Ultraviolet Photodissociation of Tryptic Peptide Backbones at 213
nm
Lars Kolbowski, Adam Belsom, and Juri Rappsilber*
Cite This: J. Am. Soc. Mass Spectrom. 2020, 31, 1282−1290
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sıSupporting Information
ABSTRACT: We analyzed the backbone fragmentation behavior of
tryptic peptides of a four-protein mixture and of E. coli lysate
subjected to ultraviolet photodissociation (UVPD) at 213 nm on a
commercially available UVPD-equipped tribrid mass spectrometer.
We obtained 15 178 unique high-confidence peptide UVPD
spectrum matches by recording a reference beam-type collision-
induced dissociation (HCD) spectrum of each precursor, ensuring
that our investigation includes a broad selection of peptides,
including those that fragmented poorly by UVPD. Type a, b, and
y ions were most prominent in UVPD spectra, and median sequence
coverage ranged from 5.8% (at 5 ms laser excitation time) to 45.0%
(at 100 ms). Overall, the sequence fragment intensity remained
relatively low (median: 0.4% (5 ms) to 16.8% (100 ms) of total
intensity), and the remaining precursor intensity, high. The sequence coverage and sequence fragment intensity ratio correlated with
the precursor charge density, suggesting that UVPD at 213 nm may suffer from newly formed fragments sticking together due to
noncovalent interactions. The UVPD fragmentation efficiency therefore might benefit from supplemental activation, as was shown
for ETD. Aromatic amino acids, most prominently tryptophan, facilitated UVPD. This points to aromatic tags as possible enhancers
of UVPD. Data are available via ProteomeXchange with identifier PXD018176 and on spectrumviewer.org/db/UVPD-213nm-
trypPep.
KEYWORDS: ultraviolet photodissociation, backbone fragmentation, tryptic peptides, collision-induced dissociation spectrum,
fragmentation behavior, precursors
■INTRODUCTION
The spread of ultraviolet photodissociation (UVPD) as an
alternative fragmentation method in mass spectrometry has
increased substantially in recent years.
1
UVPD uses photons to
achieve bond dissociation, which is a fundamentally different
mechanism from the widely used collisional-activation-based
[collision-induced dissociation (CID)
2
and beam-type CID
(HCD)
3
] and electron-based [electron transfer dissociation
(ETD)
4
and electron capture dissociation (ECD)
5
] methods.
Photons are typically produced by lasers at specific wave-
lengths over varying excitation times, tapping into a plethora of
different use cases and possible applications.
6
Coupling lasers
to mass spectrometers often requires highly specialized, custom
instrumentation with important safety considerations that must
be met. A recent key update here has been the release of a
commercially available 213 nm solid-state laser coupled to a
tribrid mass spectrometer. This has opened up the technology
to be used by a wider user base outside of specialized
laboratories who have implemented UVPD by making custom
modifications to their instruments.
So far, 213 nm UVPD has mainly been used in top-down
proteomics. For this, Fornelli et al.
7
have recently conducted a
thorough performance evaluation. Studies on peptide
fragmentation with UVPD at 213 nm have been conducted
on in-house-modified instruments by looking only at a very
small number of peptides and very specific bond cleavages.
Girod et al. described specific fragmentation behavior of
proline-containing peptides in their study on four synthetic
peptides subjected to a long (1000 ms) 213 nm UVPD
excitation time.
8
Mistarz et al. investigated the fragmentation
behavior of a single triply protonated 12-mer peptide at a 600
ms laser excitation time for localizing sites of backbone
deuteration.
9
More recently, Talbert and Julian carried out a
study on initiating bond-selective fragmentation using 213 nm
UVPD on 13 peptides.
