MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm.
MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm.
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DOI：
10.1093/nar/gki586
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发表时间：
2005-07-01
影响因子：
14.9
通讯作者：
Lin JH
中科院分区：
文献类型：
--
作者：
Chang DT;Oyang YJ;Lin JH
配体结合位点的预测是药物发现过程的重要组成部分。了解结合位点的位置可以极大地促进命中搜索、先导化合物优化过程、定点诱变实验的设计以及寻找影响结合选择性的结构特征，从而最大限度地减少药物的副作用。然而，对接仍然是此类预测的限速步骤。因此，需要更有效的算法。本文介绍了 MEDock Web 服务器的设计。该服务器的目标是提供预测配体结合位点的有效实用程序。 MEDock Web 服务器采用了全局搜索策略，该策略利用信息论背景下高斯概率分布的最大熵特性。由于采用全局搜索策略，MEDock 中采用的优化算法在处理通常具有难以克服的障碍的非常崎岖的能源景观时具有显着的优势。本文描述了四种不同的基准案例，涵盖了一组不同类型的配体结合相互作用。这些基准测试与拉马克遗传算法 (LGA) 的使用进行了比较，拉马克遗传算法是著名的 AutoDock 程序的主要工具。这些结果表明，MEDock 始终收敛于正确的结合模式，且能量评估数量明显少于 LGA 所需的能量评估数量。当以对接模拟中消耗的能量评估次数阈值来判断时，MEDock也大大提高了所有基准案例的准确预测率。 MEDock 可在 和 获取。
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 and .
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影响因子：
64.8
作者：
Lipinski, C;Hopkins, A
通讯作者：
Hopkins, A
DOI：
10.1023/b:jcam.0000017496.76572.6f
发表时间：
2003-11-01
影响因子：
3.5
作者：
Bursulaya, BD;Totrov, M;Brooks, CL
通讯作者：
Brooks, CL
影响因子：
64.8
作者：
Dobson, CM
通讯作者：
Dobson, CM
影响因子：
2.9
作者：
Lin, JH;Perryman, AL;McCammon, JA
通讯作者：
McCammon, JA
DOI：
10.1073/pnas.92.8.3626
发表时间：
1995-04-11
影响因子：
11.1
作者：
ONUCHIC, JN;WOLYNES, PG;SOCCI, ND
通讯作者：
SOCCI, ND