SemiHS: An iterative semi-supervised approach for predicting proteinprotein interaction hot spots

Protein and Peptide Letters

Volumn 18, Issue 9, 2011, Pages 896-905

  Deng, Lei ^a   Guan, Ji Hong ^a   Dong, Qi Wen ^b   Zhou, Shui Geng ^b  

^a TONGJI UNIVERSITY   (China)

^b FUDAN UNIVERSITY   (China)

Author keywords

Hot spots; Protein protein interaction; Semi supervised; SVM

Indexed keywords

PROTEIN;

ALGORITHM; ARTICLE; BINDING SITE; METABOLISM; METHODOLOGY; PROTEIN ANALYSIS; PROTEIN BINDING; PROTEIN DATABASE;

ALGORITHMS; BINDING SITES; DATABASES, PROTEIN; PROTEIN BINDING; PROTEIN INTERACTION MAPPING; PROTEINS;

EID: 79959396092     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986611796011419     Document Type: Article

References (88)

1. Alberts, B.D.; Lewis, J.; Raff, M.; Roberts, K.; Watson, J.D. Molecular Biology of the Cell, 2nd ed.; Garland Publishing: New York, 1989.
2. Xia, J.F.; Han, K.; Huang, D.S. Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor. Protein Pept. Lett., 2010, 17, 137-145.
3. Yang, J.; Jiang, X.F. A novel approach to predict protein-protein interactions related to Alzheimer's disease based on complex network. Protein Pept. Lett., 2010, 17, 356-366.
4. Hu, L.; Huang, T.; Shi, X.; Lu, W.C.; Cai, Y.D.; Chou, K.C. Predicting functions of proteins in mouse based on weighted proteinprotein interaction network and protein hybrid properties. PLoS one, 2011, 6, e14556.
5. Chou, K.C.; Cai, Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome Res., 2006, 5, 316-322.
6. Shen, Y.Z.; Ding, Y.S.; Gu, Q.; Chou, K.C. Identifying the Hub Proteins from Complicated Membrane Protein Network Systems. Medicinal Chemistry, 2010, 6, 165-173.
7. Clackson, T.; Wells, J.A. A hot spot of binding energy in a hormonereceptor interface. Science, 1995, 267(5196), 383-386.
8. Bogan, A.A.; Thorn K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol., 1998, 280(1), 1-9. (Pubitemid 28312802)
9. Moreira, I.S.; Fernandes, P.A.; Ramos M.J. Hot spots-a review of the protein-protein interface determinant amino-acid residues. Proteins, 2007, 68(4), 803-812. (Pubitemid 47434705)
10. Li, J.; Liu, Q. 'double water exclusion': a hypothesis refining the oring theory for the hot spots at protein interfaces. Bioinformatics, 2009, 25(6), 743-750.
11. DeLano, W.L. Unraveling hot spots in binding interfaces: progress and challenges. Curr. Opin. Struct. Biol., 2002, 12(1), 14-20.
12. Massova, I.; Kollman, P.A. Computational alanine scanning to probe protein-protein interactions: A novel approach to evaluate binding free energies. J. Am. Chem. Soc., 1999, 121(36), 8133-8143. (Pubitemid 29444447)
13. Huo, S.; Massova, I.; Kollman, P.A. Computational alanine scanning of the 1:1 human growth hormonecreceptor complex. J. Comput. Chem., 2002, 23(1), 15-27. (Pubitemid 34063126)
14. Grosdidier, S.; Fernndez-Recio, J. Identification of hot-spot residues in protein-protein interactions by computational docking. BMC Bioinformatics, 2008, 9, 447.
15. Brenke, R.; Kozakov, D.; Chuang, G.; Beglov, D.; Hall, D.; Landon, M.R.; Mattos, C.; Vajda, S. Fragment-based identification of druggable 'hot spots' of proteins using fourier domain correlation techniques. Bioinformatics, 2009, 25(5), 621-627.
16. Kortemme, T.; Baker, D. A simple physical model for binding energy hot spots in protein-protein complexes. Proc. Natl. Acad. Sci., 2002, 99(22), 14116-14121. (Pubitemid 35257634)
17. Guerois, R.; Nielsen, J.E.; Serrano, L. Predicting changes in the stability of proteins and protein complexes: A study of more than 1000 mutations. J. Mol. Biol., 2002, 320, 369-387. (Pubitemid 34722226)
