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计算机模拟分析揭示了喹啉基查尔酮衍生物的抗疟潜力。

In silico analysis reveals the anti-malarial potential of quinolinyl chalcone derivatives.

作者信息

Thillainayagam Mahalakshmi, Pandian Lavanya, Murugan Kumar Kalavathy, Vijayaparthasarathi Vijayakumar, Sundaramoorthy Sarveswari, Anbarasu Anand, Ramaiah Sudha

机构信息

a Medical & Biological Computing Laboratory, School of Biosciences and Technology , VIT University , Vellore 632 014 , Tamil Nadu , India.

出版信息

J Biomol Struct Dyn. 2015;33(5):961-77. doi: 10.1080/07391102.2014.920277. Epub 2014 May 28.

Abstract

In this study, the correlation between chemical structures and various parameters such as steric effects and electrostatic interactions to the inhibitory activities of quinolinyl chalcone derivatives is derived to identify the key structural elements required in the rational design of potent and novel anti-malarial compounds. The molecular docking simulations and Comparative Molecular Field Analysis (CoMFA) are carried out on 38 chalcones derivatives using Plasmodium falciparum lactate dehydrogenase (PfLDH) as potential target. Surflex-dock is used to determine the probable binding conformations of all the compounds at the active site of pfLDH and to identify the hydrogen bonding interactions which could be used to alter the inhibitory activities. The CoMFA model has provided statistically significant results with the cross-validated correlation coefficient (q(2)) of .850 and the non-cross-validated correlation coefficient (r(2)) of .912. Standard error of estimation (SEE) is .280 and the optimum number of component is five. The predictive ability of the resultant model is evaluated using a test set comprising of 13 molecules and the predicted r(2) value is .885. The results provide valuable insight for optimization of quinolinyl chalcone derivatives for better anti-malarial therapy.

摘要

在本研究中,推导了喹啉基查尔酮衍生物的化学结构与各种参数(如空间效应和静电相互作用)之间与抑制活性的相关性,以确定合理设计高效新型抗疟化合物所需的关键结构要素。使用恶性疟原虫乳酸脱氢酶(PfLDH)作为潜在靶点,对38种查尔酮衍生物进行了分子对接模拟和比较分子场分析(CoMFA)。Surflex-dock用于确定所有化合物在pfLDH活性位点的可能结合构象,并识别可用于改变抑制活性的氢键相互作用。CoMFA模型给出了具有统计学意义的结果,交叉验证相关系数(q(2))为0.850,非交叉验证相关系数(r(2))为0.912。估计标准误差(SEE)为0.280,最佳成分数为5。使用由13个分子组成的测试集评估所得模型的预测能力,预测r(2)值为0.885。这些结果为优化喹啉基查尔酮衍生物以实现更好的抗疟治疗提供了有价值的见解。

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