bioRxiv2025
Post-translational modifications (PTMs), such as SUMOylation and ubiquitination, regulate key cellular processes by covalently attaching to lysine residues. While mass spectrometry allows site-specific identification of PTMs, most existing search engines are optimized for small, non-fragmenting modifications and struggle to detect large, fragmenting protein-based modifiers. We refer to these as Sequence-Based Modifiers (SBMs). To overcome this limitation, we developed an SBM-specific search strategy within MaxQuant that accounts for the fragmentation behavior of SBMs during peptide identification. Using publicly available datasets, we validated our approach for SUMO2/3. Our analysis identified distinct diagnostic features and characteristic mass shifts associated with SBM fragmentation, referred to in this study as d-ions (diagnostic ions) and p-ions. By leveraging these features, our method improved the identification of SUMOylated peptides from human cell lines by 13%, SUMOylation sites in mouse embryonic cells by 18%, and in mouse adipocytes by 25%. Our search method improved spectral annotation of SBMs by up to 9% increase in the median Andromeda score. Taken together, we highlight the potential of our SBM search to enhance the discovery of protein-based modifications. HighlightsO_LIDevelopment of a MaxQuant module tailored for identifying Sequence-Based Modifiers (SBMs), including SUMO2/3 C_LIO_LIIncorporation of SBM-specific fragmentation patterns into search algorithms C_LIO_LIEnhanced biological discovery through improved PTM identification from mass spectrometry datasets C_LI In BriefHere, we introduce MaxSBM, an optimized framework for interpreting complex sequence-based modifiers (SBMs), particularly SUMO, within MaxQuant. Our approach incorporates SBM-specific d- and p-ion series into peptide scoring and annotation. By extending the theoretical spectral space to include fragments bearing partial SUMO (or other SBM) peptide remnants, MaxSBM provides a more comprehensive spectral annotation which enhances peptide scoring, resulting in increased identification rates at a higher confidence. Beyond methodological refinement, we validated MaxSBM via reanalysis of several physiological SUMO datasets, ultimately unlocking new insights via mapping of previously obscured modification sites.