No abstract

In nonaqueous enzymology, control of enzyme hydration is commonly approached by fixing the thermodynamic water activity of the medium. In this work, we present a strategy for evaluating the water activity in molecular dynamics simulations of proteins in water/organic solvent mixtures. The method relies on determining the water content of the bulk phase and uses a combination of Kirkwood-Buff theory and free energy calculations to determine corresponding activity coefficients. We apply the method in a molecular dynamics study of Candida antarctica lipase B in pure water and the organic solvents methanol, tert-butyl alcohol, methyl tert-butyl ether, and hexane, each mixture at five different water activities. It is shown that similar water activity yields similar enzyme hydration in the different solvents. However, both solvent and water activity are shown to have profound effects on enzyme structure and flexibility.

ABSTRACT:A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability and uncertainty of the prediction. The methodology is evaluated through development of a GC method for prediction of the heat of combustion ( ) for pure components. The results showed that robust regression lead to best performance statistics for parameter estimation. Bootstrap method is found a valid alternative to calculate parameter estimation errors when underlying distribution of residuals is unknown. Many parameters (first, second, third order groups contributions) are found unidentifiable from the typically available data, with large estimation error bounds and significant correlation. Due to this poor parameter 2 identifiability issues, reporting of the 95%-confidence intervals of the predicted property values should be mandatory as opposed to reporting only single value prediction, currently the norm in literature. Moreover, inclusion of higher order groups (additional parameters) does not always lead to improved prediction accuracy for the GC-models, in some cases it may even increase the prediction error (hence worse prediction accuracy). However, additional parameters do not affect calculated 95%-confidence interval. Last but not least, the newly developed GC model of the heat of combustion ( ) shows predictions of great accuracy and quality (the most data falling within the 95% confidence intervals) and provides additional information on the uncertainty of each prediction compared to other models reported in literature.

A systematic approach is suggested for predicting the solubility of sparingly soluble solid fine chemicals and pharmaceuticals. The procedure uses group contribution methods for computing the difference in solubility at infinite dilution in the solvent of interest from an optimal reference solvent with the aim of (1) minimizing the impact of uncertainties in pure-solute properties, (2) decreasing the number of adjustable parameters to be determined by data reduction, and (3) using appropriate experimental data to fit unknown parameters. Several examples illustrate the method.