Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

激肽释放酶 6 (KLK6) 是治疗神经系统疾病和各种癌症的一个有吸引力的药物靶点。在此，我们探索了不同计算方法和协议的准确性和效率，以预测一系列 49 种 KLK6 抑制剂的结合自由能 (ΔG_结合)。我们发现这些方法的性能随测试系统的不同而有很大差异。 仅对于三个 KLK6 数据集之一，使用 rDock 获得的对接分数与 ΔG _bond的实验值非常一致（R ² ≥ 0.5） 。基于单个最小化结构的 MM/GBSA（使用 ff14SB 力场）计算获得了类似的结果。通过自由能微扰 (FEP) 方法获得了改进的结合亲和力预测，总体 MUE 和 RMSE 分别为 0.53 和 0.68 kcal/mol。此外，在模拟现实世界的药物发现项目时，FEP 能够将最有效的化合物排在列表的首位。这些结果表明 FEP 可以成为基于结构的 KLK6 抑制剂优化的有前途的工具。