Abstract
Recent progress in structural biology and bioinformatics contributed to the increased amount of data that need to be stored and analyzed. Advances in data mining research have allowed the development of efficient methods to find interesting patterns in large databases. In this context, this work proposes a method to automatically extract detailed information from molecular docking experiments. Completely flexible molecular docking studies (including ligand and receptor explicit flexibilities) of the InhA enzyme from Mycobacterium tuberculosis in complex with NADH were performed with AutoDock3.05 using receptor snapshots generated by nanosecond molecular dynamics simulations. To analyze the results we applied our data mining method which was capable of identifying important information about intermolecular interactions and association rules. The method allowed a fast and concise analysis which led to identification of relevant residues and conformations essential to ligand binding.
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References
Wang, J., Zaki, M., Toivonen, H., Shasha, D.: Data Mining in Bioinformatics. In: Advanced Information and Knowledge Processing. Springer, Heidelberg (2005)
Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Wheeler, D.L.: GenBank. Nucl. Acids Res. 33, 34–38 (2005)
Han, J.: How can Data Mining help Bio-Data Analysis? In: Workshop on Data Mining in Bioinformatics (with SIGKDD 2002 Conference) (2002)
Alonso, H., Bliznyuk, A.A., Gready, J.E.: Combining Docking and Molecular Dynamic Simulations in Drug Design. Med. Res. Rev. 26, 531–568 (2006)
Schroeder, E.K., Basso, L.A., Santos, D.S., 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)
Waikato Environment for Knowledge, Analysis (accessed, March 2008), http://www.cs.waikato.ac.nz/ml/weka
Piatetsky-Shapiro, G., Frawley, W.J.: Knowledge Discovery in Databases. AAAI/MIT Press (1991)
Tan, P., Steinbach, M., Kumar, V.: Introduction to Data Mining. Person Addison Wesley (2006)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM 39, 27–34 (1996)
Drews, J.: Drug discovery: A historical perspective computational methods for biomolecular docking. Curr. Opin. Struct. Biol. 6, 402–406 (1996)
Lybrand, T.P.: Ligand-protein docking and rational drug design. Curr. Opin. Struct. Biol. 5, 224–228 (1995)
Kuntz, I.D.: Structure-based strategies for drug design and discovery. Science 257, 1078–1082 (1992)
Goodsell, D.S., Olson, A.J.: Automated docking of substrates to proteins by simulated annealing. Proteins 8, 195–202 (1990)
Huang, S., Zou, X.: Ensemble Docking of Multiple Protein Structures: Considering Protein Structural Variations in Molecular Docking. Proteins 66, 399–421 (2007)
Sali, A.: 100.000 Protein Structures for the Biologist. Nat. Struct. Biol. 5, 1029–1032 (1998)
van Gunsteren, W.F., Berendsen, H.J.C.: Computer Simulation of Molecular Dynamics Methodology, Applications and Perspectives in Chemistry. Angew. Chem. Int. Engl. Ed. 29, 992–1023 (1990)
Schroeder, E.K., Norberto de Souza, O., Santos, D.S., Blanchard, J.S., Basso, L.A.: Drugs that inhibit mycolic acids biosynthesis in Mycobacterium tuberculosis. Curr. Pharm. Biotech. 3, 197–225 (2002)
Machado, K.S., Schroeder, E.K., Ruiz, D.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)
Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function. J. Comput. Chem. 19, 1639–1662 (1998)
Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.H.: Data Mining in Bioinformatics using Weka. Bioinformatics 20, 2479–2481 (2004)
Guex, N., Peitsch, M.C.: SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 18, 2714–2723 (1997)
Dessen, A., Quémard, A., Blanchard, J.S., Jacobs Jr., W.R., Sacchettini, J.C.: Crystal structure and function of the isoniazid target of Mycobacterium tuberculosis. Science 267, 1638–1641 (1995)
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: PDB - Protein Data Bank. Nucl. Acids Res. 28, 235–242 (2000)
Irwin, J.J., Shoichet, B.K.: ZINC - A Free Database of Commercially Available Compounds for Virtual Screening. J. Chem. Inf. Model. 45, 177–182 (2005)
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Machado, K.S., Schroeder, E.K., Ruiz, D.D., Wink, A., Norberto de Souza, O. (2008). Extracting Information from Flexible Receptor-Flexible Ligand Docking Experiments. In: Bazzan, A.L.C., Craven, M., Martins, N.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2008. Lecture Notes in Computer Science(), vol 5167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85557-6_10
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DOI: https://doi.org/10.1007/978-3-540-85557-6_10
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