Summary
An important contribution to today's computer-aided drug design is the automated screening of large compound databases against structurally resolved protein receptors targets. The introduction of ligand flexibility has, by now, become a standardized procedure. In contrast, a general approach to treat target degrees of freedom is still to be found, a consequence of the extreme increase of computational complexity, which comes along with the relaxation of protein degrees of freedom.
In this chapter, we discuss in some detail both benefits and present limitations of target flexibility for high-throughput in silico database screens. Among the benefits are an improved diversity of binding modes, which allows one to identify a wider class of drug candidates. The limitations are related to a diminishing docking accuracy and an increased number of false hits. Using the thymidine kinase receptor and ten known inhibitors as an example, we describe in detail how target flexibility was implemented and how it affected the screening performance.
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References
1. Fischer, E. (1894) Einfluss der Konfiguration auf die Wirkung der Enzyme. Ber. Dtsch. Chem. Ges. 27:2985–2993
2. Cramer III, R.D., Patterson, D.E. and Bunce, J.D. (1998) Comparative molecular field analysis (comfa). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110:5959–5967
3. Abagyan, R. and Totrov, M. (2001) High-throughput docking for lead generation. Curr. Opin. Chem. Biol. 5:375–382
4. Merlitz, H., Burghardt, B. and Wenzel, W. (2003) Application of the stochastic tunneling method to high throughput database screening. Chem. Phys. Lett. 370:68–73
5. Jorgensen, W.L. and McDonald, N.A. (1997) Development of an all-atom force field for heterocycles. Properties of liquid pyridine and diazenes. Theochem-J. Mol. Struct. 424:145–155
6. Morris, G.M., Goodsell, D.S., Halliday, R., Huey, R., Hart, W.E., Belew, R.K. and Olson, A.J. (1998) Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19:1639–1662
7. Kirkpatrick, S., Gelatt, C.D. and Vecchi, M.P. (1983) Optimization by simulated annealing. Science 220:671–680
8. Metropolis, N. and Stanislaw, U. (1949) The Monte Carlo method. JASA 44:335–341
9. Merlitz, H., Herges, T. and Wenzel, W. (2004) Fluctuation analysis and accuracy of a largescale in silico screen. J. Comp. Chem. 25:1568–1575
10. Wenzel, W. and Hamacher, K. (1999) Stochastic tunneling approach for global optimization of complex potential energy landscapes. Phys. Rev. Lett. 82:3003–3007
11. Merlitz, H. and Wenzel, W. (2002) Comparison of stochastic optimization methods for receptor-ligand docking. Chem. Phys. Lett. 362:271–277
12. Bissantz, C., Folkerts, G. and Rognan, D. (2000) Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. J. Med. Chem. 43:4759–4767
13. Merlitz, H. and Wenzel, W. (2004) Impact of receptor flexibility on in silico screening performance. Chem. Phys. Lett. 390:500–505
14. Milne, G.W.A., Nicklaus, M.C., Driscoll, J.S., Zaharevitz, D. and Wang, S. (1994) National cancer institute drug information system 3d database. J. Chem. Inf. Comput. Sci. 34:1219–1224
15. Shi, S., Yan, L., Yang, Y., Fisher-Shaulsky, J. and Thacher, T. (2003) An extensible and systematic force field, ESFF, for molecular modeling of organic, inorganic, and organometallic systems. J. Comput. Chem. 24:1059–1076
16. Bernstein, F.C., Koetzle, T.F., Williams, G.J.B., Meyer, E.F., Brice, Jr. M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T. and Tasumi, M. (1977) The Protein Data Bank: A computer-based archival file for macromolecular structures. J. Mol. Biol. 112:535–542
17. Vogt, J., Perozzo, R., Pautsch, A., Prota, A., Schelling, P., Pilger, P., Folkerts, G., Scapozza, L., and Schulz, G.E. (2000) Nucleoside binding site of herpes simplex type 1 thymidine kinase analyzed by x-ray crystallography. Proteins 42:545–553
18. Wurth, C., Kessler, U., Vogt, J., Schulz, G.E., Folkers, G. and Scapozza, L. (2001) The effect of substrate binding on the conformation and structural stability of herpes simplex virus type 1 thymidine kinase. Protein Sci. 10:60–73
19. Knegtel, R.M.A. and Wagnet, M. (1999) Efficacy and selectivity in flexible database docking. Proteins 37:334–345
Fischer, B., Merlitz, H. and Wenzel, W. (2005) Increasing diversity in in silico screening with target flexibility. CompLife 186–197
Acknowledgments
We thank the Fond der Chemischen Industrie, the BMBF, the Deutsche Forschungsgemeinschaft (grant WE 1863/11-1), and the Kurt Eberhard Bode Stiftung for financial support.
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Fischer, B., Merlitz, H., Wenzel, W. (2008). Receptor Flexibility for Large-Scale In Silico Ligand Screens. In: Kukol, A. (eds) Molecular Modeling of Proteins. Methods Molecular Biology™, vol 443. Humana Press. https://doi.org/10.1007/978-1-59745-177-2_18
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DOI: https://doi.org/10.1007/978-1-59745-177-2_18
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