Abstract
A careful analysis of flexible-receptor molecular docking results, particularly those related to details of receptor-ligand interactions, is essential to improve the process of docking and the understanding of intermolecular recognition. Because flexible-receptor docking simulations generate large amounts of data, their manual analysis is impractical. We intend to apply classification decision trees algorithms to better understand this type of docking results. However, prior to that we need to discretize the target attribute, which in this work is the estimated Free Energy of Binding (FEB) of the flexible receptor-ligand interactions. Here we compare three different discretization methods, by equal frequency (1), by equal width (2) and our proposed method, based on the mode and standard deviation (3) of the FEB values.
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.
Similar content being viewed by others
References
Lengauer, T., Rarey, M.: Computational methods for biomolecular docking. Curr. Opin. Struct. Biol. 6, 402–406 (1996)
Morris, G., Goodsell, D., Halliday, R., Huey, R., Hart, W., Belew, R., Olson, A.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998)
Machado, K., Schroeder, E., Ruiz, D., Norberto de Souza, O.: Automating Molecular Docking with Explicit Receptor Flexibility Using Scientific Workflows. In: Sagot, M.-F., Walter, M.E.M.T. (eds.) BSB 2007. LNCS (LNBI), vol. 4643, pp. 1–11. Springer, Heidelberg (2007)
van Gunsteren, W., Berendsen, H.: Computer simulation of molecular dynamics: methodology, applications, and perspectives in chemistry. Angew. Chem. Int. Ed. Engl. 29, 992–1023 (1990)
Lin, J.-H., Perryman, A., Schames, J.R., McCammon, J.A.: Computational drug design accommodating receptor flexibility: the relaxed complex scheme. J. Am. Chem. Soc. 124, 5632–5633 (2002)
Schroeder, E., Basso, L., Santos, D., Norberto de Souza, O.: Molecular dynamics simulation studies of the wild-type, I21V, and I16T mutants of isoniazid-resistant Mycobacterium tuberculosis enoyl reductase (InhA) in complex with NADH: toward the understanding of NADH-InhA different affinities. Biophys. J. 89, 876–884 (2005)
Tan, P., Steinbach, M., Kumar, V.: Introduction to data mining. Addison Wesley, Boston (2006)
Dessen, A., Quémard, A., Blanchard, J., Jacobs Jr., W., Sacchettini, J.: Crystal structure and function of the isoniazid target of Mycobacterium tuberculosis. Science 267, 1638–1641 (1995)
Oliveira, J., Souza, E., Basso, L., Palaci, M., Dietze, R., Santos, D., Moreira, I.: An inorganic iron complex that inhibits wild-type and an isoniazid-resistant mutant 2-trans-enoyl-ACP (CoA) reductase from Mycobacterium tuberculosis. Chem. Commun. 3, 312–313 (2004)
Kuo, M., et al.: Targeting tuberculosis and malaria through inhibition of Enoyl reductase: compound activity and structural data. J. Biol. Chem. 278, 20851–20859 (2003)
Banerjee, A., Dubnau, E., Quemard, A., Balasubramanian, V., Um, K., Wilson, T., Collins, D., de Lisle, G., Jacobs Jr., W.: InhA, a gene encoding a target for isoniazid and ethionamide in Mycobacterium tuberculosis. Science 263, 227–230 (1994)
Winck, A., Machado, K., Norberto de Souza, O., Ruiz, D.: FReDD: supporting mining strategies through a flexible receptor docking database. In: Guimarães, K.S., Panchenko, A., Przytycka, T.M. (eds.) Advances in Bioinformatics and Computational Biology. LNCS, vol. 5676, pp. 143–146. Springer, Heidelberg (2009)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: The Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA, USA, pp. 194–202 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Machado, K.S., Winck, A.T., Ruiz, D.D., de Souza, O.N. (2010). Discretization of Flexible-Receptor Docking Data. In: Ferreira, C.E., Miyano, S., Stadler, P.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2010. Lecture Notes in Computer Science(), vol 6268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15060-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-15060-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15059-3
Online ISBN: 978-3-642-15060-9
eBook Packages: Computer ScienceComputer Science (R0)