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Modeling Proteolysis from Mass Spectrometry Proteomic Data

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Języki publikacji
EN
Abstrakty
EN
In this paper we propose a mathematical model of the proteolysis process. Protein digestion is modelled with the use of chemical master equation (CME), i.e. the system of stochastic differential equations corresponding to the network of enzymatic reactions. We present an efficient approach to model parameters’ estimation (i.e. enzyme activities) from time series of mass spectrometry data. These results extend previous results in three directions: by relaxing the stationarity of the proteolysis process assumption, by allowing cuts at arbitrary sites in the peptide sequence and by incorporating knowledge from biological databases.
Wydawca
Rocznik
Strony
89--104
Opis fizyczny
Bibliogr. 29 poz., wykr.
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autor
Bibliografia
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  • [8] Gelenbe, E.: Network of interacting synthetic molecules in steady state, Proceedings of the Royal Society A: Mathematical, Physical and Engine ering Science, 464(2096), 2008, 2219-2228.
  • [9] Goddard, T., Kneller, D.: SPARKY 3, TR University of California, San Francisco.
  • [10] Goldobin, D. S., Zaikin, A.: Towards quantitative prediction of proteasomal digestion patterns of pr oteins, Journal of Statistical Mechanics: Theory and Experiment, 2009(01), 2009, P01009 (14pp).
  • [11] Igarashi, Y., Eroshkin, A., Gramatikova, S., Gramatikoff, G., Zhang, Y., Smith, J., Osterman, A., Godzik, A.: CutDB: a proteolytic event database, Nucleic Acids Res., 2007.
  • [12] Jahnke, T., Huisinga, W.: Solving the chemical master equation for monomolecular reaction systems analytically, Journal of Mathematical Biology, 54(1), 2007, 1-26.
  • [13] Kluge, B., Gambin, A., Niemiro, W.: Modeling exopeptidase activity from LC-MS data, J. Comput. Biol., 16(2), 2009, 395-406.
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  • [17] Mallick, P., Schirle, M., Chen, S. S., Flory, M. R., Lee, H., Martin, D., Ranish, J., Raught, B., Schmitt, R., Werner, T., Kuster, B., Aebersold, R.: Computational prediction of proteotypic peptides for quantitative proteomics, Nat. Biotechnol., 25(1), 2007, 125-131.
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  • [24] Rawlings, N. D., Barrett, A. J.: MEROPS: the peptidase database, Nucleic Acids Res., 28(1), 2000, 323-325.
  • [25] Reznik, S. E., Fricker, L. D.: Carboxypeptidases from A to Z: implications in embryonic development and Wnt binding, Cell. Mol. Life Sci., 58(12-13), 2001, 1790-1804.
  • [26] Turner, T. E., Schnell, S., Burrage, K.: Stochastic approaches for modelling in vivo reactions, Computational Biology and Chemistry, 28, 2004, 165-178.
  • [27] Villanueva, J., Martorella, A., Lawlor, K., Philip, J., Fleisher, M., Robbins, R., P, T.: Serum peptidome patterns that distinguish metastatic thyroid carcinoma from cancer-free controls are unbiased by gender and age, Mol. Cell. Proteomics, 5, 2006, 1840-1852.
  • [28] Villanueva, J., Nazarian, A., Lawlor, K., San, S. Y., Robbins, R. J., Tempst, P.: A sequence-specific exopeptidase activity test (SSEAT) for 'functional' biomarker discovery, Mol. Cell. Proteomics, 7(3), 2008, 509-518.
  • [29] Villanueva, J., Shaffer, D. R., Philip, J., Chaparro, C. A., Erdjument-Bromage, H., Olshen, A. B., Fleisher, M., Lilja, H., Brogi, E., Boyd, J., Sanchez-Carbayo, M., Holland, E. C., Cordon-Cardo, C., Scher, H. I., Tempst, P.: Differential exoprotease activities confer tumor-specific serum peptidome patterns, J. Clin. Invest., 116(1), 2006, 271-284.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS8-0011-0005
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