dc.contributor.author
Aiche, Stephan
dc.contributor.author
Reinert, Knut
dc.contributor.author
Schütte, Christof
dc.contributor.author
Hildebrand, Diana
dc.contributor.author
Schlüter, Hartmut
dc.contributor.author
Conrad, Tim O. F.
dc.date.accessioned
2018-06-08T03:41:21Z
dc.date.available
2013-12-19T13:30:03.792Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/15711
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-19898
dc.description.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.
Conclusion: 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.
de
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/deed.de
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using
Degradation Graphs
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation
Plos one; July 2012 , Vol. 7, Issue 7
dcterms.bibliographicCitation.doi
10.1371/journal.pone.0039345
refubium.affiliation
Mathematik und Informatik
de
refubium.affiliation.other
Institut für Informatik
refubium.mycore.fudocsId
FUDOCS_document_000000019227
refubium.resourceType.isindependentpub
no
refubium.mycore.derivateId
FUDOCS_derivate_000000002849
dcterms.accessRights.openaire
open access