amino acid distance frequency; Amino acid index distribution; Pairwise kernel function; Protein protein interaction; Support vector machine

Indexed keywords

AMINO ACID SEQUENCE; ARTICLE; HYDROPHOBICITY; KERNEL METHOD; MATHEMATICAL MODEL; PREDICTION; PREDICTIVE VALUE; PRINCIPAL COMPONENT ANALYSIS; PROTEIN PROTEIN INTERACTION; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; ALGORITHM; CHEMISTRY; COMPUTER PROGRAM; INTERNET; METABOLISM;

AMINO ACID; PROTEIN;

ALGORITHMS; AMINO ACIDS; INTERNET; PROTEIN INTERACTION MAPS; PROTEINS; SOFTWARE; SUPPORT VECTOR MACHINES;

EID: 84894263238     PISSN: 16616596     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms15023220     Document Type: Article

References (50)

1. Lucy, S.; Harpreet, K.S.; Gary, D.B.; Anton, J.E. Computational prediction of protein-protein interactions. Mol. Biotechnol. 2008, 38, 1-17
2. Hu, L.; Huang, T.; Shi, X.; Lu, W.C.; Cai, Y.D.; Chou, K.C. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One 2011, 6, e14556
3. Huang, T.; Chen, L.; Cai, Y.D.; Chou, K.C. Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property. PLoS One 2011, 6, e25297
4. Jiang, Y.; Huang, T.; Chen, L.; Gao, Y.F.; Cai, Y.D.; Chou, K.C. Signal propagation in protein interaction network during colorectal cancer progression. BioMed Res. Int. 2013, 2013, doi:10.1155/2013/287019
5. Li, B.Q.; Huang, T.; Cai, Y.D.; Chou, K.C. Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network. PLoS One 2013, 7, e33393
6. Shoemaker, B.A.; Panchenko, A.R. Deciphering protein-protein interactions. Part I Experimental techniques and databases. PLoS Comput. Biol. 2007, 3, e42
7. Han, J.D.; Dupuy, D.; Bertin, N.; Cusick, M.E.; Vidal, M. Effect of sampling on topology predictions of protein-protein interaction networks. Nat. Biotechnol. 2005, 23, 839-844
8. Marcotte, E.M.; Pellegrini, M.; Ng, H.L.; Rice, D.W.; Yeates, T.O.; Eisenberg, D. Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285, 751-753
9. Juan, D.; Pazos, F.; Valencia, A. High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc. Natl. Acad. Sci.USA 2008, 105, 934-939
10. Singhal, M.; Resat, H. A domain-based approach to predict proteinprotein interactions. BMC Bioinforma. 2007, 8, 199
11. Bock, J.R.; Gough, D.A. Predicting protein-protein interactions from primary structure. Bioinformatics 2001, 17, 455-460
12. Gomez, S.M.; Noble, A.S.; Rzhetsky, A. Learning to predict protein-protein interactions from protein sequences. Bioinformatics 2003, 19, 1875-1881
13. Ben-Hur, A.; Noble, W.S. Kernel methods for predicting protein-protein interactions. Bioinformatics 2005, 21, i38-i46
14. Martin, S.; Roe, D.; Faulon, J.L. Predicting protein-protein interactions using signature products. Bioinformatics 2005, 21, 218-226
15. Chou, K.C.; Cai, Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome Res. 2006, 5, 316-322
16. Nanni, L.; Lumini, A. An ensemble of K-local hyperplanes for predicting protein-protein interactions. Bioinformatics 2006, 22, 1207-1210
17. Pitre, S.; Dehne, F.; Chan, A.; Cheetham, J.; Duong, A.; Emili, A.; Gebbia, M.; Greenblatt, J.; Jessulat, M.; Krogan, N.; et al. PIPE: A protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs. BMC Bioinforma. 2006, 7, 365
18. Li, X.L.; Tan, S.H.; Ng, S.K. Improving domain-based protein interaction prediction using biologically-significant negative dataset. Int. J. Data Min. Bioinforma. 2006, 1, 138-149
19. Shen, J.W.; Zhang, J.; Luo, X.M.; Zhu, W.L.; Yu, K.Q.; Chen, K.X.; Li, Y.X.; Jiang, H.L. Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. USA 2007, 104, 4337-4341
20. Guo, Y.Z.; Yu, L.Z.; Wen, Z.N.; Li, M.L. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res. 2008, 36, 3025-3030
21. Chen, X.W.; Han, B.; Fang, J.; Haasl, R.J. Large-scale protein-protein interaction prediction using novel kernel methods. Int. J. Data Min. Bioinforma. 2008, 2, 145-156
22. Chen, W.; Zhang, S.W.; Cheng, Y.M.; Pan, Q. Prediction of protein-protein interaction types using the decision templates based on multiple classier fusion. Math. Comput. Model. 2010, 52, 2075-2084
23. Guo, Y.; Li, M.; Pu, X.; Li, G.; Guang, X.; Xiong, W.; Li, J. PRED_PPI: A server for predicting protein-protein interactions based on sequence data with probability assignment. BMC Res. Notes 2010, 3, 145
