Generalized Fourier Transform for Non-Uniform Sampled Data

Kazimierczuk, Krzysztof; Misiak, Maria; Staněk, Jan; Zawadzka‐Kazimierczuk, Anna; Koźmiński, Wiktor

doi:10.1007/128_2011_186

Cited by 31 publications

(28 citation statements)

References 85 publications

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“…which reflects the limits of sampling [41]. For the simplicity of its FT, the step function is time-symmetric here, which does not however contradicts the causality of the signal.…”

Section: Resolution Enhancementmentioning

confidence: 95%

“…Radial sampling is utilized in projection spectroscopy, and requires the algebraic decoding of peak frequencies [29][30][31][32][33] or the reconstruction of the full multidimensional spectrum [34][35][36][37]. Sparse non-uniformly sampled data sets can be processed in some cases by zero-augmented discrete Fourier transformation (DFT) [38][39][40][41], compressed sensing (CS) [42,43], maximum entropy [44][45][46] or multidimensional decomposition [47][48][49] methods. Non-Fourier multidimensional NMR methods have been reviewed recently by Mobli and Hoch [50].…”

Section: Introductionmentioning

confidence: 99%

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Applications of high dimensionality experiments to biomolecular NMR

Nowakowski

Saxena

Staněk

et al. 2015

Progress in Nuclear Magnetic Resonance Spectroscopy

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“…which reflects the limits of sampling [41]. For the simplicity of its FT, the step function is time-symmetric here, which does not however contradicts the causality of the signal.…”

Section: Resolution Enhancementmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Applications of high dimensionality experiments to biomolecular NMR

Nowakowski

Saxena

Staněk

et al. 2015

Progress in Nuclear Magnetic Resonance Spectroscopy

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“…Spectra of both high dimensionality and high resolution reduce overlap and ensure good precision in the determination of peak positions, both of which are critical for resonance assignment procedures. Such spectra can be effectively acquired with the use of non-Cartesian sampling in evolution time space by dramatically reducing the number of acquired FIDs [recently reviewed in (Coggins et al 2010;Freeman and Kupče 2012;Hiller and Wider 2012;Hyberts et al 2012;Kazimierczuk et al 2010aKazimierczuk et al , 2012Maciejewski et al 2012;Orekhov and Jaravine 2011). One of the most general sampling schemes is random sampling, which allows for optimal resolution also in C4D experiments (Kazimierczuk et al 2009).…”

Section: Introductionmentioning

confidence: 99%

TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra

Zawadzka‐Kazimierczuk

Koźmiński

Billeter

2012

J Biomol NMR

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While NMR studies of proteins typically aim at structure, dynamics or interactions, resonance assignments represent in almost all cases the initial step of the analysis. With increasing complexity of the NMR spectra, for example due to decreasing extent of ordered structure, this task often becomes both difficult and time-consuming, and the recording of high-dimensional data with high-resolution may be essential. Random sampling of the evolution time space, combined with sparse multidimensional Fourier transform (SMFT), allows for efficient recording of very high dimensional spectra (≥4 dimensions) while maintaining high resolution. However, the nature of this data demands for automation of the assignment process. Here we present the program TSAR (Tool for SMFT-based Assignment of Resonances), which exploits all advantages of SMFT input. Moreover, its flexibility allows to process data from any type of experiments that provide sequential connectivities. The algorithm was tested on several protein samples, including a disordered 81-residue fragment of the δ subunit of RNA polymerase from Bacillus subtilis containing various repetitive sequences. For our test examples, TSAR achieves a high percentage of assigned residues without any erroneous assignments.

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“…This method can be combined with TROSY approach (Salzmann et al, 1998(Salzmann et al, , 1999Yang & Kay, 1999) for application to large systems (> 30 kDa) to overcome the line-broadening problems. Adoption of methods like longitudinal relaxation optimized technique and non-uniform sampling for fast data collection Kazimierczuk et al, 2012) can easily be employed in the present case, which can further reduce the experimental time by an order of magnitude. Since the data is acquired in the form of a 2D spectrum, high spectral/digital resolution can still be achieved easily without spending much instrument time.…”

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confidence: 99%

Identification of C-Terminal Neighbours of Residues that have only one 1Hβ Attached to 13Cβ: (Ile, Thr and Val)-specific (3,2)D-CB(CACO)NNH Experiment

