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Parsimonious Charge Deconvolution for Native Mass Spectrometry

[Image: see text] Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third...

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Autores principales: Bern, Marshall, Caval, Tomislav, Kil, Yong J., Tang, Wilfred, Becker, Christopher, Carlson, Eric, Kletter, Doron, Sen, K. Ilker, Galy, Nicolas, Hagemans, Dominique, Franc, Vojtech, Heck, Albert J. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2018
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838638/
https://www.ncbi.nlm.nih.gov/pubmed/29376659
http://dx.doi.org/10.1021/acs.jproteome.7b00839
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author Bern, Marshall
Caval, Tomislav
Kil, Yong J.
Tang, Wilfred
Becker, Christopher
Carlson, Eric
Kletter, Doron
Sen, K. Ilker
Galy, Nicolas
Hagemans, Dominique
Franc, Vojtech
Heck, Albert J. R.
author_facet Bern, Marshall
Caval, Tomislav
Kil, Yong J.
Tang, Wilfred
Becker, Christopher
Carlson, Eric
Kletter, Doron
Sen, K. Ilker
Galy, Nicolas
Hagemans, Dominique
Franc, Vojtech
Heck, Albert J. R.
author_sort Bern, Marshall
collection PubMed
description [Image: see text] Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new “parsimonious” charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma properdin glycoprotein, the new algorithm could deconvolve monomer and dimer simultaneously and, when focused on the m/z range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using m/z peaks rather than deconvolved masses. On therapeutic antibodies, the new algorithm facilitated the analysis of extensions, truncations, and Fab glycosylation. The algorithm facilitates the use of native mass spectrometry for the qualitative and quantitative analysis of protein and protein assemblies.
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spelling pubmed-58386382018-03-07 Parsimonious Charge Deconvolution for Native Mass Spectrometry Bern, Marshall Caval, Tomislav Kil, Yong J. Tang, Wilfred Becker, Christopher Carlson, Eric Kletter, Doron Sen, K. Ilker Galy, Nicolas Hagemans, Dominique Franc, Vojtech Heck, Albert J. R. J Proteome Res [Image: see text] Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new “parsimonious” charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma properdin glycoprotein, the new algorithm could deconvolve monomer and dimer simultaneously and, when focused on the m/z range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using m/z peaks rather than deconvolved masses. On therapeutic antibodies, the new algorithm facilitated the analysis of extensions, truncations, and Fab glycosylation. The algorithm facilitates the use of native mass spectrometry for the qualitative and quantitative analysis of protein and protein assemblies. American Chemical Society 2018-01-29 2018-03-02 /pmc/articles/PMC5838638/ /pubmed/29376659 http://dx.doi.org/10.1021/acs.jproteome.7b00839 Text en Copyright © 2018 American Chemical Society This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License (http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) , which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.
spellingShingle Bern, Marshall
Caval, Tomislav
Kil, Yong J.
Tang, Wilfred
Becker, Christopher
Carlson, Eric
Kletter, Doron
Sen, K. Ilker
Galy, Nicolas
Hagemans, Dominique
Franc, Vojtech
Heck, Albert J. R.
Parsimonious Charge Deconvolution for Native Mass Spectrometry
title Parsimonious Charge Deconvolution for Native Mass Spectrometry
title_full Parsimonious Charge Deconvolution for Native Mass Spectrometry
title_fullStr Parsimonious Charge Deconvolution for Native Mass Spectrometry
title_full_unstemmed Parsimonious Charge Deconvolution for Native Mass Spectrometry
title_short Parsimonious Charge Deconvolution for Native Mass Spectrometry
title_sort parsimonious charge deconvolution for native mass spectrometry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838638/
https://www.ncbi.nlm.nih.gov/pubmed/29376659
http://dx.doi.org/10.1021/acs.jproteome.7b00839
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