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Machine-Learning Methods to Predict Protein Interaction Sites in Folded Proteins

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 7548)

Abstract

A reliable predictor of protein-protein interaction sites is necessary to investigate and model protein functional interaction networks. Hidden Markov Support Vector Machines (HM-SVM) have been shown to be among the best performing methods on this task. Furthermore, it has been noted that the performance of a predictor improves when its input takes advantage of the difference between observed and predicted residue solvent accessibility. In this paper, for first time, we combine these elements and we present ISPRED2, a new HM-SVM-based method that overpasses the state of the art performance (Q2=0.71 and correlation=0.43). ISPRED2 consists of a sets of Python scripts aimed at integrating the different third-party software to obtain the final prediction.

Keywords

  • Hidden Markov Support Vector Machines
  • Prediction of Interaction sites
  • Protein-Protein Interaction
  • Machine Learning
  • Evolutionary Information
  • Solvent Accessibility

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References

  1. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology 213(3), 403–410 (1990)

    Google Scholar 

  2. Bartoli, L., Martelli, P.L., Rossi, I., Fariselli, P., Casadio, R.: The prediction of protein-protein interacting sites in genome-wide protein interaction networks: The Test Case of the Human Cell Cycle. Curr. Protein Pept. Sci. 11, 601–608 (2010)

    CrossRef  Google Scholar 

  3. Bordner, A.J., Abagyan, R.: Statistical analysis and prediction of protein-protein interfaces. Proteins 60(3), 353–366 (2005)

    CrossRef  Google Scholar 

  4. Bradford, J.R., Westhead, D.R.: Improved prediction of protein-protein binding sites using a support vector machines approach. Bioinformatics 21(8), 1487–1494 (2005)

    CrossRef  Google Scholar 

  5. Chen, X.W., Jeong, J.C.: Sequence-based prediction of protein interaction sites with an integrative method. Bioinformatics 25(5), 585–591 (2009)

    CrossRef  Google Scholar 

  6. Chen, H., Zhou, H.X.: Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data. Proteins 61(1), 21–35 (2005)

    CrossRef  Google Scholar 

  7. Chung, J.L., Wang, W., Bourne, P.E.: Exploiting sequence and structure homologs to identify protein-protein binding sites. Proteins 62(3), 630–640 (2006)

    CrossRef  Google Scholar 

  8. Deng, L., Guan, J., Dong, Q., Zhou, S.: Prediction of protein-protein interaction sites using an ensemble method. BMC Bioinformatics 10, 426 (2009)

    CrossRef  Google Scholar 

  9. DeVries, S.J., Bonvin, A.M.J.J.: How Proteins Get in Touch: Interface Prediction in the Study of Biomolecular Complexes. Current Protein and Peptide Science, 394–406 (2008)

    Google Scholar 

  10. Dong, Q., Wang, X., Lin, L., Guan, Y.: Exploiting residue-level and profile-level interface propensities for usage in binding sites prediction of proteins. BMC Bioinformatics 8, 147 (2007)

    CrossRef  Google Scholar 

  11. Ezkurdia, I., Bartoli, L., Fariselli, P., Casadio, R., Valencia, A., Tress, M.L.: Progress and challenges in predicting protein-protein interaction sites. Brief Bioinform. 10(3), 233–246 (2009)

    CrossRef  Google Scholar 

  12. Fariselli, P., Pazos, F., Valencia, A., Casadio, R.: Prediction of protein–protein interaction sites in heterocomplexes with neural networks. Eur. J. Biochem. 269(5), 1356–1361 (2002)

    CrossRef  Google Scholar 

  13. Fariselli, P., Zauli, A., Rossi, I., Finelli, M., Martelli, P.L., Casadio, R.: A neural network method to improve prediction of protein-protein interaction sites in heterocomplexes. In: IEEE Int. Workshop on Neural Network on Signal Processing 2003, Toulouse (FRANCE), pp. 33–41. IEEE Press (2003)

    Google Scholar 

  14. Henrick, K., Thornton, J.M.P.Q.S.: A protein quaternary structure file server. Trends Biochem. Sci. 23(9), 302–305 (1998)

