Shaikh Abdul Rajjak, Ismael Mohamed, Del Carpio Carlos A, Tsuboi Hideyuki, Koyama Michihisa, Endou Akira, Kubo Momoji, Broclawik Ewa, Miyamoto Akira
Department of Applied Chemistry, Graduate School of Engineering, Tohoku University, 6-6-11-1302 Aoba, Aramaki, Sendai 980-8579, Japan.
Bioorg Med Chem Lett. 2006 Nov 15;16(22):5917-25. doi: 10.1016/j.bmcl.2006.06.039. Epub 2006 Sep 20.
Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 44 (benzothiazole-2-yl) acetonitrile derivatives, inhibiting c-Jun N-terminal kinase-3 (JNK3). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 34 compounds and its predictive ability was assessed using a test set of 10 compounds. The predictive 3D-QSAR models have conventional r2 values of 0.849 and 0.766 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)2 values of 0.616 and 0.605 for MFA and RSA, respectively. The results of the QSAR model were further compared with a structure-based analysis using docking studies with crystal structure of JNK3. Ligands bind in the ATP pocket and the hydrogen bond with GLN155 was found to be crucial for selectivity among other kinases. The results of 3D-QSAR and docking studies validate each other and hence, the combination of both methodologies provides a powerful tool directed to the design of novel and selective JNK3 inhibitors.
针对44种抑制c-Jun氨基末端激酶-3（JNK3）的（苯并噻唑-2-基）乙腈衍生物构建了三维定量构效关系（3D-QSAR）模型。该模型包括分子场分析（MFA）和受体表面分析（RSA）。使用34种化合物构建了QSAR模型，并使用10种化合物的测试集评估其预测能力。预测性3D-QSAR模型中，MFA和RSA的传统r2值分别为0.849和0.766；而MFA和RSA的交叉验证系数r(cv)2值分别为0.616和0.605。将QSAR模型的结果与基于结构的分析进行了进一步比较，该分析使用了JNK3晶体结构的对接研究。配体结合在ATP口袋中，并发现与GLN155的氢键对于区分其他激酶至关重要。3D-QSAR和对接研究的结果相互验证，因此，两种方法的结合为新型选择性JNK3抑制剂的设计提供了一个强大的工具。
