Fragment Coverage: Unified Visualization and Quantitative Assessment of MS/MS Fragmentation Supports Metabolite Identification in Macromolecule Drug Metabolism Studies
Paula Cifuentes, Albert Garriga, Fabien Fontaine, Ismael ZamoraABSTRACT
Rationale
Peptides and oligonucleotides are increasingly used as therapeutic agents, making the identification and structural elucidation of their metabolites a critical step in drug development. Liquid chromatography–mass spectrometry (LC‐MS/MS) is the primary analytical technique for this purpose; however, extensive backbone fragmentation generates highly complex MS/MS spectra, making spectral interpretation challenging and limiting the exploitation of the structural information contained in MS/MS fragmentation data.
Methods
This study introduces two fragment‐based metrics, fragment coverage and complete fragment coverage, to quantify structural coverage derived from MS/MS fragmentation at metabolite and parent compound levels. Their performance was evaluated across 33 LC‐MS/MS experiments comprising peptide and oligonucleotide datasets. Ranking analysis based on mean average precision (MAP) assessed discrimination between true and false metabolite assignments. Additionally, a fragment viewer was developed to map MS/MS fragment ions onto structures, facilitating visualization and spectral interpretation.
Results
A total of 255 metabolites were identified, including 159 true metabolites and 96 false positives. True metabolites consistently exhibited higher fragment coverage and complete fragment coverage values than false positives. This trend was supported by ranking analysis using mean average precision (MAP), where complete fragment coverage achieved the highest performance (0.9737), followed by fragment coverage (0.9676), MassMetaSite score (MMS score) (0.9390), and isotopic similarity (0.9090). Two representative case studies demonstrated the applicability of the proposed metrics.
Conclusions
The proposed fragment coverage metrics enable quantitative assessment of MS/MS fragmentation, providing complementary information to support manual interpretation of LC‐MS/MS data in MetID workflows. Together with the fragment viewer tool, these approaches facilitate more efficient and interpretable analysis of complex MS/MS spectra in macromolecules.