Advertisement

Journal of Biomolecular NMR

, Volume 68, Issue 2, pp 101–118 | Cite as

Sparse multidimensional iterative lineshape-enhanced (SMILE) reconstruction of both non-uniformly sampled and conventional NMR data

  • Jinfa Ying
  • Frank Delaglio
  • Dennis A. Torchia
  • Ad Bax
Article

Abstract

Implementation of a new algorithm, SMILE, is described for reconstruction of non-uniformly sampled two-, three- and four-dimensional NMR data, which takes advantage of the known phases of the NMR spectrum and the exponential decay of underlying time domain signals. The method is very robust with respect to the chosen sampling protocol and, in its default mode, also extends the truncated time domain signals by a modest amount of non-sampled zeros. SMILE can likewise be used to extend conventional uniformly sampled data, as an effective multidimensional alternative to linear prediction. The program is provided as a plug-in to the widely used NMRPipe software suite, and can be used with default parameters for mainstream application, or with user control over the iterative process to possibly further improve reconstruction quality and to lower the demand on computational resources. For large data sets, the method is robust and demonstrated for sparsities down to ca 1%, and final all-real spectral sizes as large as 300 Gb. Comparison between fully sampled, conventionally processed spectra and randomly selected NUS subsets of this data shows that the reconstruction quality approaches the theoretical limit in terms of peak position fidelity and intensity. SMILE essentially removes the noise-like appearance associated with the point-spread function of signals that are a default of five-fold above the noise level, but impacts the actual thermal noise in the NMR spectra only minimally. Therefore, the appearance and interpretation of SMILE-reconstructed spectra is very similar to that of fully sampled spectra generated by Fourier transformation.

Keywords

Linear prediction Non-uniform sampling NUS Multi-dimensional NMR Sampling scheme Spectral reconstruction Sparse sampling 4D NMR 

Notes

Acknowledgements

We thank Alex Maltsev and Yang Shen for useful discussions, James L Baber and Dan Garrett for technical assistance, and Jung Ho Lee, Venkatraman Ramanujam, and Nikolaos Sgourakis for providing experimental data sets used for testing and illustrating the performance of SMILE. We also thank Michal Górka, Szymon Żerko, and Wiktor Koźmiński for providing the reconstructed (H)N(COCO)NH data by SSA, Victor Jaravine and Vladislav Orekhov for their help with running the MddNMR program, and Brian Coggins for helpful discussions about using SCRUB. This work was supported by the Intramural Research Program of the NIDDK and by the Intramural Antiviral Target Program of the Office of the Director, NIH. We acknowledge use of the high-performance computational capabilities of the NIH Biowulf Linux cluster. Funding was provided by National Institute of Diabetes and Digestive and Kidney Diseases (Grant No. ZIA DK029046-10).

Supplementary material

10858_2016_72_MOESM1_ESM.pdf (527 kb)
Supplementary material 1 (PDF 528  kb)

