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

Hui Yin Chang, Andy T. Kong, Felipe Da Veiga Leprevost, Dmitry M. Avtonomov, Sarah E. Haynes, Alexey I. Nesvizhskii, Alexey I. Nesvizhskii

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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.

Original languageEnglish
Pages (from-to)2511-2515
Number of pages5
JournalJournal of Proteome Research
Issue number6
StatePublished - 5 Jun 2020


  • LC-MS
  • liquid chromatography coupled to mass spectrometry
  • open search
  • peptide identification
  • post-translational modifications


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