Poisson-Gap Sampling and Forward Maximum Entropy Reconstruction for Enhancing the Resolution and Sensitivity of Protein NMR Data.

The article presents the study on the forward maximum entropy (FM) reconstruction method developed for the resolution and sensitivity of Protein Nuclear Magnetic Resonance (NMR) spectra. It assesses the sinusoidal-weighted Poison-gap sampling and FM reconstruction with linear predictions to cope up...

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Publicado en:Journal of the American Chemical Society Vol. 132; no. 7; pp. 2145 - 2148
Autores principales: Hyberts, Sven G., Takeuchi, Koh, Wagner, Gerhard
Formato: Artículo
Publicado: American Chemical Society 2/24/2010
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Poisson-Gap Sampling and Forward Maximum Entropy Reconstruction for Enhancing the Resolution and Sensitivity of Protein NMR Data.
      aug:
        au:
          Hyberts, Sven G.
          Takeuchi, Koh
          Wagner, Gerhard
        affil: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115
      su:
        Nuclear magnetic resonance spectroscopy
        Spectrometers
        Maximum entropy method
        Spectrum analysis
        Sampling (Process)
        Optical resolution
      sug:
        subj:
          Nuclear magnetic resonance spectroscopy
          Spectrometers
          Maximum entropy method
          Spectrum analysis
          Sampling (Process)
          Optical resolution
      ab: The article presents the study on the forward maximum entropy (FM) reconstruction method developed for the resolution and sensitivity of Protein Nuclear Magnetic Resonance (NMR) spectra. It assesses the sinusoidal-weighted Poison-gap sampling and FM reconstruction with linear predictions to cope up with limitations in sampling schedules for the quality of spectra. The study reveals that linear predictions suffer from lower resolution which is important in crowded spectral regions.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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