Thillainayagam Mahalakshmi, Anbarasu Anand, Ramaiah Sudha
Medical & Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore 632 014, Tamil Nadu, India.
Medical & Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore 632 014, Tamil Nadu, India.
J Theor Biol. 2016 Aug 21;403:110-128. doi: 10.1016/j.jtbi.2016.05.019. Epub 2016 May 13.
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.
以恶性疟原虫半胱氨酸蛋白酶(疟原虫蛋白酶-2)为重要靶点,对52种新型芳基查尔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,推断不同分子场效应(即空间和静电相互作用)与化学结构之间与新型芳基查尔酮衍生物抑制活性的相关性,以了解合理设计和开发有效新型抗疟先导化合物的主要结构前提。通过Surflex-dock研究确定了所有化合物在疟原虫蛋白酶-2活性位点的表观结合构象以及可用于改变抑制活性的氢键相互作用。已建立具有统计学意义的CoMFA模型,交叉验证相关系数(q(2))为0.912,非交叉验证相关系数(r(2))为0.901。估计标准误差(SEE)为0.210,最佳组分数为10。用由16种化合物组成的测试集检验所推导模型的可预测性,发现预测的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-丙烯-1-酮衍生物和合成有效的抗疟化合物提供了关键建议。