P56(lck) kinase inhibitor studies: a 3D QSAR approach towards designing new drugs from flavonoid derivatives.

Q4 Pharmacology, Toxicology and Pharmaceutics International Journal of Computational Biology and Drug Design Pub Date : 2014-01-01 Epub Date: 2014-05-28 DOI:10.1504/IJCBDD.2014.061648
Shravan Kumar Gunda, Sandeep Kumar Mulukala Narasimha, Mahmood Shaik
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引用次数: 5

Abstract

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.

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P56(lck)激酶抑制剂研究:类黄酮衍生物设计新药的3D QSAR方法
采用基于3D-QSAR (3d -定量构效关系)的比较分子场分析(CoMFA)和比较分子相似指数分析(CoMSIA)对97种黄酮类衍生物作为P56(lck)蛋白酪氨酸激酶抑制剂进行了研究。采用立体场、静电场的CoMFA标准模型(q²= 0.838,r²= 0.948)和采用立体场、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)预测效果最好。在97个分子中,76个化合物的训练集和QSAR模型的预测能力使用21个化合物的测试集进行评估。利用CoMFA和CoMSIA等高线图谱分析了这一系列类黄酮衍生物的结构特征,并在此基础上鉴定和设计了10个对P56(lck)蛋白具有较强抑制活性的新分子,为开发有效的抗该类酶药物提供了新的思路。
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来源期刊
International Journal of Computational Biology and Drug Design
International Journal of Computational Biology and Drug Design Pharmacology, Toxicology and Pharmaceutics-Drug Discovery
CiteScore
1.00
自引率
0.00%
发文量
8
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