MCR-ALS analysis of two-way UV resonance Raman spectra to resolve discrete protein secondary structural motifs
University of Missouri, Department of Chemistry, 601 S. College Ave., Columbia, MO 65211, USA.The Analyst (Impact Factor: 4.11). 02/2009; 134(1):138-47. DOI: 10.1039/b814392g
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.
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ABSTRACT: Multivariate curve resolution based on the minimization of an objective function (MCR-FMIN) defined directly from the non-fulfillment of constraints was applied for the first time as a deconvolution method to separate co-eluted gas chromatographic-mass spectrometric (GC-MS) signals. Simulated and real (standard real mixture and limon oil) GC-MS data were used to evaluate the feasibility of this method. The MCR-FMIN solutions have been obtained based on the rotation of principal component analysis (PCA) solutions using the non-linear optimization algorithms. Calculation of the initial values of R rotation matrix using model free analysis methods such as fixed-size moving window-evolving factor analysis (FSMW-EFA), evolving latent projective graphs (ELPGs), and heuristic evolving latent projection (HELP) was proposed for faster convergence and avoiding to be stuck in local minima in MCR-FMIN algorithm. The band boundaries of feasible solutions (MCR-BANDS) obtained using MCR-FMIN were calculated for simulated data to assess the reliability of the method. In addition, the results of this method were compared with those of two most common self-modeling curve resolution (SMCR) methods of multivariate curve resolution-alternating least square (MCR-ALS) and HELP. A reasonable result can be obtained by selecting proper constraints, such as non-negativity, unimodality, normalization, and selectivity. However, when the number of components or the level of noise in each peak cluster increase, the convergence of algorithm becomes difficult and the results are not reliable. For quick and accurate analysis of co-eluted multi-component problematic GC-MS data MCR-FMIN can be considered as an alternative method to the MCR-ALS and HELP methods.Chemometrics and Intelligent Laboratory Systems 03/2010; 101(1-101):1-13. DOI:10.1016/j.chemolab.2009.11.010 · 2.32 Impact Factor
Article: ChemometricsAnalytical Chemistry 06/2010; 82(12):4699-711. DOI:10.1021/ac101202z · 5.64 Impact Factor
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ABSTRACT: 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.The Analyst 03/2011; 136(6):1239-47. DOI:10.1039/c0an00774a · 4.11 Impact Factor
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