Evolution of quantitative methods in protein secondary structure determination via deep-ultraviolet resonance Raman spectroscopy.

Deep-ultraviolet resonance Raman (DUVRR) spectra is sensitive to secondary structural motifs but, similar to circular dichroism (CD) and infrared spectroscopy, requires the application of multivariate and advanced statistical analysis methods to resolve the pure secondary structure Raman spectra (PSSRS) for determination of secondary structure composition. Secondary structure motifs are selectively enhanced by different excitation wavelengths, a characteristic that inspired the first methods for quantifying secondary structures by DUVRR. This review traces the evolution of multivariate methods and their application to secondary structure composition analyses of proteins by DUVRR spectroscopy from the first experiments using two-wavelengths, and culminating with recent studies utilizing time-resolved DUVRR measurements.

Pre-processing of ultraviolet resonance Raman spectra.

The application of UV excitation sources coupled with resonance Raman have the potential to offer information unavailable with the current inventory of commonly used structural techniques including X-ray, NMR and IR analysis. However, for ultraviolet resonance Raman (UVRR) spectroscopy to become a mainstream method for the determination of protein secondary structure content and monitoring protein dynamics, the application of multivariate data analysis methodologies must be made routine. Typically, the application of higher order data analysis methods requires robust pre-processing methods in order to standardize the data arrays. The application of such methods can be problematic in UVRR datasets due to spectral shifts arising from day-to-day fluctuations in the instrument response. Additionally, the non-linear increases in spectral resolution in wavenumbers (increasing spectral data points for the same spectral region) that results from increasing excitation wavelengths can make the alignment of multi-excitation datasets problematic. Last, a uniform and standardized methodology for the subtraction of the water band has also been a systematic issue for multivariate data analysis as the water band overlaps the amide I mode. Here we present a two-pronged preprocessing approach using correlation optimized warping (COW) to alleviate spectra-to-spectra and day-to-day alignment errors coupled with a method whereby the relative intensity of the water band is determined through a least-squares determination of the signal intensity between 1750 and 1900 cm(-1) to make complex multi-excitation datasets more homogeneous and usable with multivariate analysis methods.

MCR-ALS analysis of two-way UV resonance Raman spectra to resolve discrete protein secondary structural motifs.

The ability of ultraviolet resonance Raman (UVRR) spectroscopy to monitor a host of structurally sensitive protein vibrational modes, the amide I, II, III and S regions, makes it a potentially powerful tool for the visualization of equilibrium and non-equilibrium secondary structure changes in even the most difficult peptide samples. However, it is difficult to unambiguously resolve discrete secondary structure-derived UVRR spectral signatures independently of one another as each contributes an unknown profile to each of the spectrally congested vibrational modes. This limitation is compounded by the presence of aromatic side chains, which introduce additional overlapping vibrational modes. To address this, we have exploited an often overlooked tool for alleviating this spectral overlap by utilizing the differential excitability of the vibrational modes associated with alpha-helices and coil moieties, in the deep UV. The differences in the resonance enhancements of the various structurally associated vibrational modes yields an added dimensionality in the spectral data sets making them multi-way in nature. Through a 'chemically relevant' shape-constrained multivariate curve resolution-alternating least squares (MCR-ALS) analysis, we were able to deconvolute the complex amide regions in the multi-excitation UVRR spectrum of the protein myoglobin, giving us potentially useful 'pure' secondary structure-derived contributions to these individual vibrational profiles.

Development of a gas chromatography-mass spectrometry method for the quantification of glucaric Acid derivatives in beverage substrates.

A gas chromatography-mass spectrometry (GC-MS) method using the standard addition methodology was developed for the determination of glucuronolactone (GL) and glucuronic acid (DGuA) in four beverages categorized as detoxification, recovery, or energy drinks. The method features a precolumn derivatization step with a combination of BSTFA (N,O-bis(trimethylsilyl)trifluoroacetamide) and TMCS (trimethylchlorosilane) to silylate the analytes. The sample pretreatment required no extraction, filtration, or reduction step prior to the injection. The quantification of the analytes was performed using a five-point standard addition protocol. The proposed method presented excellent intraday precision (%RSD < 10) and linearity for GL calibration curves (correlation coefficients > 0.995) and acceptable linearity for DGuA calibration curves (correlation coefficients > 0.97). The estimated limits of detection (LOD) and quantification (LOQ) for GL ranged from 0.006 ppm to 0.14 ppm, and 0.02 ppm to 0.47 ppm, respectively. The estimated LOD and LOQ for DGuA determination ranged, respectively, from 0.06 ppm to 1.1 ppm and 0.2 ppm to 3.8 ppm. The results demonstrated that the method should be regarded as a reliable alternative to the simultaneous determination of GL and DGuA.