Protein鈥揕igand-Based Pharmacophores: Generation and Utility Assessment in Computational Ligand Profiling

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• 作者：Jamel Meslamani ; Jiabo Li ; Jon Sutter ; Adrian Stevens ; Hugues-Olivier Bertrand ; Didier Rognan
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2012
• 出版时间：April 23, 2012
• 年：2012
• 卷：52
• 期：4
• 页码：943-955
• 全文大小：618K
• 年卷期：v.52,no.4(April 23, 2012)
• ISSN：1549-960X

文摘

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鈥搇igand docking. The current study describes the use of receptor鈥搇igand-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鈥搇igand 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鈥搇igand-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鈥搇igand 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.