Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors
Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors
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DOI：
10.1016/j.bmc.2007.08.019
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发表时间：
2007-12-15
影响因子：
3.5
通讯作者：
Rastelli, Giulio
中科院分区：
文献类型：
--
作者：
Ferrari, Anna Maria;Degliesposti, Gianluca;Rastelli, Giulio
在现有的预测配体与蛋白质结合自由能的方法中，分子力学泊松-玻尔兹曼表面积（MM-PBSA）和分子力学广义玻恩表面积（MM-GBSA）方法已经在相对有限的目标和化合物训练集中得到了验证。在这里，我们报告了对一系列28种醛糖还原酶抑制剂的广泛研究结果，实验确定了它们的晶体结构和抑制活性，并在许多不同的模拟条件下评估了MM-PBSA和MM-GBSA方法预测结合自由能的能力。虽然没有一种方法能够定量地预测出与实验值一致的绝对束缚自由能，但计算出的束缚自由能与实验值有显著的相关性。结果表明，MM-PBSA的结合δ G值优于MM-GBSA，在MM-PBSA方法中，琥珀的δ G值与德尔菲相似。特别地，实验和计算的结合自由能之间有显著的关系，使用琥珀PBSA和结构最小化与距离相关的介电函数。重要的是，虽然自由能预测通常是在水分子动力学过程中采样的大量平衡结构上进行的，但我们发现，如果要计算相对自由结合能，单个最小化结构是一个合理的近似值。考虑到平衡MD集合的生成和随后对多个快照的自由能分析是计算密集型的，而蛋白质-配体复合物的单个最小化结构的生成和分析相对较快，因此适合于高通量虚拟筛选研究，这一发现尤其相关。为此，我们开发了一个自动化的工作流程，它集成了生成结构和计算自由结合能所需的所有必要步骤。该方法相对快速，能够自动迭代筛选数据库和化合物库中的分子。综上所述，我们的研究结果表明，该工作流程可以成为配体识别和优化的一个有价值的工具，能够自动有效地重新确定有时可能不准确的对接姿势，并根据更准确的评分函数对化合物进行排名。(c) 2007 Elsevier Ltd.版权所有。
Among the available methods for predicting free energies of binding of ligands to a protein, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA) approaches have been validated for a relatively limited number of targets and compounds in the training set. Here, we report the results of an extensive study on a series of 28 inhibitors of aldose reductase with experimentally determined crystal structures and inhibitory activities, in which we evaluate the ability of MM-PBSA and MM-GBSA methods in predicting binding free energies using a number of different simulation conditions. While none of the methods proved able to predict absolute free energies of binding in quantitative agreement with the experimental values, calculated and experimental free energies of binding were significantly correlated. Comparing the predicted and experimental Delta G of binding, MM-PBSA proved to perform better than MM-GBSA, and within the MM-PBSA methods, the PBSA of Amber performed similarly to Delphi. In particular, significant relationships between experimental and computed free energies of binding were obtained using Amber PBSA and structures minimized with a distance-dependent dielectric function. Importantly, while free energy predictions are usually made on large collections of equilibrated structures sampled during molecular dynamics in water, we have found that a single minimized structure is a reasonable approximation if relative free energies of binding are to be calculated. This finding is particularly relevant, considering that the generation of equilibrated MD ensembles and the subsequent free energy analysis on multiple snapshots is computationally intensive, while the generation and analysis of a single minimized structure of a protein-ligand complex is relatively fast, and therefore suited for high-throughput virtual screening studies. At this aim, we have developed an automated workflow that integrates all the necessary steps required to generate structures and calculate free energies of binding. The procedure is relatively fast and able to screen automatically and iteratively molecules contained in databases and libraries of compounds. Taken altogether, our results suggest that the workflow can be a valuable tool for ligand identification and optimization, being able to automatically and efficiently re. ne docking poses, which sometimes may not be accurate, and rank the compounds based on more accurate scoring functions. (c) 2007 Elsevier Ltd. All rights reserved.