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This work considers the evaluation of density functional theory (DFT) when comparing against experimental observations of CO binding trends on the strong binding Pt(111) and intermediate binding Cu(111) and for weak binding Ag(111) and Au(111) surfaces important in electrocatalysis. By introducing thermal fluctuations using appropriate statistical mechanical NVT and NPT ensembles, we find that the RPBE and B97M-rV DFT functionals yield qualitatively better metal surface strain trends and CO enthalpies of binding for Cu(111) and Pt(111) than found at 0 K, thereby correcting the overbinding by 0.2 to 0.3 eV to yield better agreement with the enthalpies determined from experiment. The importance of dispersion effects are manifest for the weak CO binding Ag(111) and Au(111) surfaces at finite temperatures in which the RPBE functional does not bind CO at all, while the B97M-rV functional shows that the CO-metal interactions are a mixture of chemisorbed and physisorbed species with binding enthalpies that are within ∼0.05 eV of experiment. Across all M(111) surfaces, we show that the B97M-rV functional consistently predicts the correct atop site preference for all metals due to thermally induced surface distortions that preferentially favor the undercoordinated site. This study demonstrates the need to fully account for finite temperature fluctuations to make contact with the binding enthalpies from surface science experiments and electrocatalysis applications.

Ion channels are proteins that play a significant role in physiological processes, including neuronal excitability and signal transduction. However, the precise mechanisms by which these proteins facilitate ion diffusion through cell membranes are not well understood. This is because experimental techniques to characterize ion channel activity operate on a time scale too large to understand the role of the various protein conformations on diffusion. Meanwhile, computational approaches operate on a time scale too short to rationalize the observed behavior at the microscopic scale. In this paper, we present a continuous-time random walk model that aims to bridge the scales between the atomistic models of ion channels and the experimental measurement of their conductance. We show how diffusion slows down in complex systems by using 3D lattices that map out the pore geometry of two channels: Nav1.7 and gramicidin. We also introduce spatial and dynamic site disorder to account for system heterogeneity beyond the mean field approximation. Computed diffusion coefficients show that an increase in spatial disorder slows down diffusion kinetics, while dynamic disorder has the opposite effect. Our results imply that microscopic or phenomenological models based on the potential of mean force data overlook the functional importance of protein dynamics on ion diffusion through channels. [ABSTRACT FROM AUTHOR]

We review the standard model for de novo computational design of enzymes, which primarily focuses on the development of an active-site geometry, composed of protein functional groups in orientations optimized to stabilize the transition state, for a novel chemical reaction not found in nature. Its emphasis is placed on the structure and energetics of the active site embedded in an accommodating protein that serves as a physical support that shields the reaction chemistry from solvent, which is typically improved upon by laboratory-directed evolution. We also provide a review of design strategies that move beyond the standard model, by placing more emphasis on the designed enzyme as a whole catalytic construct. Starting with complete de novo enzyme design examples, we consider additional design factors such as entropy of individual residues, correlated motion between side chains (mutual information), dynamical correlations of the enzyme motions that could aid the reaction, reorganization energy, and electric fields as ways to exploit the entire protein scaffold to improve upon the catalytic rate, thereby providing directed evolution with better starting sequences for increasing biocatalytic performance.

Polarizable force fields crucially enhance the modeling of macromolecules in polar media. Here, we present new parameters to model six common monosaccharides with the polarizable AMOEBA force field. These parameters yield a thinner, but denser, hydration layer than that previously reported. This denser hydration layer results in eliminating non-physical aggregation of glucose in water—an issue that has plagued molecular dynamics simulations of carbohydrates for decades. [ABSTRACT FROM AUTHOR]

Tetrahedral assemblies of stoichiometry M4L6 have been proven to catalyze a range of chemical reactions including the carbon-carbon reductive elimination reaction from transition metals such as gold. Here, we perform quantum chemical calculations of Gold(III) transition metal complexes in vacuum, and encapsulated in Ga4L612- or Si4L68- assemblies within both a reaction field continuum solvent and in an aqueous molecular environment with counterions, to rationalize the rate enhancements observed experimentally for the reductive elimination reaction. We find that the Ga4L612- assembly lowers the energy barrier of the reaction compared to Si4L68-, which is consistent with kinetic trends observed experimentally. We have determined that the primary factor for catalytic rate acceleration stems from the electrostatic environment emanating from the Ga4L612- capsule as opposed to the water or counterions.