PR-CALC: a program for the reconstruction of NMR spectra from projections

Ultrafast Four-Dimensional Coherent Spectroscopy by Projection Reconstruction

Multidimensional coherent spectroscopy provides insights into the vibronic structure and dynamics of complex systems. In general, the higher the dimensionality, the better the spectral discrimination and the more information that may be extracted about the system. A major impediment to widespread implementation of these methods, however, is that the acquisition time generally increases exponentially with dimensionality, prohibiting practical implementation. We demonstrate the use of nonuniform sampling based on the projection-slice theorem and inverse Radon transform within the context of a fifth-order, 4D technique (GAMERS) designed to correlate the vibrational contributions to different electronic states. Projection-reconstruction (PRO GAMERS) greatly reduces the data sampling requirements without sacrificing frequency resolution. The sensitivity of this technique is demonstrated to surpass conventional uniform sampling by orders of magnitude. The incorporation of projection-reconstruction into multidimensional coherent spectroscopy opens up the possibility to study the structure of complex chemical, biological, and physical systems with unprecedented detail.

Automated projection spectroscopy and its applications

This chapter presents the NMR technique APSY (automated projection spectroscopy) and its applications for sequence-specific resonance assignments of proteins. The result of an APSY experiment is a list of chemical shift correlations for an N-dimensional NMR spectrum (N≥3). This list is obtained in a fully automated way by the dedicated algorithm GAPRO (geometric analysis of projections) from a geometric analysis of experimentally recorded, low-dimensional projections. Because the positions of corresponding peaks in multiple projections are correlated, thermal noise and other uncorrelated artifacts are efficiently suppressed. We describe the theoretical background of the APSY method and discuss technical aspects that guide its optimal use. Further, applications of APSY-NMR spectroscopy for fully automated sequence-specific backbone and side chain assignments of proteins are described. We discuss the choice of suitable experiments for this purpose and show several examples. APSY is of particular interest for the assignment of soluble unfolded proteins, which is a time-consuming task by conventional means. With this class of proteins, APSY-NMR experiments with up to seven dimensions have been recorded. Sequence-specific assignments of protein side chains in turn are obtained from a 5D TOCSY-APSY-NMR experiment.

A practical implementation of cross-spectrum in protein backbone resonance assignment

The concept of cross-spectrum is applied in protein NMR spectroscopy to assist in the backbone sequential resonance assignment. Cross-spectrum analysis is used routinely to reveal correlations in frequency domains as a means to reveal common features contained in multiple time series. Here the cross-spectrum between related NMR spectra, for example HNCO and HN(CA)CO, can be calculated with point-by-point multiplications along their common C' carbon axis. In the resulting higher order cross-spectrum, an enhanced correlation signal occurs at every common i-1 carbon frequency allowing the amide proton H(N) (and nitrogen N) resonances from residues i and i-1 to be identified. The cross-spectrum approach is demonstrated using 2D spectra H(N)CO, H(NCA)CO, H(NCO)CACB, and H(N)CACB measured on a 15N/13C double-labeled Ubiquitin sample. These 2D spectra are used to calculate two pseudo-3D cross-spectra, H(i)-H(i)(-1)-C'(i)(-1) and H(i)-H(i)(-1)-CA(i)(-1)CB(i)(-1). We show using this approach, backbone resonances of H, C', CA, and CB can be fully assigned without ambiguity. The cross-spectrum principle is expected to offer an easy, practical, and more quantitative approach for heteronuclear backbone resonance assignment.

A set of 4D NMR experiments of enhanced resolution for easy resonance assignment in proteins

This paper presents examples of techniques based on the principle of random sampling that allows acquisition of NMR spectra featuring extraordinary resolution. This is due to increased dimensionality and maximum evolution time reached. The acquired spectra of CsPin protein and maltose binding protein were analyzed statistically with the aim to evaluate each technique. The results presented include exemplary spectral cross-sections. The spectral data provided by the proposed techniques allow easy assignment of backbone and side-chain resonances.