    CrossRef  Google Scholar 

  15. Jones, S., Thornton, J.M.: Analysis of protein-protein interaction sites using surface patches. J. Mol. Biol. 272, 121–132 (1997)

    CrossRef  Google Scholar 

  16. Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1983)

    CrossRef  Google Scholar 

  17. Koike, A., Takagi, T.: Prediction of protein-protein interaction sites using support vector machines. Protein Eng. Des. Sel. 17(2), 165–173 (2004)

    CrossRef  Google Scholar 

  18. Li, M.H., Lin, L., Wang, X.L., Liu, T.: Protein-protein interaction site prediction based on conditional random fields. Bioinformatics 23(5), 597–604 (2007)

    MATH  CrossRef  Google Scholar 

  19. Li, N., Sun, Z., Jiang, F.: Prediction of protein-protein binding site by using core interface residue and support vector machine. BMC Bioinformatics 9, 553 (2008)

    CrossRef  Google Scholar 

  20. Liu, B., Wang, X., Lin, L., Tang, B., Dong, Q., Wang, X.: Prediction of protein binding sites in protein structures using hidden Markov support vector machine. BMC Bioinformatics 10, 381 (2003)

    CrossRef  Google Scholar 

  21. Nguyen, M.N., Rajapakse, J.C.: Protein-Protein Interface Residue Prediction with SVM Using Evolutionary Profiles and Accessible Surface Areas. In: CIBCB 2006, pp. 1–5 (2006)

    Google Scholar 

  22. Ofran, Y., Rost, B.: Predicted protein-protein interaction sites from local sequence information. FEBS Lett. 544(1-3), 236–239 (2003)

    CrossRef  Google Scholar 

  23. Ofran, Y., Rost, B.: ISIS: interaction sites identified from sequence. Bioinform 23(ECCB 2006), e13–e16 (2006)

    Google Scholar 

  24. Porollo, A., Meller, J.: Prediction-based fingerprints of protein-protein interactions. Proteins 66(3), 630–645 (2007)

    CrossRef  Google Scholar 

  25. Qin, S., Zhou, H.X.: meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics 23(24), 3386–3387 (2007)

    CrossRef  Google Scholar 

  26. Res, I., Mihalek, I., Lichtarge, O.: An evolution based classifier for prediction of protein interfaces without using protein structures. Bioinformatics 21(10), 2496–2501 (2005)

    CrossRef  Google Scholar 

  27. Šikić, M., Tomić, S., Vlahoviček, K.: Prediction of Protein-Protein Interaction Sites in Sequences and 3D Structures by Random Forests. PLoS Comput. Biol. 5(1), e1000278 (2009)

    Google Scholar 

  28. Tsochataridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large Margin Methods for Structured and Interdependent Output Variables. Journal of Machine Learning Research 6, 1453–1484 (2005)

    Google Scholar 

  29. Yan, C., Dobbs, D., Honavar, V.: A two-stage classifier for identification of protein-protein interface residues. Bioinformatics 20(suppl. 1), I371–I378 (2004)

    CrossRef  Google Scholar 

  30. Wagner, M., Adamczak, R., Porollo, A., Meller, J.: Linear regression models for solvent accessibility prediction in proteins. Journal of Computational Biology 12, 355–369 (2005)

    CrossRef  Google Scholar 

  31. Wang, B., Chen, P., Huang, D.S., Li, J.J., Lok, T.M., Lyu, M.R.: Predicting protein interaction sites from residue spatial sequence profile and evolution rate. FEBS Lett. 580(2), 380–384 (2006)

    CrossRef  Google Scholar 

  32. Zhou, H.X., Shan, Y.: Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44(3), 336–343 (2001)

    CrossRef  Google Scholar 

  33. Zhou, H.X., Qin, S.: Interaction-site prediction for protein complexes: a critical assessment. Bioinformatics 23(17), 2203–2220 (2007)

    CrossRef  Google Scholar 

  34. Hubbard, S.J.: ACCESS: A Computer Program Written in C. University College, London (1989)

    Google Scholar 

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Savojardo, C., Fariselli, P., Piovesan, D., Martelli, P.L., Casadio, R. (2012). Machine-Learning Methods to Predict Protein Interaction Sites in Folded Proteins. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-35686-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35685-8

  • Online ISBN: 978-3-642-35686-5

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