Inferring serum proteolytic activity from LC-MS/MS data.
BMC Bioinformatics
; 13 Suppl 5: S7, 2012 Apr 12.
Article
en En
| MEDLINE
| ID: mdl-22537011
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Asunto principal:
Péptido Hidrolasas
/
Neoplasias Colorrectales
/
Modelos Estadísticos
/
Suero
Tipo de estudio:
Health_economic_evaluation
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Polonia