Covariance Nuclear Magnetic Resonance Research Articles

Covariance nuclear magnetic resonance methods for obtaining protein assignments and novel correlations.

Protein NMR resonance assignment can be a tedious and error prone process, and it is often a limiting factor in biomolecular NMR studies. Challenges are exacerbated in larger proteins, disordered proteins, and often alpha-helical proteins, owing to an increase in spectral complexity and frequency degeneracies. Here, several multi-dimensional spectra must be inspected and compared in an iterative manner before resonances can be assigned with confidence. Over the last two decades, covariance NMR has evolved to become applicable to protein multi-dimensional spectra. The method, previously used to generate new correlations from spectra of small organic molecules, can now be used to recast assignment procedures as mathematical operations on NMR spectra. These operations result in multidimensional correlation maps combining all information from input spectra and providing direct correlations between moieties that would otherwise be compared indirectly through reporter nuclei. Thus, resonances of sequential residues can be identified and side-chain signals can be assigned by visual inspection of 4D arrays. This review highlights advances in covariance NMR that permitted to generate reliable 4D arrays and describes how these arrays can be obtained from conventional NMR spectra.

FoodPro: A Web-Based Tool for Evaluating Covariance and Correlation NMR Spectra Associated with Food Processes.

Foods from agriculture and fishery products are processed using various technologies. Molecular mixture analysis during food processing has the potential to help us understand the molecular mechanisms involved, thus enabling better cooking of the analyzed foods. To date, there has been no web-based tool focusing on accumulating Nuclear Magnetic Resonance (NMR) spectra from various types of food processing. Therefore, we have developed a novel web-based tool, FoodPro, that includes a food NMR spectrum database and computes covariance and correlation spectra to tasting and hardness. As a result, FoodPro has accumulated 236 aqueous (extracted in D2O) and 131 hydrophobic (extracted in CDCl3) experimental bench-top 60-MHz NMR spectra, 1753 tastings scored by volunteers, and 139 hardness measurements recorded by a penetrometer, all placed into a core database. The database content was roughly classified into fish and vegetable groups from the viewpoint of different spectrum patterns. FoodPro can query a user food NMR spectrum, search similar NMR spectra with a specified similarity threshold, and then compute estimated tasting and hardness, covariance, and correlation spectra to tasting and hardness. Querying fish spectra exemplified specific covariance spectra to tasting and hardness, giving positive covariance for tasting at 1.31 ppm for lactate and 3.47 ppm for glucose and a positive covariance for hardness at 3.26 ppm for trimethylamine N-oxide.

NMR Backbone Assignment of Large Proteins by Using 13Cα‐Only Triple‐Resonance Experiments

Nuclear magnetic resonance (NMR) is a powerful tool to interrogate protein structure and dynamics residue by residue. However, the prerequisite chemical-shift assignment remains a bottleneck for large proteins due to the fast relaxation and the frequency degeneracy of the (13) Cα nuclei. Herein, we present a covariance NMR strategy to assign the backbone chemical shifts by using only HN(CO)CA and HNCA spectra that has a high sensitivity even for large proteins. By using the peak linear correlation coefficient (LCC), which is a sensitive probe even for tiny chemical-shift displacements, we correctly identify the fidelity of approximately 92 % cross-peaks in the covariance spectrum, which is thus a significant improvement on the approach developed by Snyder and Brüschweiler (66 %) and the use of spectral derivatives (50 %). Thus, we calculate the 4D covariance spectrum from HN(CO)CA and HNCA experiments, in which cross-peaks with LCCs above a universal threshold are considered as true correlations. This 4D covariance spectrum enables the sequential assignment of a 42 kDa maltose binding protein (MBP), in which about 95 % residues are successfully assigned with a high accuracy of 98 %. Our LCC approach, therefore, paves the way for a residue-by-residue study of the backbone structure and dynamics of large proteins.

Facilitated assignment of large protein NMR signals with covariance sequential spectra using spectral derivatives.

Nuclear magnetic resonance (NMR) studies of larger proteins are hampered by difficulties in assigning NMR resonances. Human intervention is typically required to identify NMR signals in 3D spectra, and subsequent procedures depend on the accuracy of this so-called peak picking. We present a method that provides sequential connectivities through correlation maps constructed with covariance NMR, bypassing the need for preliminary peak picking. We introduce two novel techniques to minimize false correlations and merge the information from all original 3D spectra. First, we take spectral derivatives prior to performing covariance to emphasize coincident peak maxima. Second, we multiply covariance maps calculated with different 3D spectra to destroy erroneous sequential correlations. The maps are easy to use and can readily be generated from conventional triple-resonance experiments. Advantages of the method are demonstrated on a 37 kDa nonribosomal peptide synthetase domain subject to spectral overlap.

ChemInform Abstract: Multidimensional Correlation Spectroscopy by Covariance NMR

AbstractReview: 48 refs.

