Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

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 (& UDelta;G(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 (R-2 & GE; 0.5) with experimental values of & UDelta;G(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.

Automated summary

This study investigates the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding for a series of KLK6 inhibitors. The results show that the performance of the methods varied strongly depending on the system. The free energy perturbation (FEP) method showed improved binding affinity predictions and ranked the most potent compounds at the top of the list in a simulation of a real-world drug discovery project.