National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health Bethesda, MD 20894, USA.
J Mol Graph Model. 2011 Sep;30:135-47. doi: 10.1016/j.jmgm.2011.06.013. Epub 2011 Jul 7.
Cathepsin B has been found being responsible for many human diseases. Inhibitors of cathepsin B, a ubiquitous lysosomal cysteine protease, have been developed as a promising treatment for human diseases resulting from malfunction and over-expression of this enzyme. Through a high throughput screening assay, a set of compounds were found able to inhibit the enzymatic activity of cathepsin B. The binding structures of these active compounds were modeled through docking simulation. Three-dimensional (3D) quantitative structure-activity relationship (QSAR) models were constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on the docked structures of the compounds. Strong correlations were obtained for both CoMFA and CoMSIA models with cross-validated correlation coefficients (q²) of 0.605 and 0.605 and the regression correlation coefficients (r²) of 0.999 and 0.997, respectively. The robustness of these models was further validated using leave-one-out (LOO) method and training-test set method. The activities of eight (8) randomly selected compounds were predicted using models built from training set of compounds with prediction errors of less than 1 unit for most compounds in CoMFA and CoMSIA models. Structural features for compounds with improved activity are suggested based on the analysis of the CoMFA and CoMSIA contour maps and the property map of the protein ligand binding site. These results may help to provide better understanding of the structure-activity relationship of cathepsin B inhibitors and to facilitate lead optimization and novel inhibitor design. The multi-conformation method to build 3D QSAR is very effective approach to obtain satisfactory models with high correlation with experimental results and high prediction power for unknown compounds.
组织蛋白酶 B 已被发现与许多人类疾病有关。组织蛋白酶 B 抑制剂是一种广泛存在的溶酶体半胱氨酸蛋白酶抑制剂,已被开发为治疗因该酶功能障碍和过度表达而导致的人类疾病的一种有前途的方法。通过高通量筛选试验,发现了一组能够抑制组织蛋白酶 B 酶活性的化合物。通过对接模拟,构建了这些活性化合物的结合结构模型。使用比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)基于化合物的对接结构构建了三维(3D)定量构效关系(QSAR)模型。CoMFA 和 CoMSIA 模型均获得了很强的相关性,交叉验证相关系数(q²)分别为 0.605 和 0.605,回归相关系数(r²)分别为 0.999 和 0.997。通过留一法(LOO)和训练-测试集方法进一步验证了这些模型的稳健性。使用来自训练集的化合物构建模型,对 8 个(8)个随机选择的化合物的活性进行预测,在 CoMFA 和 CoMSIA 模型中,大多数化合物的预测误差小于 1 个单位。根据 CoMFA 和 CoMSIA 等高线图和蛋白质配体结合位点的性质图的分析,提出了具有改善活性的化合物的结构特征。这些结果可能有助于更好地理解组织蛋白酶 B 抑制剂的构效关系,并有助于先导化合物优化和新型抑制剂的设计。构建 3D QSAR 的多构象方法是一种非常有效的方法,可以获得与实验结果高度相关且对未知化合物具有高预测能力的满意模型。