The enzyme Candida Antarctica lipase B (CALB) serves here as a model for understanding connections among hydration layer dynamics, solvation shell structure, and protein surface structure. The structure and dynamics of water molecules in the hydration layer were characterized for regions of the CALB surface, divided around each α-helix, β-sheet, and loop structure. Heterogeneous hydration dynamics were observed around the surface of the enzyme, in line with spectroscopic observations of other proteins. Regional differences in the structure of the biomolecular hydration layer were found to be concomitant with variations in dynamics. In particular, it was seen that regions of higher density exhibit faster water dynamics. This is analogous to the behavior of bulk water, where dynamics (diffusion coefficients) are connected to water structure (density and tetrahedrality) by excess (or pair) entropy, detailed in the Rosenfeld scaling relationship. Additionally, effects of protein surface topology and hydrophobicity on water structure and dynamics were evaluated using multiregression analysis, showing that topology has a somewhat larger effect on hydration layer structure-dynamics. Concave and hydrophobic protein surfaces favor a less dense and more tetrahedral solvation layer, akin to a more ice-like structure, with slower dynamics. Results show that pairwise entropies of local hydration layers, calculated from regional radial distribution functions, scale logarithmically with local hydration dynamics. Thus, the Rosenfeld relationship describes the heterogeneous structure-dynamics of the hydration layer around the enzyme CALB. These findings raise the question of whether this may be a general principle for understanding the structure-dynamics of biomolecular solvation.

Solvation is critical for protein structural dynamics. Spectroscopic studies have indicated relationships between protein and solvent dynamics, and rates of gas binding to heme proteins in aqueous solution were previously observed to depend inversely on solution viscosity. In this work, the solvent-compatible enzyme Candida antarctica lipase B, which functions in aqueous and organic solvents, was modeled using molecular dynamics simulations. Data was obtained for the enzyme in acetonitrile, cyclohexane, n-butanol, and tert-butanol, in addition to water. Protein dynamics and solvation shell dynamics are characterized regionally: for each α-helix, β-sheet, and loop or connector region. Correlations are seen between solvent mobility and protein flexibility. So, does local viscosity explain the relationship between protein structural dynamics and solvation layer dynamics? Halle and Davidovic presented a cogent analysis of data describing the global hydrodynamics of a protein (tumbling in solution) that fits a model in which the protein's interfacial viscosity is higher than that of bulk water's, due to retarded water dynamics in the hydration layer (measured in NMR τ2 reorientation times). Numerous experiments have shown coupling between protein and solvation layer dynamics in site-specific measurements. Our data provides spatially-resolved characterization of solvent shell dynamics, showing correlations between regional solvation layer dynamics and protein dynamics in both aqueous and organic solvents. Correlations between protein flexibility and inverse solvent viscosity (1/η) are considered across several protein regions and for a rather disparate collection of solvents. It is seen that the correlation is consistently higher when local solvent shell dynamics are considered, rather than bulk viscosity. Protein flexibility is seen to correlate best with either the local interfacial viscosity or the ratio of the mobility of an organic solvent in a regional solvation layer relative to hydration dynamics around the same region. Results provide insight into the function of aqueous proteins, while also suggesting a framework for interpreting and predicting enzyme structural dynamics in non-aqueous solvents, based on the mobility of solvents within the solvation layer. We suggest that Kramers' theory may be used in future work to model protein conformational transitions in different solvents by incorporating local viscosity effects.

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The use of enzymes in non-aqueous solvents expands the use of biocatalysts to hydrophobic substrates, with the ability to tune selectivity of reactions through solvent selection. Non-aqueous enzymology also allows for fundamental studies on the role of water and other solvents in enzyme structure, dynamics, and function. Molecular dynamics simulations serve as a powerful tool in this area, providing detailed atomic information about the effect of solvents on enzyme properties. However, a common protocol for non-aqueous enzyme simulations does not exist. If you want to simulate enzymes in non-aqueous solutions, how many and which crystallographic waters do you keep? In the present work, this question is addressed by determining which crystallographic water molecules lead most quickly to an equilibrated protein structure. Five different methods of selecting and keeping crystallographic waters are used in order to discover which crystallographic waters lead the protein structure to reach an equilibrated structure more rapidly in organic solutions. It is found that buried waters contribute most to rapid equilibration in organic solvent, with slow-diffusing waters giving similar results.

The ability of electronic structure methods (11 density functionals, HF, and MP2 calculations; two basis sets and two solvation models) to accurately calculate the 19F chemical shifts of 31 structures of fluorinated amino acids and analogues with known experimental 19F NMR spectra has been evaluated. For this task, BH and HLYP, ωB97X, and Hartree-Fock with scaling factors (provided within) are most accurate. Additionally, the accuracy of methods to calculate relative changes in fluorine shielding across 23 sets of structural variants, such as zwitterionic amino acids vs. side chains only, was also determined. This latter criterion may be a better indicator of reliable methods for the ultimate goal of assigning and interpreting chemical shifts of fluorinated amino acids in proteins. It was found that MP2 and M062X calculations most accurately assess changes in shielding among analogues. These results serve as a guide for computational developments to calculate 19F chemical shifts in biomolecular environments.