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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 database, along with multiple searching algorithms, addresses this issue and provides a better identification tool. The currently implemented algorithms are K-mutation algorithm, spectral 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 provide a framework for high speed query processing and for efficient identification task. This new framework allows a community of users integratively to build a larger database and provide more efficient protein identification tools.