Laboratoire d'Innovation Thérapeutique, UMR7200 Université de Strasbourg/CNRS, 74 route du Rhin, 67400 Illkirch, France.
J Chem Inf Model. 2012 Apr 23;52(4):943-55. doi: 10.1021/ci300083r. Epub 2012 Apr 11.
Ligand profiling is an emerging computational method for predicting the most likely targets of a bioactive compound and therefore anticipating adverse reactions, side effects and drug repurposing. A few encouraging successes have already been reported using ligand 2-D similarity searches and protein-ligand docking. The current study describes the use of receptor-ligand-derived pharmacophore searches as a tool to link ligands to putative targets. A database of 68,056 pharmacophores was first derived from 8,166 high-resolution protein-ligand complexes. In order to limit the number of queries, a maximum of 10 pharmacophores was generated for each complex according to their predicted selectivity. Pharmacophore search was compared to ligand-centric (2-D and 3-D similarity searches) and docking methods in profiling a set of 157 diverse ligands against a panel of 2,556 unique targets of known X-ray structure. As expected, ligand-based methods outperformed, in most of the cases, structure-based approaches in ranking the true targets among the top 1% scoring entries. However, we could identify ligands for which only a single method was successful. Receptor-ligand-based pharmacophore search is notably a fast and reliable alternative to docking when few ligand information is available for some targets. Overall, the present study suggests that a workflow using the best profiling method according to the protein-ligand context is the best strategy to follow. We notably present concrete guidelines for selecting the optimal computational method according to simple ligand and binding site properties.
配体谱分析是一种新兴的计算方法，用于预测生物活性化合物最可能的靶标，从而预测不良反应、副作用和药物重新定位。使用配体 2-D 相似性搜索和蛋白-配体对接已经报告了一些令人鼓舞的成功案例。本研究描述了使用受体-配体衍生的药效团搜索作为将配体与潜在靶标联系起来的工具。首先从 8166 个高分辨率蛋白-配体复合物中衍生出 68056 个药效团数据库。为了限制查询数量，根据每个复合物的预测选择性，为每个复合物生成最多 10 个药效团。药效团搜索与配体中心（2-D 和 3-D 相似性搜索）和对接方法进行了比较，以对 157 种不同的配体进行了 profiling，这些配体针对的是一组具有已知 X 射线结构的 2556 个独特靶标。正如预期的那样，在大多数情况下，基于配体的方法在将真实靶标排在前 1%得分的条目方面优于基于结构的方法。然而，我们可以确定仅有一种方法成功的配体。当某些靶标只有很少的配体信息时，基于受体-配体的药效团搜索是对接的一种快速可靠的替代方法。总体而言，本研究表明，根据蛋白-配体的情况使用最佳 profiling 方法的工作流程是最佳策略。我们特别根据简单的配体和结合位点特性提出了选择最佳计算方法的具体指导方针。