18. Tuncbag, N.; Gursoy, A.; Keskin, O. Identification of computational hot spots in protein interfaces:combining solvent accessibility and interresidue potentials improves the accuracy. Bioinformatics, 2009, 25(12), 1513-1520.
19. Tuncbag, N.; Keskin, O.; Gursoy, A. Hotpoint: hot spot prediction server for protein interfaces. Nucleic Acids Res., 2010, 38, W402-W406.
20. Ofran, Y.; Rost, B. Protein-protein interaction hotspots carved into sequences. PLoS Comput. Biol., 2007, 3(7), e119.
21. Darnell, S.J.; Page, D.; Mitchell, J.C. An automated decision-tree approach to predicting protein interaction hot spots. Proteins, 2007, 68(4), 813-823. (Pubitemid 47434706)
22. Cho, K.; Kim, D.; Lee, D. A feature-based approach to modeling protein-protein interaction hot spots. Nucleic Acids Res., 2009, 37(8), 2672-2687.
23. Xia, J.; Zhao, X.; Song, J.; Huang, D. APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility. BMC Bioinformatics, 2010, 11, 174.
24. 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
25. Thorn, K.S.; Bogan, A.A. ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics, 2001, 17(3), 284-285. (Pubitemid 32288219)
26. Chou, K.C.; Shen, H.B. Review: Recent progresses in protein subcellular location prediction. Anal. Biochem., 2007, 370, 1-16.
27. Chou, K.C.; Shen, H.B. A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS one, 2010, 5, e9931.
28. Chou, K.C.; Shen, H.B. Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization. PLoS one, 2010, 5, e11335.
29. Chou, K.C.; Shen, H.B. Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nature Protocols, 2008, 3, 153-162.
30. Chou, K.C.; Shen, H.B. Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Natural Science, 2010, 2, 1090-1103 (openly accessible at http://www.scirp.org/ journal/NS/).
31. Toth, G.; Watts, C.R.; Murphy, R.F.; Lovas, S. Significance of aromatic-backbone amide interactions in protein structure. Proteins Struct. Funct. Genet., 2001, 43(4), 373-381. (Pubitemid 32476433)
32. Deng, L.; Guan, J.; Dong, Q.; Zhou, S. Prediction of proteinprotein interaction sites using an ensemble method. BMC Bioinformatics, 2009, 10, 426.
33. Kawashima, S.; Ogata, H.; Kanehisa, M. AAindex: amino acid index database. Nucleic Acids Res., 1999, 27(1), 368-369.
34. Landau, M.; Mayrose, I.; Rosenberg, Y.; Glaser, F.; Martz, E.; Pupko, T.; Ben-Tal, N. Consurf: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Res., 2005, 33, W299-W302. (Pubitemid 44529930)
35. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P. E. The protein data bank. Nucleic Acids Res., 2000, 28, 235-242. (Pubitemid 30047768)
36. Altschul, S.F.; Madden, T.L.; Schaffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res., 1997, 25(17), 3389-3402. (Pubitemid 27359211)
37. Edgar, R.C. Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res., 2004, 32(5), 1792-1797. (Pubitemid 38832724)
38. Mayrose, I.; Graur, D.; Ben-Tal, N.; Pupko, T. Comparison of sitespecific rate-inference methods: Bayesian methods are superior. Mol. Biol. Evol., 2004, 21(9), 1781-1791. (Pubitemid 39096894)
39. Schneider, R.; Sander, C. The hssp database of protein structuresequence alignments. Nucleic Acids Res., 1996, 24(1), 201-205. (Pubitemid 26085766)
40. Yan, C.; Wu, F.; Jernigan, R.L.; Dobbs, D.; Honavar, V. Characterization of protein-protein interfaces. Protein J., 2008, 27(1), 59-70. (Pubitemid 351229381)
41. Kabsch, W.; Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 1983, 22(12), 2577-2637.
42. Mintseris, J.; Weng, Z. Structure, function and evolution of transient and obligate protein-protein interactions. Proc. Natl. Acad. Sci., 2005, 102(31), 10930-10935. (Pubitemid 41105970)
