28 research outputs found
Activated Ion-Electron Transfer Dissociation Enables Comprehensive Top-Down Protein Fragmentation
Here
we report the first demonstration of near-complete sequence
coverage of intact proteins using activated ion-electron transfer
dissociation (AI-ETD), a method that leverages concurrent infrared
photoactivation to enhance electron-driven dissociation. AI-ETD produces
mainly c/z-type product ions and provides comprehensive (77ā97%)
protein sequence coverage, outperforming HCD, ETD, and EThcD for all
proteins investigated. AI-ETD also maintains this performance across
precursor ion charge states, mitigating charge-state dependence that
limits traditional approaches
Full-Featured Search Algorithm for Negative Electron-Transfer Dissociation
Negative electron-transfer
dissociation (NETD) has emerged as a
premier tool for peptide anion analysis, offering access to acidic
post-translational modifications and regions of the proteome that
are intractable with traditional positive-mode approaches. Whole-proteome
scale characterization is now possible with NETD, but proper informatic
tools are needed to capitalize on advances in instrumentation. Currently
only one database search algorithm (OMSSA) can process NETD data.
Here we implement NETD search capabilities into the Byonic platform
to improve the sensitivity of negative-mode data analyses, and we
benchmark these improvements using 90 min LCāMS/MS analyses
of tryptic peptides from human embryonic stem cells. With this new
algorithm for searching NETD data, we improved the number of successfully
identified spectra by as much as 80% and identified 8665 unique peptides,
24āÆ639 peptide spectral matches, and 1338 proteins in activated-ion
NETD analyses, more than doubling identifications from previous negative-mode
characterizations of the human proteome. Furthermore, we reanalyzed
our recently published large-scale, multienzyme negative-mode yeast
proteome data, improving peptide and peptide spectral match identifications
and considerably increasing protein sequence coverage. In all, we
show that new informatics tools, in combination with recent advances
in data acquisition, can significantly improve proteome characterization
in negative-mode approaches
Implementation of Activated Ion Electron Transfer Dissociation on a Quadrupole-Orbitrap-Linear Ion Trap Hybrid Mass Spectrometer
Using
concurrent IR photoactivation during electron transfer dissociation
(ETD) reactions, i.e., activated ion ETD (AI-ETD), significantly increases
dissociation efficiency resulting in improved overall performance.
Here we describe the first implementation of AI-ETD on a quadrupole-Orbitrap-quadrupole
linear ion trap (QLT) hybrid MS system (Orbitrap Fusion Lumos) and
demonstrate the substantial benefits it offers for peptide characterization.
First, we show that AI-ETD can be implemented in a straightforward
manner by fastening the laser and guiding optics to the instrument
chassis itself, making alignment with the trapping volume of the QLT
simple and robust. We then characterize the performance of AI-ETD
using standard peptides in addition to a complex mixtures of tryptic
peptides using LCāMS/MS, showing not only that AI-ETD can nearly
double the identifications achieved with ETD alone but also that it
outperforms the other available supplemental activation methods (ETcaD
and EThcD). Finally, we introduce a new activation scheme called AI-ETD+
that combines AI-ETD in the high pressure cell of the QLT with a short
infrared multiphoton dissociation (IRMPD) activation in the low-pressure
cell. This reaction scheme introduces no addition time to the scan
duty cycle but generates MS/MS spectra rich in b/y-type and c/z<sup>ā¢</sup>-type product ions. The extensive generation of fragment
ions in AI-ETD+ substantially increases peptide sequence coverage
while also improving peptide identifications over all other ETD methods,
making it a valuable new tool for hybrid fragmentation approaches
Top-Down Characterization of Proteins with Intact Disulfide Bonds Using Activated-Ion Electron Transfer Dissociation
Here we report the
fragmentation of disulfide linked intact proteins
using activated-ion electron transfer dissociation (AI-ETD) for top-down
protein characterization. This fragmentation method is then compared
to the alternative methods of beam-type collisional activation (HCD),
electron transfer dissociation (ETD), and electron transfer and higher-energy
collision dissociation (EThcD). We analyzed multiple precursor charge
states of the protein standards bovine insulin, Ī±-lactalbumin,
lysozyme, Ī²-lactoglobulin, and trypsin inhibitor. In all cases,
we found that AI-ETD provides a boost in protein sequence coverage
information and the generation of fragment ions from within regions
enclosed by disulfide bonds. AI-ETD shows the largest improvement
over the other techniques when analyzing highly disulfide linked and
low charge density precursor ions. This substantial improvement is
attributed to the concurrent irradiation of the gas phase ions while
the electron-transfer reaction is taking place, mitigating nondissociative
electron transfer, helping unfold the gas phase protein during the
electron transfer event, and preventing disulfide bond reformation.