24. Yu, C.Y.; Chou, L.C.; Chang, D.T.H. Predicting protein-protein interactions in unbalanced data using the primary structure of proteins. BMC Bioinforma. 2010, 11, 167
25. Pan, X.Y.; Zhang, Y.N.; Shen, H.B. Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features. J. Proteome Res. 2010, 9, 4992-5001
26. Liu, C.H.; Li, K.C.; Yuan, S. Human protein-protein interaction prediction by a novel sequence-based co-evolution method: Co-evolutionary divergence. Bioinformatics 2013, 29, 92-98
27. Hsu, C.; Lin, C.J. A comparision of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 2002, 3, 415-425
28. Chou, K.C.; Zhang, C.T. Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol. 1995, 30, 275-349
29. Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J. Theor. Biol. 2011, 273, 236-247
30. Esmaeili, M.; Mohabatkar, H.; Mohsenzadeh, S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J. Theor. Biol. 2010, 263, 203-209
31. Hajisharifi, Z.; Piryaiee, M.; Mohammad Beigi, B.; Mandana, B.; Hassan, M. Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test. J. Theor. Biol. 2014, 341, 34-40
32. Mohabatkar, H.; Mohammad Beigi, M.; Esmaeili, A. Prediction of GABA(A) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine. J. Theor. Biol. 2011, 281, 18-23
33. Xu, Y.; Ding, J.; Wu, L.Y.; Chou, K.C. iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition. PLoS One 2013, 8, e55844
34. Xu, Y.; Shao, X.J.; Wu, L.Y.; Deng, N.Y.; Chou, K.C. iSNO-AAPair: Incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins. PeerJ 2013, 1, e171
35. Chen, W.; Feng, P.M.; Lin, H.; chou, K.C. iRSpot-PseDNC: Identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res. 2013, 41, e69
36. Qiu, W.R.; Xiao, X.; Chou, K.C. iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components. Int. J. Mol. Sci. 2014, 15, 1746-1766
37. Min, J.L.; Xiao, X.; Chou, K.C. iEzy-Drug: A web server for identifying the interaction between enzymes and drugs in cellular networking. Biomed. Res. Int. 2013, 2013, 701317
38. Zhang, S.W.; Liu, Y.F.; Yu, Y.; Zhang, T.H.; Fan, X.N. MSLoc-DT: A new method for predicting the protein subcellular location of multispecies based on decision templates. Anal. Biochem. 2014, 449, 164-171
39. Chen, W.; Zhang, S.W.; Cheng, Y.M.; Pan, Q. Identification of protein-RNA interaction sites using the information of spatial adjacent residues. Proteome Sci. 2011, 9, S16
40. Zhang, S.W.; Zhang, Y.L.; Yang, H.F.; Zhao, C.H.; Pan, Q. Using the concept of Chou's pseudo amino acid composition to predict protein subcellular localization: An approach by incorporating evolutionary information and von Neumann entropies. Amino Acids 2008, 34, 565-572
41. Zhang, S.W.; Chen, W.; Yang, F.; Pan, Q. Using Chou's pseudo amino acid composition to predict protein quaternary structure: A sequence-segmented PseAAC approach. Amino Acids 2008, 35, 591-598
42. Tomii, K.; Kanehisa, M. Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins. Protein Eng. 1996, 9, 27-36
43. Ogmen, U.; Keskin, O.; Aytuna, A.S.; Nussinov, R.; Gürsoy, A. PRISM: Protein interactions by structural matching. Nucleic Acids Res. 2005, 33, 331-336
44. Matsuda, S.; Vert, J.P.; Saigo, H.; Ueda, N.; Toh, H.; Akutsu, T. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 2005, 14, 2804-2813
45. Dubchak, I.; Muchnik, I.; Mayor, C.; Dralyuk, I.; Kim, S.H. Recognition of a protein fold in the context of the SCOP classification. Proteins 1999, 35, 401-407
46. Chothia, C.; Finkelstein, A.V. The classification and origins of protein folding patterns. Annu. Rev. Biochem. 1999, 59, 1007-1039
47. Fauchere, J.L.; Charton, M.; Kier, L.B.; Verloop, A.; Pliska, V. Amino acid side chain parameters for correlation studies in biology and pharmacology. Int. J. Peptide Protein Res. 1998, 32, 269-278
48. Grantham, R. Amino acid difference formula to help explain protein evolution. Science 1974, 185, 862-864
49. Charton, M.; Charton, B.I. The structural dependence of amino acid hydrophobicity parameters. J. Theor. Biol. 1982, 99, 629-644
50. Vert, J.P.; Qiu, J.; Noble, W.S. A new pairwise kernel for biological network inference with support vector machines. BMC Bioinforma. 2007, 8, S8.