Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, Connecticut 06269, USA.
J Chem Inf Model. 2009 Dec;49(12):2813-9. doi: 10.1021/ci9003078.
Ensembles of protein structures to simulate protein flexibility are widely used throughout several applications including virtual lead optimization where they have been shown to improve ligand ranking. Yet, there is no established convention for weighting individual scores generated from ensemble members. To investigate the best method for weighting ensemble scores for proper ligand ranking, a series of dihydrofolate reductase inhibitors was docked to ensembles of Candida albicans dihydrofolate reductase (CaDHFR) structures created from a molecular dynamics (MD) simulation. From a single MD simulation, two ensemble collections were generated, one of which was subjected to a minimization procedure to create a group of structures of equal probability. As expected, ligand ranking accuracy was significantly improved when Boltzmann weighting was applied to the energies of the ensemble without structural minimization (60%), relative to that achieved with averaging (36%). However, accuracy was further improved (72%) by averaging docking scores across a minimized ensemble. To examine whether this accuracy results from structural variation in the single trajectory versus the possibility that error is minimized by averaging, a third collection of receptor structures was created in which each member was taken from an independent molecular dynamics simulation after minimization. Comparison of the docking accuracy results from the single trajectory (72%) to this third collection (61%) showed decreased accuracy, suggesting that ligands are more accurately oriented and assessed when docked to the minimized ensemble from a single MD trajectory, an effect that is more than simply error minimization. Averaging docking scores over a minimized ensemble of another target, influenza A neuraminidase, yielded a ligand ranking accuracy of 83%, representing a 24% improvement over other methods tested.
用于模拟蛋白质柔性的蛋白质结构集合在多个应用中得到了广泛应用，包括虚拟先导化合物优化，在这些应用中，它们已被证明可以提高配体的排名。然而，对于从集合成员生成的各个得分进行加权还没有既定的惯例。为了研究加权集合得分以正确排名配体的最佳方法，对一系列二氢叶酸还原酶抑制剂进行了对接，这些抑制剂是从分子动力学 (MD) 模拟创建的假丝酵母二氢叶酸还原酶 (CaDHFR) 结构的集合中得到的。从单个 MD 模拟中，生成了两个集合集合，其中一个集合经过最小化过程处理，以创建一组具有相同概率的结构。正如预期的那样，当将 Boltzmann 加权应用于未经结构最小化的集合的能量时（60%），与使用平均值（36%）相比，配体排名的准确性得到了显著提高。然而，通过对最小化集合进行平均 docking 得分，准确性进一步提高（72%）。为了检查这种准确性是否是由于单个轨迹中的结构变化导致的，还是由于平均化可以最小化误差的可能性导致的，创建了第三个受体结构集合，其中每个成员都是在最小化后从独立的分子动力学模拟中获得的。将单个轨迹的 docking 准确性结果（72%）与第三个集合（61%）进行比较表明，准确性降低，这表明当配体从单个 MD 轨迹对接至最小化集合时，配体的定向和评估更为准确，这种效果不仅仅是简单的误差最小化。对另一个靶标，流感 A 神经氨酸酶的最小化集合的 docking 得分进行平均，得到了 83%的配体排名准确性，比其他测试方法提高了 24%。