We also show that AI-ETD is able to yield comparable sequence coverage
information when disulfide bonds are left intact relative to proteins
that have been reduced and alkylated. This work demonstrates that
AI-ETD is an effective fragmentation method for the analysis of proteins
with intact disulfide bonds, dramatically enhancing sequence ion generation
and total sequence coverage compared to HCD and ETD
Infrared Multiphoton Dissociation for Quantitative Shotgun Proteomics
We modified a dual-cell linear ion trap mass spectrometer
to perform
infrared multiphoton dissociation (IRMPD) in the low-pressure trap
of a dual-cell quadrupole linear ion trap (dual-cell QLT) and perform
large-scale IRMPD analyses of complex peptide mixtures. Upon optimization
of activation parameters (precursor <i>q</i>-value, irradiation
time, and photon flux), IRMPD subtly, but significantly, outperforms
resonant-excitation collisional-activated dissociation (CAD) for peptides
identified at a 1% false-discovery rate (FDR) from a yeast tryptic
digest (95% confidence, <i>p</i> = 0.019). We further demonstrate
that IRMPD is compatible with the analysis of isobaric-tagged peptides.
Using fixed QLT rf amplitude allows for the consistent retention of
reporter ions, but necessitates the use of variable IRMPD irradiation
times, dependent upon precursor mass to charge (<i>m</i>/<i>z</i>). We show that IRMPD activation parameters can
be tuned to allow for effective peptide identification and quantitation
simultaneously. We thus conclude that IRMPD performed in a dual-cell
ion trap is an effective option for the large-scale analysis of both
unmodified and isobaric-tagged peptides
Automated Gas-Phase Purification for Accurate, Multiplexed Quantification on a Stand-Alone Ion-Trap Mass Spectrometer
Isobaric tagging enables the acquisition of highly multiplexed
proteome quantification, but it is hindered by the pervasive problem
of precursor interference. The elimination of coisolated contaminants
prior to reporter tag generation can be achieved through the use of
gas-phase purification via proton transfer ion/ion reactions (QuantMode);
however, the original QuantMode technique was implemented on the high-resolution
linear ion-trapāOrbitrap hybrid mass spectrometer enabled with
electron transfer dissociation (ETD). Here we extend this technology
to stand-alone linear ion-trap systems (trapQuantMode, trapQM). Facilitated
by the use of inlet beam-type activation (i.e., trapHCD) for production
and observation of the low mass-to-charge reporter region, this scan
sequence comprises three separate events to maximize peptide identifications,
minimize duty cycle requirements, and increase quantitative accuracy,
precision, and dynamic range. Significant improvements in quantitative
accuracy were attained over standard methods when using trapQM to
analyze an interference model system comprising tryptic peptides of
yeast that we contaminated with human peptides. Finally, we demonstrate
practical benefits of this method by analysis of the proteomic changes
that occur during mouse skeletal muscle myoblast differentiation.
While the reduced duty cycle of trapQM led to the identification of
fewer proteins than conventional operation (4050 vs 2964), trapQM
identified more significant differences (>1.5 fold, 1362 vs 1132,
respectively; <i>p</i> < 0.05) between the proteomes
of undifferentiated myoblasts and differentiated myotubes and nearly
10-fold more differences with changes greater than 5-fold (96 vs 12).
We further show that our trapQM dataset is superior for identifying
changes in protein abundance that are consistent with the metabolic
and structural changes known to accompany myotube formation
Phosphoproteomics with Activated Ion Electron Transfer Dissociation
The
ability to localize phosphosites to specific amino acid residues
is crucial to translating phosphoproteomic data into biological meaningful
contexts. In a companion manuscript (Anal. Chem. 2017, DOI: 10.1021/acs.analchem.7b00213), we described a new implementation of activated ion
electron transfer dissociation (AI-ETD) on a quadrupole-Orbitrap-linear
ion trap hybrid MS system (Orbitrap Fusion Lumos), which greatly improved
peptide fragmentation and identification over ETD and other supplemental
activation methods. Here we present the performance of AI-ETD for
identifying and localizing sites of phosphorylation in both phosphopeptides
and intact phosphoproteins. Using 90 min analyses we show that AI-ETD
can identify 24,503 localized phosphopeptide spectral matches enriched
from mouse brain lysates, which more than triples identifications
from standard ETD experiments and outperforms ETcaD and EThcD as well.
