Assessing the Performance of MM/PBSA, MM/GBSA, and QM-MM/GBSA Approaches on Protein/Carbohydrate Complexes: Effect of Implicit Solvent Models, QM Methods, and Entropic Contributions

Rapid and accurate binding affinity prediction of protein-carbohydrate complexes is a major challenge in glycomimetics design. Among the existing computational techniques, endpoint methods have received considerable interest because of their low computational cost. However, significant obstacles remain when such methods are applied to protein-glycan complexes. This article reports the performance of end-point free-energy calculation methods: molecular mechanics/Poisson-Boltzmann surface area (MM/ PBSA), MM/generalized Born surface area (MM/GBSA), and quantum mechanics-MM/GBSA (QM-MM/GBSA) on monosaccharides bound to RSL lectin from Ralstonia solanacearum. A careful investigation of the molecular dynamics simulation length, van der Waals radii sets, GB models, QM Hamiltonians, and entropic compensation has been made, and the results are compared with the experimental binding free energies from isothermal titration calorimetry/surface plasmon resonance measurements. The binding free energies using implicit solvent methods are found to be sensitive to the simulation length, radii set, GB model, and QM Hamiltonian. A simulation length of 10 ns using the radii set mbondi provides the best agreement with the experimental values (r(2) = 0.96) by MM/PBSA. The GB(HCT) model is in accord with the experimental values in MM/GBSA (r(2) = 0.91) or in combination with parameterized model number 6 (PM6) (r(2) = 0.98) in QM-MM/GBSA. Out of 12 QM Hamiltonians tested, PM6, density functional theory-based tight binding (DFTB), and their variants proved to be more efficient than other semiempirical methods. These methods perform equally well in predicting both absolute and relative binding free energies.