Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
J Chem Inf Model. 2010 Sep 27;50(9):1574-88. doi: 10.1021/ci1002153.
Prediction of the binding strength of untested ligands is a central issue in structure-based drug design. In order to rapidly screen large compound databases, simple scoring schemes are often used in target-based virtual screening. The resulting scores often correlate poorly with biological affinities. More rigorous scoring methods, such as MM-PB/SA, correlate better with biological data by considering solvation effects and protein flexibility in the calculation of the binding free energy of a ligand. Here we describe the performance of a modified MM-PB/SA method on 222 Wee1 kinase inhibitors (48 pyridopyrimidine and 174 pyrrolocarbazole derivatives). Docking of these inhibitors into the available Wee1 kinase crystal structure yielded a consistent binding mode, and the derived MM-PB/SA models showed a significant correlation between calculated and experimental data (r(2) values between 0.64 and 0.67). Further study of these models on external test sets of Wee1 kinase inhibitors and structurally related decoys showed that a model based on a single kinase-inhibitor conformation can discriminate the active inhibitors from decoys. We also tested whether the linear interaction energy method with continuum electrostatics (LIECE) yields comparable results to MM-PB/SA and whether the LIECE and MM-PB/SA models can be applied for virtual screening of compound libraries.
预测未经测试的配体的结合强度是基于结构的药物设计中的一个核心问题。为了快速筛选大型化合物数据库,基于靶标的虚拟筛选中通常使用简单的评分方案。由此产生的分数通常与生物亲和力相关性较差。更严格的评分方法,如 MM-PB/SA,通过在计算配体的结合自由能时考虑溶剂化效应和蛋白质柔性,与生物数据相关性更好。在这里,我们描述了经过修改的 MM-PB/SA 方法在 222 种 Wee1 激酶抑制剂(48 种吡啶并嘧啶和 174 种吡咯并咔唑衍生物)上的性能。将这些抑制剂对接入可用的 Wee1 激酶晶体结构中,得到了一致的结合模式,并且推导的 MM-PB/SA 模型显示出计算数据与实验数据之间存在显著相关性(r(2) 值在 0.64 到 0.67 之间)。进一步对这些模型进行 Wee1 激酶抑制剂和结构相关的诱饵的外部测试集研究表明,基于单个激酶抑制剂构象的模型可以区分活性抑制剂和诱饵。我们还测试了连续静电的线性相互作用能方法(LIECE)是否可以得到与 MM-PB/SA 相当的结果,以及 LIECE 和 MM-PB/SA 模型是否可以用于化合物库的虚拟筛选。