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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2018
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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. |
format | Online Article Text |
id | pubmed-5838638 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
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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