AI-ETD achieves these gains through improved quality of fragmentation
and MS/MS success rates for all precursor charge states, especially
for doubly protonated species. We also evaluate the degree to which
phosphate neutral loss occurs from phosphopeptide product ions due
to the infrared photoactivation of AI-ETD and show that modifying
phosphoRS (a phosphosite localization algorithm) to include phosphate
neutral losses can significantly improve localization in AI-ETD spectra.
Finally, we demonstrate the utility of AI-ETD in localizing phosphosites
in Ī±-casein, an ā¼23.5 kDa phosphoprotein that showed
eight of nine known phosphorylation sites occupied upon intact mass
analysis. AI-ETD provided the greatest sequence coverage for all five
charge states investigated and was the only fragmentation method to
localize all eight phosphosites for each precursor. Overall, this
work highlights the analytical value AI-ETD can bring to both bottom-up
and top-down phosphoproteomics
Segmentation of Precursor Mass Range Using āTilingā Approach Increases Peptide Identifications for MS<sup>1</sup>āBased Label-Free Quantification
Label-free quantification is a powerful tool for the
measurement
of protein abundances by mass spectrometric methods. To maximize quantifiable
identifications, MS<sup>1</sup>-based methods must balance the collection
of survey scans and fragmentation spectra while maintaining reproducible
extracted ion chromatograms (XIC). Here we present a method which
increases the depth of proteome coverage over replicate data-dependent
experiments without the requirement of additional instrument time
or sample prefractionation. Sampling depth is increased by restricting
precursor selection to a fraction of the full MS<sup>1</sup> mass
range for each replicate; collectively, the <i>m</i>/<i>z</i> segments of all replicates encompass the full MS<sup>1</sup> range. Although selection windows are narrowed, full MS<sup>1</sup> spectra are obtained throughout the method, enabling the collection
of full mass range MS<sup>1</sup> chromatograms such that label-free
quantitation can be performed for any peptide in any experiment. We
term this approach ābinningā or ātilingā
depending on the type of <i>m</i>/<i>z</i> window
utilized. By combining the data obtained from each segment, we find
that this approach increases the number of quantifiable yeast peptides
and proteins by 31% and 52%, respectively, when compared to normal
data-dependent experiments performed in replicate
Neucode Labels for Multiplexed, Absolute Protein Quantification
We
describe a new method to accomplish multiplexed, absolute protein
quantification in a targeted fashion. The approach draws upon the
recently developed neutron encoding (NeuCode) metabolic labeling strategy
and parallel reaction monitoring (PRM). Since PRM scanning relies
upon high-resolution tandem mass spectra for targeted protein quantification,
incorporation of multiple NeuCode labeled peptides permits high levels
of multiplexing that can be accessed from high-resolution tandem mass
spectra. Here we demonstrate this approach in cultured cells by monitoring
a viral infection and the corresponding viral protein production over
many infection time points in a single experiment. In this context
the NeuCode PRM combination affords up to 30 channels of quantitative
information in a single MS experiment
The Peripheral Blood Eosinophil Proteome
A system-wide
understanding of biological processes requires a
comprehensive knowledge of the proteins in the biological system.
The eosinophil is a type of granulocytic leukocyte specified early
in hematopoietic differentiation that participates in barrier defense,
innate immunity, and allergic disease. The proteome of the eosinophil
is largely unannotated with under 500 proteins identified. We now
report a map of the nonstimulated peripheral blood eosinophil proteome
assembled using two-dimensional liquid chromatography coupled with
high-resolution mass spectrometry. Our analysis yielded 100,892 unique
peptides mapping to 7,086 protein groups representing 6,813 genes
as well as 4,802 site-specific phosphorylation events. We account
for the contribution of platelets that routinely contaminate purified
eosinophils and report the variability in the eosinophil proteome
among five individuals and proteomic changes accompanying acute activation
of eosinophils by interleukin-5. Our deep coverage and quantitative
analyses fill an important gap in the existing maps of the human proteome
and will enable the strategic use of proteomics to study eosinophils
in human diseases