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Degradation graphs reveal hidden proteolytic activity in peptidomes

Fig 2

Definition and probabilistic formulation of the degradation graph.

a A biological sample is analyzed with the mass spectrometer followed by peptide identification and quantification, resulting in a peptide distribution. b A degradation graph represents proteolysis as a directed acyclic graph in which each node corresponds to a peptide (including the intact protein, Ω), and each directed edge denotes a proteolytic event where peptide v is cleaved into a shorter peptide u. The graph thereby encodes sequential cleavage relationships that describe how a protein is progressively degraded into smaller fragments. b Each edge is associated with a transition probability describing the likelihood that v degrades into u. The probability that v remains intact, its absorption probability, is modeled as − , where is the set of child nodes. This defines a Markov chain in which transitions correspond to cleavage events and self-loops to peptide stability. c The overall peptide distribution, , can be obtained by propagating probability mass from the protein node Ω through the graph in a forward pass. At each node, a fraction of the mass is absorbed, and the remainder distributed according to the outgoing transition probabilities, yielding a marginal distribution that reflects the steady-state abundances of peptides generated by the degradation process.

doi: https://doi.org/10.1371/journal.pcbi.1013972.g002