Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda, 151001, India.
Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda,151001, India.
Curr Top Med Chem. 2018;18(32):2800-2815. doi: 10.2174/1568026619666190208164801.
The failure of the Integrase Strand Transfer Inhibitors (INSTIs) due to the mutations occurring at the catalytic site of HIV integrase (IN) has led to the design of allosteric integrase inhibitors (ALLINIs). Lens epithelium derived growth factor (LEDGF/p75) is the host cellular cofactor which helps chaining IN to the chromatin. The protein-protein interactions (PPIs) were observed at the allosteric site (LEDGF/p75 binding domain) between LEDGF/p75 of the host cell and IN of virus. In recent years, many small molecules such as CX04328, CHIBA-3053 and CHI-104 have been reported as LEDGF/p75-IN interaction inhibitors (LEDGINs). LEDGINs have emerged as promising therapeutics to halt the PPIs by binding at the interface of both the proteins. In the present work, we correlated the docking scores for the reported LEDGINs containing quinoline scaffold with the in vitro biological data. The hierarchal clustering method was used to divide the compounds into test and training set. The robustness of the generated model was validated by q2 and r2 for the predicted set of compounds. The generated model between the docking score and biological data was assessed to predict the activity of the hits (quinoline scaffold) obtained from virtual screening of LEDGINs providing their structureactivity relationships to aim for the generation of potent agents.
由于 HIV 整合酶 (IN) 催化位点发生突变,导致整合酶链转移抑制剂 (INSTIs) 失效,从而设计了别构整合酶抑制剂 (ALLINIs)。晶状体上皮衍生生长因子 (LEDGF/p75) 是宿主细胞的辅助因子,有助于将 IN 链接到染色质上。在宿主细胞的 LEDGF/p75 和病毒的 IN 之间的别构位点 (LEDGF/p75 结合域) 观察到蛋白质-蛋白质相互作用 (PPIs)。近年来,许多小分子如 CX04328、CHIBA-3053 和 CHI-104 已被报道为 LEDGF/p75-IN 相互作用抑制剂 (LEDGINs)。LEDGINs 通过与两种蛋白质的界面结合,成为阻止 PPIs 的有前途的治疗药物。在本工作中,我们将报道的含喹啉支架的 LEDGINs 的对接评分与体外生物学数据相关联。使用层次聚类方法将化合物分为测试集和训练集。通过 q2 和 r2 对预测化合物集验证了所生成模型的稳健性。评估了对接评分和生物数据之间的生成模型,以预测从 LEDGINs 的虚拟筛选中获得的命中物(喹啉支架)的活性,从而获得结构-活性关系,以生成有效的药物。