Department of Chemistry, Carleton University, 1125 Colonel By Dr., Ottawa, K1S 5B6, Canada,
J Comput Aided Mol Des. 2013 Aug;27(8):707-21. doi: 10.1007/s10822-013-9670-6. Epub 2013 Aug 24.
The computational determination of binding modes for a ligand into a protein receptor is much more successful than the prediction of relative binding affinities (RBAs) for a set of ligands. Here we consider the binding of a set of 26 synthetic A-CD ligands into the estrogen receptor ERα. We show that the MOE default scoring function (London dG) used to rank the docked poses leads to a negligible correlation with experimental RBAs. However, switching to an energy-based scoring function, using a multiple linear regression to fit experimental RBAs, selecting top-ranked poses and then iteratively repeating this process leads to exponential convergence in 4-7 iterations and a very strong correlation. The method is robust, as shown by various validation tests. This approach may be of general use in improving the quality of predicted binding affinities.
计算确定配体与蛋白质受体的结合模式比预测一组配体的相对结合亲和力 (RBA) 要成功得多。在这里，我们考虑了一组 26 个合成 A-CD 配体与雌激素受体 ERα 的结合。我们表明，用于对对接构象进行排序的 MOE 默认评分函数（伦敦 dG）与实验 RBA 几乎没有相关性。然而，切换到基于能量的评分函数，使用多元线性回归拟合实验 RBA，选择排名最高的构象，然后迭代重复这个过程，在 4-7 次迭代后会出现指数收敛，并得到非常强的相关性。该方法是稳健的，正如各种验证测试所表明的那样。这种方法可能在提高预测结合亲和力的质量方面具有普遍的用途。
