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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/29122

Title: MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics
Authors: Lermyte, Frederik
Dittwald, Piotr
Claesen, Jurgen
Baggerman, Geert
Sobott, Frank
O'Connor, Peter B.
Laukens, Kris
Hooyberghs, Jef
Gambin, Anna
Valkenborg, Dirk
Issue Date: 2019
Publisher: AMER CHEMICAL SOC
Citation: ANALYTICAL CHEMISTRY, 91(15), p. 10310-10319
Abstract: Top-down proteomics approaches are becoming ever more popular, due to the advantages offered by knowledge of the intact protein mass in correctly identifying the various proteoforms that potentially arise due to point mutation, alternative splicing, post-translational modifications, etc. Usually, the average mass is used in this context; however, it is known that this can fluctuate significantly due to both natural and technical causes. Ideally, one would prefer to use the monoisotopic precursor mass, but this falls below the detection limit for all but the smallest proteins. Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes. This linear model was tested experimentally, as well as in silico, and typically predicts monoisotopic masses with an accuracy of only a few parts per million. A confidence measure is associated with the predicted monoisotopic mass to handle the off-by-one-Da prediction error. Furthermore, we introduce a correction function to extract the "true" (i.e., theoretically) most-abundant isotope peak from a spectrum, even if the observed isotope distribution is distorted by noise or poor ion statistics. The method is available online as an R shiny app: https:// valkenborg-lab.shinyapps.io/mind/
Notes: [Lennyte, Frederik; Sobott, Frank] Univ Antwerp, Dept Chem, Biomol & Analyt Mass Spectrometry Grp, B-2000 Antwerp, Belgium. [Lennyte, Frederik; Baggerman, Geert; Valkenborg, Dirk] Univ Antwerp, UA VITO Ctr Prote, B-2000 Antwerp, Belgium. [Lennyte, Frederik] Univ Warwick, Sch Engn, Coventry CV4 7AL, W Midlands, England. [Lennyte, Frederik; O'Connor, Peter B.] Univ Warwick, Dept Chem, Coventry CV4 7AL, W Midlands, England. [Dittwald, Piotr; Gambin, Anna] Univ Warsaw, Inst Informat, PL-00927 Warsaw, Poland. [Claesen, Jurgen; Valkenborg, Dirk] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, BE-3500 Hasselt, Belgium. [Baggerman, Geert; Hooyberghs, Jef; Valkenborg, Dirk] Flemish Inst Technol Res VITO, Appl Bio & Mol Syst, B-2400 Mol, Belgium. [Sobott, Frank] Univ Leeds, Astbury Ctr Struct Mol Biol, Leeds LS2 9JT, W Yorkshire, England. [Sobott, Frank] Univ Leeds, Sch Mol & Cellular Biol, Leeds LS2 9JT, W Yorkshire, England. [Laukens, Kris] Univ Antwerp, Dept Math & Comp Sci, Adrem Data Lab, B-2000 Antwerp, Belgium. [Laukens, Kris] Univ Antwerp, Biomed Informat Network Antwerp Biomina, B-2000 Antwerp, Belgium.
URI: http://hdl.handle.net/1942/29122
DOI: 10.1021/acs.analchem.9b02682
ISI #: 000480499200131
ISSN: 0003-2700
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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