Gunda Shravan Kumar, Narasimha Sandeep Kumar Mulukala, Shaik Mahmood
Bioinformatics Division, Osmania University, Hyderabad 500007, Andhra Pradesh, India.
Int J Comput Biol Drug Des. 2014;7(2-3):278-94. doi: 10.1504/IJCBDD.2014.061648. Epub 2014 May 28.
Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on 3D-QSAR (3D-quantitative structure activity relationship) studies were carried out on 97 flavonoid derivatives as potent P56(lck) protein tyrosine kinase inhibitors. The best prediction was obtained with CoMFA standard model (q² = 0.838, r² = 0.948) using steric, electrostatic along with CoMSIA standard model (q² = 0.714, r² = 0.921) using steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor fields. Of the 97 molecules a training set of 76 compounds and the predictive ability of the QSAR model were assessed employing a test set of 21 compounds. The resulting CoMFA and CoMSIA contour maps were used to identify the structural features relevant to the biological activity in this series of flavonoid derivatives, based upon which we identified and designed 10 novel molecules that showed superior inhibitory activity against P56(lck) protein which shed new light on effective therapeutic agents against these classes of enzymes.
基于三维定量构效关系（3D-QSAR）研究，对97种黄酮类衍生物作为有效的P56（lck）蛋白酪氨酸激酶抑制剂进行了比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）。使用空间场、静电场的CoMFA标准模型（q² = 0.838，r² = 0.948）以及使用空间场、静电场、疏水场、氢键供体场和受体场的CoMSIA标准模型（q² = 0.714，r² = 0.921）获得了最佳预测结果。在97个分子中，使用76个化合物的训练集，并采用21个化合物的测试集评估QSAR模型的预测能力。所得的CoMFA和CoMSIA等高线图用于确定该系列黄酮类衍生物中与生物活性相关的结构特征，在此基础上，我们鉴定并设计了10种对P56（lck）蛋白具有优异抑制活性的新型分子，这为针对这类酶的有效治疗剂提供了新的思路。
