Rolland Catherine, Gozalbes Rafael, Nicolaï Eric, Paugam Marie-France, Coussy Laurent, Barbosa Frédérique, Horvath Dragos, Revah Frédéric
Cerep, 128 rue Danton, 92500 Rueil-Malmaison, France, and Cerep, Le Bois l'Evêque, 86600 Celle l'Evescault, France.
J Med Chem. 2005 Oct 20;48(21):6563-74. doi: 10.1021/jm0500673.
A QSAR model accounting for "average" G-protein-coupled receptor (GPCR) binding was built from a large set of experimental standardized binding data (1939 compounds systematically tested over 40 different GPCRs) and applied to the design of a library of "GPCR-predicted" compounds. Three hundred and sixty of these compounds were randomly selected and tested in 21 GPCR binding assays. Positives were defined by their ability to inhibit by more than 70% the binding of reference compounds at 10 microM. A 5.5-fold enrichment in positives was observed when comparing the "GPCR-predicted" compounds with 600 randomly selected compounds predicted as "non-GPCR" from a general collection. The model was efficient in predicting strongest binders, since enrichment was greater for higher cutoffs. Significant enrichment was also observed for peptidic GPCRs and receptors not included to develop the QSAR model, suggesting the usefulness of the model to design ligands binding with newly identified GPCRs, including orphan ones.
基于大量实验标准化结合数据（1939种化合物在40种不同的G蛋白偶联受体（GPCR）上进行系统测试）构建了一个考虑“平均”GPCR结合的定量构效关系（QSAR）模型，并将其应用于“GPCR预测”化合物库的设计。从这些化合物中随机选择360种，并在21种GPCR结合试验中进行测试。阳性定义为在10微摩尔浓度下能够抑制参考化合物结合超过70%的能力。将“GPCR预测”化合物与从一般库中随机选择的600种预测为“非GPCR”的化合物进行比较时，观察到阳性化合物有5.5倍的富集。该模型在预测最强结合剂方面很有效，因为对于更高的截断值，富集程度更高。对于肽类GPCR和未纳入QSAR模型构建的受体，也观察到了显著的富集，这表明该模型对于设计与新鉴定的GPCR（包括孤儿受体）结合的配体是有用的。
