Motivation: A detailed analysis of multidimensional NMR spectra of macromolecules requires the identification of individual resonances (peaks). This task can be tedious and time-consuming and often requires support by experienced users. Automated peak picking algorithms were introduced more than 25 years ago, but there are still major deficiencies/flaws that often prevent complete and error free peak picking of biological macromolecule spectra. The major challenges of automated peak picking algorithms is both the distinction of artifacts from real peaks particularly from those with irregular shapes and also picking peaks in spectral regions with overlapping resonances which are very hard to resolve by existing computer algorithms. In both of these cases a visual inspection approach could be more effective than a "blind" algorithm.

Results: We present a novel approach using computer vision (CV) methodology which could be better adapted to the problem of peak recognition. After suitable "training" we successfully applied the CV algorithm to spectra of medium sized soluble proteins up to molecular weights of 26 kDa and to a 130 kDa complex of a tetrameric membrane protein in detergent micelles. Our CV approach outperforms commonly used programs. With suitable training data sets the application of presented method can be extended to automated peak picking in multidimensional spectra of nucleic acids or carbohydrates and adapted to solid state NMR spectra.

Availability: CV-Peak Picker is available upon request from the authors.