ArticleJournal of biomolecular NMR2017
Automation of peak-tracking analysis of stepwise perturbed NMR spectra.
Article in Journal of biomolecular NMR, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Harmonizing Peak Matching between Multidimensional NMR Spectra.Journal of chemical information and modeling · 2026Article
- Fully Automated Characterization of Protein-Peptide Binding by Microfluidic 2D NMR.Journal of the American Chemical Society · 2023Article
- Chemical shift transfer: an effective strategy for protein NMR assignment with ARTINA.Frontiers in molecular biosciences · 2023Article
- Automated Determination of Nuclear Magnetic Resonance Chemical Shift Perturbations in Ligand Screening Experiments: The PICASSO Web Server.Journal of chemical information and modeling · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We describe a new algorithmic approach able to automatically pick and track the NMR resonances of a large number of 2D NMR spectra acquired during a stepwise variation of a physical parameter. The method has been named Trace in Track (TINT), referring to the idea that a gaussian decomposition traces peaks within the tracks recognised through 3D mathematical morphology. It is capable of determining the evolution of the chemical shifts, intensity and linewidths of each tracked peak.The performances obtained in term of track reconstruction and correct assignment on realistic synthetic spectra were high above 90% when a noise level similar to that of experimental data were considered. TINT was applied successfully to several protein systems during a temperature ramp in isotope exchange experiments. A comparison with a state-of-the-art algorithm showed promising results for great numbers of spectra and low signal to noise ratios, when the graduality of the perturbation is appropriate. TINT can be applied to different kinds of high throughput chemical shift mapping experiments, with quasi-continuous variations, in which a quantitative automated recognition is crucial.
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