The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Ulrich L. Günther - One of the best experts on this subject based on the ideXlab platform.
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metabolab advanced NMR Data processing and analysis for metabolomics
BMC Bioinformatics, 2011Co-Authors: Christian Ludwig, Ulrich L. GüntherAbstract:Despite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR Data sets for statistical analysis. Here we present MetaboLab, a MATLAB based software package that facilitates NMR Data processing by providing automated algorithms for processing series of spectra in a reproducible fashion. A graphical user interface provides easy access to all steps of Data processing via a script builder to generate MATLAB scripts, providing an option to alter code manually. The analysis of two-dimensional spectra (1H,13C-HSQC spectra) is facilitated by the use of a spectral library derived from publicly available Databases which can be extended readily. The software allows to display specific metabolites in small regions of interest where signals can be picked. To facilitate the analysis of series of two-dimensional spectra, different spectra can be overlaid and assignments can be transferred between spectra. The software includes mechanisms to account for overlapping signals by highlighting neighboring and ambiguous assignments. The MetaboLab software is an integrated software package for NMR Data processing and analysis, closely linked to the previously developed NMRLab software. It includes tools for batch processing and gives access to a wealth of algorithms available in the MATLAB framework. Algorithms within MetaboLab help to optimize the flow of metabolomics Data preparation for statistical analysis. The combination of an intuitive graphical user interface along with advanced Data processing algorithms facilitates the use of MetaboLab in a broader metabolomics context.
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NMRLAB—Advanced NMR Data Processing in Matlab
Journal of Magnetic Resonance, 2000Co-Authors: Ulrich L. Günther, Christian Ludwig, Heinz RüterjansAbstract:Abstract NMRLAB is a toolbox for NMR Data processing in MATLAB (The Mathworks). MATLAB is a matrix-oriented high-level programming environment which gives access to fast algorithms for a large number of numerical tasks on many common computer platforms. To take advantage of fast matrix operations in MATLAB most processing commands in NMRLAB have been vectorized. Data processing can be achieved either by scripts or by a user-friendly command structure. An interface to WaveLab enables spectral denoising employing wavelet transforms. The use of wavelet denoising is demonstrated for one- and two-dimensional Data.
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NMRLAB—Advanced NMR Data Processing in Matlab
Journal of Magnetic Resonance, 2000Co-Authors: Ulrich L. Günther, Christian Ludwig, Heinz RüterjansAbstract:NMRLAB is a toolbox for NMR Data processing in MATLAB (The Mathworks). MATLAB is a matrix-oriented high-level programming environment which gives access to fast algorithms for a large number of numerical tasks on many common computer platforms. To take advantage of fast matrix operations in MATLAB most processing commands in NMRLAB have been vectorized. Data processing can be achieved either by scripts or by a user-friendly command structure. An interface to WaveLab enables spectral denoising employing wavelet transforms. The use of wavelet denoising is demonstrated for one- and two-dimensional Data. Copyright 2000 Academic Press.
Rongbiao Tong - One of the best experts on this subject based on the ideXlab platform.
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structural revision of uprolide g acetate effective interplay between NMR Data analysis and chemical synthesis
ChemInform, 2015Co-Authors: Rongbiao TongAbstract:On the basis of NMR Data and chemical synthesis, a revised structure is given for uprolide G acetate (I).
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structural revision of uprolide g acetate effective interplay between NMR Data analysis and chemical synthesis
Synlett, 2015Co-Authors: Rongbiao TongAbstract:The molecular structure of the cytotoxic cembranolide uprolide G acetate (UGA) was proposed in 1995 and subsequently revised in 2000 on the basis that NMR Data for UGA were very similar to those of a synthetic analogue that was unambiguously confirmed by X-ray diffraction analysis. Our synthetic studies of UGA suggested that the revised structure for UGA was still incorrect. Therefore, two new possible structures for UGA were proposed based on comprehensive NMR Data analysis. The proposed structures were synthesized in 33 steps by exploitation of Achmatowicz rearrangement, ring-closing metathesis, and Sharpless asymmetric dihydroxylation as the key steps. Their analysis led to the identification of the correct structure for UGA. The success of structural revision of UGA illustrated well the importance of the interplay between NMR Data analysis and chemical synthesis.
Christian Ludwig - One of the best experts on this subject based on the ideXlab platform.
