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Peptidomics and degradomics enables large-scale measurement of these peptides, yet most data analyses approaches treat peptides as isolated endpoints rather than intermediates produced by sequential cleavage. Here, we introduce degradation graphs, a probabilistic framework that represents proteolysis as a directed acyclic network of cleavage events with explicit absorption. From single-snapshot peptidomes, we infer graph weights by gradient descent or linear-flow optimization, quantify flows through branches and bottlenecks, and correct a core bias in conventional quantification. Across three biological datasets, failure to model downstream trimming leads to 3\u20134-fold underestimation of upstream proteolytic activity. Moreover, degradation graphs provide graph-structured features that enable machine learning models to capture protease-specific signatures from both graph topology and sequence context. Taken together, these findings establish explicit degradation modeling as a practical approach to mechanistic and interpretable peptidomics, bridging the fields of degradomics and peptidomics.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013972","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:43:43Z","timestamp":1771613023000},"page":"e1013972","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Degradation graphs reveal hidden proteolytic activity in peptidomes"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9997-2405","authenticated-orcid":true,"given":"Erik","family":"Hartman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johan","family":"Malmstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Wallin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"pcbi.1013972.ref001","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/978-1-4939-6952-4_4","article-title":"Degradomics in neurotrauma: profiling traumatic brain injury","volume":"1598","author":"H Abou-El-Hassan","year":"2017","journal-title":"Methods Mol Biol."},{"issue":"43","key":"pcbi.1013972.ref002","doi-asserted-by":"crossref","first-page":"43353","DOI":"10.1021\/acsomega.4c01959","article-title":"Unveiling encrypted antimicrobial peptides from cephalopods\u2019 salivary glands: a proteolysis-driven virtual approach","volume":"9","author":"G Ag\u00fcero-Chapin","year":"2024","journal-title":"ACS Omega."},{"issue":"7","key":"pcbi.1013972.ref003","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0040656","article-title":"Inferring proteolytic processes from mass spectrometry time series data using degradation graphs","volume":"7","author":"S Aiche","year":"2012","journal-title":"PLoS One."},{"key":"pcbi.1013972.ref004","doi-asserted-by":"crossref","unstructured":"Bhutada S, Martin DR, Cikach F, da Silva EG, Willard BB, Ramkhelawon B, et al. 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