10
In this study, we investigated the 213 nm UVPD
fragmentation behavior of a large number of tryptic peptides
Received: March 25, 2020
Revised: April 30, 2020
Accepted: April 30, 2020
Published: April 30, 2020
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in bottom-up proteomics experiments using a well-defined
four-protein mix as well as an E. coli lysate. We deployed a
systematic dual MS2 acquisition scheme, utilizing reference
HCD spectra for identification and to obtain UVPD peptide-
spectra matches (PSMs) independent of the UVPD
fragmentation efficiency. We characterized the peptide back-
bone ion-types present and compared the sequence coverage
and the sequence fragment intensity ratio across a range of
UVPD excitation times and other fragmentation techniques. As
the speed of acquisition plays a crucial role in the number of
possible identifications,
11
we investigated excitation times
comparable to typical ETD reaction time scales
12
to acquire
more practical data points. Finally, we probed our data to find
precursor-dependent properties that facilitate good UVPD
fragmentation.
■EXPERIMENTAL METHODS
Materials/Reagents. Human serum albumin (HSA),
ovotransferrin (chicken), and myoglobin (equine) were
purchased from Sigma-Aldrich (St. Louis, MO). Creatine
kinase (rabbit) was purchased from Roche (Basel, Switzer-
land).
Sample Preparation. HSA, ovotransferrin, myoglobin,
and creatine kinase were dissolved in 8 M urea with 50 mM
ammonium bicarbonate to a concentration of 2 mg/mL each.
Proteins were reduced by adding dithiothreitol at 2.5 mM
followed by incubation for 30 min at 20 °C. Samples were
derivatized using iodoacetamide at 5 mM concentration for 20
min in the dark at 20 °C, diluted 1:5 with 50 mM ammonium
bicarbonate and digested with trypsin (Pierce Biotechnology,
Waltham, MA) at a protease-to-protein ratio of 1:100 (w/w)
during a 16 h incubation period at 37 °C. Digestion was
stopped by adding 10% TFA at a concentration of 0.5%.
E. coli K12 cells were cultured in LB medium, harvested at
OD 0.6, formed into pellets, frozen using liquid nitrogen, and
stored at −80 °C. Cell pellets were then resuspended in ice-
cold lysis buffer (20 mM HEPES, 150 mM NaCl, pH 7.5, and
protease inhibitors (Roche)) and sonicated on ice at 30%
amplitude, 30 s on/offfor 10 cycles (total time 5 min) using a
Branson digital sonifier. Liquid was collected in a centrifuge
tube (the upper foaming with DNA proteins was discarded),
and the sample was clarified by centrifugation at 15 500 rpm
for 30 min. The protein concentration was measured using the
Pierce BCA protein assay. The soluble lysate was then run into
the first centimeter of an SDS-PAGE gel, and the protein band
was excised and subjected to an in-gel digestion protocol:
reduction in 20 mM DTT for 30 min at room temperature,
alkylation with 55 mM iodoacetamide for 30 min at room
temperature in the dark, and digestion with 12.5 ng/μL trypsin
overnight at 37 °C.
Digests for all proteins were cleaned up using the StageTip
protocol.
13
Peptides were eluted using 80% v/v ACN and 0.1%
v/v TFA, partially evaporated using a Vacufuge concentrator
(Eppendorf, Germany) to <5% ACN, and resuspended in
mobile phase A (0.1% formic acid) prior to mass spectrometry
analysis.
