Theoretical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Parana State University-Unioeste, 2069 Universitaria St, Cascavel, PR 85819-110, Brazil.
J Chem Inf Model. 2012 Jul 23;52(7):1722-32. doi: 10.1021/ci300039a. Epub 2012 Jun 21.
Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN strand transfer inhibitors (INSTI), containing the β-diketo acid (DKA) substructure, was carried out. The GROMACS molecular dynamic package was used to obtain a conformational ensemble profile (CEP) and LQTA-QSAR was employed to calculate Coulomb and Lennard-Jones potentials and to generate the field descriptors. The partial least-squares (PLS) regression model using 14 field descriptors and 8 latent variables (LV) yielded satisfactory statistics (R2= 0.897, SEC = 0.270, and F = 72.827), good performance in internal (QLOO2 = 0.842 and SEV = 0.314) and external prediction (Rpred2 = 0.839, SEP = 0.384, AREpred = 4.942%, k = 0.981, k′ = 1.016, and |R02 – R0′2 = 0.0257). The QSAR model was shown to be robust (leave-N-out cross validation; average QLNO2 = 0.834) and was not built by chance (y-randomization test; R2 intercept = 0.109; Q2 intercept = -0.398). Fair chemical interpretation of the model could be traced, including descriptors related to interaction with the metallic cofactors and the hydrophobic loop. The model obtained has a good potential for aid in the design of new INSTI, and it is a successful example of application of LQTA-QSAR as an useful tool to be used in computer-aided drug design (CADD).
尽管已经实施了高效抗逆转录病毒疗法（HAART），但仍需要不断寻找新的抗 HIV 药物。HIV-1 整合酶（HIV-1 IN）是一种最近经过验证的艾滋病治疗生物靶标。在这项工作中，使用一种名为 LQTA-QSAR 方法的新方法对 85 种 HIV-1 IN 链转移抑制剂（INSTI）进行了四维定量构效关系（4D-QSAR）研究，其中包含β-二酮酸（DKA）结构。使用 GROMACS 分子动力学包获得构象系综分布（CEP），并使用 LQTA-QSAR 计算库仑和 Lennard-Jones 势能并生成场描述符。使用 14 个场描述符和 8 个潜在变量（LV）的偏最小二乘（PLS）回归模型产生了令人满意的统计数据（R2=0.897，SEC=0.270，F=72.827），在内部（QLOO2=0.842 和 SEV=0.314）和外部预测（Rpred2=0.839，SEP=0.384，AREpred=4.942%，k=0.981，k′=1.016，|R02-R0′2|=0.0257）方面表现良好。该 QSAR 模型被证明是稳健的（留一法交叉验证；平均 QLNO2=0.834），并且不是偶然构建的（y-随机化测试；R2 截距=0.109；Q2 截距=-0.398）。可以追溯到对模型的合理化学解释，包括与金属辅因子和疏水性环相互作用相关的描述符。所获得的模型具有辅助设计新 INSTI 的良好潜力，并且是 LQTA-QSAR 作为有用工具在计算机辅助药物设计（CADD）中应用的成功示例。
