Glen Robert C, Cole Jason C, Stewart James J P
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB21EW, UK.
Department of Metabolism Digestion and Reproduction, Imperial College London, Hammersmith Hospital Campus, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK.
J Mol Model. 2025 Jul 12;31(8):209. doi: 10.1007/s00894-025-06423-7.
The ability to predict the relative binding energies of ligands to a biological receptor would be of great value in drug discovery. However, accurately calculating the predicted binding energies is limited by the high accuracy required, by the presence of multiple minima on the potential energy surface, and by issues specific to the intrinsic properties of the binding site, such as details of the geometry of the ligand-protein complex. To address these issues, a systematic analysis of potential sources of error was carried out which resulted in a few relatively small changes being made to the MOPAC program.
A set of 77 ligands was constructed for which experimentally determined IC values were available. For each of the ligands, prediction of the protein-ligand interaction energy was carried out in two distinct stages. In the first stage, the Protein-Ligand docking program GOLD was used to generate several distinct conformations of the ligand bound to a protein. The geometries of these systems were then optimised using the MOPAC program. A comparison of the relative binding energies of the ligands with the reported IC values showed a very poor predictive power. By partitioning the ligand set into two subsets, and eliminating six ligands that were inconsistent with the experimental results, a large increase in accuracy was obtained.
预测配体与生物受体的相对结合能的能力在药物发现中具有重要价值。然而，准确计算预测的结合能受到所需高精度的限制，受到势能面上多个极小值的存在的限制，以及受到结合位点固有特性所特有的问题的限制，例如配体 - 蛋白质复合物几何结构的细节。为了解决这些问题，对潜在的误差来源进行了系统分析，结果对MOPAC程序进行了一些相对较小的修改。
构建了一组77种配体，其具有可获得的实验测定的IC值。对于每种配体，在两个不同阶段进行蛋白质 - 配体相互作用能的预测。在第一阶段，使用蛋白质 - 配体对接程序GOLD生成与蛋白质结合的配体的几种不同构象。然后使用MOPAC程序优化这些系统的几何结构。将配体的相对结合能与报道的IC值进行比较，结果显示预测能力非常差。通过将配体集划分为两个子集，并剔除六个与实验结果不一致的配体，准确性得到了大幅提高。
