A general algorithm for peak-tracking in multi-dimensional NMR experiments

We present an algorithmic method allowing automatic tracking of NMR peaks in a series of spectra. It consists in a two phase analysis. The first phase is a local modeling of the peak displacement between two consecutive experiments using distance matrices. Then, from the coefficients of these matrices, a value graph containing the a priori set of possible paths used by these peaks is generated. On this set, the minimization under constraint of the target function by a heuristic approach provides a solution to the peak-tracking problem. This approach has been named GAPT, standing for General Algorithm for NMR Peak Tracking. It has been validated in numerous simulations resembling those encountered in NMR spectroscopy. We show the robustness and limits of the method for situations with many peak-picking errors, and presenting a high local density of peaks. It is then applied to the case of a temperature study of the NMR spectrum of the Lipid Transfer Protein (LTP).     

StePSIM – a method for stepwise peak selection and identification of metabolites in 1 H NMR spectra

A method for stepwise selection of peaks in NMR spectra from multiple groups is described. This method is based on initial peak-finding among the spectra and uses jacknife classification performance as the basis for selection of peaks. The selection process is followed by the construction of correlation maps to identify sets of multiplets that are related to each of the selected peaks, aiding in the identification of metabolites that are responsible for differences among the groups. For illustrative purposes, this methodology is applied to a data set that contains 52 spectra from renal cell carcinoma and normal renal tissue samples. The new method is denoted as StePSIM, Stepwise Peak Selection and Identification of Metabolites. Research partially supported by NCI 1 R21 CA89671-01A1 and NIH NCRR 02584     

Line-shape analysis of NMR difference spectra of an anti-spin-label antibody

Specifically deuteriated Fab fragments of the anti-spin-label antibody AN02 were prepared. NMR difference spectra were obtained, in which the spectrum of Fab with some fraction of the binding sites occupied with spin-label hapten was subtracted from the spectrum of Fab with no spin-label. The peak heights were analyzed as a function of the fractional occupation of the binding site, using a computer program that calculates a best fit to the observed spectra. This method treats all of the peaks in the spectra simultaneously. Analyzing all peaks at once allows for the interdependencies in the spectra arising from overlap of positive and negative signals from different peaks. The fitting program calculates line widths for the peaks arising from protons in the binding site region. Almost all of the line widths calculated for the spectrum of the Fab complex with diamagnetic hapten dinitrophenyldiglycine were found to be narrower than the line widths of the corresponding resonances in the spectrum of Fab with an empty binding site. The distances of the binding site region protons from the unpaired electron of the hapten were also obtained from this calculation. Two tyrosine protons were found to be close (less than A) to this electron. These line-width and distance results are discussed with respect to the structure and dynamics of the antibody binding site.     

AURELIA,a program for computer-aided analysis of multidimensional NMR spectra

Summary AURELIA is an advanced program for the computer-aided evaluation of two-, three- and four-dimensional NMR spectra of any type of molecule. It can be used for the analysis of spectra of small molecules as well as for evaluation of complicated spectra of biological macromolecules such as proteins. AURELIA is highly interactive and offers a large number of tools, such as artefact reduction, cluster and multiplet analysis, spin system searches, resonance assignments, automated calculation of volumes in multidimensional spectra, calculation of distances with different approaches, including the full relaxation matrix approach, Bayesian analysis of peak features, correlation of molecular structures with NMR data, comparison of spectra via spectral algebra and pattern match techniques, automated sequential assignments on the basis of triple resonance spectra, and automatic strip calculation. In contrast to most other programs, many tasks are performed automatically.     

NMR spectra of some cyanosteroids

The MMR Spectra of thirty-four cyanosteroids and of thirty-four model steroids are discussed. The chemical shifts of the protons at C-18, C-19 and C-21 are used to study the cyanosteroid structures and the specific corrections Δδ^∗ are calculated.     

