Peak picking NMR spectral data using non-negative matrix factorization

  • Background: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. Results: To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. Conclusions: Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap.

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Metadaten
Author:Suhas Tikole, Victor Jaravine, Vladimir V. Rogov, Volker DötschORCiDGND, Peter Güntert
URN:urn:nbn:de:hebis:30:3-330323
DOI:https://doi.org/10.1186/1471-2105-15-46
ISSN:1471-2105
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/24511909
Parent Title (English):BMC bioinformatics
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Language:English
Date of Publication (online):2014/02/11
Date of first Publication:2014/02/11
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/03/03
Tag:NMR spectrum; Non-negative matrix factorization; Peak overlap; Peak picking
Volume:15
Issue:46
Page Number:7
Note:
© 2014 Tikole et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
HeBIS-PPN:364160446
Institutes:Biochemie, Chemie und Pharmazie / Biochemie und Chemie
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Sammlung Biologie / Sondersammelgebiets-Volltexte
Licence (German):License LogoCreative Commons - Namensnennung 2.0