Abstract
BackgroundSimple 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.ResultsTo 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.ConclusionsApplication 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.
Highlights
Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional nuclear magnetic resonance (NMR) spectra, but often produce wrong intensities and frequencies for overlapping peak clusters
The precise estimation of the frequencies of peaks in nuclear magnetic resonance (NMR) spectra is often complicated by poor signal-to-noise ratio and peak overlap
The peak list of 201 peaks is given in Additional file 1: Table S1
Summary
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 be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have