NMR spectra of fluorinated carbohydrates

Recent advances in structural and conformational analysis of fluorinated carbohydrates by NMR spectroscopy are reviewed. Characteristic 1H, 13C, and 19F NMR chemical shifts and coupling constants for selected examples are given and the spectral data of a series of fluorinated carbohydrates were collected in continuation of the review of Csuk and Gl?nzer [Adv. Carbohydr. Chem. Biochem., 46 (1988) 73-177].     

Automation of NMR structure determination of proteins

The automation of protein structure determination using NMR is coming of age. The tedious processes of resonance assignment, followed by assignment of NOE (nuclear Overhauser enhancement) interactions (now intertwined with structure calculation), assembly of input files for structure calculation, intermediate analyses of incorrect assignments and bad input data, and finally structure validation are all being automated with sophisticated software tools. The robustness of the different approaches continues to deal with problems of completeness and uniqueness; nevertheless, the future is very bright for automation of NMR structure generation to approach the levels found in X-ray crystallography. Currently, near completely automated structure determination is possible for small proteins, and the prospect for medium-sized and large proteins is good.     

Characterization of bright tobaccos by multivariate analysis of C CPMAS NMR spectra

Univariate and multivariate statistics were applied to characterize cured bright tobacco samples on the basis of their ¹³C CPMAS NMR spectra and leaf constituent analysis. NMR spectra were obtained for 55 samples selected from a set of 134 samples of graded bright tobacco leaves from crop year 1999. Historical leaf constituent analyses were available for total alkaloids, reducing sugars, total nitrogen, and insoluble ash. In addition, we applied HPLC to quantify the two abundant plant polyphenols, chlorogenic acid, and rutin. Principal component analysis (PCA) and partial least squares (PLS) of the NMR spectra revealed systematic relationships between groups of samples related to these substances and afforded predictive quantitative models for the analyzed constituents. Analysis of the PLS significant variables showed that leaf polysaccharides, alkaloids, and minerals are major determinants influencing the grading of cured bright tobacco leaves.     

Constant-time NOESY: An aid in the analysis of protein NMR spectra

Summary A constant-time version of the homonuclear NOESY experiment (CT-NOESY) is described. The experiment yields simplified protein spectra, in which cross peaks arising from protons with zero or small couplings are differentiated from other cross peaks, thus partially overcoming the problem of signal overlap. In addition, the CT-NOESY spectrum provides information on the magnitude of³J_NH- and³J coupling' constants, and is thus useful to determine torsion angle constraints and to perform stereospecific assignments of CHH protons in the case of³J constants.     

Efficient analysis of protein 2D NMR spectra using the software packageEASY

Summary The programEASY supports the spectral analysis of biomacromolecular two-dimensional (2D) nuclear magnetic resonance (NMR) data. It provides a user-friendly, window-based environment in which to view spectra for interactive interpretation. In addition, it includes a number of automated routines for peakpicking, spin-system identification, sequential resonance assignment in polypeptide chains, and cross peak integration. In this uniform environment, all resulting parameter lists can be recorded on disk, so that the paper plots and handwritten notes which normally accompany manual assignment of spectra can be largely eliminated. For example, in a protein structure determination by 2D¹H NMR,EASY accepts the frequency domain datasets as input, and after combined use of the automated and interactive routines it can yield a listing of conformational constraints in the format required as input for the calculation of the 3D structure. The program was extensively tested with current protein structure determinations in our laboratory. In this paper, its main features are illustrated with data on the protein basic pancreatic trypsin inhibitor.     

Protein–RNA specificity by high-throughput principal component analysis of NMR spectra

Defining the RNA target selectivity of the proteins regulating mRNA metabolism is a key issue in RNA biology. Here we present a novel use of principal component analysis (PCA) to extract the RNA sequence preference of RNA binding proteins. We show that PCA can be used to compare the changes in the nuclear magnetic resonance (NMR) spectrum of a protein upon binding a set of quasi-degenerate RNAs and define the nucleobase specificity. We couple this application of PCA to an automated NMR spectra recording and processing protocol and obtain an unbiased and high-throughput NMR method for the analysis of nucleobase preference in protein–RNA interactions. We test the method on the RNA binding domains of three important regulators of RNA metabolism.     

