Comparison of radii sets, entropy, QM methods, and sampling on MM-PBSA, MM-GBSA, and QM/MM-GBSA ligand binding energies of F. tularensis enoyl-ACP reductase (FabI)

To validate a method for predicting the binding affinities of FabI inhibitors, three implicit solvent methods, MM‐PBSA, MM‐GBSA, and QM/MM‐GBSA were carefully compared using 16 benzimidazole inhibitors in complex with Francisella tularensis FabI. The data suggests that the prediction results are sensitive to radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length, if only one simulation trajectory is used for each ligand. In this case, QM/MM‐GBSA using 6 ns MD simulation trajectories together with GBneck2, PM3, and the mbondi2 radii set, generate the closest agreement with experimental values (r2 = 0.88). However, if the three implicit solvent methods are averaged from six 1 ns MD simulations for each ligand (called “multiple independent sampling”), the prediction results are relatively insensitive to all the tested parameters. Moreover, MM/GBSA together with GBHCT and mbondi, using 600 frames extracted evenly from six 0.25 ns MD simulations, can also provide accurate prediction to experimental values (r2 = 0.84). Therefore, the multiple independent sampling method can be more efficient than a single, long simulation method. Since future scaffold expansions may significantly change the benzimidazole's physiochemical properties (charges, etc.) and possibly binding modes, which may affect the sensitivities of various parameters, the relatively insensitive “multiple independent sampling method” may avoid the need of an entirely new validation study. Moreover, due to large fluctuating entropy values, (QM/)MM‐P(G)BSA were limited to inhibitors’ relative affinity prediction, but not the absolute affinity. The developed protocol will support an ongoing benzimidazole lead optimization program. © 2015 Wiley Periodicals, Inc. Computation prediction on ligand‐protein binding activities (Molecular Mechanics Poisson‐Boltzmann surface area (MM‐PBSA), and Quantum Mechanics/MM‐Generalized Born surface area (QM/MM‐GBSA)), has gained attention in the pharmaceutical community due to its efficiency and accuracy. However, the effect of radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length on the MM‐PBSA and QM/MM‐GBSA performance has not been extensively studied. The article highlights the importance of these factors in the MM‐PBSA and QM/MM‐GBSA performance using the bacterial enoyl‐ACP reductase and benzimidazole inhibitors as a test case.