43. Keskin, O.; Ma, B.; Nussinov, R. Hot regions in protein-protein interactions: the organization and contribution of structurally conserved hot spot residues. J. Mol. Biol., 2005, 345, 1281-1294. (Pubitemid 40092239)
44. Sol, A.; Fujihashi, H.; Amoros, D.; Nussinov, R. Residue centrality, functionally important residues and active site shape: analysis of enzyme and non-enzyme families. Protein Sci., 2006, 15(9), 2120-2128. (Pubitemid 44316014)
45. Fauchere, J.L.; Pliska, V. Hydrophobic paramaters p of amino acid side chains from the partitioning of n-acetyl-amino-acid amides. Chem. Eur. J. Med., 1983, 18, 369-375.
46. Fernandez-Recio, J.; Totrov, M.; Abagyan, R. Identification of protein-protein interaction sites from docking energy landscapes. J. Mol. Biol., 2004, 335(3), 843-865. (Pubitemid 38352824)
47. Fernandez-Recio, J.; Totrov, M.; Skorodumov, C.; Abagyan, R. Optimal docking area:a new method for predicting proteincprotein interaction sites. Proteins, 2005, 58(1), 134-143. (Pubitemid 39665624)
48. Keskin, O.; Bahar, I.; Badretdinov, A.Y.; Ptitsyn, O.B.; Jernigan, R.L. Empirical solvent-mediated potentials hold for both intramolecular and inter-molecular inter-residue interactions. Protein Sci., 1998, 7, 2578-2586. (Pubitemid 28565699)
49. Bahar, I.; Jernigan, R.L. Coordination geometry of nonbonded residues in globular proteins. Fold Des., 1996, 1(5), 357-370. (Pubitemid 126748232)
50. Vapnik, V. The Nature of Statistical Learning Theory. Springer-Verlag: New York, 2000.
51. Cai, Y.D.; Zhou, G.P.; Chou, K.C. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J., 2003, 84, 3257-3263.
52. Cai, Y.D.; Pong-Wong, R.; Feng, K.; Jen, J.C.H.; Chou, K.C. Application of SVM to predict membrane protein types. J. Theor. Biol., 2004, 226, 373-376.
53. Chou, K.C.; Cai, Y.D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem., 2002, 277, 45765-45769.
54. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Prediction of protein structural classes by support vector machines. Comput. Chem., 2002, 26, 293-296.
55. Ding, Y.S.; Zhang, T.L.; Chou, K.C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept. Lett., 2007, 14, 811-815.
56. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support vector machines for predicting the specificity of GalNAc-transferase. Peptides, 2002, 23, 205-208.
57. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support Vector Machines for predicting HIV protease cleavage sites in protein. J. Comput. Chem., 2002, 23, 267-274.
58. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support vector machines for the classification and prediction of beta-turn types. J. Pept. Sci., 2002, 8, 297-301.
59. Cai, Y.D.; Lin, S.; Chou, K.C. Support vector machines for prediction of protein signal sequences and their cleavage sites. Peptides, 2003, 24, 159-161. (Pubitemid 36165187)
60. Cai, Y.D.; Feng, K.Y.; Li, Y.X.; Chou, K.C. Support vector machine for predicting alpha-turn types. Peptides, 2003, 24, 629-630.
61. Cai, Y.D.; Zhou, G.P.; Jen, C.H.; Lin, S.L.; Chou, K.C. Identify catalytic triads of serine hydrolases by support vector machines. J. Theor. Biol., 2004, 228, 551-557.
62. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 1993, Neural Networks, 6(4), 525-533
63. Nigam, K.; McCallum, A.K.; Thrun, S.; Mitchell, T. Text classification from labeled and unlabeled documents using EM, Machine Learning, 1995, 39(2-3), 103-134. (Pubitemid 30594822)
64. Fujino, A.; Ueda, N.; Saito, K. A hybrid generative/discriminative approach to semi-supervised classifier design, Proceedings of the 20th national conference on Artificial intelligence, Pittsburgh, Pennsylvania, 2005, pp. 764-769.