Data Acquisition. Samples were analyzed using an
UltiMate 3000 Nano LC system coupled to an Orbitrap
Fusion Lumos Tribrid mass spectrometer equipped with an
EasySpray source and a UVPD module (Thermo Fisher
Scientific, San Jose, CA) comprising a solid-state Nd:YAG laser
head (CryLaS GmbH) that generates a pulsed 213 nm beam
(fifth harmonic). Mobile phase A consisted of 0.1% formic acid
in water, and mobile phase B consisted of 80% acetonitrile,
0.1% formic acid, and 19.9% water. Peptides (1 μg equiv) were
loaded onto a 500 mm C-18 EasySpray column (75 μm i.d., 2
μm particles, 100 Å pore size) and eluted using the following
nonlinear gradient at a 300 nL/min flow rate: 2−4% (1 min);
4−6% B (2 min); 6−37.5% B (97 min); 37.5−42.5% B (10
min); and 42.5−47.5% B (5 min). MS1 spectra were acquired
in the Orbitrap using a scanning range from 300 to 1500 m/z
at 120 000 resolution. The AGC target was set to 8 ×105, and
the maximum injection time was 50 ms. The filter that was
used was a peptide monoisotopic precursor selection (MIPS)
with an intensity threshold of 5 ×104, precursor charge states
of 2−7+, and a dynamic exclusion of 60 s. MS2 spectra were
acquired using a top speed with a 3 s cycle time at 30 000
Orbitrap resolution. Precursors were isolated using the
Quadrupole with an isolation window of 1.4 m/z. The MS2
AGC target was set to “Standard”using the “Auto”maximum
injection time mode. For each precursor, two MS2 scans were
acquired (dual MS2): one with HCD at NCE 30% and the
other utilizing UVPD at 213 nm with varying laser excitation
times between experiments (5, 10, 15, 20, 30, 40, 50, and 100
ms). As reference, dual MS2 with HCD-HCD, HCD-ETD,
and HCD-EThcD (supplemental activation with 30% NCE)
were acquired. For the ETD experiments, calibrated charge-
dependent reaction times were used. For each dual MS2
method, 2 replicas were acquired, resulting in a total of 22
acquisitions per sample.
Data Analysis. Raw files were preprocessed using a custom
python script (https://github.com/Rappsilber-Laboratory/
preprocessing). Preprocessing included conversion to MGF
format, m/zrecalibration of both precursor and fragment
peaks, and splitting the primary (HCD) and secondary
(UVPD, ETD, EThcD, or HCD) MS2 spectra into separate
Figure 1. Dual MS2 workflow. For each selected precursor, two MS2
spectra are acquired, one with HCD and one with UVPD
fragmentation. The HCD spectrum is used for identification via a
database search. The identified peptide is then used to annotate the
UVPD spectra, allowing evaluation even when the extent of peptide
fragmentation in a UVPD spectrum alone does not enable
unambiguous fragment-based identification.
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MGF files. The primary MS2 spectra were denoised with the
default MS2Denoise settings in MSconvert
14
and then
analyzed with xiSEARCH
15
using the following settings: MS
accuracy, 3 ppm; MS2 accuracy, 15 ppm; missing mono-
isotopic precursor peaks, two; enzyme, trypsin; maximum
missed cleavages, four; maximum number of modifications,
three;ions:precursor,b-andy-type;modifications:
carbamidomethylation on cysteine as fixed and oxidation on
methionine as variable modification. The false discovery rate
was estimated separately for each fragmentation parameter set
(2 acquisition replicas per fragmentation parameter) using
xiFDR
16
on a unique PSM level to 1% for the 4 PM and 0.1%
for the E. coli lysate. The larger number of identified spectra of
the E. coli sample allowed for a lower FDR while maintaining a
sufficient number of decoy hits (>9) to reliably estimate the
FDR. Additionally, we excluded PSMs that were not identified
in at least 6 of the 11 different fragmentation parameter set
duplicates. This minimizes the number of false positives in our
data set because of the improbability of any given random
match to occur in more than half of the experiments. From the
secondary peak list files, the corresponding scans to the
primary HCD PSMs were extracted and subsequently
annotated with pyXiAnnotator (https://github.com/
Rappsilber-Laboratory/pyXiAnnotator/) using the identifica-
tions from their primary counterparts. Secondary spectra were
annotated with peptide, a-, b-, c-, x-, y-, and z-type ions with a
maximum tolerance of 15 ppm. Additionally, a hydrogen loss
was defined, and the missing monoisotopic fragment peak
feature was used to enable the matching of ions with the
absence or presence of an extra hydrogen respectively (e.g., y−,
a+). For comparison, we additionally searched the secondary
spectra alone with the same settings described above for HCD,
with the exception of the searched ion types, where we tested
several ion type combinations and used the ones that gave the
highest number of results (a, b, c, x, y, and z for UVPD; c, y,
and z for ETD; and a, b, c, y, and z for EThcD).
