Crystal-C: A Computational Tool for Refinement of Open Search Results

Published in Journal of Proteome Research, 2020

Recommended citation: Chang HY, Kong AT, da Veiga Leprevost F, Avtonomov DM, Haynes SE, Nesvizhskii AI. Crystal-C: A Computational Tool for Refinement of Open Search Results. J Proteome Res. 2020;19(6):2511-2515. doi:10.1021/acs.jproteome.0c00119

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Shotgun proteomics using liquid chromatography coupled to mass spectrometry (LC-MS) is commonly used to identify peptides containing post-translational modifications. With the emergence of fast database search tools such as MSFragger, the approach of enlarging precursor mass tolerances during the search (termed "open search") has been increasingly used for comprehensive characterization of post-translational and chemical modifications of protein samples. However, not all mass shifts detected using the open search strategy represent true modifications, as artifacts exist from sources such as unaccounted missed cleavages or peptide co-fragmentation (chimeric MS/MS spectra). Here, we present Crystal-C, a computational tool that detects and removes such artifacts from open search results. Our analysis using Crystal-C shows that, in a typical shotgun proteomics data set, the number of such observations is relatively small. Nevertheless, removing these artifacts helps to simplify the interpretation of the mass shift histograms, which in turn should improve the ability of open search-based tools to detect potentially interesting mass shifts for follow-up investigation.