Inferring serum proteolytic activity from LC–MS/MS data

This webpage contains the supplementary information for the research paper "Inferring serum proteolytic activity from LC–MS/MS data" (BMC Bioinformatics).


In this paper we deal with modeling serum proteolysis process from tandem mass spectrometry data. The model significantly extends those proposed recently in [Kluge et al]. The parameters of peptide degradation process inferred from LCMS/ MS data correspond directly to the activity of specific enzymes present in the serum samples of patients and healthy donnors. Our approach integrate the existing knowledge about peptidases’ activity stored in MEROPS database with the efficient procedure for estimation the model parameters. Taking into account the inherent stochasticity of the process, the proteolytic activity is modeled with the use of Chemical Master Equation (CME). Assuming the stationarity of the Markov process we calculate the expected values of digested peptides in the model. The parameters are fitted to minimize the discrepancy between those expected values and the peptide activities observed in the MS data. Constrained optimization problem is solved by Levenberg-Marquadt algorithm.

[Kluge et al] - B. Kluge, A. Gambin, and W. Niemiro, “Modeling exopeptidase activity from LC-MS data,” J. Comput. Biol., vol. 16, no. 2, pp. 395–406, 2009.


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