ALiBERO: evolving a team of complementary pocket conformations rather than a single leader.
ALiBERO: evolving a team of complementary pocket conformations rather than a single leader.
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DOI：
10.1021/ci3001088
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发表时间：
2012-10-22
影响因子：
5.6
通讯作者：
Abagyan R
中科院分区：
文献类型：
--
作者：
Rueda M;Totrov M;Abagyan R
当受体显示出精确配体结合所需的结构变化时，对接和虚拟筛选（VS）达到最大潜力。不幸的是，这些构象变化往往在实验结构或同源模型中表现不佳，削弱了它们的对接性能。最近，我们已经表明，用我们的LiBERO方法优化的受体（配体引导的骨架包被受体优化）能够在柔性配体VS对接实验中更好地区分活性配体和非活性配体。LiBERO方法依赖于配体信息的使用，用于从来自正常模式分析或蒙特卡罗的集合中选择表现最佳的单个口袋。在这里，我们介绍了ALiBERO，这是一种新的计算工具，它将口袋选择从单个扩展到多个，允许采样选择过程的自动迭代。口袋的选择是由一个双重的方法，使用穷举组合搜索加上单独添加口袋，只选择那些最大限度地从诱饵的已知活性化合物的歧视。所得的优化口袋显示出增加的VS性能，当以后用于由生物活性和非活性配体组成的大得多的无关测试集时。在本文中，我们将描述的算法的设计和实现，作为参考的人类雌激素受体α。
Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha.
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