Improvement of the Protein–Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: An Application to Interaction Pathway Analysis

  • Masahito Ohue
  • Yuri Matsuzaki
  • Takashi Ishida
  • Yutaka Akiyama
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7632)


We propose a new hydrophobic interaction model that applies atomic contact energy for our protein–protein docking software, MEGADOCK. Previously, this software used only two score terms, shape complementarity and electrostatic interaction. We develop a modified score function incorporating the hydrophobic interaction effect. Using the proposed score function, MEGADOCK can calculate three physico-chemical effects with only one correlation function. We evaluate the proposed system against three other protein–protein docking score models, and we confirm that our method displays better performance than the original MEGADOCK system and is faster than both ZDOCK systems. Thus, we successfully improve accuracy without loosing speed.


Protein–Protein Docking MEGADOCK Hydrophobic Interaction Fast Fourier Transform Protein–Protein Interaction 


  1. 1.
    Pons, C., Grosdidier, S., Solernou, A., Pérez-Cano, L., Fernández-Recio, J.: Present and future challenges and limitations in protein–protein docking. Proteins 78(1), 95–108 (2010)CrossRefGoogle Scholar
  2. 2.
    Wass, M.N., David, A., Sternberg, M.J.E.: Challenges for the prediction of macromolecular interactions. Curr. Opin. Struct. Biol. 21(3), 382–390 (2011)CrossRefGoogle Scholar
  3. 3.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., et al.: The Protein Data Bank. Nucleic Acids Res. 28(1), 235–242 (2000)CrossRefGoogle Scholar
  4. 4.
    Stein, A., Mosca, R., Aloy, P.: Three-dimensional modeling of protein interactions and complexes is going ‘omics. Curr. Opin. Struct. Biol. 21(2), 200–208 (2011)CrossRefGoogle Scholar
  5. 5.
    Katchalski-Katzir, E., Shariv, I., Eisenstein, M., Friesem, A.A., Aflalo, C., et al.: Molecular surface recognition: Determination of geometric fit between proteins and their ligands by correlation techniques. Proc. Natl. Acad. Sci. USA 89, 2195–2199 (1992)CrossRefGoogle Scholar
  6. 6.
    Gabb, H.A., Jackson, R.M., Sternberg, M.J.E.: Modelling protein docking using shape complementarity, electrostatics and biochemical information. J. Mol. Biol. 272(1), 106–120 (1997)CrossRefGoogle Scholar
  7. 7.
    Vakser, I.A.: Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex. Proteins (suppl. 1), 226–230 (1997)Google Scholar
  8. 8.
    Mandell, J.G., Roberts, V.A., Pique, M.E., Kotlovyi, V., Mitchell, J.C., et al.: Protein docking using continuum electrostatics and geometric fit. Protein Eng. 14(2), 105–113 (2001)CrossRefGoogle Scholar
  9. 9.
    Cheng, T.M.-K., Blundell, T.L., Fernández-Recio, J.: pyDock: electrostatics and desolvation for effective scoring of rigid-body protein–protein docking. Proteins 68(2), 503–515 (2007)CrossRefGoogle Scholar
  10. 10.
    Kozakov, D., Brenke, R., Comeau, S.R., Vajda, S.: PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65(2), 392–406 (2006)CrossRefGoogle Scholar
  11. 11.
    Chen, R., Li, L., Weng, Z.: ZDOCK: an initial-stage protein-docking algorithm. Proteins 52(1), 80–87 (2003)CrossRefGoogle Scholar
  12. 12.
    Mintseris, J., Pierce, B., Wiehe, K., Anderson, R., Chen, R., et al.: Integrating statistical pair potentials into protein complex prediction. Proteins 69(3), 511–520 (2007)CrossRefGoogle Scholar
  13. 13.
    Hwang, H., Vreven, T., Pierce, B.G., Hung, J.-H., Weng, Z.: Performance of ZDOCK and ZRANK in CAPRI rounds 13–19. Proteins 78(15), 3104–3110 (2010)CrossRefGoogle Scholar
