A model-based method for the prediction of the isotopic distribution of peptides
Faculty of Sciences. Chemistry
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
New York, N.Y.
Journal of the American Society for Mass Spectrometry. - New York, N.Y.
, p. 703-712
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) , using a scaled version of the average amino acid Averagine (Senko et al. 1995) . Another method, introduced by Breen et al., approximates the result of the multinomial expansion by a Poisson model (Breen et al. 2000) . 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) . 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.