“Parallel factor analysis of multi-excitation ultraviolet resonance Raman spectra for protein secondary structure determination”

Abstract

Protein secondary structural analysis is important for understanding the relationship between protein structure and function, or more importantly how changes in structure relate to loss of function. The structurally sensitive protein vibrational modes (amide I, II, III and S) in deep-ultraviolet resonance Raman (DUVRR) spectra resulting from the backbone C-O and N-H vibrations make DUVRR a potentially powerful tool for studying secondary structure changes. Experimental studies reveal that the position and intensity of the four amide modes in DUVRR spectra of proteins are largely correlated with the varying fractions of α-helix, β-sheet and disordered structural content of proteins. Employing multivariate calibration methods and DUVRR spectra of globular proteins with varying structural compositions, the secondary structure of a protein with unknown structure can be predicted. A disadvantage of multivariate calibration methods is the requirement of known concentration or spectral profiles. Second-order curve resolution methods, such as parallel factor analysis (PARAFAC), do not have such a requirement due to the "second-order advantage." An exceptional feature of DUVRR spectroscopy is that DUVRR spectra are linearly dependent on both excitation wavelength and secondary structure composition. Thus, higher order data can be created by combining protein DUVRR spectra of several proteins collected at multiple excitation wavelengths to give multi-excitation ultraviolet resonance Raman data (ME-UVRR). PARAFAC has been used to analyze ME-UVRR data of nine proteins to resolve the pure spectral, excitation and compositional profiles. A three factor model with non-negativity constraints produced three unique factors that were correlated with the relative abundance of helical, β-sheet and poly-proline II dihedral angles. This is the first empirical evidence that the typically resolved "disordered" spectrum represents the better defined poly-proline II type structure.

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

“…58 Spectral deconvolution: This involves following any identified changes in molecular composition using curve resolution methods such as multivariate curve resolution (MCR) 103,119,120 and/or parallel factor analysis (PARAFAC). 121 Both methods are commonly applied to the analysis of Raman data and can be used for both reaction monitoring (most significantly for small molecule synthesis) and structural analysis of proteins. They have not, however, been widely applied to complex biogenic samples (media and broths) because there are usually too many components present to provide unique and robust solutions. Quantitative regression analysis: This involves the development of quantitative models for prediction of important process or product parameters (e.g.…”

“…121 Both methods are commonly applied to the analysis of Raman data and can be used for both reaction monitoring (most significantly for small molecule synthesis) and structural analysis of proteins. They have not, however, been widely applied to complex biogenic samples (media and broths) because there are usually too many components present to provide unique and robust solutions.…”

Binding mechanism of lipase with Lentinus edodes mycelia polysaccharide by multi‐spectroscopic methods

“…Lipase solution with the concentration of 0.1 U/mL mixed with the LMP (1:1). Scanning time was 10 seconds, scanning was performed at 523 nm with a scanning range of 3000 to 400 cm −1 29 …”

Deep ultraviolet resonance Raman spectroscopy as a tool for biomolecule analysis

“…A core consistency diagnostic 106 was used to determine the PARAFAC model parameters. This type of diagnostic on a similar data set has been described previously 99 . A three-factor model with non-negativity restraints was chosen because this model yielded the best core consistency (34.3%).…”

“…There have been many studies that employ multivariate calibration and higher order statical techniques to the dUVRR analysis of proteins 27,78,[98][99] . Analyses like multivariate curve resolution-alternating least squares (MCR-ALS) and parallel factor analysis (PARAFAC) can analyze complex data sets and reveal underlying spectral and chromatographic components when the composition of the analyte is not known.…”