Analytical solution to the coupled evolution of multidimensional NMR data

A substantial time savings in the collection of multidimensional NMR data can be achieved by coupling the evolution of nuclei in the indirect dimensions. In order to save time, the sampling of the indirect dimensions is inherently incomplete. Therefore, many algorithms and samplings schemes have been developed aimed at separating the coevolved frequencies into analyzable data with limited artifacts. This paper extends the use of circulant matrices to describe coupled evolution with convolutions. By understanding the data in terms of convolutions, there is an exact solution to the inversion problem of extracting the orthogonal vectors from the coupled dimensions. Previously, this inversion problem has been solved using peak coordinates extracted from spectra. In contrast, the method described here uses spectra directly. This solution suggests a simple sampling scheme of collecting N orthogonal spectra, and N + 1 projections at specific projection angles, however, the theory developed can be extended generally to arbitrary projection angles. The circulant matrix methodology is demonstrated for simulated and real data. Further, an algorithm for separating overlapped signals in the detected dimension is presented. The algorithm involves the forward calculation of the coupled spectra from the orthogonal spectra, followed by back calculation of the orthogonal spectra from the coupled spectra, thus permitting rigorous cross-validation. This algorithm is shown to be robust in that erroneous solutions give rise to large artifacts.

Narrow peaks and high dimensionalities: exploiting the advantages of random sampling

Level of artifacts in spectra obtained by Multidimensional Fourier Transform has been studied, considering randomly sampled signals of high dimensionality and long evolution times. It has been shown theoretically and experimentally, that this level is dependent on the number of time domain samples, but not on its relation to the number of points required in appropriate conventional experiment. Independence of the evolution time domain size (in the terms of both: dimensionality and evolution time reached), suggests that random sampling should be used rather to design new techniques with large time domain than to accelerate standard experiments. 5D HC(CC-TOCSY)CONH has been presented as the example of such approach. The feature of Multidimensional Fourier Transform, namely the possibility of calculating spectral values at arbitrary chosen frequency points, allowed easy examination of resulting spectrum. We present the example of such approach, referred to as Sparse Multidimensional Fourier Transform.

On projection-reconstruction NMR

Three most simple Projection-Reconstruction algorithms, namely, the Lowest-Value, Additive Back-Projection and Hybrid Back-Projection/Lowest-Value algorithms, are analyzed. A new, also simple, algorithm that reconstructs the spectrum by utilizing the amplitude histogram at each reconstruction point, is explored. The algorithms are tested using simulated spectra. While all the algorithms considered can potentially result in substantial reduction of the amount of data needed for reconstruction, they can suffer from a number of drawbacks. In particular, they often fail when the spectra are noisy and/or contain overlapping peaks. When compared to the existing algorithms, the new, histogram-based algorithm has the potential advantage of being able to deal with spectra containing peaks of opposite phase.

Enhanced spectral resolution by high-dimensional NMR using the filter diagonalization method and "hidden" dimensions

High-dimensional (HD) NMR spectra have poorer digital resolution than low-dimensional (LD) spectra, for a fixed amount of experiment time. This has led to "reduced-dimensionality" strategies, in which several LD projections of the HD NMR spectrum are acquired, each with higher digital resolution; an approximate HD spectrum is then inferred by some means. We propose a strategy that moves in the opposite direction, by adding more time dimensions to increase the information content of the data set, even if only a very sparse time grid is used in each dimension. The full HD time-domain data can be analyzed by the filter diagonalization method (FDM), yielding very narrow resonances along all of the frequency axes, even those with sparse sampling. Integrating over the added dimensions of HD FDM NMR spectra reconstitutes LD spectra with enhanced resolution, often more quickly than direct acquisition of the LD spectrum with a larger number of grid points in each of the fewer dimensions. If the extra-dimensions do not appear in the final spectrum, and are used solely to boost information content, we propose the moniker hidden-dimension NMR. This work shows that HD peaks have unmistakable frequency signatures that can be detected as single HD objects by an appropriate algorithm, even though their patterns would be tricky for a human operator to visualize or recognize, and even if digital resolution in an HD FT spectrum is very coarse compared with natural line widths.

