Methodology

Analytical model of peptide mass cluster centres with applications

Witold E Wolski^1,2 , Malcolm Farrow¹ , Anne-Katrin Emde² , Hans Lehrach⁴ , Maciej Lalowski³ and Knut Reinert²

¹School of Mathematics and Statistics, Merz Court, University of Newcastle upon Tyne, NE1 7RU, UK

²Institute for Computer Science, Free University Berlin, Takustr. 9, 14195 Berlin, Germany

³Max Delbrück Center for Molecular Medicine, Robert-Roessle-Str. 10, D-13125 Berlin-Buch, Germany

⁴Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, D-14195 Berlin, Germany

author email corresponding author email

Proteome Science 2006, 4:18doi:10.1186/1477-5956-4-18

Published:	23 September 2006

Abstract

Background

The elemental composition of peptides results in formation of distinct, equidistantly spaced clusters across the mass range. The property of peptide mass clustering is used to calibrate peptide mass lists, to identify and remove non-peptide peaks and for data reduction.

Results

We developed an analytical model of the peptide mass cluster centres. Inputs to the model included, the amino acid frequencies in the sequence database, the average length of the proteins in the database, the cleavage specificity of the proteolytic enzyme used and the cleavage probability. We examined the accuracy of our model by comparing it with the model based on an in silico sequence database digest. To identify the crucial parameters we analysed how the cluster centre location depends on the inputs. The distance to the nearest cluster was used to calibrate mass spectrometric peptide peak-lists and to identify non-peptide peaks.

Conclusion

The model introduced here enables us to predict the location of the peptide mass cluster centres. It explains how the location of the cluster centres depends on the input parameters. Fast and efficient calibration and filtering of non-peptide peaks is achieved by a distance measure suggested by Wool and Smilansky.