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

Abstract

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 engines report only the overall mass difference between precursor and matched peptide, without explicitly identifying or scoring combinations of modifications at the PSM level. Here, we introduce MS Andrea, a novel OMS search engine that directly identifies and scores combinations of modifications without predefining them. MS Andrea uses a sequence tag-based strategy to filter candidate peptides, evaluated using the MS Amanda scoring function. First fixed modifications only, then combinations of modifications from the Unimod database based on the observed mass shift. We evaluated MS Andrea using a human histone dataset and two phosphopeptide datasets (HeLa cells and Arabidopsis thaliana), comparing its performance with MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1 % FDR using the standard target-decoy approach while achieving higher or comparable numbers using model-based FDR estimation. Importantly, MS Andrea reports modification identities and sites for up to four modifications at the PSM level. Together, these results demonstrate that MS Andrea enables more detailed, interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomics.