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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8048

Title: A model-based method for the prediction of the isotopic distribution of peptides
Authors: VALKENBORG, Dirk
JANSEN, Ivy
BURZYKOWSKI, Tomasz
Issue Date: 2008
Publisher: Elsevier
Citation: JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY, 19(5). p. 703-712
Abstract: The process of monoisotopic mass determination, i.e., nomination of the correct peak of an isotopically resolved group of peptide peaks as a monoisotopic peak, requires prior information about the isotopic distribution of the peptide. This points immediately to the difficulty of monoisotopic mass determination, whereas a single mass spectrum does not contain information about the atomic composition of a peptide and therefore the isotopic distribution of the peptide remains unknown. To solve this problem a technique is required, which is able to estimate the isotopic distribution given the information of a single mass spectrum. Senko et al. calculated the average isotopic distribution for any mass peptide via the multinomial expansion (Yergey 1983) [1], using a scaled version of the average amino acid Averagine (Senko et al. 1995) [2]. Another method, introduced by Breen et al., approximates the result of the multinomial expansion by a Poisson model (Breen et al. 2000) [3]. Although both methods perform well, they have their specific limitations. In this manuscript, we propose an alternative method for the prediction of the isotopic distribution based on a model for consecutive ratios of peaks from the isotopic distribution, similar in spirit to the approach introduced by Gay et al. (1999) [5]. The presented method is computationally simple and accurate in predicting the expected isotopic distribution. Further, we extend our method to estimate the isotopic distribution of sulphur-containing peptides. This is important because the naturally occurring isotopes of sulphur have an impact on the isotopic distribution of a peptide.
URI: http://hdl.handle.net/1942/8048
DOI: 10.1016/j.jasms.2008.01.009
ISI #: 000255722200010
ISSN: 1044-0305
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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