Poisson-gap sampling and forward maximum entropy reconstruction for enhancing the resolution and sensitivity of protein NMR data.
The Fourier transform has been the gold standard for transforming data from the time domain to the frequency domain in many spectroscopic methods, including NMR spectroscopy. While reliable, it has the drawback that it requires a grid of uniformely sampled data points, which is not efficient for decaying signals, and it also suffers from artifacts when dealing with nondecaying signals. Over several decades, many alternative sampling and transformation schemes have been proposed. Their common problem is that relative signal amplitudes are not well-preserved. Here we demonstrate the superior performance of a sine-weighted Poisson-gap distribution sparse-sampling scheme combined with forward maximum entropy (FM) reconstruction. While the relative signal amplitudes are well-preserved, we also find that the signal-to-noise ratio is enhanced up to 4-fold per unit of data acquisition time relative to traditional linear sampling.
Hyberts
Sven G
SG
Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Avenue, Boston, Massachusetts 02115, USA.
Takeuchi
Koh
K
Wagner
Gerhard
G
D019277:Entropy; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D016012:Poisson Distribution; D011506:Proteins / Q000737:chemistry*; D012680:Sensitivity and Specificity
D011506:Proteins