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Degradation graphs reveal hidden proteolytic activity in peptidomes

Fig 3

Inferring degradation graphs from observed peptidomes.

a The degradation graph is a latent structure that describes the proteolytic relationships giving rise to the observed peptidome. Observed peptide abundances are used to infer the most plausible graph structure and transition probabilities. b Inference is formulated as an optimization problem in which the modeled marginal peptide distribution is fitted to the measured distribution. Edge transition probabilities are iteratively updated by gradient descent to minimize the loss , or equivalently inferred as a linear-flow system solved by linear programming under mass-conservation constraints. c Example of graph optimization by gradient descent. The mean-squared-error loss (blue) decreases over iterations as the inferred graph converges toward the true degradation graph, measured by the total deviation of edge weights (orange). d The coefficient of variation of edge weights when applying gradient descent to graphs of increasing sizes. The shaded region shows ± 1 standard deviation. e Experimental validation using in vitro trypsin digestion of human pharyngeal epithelial cells (Detroit 562; dataset PXD037803 [14]). Peptides were identified and quantified by LC–MS/MS and analyzed to reconstruct the underlying degradation graph. f Peptides mapped onto the β-actin backbone and colored by their number of descendants in the inferred graph, illustrating hierarchical fragmentation patterns. g Relationship between peptide abundance and total inflow (inflow) for each peptide. The solid line indicates where the modeled inflow and measured abundance are equivalent, demonstrating quantitative agreement between the inferred degradation flow and experimental intensities.

doi: https://doi.org/10.1371/journal.pcbi.1013972.g003