Automated evaluation of chemical shift perturbation spectra: New approaches to quantitative analysis of receptor-ligand interaction NMR spectra

This paper presents new methods designed for quantitative analysis of chemical shift perturbation NMR spectra. The methods automatically trace the displacements of cross peaks between a perturbed test spectrum and the reference spectrum (or among a series of titration spectra), and measure the changes of chemical shifts, heights, and widths of the altered peaks. The methods are primary aimed at the (1)H-(15)N HSQC spectra of relatively small proteins (<15 kDa) assuming fast exchange between free and ligand-bound states on the chemical shift time scale, or for comparing spectra of free and fully bound states in the slow exchange situation. Using the (1)H-(15)N HSQC spectra from a titration experiment of the 74-residue Pex13p SH3 domain with a Pex14p peptide ligand (14 residues, K (d)= approximately 40 microM), we demonstrate the scope and limits of our automatic peak tracing (APET) algorithm for efficient scoring of high-throughput SAR by NMR type HSQC spectra, and progressive peak tracing (PROPET) algorithm for detailed analysis of ligand titration spectra. Simulated spectra with low signal-to-noise ratios (S/N ranged from 20 to 1) were used to demonstrate the reliability and reproducibility of the results when dealing with poor quality spectra. These algorithms have been implemented in a new software module, FELIX-Autoscreen, for streamlined processing, analysis and visualization of SAR by NMR and other high-throughput receptor/ligand interaction experiments.     

Glycosaminoglycan origin and structure revealed by multivariate analysis of NMR and CD spectra

Principal component analysis (PCA) is a method of simplifying complex datasets to generate a lower number of parameters, while retaining the essential differences and allowing objective comparison of large numbers of datasets. Glycosaminoglycans (GAGs) are a class of linear sulfated carbohydrates with diverse sequences and consequent complex conformation and structure. Here, PCA is applied to three problems in GAG research: (i) distinguishing origins of heparin preparations, (ii) structural analysis of heparin derivatives, and (iii) classification of chondroitin sulfates (CS). The results revealed the following. (i) PCA of heparin (13)C NMR spectra allowed their origins to be distinguished and structural differences were identified. (ii) Analysis of the information-rich (1)H and (13)C NMR spectra of a series of systematically modified heparin derivatives uncovered underlying properties. These included the presence of interactions between residues, providing evidence that a degree of degeneracy exists in linkage geometry and that a different degree of variability exists for the two types of glycosidic linkage. The relative sensitivity of each position (C or H nucleus) in the disaccharide repeating unit to changes in O-, N-sulfation and N-acetylation was also revealed. (iii) Analysis of the (1)H NMR and CD spectra of a series of CS samples from different origins allowed their structural classification and highlighted the power of employing complementary spectroscopic methods in concert with PCA.     

Automated structure determination from NMR spectra

Automated methods for protein structure determination by NMR have increasingly gained acceptance and are now widely used for the automated assignment of distance restraints and the calculation of three-dimensional structures. This review gives an overview of the techniques for automated protein structure analysis by NMR, including both NOE-based approaches and methods relying on other experimental data such as residual dipolar couplings and chemical shifts, and presents the FLYA algorithm for the fully automated NMR structure determination of proteins that is suitable to substitute all manual spectra analysis and thus overcomes a major efficiency limitation of the NMR method for protein structure determination.     

The 220 MHz NMR spectra of phytosterols

The 220MHz NMR spectra of forty two steroids are reported. Eight pairs of C-24 epimers (24α- and 24β) and two pairs of double bond isomers (cis and trans) can be distinguished by this technique. The influence of substituents, solvents and stereochemistry on methyl group chemical shifts is discussed.     

