Combining Tags of Various Lengths Benefits Peptide Identification in Bottom-up Proteomics.

Peptide identification provides key information for protein inference in bottom-up proteomics. Post-translational modifications (PTMs) are essential to understand cellular activities at the protein level. In current database search methods for peptide identification, precursor mass is a critical parameter to narrow down the search space. However, true peptides may be excluded from the search space if precursor masses are modified by PTMs. Thus, many researchers use peptide sequence segments called tags which are invariant to PTMs in database search. Shorter tags are more sensitive but less accurate, whereas longer tags are more accurate but less frequent. Current methods use tags of fixed lengths, ignoring the effect of different tag lengths. To address this issue, we propose to combine tags of various lengths to improve tag-based peptide identification methods. Using combined tags, true peptides are included in the search space in more cases, resulting in at least 35% and 49% more peptide identifications and PTM results compared to benchmark methods using the same quality control parameters.