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This article is part of the supplement: Selected articles from the First IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2011): Bioinformatics

Open Access Research

Inferring serum proteolytic activity from LC-MS/MS data

Piotr Dittwald15*, Jerzy Ostrowski34, Jakub Karczmarski3 and Anna Gambin12

Author Affiliations

1 Institute of Informatics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland

2 Mossakowski Medical Research Centre PAS, Pawinskiego 5, 02-106 Warsaw, Poland

3 Department of Oncological Genetics, Maria Skłodowska-Curie Memorial Cancer Center and Institute of Oncology, 02-781 Warsaw, Poland

4 Department of Gastroenterology, Medical Center for Postgraduate Education, 01-813 Warsaw, Poland

5 College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Zwirki i Wigury 93, 02-089 Warsaw. Poland

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BMC Bioinformatics 2012, 13(Suppl 5):S7  doi:10.1186/1471-2105-13-S5-S7

Published: 12 April 2012

Abstract

Background

In this paper we deal with modeling serum proteolysis process from tandem mass spectrometry data. The parameters of peptide degradation process inferred from LC-MS/MS data correspond directly to the activity of specific enzymes present in the serum samples of patients and healthy donors. Our approach integrate the existing knowledge about peptidases' activity stored in MEROPS database with the efficient procedure for estimation the model parameters.

Results

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.

Conclusions

Our results demonstrates the feasibility and potential of high-level analysis for LC-MS proteomic data. The estimated enzyme activities give insights into the molecular pathology of colorectal cancer. Moreover the developed framework is general and can be applied to study proteolytic activity in different systems.