Spectral Analysis


Peak picking and quantitative analysis of NMR spectra is an important step towards providing a biological representation of the outcomes of NMR spectroscopy. Identification and quantification of the peaks in 1D and 2D NMR spectra provides a powerful platform for small molecule studies, where recognition of the possible peaks overlaps and calculating the intensities of such peaks are the most challenging problems towards constructing such platform.

The Newton package provides a graphical user interface (GUI) for quantitative studies of 1D and 2D NMR spectra. This package is developed using a “Spectral Convolution Method based on the Fast Maximum Likelihood Reconstruction (FMLR)” [Ref 1].

The FMLR method is capable of constructing a model peak in the time domain, which will be used in identification of the peaks overlaps in the frequency domain [Ref 2]. In addition, the capability of defining regions of interest (ROI) in batch processing provides the opportunity of analyzing only these regions in a dataset of spectral data. The use of ROIs has proven a very powerful tool in metabolomics studies [Ref 2, 3, 4].

 

Figure from [Ref 2]. Overview of fast maximum likelihood reconstruction.

 

1)    Roger A. Chylla, John L. Markley, “Theory and application of the maximum likelihood principle to NMR parameter estimation of multidimensional NMR data”, Journal of Biomolecular NMR, April 1995, Volume 5, Issue 3, pp 245-258. [url: http://link.springer.com/article/10.1007%2FBF00211752]

2)    Roger A. Chylla, Kaifeng Hu, James J. Ellinger, and John L. Markley, “Deconvolution of Two-Dimensional NMR Spectra by Fast Maximum Likelihood Reconstruction: Application to Quantitative Metabolomics”,  Anal. Chem. 2011, 83, 4871–4880. [url: http://www.ncbi.nlm.nih.gov/pubmed/21526800].

3)    Chylla RA, Van Acker R, Kim H, Azapira A, Mukerjee P, Markley JL, Storme V, Boerjan W, Ralph J. “Plant cell wall profiling by fast maximum likelihood reconstruction (FMLR) and region-of-interest (ROI) segmentation of solution-state 2D 1H-13C NMR spectra.”, Biotechnol Biofuels. 2013 Apr 26;6(1):45. doi: 10.1186/1754-6834-6-45. [url: http://www.ncbi.nlm.nih.gov/pubmed/23622232]

4)    Hu K,  Ellinger JJ, Chylla RA, Markley JL, “Measurement of absolute concentrations of individual compounds in metabolite mixtures by gradient-selective time-zero 1H-13C HSQC with two concentration references and fast maximum likelihood reconstruction analysis.”, Anal Chem. 2011 Dec 15;83(24):9352-60. doi: 10.1021/ac201948f. Epub 2011 Nov 15. [url: http://www.ncbi.nlm.nih.gov/pubmed/22029275]

 

 

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Biochemistry 800 - Practical Nuclear Magnetic Resonance Theory 

Biochemistry 801 - Biochemical Applications of Nuclear Magnetic Resonance 

"The Future of NMR-Based Metabolomics, Current Opinion in Biotechnology (2017), pp. 34-40

Documents on the use of the Bruker-Axs Nanostar,SAXS instrument and analysis of SAXS data are now available.

NMRFAM-SPARKY Distribution - the popular NMR analysis program SPARKY recompiled (including updated python and Tcl/Tk) with incorporation of PINE-Sparky, enhancements to import/export to the structural analysis program CYANA, and other useful python extensions.

ADAPT-NMR Enhancer: Complete Package for Reduced Dimensionality in Protein NMR Spectroscopy

RNA-PAIRS: RNA Probabilistic Assignment of Imino Resonance Shifts

PACSY, a Relational Database Management System for Protein Structure and Chemical Shift Analysis 

 

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1st: Lai Bergeman 
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