Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs

Background: Proteases play an essential part in a variety of biological processes. Besides their importance under healthy conditions they are also known to have a crucial role in complex diseases like cancer. In recent years, it has been shown that not only the fragments produced by proteases but also their dynamics, especially ex vivo, can serve as biomarkers. But so far, only a few approaches were taken to explicitly model the dynamics of proteolysis in the context of mass spectrometry. Results: We introduce a new concept to model proteolytic processes, the degradation graph. The degradation graph is an extension of the cleavage graph, a data structure to reconstruct and visualize the proteolytic process. In contrast to previous approaches we extended the model to incorporate endoproteolytic processes and present a method to construct a degradation graph from mass spectrometry time series data. Based on a degradation graph and the intensities extracted from the mass spectra it is possible to estimate reaction rates of the underlying processes. We further suggest a score to rate different degradation graphs in their ability to explain the observed data. This score is used in an iterative heuristic to improve the structure of the initially constructed degradation graph. Conclusion: We show that the proposed method is able to recover all degraded and generated peptides, the underlying reactions, and the reaction rates of proteolytic processes based on mass spectrometry time series data. We use simulated and real data to demonstrate that a given process can be reconstructed even in the presence of extensive noise, isobaric signals and false identifications. While the model is currently only validated on peptide data it is also applicable to proteins, as long as the necessary time series data can be produced.