The use of 1H–31P GHMBC and covariance NMR to unambiguously determine phosphate ester linkages in complex polysaccharide mixtures

Poly- and oligo-saccharides are commonly employed as antigens in many vaccines. These antigens contain phosphoester structural elements that are crucial to the antigenicity, and hence the effectiveness of the vaccine. Nuclear Magnetic Resonance (NMR) is a powerful tool for the site-specific identification of phosphoesters in saccharides. We describe here two advances in the characterization of phosphoesters in saccharides: (1) the use of (1)H-(31)P GHMBC to determine the site-specific identity of phosphoester moieties in heterogeneous mixtures and (2) the use of Unsymmetrical/Generalized Indirect Covariance (U/GIC) to calculate a carbon-phosphorus 2D spectrum. The sensitivity of the (1)H-(31)P GHMBC is far greater than the "standard" (1)H-(31)P GHSQC and allows long-range (3-5)J(HP) couplings to be readily detected. This is the first example to be reported of using U/GIC to calculate a carbon-phosphorus spectrum. The U/GIC processing affords, in many cases, a fivefold to tenfold or greater increase in signal-to-noise ratios in the calculated spectrum. When coupled with the high sensitivity of (1)H-(31)P HMBC, U/GIC processing allows the complete and unambiguous assignments of phosphoester moieties present in heterogeneous samples at levels of ~5% (or less) of the total sample, expanding the breadth of samples that NMR can be used to analyze. This new analytical technique is generally applicable to any NMR-observable phosphoester.

Web server suite for complex mixture analysis by covariance NMR

Elucidation of the chemical composition of biological samples is a main focus of systems biology and metabolomics. Their comprehensive study requires reliable, efficient, and automatable methods to identify and quantify the underlying metabolites. Because nuclear magnetic resonance (NMR) spectroscopy is a rich source of molecular information, it has a unique potential for this task. Here we present a suite of public web servers (http://spinportal.magnet.fsu.edu), termed COLMAR, which facilitates complex mixture analysis by NMR. The COLMAR web portal presently consists of three servers: COLMAR covariance calculates the covariance NMR spectrum from an NMR input dataset, such as a TOCSY spectrum; COLMAR DemixC method decomposes the 2D covariance TOCSY spectrum into a reduced set of nonredundant 1D cross sections or traces, which belong to individual mixture components; and COLMAR query screens the traces against a NMR spectral database to identify individual compounds. Examples are presented that illustrate the utility of this web server suite for complex mixture analysis.

Generalized indirect covariance NMR formalism for establishment of multidimensional spin correlations.

Multidimensional nuclear magnetic resonance (NMR) experiments measure spin-spin correlations, which provide important information about bond connectivities and molecular structure. However, direct observation of certain kinds of correlations can be very time-consuming due to limitations in sensitivity and resolution. Covariance NMR derives correlations between spins via the calculation of a (symmetric) covariance matrix, from which a matrix-square root produces a spectrum with enhanced resolution. Recently, the covariance concept has been adopted to the reconstruction of nonsymmetric spectra from pairs of 2D spectra that have a frequency dimension in common. Since the unsymmetric covariance NMR procedure lacks the matrix-square root step, it does not suppress relay effects and thereby may generate false positive signals due to chemical shift degeneracy. A generalized covariance formalism is presented here that embeds unsymmetric covariance processing within the context of the regular covariance transform. It permits the construction of unsymmetric covariance NMR spectra subjected to arbitrary matrix functions, such as the square root, with improved spectral properties. This formalism extends the domain of covariance NMR to include the reconstruction of nonsymmetric NMR spectra at resolutions or sensitivities that are superior to the ones achievable by direct measurements.

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

Due to the limited sensitivity of many nuclear magnetic resonance (NMR) 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 (HSQC) and (13)C-(1)H heteronuclear, multiple bond correlation (HMBC) 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.

Homonuclear long‐range correlation spectra from HMBC experiments by covariance processing

We present a new application of covariance nuclear magnetic resonance processing based on 1H--13C-HMBC experiments which provides an effective way for establishing indirect 1H--1H and 13C--13C nuclear spin connectivity at natural isotope abundance. The method, which identifies correlated spin networks in terms of covariance between one-dimensional traces from a single decoupled HMBC experiment, derives 13C--13C as well as 1H--1H spin connectivity maps from the two-dimensional frequency domain heteronuclear long-range correlation data matrix. The potential and limitations of this novel covariance NMR application are demonstrated on two compounds: eugenyl-beta-D-glucopyranoside and an emodin-derivative.

Covariance nuclear magnetic resonance spectroscopy.

Covariance nuclear magnetic resonance (NMR) spectroscopy is introduced, which is a new scheme for establishing nuclear spin correlations from NMR experiments. In this method correlated spin dynamics is directly displayed in terms of a covariance matrix of a series of one-dimensional (1D) spectra. In contrast to two-dimensional (2D) Fourier transform NMR, in a covariance spectrum the spectral resolution along the indirect dimension is determined by the favorable spectral resolution obtainable along the detection dimension, thereby reducing the time-consuming sampling requirement along the indirect dimension. The covariance method neither involves a second Fourier transformation nor does it require separate phase correction or apodization along the indirect dimension. The new scheme is demonstrated for cross-relaxation (NOESY) and J-coupling based magnetization transfer (TOCSY) experiments.