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Degradation graphs reveal hidden proteolytic activity in peptidomes

Hartman, Erik LU orcid ; Malmström, Johan LU orcid and Wallin, Jonas LU (2026) In PLoS Computational Biology 22(2). p.1-21
Abstract

Protein degradation is a regulated process that reshapes the proteome and generates bioactive peptides. 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... (More)

Protein degradation is a regulated process that reshapes the proteome and generates bioactive peptides. 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 3-4-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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS Computational Biology
volume
22
issue
2
article number
e1013972
pages
1 - 21
publisher
Public Library of Science (PLoS)
external identifiers
  • scopus:105030785979
  • pmid:41719358
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1013972
language
English
LU publication?
yes
additional info
Publisher Copyright: Copyright: © 2026 Hartman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
id
4e54816c-1066-4cbd-ab69-1c5541826827
date added to LUP
2026-03-04 07:09:04
date last changed
2026-03-18 12:37:15
@article{4e54816c-1066-4cbd-ab69-1c5541826827,
  abstract     = {{<p>Protein degradation is a regulated process that reshapes the proteome and generates bioactive peptides. 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 3-4-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.</p>}},
  author       = {{Hartman, Erik and Malmström, Johan and Wallin, Jonas}},
  issn         = {{1553-7358}},
  language     = {{eng}},
  month        = {{02}},
  number       = {{2}},
  pages        = {{1--21}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS Computational Biology}},
  title        = {{Degradation graphs reveal hidden proteolytic activity in peptidomes}},
  url          = {{http://dx.doi.org/10.1371/journal.pcbi.1013972}},
  doi          = {{10.1371/journal.pcbi.1013972}},
  volume       = {{22}},
  year         = {{2026}},
}