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metabolab advanced NMR Data processing and analysis for metabolomics
BMC Bioinformatics, 2011Co-Authors: Christian Ludwig, Ulrich L. GüntherAbstract:Despite wide-spread use of Nuclear Magnetic Resonance (NMR) in metabolomics for the analysis of biological samples there is a lack of graphically driven, publicly available software to process large one and two-dimensional NMR Data sets for statistical analysis. Here we present MetaboLab, a MATLAB based software package that facilitates NMR Data processing by providing automated algorithms for processing series of spectra in a reproducible fashion. A graphical user interface provides easy access to all steps of Data processing via a script builder to generate MATLAB scripts, providing an option to alter code manually. The analysis of two-dimensional spectra (1H,13C-HSQC spectra) is facilitated by the use of a spectral library derived from publicly available Databases which can be extended readily. The software allows to display specific metabolites in small regions of interest where signals can be picked. To facilitate the analysis of series of two-dimensional spectra, different spectra can be overlaid and assignments can be transferred between spectra. The software includes mechanisms to account for overlapping signals by highlighting neighboring and ambiguous assignments. The MetaboLab software is an integrated software package for NMR Data processing and analysis, closely linked to the previously developed NMRLab software. It includes tools for batch processing and gives access to a wealth of algorithms available in the MATLAB framework. Algorithms within MetaboLab help to optimize the flow of metabolomics Data preparation for statistical analysis. The combination of an intuitive graphical user interface along with advanced Data processing algorithms facilitates the use of MetaboLab in a broader metabolomics context.
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NMRLAB—Advanced NMR Data Processing in Matlab
Journal of Magnetic Resonance, 2000Co-Authors: Ulrich L. Günther, Christian Ludwig, Heinz RüterjansAbstract:Abstract NMRLAB is a toolbox for NMR Data processing in MATLAB (The Mathworks). MATLAB is a matrix-oriented high-level programming environment which gives access to fast algorithms for a large number of numerical tasks on many common computer platforms. To take advantage of fast matrix operations in MATLAB most processing commands in NMRLAB have been vectorized. Data processing can be achieved either by scripts or by a user-friendly command structure. An interface to WaveLab enables spectral denoising employing wavelet transforms. The use of wavelet denoising is demonstrated for one- and two-dimensional Data.
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NMRLAB—Advanced NMR Data Processing in Matlab
Journal of Magnetic Resonance, 2000Co-Authors: Ulrich L. Günther, Christian Ludwig, Heinz RüterjansAbstract:NMRLAB is a toolbox for NMR Data processing in MATLAB (The Mathworks). MATLAB is a matrix-oriented high-level programming environment which gives access to fast algorithms for a large number of numerical tasks on many common computer platforms. To take advantage of fast matrix operations in MATLAB most processing commands in NMRLAB have been vectorized. Data processing can be achieved either by scripts or by a user-friendly command structure. An interface to WaveLab enables spectral denoising employing wavelet transforms. The use of wavelet denoising is demonstrated for one- and two-dimensional Data. Copyright 2000 Academic Press.
David Baker - One of the best experts on this subject based on the ideXlab platform.
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rapid protein fold determination using unassigned NMR Data
Proceedings of the National Academy of Sciences of the United States of America, 2003Co-Authors: Jens Meiler, David BakerAbstract:Experimental structure determination by x-ray crystallography and NMR spectroscopy is slow and time-consuming compared with the rate at which new protein sequences are being identified. NMR spectroscopy has the advantage of rapidly providing the structurally relevant information in the form of unassigned chemical shifts (CSs), intensities of NOESY crosspeaks [nuclear Overhauser effects (NOEs)], and residual dipolar couplings (RDCs), but use of these Data are limited by the time and effort needed to assign individual resonances to specific atoms. Here, we develop a method for generating low-resolution protein structures by using unassigned NMR Data that relies on the de novo protein structure prediction algorithm, rosetta [Simons, K. T., Kooperberg, C., Huang, E. & Baker, D. (1997) J. Mol. Biol. 268, 209–225] and a Monte Carlo procedure that searches for the assignment of resonances to atoms that produces the best fit of the experimental NMR Data to a candidate 3D structure. A large ensemble of models is generated from sequence information alone by using rosetta, an optimal assignment is identified for each model, and the models are then ranked based on their fit with the NMR Data assuming the identified assignments. The method was tested on nine protein sequences between 56 and 140 amino acids and published CS, NOE, and RDC Data. The procedure yielded models with rms deviations between 3 and 6 A, and, in four of the nine cases, the partial assignments obtained by the method could be used to refine the structures to high resolution (0.6–1.8 A) by repeated cycles of structure generation guided by the partial assignments, followed by reassignment using the newly generated models.
