Proteome Discoverer

Proteome Discoverer includes the following:

• Industry-standard database search algorithms SEQUEST and Mascot (optional) for confident and comprehensive protein identification and PTM characterization
• Z·Core database search algorithm for quick and easy searching of ETD data
• SRF file import wizard for seamless transition from previous BioWorks™ platforms
• Ability to easily combine results of multiple search engines and dissociation techniques (CID, HCD, ETD) in a single consensus report
• Relative quantitation capability for isotopically-labeled peptides
• False Discovery Rate (FDR) for each search for validation of protein IDs
• Simple wizards and customizable workflows to analyze MSn data from raw spectra through protein annotation
• Support for data standards developed by the HUPO Proteomics Standards Initiative (PSI)
• Annotation capability to provide biological information about proteins identified.

Proteome Discoverer contains several database search engines to complement the breadth of dissociation techniques such as CID, ETD, and HCD available with Thermo Scientific LTQ™ series linear ion trap-based mass spectrometers. Different dissociation techniques provide complementary results and often increase protein coverage and the number of identified proteins, allowing users to exploit the benefits of their high mass accuracy, high resolution data.

The Peptide Consensus View in Proteome Discoverer facilitates interpretation of database search results by combining CID and ETD fragment ion matches to increase peptide coverage. Multiple complex data sets are easily organized and advanced visualization tools facilitate data interpretation and results sharing.

Proteome Discoverer’s Workflow Editor allows the user to select from a comprehensive menu of search algorithms, dissociation methods, results filters and quantitative methods to set up a custom workflow, and even consolidate the results in a single easy-to-read report. In addition, the PMF Workflow facilitates analysis of high mass accuracy MALDI data.

The novel database search algorithm, Z·Core, specifically takes into account the unique characteristics of ETD spectra. It includes a data pre-processing step that assigns a charge state to precursor ions from unit resolution measurements according to the characteristics of ETD spectra, such as intense charge-reduced precursor ion peaks. Z·Core increases the throughput of ETD data analysis, maximizes database search efficiency, increases the confidence of the subsequent identification results and reduces the time necessary for ETD data interrogation.

Proteome Discoverer accommodates several relative quantitation methods, including common isobaric mass tagging techniques such as Tandem Mass Tags® (TMT), reporting the peptides identified and their relative expression ratios in a simple graphical display. Customizable filters can be designed to use only unique peptides to calculate accurate relative protein quantitation, and normalization of peptide concentration corrects for experimental errors.

Proteome Discoverer provides annotation workflows for LC-MS/MS data through the InforSense Platform that automatically retrieves pertinent information about each identified protein from public databases, providing annotation including GO (Gene Ontology) classifications, sites of posttranslational modifications (PTMs), and literature references. Annotation capabilities such as these provide specific biological information about proteins and their post-translational modifications and can be used to help design iterative and targeted follow-up experiments.

Additionally, identification results annotated with Meta information from public databases are easily exported to Excel and the data can be further interrogated to verify known and predicted post-translational modifications and view other descriptive information for each.

LC-MSn data acquisition can now be combined with creative data interrogation strategies via creation of inclusion and exclusion lists for direct import into Xcalibur instrument methods, and iterative workflows can be created to specifically look for low-level sample components.