Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26590
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dc.contributor.authorCIACH, Michal-
dc.contributor.authorLacki, Mateusz Krzysztof-
dc.contributor.authorMiasojedow, Blazej-
dc.contributor.authorLermyte, Frederik-
dc.contributor.authorVALKENBORG, Dirk-
dc.contributor.authorSobott, Frank-
dc.contributor.authorGambin, Anna-
dc.date.accessioned2018-08-06T09:01:50Z-
dc.date.available2018-08-06T09:01:50Z-
dc.date.issued2018-
dc.identifier.citationJOURNAL OF COMPUTATIONAL BIOLOGY, 25(3), p. 282-301-
dc.identifier.issn1066-5277-
dc.identifier.urihttp://hdl.handle.net/1942/26590-
dc.description.abstractElectron transfer dissociation (ETD) is a versatile technique used in mass spectrometry for the high-throughput characterization of proteins. It consists of several concurrent reactions triggered by the transfer of an electron from its anion source to sample cations. Transferring an electron causes peptide backbone cleavage while leaving labile post-translational modifications intact. The obtained fragmentation spectra provide valuable information for sequence and structure analyses. In this study, we propose a formal mathematical model of the ETD fragmentation process in the form of a system of stochastic differential equations describing its joint dynamics. Parameters of the model correspond to the rates of occurring reactions. Their estimates for various experimental settings give insight into the dynamics of the ETD process. We estimate the model parameters from the relative quantities of fragmentation products in a given mass spectrum by solving a nonlinear optimization problem. The cost function penalizes for the differences between the analytically derived average number of reaction products and their experimental counterparts. The presented method proves highly robust to noise in silico. Moreover, the model can explain a considerable amount of experimental results for a wide range of instrumentation settings. The implementation of the presented workflow, code-named ETDetective, is freely available under the two-clause BSD license.-
dc.description.sponsorshipThis work was partially supported by the National Science Centre grant numbers 2013/09/B/ST6/01575, 2014/12/W/ST5/00592, and 2015/17/N/ST6/03565 and the SBO grant InSPECtor (120025) of the Flemish Agency for Innovation by Science and Technology (IWT). The authors thank the Research Foundation-Flanders (FWO) for funding a PhD fellowship (F.L.). The Synapt G2 mass spectrometer is funded by a grant from the Hercules Foundation-Flanders.-
dc.language.isoen-
dc.rights(C) Mary Ann Liebert, Inc.-
dc.subject.otherBFGS; electron transfer dissociation; mass spectrometry; Markov Jump Process; ODEs-
dc.titleEstimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry-
dc.typeJournal Contribution-
dc.identifier.epage301-
dc.identifier.issue3-
dc.identifier.spage282-
dc.identifier.volume25-
local.bibliographicCitation.jcatA1-
dc.description.notesCiach, MA; Lacki, MK (reprint author), Univ Warsaw, Fac Math Informat & Mech, Banocho 2, PL-02097 Warsaw, Poland. m_ciach@student.uw.edu.pl; mateusz.lacki@biol.uw.edu.pl-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1089/cmb.2017.0156-
dc.identifier.isi000429742800004-
item.fulltextWith Fulltext-
item.accessRightsRestricted Access-
item.fullcitationCIACH, Michal; Lacki, Mateusz Krzysztof; Miasojedow, Blazej; Lermyte, Frederik; VALKENBORG, Dirk; Sobott, Frank & Gambin, Anna (2018) Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry. In: JOURNAL OF COMPUTATIONAL BIOLOGY, 25(3), p. 282-301.-
item.validationecoom 2019-
item.contributorCIACH, Michal-
item.contributorLacki, Mateusz Krzysztof-
item.contributorMiasojedow, Blazej-
item.contributorLermyte, Frederik-
item.contributorVALKENBORG, Dirk-
item.contributorSobott, Frank-
item.contributorGambin, Anna-
crisitem.journal.issn1066-5277-
crisitem.journal.eissn1557-8666-
Appears in Collections:Research publications
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