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.
Coggins
Brian E
BE
Department of Biochemistry, Duke University Medical Center, Durham, NC 27710, USA.
Zhou
Pei
P
D000465:Algorithms; D005583:Fourier Analysis; D008958:Models, Molecular; D009419:Nerve Tissue Proteins / Q000737:chemistry; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013997:Time Factors
C029152:G-substrate; D009419:Nerve Tissue Proteins