High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN

Recent efforts to reduce the measurement time for multidimensional NMR experiments have fostered the development of a variety of new procedures for sampling and data processing. We recently described concentric ring sampling for 3-D NMR experiments, which is superior to radial sampling as input for processing by a multidimensional discrete Fourier transform. Here, we report the extension of this approach to 4-D spectroscopy as Randomized Concentric Shell Sampling (RCSS), where sampling points for the indirect dimensions are positioned on concentric shells, and where random rotations in the angular space are used to avoid coherent artifacts. With simulations, we show that RCSS produces a very low level of artifacts, even with a very limited number of sampling points. The RCSS sampling patterns can be adapted to fine rectangular grids to permit use of the Fast Fourier Transform in data processing, without an apparent increase in the artifact level. These artifacts can be further reduced to the noise level using the iterative CLEAN algorithm developed in radioastronomy. We demonstrate these methods on the high resolution 4-D HCCH-TOCSY spectrum of protein G's B1 domain, using only 1.2% of the sampling that would be needed conventionally for this resolution. The use of a multidimensional FFT instead of the slow DFT for initial data processing and for subsequent CLEAN significantly reduces the calculation time, yielding an artifact level that is on par with the level of the true spectral noise.

APSY-NMR with proteins: practical aspects and backbone assignment

Automated projection spectroscopy (APSY) is an NMR technique for the recording of discrete sets of projection spectra from higher-dimensional NMR experiments, with automatic identification of the multidimensional chemical shift correlations by the dedicated algorithm GAPRO. This paper presents technical details for optimizing the set-up and the analysis of APSY-NMR experiments with proteins. Since experience so far indicates that the sensitivity for signal detection may become the principal limiting factor for applications with larger proteins or more dilute samples, we performed an APSY-NMR experiment at the limit of sensitivity, and then investigated the effects of varying selected experimental parameters. To obtain the desired reference data, a 4D APSY-HNCOCA experiment with a 12-kDa protein was recorded in 13 min. Based on the analysis of this data set and on general considerations, expressions for the sensitivity of APSY-NMR experiments have been generated to guide the selection of the projection angles, the calculation of the sweep widths, and the choice of other acquisition and processing parameters. In addition, a new peak picking routine and a new validation tool for the final result of the GAPRO spectral analysis are introduced. In continuation of previous reports on the use of APSY-NMR for sequence-specific resonance assignment of proteins, we present the results of a systematic search for suitable combinations of a minimal number of four- and five-dimensional APSY-NMR experiments that can provide the input for algorithms that generate automated protein backbone assignments.

A HAUSDORFF-BASED NOE ASSIGNMENT ALGORITHM USING PROTEIN BACKBONE DETERMINED FROM RESIDUAL DIPOLAR COUPLINGS AND ROTAMER PATTERNS

High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue37, 39, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn(3) +tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 Å and all-heavy-atom RMSD < 2.5 Å from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.

Sampling of the NMR time domain along concentric rings

We present a novel approach to sampling the NMR time domain, whereby the sampling points are aligned on concentric rings, which we term concentric ring sampling (CRS). Radial sampling constitutes a special case of CRS where each ring has the same number of points and the same relative orientation. We derive theoretically that the most efficient CRS approach is to place progressively more points on rings of larger radius, with the number of points growing linearly with the radius, a method that we call linearly increasing CRS (LCRS). For cases of significant undersampling to reduce measurement time, a randomized LCRS (RLCRS) is also described. A theoretical treatment of these approaches is provided, including an assessment of artifacts and sensitivity. The analytical treatment of sensitivity also addresses the sensitivity of radially sampled data processed by Fourier transform. Optimized CRS approaches are found to produce artifact-free spectra of the same resolution as Cartesian sampling, for the same measurement time. Additionally, optimized approaches consistently yield fewer and smaller artifacts than radial sampling, and have a sensitivity equal to Cartesian and better than radial sampling. We demonstrate the method using numerical simulations, as well as a 3D HNCO experiment on protein G B1 domain.

Polar Fourier transforms of radially sampled NMR data

Radial sampling of the NMR time domain has recently been introduced to speed up data collection significantly. Here, we show that radially sampled data can be processed directly using Fourier transforms in polar coordinates. We present a comprehensive theoretical analysis of the discrete polar Fourier transform, and derive the consequences of its application to radially sampled data using linear response theory. With adequate sampling, the resulting spectrum using a polar Fourier transform is indistinguishable from conventionally processed spectra with Cartesian sampling. In the case of undersampling in azimuth--the condition that provides significant savings in measurement time-the correct spectrum is still produced, but with limited distortion of the baseline away from the peaks, taking the form of a summation of high-order Bessel functions. Finally, we describe an intrinsic connection between the polar Fourier transform and the filtered backprojection method that has recently been introduced to process projection-reconstruction NOESY data. Direct polar Fourier transformation holds great potential for producing quantitatively accurate spectra from radially sampled NMR data.