Modeling peptide mass fingerprinting data using the atomic composition of peptides

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 characteriza tion of proteins using this technique. We present here the first step towar ds 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 datab ase were used for the calculation. To validate the model, the variability o f the peptide mass distribution in SWISS-PROT was compared to two theoretic al, randomly generated databases. Functions have been built that describe t he behavior of the isotopic distribution according to the mass of peptides. The variability of these functions was analyzed. In particular, the influe nce 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 i n MS spectra used by protein identification algorithms.