Accurately computing molecular Raman spectra would enable rapid development of inexpensive and extensive Raman libraries. This is especially beneficial for chemicals that are regulated, toxic, or otherwise difficult to handle. Numerous quantum mechanical methods have been developed that enable computation of Raman spectra. Here, we study the B3LYP exchange correlation functional with various combinations of basis sets, polarization functions, and diffuse functions to determine which combination best computes the Raman spectra for explosive and nonexplosive molecules. In comparing spectra, three metrics were utilized: the root mean square error, the earth mover's distance, and the weighted cross-correlation average. The earth mover's distance and weighted cross-correlation metrics are shown to have significantly greater power at detecting spectral similarities and differences than the root mean square error. Across all methods and molecules examined, B3LYP/6-311++G(d,p) was found to provide the best match between measured and computed Raman spectra. Spectra generated at the B3LYP/6-311++G(d,p) level were found to be accurate enough to correctly identify each molecule out of a set of measured molecular spectra.

Human Serum paraoxonase 1 (HuPON1) is an enzyme that has been shown to hydrolyze a variety of chemicals including the nerve agent VX. While wildtype HuPON1 does not exhibit sufficient activity against VX to be used as an in vivo countermeasure, it has been suggested that increasing HuPON1's organophosphorous hydrolase activity by one or two orders of magnitude would make the enzyme suitable for this purpose. The binding interaction between HuPON1 and VX has recently been modeled, but the mechanism for VX hydrolysis is still unknown. In this study, we created a transition state model for VX hydrolysis (VXts) in water using quantum mechanical/molecular mechanical simulations, and docked the transition state model to 22 experimentally characterized HuPON1 variants using AutoDock Vina. The HuPON1-VXts complexes were grouped by reaction mechanism using a novel clustering procedure. The average Vina interaction energies for different clusters were compared to the experimentally determined activities of HuPON1 variants to determine which computational procedures best predict how well HuPON1 variants will hydrolyze VX. The analysis showed that only conformations which have the attacking hydroxyl group of VXts coordinated by the sidechain oxygen of D269 have a significant correlation with experimental results. The results from this study can be used for further characterization of how HuPON1 hydrolyzes VX and design of HuPON1 variants with increased activity against VX.

Human serum paraoxonase 1 (HuPON1) is an enzyme that can hydrolyze various chemical warfare nerve agents including VX. A previous study has suggested that increasing HuPON1's VX hydrolysis activity one to two orders of magnitude would make the enzyme an effective countermeasure for in vivo use against VX. This study helps facilitate further engineering of HuPON1 for enhanced VX-hydrolase activity by computationally characterizing HuPON1's tertiary structure and how HuPON1 binds VX. HuPON1's structure is first predicted through two homology modeling procedures. Docking is then performed using four separate methods, and the stability of each bound conformation is analyzed through molecular dynamics and solvated interaction energy calculations. The results show that VX's lone oxygen atom has a strong preference for forming a direct electrostatic interaction with HuPON1's active site calcium ion. Various HuPON1 residues are also detected that are in close proximity to VX and are therefore potential targets for future mutagenesis studies. These include E53, H115, N168, F222, N224, L240, D269, I291, F292, and V346. Additionally, D183 was found to have a predicted pKa near physiological pH. Given D183's location in HuPON1's active site, this residue could potentially act as a proton donor or accepter during hydrolysis. The results from the binding simulations also indicate that steered molecular dynamics can potentially be used to obtain accurate binding predictions even when starting with a closed conformation of a protein's binding or active site.

SUMMARYWhile principal component regression (PCR) is often performed with eigenvectors ordered by decreasing singular values, PCR models have been formed using other eigenvector arrangements. A common criterion for organizing eigenvectors involves absolute correlations between respective eigenvectors and the prediction property being modeled. However, correlation cut-off values for eigenvector selection are inconsistent between data sets, and additional criteria are needed such as the root mean square error of cross-validation (RMSECV). Furthermore, correlations for some selected eigenvectors are often extremely low (e.g. values of 0⋅1 have been considered acceptable) and it is difficult to justify inclusion of these eigenvectors. Relative standard deviations (RSDs) of correlations are evaluated in this paper as an alternative method of eigenvector selection. This paper reveals distinct advantages for using eigenvectors ordered by RSDs of correlations compared to eigenvectors ordered by absolute correlations. In particular, RSDs can be used to determine significant eigenvectors without resorting to additional criteria such as the RMSECV. Additionally, inspection of RSD values explains why different correlation cut-off values are obtained for different data sets as well as why correlations can be small.

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