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Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR data sets

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Abstract

Spectra obtained by application of multidimensional Fourier Transformation (MFT) to sparsely sampled nD NMR signals are usually corrupted due to missing data. In the present paper this phenomenon is investigated on simulations and experiments. An effective iterative algorithm for artifact suppression for sparse on-grid NMR data sets is discussed in detail. It includes automated peak recognition based on statistical methods. The results enable one to study NMR spectra of high dynamic range of peak intensities preserving benefits of random sampling, namely the superior resolution in indirectly measured dimensions. Experimental examples include 3D 15N- and 13C-edited NOESY-HSQC spectra of human ubiquitin.

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Acknowledgments

Special thanks are addressed at Maxim Mayzel from Swedish NMR Centre for his help in using MDD package, and at prof. J.C. Hoch from University of Connecticut Health Center for providing access to Rowland NMR Toolkit v.3. This work was supported by grant number: N301 07131/2159, founded by Ministry of Science and Higher Education in years 2006-2009. Research cofinanced by the European Social Fund and State funds under the Integrated Regional Operational Programme, Measure 2.6 “Regional Innovation Strategies and transfer of knowledge”, Mazovian Voivodship project “Mazovian Ph.D. Scholarship”.

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  1. Faculty of Chemistry, University of Warsaw, Pasteura 1, 02-093, Warsaw, Poland

    Jan Stanek & Wiktor Koźmiński

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  1. Jan Stanek
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  2. Wiktor Koźmiński
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Correspondence to Wiktor Koźmiński.

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Stanek, J., Koźmiński, W. Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR data sets. J Biomol NMR 47, 65–77 (2010). https://doi.org/10.1007/s10858-010-9411-2

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