A semiautomated Nwat-MM-GBSA workflow for fast and accurate predictions of relative binding free energies

Despite the development of high-throughput computational methods able to screen very large libraries in a short time, the reliable prediction of binding free energy can still be important in drug design.1,2 Although quite computationally expensive, molecular dynamics (MD), providing a statistically meaningful conformational ensemble for thermodynamic calculations, are within the most accurate tecqniques to predict interaction free energies of biomolecules. Among MD-based methods, one of the most popular is Molecular Mechanics Poisson−Boltzmann/Generalized Born Surface Area (MM-PB/GBSA).3 We recently reported on how the inclusion of a certain number of explicit waters (Nwat), chosen to be the closest to the ligand atoms, can improve the correlation between MM-PB and GBSA computed binding energy and experimental activities (Fig. 1).4 Fig. : Effect of the inclusion of explicit waters in the correlation of computed and experimental activities for a set of topoisomerase inhibitors Here, we will present a semiautomated workflow to compute MM-GBSA relative binding energies starting from a set of complexes, either obtained through X-ray crystallography, homology modelling or docking simulations, by taking advantage of GPU calculations and with a minimal effort by the user. We will also discuss specific examples of application on protein-ligand and protein-protein complexes. REFERENCES 1. Durrant, J.D.; McCammon, J.A. Molecular dynamics simulations and drug discovery. BMC Biology 2011, 9:71 2. Zhao, H.; Caflish, A. Molecular dynamics in drug design. Eur. J. Med. Chem. 2014, doi:10.1016/j.ejmech.2014.08.004 3. Massova, I.; Kollman, P. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. 2000, 18 (1), 113-135 4. Maffucci, I.; Contini, A. Explicit Ligand Hydration Shells Improve the Correlation between MM-PB/GBSA Binding Energies and Experimental Activities J. Chem. Theory Comput. 2013, 9, 2706-2717.