  14. 14.
    Uchikoga, N., Hirokawa, T.: Analysis of protein–protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes. BMC Bioinformatics 11(236) (2010)Google Scholar
  15. 15.
    Fleishman, S.J., Whitehead, T.A., Strauch, E.-M., Corn, J.E., Qin, S., et al.: Community-wide assessment of protein-interface modeling suggests improvements to design methodology. J. Mol. Biol. 414(2), 289–302 (2011)CrossRefGoogle Scholar
  16. 16.
    Wass, M.N., Fuentes, G., Pons, C., Pazos, F., Valencia, A.: Towards the prediction of protein interaction partners using physical docking. Mol. Syst. Biol. 7(469) (2011)Google Scholar
  17. 17.
    Matsuzaki, Y., Matsuzaki, Y., Sato, T., Akiyama, Y.: In silico screening of protein–protein interactions with all-to-all rigid docking and clustering: an application to pathway analysis. J. Bioinform. Comput. Biol. 7(6), 991–1012 (2009)CrossRefGoogle Scholar
  18. 18.
    Tsukamoto, K., Yoshikawa, T., Hourai, Y., Fukui, K., Akiyama, Y.: Development of an affinity evaluation and prediction system by using the shape complementarity characteristic between proteins. J. Bioinform. Comput. Biol. 6(6), 1133–1156 (2008)CrossRefGoogle Scholar
  19. 19.
    Yoshikawa, T., Tsukamoto, K., Hourai, Y., Fukui, K.: Improving the accuracy of an affinity prediction method by using statistics on shape complementarity between proteins. J. Chem. Inf. Model. 49(3), 693–703 (2009)CrossRefGoogle Scholar
  20. 20.
    Chaleil, R.A.G., Tournier, A.L., Bates, P.A., Kro, M.: Implicit flexibility in protein docking: Cross-docking and local refinement. Proteins 69(4), 750–757 (2007)CrossRefGoogle Scholar
  21. 21.
    Dobbins, S.E., Lesk, V.I., Sternberg, M.J.E.: Insights into protein flexibility: The relationship between normal modes and conformational change upon protein–protein docking. Proc. Natl. Acad. Sci. USA 105(30), 10390–10395 (2008)CrossRefGoogle Scholar
  22. 22.
    Venkatraman, V., Ritchie, D.W.: Flexible protein docking refinement using pose-dependent normal mode analysis. Proteins 80(9), 2262–2274 (2012)Google Scholar
  23. 23.
    Ohue, M., Matsuzaki, Y., Matsuzaki, Y., Sato, T., Akiyama, Y.: MEGADOCK: an all-to-all protein–protein interaction prediction system using tertiary structure data and its application to systems biology study. IPSJ TOM 3(3), 91–106 (2010) (in Japanese)Google Scholar
  24. 24.
    Ohue, M., Matsuzaki, Y., Akiyama, Y.: Docking-calculation-based method for predicting protein-RNA interactions. Genome Inform. 25(1), 25–39 (2011)Google Scholar
  25. 25.
    Reiher III, W.H.: Theoretical studies of hydrogen bonding. Ph.D. Thesis at Harvard University (1985)Google Scholar
  26. 26.
    Zhang, C., Vasmatzis, G., Cornette, J.L., DeLisi, C.: Determination of atomic desolvation energies from the structures of crystallized proteins. J. Mol. Biol. 267(3), 707–726 (1997)CrossRefGoogle Scholar
  27. 27.
    Hwang, H., Vreven, T., Janin, J., Weng, Z.: Protein–protein docking benchmark version 4.0. Proteins 78(15), 3111–3114 (2010)Google Scholar
  28. 28.
    Baker, M.D., Wolanin, P.M., Stock, J.B.: Systems biology of bacterial chemotaxis. Curr. Opin. Microbiol. 9(2), 187–192 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Masahito Ohue
    • 1
    • 2
  • Yuri Matsuzaki
    • 1
  • Takashi Ishida
    • 1
  • Yutaka Akiyama
    • 1
  1. 1.Graduate School of Information Science and EngineeringTokyo Institute of TechnologyTokyoJapan
  2. 2.Japan Society for the Promotion of ScienceJapan

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