Evaluation of Advanced Structure-Based Virtual Screening Methods for Computer-Aided Drug Discovery

  • Lee, Hui-Sun (Department of Biological Sciences, Research Center for Women’s Diseases (RCWD), Sookmyung Women’s University) ;
  • Choi, Ji-Won (Department of Biological Sciences, Research Center for Women’s Diseases (RCWD), Sookmyung Women’s University) ;
  • Yoon, Suk-Joon (1Department of Biological Sciences, Research Center for Women’s Diseases (RCWD), Sookmyung Women’s University)
  • Published : 2007.03.31

Abstract

Computational virtual screening has become an essential platform of drug discovery for the efficient identification of active candidates. Moleculardocking, a key technology of receptor-centric virtual screening, is commonly used to predict the binding affinities of chemical compounds on target receptors. Despite the advancement and extensive application of these methods, substantial improvement is still required to increase their accuracy and time-efficiency. Here, we evaluate several advanced structure-based virtual screening approaches for elucidating the rank-order activity of chemical libraries, and the quantitative structureactivity relationship (QSAR). Our results show that the ensemble-average free energy estimation, including implicit solvation energy terms, significantly improves the hit enrichment of the virtual screening. We also demonstrate that the assignment of quantum mechanical-polarized (QM-polarized) partial charges to docked ligands contributes to the reproduction of the crystal pose of ligands in the docking and scoring procedure.

Keywords

References

  1. Blair, R. M., Fang, H., Branham, W. S., Hass, B. S., Dial, S. L., Moland, C. L., Tong, W., Shi, L., Perkins, R., and Sheehan, D. M. (2000). The estrogen receptor relative binding affinities of 188 natural and xenochemicals: structural diversity of ligands. Toxicol. Sci. 54, 138-153 https://doi.org/10.1093/toxsci/54.1.138
  2. Chen, B., Harrison, R. F., Papadatos, G., Willett, P., Wood, D. J., Lewell, X. Q., Greenidge, P., and Stiefl, N. (2007). Evaluation of machine-learning methods for ligand-based virtual screening. Journal of computer-aided molecular design 21, 53-62 https://doi.org/10.1007/s10822-006-9096-5
  3. Cherkasov, A., Ban, F., Li, Y., Fallahi, M., and Hammond, G. L. (2006). Progressive docking: a hybrid QSAR/docking approach for accelerating in silico high throughput screening. Journal of medicinal chemistry 49, 7466-7478 https://doi.org/10.1021/jm060961+
  4. Cho, A. E., Guallar, V., Berne, B. J., and Friesner, R. (2005). Importance of accurate charges in molecular docking: Quantum mechanical/molecular mechanical (QM/MM) approach. J. Comput. Chem. 26, 915-931 https://doi.org/10.1002/jcc.20222
  5. Cleves, A. E. and Jain, A. N. (2006). Robust ligand-based modeling of the biological targets of known drugs. Journal of medicinal chemistry 49, 2921-2938 https://doi.org/10.1021/jm051139t
  6. Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., and Mee, R. P. (1997). Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. Journal of computer-aided molecular design 11, 425-445 https://doi.org/10.1023/A:1007996124545
  7. Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., Shaw, D. E., Francis, P., and Shenkin, P. S. (2004). Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of medicinal chemistry 47, 1739-1749 https://doi.org/10.1021/jm0306430
  8. Hand, D., Mannila, H., and Smyth, P. (2001). Principles of Data Mining. (Cambridge, Massachsetts: The MIT Press)
  9. Hawkins, P. C., Skillman, A. G., and Nicholls, A. (2007). Comparison of shape-matching and docking as virtual screening tools. Journal of medicinal chemistry 50, 74-82 https://doi.org/10.1021/jm0603365
  10. Hong, H., Tong, W., Fang, H., Shi, L., Xie, Q., Wu, J., Perkins, R., Walker, J. D., Branham, W., and Sheehan, D. M. (2002). Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts. Environmental health perspectives 110, 29-36 https://doi.org/10.1289/ehp.1104c29
  11. Kitchen, D. B., Decornez, H., Furr, J. R., and Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3, 935-949 https://doi.org/10.1038/nrd1549
  12. Klebe, G. (2006). Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11, 580-594 https://doi.org/10.1016/j.drudis.2006.05.012
  13. Oprea, T.I. and Matter, H. (2004). Integrating virtual screening in lead discovery. Curr. Opin. Chem. Biol. 8, 349-358 https://doi.org/10.1016/j.cbpa.2004.06.008
  14. Sousa, S. F., Fernandes, P. A., and Ramos, M. J. (2006). Protein-ligand docking: current status and future challenges. Proteins 65, 15-26 https://doi.org/10.1002/prot.21082
  15. Stahura, F. L. and Bajorath, J. (2004). Virtual screening methods that complement HTS. Comb. Chem. High Throughput Screen. 7, 259-269 https://doi.org/10.2174/1386207043328706
  16. Warren, G. L., Andrews, C. W., Capelli, A. M., Clarke, B., LaLonde, J., Lambert, M. H., Lindvall, M., Nevins, N., Semus, S. F., Senger, S., Tedesco, G., Wall, I. D., Woolven, J. M., Peishoff, C. E., and Head, M. S. (2006). A critical assessment of docking programs and scoring functions. Journal of medicinal chemistry 49, 5912-5931 https://doi.org/10.1021/jm050362n
  17. Yoon, S., Smellie, A., Hartsough, D., and Filikov, A. (2005a). Computational identification of proteins for selectivity assays. Proteins 59, 434-443 https://doi.org/10.1002/prot.20428
  18. Yoon, S., Smellie, A., Hartsough, D., and Filikov, A. (2005b). Surrogate docking: structure-based virtual screening at high throughput speed. Journal of computer-aided molecular design 19, 483-497 https://doi.org/10.1007/s10822-005-9002-6
  19. Yoon, S. and Welsh, W. J. (2004). Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring. J. Chem. Inf. Comput. Sci. 44, 88-96 https://doi.org/10.1021/ci0341619