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Open Access Methodology article

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Wiebke Timm14*, Alexandra Scherbart1, Sebastian Böcker2, Oliver Kohlbacher3 and Tim W Nattkemper1

Author Affiliations

1 Applied Neuroinformatics Group, Bielefeld University, Germany

2 Friedrich-Schiller-University, Jena, Germany

3 Simulation of biological systems, Center for Bioinformatics Tübingen, Eberhard Karls University, Tübingen, Germany

4 Intl. NRW Graduate School for Bioinformatics and Genome Research, Bielefeld University, Germany

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BMC Bioinformatics 2008, 9:443  doi:10.1186/1471-2105-9-443

Published: 20 October 2008

Abstract

Background

Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e.g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification.

Results

In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation).

Conclusion

The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics.