Protein sequence databases are indispensable tools for life science research including mass spectrometry (MS)-based proteomics. In current database construction processes, sequence similarity clustering is used to reduce redundancies in the source data. Albeit powerful, it ignores the peptide centric nature of proteomic data and the fact that MS is able to distinguish similar sequences. Therefore, we introduce an approach that structures the protein sequence space at the peptide level using theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry centric protein sequence database (MScDB). The core modules of MScDB are an in-silico proteolytic digest and a peptide centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry.
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