MEDock: a Web Server for Efficient Prediction of Ligand Binding Sites Based on a Novel Optimization Algorithm
Resource
Nucleic Acids Research 33: W233-W238
Journal
Nucleic Acids Research
Pages
W233-W238
Date Issued
2005
Date
2005
Author(s)
Abstract
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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Type
journal article
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