The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

Single-atom catalysts provide an effective approach to reduce the amount of precious metals meanwhile maintain their catalytic activity. However, the sluggish activity of the catalysts for alkaline water dissociation has hampered advances in highly efficient hydrogen production. Herein, we develop a single-atom platinum immobilized NiO/Ni heterostructure (PtSA-NiO/Ni) as an alkaline hydrogen evolution catalyst. It is found that Pt single atom coupled with NiO/Ni heterostructure enables the tunable binding abilities of hydroxyl ions (OH*) and hydrogen (H*), which efficiently tailors the water dissociation energy and promotes the H* conversion for accelerating alkaline hydrogen evolution reaction. A further enhancement is achieved by constructing PtSA-NiO/Ni nanosheets on Ag nanowires to form a hierarchical three-dimensional morphology. Consequently, the fabricated PtSA-NiO/Ni catalyst displays high alkaline hydrogen evolution performances with a quite high mass activity of 20.6 A mg−1 for Pt at the overpotential of 100 mV, significantly outperforming the reported catalysts.

MMPBSA methods have become widely adopted in estimating protein-ligand binding affinities due to their efficiency and high correlation with experiment. Here we investigated different computational alternatives on their impact to the agreement of MMPBSA calculations with experiment. Seven receptor families with both high-quality crystal structures and binding affinities were selected. We first studied the performance of nonpolar solvation models and found that the modern approach that separately models hydrophobic and dispersion interactions dramatically reduces RMSD's of computed relative binding affinities. The numerical setup of the PoissonBoltzmann methods was analyzed next. Our data shows that the impact of grid spacing to the quality of MMPBSA calculations is small: the numerical error at the grid spacing of 0.5 Angstrom is already small enough to be negligible. We further analyzed the impact of different atomic radius sets and different molecular surface definitions and found weak influences on the agreement with experiment. The influence of solute dielectric constant was also analyzed: a higher dielectric constant generally improves the overall agreement with experiment, especially for highly charged binding pockets. Our data also show that the converged simulations cause slight reduction in the agreement with experiment. Finally we briefly explored the direction of estimating absolute binding free energies. Upon correction of the binding-induced rearrangement free energy and the binding entropy lost, the errors in absolute binding affinities are also reduced dramatically when the modern nonpolar solvent model was used, though further developments are apparently necessary to further improve the MMPBSA methods. Graphical abstract HHS Public Access Author Manuscript Author ManuscriptAuthor Manuscript Author ManuscriptThe modern nonpolar solvent model that separately models solvation hydrophobic and dispersion interactions dramatically reduces RMSDs of computed relative binding affinities in Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) methods.

Single-atom catalysts (SACs) have exhibited extraordinary catalytic performance due to the utmost atom utilization efficiency and unique electronic states by metal-support interaction. Rationally designing SACs at the atomic level by...

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