It has been noted [1] that only a handful of a protein’s possible tryptic peptides are consistently observed in proteomics experiments. We denote these consistently observed peptides to be proteotypic peptides. Such peptides have a variety of potential applications in proteomic research including improving protein identification scoring functions of database search software, providing a panel of reagents for protein quantification as well as the annotation of genomes for coding sequences of e.g. the hundreds of sequenced bacterial genomes some of which are important model organisms in systems biology and a guide for peptide selection in targeted proteomics experiments. Here we present PeptideSieve, an alpha version of a computational tool to predict a peptide’s proteotypic propensity based on its physico-chemical properties. The resulting predictors have the ability to accurately identify proteotypic peptides from any protein sequence and offer starting points for generating a physical model describing the factors that govern elements of proteomic workflows such as digestion, chromatography, ionization and fragmentation.

The software consists of a single C++ program. The input is either a FASTA file of protein sequences or a TXT file of peptide sequences. The program then returns which of a protein's peptides are most likely to be proteotypic for each of four common experimental designs.