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de novo protein structure determination using sparse NMR Data
Journal of Biomolecular NMR, 2000Co-Authors: Peter M Bowers, Charlie E M Strauss, David BakerAbstract:We describe a method for generating moderate to high-resolution protein structures using limited NMR Data combined with the ab initio protein structure prediction method Rosetta. Peptide fragments are selected from proteins of known structure based on sequence similarity and consistency with chemical shift and NOE Data. Models are built from these fragments by minimizing an energy function that favors hydrophobic burial, strand pairing, and satisfaction of NOE constraints. Models generated using this procedure with ∼1 NOE constraint per residue are in some cases closer to the corresponding X-ray structures than the published NMR solution structures. The method requires only the sparse constraints available during initial stages of NMR structure determination, and thus holds promise for increasing the speed with which protein solution structures can be determined.
Anne S Ulrich - One of the best experts on this subject based on the ideXlab platform.
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orientation and dynamics of peptides in membranes calculated from 2h NMR Data
Biophysical Journal, 2009Co-Authors: Erik Strandberg, Santi Estebanmartin, Jesus Salgado, Anne S UlrichAbstract:Solid-state 2H-NMR is routinely used to determine the alignment of membrane-bound peptides. Here we demonstrate that it can also provide a quantitative measure of the fluctuations around the distinct molecular axes. Using several dynamic models with increasing complexity, we reanalyzed published 2H-NMR Data on two representative α-helical peptides: 1), the amphiphilic antimicrobial peptide PGLa, which permeabilizes membranes by going from a monomeric surface-bound to a dimeric tilted state and finally inserting as an oligomeric pore; and 2), the hydrophobic WALP23, which is a typical transmembrane segment, although previous analysis had yielded helix tilt angles much smaller than expected from hydrophobic mismatch and molecular dynamics simulations. Their 2H-NMR Data were deconvoluted in terms of the two main helix orientation angles (representing the time-averaged peptide tilt and azimuthal rotation), as well as the amplitudes of fluctuation about the corresponding molecular axes (providing the dynamic picture). The mobility of PGLa is found to be moderate and to correlate well with the respective oligomeric states. WALP23 fluctuates more vigorously, now in better agreement with the molecular dynamics simulations and mismatch predictions. The analysis demonstrates that when 2H-NMR Data are fitted to extract peptide orientation angles, an explicit representation of the peptide rigid-body angular fluctuations should be included.
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Influence of Dynamics on The Analysis of Solid-State NMR Data From Membrane-bound Peptides
Biophysical Journal, 2009Co-Authors: Erik Strandberg, Jesus Salgado, Santi Esteban-martín, Anne S UlrichAbstract:By isotope labeling of membrane-bound peptides, typically with 2H, 19F, or 15N, solid-state NMR experiments can yield Data from which the orientation of peptides in a native membrane environment can be determined. Such an orientation is defined by a tilt angle and an azimuthal rotation angle.Here we show that to obtain correct values of the orientation angles, it is important to include dynamics in the analysis of the NMR Data. Nevertheless the effects of dynamics are different depending on the type of isotope labeling and NMR experiment considered.To analyze the influence of dynamics in detail, we generated virtual NMR observables using a model peptide undergoing explicit Gaussian fluctuations of the orientation angles. For simulated 2H- or 19F-NMR Data, even moderate motions were found to have a large influence, as calculated tilt values are consistently much too small, unless dynamics is properly considered. A simple dynamic model, including a molecular order parameter scaling factor, gives good results only for moderately mobile peptides, while for high mobility cases the correct tilt is only obtained by re-introducing the explicit Gaussian fluctuations in the fitting functions.In contrast, 15N-NMR Data appear to be less sensitive to rigid-body peptide motions, and PISEMA spectra can give correct orientations even for highly mobile peptides, and assuming a static model for the analysis. The differences are due to the different orientation of the tensors of 2H- and 19F-labels, placed on peptide side chains, compared to the orientation of the 15N tensor, placed on amide backbone groups.We conclude that dynamics should be included in the analysis of solid-state NMR Data of membrane-bound peptides. Not only does this give more accurate orientations, but it can also provide information about the dynamics of the peptide.
