ArticlePloS one2025
An automated software-assisted approach for exploring metabolic susceptibility and degradation products in macromolecules using high-resolution mass spectrometry.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Predicting enzymatic cleavage sites in cyclic peptides with non-canonical amino acids using a Graphormer model trained on MetID user data.Scientific reports · 2026Article
- Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows.Analytical chemistry · 2025Article
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Authors and funding
9 authors.
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Abstract
A comprehensive understanding of drug metabolism is crucial for advancements in drug development. Automation has improved various stages of this process, from compound procurement to data analysis, but significant challenges persist in the metabolite identification (MetID) of macromolecules due to their size, structural complexity, and associated computational demands. This study introduces new algorithms for automated Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) data analysis applicable to macromolecules. A novel peak detection approach based on the most abundant mass (MaM) is presented and systematically compared with the monoisotopic mass (MiM) approach, commonly used in small molecules MetID. Additionally, three structure visualization strategies, expanded (atom-level), non-expanded (monomer-level), and a hybrid mode, are evaluated for their impact on computation data processing time and interpretability, based on their distinct fragmentation strategies. The workflow was validated using six diverse datasets, comprising linear and cyclic peptides and oligonucleotides with both natural and unnatural monomers, covering a molecular weight range of 700-7630 Da. A total of 970 metabolites were identified under various experimental and ionization conditions. The MaM algorithm demonstrated higher scores and a greater number of matches, instilling greater confidence in the accurate prediction of metabolite structures, while the non-expanded visualization significantly reduced processing times (ranging from minutes to under an hour for most peptides). Furthermore, the visualization algorithm, which integrates monomer-level and atom/bond notation, enables clear localization of metabolic biotransformations. Compared to previous studies, the proposed workflow demonstrated reduced processing time, consistent detection of degradation products, and enhanced visualization capabilities, advancing automated MetID for macromolecules.
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Registered trials
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