65. Yarowsky, D. Unsupervised word sense disambiguation rivaling supervised methods, Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, Cambridge, Massachusetts, 1995; pp. 189-196.
66. Nigam, K.; Ghani, R. Analyzing the effectiveness and applicability of co-training, Ninth International Conference on Information and Knowledge Management, McLean, Virginia, United States, 2000; pp. 86-93.
67. Blum, A.; Mitchell, T. Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory, Madison, Wisconsin, United States, 1998; pp. 92-100.
68. Zhou, D.; Scholkopf, B.; Hofmannz, T. Semi-supervised learning on directed graphs. MIT Press: Cambridge, MA, 2004.
69. Xu, L.; Jordan, M.I. On convergence properties of the em algorithm for gaussian mixtures. Neural Comput., 1996, 8(1), 129-151. (Pubitemid 126449919)
70. Chou, K. C.; Zhang, C. T. Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30, 275-349.
71. Gu, Q.; Ding, Y.S.; Zhang, T.L. Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chou's Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns. Protein Pept. Lett., 2010, 17, 559-567.
72. Mohabatkar, H. Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein Pept. Lett.,, 2010, 17, 1207-1214.
73. Lin, H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol., 2008, 252, 350-356.
74. Zeng, Y.H.; Guo, Y.Z.; Xiao, R.Q.; Yang, L.; Yu, L.Z.; Li, M.L. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J. Theor. Biol., 2009, 259, 366-372.
75. Masso, M.; Vaisman, I.I. Knowledge-based computational mutagenesis for predicting the disease potential of human nonsynonymous single nucleotide polymorphisms. J. Theor. Biol., 2010, 266, 560-568.
76. Xiao, X.; Wang, P.; Chou, K.C. GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J. Comput. Chem., 2009, 30, 1414-1423.
77. Xiao, X.; Wang, P.; Chou, K.C. GPCR-2L: Predicting G proteincoupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Molecular Biosystems, 2011, 7, 911-919.
78. Ding, H.; Liu, L.; Guo, F.B.; Huang, J.; Lin, H. Identify Golgi protein types with modified mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept. Lett., 2011, 18, 58-63.
79. Hu, L.L.; Niu, S.; Shi, X.H.; Cai, Y.D.; Chou, K.C. Prediction and analysis of protein palmitoylation sites. Biochimie, 2011, 93, 489-496.
80. Kandaswamy, K.K.; Pugalenthi, G.; Moller, S.; Hartmann, E.; Kalies, K.U.; Suganthan, P.N.; Martinetz, T. Prediction of Apoptosis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composition. Protein Pept. Lett., 2010, 17, 1473-1479.
81. Van Rijsbergen C.J. Information Retrieval, 2nd, Butterworth-Heinemann, Newton, MA, 1979
82. Pearl, J. Bayesian networks: A model of self-activated memory for evidential reasoning, Proceedings of the 7th Conference of the Cognitive Science Society, 1985; pp. 329-334.
83. Breiman, L. Random forests, Machine Learning, 2001, 45(1), 5-32. (Pubitemid 32933532)
84. Quinlan, J. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers: California, 1993.
85. Menard, S. Applied logistic regression analysis. Sage Publications: California, 1995.
86. Fischer, T.; Arunachalam, K.; Bailey, D.; Mangual, V.; Bakhru, S. The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces. Bioinformatics, 2003, 19(11), 1453-1454. (Pubitemid 36939831)
87. Atwell, S.; Ultsch, M.; Vos, A.; Wells, J. Structural plasticity in a remodeled protein-protein interface. Science, 1997, 278(5340), 1125-1128. (Pubitemid 27517894)
88. Chou, K.C.; Shen, H.B. Review: recent advances in developing web-servers for predicting protein attributes. Natural Science, 2009, 2, 63-92 (openly accessible at http://www.scirp.org/journal/NS/).