Abstract
Proteins with deamidated/citrullinated amino acids play critical roles in the pathogenesis of many human diseases; however, identifying these modifications in complex biological samples has been an ongoing challenge. Herein we present a method to accurately identify these modifications from shotgun proteomics data generated by a deep proteome profiling study of human pancreatic islets obtained by laser capture microdissection. All MS/MS spectra were searched twice using MSGF+ database matching, with and without a dynamic +0.9840 Da mass shift modification on amino acids asparagine, glutamine, and arginine (NQR). Consequently, each spectrum generates two peptide-to-spectrum matches (PSMs) with MSGF+ scores, which were used for the Delta Score calculation. It was observed that all PSMs with positive Delta Score values were clustered with mass errors around 0 ppm, while PSMs with negative Delta Score values were distributed nearly equally within the defined mass error range (20 ppm) for database searching. To estimate false discovery rate (FDR) of modified peptides, a "target-mock" strategy was applied in which data sets were searched against a concatenated database containing "real-modified" (+0.9840 Da) and "mock-modified" (+1.0227 Da) peptide masses. The FDR was controlled to ∼2% using a Delta Score filter value greater than zero. Manual inspection of spectra showed that PSMs with positive Delta Score values contained deamidated/citrullinated fragments in their MS/MS spectra. Many citrullinated sites identified in this study were biochemically confirmed as autoimmunogenic epitopes of autoimmune diseases in literature. The results demonstrated that in situ deamidated/citrullinated peptides can be accurately identified from shotgun tissue proteomics data using this dual-search Delta Score strategy. Raw MS data is available at ProteomeXchange (PXD010150).