References

  1. Balsgart NM, Vosegaard T (2012) Fast forward maximum entropy reconstruction of sparsely sampled data. J Magn Reson 223:164–169ADSCrossRefGoogle Scholar
  2. Barna JCJ, Laue ED, Mayger MR, Skilling J, Worrall SJP (1987) Exponential sampling, an alternative method for sampling in two-dimensional NMR experiments. J Magn Reson 73:69–77ADSGoogle Scholar
  3. Bax A, Pochapsky SS (1992) Optimized recording of heteronuclear multidimensional NMR-spectra using pulsed field gradients. J Magn Reson 99:638–643ADSGoogle Scholar
  4. Bermel W, Felli IC, Gonnelli L, Kozminski W, Piai A, Pierattelli R, Zawadzka-Kazimierczuk A (2013) High-dimensionality C-13 direct-detected NMR experiments for the automatic assignment of intrinsically disordered proteins. J Biomol NMR 57:353–361CrossRefGoogle Scholar
  5. Bostock MJ, Holland DJ, Nietlispach D (2012) Compressed sensing reconstruction of undersampled 3D NOESY spectra: application to large membrane proteins. J Biomol NMR 54:15–32CrossRefGoogle Scholar
  6. Clore GM, Kay LE, Bax A, Gronenborn AM (1991) 4-Dimensional C-13/C-13-edited nuclear overhauser enhancement spectroscopy of a protein in solution—application to interleukin 1-Beta. Biochemistry 30:12–18CrossRefGoogle Scholar
  7. Coggins BE, Zhou P (2008) High resolution 4-D spectroscopy with sparse concentric shell sampling and FFT-CLEAN. J Biomol NMR 42:225–239CrossRefGoogle Scholar
  8. Coggins BE, Venters RA, Zhou P (2010) Radial sampling for fast NMR: concepts and practices over three decades. Prog Nucl Magn Reson Spectrosc 57:381–419CrossRefGoogle Scholar
  9. Coggins BE, Werner-Allen JW, Yan A, Zhou P (2012) Rapid protein global fold determination using ultrasparse sampling, high-dynamic range artifact suppression, and time-shared NOESY. J Am Chem Soc 134:18619–18630CrossRefGoogle Scholar
  10. Delaglio F, Grzesiek S, Vuister GW, Zhu G, Pfeifer J, Bax A (1995) NMRpipe—a multidimensional spectral processing system based on UNIX pipes. J Biomol NMR 6:277–293CrossRefGoogle Scholar
  11. Delsuc MA, Tramesel D (2006) Application of maximum-entropy processing to NMR multidimensional datasets, partial sampling case. C R Chim 9:364–373CrossRefGoogle Scholar
  12. Eghbalnia HR, Bahrami A, Tonelli M, Hallenga K, Markley JL (2005) High-resolution iterative frequency identification for NMR as a general strategy for multidimensional data collection. J Am Chem Soc 127:12528–12536CrossRefGoogle Scholar
  13. Fiorito F, Hiller S, Wider G, Wuthrich K (2006) Automated resonance assignment of proteins: 6D APSY-NMR. J Biomol NMR 35:27–37CrossRefGoogle Scholar
  14. Hiller S, Ibraghimov I, Wagner G, Orekhov VY (2009) Coupled decomposition of four-dimensional NOESY spectra. J Am Chem Soc 131:12970–12978CrossRefGoogle Scholar
  15. Hoch JC, Maciejewski MW, Mobli M, Schuyler AD, Stern AS (2014) Nonuniform sampling and maximum entropy reconstruction in multidimensional NMR. Acc Chem Res 47:708–717CrossRefGoogle Scholar
  16. Holland DJ, Bostock MJ, Gladden LF, Nietlispach D (2011) Fast multidimensional NMR spectroscopy using compressed sensing. Angew Chem Int Ed 50:6548–6551CrossRefGoogle Scholar
  17. Hu JS, Bax A (1996) Measurement of three-bond C–13–C–13 J couplings between carbonyl and carbonyl/carboxyl carbons in isotopically enriched proteins. J Am Chem Soc 118:8170–8171CrossRefGoogle Scholar
  18. Hyberts SG, Takeuchi K, Wagner G (2010) Poisson-gap sampling and forward maximum entropy reconstruction for enhancing the resolution and sensitivity of protein NMR data. J Am Chem Soc 132:2145–2147CrossRefGoogle Scholar
  19. Hyberts SG, Milbradt AG, Wagner AB, Arthanari H, Wagner G (2012) Application of iterative soft thresholding for fast reconstruction of NMR data non-uniformly sampled with multidimensional Poisson Gap scheduling. J Biomol NMR 52:315–327CrossRefGoogle Scholar
  20. Hyberts SG, Robson SA, Wagner G (2013) Exploring signal-to-noise ratio and sensitivity in non-uniformly sampled multi-dimensional NMR spectra. J Biomol NMR 55:167–178CrossRefGoogle Scholar
  21. Kay LE, Clore GM, Bax A, Gronenborn AM (1990) Four-dimensional heteronuclear triple-resonance NMR spectroscopy of interleukin-1B in solution. Science 249:411–414ADSCrossRefGoogle Scholar
