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Protein-Protein Interaction Affinity Prediction Based on Interface Descriptors and Machine Learning

  • Xue-Ling Li
  • Min Zhu
  • Xiao-Lai Li
  • Hong-Qiang Wang
  • Shulin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

Knowing the protein-protein interaction affinity is important for accurately inferring the time dimensionality of the dynamic protein-protein interaction networks from a viewpoint of systems biology. The accumulation of the determined protein complex structures with high resolution facilitates to realize this ambitious goal. Previous methods on protein-protein interaction affinity (PPIA) prediction have achieved great success. However, there is still a great space to improve prediction accuracy. Here, we develop a support vector regression method to infer highly heterogeneous protein-protein interaction affinities based on interface properties. This method takes full advantage of the labels of the interaction pairs and greatly reduces the dimensionality of the input features. Results show that the supervised machine leaning methods are effective with R=0.80 and SD=1.41 and perform well when applied to the prediction of highly heterogeneous or generic PPIA. Comparison of different types of interface properties shows that the global interface properties have a more stable performance while the smoothed PMF obtains the best prediction accuracy.

Keywords

Protein-protein interaction affinity Potential of Mean Force protein complex interface descriptors Machine Learning two-layer Support Vectors 

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References

  1. 1.
    Zhang, J.S., Maslov, S., Shakhnovich, E.I.: Constraints Imposed by Non-functional Protein-protein Interactions on Gene Expression and Proteome Size. Molecular Systems Biology 4, 210 (2008)CrossRefGoogle Scholar
  2. 2.
    Kollman, P.A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., Donini, O., Cieplak, P., Srinivasan, J., Case, D.A., Cheatham, T.E.: 3rd: Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Acc Chem. Res. 33, 889–897 (2000)CrossRefGoogle Scholar
  3. 3.
    Bohm, H.J.: Prediction of Binding Constants of Protein Ligands: A Fast Method for The Prioritization of Hits Obtained from De Novo Design or 3D Database Search Programs. J. Comput. Aided Mol. Des. 12, 309–323 (1998)CrossRefGoogle Scholar
  4. 4.
    Melo, F., Feytmans, E.: Novel Knowledge-based Mean Force Potential at Atomic Level. J. Mol. Biol. 267, 207–222 (1997)CrossRefGoogle Scholar
  5. 5.
    Su, Y., Zhou, A., Xia, X., Li, W., Sun, Z.: Quantitative Prediction of Protein-Protein Binding Affinity with a Potential of Mean Force Considering Volume Correction. Protein Sci. 18, 2550–2558 (2009)CrossRefGoogle Scholar
  6. 6.
    Oda, A., Tsuchida, K., Takakura, T., Yamaotsu, N., Hirono, S.: Comparison of Consensus Scoring Strategies for Evaluating Computational Models of Protein-ligand Complexes. Journal of Chemical Information and Modeling 46, 380–391 (2006)CrossRefGoogle Scholar
  7. 7.
    Kastritis, P.L., Bonvin, A.M.J.J.: Are Scoring Functions in Protein-Protein Docking Ready To Predict Interactomes? Clues from a Novel Binding Affinity Benchmark. Journal of Proteome Research 9, 2216–2225 (2010)CrossRefGoogle Scholar
  8. 8.
    Sotriffer, C.A., Sanschagrin, P., Matter, H., Klebe, G.: SFCscore: Scoring Functions for Affinity Prediction of Protein-ligand Complexes. Proteins-Structure Function and Bioinformatics 73, 395–419 (2008)CrossRefGoogle Scholar
  9. 9.
    Xia, J.F., Zhao, X.M., Huang, D.S.: Predicting Protein-protein Interactions from Protein Sequences Using Meta Predictor. Amino Acids 39, 1595–1599 (2010)CrossRefGoogle Scholar
  10. 10.
    Li, X.-L., Hou, M.-L., Wang, S.-L.: A Residual Level Potential of Mean Force Based Approach to Predict Protein-Protein Interaction Affinity. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 680–686. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Li, X.L., Wang, S.L., Hou, M.L.: Specificity of Transporter Associated with Antigen Processing Protein as Revealed by Feature Selection Method. Protein and Peptide Letters 17, 1129–1135 (2010)CrossRefGoogle Scholar
  12. 12.
    Li, X.-L., Wang, S.-L.: A Comparative Study on Feature Selection in Regression for Predicting the Affinity of TAP Binding Peptides. In: Huang, D.-S., Zhang, X., Reyes García, C.A., Zhang, L. (eds.) ICIC 2010. LNCS (LNAI), vol. 6216, pp. 69–75. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Moal, L.H., Agius, R., Bates, P.A.: Protein-protein Binding Affinity Prediction on a Diverse Set of Structures. Bioinformatics 27(21), 3002–3009 (2011)CrossRefGoogle Scholar
  14. 14.
    Wang, R.X., Fang, X.L., Lu, Y.P., Yang, C.Y., Wang, S.M.: The PDBbind Database: Methodologies and Updates. Journal of Medicinal Chemistry 48, 4111–4119 (2005)CrossRefGoogle Scholar
  15. 15.
    Vapnik, V.N.: Statistical Learning Theory. Springer, New York (1998)zbMATHGoogle Scholar
  16. 16.
    Wolpert, D.H.: Stacked Generalization. Neural Network 5, 241–259 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xue-Ling Li
    • 1
  • Min Zhu
    • 2
  • Xiao-Lai Li
    • 1
  • Hong-Qiang Wang
    • 1
  • Shulin Wang
    • 3
  1. 1.Intelligent Computing LabHefei Institute of Intelligent Machines, Chinese Academy of SciencesHefeiP.R. China
  2. 2.Robot Sensor and Human-Machine Interaction LaboratoryHefei Institute of Intelligent Machines, Chinese Academy of SciencesHefeiP.R. China
  3. 3.School of Computer and CommunicationHunan UniversityChangshaP.R. China

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