Peptide identification relies in the majority of mass spectrometry-based proteomics experiments on matching of experimental data against peptide and fragment ion masses derived from in silico digests of protein databases. One of the main drawbacks of this approach is that modifications have to be defined for database searching and therefore no unexpected modifications can be identified in a standard setup. Consequently, in many bottom-up proteomics experiments, unexpected modifications are not identified, even if high-quality fragment ion spectra of the modified peptides were acquired. It is therefore often not straightforward to identify unexpected modifications. In this protocol, we describe a stepwise procedure to identify unexpected modifications at peptides using the database search algorithm Mascot. The workflow includes parallel searches for the identification of known modifications at unexpected amino acids, error tolerant searches for modifications unexpected in the sample but known to the community, and mass tolerant searches for entirely unknown modifications. Furthermore, we suggest a follow-up strategy consisting of (1) verification of identified modifications in the initial dataset and (2) targeted experiments using synthetic peptides.
Keywords: Bottom-up proteomics; Data analysis; Error tolerant search; Mascot; Mass spectrometry; Mass tolerant search; Posttranslational modifications; Unexpected modifications.