Beyond Delta Masses: MS Andrea Directly Resolves Combinatorial Peptide Modifications in Open Searches

Open modification search (OMS) strategies have gained popularity in mass spectrometry-based proteomics for identification of peptides carrying unknown or unexpected post-translational modifications. However, most OMS search engines report only the overall mass difference between the precursor and the matched peptide and do not explicitly identify or score combinations of multiple modifications at the peptide-spectrum match (PSM) level, leaving the interpretation of mass shifts up to the end user and to using downstream analysis tools. Here, we introduce MS Andrea, a novel OMS search engine developed to directly identify and score combinations of up to four variable modifications per peptide without having to predefine them. MS Andrea uses a sequence tag-based strategy to efficiently filter candidate peptides prior to scoring. Remaining candidates are evaluated using the MS Amanda scoring function, first considering fixed modifications only, followed by a second scoring stage in which combinations of modifications from the Unimod database are considered based on the observed mass difference and matched to the spectrum. We evaluated MS Andrea using phosphopeptide datasets from HeLa cells and Arabidopsis thaliana and compared its performance with the widely used OMS engines MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1 % false discovery rate while achieving comparable peptide-level identifications. Importantly, MS Andrea directly reports modification identities and sites at the PSM level and enables the identification of peptides having up to four variable modifications. Together, these results demonstrate that MS Andrea facilitates more detailed and interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomic analyses.