The mass spectrometry raw files, peak lists, search engine
results, and FASTA files have been deposited to the
Figure 2. Fragmentation analysis of 14 511 unique PSMs from 11 279 unique peptides subjected to different UVPD excitation times and ETD,
HCD, and EThcD for comparison. Data from the E. coli data set. (A) Bar plots showing the sequence fragment ion type counts (normalized by
peptide length). Error bars represent 0.95 confidence intervals. (B) Box plots showing sequence coverage of precursors. (C) Box plots of MS2
intensity ratios of remaining precursor and sequence fragments with respect to the total MS2 intensity. In all box plots, whiskers extend to the 1.5
interquartile range past the low and high quartiles. (D) Example MS2 spectra of YLDLIANDK (charge 2+) subjected to 5, 30, and 100 ms of
UVPD. All three spectra are zoomed in on the yaxis (5×). Annotated fragment peaks are shown in red, and the unfragmented precursor is
highlighted with a gray outline.
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ProteomeXchange Consortium via the PRIDE
17
partner
repository with the PXD018176 data set identifier and
10.6019/PXD018176. Additionally, we have made all
annotated spectra available through xiSPEC
18
on
spectrumviewer.org/db/UVPD-213nm-trypPep.
■RESULTS/DISCUSSION
Dual MS2 Data Acquisition. We aimed to evaluate the
UVPD efficiency of peptides in tryptic digests at different
excitation times. For some settings and peptides, the resulting
MS2 spectra themselves may not allow the unambiguous
identification of the peptide due to poor fragmentation
efficiency. We therefore designed a dual MS2 acquisition
approach by acquiring two MS2 spectra for each precursor:
one fragmented with HCD and then another fragmented with
UVPD (alternatively ETD, EThcD, or HCD as controls). This
allowed us to use the well-established collisional fragmentation
method to reliably identify precursors and then annotate and
evaluate the UVPD spectra with those identifications, even
when the extent of peptide fragmentation in a UVPD spectrum
alone did not enable unambiguous fragment-based identi-
fication (Figure 1).
Using the HCD scans for identification gave us a large
number of PSMs over all tested conditions (Figure S1). This
becomes especially apparent for the short excitation time
acquisitions of the more complex E. coli sample. From the 5 ms
acquisitions 17 536 HCD spectra could be identified, whereas
relying on UVPD alone would have yielded only 1047 PSMs.
Additionally, we checked the overlap of UVPD and HCD
identifications to make sure that we are not missing peptides
that fragment well using UVPD but not HCD, yet we found
almost none. Furthermore, analyzing the subset of UVPD
spectra that led to peptide identifications would have
introduced a strong bias toward peptides that fragment well.
The HCD-HCD control samples led to almost identical
numbers of PSMs from both the reference HCD and the
secondary HCD control scan, showing that the quality of the
second spectra is not adversely affected, thus demonstrating
the viability of this approach.
To further improve the quality of our data we applied the
additional restriction to include only PSMs that were seen in
more than half of our acquired fragmentation parameter sets.
This rather conservative selection of PSMs (MS1 plus MS2
HCD fragment-based identification with stringent FDR cutoff
and the additional requirement mentioned above) combines
the benefits of using a real-world sample from a protein digest
while approaching the confidence level of a synthetic peptide
library.
Fragmentation Analysis of Different UVPD Excitation
Times. We investigated the UVPD-induced peptide fragmen-
tation behavior of tryptic digests from a lower-complexity
sample consisting of four model proteins (4 PM) and a
complex E. coli lysate sample and compared it with HCD,
ETD, and EThcD fragmentation.
