MUNIN: A new approach to multi-dimensional NMR spectra interpretation

A new method, MUNIN (Multi-dimensional NMR spectra interpretation), is introduced for the automated interpretation of three-dimensional NMR spectra. It is based on a mathematical concept referred to as three-way decomposition. An NMR spectrum is decomposed into a sum of components, with each component corresponding to one or a group of peaks. Each component is defined as the direct product of three one-dimensional shapes. A consequence is reduction in dimensionality of the spectral data used in further analysis. The decomposition may be applied to frequency-domain or time-domain data, or to a mixture of these. Features of MUNIN include good resolution in crowded regions and the absence of assumptions about line shapes. Uniform sampling of time-domain data, a prerequisite for discrete Fourier transform, is not required. This opens an avenue for the processing of NMR data that do not follow oscillating behaviour, e.g. from relaxation measurements. The application of MUNIN is illustrated for a ¹H-¹⁵N-NOESY-HSQC, where each component is defined as the set of all NOE peaks formed by a given amide group. As a result, the extraction of structural information simply consists of one-dimensional peak picking of the shape along the NOE-axis obtained for each amide group.     

VirtualSpectrum,a tool for simulating peak list for multi-dimensional NMR spectra

NMR spectroscopy is a widely used technique for characterizing the structure and dynamics of macromolecules. Often large amounts of NMR data are required to characterize the structure of proteins. To save valuable time and resources on data acquisition, simulated data is useful in the developmental phase, for data analysis, and for comparison with experimental data. However, existing tools for this purpose can be difficult to use, are sometimes specialized for certain types of molecules or spectra, or produce too idealized data. Here we present a fast, flexible and robust tool, VirtualSpectrum, for generating peak lists for most multi-dimensional NMR experiments for both liquid and solid state NMR. It is possible to tune the quality of the generated peak lists to include sources of artifacts from peak overlap, noise and missing signals. VirtualSpectrum uses an analytic expression to represent the spectrum and derive the peak positions, seamlessly handling overlap between signals. We demonstrate our tool by comparing simulated and experimental spectra for different multi-dimensional NMR spectra and analyzing systematically three cases where overlap between peaks is particularly relevant; solid state NMR data, liquid state NMR homonuclear ¹H and ¹⁵N-edited spectra, and 2D/3D heteronuclear correlation spectra of unstructured proteins. We analyze the impact of protein size and secondary structure on peak overlap and on the accuracy of structure determination based on data of different qualities simulated by VirtualSpectrum.     

Fast acquisition of NMR spectra using Fourier transform of non-equispaced data

Rapid acquisition of high-resolution 2D and 3D NMR spectra is essential for studying biological macromolecules. In order to minimize the experimental time, a non-linear sampling scheme is proposed for the indirect dimensions of multidimensional experiments. These data can be processed using the algorithm proposed by Dutt and Rokhlin (Appl. Comp. Harm. Anal. 1995, 2, 85–100) for fast Fourier transforms of non equispaced data. Examples of ¹H−¹⁵N HSQC spectra are shown, where crowded correlation peaks can be resolved using non-linear acquisition. Simulated data have been used to analyze the artefacts produced by the Lagrange interpolation. As compared to non-linear processing methods, this algorithm is simple and highly robust since no parameters need to be adjusted by the user.     

Fast multi-dimensional NMR of proteins

Three-dimensional HNCO and HNCA subspectra from a small protein (agitoxin, 4 kDa, enriched in carbon-13 and nitrogen-15), have been obtained by direct frequency-domain excitation of selected carbon and nitrogen sites. This new technique applies an array of several simultaneous soft radiofrequency spin-inversion pulses, encoded (on or off) according to nested Hadamard matrices, and the resulting responses are extracted by reference to the same matrices. This not only simplifies the spectra but, by avoiding extensive sampling in the traditional evolution dimensions, affords a speed advantage of more than two orders of magnitude.     

Parallel acquisition of 3D-HA(CA)NH and 3D-HACACO spectra

We present here an NMR pulse sequence with 5 independent incrementable time delays within the frame of a 3-dimensional experiment, by incorporating polarization sharing and dual receiver concepts. This has been applied to directly record 3D-HA(CA)NH and 3D-HACACO spectra of proteins simultaneously using parallel detection of ¹H and ¹³C nuclei. While both the experiments display intra-residue backbone correlations, the 3D-HA(CA)NH provides also sequential ‘i ? 1 → i’ correlation along the ¹Hα dimension. Both the spectra contain special peak patterns at glycine locations which serve as check points during the sequential assignment process. The 3D-HACACO spectrum contains, in addition, information on prolines and side chains of residues having H–C–CO network (i.e., ¹Hβ, ¹³Cβ and ¹³COγ of Asp and Asn, and ¹Hγ, ¹³Cγ and ¹³COδ of Glu and Gln), which are generally absent in most conventional proton detected experiments.     

