Abstract
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 (ΔGbind) 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 ΔGbind. 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.
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22 July 2023
A Correction to this paper has been published: https://doi.org/10.1007/s10822-023-00521-5
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Acknowledgements
W.J.L.S. and R.F.F. gratefully acknowledges funding from São Paulo Research Foundation – FAPESP (2021/04450-7, 2018/11011-7 and 2019/08603-2). We are grateful to OpenEye Scientific Software, Inc. for providing us with an academic license for Omega. We thank D.E. Shaw Research for providing us with an academic license for Desmond.
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W.J.L.S. and R.F.F. designed the study and analyzed the data. W.J.L.S. performed the simulations. W.J.L.S. and R.F.F. wrote the manuscript. R.F.F. was responsible for the project. All authors reviewed the manuscript.
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Lima Silva, W.J., Ferreira de Freitas, R. Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors. J Comput Aided Mol Des 37, 407–418 (2023). https://doi.org/10.1007/s10822-023-00515-3
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DOI: https://doi.org/10.1007/s10822-023-00515-3