Improving the reliability of MM-PBSA and MM-GBSA binding energy predictions by explicitly considering ligand solvation shells

Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) are interesting techniques for drug design/discovery applications, but sometimes the correlation between predicted and experimental binding energies might result unsatisfactory. Nowadays, a certain effort is focused on ameliorating the solvation term in MM-PB/GBSA calculations and some strategies were applied to obtain a better correlation between calculations and experiments. Some authors reported that the predictivity of MM-PB/GBSA calculations might be improved by modulating the internal dielectric constant (εin).1 Unfortunately, a universal εin, suitable for all systems was not found and a thorough analysis of the binding pocket is needed to choose the proper value of εin. MM-PB/GBSA binding energy predictions might also be improved by explicitly considering selected water molecules in the calculation, however this strategy is controversial.2-5 Herein, we report on how the explicit inclusion of variably populated ligand hydration shells might improve the correlation between MM-PB/GBSA computed binding energy and experimental activities. DNA-topoisomerase, α-thrombin, penicillopepsin, avidin, and neuraminidase complexes with different ligands were considered as test sets, and ligand hydration shells populated by an increasing number of water molecules were systematically evaluated. We found that the consideration of a hydration shell populated by a number of water residues (Nwat) between 30 and 70 provided in all the considered examples a positive effect on correlation between MM-PB/GBSA calculated binding affinities and experimental activities, with a negligible increment of computational cost.6 REFERENCES 1. Hou, T.; Wang, J.; Li, Y.; Wang, W., J. Chem. Inf. Model. 2011, 51, 69-82. 2. Wong, S.; Amaro, R. E.; McCammon, J. A., J. Chem. Theory Comput. 2009, 5, 422-429. 3. Hayes, J. M.; Skamnaki, V. T.; Archontis, G.; Lamprakis, C.; Sarrou, J.; Bischler, N.; Skaltsounis, A.-L.; Zographos, S. E.; Oikonomakos, N. G., Proteins 2011, 79, 703-19. 4. Freedman, H.; Huynh, L. P.; Le, L.; Cheatham, I. I. I. T. E.; Tuszynski, J. A.; Truong, T. N., J. Phys. Chem. B 2010, 114, 2227-2237. 5. Checa, A.; Ortiz, A. R.; de Pascual-Teresa, B.; Gago, F., J. Med. Chem. 1997, 40 (25), 4136-45. 6. Maffucci, I.; Contini, A., J. Chem. Theory Comput. 2013, 9 (6), 2706-2717.