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Modeling peptide mass fingerprinting data using the atomic composition of peptides
Author(s) -
Gay Steven,
Binz PierreAlain,
Hochstrasser Denis F.,
Appel Ron D.
Publication year - 1999
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/(sici)1522-2683(19991201)20:18<3527::aid-elps3527>3.0.co;2-9
Subject(s) - peptide mass fingerprinting , mass spectrometry , chemistry , peptide , mass spectrum , bottom up proteomics , composition (language) , sequence database , biological system , protein mass spectrometry , isobaric labeling , analytical chemistry (journal) , chromatography , computational biology , tandem mass spectrometry , proteomics , biochemistry , biology , gene , linguistics , philosophy
The peptide mass fingerprinting technique is commonly used for identifying proteins analyzed by mass spectrometry (MS) after enzymatic digestion. Our goal is to build a theoretical model that predicts the mass spectra of such digestion products in order to improve the identification and characterization of proteins using this technique. We present here the first step towards a full MS model. We have modeled MS spectra using the atomic composition of peptides and evaluated the influence that this composition may have on the MS signals. Peptides deduced from the SWISS‐PROT protein sequence database were used for the calculation. To validate the model, the variability of the peptide mass distribution in SWISS‐PROT was compared to two theoretical, randomly generated databases. Functions have been built that describe the behavior of the isotopic distribution according to the mass of peptides. The variability of these functions was analyzed. In particular, the influence of sulfur was studied. This work, while representing only a first step in the construction of an MS model, yields immediate practical results, as the new isotopic distribution model significantly improves peak detection in MS spectra used by protein identification algorithms.