  22. Kazimierczuk K, Orekhov VY (2011) Accelerated NMR spectroscopy by using compressed sensing. Angew Chem Int Ed 50:5556–5559CrossRefGoogle Scholar
  23. Kazimierczuk K, Stanek J, Zawadzka-Kazimierczuk A, Kozminski W (2010) Random sampling in multidimensional NMR spectroscopy. Prog Nucl Magn Reson Spectrosc 57:420–434CrossRefGoogle Scholar
  24. Kim S, Szyperski T (2003) GFT NMR, a new approach to rapidly obtain precise high-dimensional NMR spectral information. J Am Chem Soc 125:1385–1393CrossRefGoogle Scholar
  25. Kontaxis G, Clore GM, Bax A (2000) Evaluation of cross-correlation effects and measurement of one-bond couplings in proteins with short transverse relaxation times. J Magn Reson 143:184–196ADSCrossRefGoogle Scholar
  26. Krishnamurthy K (2013) CRAFT (complete reduction to amplitude frequency table)—robust and time-efficient Bayesian approach for quantitative mixture analysis by NMR. Magn Reson Chem 51:821–829CrossRefGoogle Scholar
  27. Led JJ, Gesmar H (1991) Application of the linear prediction method to NMR spectroscopy. Chem Rev 91:1413–1426CrossRefGoogle Scholar
  28. Lee JH, Li F, Grishaev A, Bax A (2015) Quantitative residue-specific protein backbone torsion angle dynamics from concerted measurement of 3 J couplings. J Am Chem Soc 137:1432–1435CrossRefGoogle Scholar
  29. Levitt MH, Bodenhausen G, Ernst RR (1984) Sensitivity of two-dimensional spectra. J Magn Reson 58:462–472ADSGoogle Scholar
  30. Li F, Lee JH, Grishaev A, Ying J, Bax A (2015) High accuracy of karplus equations for relating three-bond J couplings to protein backbone torsion angles. ChemPhysChem 16:572–578CrossRefGoogle Scholar
  31. Linser R, Gelev V, Hagn F, Arthanari H, Hyberts SG, Wagner G (2014) Selective methyl labeling of eukaryotic membrane proteins using cell-free expression. J Am Chem Soc 136:11308–11310CrossRefGoogle Scholar
  32. Mayzel M, Kazimierczuk K, Orekhov VY (2014) The causality principle in the reconstruction of sparse NMR spectra. Chem Commun 50:8947–8950CrossRefGoogle Scholar
  33. Mobli M, Hoch JC (2014) Nonuniform sampling and non-Fourier signal processing methods in multidimensional NMR. Prog Nucl Magn Reson Spectrosc 83:21–41CrossRefGoogle Scholar
  34. Mobli M, Maciejewski MW, Gryk MR, Hoch JC (2007) Automatic maximum entropy spectral reconstruction in NMR. J Biomol NMR 39:133–139CrossRefGoogle Scholar
  35. Nowakowski M, Saxena S, Stanek J, Zerko S, Kozminski W (2015) Applications of high dimensionality experiments to biomolecular NMR. Prog Nucl Magn Reson Spectrosc 90–91:49–73CrossRefGoogle Scholar
  36. OpenMP Architecture Review Board, OpenMP Application Program Interface, Version 3.1, July 2011, http://www.openmp.org
  37. Orekhov VY, Jaravine VA (2011) Analysis of non-uniformly sampled spectra with multi-dimensional decomposition. Prog Nucl Magn Reson Spectrosc 59:271–292CrossRefGoogle Scholar
  38. Orekhov VY, Ibraghimov I, Billeter M (2003) Optimizing resolution in multidimensional NMR by three-way decomposition. J Biomol NMR 27:165–173CrossRefGoogle Scholar
  39. Otting G, Widmer H, Wagner G, Wüthrich K (1986) Origin of t 1 and t 2 ridges in 2D NMR spectra and procedures for suppresion. J Magn Reson 66:187–193ADSGoogle Scholar
  40. Piai A, Hosek T, Gonnelli L, Zawadzka-Kazimierczuk A, Kozminski W, Brutscher B, Bermel W, Pierattelli R, Felli IC (2014) “CON–CON’’ assignment strategy for highly flexible intrinsically disordered proteins. J Biomol NMR 60:209–218CrossRefGoogle Scholar
  41. Rovnyak D, Frueh DP, Sastry M, Sun ZYJ, Stern AS, Hoch JC, Wagner G (2004) Accelerated acquisition of high resolution triple-resonance spectra using non-uniform sampling and maximum entropy reconstruction. J Magn Reson 170:15–21ADSCrossRefGoogle Scholar
  42. Sgourakis NG, Natarajan K, Ying J, Vogeli B, Boyd LF, Margulies DH, Bax A (2014) The structure of mouse cytomegalovirus m04 protein obtained from sparse NMR data reveals a conserved fold of the m02–m06 viral immune modulator family. Structure 22:1263–1273CrossRefGoogle Scholar
  43. Sheppard D, Guo CY, Tugarinov V (2009) 4D H-1-C-13 NMR spectroscopy for assignments of alanine methyls in large and complex protein structures. J Am Chem Soc 131:1364–1365CrossRefGoogle Scholar
  44. Stanek J, Kozminski W (2010) Iterative algorithm of discrete Fourier transform for processing randomly sampled NMR data sets. J Biomol NMR 47:65–77CrossRefGoogle Scholar
  45. Stanek J, Augustyniak R, Kozminski W (2012) Suppression of sampling artefacts in high-resolution four-dimensional NMR spectra using signal separation algorithm. J Magn Reson 214:91–102ADSCrossRefGoogle Scholar
  46. Stern AS, Hoch JC (2015) A new approach to compressed sensing for NMR. Magn Reson Chem 53:908–912CrossRefGoogle Scholar
  47. Stern AS, Li KB, Hoch JC (2002) Modern spectrum analysis in multidimensional NMR spectroscopy: comparison of linear-prediction extrapolation and maximum-entropy reconstruction. J Am Chem Soc 124:1982–1993CrossRefGoogle Scholar
  48. Stern AS, Donoho DL, Hoch JC (2007) NMR data processing using iterative thresholding and minimum l(1)-norm reconstruction. J Magn Reson 188:295–300ADSCrossRefGoogle Scholar
  49. Sun SJ, Gill M, Li YF, Huang M, Byrd RA (2015) Efficient and generalized processing of multidimensional NUS NMR data: the NESTA algorithm and comparison of regularization terms. J Biomol NMR 62:105–117CrossRefGoogle Scholar
  50. Tjandra N, Grzesiek S, Bax A (1996) Magnetic field dependence of nitrogen-proton J splittings in N- 15-enriched human ubiquitin resulting from relaxation interference and residual dipolar coupling. J Am Chem Soc 118:6264–6272CrossRefGoogle Scholar
  51. Tugarinov V, Kay LE, Ibraghimov I, Orekhov VY (2005) High-resolution four-dimensional H-1-C-13 NOE spectroscopy using methyl-TROSY, sparse data acquisition, and multidimensional decomposition. J Am Chem Soc 127:2767–2775CrossRefGoogle Scholar
  52. Undheim EAB, Grimm LL, Low CF, Morgenstern D, Herzig V, Zobel-Thropp P, Pineda SS, Habib R, Dziemborowicz S, Fry BG, Nicholson GM, Binford GJ, Mobli M, King GF (2015) Weaponization of a hormone: convergent recruitment of hyperglycemic hormone into the venom of arthropod predators. Structure 23:1283–1292CrossRefGoogle Scholar
  53. Werner-Allen JW, Coggins BE, Zhou P (2010) Fast acquisition of high resolution 4-D amide–amide NOESY with diagonal suppression, sparse sampling and FFT-CLEAN. J Magn Reson 204:173–178ADSCrossRefGoogle Scholar
  54. Xiao Y, Warner LR, Latham MP, Ahn NG, Pardi A (2015) Structure-based assignment of ile, leu, and val methyl groups in the active and inactive forms of the mitogen-activated protein kinase extracellular signal-regulated kinase. Biochemistry 54:4307–4319CrossRefGoogle Scholar
  55. Ying JF, Chill JH, Louis JM, Bax A (2007) Mixed-time parallel evolution in multiple quantum NMR experiments: sensitivity and resolution enhancement in heteronuclear NMR. J Biomol NMR 37:195–204CrossRefGoogle Scholar
  56. Yoon JW, Godsill S, Kupce E, Freeman R (2006) Deterministic and statistical methods for reconstructing multidimensional NMR spectra. Magn Reson Chem 44:197–209CrossRefGoogle Scholar
  57. Zhu G, Bax A (1990) Improved linear prediction for truncated signals of known phase. J Magn Reson 90:405–410ADSGoogle Scholar
  58. Zhu G, Torchia DA, Bax A (1993) Discrete Fourier transformation of NMR signals. The relationship between sampling delay time and spectral baseline. J Magn Reson Ser A 105:219–222ADSCrossRefGoogle Scholar
  59. Zuiderweg ERP, Petros AM, Fesik SW, Olejniczak ET (1991) 4-Dimensional [C-13, H-1, C-13, H-1] HMQC-NOE-HMQC NMR spectroscopy—resolving tertiary NOE distance constraints in the spectra of larger proteins. J Am Chem Soc 113:370–372CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2016

Authors and Affiliations

  1. 1.Laboratory of Chemical Physics, National Institute of Digestive and Diabetic and Kidney DiseasesNational Institutes of HealthBethesdaUSA
  2. 2.Institute for Bioscience and Biotechnology ResearchNational Institute of Standards and Technology and the University of MarylandRockvilleUSA
  3. 3.National Institute of Dental and Craniofacial ResearchNational Institutes of HealthBethesdaUSA

Personalised recommendations