NMR – Brian E. Coggins

Because my professional focus has shifted from NMR research to biochemical education, I am no longer actively researching and developing NMR technologies or updating/enhancing NMR software. However, I am pleased to make available the source code to previously released software and to share links to my NMR research publications.

Software

The following software packages are available as git source code repositories. Unfortunately, I am not able to prepare and distribute precompiled binaries at this time.

NMR spectra calculated from sparsely sampled data are corrupted by artifacts that look like random noise (left). The SCRUB algorithm reduces these artifacts by more than 10000x, allowing very weak signals to be detected (center). Applying this to a rapidly collected NOESY dataset, it is possible to determine accurate protein structures automatically (right).

Research Publications on NMR Sparse Sampling: Current Methods

The current state of the art for sparse sampling in multidimensional solution state NMR experiments is to measure the time domain with sampling at a small number of randomly selected positions, and then process the data into a full frequency-domain spectrum using one of several signal processing algorithms. Pei Zhou and I worked on the use of the CLEAN algorithm, originally developed for radioastronomy data, as a tool for processing randomized sparse NMR data. The original CLEAN algorithm has some limitations that reduce its effectiveness for NMR, which we addressed with the modified algorithm SCRUB. The following two papers describe our SCRUB method, and a quantitative measure we developed for assessing the quality of processed spectra, whether from SCRUB or other methods.

B.E. Coggins, J.W. Werner-Allen, A. Yan, P. Zhou. “Rapid protein global fold determination using ultrasparse sampling, high-dynamic range artifact suppression, and time-shared NOESY.” J. Am. Chem. Soc. 134: 18619-18630 (2012). Introduced the SCRUB method for processing sparse NMR data, derived from the CLEAN algorithm used in radioastronomy, and demonstrated its application to rapid structure determination from highly sparse 4-D NOESY spectra. PubMed
Q. Wu, B.E. Coggins, P. Zhou. “Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra.” Nat. Commun. 7: 12281 (2016). Introduced a quantitative tool for assessing the quality of spectra reconstructed from sparse data. PubMed

Much of our earlier work on rapid NMR data collection focused on radial sampling. Radial sampling point-spread functions show aliasing artifacts in the form of radial spokes (top left). Collecting additional vectors of samples at more angles results in an artifact-free zone surrounding each signal, which can be seen by following the progression down the left-hand column and then down the right-hand column. With sampling in enough directions, the directional artifacts are pushed beyond the boundaries of the spectrum, leaving only concentric ripples from truncation (bottom right).

Review Papers and Book Chapters on NMR Sparse Sampling Techniques

The following three publications were written as introductions to the techniques I have worked on with Pei Zhou for sparse sampling in NMR. Much of our earlier work was focused on radial sampling, and the first publication explains the theory of radial sampling, as well as reviewing the many ways it has been applied to NMR by others and by ourselves. The two latter publications are from an edited volume on techniques for accelerated NMR and explain the theory and practice of reconstructing full spectra from sparse data using backprojection methods (for radial data) and CLEAN (for randomized data).

B.E. Coggins, R.A. Venters, P. Zhou. “Radial sampling for fast NMR: Concepts and practices over three decades.” Prog. Nucl. Magn. Reson. Spectrosc. 57: 381-419 (2010). Explains the theory of radial sampling approaches in NMR, and reviews the long history of their use. PubMed
B.E. Coggins, P. Zhou. “Backprojection and Related Methods.” In Fast NMR Data Acquisition: Beyond the Fourier Transform, ed. J. Hoch and M. Mobli. Royal Society of Chemistry: London, 2017, pp. 119-168. Explains methods for reconstructing NMR spectra from projections. RSC DOI
B.E. Coggins, P. Zhou. “CLEAN.” In Fast NMR Data Acquisition: Beyond the Fourier Transform, ed. J. Hoch and M. Mobli. Royal Society of Chemistry: London, 2017, pp. 169-219. Explains methods for reconstructing NMR spectra from sparse data using the CLEAN algorithm from radioastronomy, and algorithms derived from CLEAN. RSC DOI

Significant Earlier Research Publications on NMR Sparse Sampling

J.W. Werner-Allen, B.E. Coggins, P. Zhou. “Fast acquisition of high resolution 4-D amide-amide NOESY with diagonal suppression, sparse sampling and FFT-CLEAN.” J. Magn. Reson. 204: 173-178 (2010). PubMed
B.E. Coggins, P. Zhou. “High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN.” J. Biomol. NMR 42: 225-239 (2008). PubMed
B.E. Coggins, P. Zhou. “Sampling of the NMR time domain along concentric rings.” J. Magn. Reson. 184: 207-221 (2007). PubMed
B.E. Coggins, P. Zhou. “Polar Fourier transforms of radially sampled NMR data.” J. Magn. Reson. 182: 84-95 (2006). PubMed
B.E. Coggins, R.A. Venters, P. Zhou. “Filtered backprojection for the reconstruction of a high-resolution (4,2)D CH3-NH NOESY spectrum on a 29 kDa protein.” J. Am. Chem. Soc. 127: 11562-11563 (2005). PubMed
R.A. Venters, B.E. Coggins, D. Kojetin, J. Cavanagh, P. Zhou. “(4,2)D Projection–reconstruction experiments for protein backbone assignment: application to human carbonic anhydrase II and calbindin D(28K).” J. Am. Chem. Soc. 127: 8785-8795 (2005). PubMed
B.E. Coggins, R.A. Venters, P. Zhou. “Generalized reconstruction of n-D NMR spectra from multiple projections: application to the 5-D HACACONH spectrum of protein G B1 domain.” J. Am. Chem. Soc. 126: 1000-1001 (2004). PubMed