Using wavelet de-noised spectra in NMR screening

Nikola Trbovic, Felician Dancea, Thomas Langer, Ulrich Günther*

*Corresponding author for this work
23 Citations (Scopus)


Principal component analysis (PCA) is a commonly used algorithm in multivariate analysis of NMR screening data. PCA substantially reduces the complexity of data in which a large number of variables are interrelated. For series of NMR spectra obtained for ligand binding, it is commonly used to visually group spectra with a similar response to ligand binding. A series of filters are applied to the experimental data to obtain suitable descriptors for PCA which optimize computational efficiency and minimize the weight of small chemical shift variations. The most common filter is bucketing where adjacent points are summed to a bucket. To overcome some inherent disadvantages of the bucketing procedure we have explored the effect of wavelet de-noising on multivariate analysis, using a series of HSQC spectra of proteins with different ligands present. The combination of wavelet de-noising and PCA is most efficient when PCA is applied to wavelet coefficients. This new algorithm yields good clustering and can be applied to series of one- or two-dimensional spectra.

Original languageEnglish
JournalJournal of Magnetic Resonance
Issue number2
Pages (from-to)280-287
Number of pages8
Publication statusPublished - 04.2005

Research Areas and Centers

  • Academic Focus: Center for Infection and Inflammation Research (ZIEL)


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