Structure-aware algorithm enhances MS/MS interpretation for modified and cyclic peptides like liraglutide and cyclosporine

Conventional tandem mass spectrometry (MS/MS) struggles with peptidomimetics such as fatty-acid-modified, head-to-tail cyclic, and disulfide-constrained peptides. Their intricate structures lead to fragmentation pathways that extend beyond canonical backbone cleavages, often requiring multiple bond breakages to generate sequence-informative ions. This analytical gap hinders comprehensive characterization and quality control for novel peptide therapeutics, which frequently incorporate these modifications for improved stability, bioavailability, and target selectivity.

Researchers developed a structure-aware algorithm designed to calculate and label theoretical fragment ions across modified and cyclic peptides, including side-chain and disulfide-related fragments. To evaluate assignment accuracy, they integrated three numerical metrics: sequence coverage, intensity coverage, and signal coverage, assessing their behavior across m/z tolerances, intensity thresholds, and charge states. The algorithm was tested using representative MS/MS data from liraglutide, semaglutide, cyclosporine, oxytocin, somatostatin, and angiotensin-related peptides.

The structure-aware algorithm reliably distinguished correct from incorrect assignments, even for closely related sequences. Incorporating fatty-acid-specific fragments significantly increased intensity coverage for liraglutide and semaglutide. For the cyclic peptide cyclosporine, MS3 improved sequence coverage relative to MS2, but no additional benefit was observed at MS4 level. For disulfide-bonded peptides, the combination of electron-transfer dissociation (ETD) and collision-induced dissociation (CID) shifted fragment distributions toward disulfide-cleavage products compared to CID alone, which the algorithm's dedicated disulfide labels effectively captured. These results demonstrate that structure-aware fragment calculation, coupled with explicit assignment metrics, enables more comprehensive and standardized interpretation of complex peptidomimetic MS/MS data. This approach lays a foundation for reproducible benchmarking, extendable to additional cyclization, modification, and fragmentation techniques.

The algorithm reliably distinguished correct from incorrect assignments, including closely related sequences, for complex peptidomimetics.