Abstract
Accurately predicting generic protein-protein binding affinities (PPBA) is essential to analyze the outputs of protein docking and may help infer real status of cellular protein-protein interaction sub-networks. However, accurate PPBA prediction is still extremely challenging. Machine learning methods are promising to address this problem. We propose a two-layer support vector regression (TLSVR) model to implicitly capture binding contributions that are hard to explicitly model. The TLSVR circumvents both the descriptor compatibility problem and the need for problematic modeling assumptions. Input features for TLSVR in first layer are scores of 2209 interacting atom pairs within each distance bin. The base SVRs are combined by the second layer to infer the final affinities. Leave-one-out validation on our heterogeneous data shows that the TLSVR method obtains a very good result of R=0.80 and SD=1.32 with real affinities. Comparison experiment further demonstrates that TLSVR is superior to the previous state-of-art methods in predicting generic PPBA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Melo, F., Feytmans, E.: Novel Knowledge-based Mean force Potential at Atomic Level. J. Mol. Biol. 267, 207–222 (1997)
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)
Lu, H., Lu, L., Skolnick, J.: Development of Unified Statistical Potentials Describing Protein-protein Interactions. Biophysical Journal 84, 1895–1901 (2003)
Muegge, I.: PMF Scoring Revisited. J. Med. Chem. 49, 5895–5902 (2006)
Englebienne, P., Moitessier, N.: Docking Ligands into Flexible and Solvated Macromolecules. 4. Are Popular Scoring Functions Accurate for this Class of Proteins? Journal of Chemical Information and Modeling 49, 1568–1580 (2009)
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)
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)
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)
Wolpert, D.H.: Stacked Generalization. Neural Network 5, 241–259 (1992)
Xia, J.-F., Zhao, X.-M., Huang, D.-S.: Predicting Protein-protein Interactions from Protein Sequences Using Meta Predictor. Amino. Acids 39, 1595–1599
Teramoto, R., Kashima, H.: Prediction of Protein-ligand Binding Affinities Using Multiple Instance Learning. Journal of Molecular Graphics and Modelling 29, 492–497
Ballester, P.J., Mitchell, J.B.O.: A Machine Learning Approach to Predicting Protein-ligand Binding Affinity with Applications to Molecular Docking. Bioinformatics 26, 1169–1175 (2010)
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 GarcÃa, C.A., Zhang, L. (eds.) ICIC 2010. LNCS, vol. 6216, pp. 69–75. Springer, Heidelberg (2010)
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)
Wang, R.X., Fang, X.L., Lu, Y.P., Wang, S.M.: The PDBbind Database: Collection of Binding Affinities for Protein-ligand Complexes with Known Three-dimensional Structures. Journal of Medicinal Chemistry 47, 2977–2980 (2004)
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)
Vapnik, V.N.: Statistical learning theory. Springer, New York (1998)
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
Li, X., Zhu, M., Li, X., Wang, HQ., Wang, S. (2012). Protein-Protein Binding Affinity Prediction Based on an SVR Ensemble. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-31588-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31587-9
Online ISBN: 978-3-642-31588-6
eBook Packages: Computer ScienceComputer Science (R0)