Implementing Spectral Similarity Algorithms for Protein Identification

Database searching for protein identification is an efficient approach for MS/MS spectra identification compared to the existing approaches, such as protein identification with antibodies, and chemical degradation. In order for a database search to give reliable results, the database itself, as well as the searching algorithm , must be reliable. However, MS/MS spectra identification is still imperfect. The Illinois Bio-Grid Mass Spectrometry Database (IBG-MSD), an empirically derived curated and annotated databa se, along with multiple searching algorithms, addresses this issue and provides a bet ter identification tool. The currently implemented algorithms are K-mutation algorithm, sp ectral contrast angle, and similarity index algorithm. The search engine allows users to search for post-translationally modified peptide using the K-mutation algorithm implemented. We pro vide a framework for high speed query processing and for efficient identification task. T his new framework allows a community of users integratively to build a larger database and provid e more efficient protein identification tools.