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      Deconvolution of Two-Dimensional NMR Spectra by Fast Maximum Likelihood Reconstruction: Application to Quantitative Metabolomics

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          Abstract

          We have developed an algorithm called fast maximum likelihood reconstruction (FMLR) that performs spectral deconvolution of 1D–2D NMR spectra for the purpose of accurate signal quantification. FMLR constructs the simplest time-domain model (e.g., the model with the fewest number of signals and parameters) whose frequency spectrum matches the visible regions of the spectrum obtained from identical Fourier processing of the acquired data. We describe the application of FMLR to quantitative metabolomics and demonstrate the accuracy of the method by analysis of complex, synthetic mixtures of metabolites and liver extracts. The algorithm demonstrates greater accuracy (0.5–5.0% error) than peak height analysis and peak integral analysis with greatly reduced operator intervention. FMLR has been implemented in a Java-based framework that is available for download on multiple platforms and is interoperable with popular NMR display and processing software. Two-dimensional 1H– 13C spectra of mixtures can be acquired with acquisition times of 15 min and analyzed by FMLR in the range of 2–5 min per spectrum to identify and quantify constituents present at concentrations of 0.2 mM or greater.

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          New bioinformatics resources for metabolomics.

          We recently developed two databases and a laboratory information system as resources for the metabolomics community. These tools are freely available and are intended to ease data analysis in both MS and NMR based metabolomics studies. The first database is a metabolomics extension to the BioMagResBank (BMRB, http://www.bmrb.wisc.edu), which currently contains experimental spectral data on over 270 pure compounds. Each small molecule entry consists of five or six one- and two-dimensional NMR data sets, along with information about the source of the compound, solution conditions, data collection protocol and the NMR pulse sequences. Users have free access to peak lists, spectra, and original time-domain data. The BMRB database can be queried by name, monoisotopic mass and chemical shift. We are currently developing a deposition tool that will enable people in the community to add their own data to this resource. Our second database, the Madison Metabolomics Consortium Database (MMCD, available from http://mmcd.nmrfam.wisc.edu/), is a hub for information on over 10,000 metabolites. These data were collected from a variety of sites with an emphasis on metabolites found in Arabidopsis. The MMC database supports extensive search functions and allows users to make bulk queries using experimental MS and/or NMR data. In addition to these databases, we have developed a new module for the Sesame laboratory information management system (http://www.sesame.wisc.edu) that captures all of the experimental protocols, background information, and experimental data associated with metabolomics samples. Sesame was designed to help coordinate research efforts in laboratories with high sample throughput and multiple investigators and to track all of the actions that have taken place in a particular study.
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            Theory and application of the maximum likelihood principle to NMR parameter estimation of multidimensional NMR data.

            A general theory has been developed for the application of the maximum likelihood (ML) principle to the estimation of NMR parameters (frequency and amplitudes) from multidimensional time-domain NMR data. A computer program (ChiFit) has been written that carries out ML parameter estimation in the D-1 indirectly detected dimensions of a D-dimensional NMR data set. The performance of this algorithm has been tested with experimental three-dimensional (HNCO) and four-dimensional (HN(CO)-CAHA) data from a small protein labeled with 13C and 15N. These data sets, with different levels of digital resolution, were processed using ChiFit for ML analysis and employing conventional Fourier transform methods with prior extrapolation of the time-domain dimensions by linear prediction. Comparison of the results indicates that the ML approach provides superior frequency resolution compared to conventional methods, particularly under conditions of limited digital resolution in the time-domain input data, as is characteristic of D-dimensional NMR data of biomolecules. Close correspondence is demonstrated between the results of analyzing multidimensional time-domain NMR data by Fourier transformation, Bayesian probability theory [Chylla, R.A. and Markley, J.L. (1993) J. Biomol. NMR, 3, 515-533], and the ML principle.
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              Anal. Chem.

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                Author and article information

                Journal
                Anal Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                28 April 2011
                15 June 2011
                : 83
                : 12
                : 4871-4880
                Affiliations
                [1] National Magnetic Resonance Facility at Madison and Department of Biochemistry, simpleUniversity of Wisconsin-Madison , 433 Babcock Drive, Madison, Wisconsin 53706, United States
                Author notes
                [* ]To whom correspondence should be addressed. Tel:(608) 262-0459. E-mail: rchylla@ 123456wisc.edu .
                Article
                10.1021/ac200536b
                3114465
                21526800
                cbcf4957-3428-433d-8179-3e6d057b60ef
                Copyright © 2011 American Chemical Society

                This is an open-access article distributed under the ACS AuthorChoice Terms & Conditions. Any use of this article, must conform to the terms of that license which are available at http://pubs.acs.org.

                History
                : 03 March 2011
                : 28 April 2011
                : 26 May 2011
                : 15 June 2011
                : 28 April 2011
                Funding
                National Institutes of Health, United States
                Categories
                Article
                Custom metadata
                ac200536b
                ac-2011-00536b

                Analytical chemistry
                Analytical chemistry

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