Summary
Peptide and protein identification plays an important role in proteomics research. Database search tools such as Mascot, Sequest, X!Tandem and OMMSA as well as De Novo peptide identification tools such as Peaks, Lutefisk, Mascot Distiller and PepNovo are just an example of tools already available to aid in this step. Although a plethora of these tools can be used in peptide identification, still there is need for algorithms that are tailored for specific instruments or proteomics applications. One example is the current application of a metalloendopeptidase (Lys-N), which cleaves a peptide bond N-terminally of the basic Lysine residue (Taouatas et al, 2008). As a result this application yields a large amount of fragmentation spectra which are dominated by b-ions (when CID is applied) or c-ions (when ETD is applied) lacking their counter ions (y or z-ions). These spectra are in general less complex and contain a more complete sequence ladder when compared to the more conventional application of Trypsin and CID to generate MS/MS spectra. Even though these spectra are less complex, most if not all peptides search tools fail to identify peptides with a high confidence.
In this project we aim to develop a framework for protein identification integrating several algorithms and tools that help in a better identification of peptides obtained after Lys-N cleavage from (MS/MS) fragmentation spectra. Additionally we aim to develop tools that integrate efficiently MS/MS data of multiple experiments. For example by setting up a repository for MS(MS) data, such as the PRIDE repository for protein identifications, we will be able to create filters and search tools that use previously identified peptides and their corresponding spectra to annotate quickly with higher confidence spectra from new experiments. Finally we aim to develop a library of algorithms for (pre) processing of MS(MS) spectra.