title: Inferring proteolytic processes from mass spectrometry time series data using degradation graphs.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors: Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany. stephan.aiche@fu-berlin.de
authors: Stephan
authors: S
authors: Aiche
authors: Knut
authors: K
authors: Reinert
authors: Christof
authors: C
authors: Schütte
authors: Diana
authors: D
authors: Hildebrand
authors: Hartmut
authors: H
authors: Schlüter
authors: Tim O F
authors: TO
authors: Conrad
mesh_terms: D003198:Computer Simulation; D030562:Databases, Protein; D005344:Fibrinopeptide A / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D008954:Models, Biological; D010446:Peptide Fragments / Q000097:blood / Q000737:chemistry / Q000378:metabolism; D059748:Proteolysis; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D013997:Time Factors; D001613:beta 2-Microglobulin / Q000737:chemistry / Q000378:metabolism
chemical_list: D010446:Peptide Fragments; D001613:beta 2-Microglobulin; D005344:Fibrinopeptide A