Abstract
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.
Chapter PDF
Similar content being viewed by others
Keywords
References
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)
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)
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)
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)
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)
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)
Vakser, I.A.: Evaluation of GRAMM low-resolution docking methodology on the hemagglutinin-antibody complex. Proteins (suppl. 1), 226–230 (1997)
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)
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)
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)
Chen, R., Li, L., Weng, Z.: ZDOCK: an initial-stage protein-docking algorithm. Proteins 52(1), 80–87 (2003)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Venkatraman, V., Ritchie, D.W.: Flexible protein docking refinement using pose-dependent normal mode analysis. Proteins 80(9), 2262–2274 (2012)
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)
Ohue, M., Matsuzaki, Y., Akiyama, Y.: Docking-calculation-based method for predicting protein-RNA interactions. Genome Inform. 25(1), 25–39 (2011)
Reiher III, W.H.: Theoretical studies of hydrogen bonding. Ph.D. Thesis at Harvard University (1985)
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)
Hwang, H., Vreven, T., Janin, J., Weng, Z.: Protein–protein docking benchmark version 4.0. Proteins 78(15), 3111–3114 (2010)
Baker, M.D., Wolanin, P.M., Stock, J.B.: Systems biology of bacterial chemotaxis. Curr. Opin. Microbiol. 9(2), 187–192 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ohue, M., Matsuzaki, Y., Ishida, T., Akiyama, Y. (2012). Improvement of the Protein–Protein Docking Prediction by Introducing a Simple Hydrophobic Interaction Model: An Application to Interaction Pathway Analysis. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-34123-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34122-9
Online ISBN: 978-3-642-34123-6
eBook Packages: Computer ScienceComputer Science (R0)