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Calibration of mass spectrometric peptide mass fingerprint data without specific external or internal calibrants

Lookup NU author(s): Eryk Wolski

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Abstract

Background: Peptide Mass Fingerprinting (PMF) is a widely used mass spectrometry (MS) method of analysis of proteins and peptides. It relies on the comparison between experimentally determined and theoretical mass spectra. The PMF process requires calibration, usually performed with external or internal calibrants of known molecular masses. Results: We have introduced two novel MS calibration methods. The first method utilises the local similarity of peptide maps generated after separation of complex protein samples by two-dimensional gel electrophoresis. It computes a multiple peak-list alignment of the data set using a modified Minimum Spanning Tree (MST) algorithm. The second method exploits the idea that hundreds of MS samples are measured in parallel on one sample support. It improves the calibration coefficients by applying a two-dimensional Thin Plate Splines (TPS) smoothing algorithm. We studied the novel calibration methods utilising data generated by three different MALDI-TOF-MS instruments. We demonstrate that a PMF data set can be calibrated without resorting to external or relying on widely occurring internal calibrants. The methods developed here were implemented in R and are part of the BioConductor package mscalib available from http://www.bioconductor.org. Conclusion: The MST calibration algorithm is well suited to calibrate MS spectra of protein samples resulting from two-dimensional gel electrophoretic separation. The TPS based calibration algorithm might be used to correct systematic mass measurement errors observed for large MS sample supports. As compared to other methods, our combined MS spectra calibration strategy increases the peptide/protein identification rate by an additional 5 - 15%. © 2005 Wolski et al; licensee BioMed Central Ltd.


Publication metadata

Author(s): Wolski WE, Lalowski M, Jungblut P, Reinert K

Publication type: Article

Publication status: Published

Journal: BMC Bioinformatics

Year: 2005

Volume: 6

Issue: 203

Pages: 17

Print publication date: 15/08/2005

ISSN (electronic): 1471-2105

Publisher: BioMed Central

URL: http://dx.doi.org/10.1186/1471-2105-6-203

DOI: 10.1186/1471-2105-6-203


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