Free energy perturbation (FEP) methodologies have become commonplace methods for modeling potency in hit-to-lead and lead optimization stages of drug discovery. The conformational states of the initial poses of compounds for FEP+ calculations are often set up by alignment to a cocrystal structure ligand, but it is not clear if this method provides the best result for all proteins or all ligands. Not only are ligand conformational states potential variables in modeling compound potency in FEP but also the selection of crystallographic water molecules for inclusion in the FEP input structures can impact FEP models. Here, we report the results of FEP calculations using FEP+ from Schrödinger and starting from maximum common substructure alignment and docked poses generated with an array of docking methodologies. As a benchmark data set, we use monoacylglycerol lipase (MAGL), an important clinical drug target in cancer malignancy, neurological diseases, and metabolic disorders, and a set of 17 MAGL inhibitors. We found a large variation among FEP+ correlations to experimental IC50 values depending on the method used to generate the input pose and that the inclusion of ligand-based information in the docking process, with some methods, increases the correlation between FEP+ free energies and IC50 values. Upon analysis of the initial poses, we found that the differences in FEP+ correlations stemmed from rotation around a tertiary amide bond as well as translation of the compound toward the more hydrophobic side of the MAGL pocket. FEP+ estimation improved across all pose modeling methods when hydrogen bond constraint information was added. However, simple maximum common substructure alignment in the presence of all crystallographic water molecules outperformed all other methods in correlation between estimated and experimental IC50 values. Taken together, these findings suggest that pose selection and crystallographic water inclusion greatly impact how well FEP+ estimated IC50 values align with experimental IC50 values and that modelers should benchmark a few different pose generation methodologies and different water inclusion strategies for their hit-to-lead and lead optimization drug discovery projects.

中文翻译：

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输入姿势是自由能扰动性能的关键：使用单酰基甘油脂肪酶进行基准测试。


自由能扰动 （FEP） 方法已成为在药物发现的苗头化合物到先导化合物和先导化合物优化阶段对效价进行建模的常见方法。用于 FEP+ 计算的化合物初始位姿的构象状态通常是通过与共晶结构配体的对齐来设置的，但尚不清楚这种方法是否为所有蛋白质或所有配体提供最佳结果。不仅配体构象状态是 FEP 中化合物效力建模的潜在变量，而且选择用于 FEP 输入结构的晶体水分子也会影响 FEP 模型。在这里，我们报告了使用 Schrödinger 的 FEP+ 进行 FEP 计算的结果，并从使用一系列对接方法生成的最大公共子结构对齐和对接姿势开始。作为基准数据集，我们使用单酰基甘油脂肪酶 （MAGL），这是癌症恶性肿瘤、神经系统疾病和代谢紊乱的重要临床药物靶点，以及一组 17 种 MAGL 抑制剂。我们发现 FEP+ 与实验 IC50 值的相关性存在很大差异，具体取决于用于生成输入姿势的方法，并且在对接过程中包含基于配体的信息，对于某些方法，增加了 FEP+ 自由能和 IC50 值之间的相关性。在分析初始姿势后，我们发现 FEP+ 相关性的差异源于围绕叔酰胺键的旋转以及化合物向 MAGL 口袋更疏水的一侧的平移。添加氢键约束信息后，所有姿态建模方法的 FEP+ 估计都得到了改进。 然而，在存在所有晶体学水分子的情况下，简单的最大公共子结构对齐在估计和实验 IC50 值之间的相关性方面优于所有其他方法。综上所述，这些发现表明，位姿选择和晶体学水夹杂极大地影响了 FEP+ 估计的 IC50 值与实验 IC50 值的一致性，建模者应该为他们的苗头化合物到先导化合物和先导化合物优化药物发现项目对几种不同的姿态生成方法和不同的水夹杂策略进行基准测试。