Department of Chemistry, Umeå University, Umeå, Sweden.
J Chem Inf Model. 2011 Feb 28;51(2):267-82. doi: 10.1021/ci100354x. Epub 2011 Feb 10.
Molecular docking plays an important role in drug discovery as a tool for the structure-based design of small organic ligands for macromolecules. Possible applications of docking are identification of the bioactive conformation of a protein-ligand complex and the ranking of different ligands with respect to their strength of binding to a particular target. We have investigated the effect of implicit water on the postprocessing of binding poses generated by molecular docking using MM-PB/GB-SA (molecular mechanics Poisson-Boltzmann and generalized Born surface area) methodology. The investigation was divided into three parts: geometry optimization, pose selection, and estimation of the relative binding energies of docked protein-ligand complexes. Appropriate geometry optimization afforded more accurate binding poses for 20% of the complexes investigated. The time required for this step was greatly reduced by minimizing the energy of the binding site using GB solvation models rather than minimizing the entire complex using the PB model. By optimizing the geometries of docking poses using the GB(HCT+SA) model then calculating their free energies of binding using the PB implicit solvent model, binding poses similar to those observed in crystal structures were obtained. Rescoring of these poses according to their calculated binding energies resulted in improved correlations with experimental binding data. These correlations could be further improved by applying the postprocessing to several of the most highly ranked poses rather than focusing exclusively on the top-scored pose. The postprocessing protocol was successfully applied to the analysis of a set of Factor Xa inhibitors and a set of glycopeptide ligands for the class II major histocompatibility complex (MHC) A(q) protein. These results indicate that the protocol for the postprocessing of docked protein-ligand complexes developed in this paper may be generally useful for structure-based design in drug discovery.
分子对接在药物发现中起着重要作用，是设计与大分子结合的小分子有机配体的结构基础。对接的可能应用包括识别蛋白质-配体复合物的生物活性构象和根据与特定靶标的结合强度对不同配体进行排序。我们研究了使用 MM-PB/GB-SA（分子力学泊松-玻尔兹曼和广义 Born 表面区域）方法对接产生的结合构象进行后处理时隐式水的影响。该研究分为三个部分：几何优化、构象选择和对接蛋白-配体复合物相对结合能的估计。适当的几何优化为 20%的复合物提供了更准确的结合构象。通过使用 GB 溶剂化模型而不是 PB 模型最小化整个复合物的能量来最小化结合位点的能量，可以大大减少此步骤所需的时间。使用 GB(HCT+SA)模型优化对接构象的几何形状，然后使用 PB 隐式溶剂模型计算它们的结合自由能，从而获得与晶体结构中观察到的构象相似的结合构象。根据它们计算的结合能对这些构象进行重新评分可提高与实验结合数据的相关性。通过对几个排名最高的构象而不是仅关注得分最高的构象应用后处理，可以进一步提高这些相关性。该后处理协议已成功应用于一组 Factor Xa 抑制剂和一组 II 类主要组织相容性复合体 (MHC) A(q) 蛋白的糖肽配体的分析。这些结果表明，本文开发的对接蛋白-配体复合物后处理协议可能对药物发现中的基于结构的设计具有普遍意义。
