Covariance NMR spectroscopy by singular value decomposition

Trbovic, Nikola; Smirnov, S. K.; Zhang, Fengli; Brüschweiler, Rafael

doi:10.1016/j.jmr.2004.08.007

Cited by 97 publications

(115 citation statements)

References 12 publications

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“…After Fourier transformation along the t 2 dimension and phase correction, the resulting data matrix was used for covariance processing as previously reported (18,37). The covariance processing step was accelerated by singular value decomposition (38).…”

Section: Methodsmentioning

confidence: 99%

Site-specific Inhibitory Mechanism for Amyloid β42 Aggregation by Catechol-type Flavonoids Targeting the Lys Residues

Satô

Murakami²,

Uno³

et al. 2013

Journal of Biological Chemistry

204

227

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Background:The inhibitory mechanism of A␤42 aggregation by flavonoid is fully unknown. Results: The oxidant enhanced the inhibitory activity of (ϩ)-taxifolin against A␤42 aggregation by forming A␤42-taxifolin adducts between the Lys residues and oxidized (ϩ)-taxifolin. Conclusion:The inhibitory activity of catechol-type flavonoids requires autoxidation to form an o-quinone to react with Lys. Significance: These may help design promising inhibitors against A␤42 aggregation for Alzheimer disease therapy.

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Section: Methodsmentioning

confidence: 99%

Site-specific Inhibitory Mechanism for Amyloid β42 Aggregation by Catechol-type Flavonoids Targeting the Lys Residues

Satô

Murakami²,

Uno³

et al. 2013

Journal of Biological Chemistry

204

227

View full text Add to dashboard Cite

show abstract

“…As the number of mixture components increase, some degree of overlap of the signals even in the 2D experiments becomes inevitable, so that it is all natural to try to further improve their resolution by data processing, covariance analysis [18,19,20,21,22,23,24,25,26,27] or pure-shift spectroscopy [28,29,30,31,32,33,34,35] being notable examples.…”

Section: Introductionmentioning

confidence: 99%

A review of blind source separation in NMR spectroscopy

Toumi

Caldarelli

Torrésani

2014

Progress in Nuclear Magnetic Resonance Spectroscopy

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Fourier transform is the data processing naturally associated to most NMR experiments. Notable exceptions are Pulse Field Gradient and relaxation analysis, the structure of which is only partially suitable for FT. With the revamp of NMR of complex mixtures, fueled by analytical challenges such as metabolomics, alternative and more apt mathematical methods for data processing have been sought, with the aim of decomposing the NMR signal into simpler bits. Blind Source Separation is a very broad definition regrouping several classes of mathematical methods for complex signal decomposition that use no hypothesis on the form of the data. Developed outside NMR , these algorithms have been increasingly tested on spectra of mixtures. In this review, we shall provide an historical overview of the application of Blind Source Separation methodologies to NMR , including methods specifically designed for the specificity of this spectroscopy.

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“…While for standard Fourier transform ͑FT͒ the effect of noise on spectra is well understood, 1 more recent processing methods can have advantages, in particular, when the shortening of measurement time of multidimensional spectra is essential. [2][3][4][5][6] However, many of these methods affect the noise signature resulting in changes in both the apparent and the actual sensitivity. 4,7 Due to its linear nature, the FT method converts a free induction decay that includes additive white noise into a spectrum that is superimposed on a homogeneous noise floor.…”

Section: Introductionmentioning

confidence: 99%

“…9 When the covariance spectrum is symmetric, application of the matrix square root strongly attenuates or eliminates artifacts due to relay effects and chemical shift near degeneracy. 6 In fact, with regularization 10 applied as necessary, the covariance transform followed by a matrix square root leaves the signal-to-noise properties of a spectrum essentially unperturbed. The covariance NMR concept can be generalized to pairs of spectra, which has been referred to as "unsymmetric" covariance NMR, by multiplying the matrices belonging to two spectra along a common dimension resulting in a spectrum that is generally nonsymmetric.…”

Section: Introductionmentioning

confidence: 99%

Z -matrix formalism for quantitative noise assessment of covariance nuclear magnetic resonance spectra

Snyder

Ghosh

Zhang

et al. 2008

The Journal of Chemical Physics

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Due to the limited sensitivity of many nuclear magnetic resonance ͑NMR͒ applications, careful consideration must be given to the effect of NMR data processing on spectral noise. This work presents analytical relationships as well as simulated and experimental results characterizing the propagation of noise by unsymmetric covariance NMR processing, which concatenates two NMR spectra along a common dimension, resulting in a new spectrum showing spin correlations as cross peaks that are not directly measured in either of the two input spectra. It is shown how the unsymmetric covariance spectrum possesses an inhomogeneous noise distribution across the spectrum with the least amount of noise in regions whose rows and columns do not contain any cross or diagonal peaks and with the largest amount of noise on top of signal peaks. Therefore, methods of noise estimation commonly used in Fourier transform spectroscopy underestimate the amount of uncertainty in unsymmetric covariance spectra. Different data processing procedures, including the Z-matrix formalism, thresholding, and maxima ratio scaling, are described to assess noise contributions and to reduce noise inhomogeneity. In particular, determination of a Z score, which measures the difference in standard deviations of a statistic from its mean, for each spectral point yields a Z matrix, which indicates whether a given peak intensity above a threshold arises from the covariance of signals in the input spectra or whether it is likely to be caused by noise. Application to an unsymmetric covariance spectrum, obtained by concatenating two 2D 13 C-1 H heteronuclear, single quantum coherence ͑HSQC͒ and 13 C-1 H heteronuclear, multiple bond correlation ͑HMBC͒ spectra of a metabolite mixture along their common proton dimension, reveals that for sufficiently sensitive input spectra the reduction in sensitivity due to covariance processing is modest.

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Covariance NMR spectroscopy by singular value decomposition

Cited by 97 publications

References 12 publications

Site-specific Inhibitory Mechanism for Amyloid β42 Aggregation by Catechol-type Flavonoids Targeting the Lys Residues

Site-specific Inhibitory Mechanism for Amyloid β42 Aggregation by Catechol-type Flavonoids Targeting the Lys Residues

A review of blind source separation in NMR spectroscopy

Z -matrix formalism for quantitative noise assessment of covariance nuclear magnetic resonance spectra

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