Protein pharmacophore selection using hydration-site analysis.
Protein pharmacophore selection using hydration-site analysis.
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DOI：
10.1021/ci200620h
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发表时间：
2012-04-23
影响因子：
5.6
通讯作者：
Lill MA
中科院分区：
文献类型：
--
作者：
Hu B;Lill MA
使用药效团模型的虚拟筛选是识别目标蛋白的潜在先导化合物的有效方法。基于蛋白质结构的药效团模型是有利的，因为不需要活性配体的先验知识，并且模型不会因先前识别的活性物的化学空间而产生偏差。然而，为了捕获所有潜在结合配体和蛋白质之间的大多数潜在相互作用，药效团模型的大小（即药效团元件的数量）通常相当大，因此降低了基于药效团的筛选的效率。我们开发了一种利用水合位点信息选择重要药效基团元素的新方法。基本前提是，由于将水分子释放到本体溶剂中而获得了额外的自由能，因此取代 apo 蛋白中水分子的配体官能团对配体的整体结合亲和力有很大贡献。我们使用分子动力学 (MD) 模拟的热力学分析计算了从每个水合位点的结合位点释放的水的自由能。选择与水合位点共定位且对结合自由能具有估计有利贡献的药效团来生成简化的药效团模型。我们为三种蛋白质系统构建了简化的药效团模型，并证明了良好的富集质量和高效率。与使用所有蛋白质药效团元件相比，药效团模型尺寸的减小将所需的筛选时间减少了 200-500 倍。我们还描述了使用一小组已知活性物质的训练过程，以可靠地为每个蛋白质系统的药效团选择选择最佳标准集。
Virtual screening using pharmacophore models is an efficient method to identify potential lead compounds for target proteins. Pharmacophore models based on protein structures are advantageous because a priori knowledge of active ligands is not required and the models are not biased by the chemical space of previously identified actives. However, in order to capture most potential interactions between all potentially binding ligands and the protein, the size of the pharmacophore model, i.e. number of pharmacophore elements, is typically quite large and therefore reduces the efficiency of pharmacophore based screening. We have developed a new method to select important pharmacophore elements using hydration-site information. The basic premise is that ligand functional groups that replace water molecules in the apo protein contribute strongly to the overall binding affinity of the ligand, due to the additional free energy gained from releasing the water molecule into the bulk solvent. We computed the free energy of water released from the binding site for each hydration site using thermodynamic analysis of molecular dynamics (MD) simulations. Pharmacophores which are co-localized with hydration sites with estimated favorable contributions to the free energy of binding are selected to generate a reduced pharmacophore model. We constructed reduced pharmacophore models for three protein systems and demonstrated good enrichment quality combined with high efficiency. The reduction in pharmacophore model size reduces the required screening time by a factor of 200–500 compared to using all protein pharmacophore elements. We also describe a training process using a small set of known actives to reliably select the optimal set of criteria for pharmacophore selection for each protein system.