Molecular Chemistry and Natural Substances Laboratory, Moulay Ismail University, Faculty of Science, Meknes, Morocco.
Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Gondur, Dhule 424002, Maharashtra, India; Division of Computer Aided Drug Design, Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur 425405, Maharashtra, India.
Comput Biol Chem. 2023 Jun;104:107855. doi: 10.1016/j.compbiolchem.2023.107855. Epub 2023 Mar 26.
Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.
已建立吡咯烷衍生物的定量构效关系(QSAR)研究,使用 CoMFA、CoMSIA 和 Hologram QSAR 分析来估计明胶酶抑制剂的数值(pIC)。当 CoMFA 交叉验证值 Q²为 0.625 时,训练集系数确定 R²为 0.981。在 CoMSIA 中,Q²为 0.749,R²为 0.988。在 HQSAR 中,Q²为 0.84,R²为 0.946。通过显示活性有利和不利区域的等高线图来可视化这些模型,而 HQSAR 模型的可视化则通过彩色原子贡献图来实现。基于外部验证的结果,CoMSIA 模型在统计学上更为显著和稳健,被选为预测新的、更活跃抑制剂的最佳模型。为了研究预测化合物在 MMP-2 和 MMP-9 活性部位的相互作用模式,实现了分子对接的模拟。还进行了 MD 模拟和自由结合能计算的综合研究,以验证在数据集和作为对照化合物的 NNGH 中最佳预测和最活跃化合物上获得的结果。结果证实了分子对接结果,并表明预测配体在 MMP-2 和 MMP-9 的结合位点稳定。