Rapid prediction of multi-dimensional NMR data sets

We present a computational environment for Fast Analysis of multidimensional NMR DAta Sets (FANDAS) that allows assembling multidimensional data sets from a variety of input parameters and facilitates comparing and modifying such ??in silico?? data sets during the various stages of the NMR data analysis. The input parameters can vary from (partial) NMR assignments directly obtained from experiments to values retrieved from in silico prediction programs. The resulting predicted data sets enable a rapid evaluation of sample labeling in light of spectral resolution and structural content, using standard NMR software such as Sparky. In addition, direct comparison to experimental data sets can be used to validate NMR assignments, distinguish different molecular components, refine structural models or other parameters derived from NMR data. The method is demonstrated in the context of solid-state NMR data obtained for the cyclic nucleotide binding domain of a bacterial cyclic nucleotide-gated channel and on membrane-embedded sensory rhodopsin II. FANDAS is freely available as web portal under WeNMR (http://www.wenmr.eu/services/FANDAS).     

Molecular interactions investigated by multi-dimensional solid-state NMR

Solid-state NMR (ssNMR) offers insight into the formation of protein complexes and ligand binding for a large range of molecular sizes and binding affinities. Recent instrumental and methodological progress has enabled novel possibilities for using multi-dimensional ssNMR to study molecular 3D structures and interactions in noncrystalline systems. Two-dimensional ssNMR correlation experiments were applied to study ligand binding in globular and membrane proteins and have enabled the investigation of molecular interfaces in the context of protein folding and aggregation. In lipid bilayers, a versatile set of ssNMR experiments has been developed to study molecular structure, topology and complex formation in a functional environment.     

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.     

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).     

DOTA-amide lanthanide tag for reliable generation of pseudocontact shifts in protein NMR spectra

Structural studies of proteins and protein-ligand complexes by nuclear magnetic resonance (NMR) spectroscopy can be greatly enhanced by site-specific attachment of lanthanide ions to create paramagnetic centers. In particular, pseudocontact shifts (PCS) generated by paramagnetic lanthanides contain important and unique long-range structure information. Here, we present a high-affinity lanthanide binding tag that can be attached to single cysteine residues of proteins. The new tag has many advantageous features that are not available in this combination from previously published tags: (i) it binds lanthanide ions very tightly, minimizing the generation of nonspecific effects, (ii) it produces PCSs with high reliability as its bulkiness prevents complete motional averaging of PCSs, (iii) it can be attached to single cysteine residues, alleviating the need of detailed prior knowledge of the 3D structure of the target protein, and (iv) it does not display conformational exchange phenomena that would increase the number of signals in the NMR spectrum. The performance of the tag is demonstrated with the N-terminal domain of the E. coli arginine repressor and the A28C mutant of human ubiquitin.     

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.     

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].     

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.     

Processing of multi-dimensional NMR data with the new software PROSA

Summary The new program PROSA is an efficient implementation of the common data-processing steps for multi-dimensional NMR spectra. PROSA performs linear prediction, digital filtering, Fourier transformation, automatic phase correction, and baseline correction. High efficiency is achieved by avoiding disk storage of intermediate data and by the absence of any graphics display, which enables calculation in the batch mode and facilitates porting PROSA on a variety of different computer systems; including supercomputers. Furthermore, all time-consuming routines are completely vectorized. The elimination of a graphics display was made possible by the use of a new, reliable automatic phase-correction routine. CPU times for complete processing of a typical heteronuclear three-dimensional NMR data set of a protein vary between less than 1 min on a NEC SX3 supercomputer and 40 min on a Sun-4 computer system.     

Automatic assignment of protein backbone resonances by direct spectrum inspection in targeted acquisition of NMR data

The necessity to acquire large multidimensional datasets, a basis for assignment of NMR resonances, results in long data acquisition times during which substantial degradation of a protein sample might occur. Here we propose a method applicable for such a protein for automatic assignment of backbone resonances by direct inspection of multidimensional NMR spectra. In order to establish an optimal balance between completeness of resonance assignment and losses of cross-peaks due to dynamic processes/degradation of protein, assignment of backbone resonances is set as a stirring criterion for dynamically controlled targeted nonlinear NMR data acquisition. The result is demonstrated with the 12 kDa ¹³C,¹⁵ N-labeled apo-form of heme chaperone protein CcmE, where hydrolytic cleavage of 29 C-terminal amino acids is detected. For this protein, 90 and 98% of manually assignable resonances are automatically assigned within 10 and 40 h of nonlinear sampling of five 3D NMR spectra, respectively, instead of 600 h needed to complete the full time domain grid. In addition, resonances stemming from degradation products are identified. This study indicates that automatic resonance assignment might serve as a guiding criterion for optimal run-time allocation of NMR resources in applications to proteins prone to degradation. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.