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Comparative molecular field analysis and molecular docking studies on novel aryl chalcone derivatives against an important drug target cysteine protease in Plasmodium falciparum

机译:新型芳基查尔酮衍生物对恶性疟原虫重要药物靶标半胱氨酸蛋白酶的比较分子场分析和分子对接研究

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摘要

The computational studies namely molecular docking simulations and Comparative Molecular Field Analysis (CoMFA) are executed on series of 52 novel aryl chalcones derivatives using Plasmodium falciparum cysteine proteases (falcipain - 2) as vital target. In the present study, the correlation between different molecular field effects namely steric and electrostatic interactions and chemical structures to the inhibitory activities of novel aryl chalcone derivatives is inferred to perceive the major structural prerequisites for the rational design and development of potent and novel lead anti-malarial compound. The apparent binding conformations of all the compounds at the active site of falcipain-2 and the hydrogen-bond interactions which could be used to modify the inhibitory activities are identified by using Surflex-dock study. Statistically significant CoMFA model has been developed with the cross-validated correlation coefficient (q(2)) of 0.912 and the non-cross-validated correlation coefficient (r(2)) of 0.901. Standard error of estimation (SEE) of 0.210, with the optimum number of components is ten. The predictability of the derived model is examined with a test set consists of sixteen compounds and the predicted r(2) value is found to be 0.924. The docking and QSAR study results confer crucial suggestions for the optimization of novel 1,3-diphenyl-2-propen-1-one derivatives and synthesis of effective anti-malarial compounds. (C) 2016 Elsevier Ltd. All rights reserved.
机译:以恶性疟原虫半胱氨酸蛋白酶(falcipain-2)为主要靶标,对一系列52种新型芳基查耳酮衍生物进行了计算研究,即分子对接模拟和比较分子场分析(CoMFA)。在本研究中,空间和静电相互作用以及化学结构与新型芳基查尔酮衍生物的抑制活性的不同分子场效应之间的相关性被推断为感知到有效设计和开发有效和新型铅类抗铅剂的主要结构前提。疟疾化合物。通过Surflex-dock研究鉴定了所有化合物在falcipain-2活性位点上的表观结合构象以及可用于修饰抑制活性的氢键相互作用。已经开发出具有统计学意义的CoMFA模型,其交叉验证的相关系数(q(2))为0.912,非交叉验证的相关系数(r(2))为0.901。最佳组件估计数为0.210的标准估计误差(SEE)为10。使用由16种化合物组成的测试集检查了衍生模型的可预测性,并且预测的r(2)值为0.924。对接和QSAR研究结果为优化新型1,3-二苯基-2-丙-1-酮衍生物和合成有效的抗疟疾化合物提供了重要建议。 (C)2016 Elsevier Ltd.保留所有权利。

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