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

Abstract

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.

UV Resonance Raman Investigations of Peptide and Protein Structure and Dynamics

“…126 They also used constrained multivariate curve resolution-alternating least squares (MCR-ALS) analysis 127 and parallel factor (PARAFAC) analysis 126 to resolve discrete protein secondary structural motifs.…”

Molecular component distribution imaging of living cells by multivariate curve resolution analysis of space-resolved Raman spectra

“…25,29,31,[36][37][38][39][40][41] It has also been exploited in many kinds of spectroscopy, high performance liquid chromatography, 42,43 gas chromatograph/mass spectrometry, 44 UV-VIS, 45 near-infrared, 46,47 FT-IR, 48,49 fluorescence imaging, 50 etc. In the MCR method, the experimental data is approximated by a linear combination of several spectral components.…”

Evaluation of conversion during the synthesis of aluminum (III) methacrylate-based copolymers using Raman spectroscopy and multivariate curve resolution

“…MCR-ALS has been applied to Raman spectroscopy monitoring and some examples of representative studies are available in the open literature [35,36,39,42,48]. Among them, the following works deserve more emphasis.…”

“…Basically, MCR-ALS is characterized by a bilinear decomposition of an original data matrix (D) into two matrices [31][32][33][34][35][36][37][38][39][40][41][42][43]: i) a matrix corresponding to the concentration profiles (C) and; ii) a second matrix related to the pure spectra profiles (S). MCR model can be expressed as D = CS T + ε, where the matrix D (dimensions m different individual spectra by n intensities measured at each spectral points) is approximated by the product of the matrix C (dimensions k components in the different by m rows of the data matrix) and matrix S T (transpose of matrix pure spectral profiles of dimensions k components by n columns of the original data matrix); and ε is the matrix of residuals (dimensions m different individual spectra by n intensities) containing the information not explained by the model.…”