Capillary Zone Electrophoresis-Tandem Mass Spectrometry with Activated Ion Electron Transfer Dissociation for Large-scale Top-down Proteomics

McCool, Elijah N.; Lodge, Jean M.; Basharat, Abdul Rehman; Liu, Xiaowen; Coon, Joshua J.; Sun, Liangliang

doi:10.1007/s13361-019-02206-6

Cited by 19 publications

(23 citation statements)

References 42 publications

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“…By contrast, activated ion ETD (AI-ETD) uses infrared photoactivation during the ETD reaction to disrupt the gas-phase non-covalent interactions that prevent formation of c and ⋅ z -type product ions, thereby greatly reducing ETnoD. This benefits glycoproteomics ( Riley et al., 2019 ), top-down proteomics ( McCool et al., 2019 ; Riley et al., 2018a , 2018b ), and phosphoproteomics ( Riley et al., 2017a ). We therefore set out to investigate the utility of AI-ETD for identification and confident localization of ADPr sites.…”

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confidence: 99%

Mapping Physiological ADP-Ribosylation Using Activated Ion Electron Transfer Dissociation

et al. 2020

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ADP-ribosylation (ADPr) is a post-translational modification that plays pivotal roles in a wide range of cellular processes. Mass spectrometry (MS)-based analysis of ADPr under physiological conditions, without relying on genetic or chemical perturbation, has been hindered by technical limitations. Here, we describe the applicability of activated ion electron transfer dissociation (AI-ETD) for MS-based proteomics analysis of physiological ADPr using our unbiased Af1521 enrichment strategy. To benchmark AI-ETD, we profile 9,000 ADPr peptides mapping to >5,000 unique ADPr sites from a limited number of cells exposed to oxidative stress and identify 120% and 28% more ADPr peptides compared to contemporary strategies using ETD and electron-transfer higher-energy collisional dissociation (EThcD), respectively. Under physiological conditions, AI-ETD identifies 450 ADPr sites on low-abundant proteins, including in vivo cysteine modifications on poly(ADP-ribosyl)polymerase (PARP) 8 and tyrosine modifications on PARP14, hinting at specialist enzymatic functions for these enzymes. Collectively, our data provide insights into the physiological regulation of ADPr.

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Section: Introductionmentioning

confidence: 99%

Mapping Physiological ADP-Ribosylation Using Activated Ion Electron Transfer Dissociation

et al. 2020

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“…By contrast, activated ion-ETD (AI-ETD) uses infrared photoactivation during the ETD reaction to overcome ETnoD, and has been shown to benefit glycoproteomics (Riley et al, 2019), top-down proteomics (McCool et al, 2019;Riley et al, 2018a;Riley et al, 2018b), as well as phosphoproteomics (Riley et al, 2017a). We therefore set out to investigate the utility of AI-ETD on an Orbitrap Fusion Lumos mass spectrometer for identification and confident localization of ADPr sites.…”

Section: Introductionmentioning

confidence: 99%

Mapping physiological ADP-ribosylation using Activated Ion Electron Transfer Dissociation (AI-ETD)

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Lodge

et al. 2020

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max. 150 words)ADP-ribosylation (ADPr) is a post-translational modification that plays pivotal roles in a wide range of cellular processes. Mass spectrometry (MS)-based analysis of ADPr under physiological conditions, without relying on genetic or chemical perturbation, has been hindered by technical limitations. Here, we describe the applicability of Activated Ion Electron Transfer Dissociation (AI-ETD) for MS-based proteomics analysis of physiological ADPr using our unbiased Af1521 enrichment strategy. To benchmark AI-ETD, we profiled 9,000 ADPr peptides mapping to >5,000 unique ADPr sites from a limited number of cells exposed to oxidative stress, corresponding to 120% and 28% more ADPr peptides compared to contemporary strategies using ETD and EThcD, respectively. Under physiological conditions AI-ETD identified 450 ADPr sites on low-abundant proteins, including in vivo cysteine automodifications on PARP8 and tyrosine auto-modifications on PARP14, hinting at specialist enzymatic functions for these enzymes. Collectively, our data provides new insights into the physiological regulation of ADP-ribosylation.

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“…Second, proteins/proteoforms are much larger than peptides, leading to potential difficulties in calculating their size and charge accurately. In the last 5 years, CZE–MS has been recognized as an important approach for large-scale top-down proteomics due to the improvement in CE–MS interfaces, capillary coatings, and online sample stacking techniques. − For instance, we identified nearly 600 proteoforms from an E. coli cell lysate in a single-shot CZE–MS/MS analysis . In that study, we employed a commercialized electrokinetically pumped sheath-flow CE–MS interface, , a 1-m-long linear polyacrylamide (LPA)-coated capillary, and a dynamic pH junction-based proteoform stacking method to boost the sample loading capacity, separation window, and overall sensitivity of the CZE–MS system.…”

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Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

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Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)–tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)–MS/MS. The false discovery rate (FDR) from the target–decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after the database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics data sets. In this pilot study, for the first time, we evaluated various semiempirical models for predicting proteoforms’ electrophoretic mobility (μef) using large-scale top-down proteomics data sets from CZE–MS/MS. We achieved a linear correlation between experimental and predicted μef of E. coli proteoforms (R 2 = 0.98) with a simple semiempirical model, which utilizes the number of charges and molecular mass of each proteoform as the parameters. Our modeling data suggest that the complete unfolding of proteoforms during CZE separation benefits the prediction of their μef. Our results also indicate that N-terminal acetylation and phosphorylation both decrease the proteoforms’ charge by roughly one charge unit.

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Capillary Zone Electrophoresis-Tandem Mass Spectrometry with Activated Ion Electron Transfer Dissociation for Large-scale Top-down Proteomics

Cited by 19 publications

References 42 publications

Mapping Physiological ADP-Ribosylation Using Activated Ion Electron Transfer Dissociation

Mapping Physiological ADP-Ribosylation Using Activated Ion Electron Transfer Dissociation

Mapping physiological ADP-ribosylation using Activated Ion Electron Transfer Dissociation (AI-ETD)

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics

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