Latent variable analysis of Raman spectra for structural characterization of proteins

Shashilov, Victor A.; Xu, Min; Ermolenkov, Vladimir V.; Lednev, Igor K.

doi:10.1016/j.jqsrt.2006.02.049

Cited by 31 publications

(43 citation statements)

References 81 publications

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“…Advanced statistical methods allow for retrieving of reliable information from data sets that is not otherwise evident. We have been successful in developing and applying advanced statistical analysis of Raman spectra for biochemical [19][20][21][22][23][24][25][26][27] and forensic purposes [7][8][9]11,13].…”

Section: Open Accessmentioning

confidence: 99%

Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

Sikirzhytski

Virkler

Lednev

et al. 2010

AIP Conference Proceedings

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Detection and identification of blood, semen and saliva stains, the most common body fluids encountered at a crime scene, are very important aspects of forensic science today. This study targets the development of a nondestructive, confirmatory method for body fluid identification based on Raman spectroscopy coupled with advanced statistical analysis. Dry traces of blood, semen and saliva obtained from multiple donors were probed using a confocal Raman microscope with a 785-nm excitation wavelength under controlled laboratory conditions. Results demonstrated the capability of Raman spectroscopy to identify an unknown substance to be semen, blood or saliva with high confidence.

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Section: Open Accessmentioning

confidence: 99%

Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

Sikirzhytski

Virkler

Lednev

et al. 2010

AIP Conference Proceedings

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“…Thus it becomes very difficult to choose the proper value of dispersion constant  in the clustering function (11) and presence of dummy peaks in the clustering function is very likely to occur. As already mentioned, such case is illustrated in Figure 8.…”

Section: Case 2: 1 H Nmr Spectramentioning

confidence: 99%

“…The 1 H NMR spectrum of any biological fluid is a superposition of the spectra of great number of compounds. Quantification and identification of the components present in the mixture is a traditional problem not only in NMR spectroscopy [3][4][5] but also in infrared (IR) spectroscopy [6,12], EPR spectroscopy [7,8], mass spectrometry [9,10,12], Raman spectroscopy [11], etc. Identification of the spectra of mixtures proceeds in majority of the cases by matching the mixture's spectra with a library reference compounds, [3,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Provided that available number of linearly independent mixtures is equal or greater than the number of components, it is possible to separate mixture's spectra into component spectra using only the measurements of the mixture's spectra. This problem is generally known as blind source separation (BSS) and is for described case (more measured mixture's spectra than component spectra) solved by algorithms of independent component analysis (ICA), [11][12][13][14][15][16][17][18]. ICA assumes that pure components are statistically independent and that at most one is normally distributed.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

Kopriva

Jerić

Smrečki

2009

Analytica Chimica Acta

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Sparse Component Analysis (SCA) is demonstrated for blind extraction of three pure component spectra from only two measured mixed spectra in 13 C and 1 H nuclear magnetic resonance (NMR) spectroscopy. This appears to be the first time to report such results and that is the first novelty of the paper. Presented concept is general and directly applicable to experimental scenarios that possibly would require use of more than two mixtures. However, it is important to emphasize that number of required mixtures is always less than number of components present in these mixtures. The second novelty is formulation of blind NMR spectra decomposition exploiting sparseness of the pure components in the wavelet basis defined by either Morlet or Mexican hat wavelet. This enabled accurate estimation of the concentration matrix and number of pure components by means of data clustering algorithm and pure components § Patent pending under the number PCT/HR2008/000037. 2 spectra by means of linear programming with constraints from both 1 H and 13 C NMR experimental data.The third novelty is capability of proposed method to estimate number of pure components in demanding underdetermined blind source separation (uBSS) scenario. This is in contrast to majority of the BSS algorithms that assume this information to be known in advance. Presented results are important for the NMR spectroscopy-associated data analysis in pharmaceutical industry, medicine diagnostics and natural products research.

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“…This means that the formations of b-sheet and unordered structures might be correlated. The development of statistical methods of analysis, which would allow for distinguishing species with a partially correlated behavior, is currently underway in our laboratory (Shashilov et al 2006b). Figure 8.…”

Section: Duvrr Spectra Of Lsozyme: Changes With the Incubation Time Amentioning

confidence: 99%

The first step of hen egg white lysozyme fibrillation, irreversible partial unfolding, is a two‐state transition

Shashilov

Ermolenkov

et al. 2007

Protein Science

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Amyloid fibril depositions are associated with many neurodegenerative diseases as well as amyloidosis. The detailed molecular mechanism of fibrillation is still far from complete understanding. In our previous study of in vitro fibrillation of hen egg white lysozyme, an irreversible partially unfolded intermediate was characterized. A similarity of unfolding kinetics found for the secondary and tertiary structure of lysozyme using deep UV resonance Raman (DUVRR) and tryptophan fluorescence spectroscopy leads to a hypothesis that the unfolding might be a two-state transition. In this study, chemometric analysis, including abstract factor analysis (AFA), target factor analysis (TFA), evolving factor analysis (EFA), multivariate curve resolutionalternating least squares (ALS), and genetic algorithm, was employed to verify that only two principal components contribute to the DUVRR and fluorescence spectra of soluble fraction of lysozyme during the fibrillation process. However, a definite conclusion on the number of conformers cannot be made based solely on the above spectroscopic data although chemometric analysis suggested the existence of two principal components. Therefore, electrospray ionization mass spectrometry (ESI-MS) was also utilized to address the hypothesis. The protein ion charge state distribution (CSD) envelopes of the incubated lysozyme were well fitted with two principal components. Based on the above analysis, the partial unfolding of lysozyme during in vitro fibrillation was characterized quantitatively and proven to be a two-state transition. The combination of ESI-MS and Raman and fluorescence spectroscopies with advanced statistical analysis was demonstrated to be a powerful methodology for studying protein structural transformations.

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Latent variable analysis of Raman spectra for structural characterization of proteins

Cited by 31 publications

References 81 publications

Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

Discriminant Analysis of Raman Spectra for Body Fluid Identification for Forensic Purposes

Extraction of multiple pure component 1H and 13C NMR spectra from two mixtures: Novel solution obtained by sparse component analysis-based blind decomposition

The first step of hen egg white lysozyme fibrillation, irreversible partial unfolding, is a two‐state transition

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