Software:PeptideSieve
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| ===Getting the software=== | ===Getting the software=== | ||
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| - | <!--<FONT COLOR=RED>A new, re-written version of PeptideSieve will be released on or around October 8th 2007. This version fixes a known bug in converting some datasets, and will be available here as soon as it is released.</FONT>--> | ||
Revision as of 18:09, 15 April 2008
Peptide Sieve Package & Server
Contents |
Description
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.
Method outline
The program first performs an in silico digest of the protein and then converts each of the peptides into chemical property strings. The C++ program then computes a likelihood function, which scores the likelihood each peptide is proteotypic. It is important to note that the predictors are specific for particular experimental designs.
Reference
[1] Nat Biotechnol. 2007 Jan;25(1):125-31. Computational prediction of proteotypic peptides for quantitative proteomics. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R.
Getting the software
New native C++ version released 2/2008 Download Build instructions are available here.
Predictions and Supplementary Materials
AEBERSOLD_TABLE_S1_Observed_Proteins
AEBERSOLD_TABLE_S2_Proteotypic_Peptides
AEBERSOLD_TABLE_S11_YEAST_PREDICTIONS
AEBERSOLD_TABLE_S12_HUMAN_PREDICTIONS
