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Nucleic Acids Research 2005 33(Web Server Issue):W233-W238; doi:10.1093/nar/gki586
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© The Author 2005. Published by Oxford University Press. All rights reserved
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Article

MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm

Darby Tien-Hau Chang1, Yen-Jen Oyang1,2 and Jung-Hsin Lin3,4,*

1Department of Computer Science and Information Engineering, National Taiwan University Taipei 106, Taiwan, ROC 2Graduate Institute of Networking and Multimedia, National Taiwan University Taipei 106, Taiwan, ROC 3School of Pharmacy, National Taiwan University Taipei 100, Taiwan, ROC 4Institute of Biomedical Sciences, Academia Sinica Taipei, 115, Taiwan, ROC

*To whom correspondence should be addressed. Tel: +886 2 2312 3456, ext. 8404; Fax: +886 2 2391 9098; Email: jlin{at}rx.mc.ntu.edu.tw

Received February 15, 2005. Revised April 7, 2005. Accepted May 2, 2005.

The prediction of ligand binding sites is an essential part of the drug discovery process. Knowing the location of binding sites greatly facilitates the search for hits, the lead optimization process, the design of site-directed mutagenesis experiments and the hunt for structural features that influence the selectivity of binding in order to minimize the drug's adverse effects. However, docking is still the rate-limiting step for such predictions; consequently, much more efficient algorithms are required. In this article, the design of the MEDock web server is described. The goal of this sever is to provide an efficient utility for predicting ligand binding sites. The MEDock web server incorporates a global search strategy that exploits the maximum entropy property of the Gaussian probability distribution in the context of information theory. As a result of the global search strategy, the optimization algorithm incorporated in MEDock is significantly superior when dealing with very rugged energy landscapes, which usually have insurmountable barriers. This article describes four different benchmark cases that span a diverse set of different types of ligand binding interactions. These benchmarks were compared with the use of the Lamarckian genetic algorithm (LGA), which is the major workhorse of the well-known AutoDock program. These results demonstrate that MEDock consistently converged to the correct binding modes with significantly smaller numbers of energy evaluations than the LGA required. When judged by a threshold of the number of energy evaluations consumed in the docking simulation, MEDock also greatly elevates the rate of accurate predictions for all benchmark cases. MEDock is available at http://medock.csie.ntu.edu.tw/ and http://bioinfo.mc.ntu.edu.tw/medock/.


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