Islam Md Ataul, Pillay Tahir S
Department of Chemical Pathology, Faculty of Health Sciences, University of Pretoria and National Health Laboratory Service Tshwane Academic Division, Private Bag X323, Arcadia, Pretoria, 0007, South Africa.
Mol Biosyst. 2016 Mar;12(3):982-93. doi: 10.1039/c5mb00767d.
Acquired immunodeficiency syndrome (AIDS) is a life-threatening disease which is a collection of symptoms and infections caused by a retrovirus, human immunodeficiency virus (HIV). There is currently no curative treatment and therapy is reliant on the use of existing anti-retroviral drugs. Pharmacoinformatics approaches have already proven their pivotal role in the pharmaceutical industry for lead identification and optimization. In the current study, we analysed the binding preferences and inhibitory activity of HIV-integrase inhibitors using pharmacoinformatics. A set of 30 compounds were selected as the training set of a total 540 molecules for pharmacophore model generation. The final model was validated by statistical parameters and further used for virtual screening. The best mapped model (R = 0.940, RMSD = 2.847, Q(2) = 0.912, se = 0.498, Rpred(2) = 0.847 and rm(test)(2) = 0.636) explained that two hydrogen bond acceptor and one aromatic ring features were crucial for the inhibition of HIV-integrase. From virtual screening, initial hits were sorted using a number of parameters and finally two compounds were proposed as promising HIV-integrase inhibitors. Drug-likeness properties of the final screened compounds were compared to FDA approved HIV-integrase inhibitors. HIV-integrase structure in complex with the most active and final screened compounds were subjected to 50 ns molecular dynamics (MD) simulation studies to check comparative stability of the complexes. The study suggested that the screened compounds might be promising HIV-integrase inhibitors. The new chemical entities obtained from the NCI database will be subjected to experimental studies to confirm potential inhibition of HIV integrase.
获得性免疫缺陷综合征（艾滋病）是一种危及生命的疾病，它是由逆转录病毒人类免疫缺陷病毒（HIV）引起的一系列症状和感染。目前尚无治愈性治疗方法，治疗依赖于现有的抗逆转录病毒药物。药物信息学方法已在制药行业中证明了其在先导物识别和优化方面的关键作用。在本研究中，我们使用药物信息学分析了HIV整合酶抑制剂的结合偏好和抑制活性。从总共540个分子中选择了一组30种化合物作为生成药效团模型的训练集。最终模型通过统计参数进行验证，并进一步用于虚拟筛选。最佳映射模型（R = 0.940，RMSD = 2.847，Q(2) = 0.912，se = 0.498，Rpred(2) = 0.847和rm(test)(2) = 0.636）表明，两个氢键受体和一个芳香环特征对于抑制HIV整合酶至关重要。通过虚拟筛选，使用多个参数对初步命中物进行排序，最终提出两种化合物作为有前景的HIV整合酶抑制剂。将最终筛选化合物的类药性质与FDA批准的HIV整合酶抑制剂进行比较。将HIV整合酶与活性最高的最终筛选化合物形成的复合物结构进行50纳秒的分子动力学（MD）模拟研究，以检查复合物的相对稳定性。该研究表明，筛选出的化合物可能是有前景的HIV整合酶抑制剂。从美国国立癌症研究所（NCI）数据库获得的新化学实体将进行实验研究，以确认其对HIV整合酶的潜在抑制作用。