Maximal clique method for the automated analysis of NMR TOCSY spectra of complex mixtures

Characterization of the chemical components of complex mixtures in solution is important in many areas of biochemistry and chemical biology, including metabolomics. The use of 2D NMR total correlation spectroscopy (TOCSY) experiments has proven very useful for the identification of known metabolites as well as for the characterization of metabolites that are unknown by taking advantage of the good resolution and high sensitivity of this homonuclear experiment. Due to the complexity of the resulting spectra, automation is critical to facilitate and speed-up their analysis and enable high-throughput applications. To better meet these emerging needs, an automated spin-system identification algorithm of TOCSY spectra is introduced that represents the cross-peaks and their connectivities as a mathematical graph, for which all subgraphs are determined that are maximal cliques. Each maximal clique can be assigned to an individual spin system thereby providing a robust deconvolution of the original spectrum for the easy extraction of critical spin system information. The approach is demonstrated for a complex metabolite mixture consisting of 20 compounds and for E. coli cell lysate.     

Prediction of peak overlap in NMR spectra

Peak overlap is one of the major factors complicating the analysis of biomolecular NMR spectra. We present a general method for predicting the extent of peak overlap in multidimensional NMR spectra and its validation using both, experimental data sets and Monte Carlo simulation. The method is based on knowledge of the magnetization transfer pathways of the NMR experiments and chemical shift statistics from the Biological Magnetic Resonance Data Bank. Assuming a normal distribution with characteristic mean value and standard deviation for the chemical shift of each observable atom, an analytic expression was derived for the expected overlap probability of the cross peaks. The analytical approach was verified to agree with the average peak overlap in a large number of individual peak lists simulated using the same chemical shift statistics. The method was applied to eight proteins, including an intrinsically disordered one, for which the prediction results could be compared with the actual overlap based on the experimentally measured chemical shifts. The extent of overlap predicted using only statistical chemical shift information was in good agreement with the overlap that was observed when the measured shifts were used in the virtual spectrum, except for the intrinsically disordered protein. Since the spectral complexity of a protein NMR spectrum is a crucial factor for protein structure determination, analytical overlap prediction can be used to identify potentially difficult proteins before conducting NMR experiments. Overlap predictions can be tailored to particular classes of proteins by preparing statistics from corresponding protein databases. The method is also suitable for optimizing recording parameters and labeling schemes for NMR experiments and improving the reliability of automated spectra analysis and protein structure determination.     

NMR spectra of C-24 isomeric sterols

Cholesterol and four pairs of C-24 isomeric sterols, campesterol-22,23-dihydrobrassicasterol, α-spinasterol-chondrillasterol, stigmasterol-poriferasterol, and sitosterol-22,23-dihydroporiferasterol were studied by NMR spectroscopy and their spectra are presented. The NMR spectra of three of the pairs of isomeric sterols recorded at 100 MHz could be differentiated from each other, although at 60 MHz only the spectra of campesterol (24α-methylcholesterol) and 22,23-dihydrobrassicasterol (24β-methylcholesterol) showed differences. Sitosterol and 22,23-dihydroporiferasterol, the pair of sterols that showed no differences in their NMR spectra are readily differentiated by the physical properties of their acetates. The practical application of NMR spectroscopy to several problems concerning the C-24 isomeric sterols is demonstrated.     

Tellurium-125 NMR and mass spectra of dithiotellurides

The preparation, ¹²⁵Te NMR and mass spectra of some dithiotellurides, Te(RS)₂ (R = Ph, 2-PhCOOH, CPh₃, CH₂Ph, 3-PrCOOH, n-Bu, i-Pr, t-Bu) are discussed. The ¹²⁵Te chemical shifts have been found to lie within a range spanning ca. 690 ppm and correlate with the pK_a values of the parent thiols. The mass spectra of the alkyl derivatives (R = n-Bu, i-Pr, t-Bu) indicate an initial step-wise loss of alkenyl groups followed by the elimination of hydrogen sulfide from the resultant bis(hydrosulfido)-tellurium ion.