Lee Juyong, Seok Chaok
Department of Chemistry, College of Natural Sciences, Seoul National University, Seoul 151-747, Republic of Korea.
Proteins. 2008 Feb 15;70(3):1074-83. doi: 10.1002/prot.21844.
Computational prediction of protein-ligand binding modes provides useful information on the relationship between structure and activity needed for drug design. A statistical rescoring method that incorporates entropic effect is proposed to improve the accuracy of binding mode prediction. A probability function for two sampled conformations to belong to the same broad basin in the potential energy surface is introduced to estimate the contribution of the state represented by a sampled conformation to the configurational integral. The rescoring function is reduced to the colony energy introduced by Xiang et al. (Proc Natl Acad Sci USA 2002;99:7432-7437) when a particular functional form for the probability function is used. The scheme is applied to rescore protein-ligand complex conformations generated by AutoDock. It is demonstrated that this simple rescoring improves prediction accuracy substantially when tested on 163 protein-ligand complexes with known experimental structures. For example, the percentage of complexes for which predicted ligand conformations are within 1 A root-mean-square deviation from the native conformations is doubled from about 20% to more than 40%. Rescoring with 11 different scoring functions including AutoDock scoring functions were also tested using the ensemble of conformations generated by Wang et al. (J Med Chem 2003;46:2287-2303). Comparison with other methods that use clustering and estimation of conformational entropy is provided. Examination of the docked poses reveals that the rescoring corrects the predictions in which ligands are tightly fit into the binding pockets and have low energies, but have too little room for conformational freedom and thus have low entropy.
蛋白质-配体结合模式的计算预测为药物设计所需的结构与活性之间的关系提供了有用信息。提出了一种纳入熵效应的统计重打分方法，以提高结合模式预测的准确性。引入了两个采样构象属于势能面中同一宽泛盆地的概率函数，以估计采样构象所代表的状态对构型积分的贡献。当使用概率函数的特定函数形式时，重打分函数简化为Xiang等人（《美国国家科学院院刊》2002年；99:7432 - 7437）引入的群体能量。该方案应用于对AutoDock生成的蛋白质-配体复合物构象进行重打分。结果表明，在对163个具有已知实验结构的蛋白质-配体复合物进行测试时，这种简单的重打分显著提高了预测准确性。例如，预测的配体构象与天然构象的均方根偏差在1 Å以内的复合物百分比从约20%增加了一倍，达到40%以上。还使用Wang等人（《药物化学杂志》2003年；46:2287 - 2303）生成的构象集合，测试了包括AutoDock打分函数在内的11种不同打分函数的重打分情况。提供了与其他使用聚类和构象熵估计方法的比较。对对接姿势的检查表明，重打分纠正了那些配体紧密契合结合口袋且能量较低，但构象自由度空间太小因而熵较低的预测。
