Filling the gaps in peptide maps with a platform assay for top-down characterization of purified protein samples.
denaturing size exclusion chromatography
online buffer exchange
positional isomer
protein characterization
top‐down mass spectrometry
Journal
Proteomics
ISSN: 1615-9861
Titre abrégé: Proteomics
Pays: Germany
ID NLM: 101092707
Informations de publication
Date de publication:
14 Jul 2024
14 Jul 2024
Historique:
revised:
17
06
2024
received:
24
03
2024
accepted:
18
06
2024
medline:
15
7
2024
pubmed:
15
7
2024
entrez:
15
7
2024
Statut:
aheadofprint
Résumé
Liquid chromatography-mass spectrometry (LC-MS) intact mass analysis and LC-MS/MS peptide mapping are decisional assays for developing biological drugs and other commercial protein products. Certain PTM types, such as truncation and oxidation, increase the difficulty of precise proteoform characterization owing to inherent limitations in peptide and intact protein analyses. Top-down MS (TDMS) can resolve this ambiguity via fragmentation of specific proteoforms. We leveraged the strengths of flow-programmed (fp) denaturing online buffer exchange (dOBE) chromatography, including robust automation, relatively high ESI sensitivity, and long MS/MS window time, to support a TDMS platform for industrial protein characterization. We tested data-dependent (DDA) and targeted strategies using 14 different MS/MS scan types featuring combinations of collisional- and electron-based fragmentation as well as proton transfer charge reduction. This large, focused dataset was processed using a new software platform, named TDAcquireX, that improves proteoform characterization through TDMS data aggregation. A DDA-based workflow provided objective identification of αLac truncation proteoforms with a two-termini clipping search. A targeted TDMS workflow facilitated the characterization of αLac oxidation positional isomers. This strategy relied on using sliding window-based fragment ion deconvolution to generate composite proteoform spectral match (cPrSM) results amenable to fragment noise filtering, which is a fundamental enhancement relevant to TDMS applications generally.
Identifiants
pubmed: 39004851
doi: 10.1002/pmic.202400036
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2400036Subventions
Organisme : Cancer Prevention Research Institute of Texas (CPRIT)
ID : RP190682
Informations de copyright
© 2024 Wiley‐VCH GmbH.
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