BACKGROUND:

In the last decade, techniques were established for the large scale genome-wide analysis of proteins, RNA, and metabolites, and database solutions have been developed to manage the generated data sets. The Golm Metabolome Database for metabolite data (GMD) represents one such effort to make these data broadly available and to interconnect the different molecular levels of a biological system 1. As data interpretation in the light of already existing data becomes increasingly important, these initiatives are an essential part of current and future systems biology.

RESULTS:

A mass spectral library consisting of experimentally derived tryptic peptide product ion spectra was generated based on liquid chromatography coupled to ion trap mass spectrometry (LC-IT-MS). Protein samples derived from Arabidopsis thaliana, Chlamydomonas reinhardii, Medicago truncatula, and Sinorhizobium meliloti were analysed. With currently 4,557 manually validated spectra associated with 4,226 unique peptides from 1,367 proteins, the database serves as a continuously growing reference data set and can be used for protein identification and quantification in uncharacterized biological samples. For peptide identification, several algorithms were implemented based on a recently published study for peptide mass fingerprinting 2 and tested for false positive and negative rates. An algorithm which considers intensity distribution for match correlation scores was found to yield best results. For proof of concept, an LC-IT-MS analysis of a tryptic leaf protein digest was converted to mzData format and searched against the mass spectral library. The utility of the mass spectral library was also tested for the identification of phosphorylated tryptic peptides. We included in vivo phosphorylation sites of Arabidopsis thaliana proteins and the identification performance was found to be improved compared to genome-based search algorithms. Protein identification by ProMEX is linked to other levels of biological organization such as metabolite, pathway, and transcript data. The database is further connected to annotation and classification services via BioMoby.

CONCLUSION:

The ProMEX protein/peptide database represents a mass spectral reference library with the capability of matching unknown samples for protein identification. The database allows text searches based on metadata such as experimental information of the samples, mass spectrometric instrument parameters or unique protein identifier like AGI codes. ProMEX integrates proteomics data with other levels of molecular organization including metabolite, pathway, and transcript information and may thus become a useful resource for plant systems biology studies. The ProMEX mass spectral library is available at http://promex.mpimp-golm.mpg.de/.