Peak picking NMR spectral data using non-negative matrix factorization

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 lea...

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Bibliographic Details
Published in:BMC bioinformatics 2014-02, Vol.15 (1), p.46-46, Article 46
Main Authors: Tikole, Suhas, Jaravine, Victor, Rogov, Vladimir, Dötsch, Volker, Güntert, Peter
Format: Article
Language:English
Subjects:
Algorithms
Automation
Bioinformatics
Data analysis
Data processing
Decomposition
Factorization
Fourier Analysis
Lists
Methodology
Models, Chemical
Models, Statistical
NMR
Nuclear magnetic resonance
Nuclear Magnetic Resonance, Biomolecular - methods
Picking
Proteins - chemistry
Spectra
Three dimensional
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Summary: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. 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. 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.
ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-15-46