Abstract
PythDock is a heuristic docking program that uses Python programming language with a simple scoring function and a population based search engine. The scoring function considers electrostatic and dispersion/repulsion terms. The search engine utilizes a particle swarm optimization algorithm. A grid potential map is generated using the shape information of a bound ligand within the active site. Therefore, the searching area is more relevant to the ligand binding. To evaluate the docking performance of PythDock, two well-known docking programs (AutoDock and DOCK) were also used with the same data. The accuracy of docked results were measured by the difference of the ligand structure between x-ray structure, and docked pose, i.e., average root mean squared deviation values of the bound ligand were compared for fourteen protein-ligand complexes. Since the number of ligands’ rotational flexibility is an important factor affecting the accuracy of a docking, the data set was chosen to have various degrees of flexibility. Although PythDock has a scoring function simpler than those of other programs (AutoDock and DOCK), our results showed that PythDock predicted more accurate poses than both AutoDock4.2 and DOCK6.2. This indicates that PythDock could be a useful tool to study ligand-receptor interactions and could also be beneficial in structure based drug design.
Similar content being viewed by others
References
Böhm, H. J., On the use of LUDI to search the Fine Chemicals Directory for ligands of proteins of known three-dimensional structure. J. Comput. Aided Mol. Des., 8, 623–632 (1994).
Brooijmans, N. and Kuntz, I. D., Molecular recognition and docking algorithms. Annu. Rev. Biophys. Biomol. Struct., 32, 335–373 (2003).
Chen, H. M., Liu, B. F., Huang, H. L., Hwang, S. F., and Ho, S. Y., SODOCK: swarm optimization for highly flexible protein-ligand docking. J. Comput. Chem., 28, 612–623 (2007).
Chung, J. Y., Hah, J. M., and Cho, A. E., Correlation between performance of QM/MM docking and simple classification of binding sites. J. Chem. Inf. Model., 49, 2382–2387 (2009).
Confgen V2.0, S. D., Llc, New York, NY (2008).
Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., and Mee, R. P., Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aided Mol. Des., 11, 425–445 (1997).
Ewing, T. J., Makino, S., Skillman, A. G., and Kuntz, I. D., DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des., 15, 411–428 (2001).
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., Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 47, 1739–1749 (2004).
Gilson, M. K. and Zhou, H. X., Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct., 36, 21–42 (2007).
Hawkins, P. C., Skillman, A. G., and Nicholls, A., Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem., 50, 74–82 (2007).
Helmer-Citterich, M. and Tramontano, A., PUZZLE: a new method for automated protein docking based on surface shape complementarity. J. Mol. Biol., 235, 1021–1031 (1994).
Jain, A. N., Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J. Med. Chem., 46, 499–511 (2003).
Kitchen, D. B., Decornez, H., Furr, J. R., and Bajorath, J., Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov., 3, 935–949 (2004).
Kontoyianni, M., McClellan, L. M., and Sokol, G. S., Evaluation of docking performance: comparative data on docking algorithms. J. Med. Chem., 47, 558–565 (2004).
Korb, O., Stützle, T., and Exner, T. E., Empirical scoring functions for advanced protein-ligand docking with PLANTS. J. Chem. Inf. Model., 49, 84–96 (2009).
Leach, A. R., Shoichet, B. K., and Peishoff, C. E., Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. J. Med. Chem., 49, 5851–5855 (2006).
Lee, H. S., Choi, J., Kufareva, I., Abagyan, R., Filikov, A., Yang, Y., and Yoon, S., Optimization of high throughput virtual screening by combining shape-matching and docking methods. J. Chem. Inf. Model., 48, 489–497 (2008).
Luo, W., Pei, J., and Zhu, Y., A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity. J. Mol. Model., 16, 903–913 (2010).
Mehler, E. L. and Solmajer, T., Electrostatic effects in proteins: comparison of dielectric and charge models. Protein Eng., 4, 903–910 (1991).
Meng, E. C., Shoichet, B. K., and Kuntz, I. D., Automated docking with grid-based energy evaluation. J. Comput. Chem., 13, 505–524 (1992).
Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K., and Olson, A. J. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem., 19, 1639–1662 (1998).
Rester, U., Dock around the Clock — Current Status of Small Molecule Docking and Scoring. QSAR Comb. Sci., 25, 605–615 (2006).
Shoichet, B. K. and Kuntz, I. D., Matching chemistry and shape in molecular docking. Protein Eng., 6, 723–732 (1993).
Tame, J. R., Scoring functions: a view from the bench. J. Comput. Aided Mol. Des., 13, 99–108 (1999).
Taylor, R. D., Jewsbury, P. J., and Essex, J. W., A review of protein-small molecule docking methods. J. Comput. Aided Mol. Des., 16, 151–166 (2002).
Venkatachalam, C. M., Jiang, X., Oldfield, T., and Waldman, M., LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graph. Model., 21, 289–307 (2003).
Viji, S. N., Prasad, P. A., and Gautham, N., Protein-ligand docking using mutually orthogonal Latin squares (MOLSDOCK). J. Chem. Inf. Model., 49, 2687–2694 (2009).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chung, J.Y., Cho, S.J. & Hah, JM. A python-based docking program utilizing a receptor bound ligand shape: PythDock. Arch. Pharm. Res. 34, 1451–1458 (2011). https://doi.org/10.1007/s12272-011-0906-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12272-011-0906-5