Speaker
Description
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 graphstructured 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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