Chandra¹,

Patel²,

Atreya³

et al. 2014

Proceedings of the Indian National Science Academy

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Sequence specific resonance assignment is the first step in determining the three-dimensional (3D) structure of proteins. Several double and triple-resonance NMR experiments were proposed in this direction for achieving unambiguous assignments. However, these methodologies are non-trivial for proteins with molecular weight beyond 15 kDa. Common problems in this process are spectral overlaps and rapid relaxation rates of the nuclei that result in broad cross peaks. Further, Pro residues, which lack 1 H N , complicate the scenario. This led to identify maximum possible starting points along the polypeptide chain of a given protein to aid in the sequence-specific resonance assignment procedure. In this paper, we propose a fast NMR methodology namely Ile, Thr and Val (ITV) specific (3, 2) D-CB(CACO)NNH, which shows the spectral signatures of the C-terminal sequential neighbors of Ile, Thr and Val residues in highly resolved manner. This aids in the generation of additional starting points along a given polypeptide chain, apart from the known canonical Ala, Gly, Ser and Thr residues, and thus accelerate resonance assignment process significantly. IntroductionAdvent of labeling schemes to produce 13 C-or/and 15 N-enriched proteins paved the way to determine their 3D structures of medium size proteins (upto a M r = 50 kDa) by 3D triple resonance NMR. In this endeavor, 1 H, 13 C and 15 N sequence-specific resonance assignments remain the first step. Till date, several double-and triple-resonance experiments were proposed to carry out these assignments in isotope-labeled proteins. However, these methodologies are non-trivial for proteins with molecular weights even beyond 15 kDa. Common problems in this process are spectral overlaps and rapid relaxation rates of the nuclei that result in broad spectral signatures. Further, Pro residues, which lack 1 H N , complicate the situation. Hence maximum possible number of starting points is required along the polypeptide chain of a given protein to facilitate the sequence-specific resonance assignment procedure.It is a well-known fact that, spectral signatures of Ala, Gly, Ser and Thr residues are easily identifiable residues because of their characteristic 13 C α and 13 C β chemical shifts. The 13 C α chemical shifts of Gly resonate around 45.4±1.8 ppm, which is well separated from the 13 C α chemical shifts of the rest of the residues. On the other hand, the 13 (Atreya et al., 2000;Atreya et al., 2002). Thus, in principle, four tripleresonance experiments HNCACB, CBCA(CO)NH, HNCO and HN(CA)CO, should suffice for the resonance assignment of 1 H N , 15 N, 13 C α , 13 C β and 13 C' spins (Atreya et al., 2000;Atreya et al., 2002). However, in practice, the success rate in any automated resonance assignment procedure turns out to be low, primarily because of incomplete spectral signatures or/and spectral overlap. Hence, the quest has been to identify spectral signatures arising from residues other than Ala, Ser, Thr and Gly. Based on the analysis of BMRB data of 9000 odd protein...

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Generalized Fourier Transform for Non-Uniform Sampled Data

Cited by 31 publications

References 85 publications

Applications of high dimensionality experiments to biomolecular NMR

Applications of high dimensionality experiments to biomolecular NMR

TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra

Identification of C-Terminal Neighbours of Residues that have only one <SUP>1</SUP>H<SUP>β</SUP> Attached to <SUP>13</SUP>C<SUP>β</SUP>: (Ile, Thr and Val)-specific (3,2)D-CB(CACO)NNH Experiment

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Generalized Fourier Transform for Non-Uniform Sampled Data

Cited by 31 publications

References 85 publications

Applications of high dimensionality experiments to biomolecular NMR

Applications of high dimensionality experiments to biomolecular NMR

TSAR: a program for automatic resonance assignment using 2D cross-sections of high dimensionality, high-resolution spectra

Identification of C-Terminal Neighbours of Residues that have only one <SUP>1</SUP>H<SUP>&#946;</SUP> Attached to <SUP>13</SUP>C<SUP>&#946;</SUP>: (Ile, Thr and Val)-specific (3,2)D-CB(CACO)NNH Experiment

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Identification of C-Terminal Neighbours of Residues that have only one <SUP>1</SUP>H<SUP>β</SUP> Attached to <SUP>13</SUP>C<SUP>β</SUP>: (Ile, Thr and Val)-specific (3,2)D-CB(CACO)NNH Experiment