Reference Deconvolution, Phase Correction, and Line Listing of NMR Spectra by the 1D Filter Diagonalization Method

Hu, Haitao; Van, Que N.; Mandelshtam, Vladimir A.; Shaka, A.J.

doi:10.1006/jmre.1998.1516

Cited by 67 publications

(88 citation statements)

References 21 publications

(48 reference statements)

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“…Considering that (i) sensitivity and sweep widths increase with increasing magnetic fields and that (ii) the widespread use of cryogenic probes will greatly boost the sensitivity of our spectrometers, we expect a ''change in paradigm'' in biological NMR spectroscopy with a new focus on research addressing the caveat of sampling limitation. Data processing protocols reducing the number of data points in the indirect dimensions without sacrificing spectral resolution, such as linear prediction and maximum entropy methods (24,25), nonlinear sampling (25,26), and possibly the recently introduced filter diagonalization method (27,28), appear to be of keen interest to further enhance the impact of RD NMR. N )]-strips taken from 3D H ␣/␤ C ␣/␤ (CO)NHN comprising the 1( 13 C͞ 1 H) peaks of all aromatic side chains in the polypeptide segment Ϫ5 to 58 of Z-domain, the 2D HBCB(CGCD)HD spectrum (B), and a spectral region taken from 2D 1 H-TOCSY-relayed HCH-COSY (C) are shown.…”

Section: Resultsmentioning

confidence: 99%

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

Szyperski

Yeh²,

Sukumaran³

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

167

161

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A suite of reduced-dimensionality 13 C, 15 N, 1 H-triple-resonance NMR experiments is presented for rapid and complete protein resonance assignment. Even when using short measurement times, these experiments allow one to retain the high spectral resolution required for efficient automated analysis. ''Sampling limited'' and ''sensitivity limited'' data collection regimes are defined, respectively, depending on whether the sampling of the indirect dimensions or the sensitivity of a multidimensional NMR experiments per se determines the minimally required measurement time. We show that reduced-dimensionality NMR spectroscopy is a powerful approach to avoid the ''sampling limited regime''-i.e., a standard set of ten experiments proposed here allows one to effectively adapt minimal measurement times to sensitivity requirements. This is of particular interest in view of the greatly increased sensitivity of NMR spectrometers equipped with cryogenic probes. As a step toward fully automated analysis, the program AUTOASSIGN has been extended to provide sequential backbone and 13 C ␤ resonance assignments from these reduced-dimensionality NMR data. R apid resonance assignment is a prerequisite for highthroughput (HTP) structure determination and structural genomics (1). The aims of structural genomics are to (i) explore the naturally occurring ''protein fold space '' and (ii) contribute to the characterization of function through the assignment of atomic-resolution three-dimensional (3D) structures to proteins. The ultimate goal is to provide one or more representative 3D structures for every structural domain family in nature. It is now generally acknowledged that NMR will play an important role in this endeavor (1). The resulting demand for HTP structure determination requires fast and automated NMR data collection and analysis protocols. This impetus for the development of new methods will have broad impact in the technological infrastructure for structural biology and molecular biophysics.Two key objectives for NMR data collection can be identified. Firstly, the measurement time should be minimized so as to lower the cost per structure and relax the constraint that NMR samples need to be stable over long time periods. Secondly, automated analysis requires recording of a redundant set of NMR spectra each affording good resolution, while it is also desirable to keep the total number of spectra small to reduce complications due to interspectral variations of chemical shifts (2). This second objective can be addressed by maximizing the dimensionality of the spectra. However, the joint realization of the first and second objective is impeded by the large lower bounds for measurement times of four (or higher) dimensional NMR spectra arising from the independent sampling of three (or more) indirect dimensions.We distinguish ''sampling limited'' and ''sensitivity limited'' data collection regimes, depending on whether the sampling of the indirect dimensions or the sensitivity of the multidimensional NMR experiments per se det...

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Section: Resultsmentioning

confidence: 99%

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

Szyperski

Yeh²,

Sukumaran³

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

167

161

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“…Fitting the experimental RC signal region and use of its analytical model in the RD procedure 34 leads to only minor improvements.…”

Section: Mathematical Analysis Of the Deconvoluted Datamentioning

confidence: 99%

“…We selected two methods which best fulfill our tasks and used them in combination: (i) the Bayesian method 33 (implemented in Bruker's XWINNMR program) in the 'parameter optimization' regime; (ii) the filter diagonalization method (FDM). 34 It should be noted that methods for time domain data analysis model the FID as a mixture of exponentially damped sinusoids plus white noise, i.e. Lorentzian lines in the frequency domain.…”

Section: Mathematical Analysis Of the Deconvoluted Datamentioning

confidence: 99%

Extraction of T₂ from NMR linewidths in simple spin systems by use of reference deconvolution

Mladenov

Dimitrov

2001

Magnetic Reson in Chemistry

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We investigated the possibility of obtaining transverse relaxation times through linewidth measurements in NMR spectra of simple spin systems (subsystems). The spectra are subjected to reference deconvolution and subsequently to time domain data analysis. The time for the experiment is usually short; stringent instrumental stability should be maintained during the experiment. The approach described here produces correct results if the lineshape is Lorentzian. Rough width estimation is achievable in other cases. As an additional result, detection of very small couplings is possible.

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“…1), was borrowed from Ref. (10) in which the signal was synthesized by Eq. [3] using a set of 50 triplets with both the peak widths and the splittings gradually decreasing from the right to the left, allowing examination of the breakdown of the resolution for any fixed signal size.…”

Section: D Rrtmentioning

confidence: 99%

“…A convenient choice corresponds to a direct product grid with K win ϭ K 1win ϫ K 2win in which the spacing between the grid points in an lth dimension is defined according to Eq. [10].…”

Section: Figmentioning

confidence: 99%

RRT: The Regularized Resolvent Transform for High-Resolution Spectral Estimation

Chen

Shaka

Mandelshtam

2000

Journal of Magnetic Resonance

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A new numerical expression, called the regularized resolvent transform (RRT), is presented. RRT is a direct transformation of the truncated time-domain data into a frequency-domain spectrum and is suitable for high-resolution spectral estimation of multidimensional time signals. One of its forms, under the condition that the signal consists only of a finite sum of damped sinusoids, turns out to be equivalent to the exact infinite time discrete Fourier transformation. RRT naturally emerges from the filter diagonalization method, although no diagonalization is required. In RRT the spectrum at each frequency s is expressed in terms of the resolvent R(s)؊1 of a small data matrix R(s), that is constructed from the time signal. Generally, R is singular, which requires certain regularization. In particular, the Tikhonov regu-؊1 R † with regularization parameter q, appears to be computationally both efficient and very stable. Numerical implementation of RRT is very inexpensive because even for extremely large data sets the matrices involved are small. RRT is demonstrated using model 1D and experimental 2D NMR signals.

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Reference Deconvolution, Phase Correction, and Line Listing of NMR Spectra by the 1D Filter Diagonalization Method

Cited by 67 publications

References 21 publications

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

Extraction of T₂ from NMR linewidths in simple spin systems by use of reference deconvolution

RRT: The Regularized Resolvent Transform for High-Resolution Spectral Estimation

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Reference Deconvolution, Phase Correction, and Line Listing of NMR Spectra by the 1D Filter Diagonalization Method

Cited by 67 publications

References 21 publications

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

Extraction of T2 from NMR linewidths in simple spin systems by use of reference deconvolution

RRT: The Regularized Resolvent Transform for High-Resolution Spectral Estimation

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Extraction of T₂ from NMR linewidths in simple spin systems by use of reference deconvolution