A general Bayesian method for an automated signal class recognition in 2D NMR spectra combined with a multivariate discriminant analysis |
| |
Authors: | Christoph Antz Klaus-Peter Neidig Hans Robert Kalbitzer |
| |
Institution: | (1) Department of Biophysics, Max-Planck-Institute for Medical Research, P.O. Box 103820, D-69028 Heidelberg, Germany;(2) Bruker Analytische Messtechnik, D-76287 Rheinstetten, Germany |
| |
Abstract: | Summary A generally applicable method for the automated classification of 2D NMR peaks has been developed, based on a Bayesian approach coupled to a multivariate linear discriminant analysis of the data. The method can separate true NMR signals from noise signals, solvent stripes and artefact signals. The analysis relies on the assumption that the different signal classes have different distributions of specific properties such as line shapes, line widths and intensities. As to be expected, the correlation network of the distributions of the selected properties affects the choice of the discriminant function and the final selection of signal properties. The classification rule for the signal classes was deduced from Bayes's theorem. The method was successfully tested on a NOESY spectrum of HPr protein from Staphylococcus aureus. The calculated probabilities for the different signal class memberships are realistic and reliable, with a high efficiency of discrimination between peaks that are true NOE signals and those that are not. |
| |
Keywords: | Multivariate discriminant analysis 2D NMR spectroscopy NOESY Bayesian analysis Peak recognition NMR molecular structure |
本文献已被 PubMed SpringerLink 等数据库收录! |
|