Our data set consists of 15 178 unique precursors (unique
sequence and charge state combination; 4 PM: 667; E. coli:
14 511; Table S1). These originate from 11 663 unique
peptides (4 PM: 384; E. coli: 11 279) subjected to UVPD at
213 nm with varying laser excitation times (5−100 ms). We
analyzed both data sets separately to test if we can reproduce
the findings from our more complex E. coli lysate sample also
in the small, well-defined 4 PM sample. The evaluation of
fragmentation characteristics from both data sets led indeed to
very similar results (compare Figure 2 and Figure S2). Our
analysis found that UVPD at 213 nm results in all possible
types of backbone fragmentation ions in tryptic peptides, with
a-, b-, and y-type ions being the most prominent over all
excitation times (Figure 2A). In contrast to our observation for
tryptic peptides, UVPD of intact proteins has been shown to
produce predominantly a and x ions.
7
Intact proteins produce
much larger fragments. One could speculate that for large
fragments, b- to a-ion and y- to x-ion conversion by CO-
elimination might be favored. Ion counts were normalized per
peptide by length for ease of comparison. Ion counts increase
with increasing excitation time and start to level offat the
maximum acquired time for all ion types. Doubling the
excitation time from 50 to 100 ms resulted only in a very small
increase in fragment count.
WecalculatedthesequencecoverageforourPSMs
conservatively, as the ratio of matched N-terminal and C-
terminal sequence fragments to the number of theoretically
possible sequence fragments (i.e., 100% sequence coverage
would mean the detection of at least one fragment from the N-
terminal (a, b, or c) and one from the C-terminal series (x, y,
or z) between all amino acid residues of a peptide). Sequence
coverage of UVPD was low at 5 ms (median: 5.8%) but
increased steadily with excitation time to a median of 45.0% for
100 ms. While still on the low side compared to the HCD at
68.2% and EThcD at 81.6%, UVPD started to give better
median sequence coverage than ETD with excitation times
longer than 15 ms. The low median sequence coverage from
ETD is partly due to its dependency on higher charge states
(median sequence coverage for charge 2+: 12.5% vs 41.2% for
≥3+). Notably even for the lowest UVPD excitation time,
there are some precursors that fragmented reasonably well
(showing a sequence coverage of >50%) while others showed
almost no fragmentation even at 100 ms. This suggested that
precursor-dependent properties play an important role in
UVPD. Also, comparing the last two UVPD data points
showed that doubling the excitation time had only a marginal
effect on the resulting sequence coverage.
As a way of assessing the fragmentation efficiency, we
calculated the ratio of the sum of all sequence fragment isotope
cluster peak intensities to the total MS2 intensity. This
Figure 3. Sequence fragment complementarity of acquired
fragmentation methods to the HCD reference spectra. Plotted is
the sequence coverage gain for all matches from the E. coli and 4 PM
data sets when combining annotated fragments from the secondary
MS2 scan (HCD, UVPD 5−100 ms, ETD, or EThcD) with the
reference HCD scan versus the reference HCD scan alone.
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sequence fragment intensity ratio remained low over all tested
UVPD excitation times, ranging from a median of 0.4% at 5 ms
to 16.8% at 100 ms (Figure 2C). In contrast to the ion count
and the sequence coverage, doubling the excitation time from
50 to 100 ms led to higher fragment intensity but few
additional fragments.
The high remaining precursor intensity revealed that the low
sequence fragment intensity was due to the low fragmentation
efficiency, similar to what has been described for ETD.
19
This
can be seen in the representative spectra where even at 100 ms
of UVPD the unfragmented precursor is the most intense peak
by far (Figure 2D). In the HCD spectra, on the other hand, a
median of 57.0% MS2 intensity stems from sequence
fragments with almost no leftover precursor. This suggests
that here the remaining intensity most likely stems from
internal fragmentation events, commonly seen in HCD.
20
Next, we checked the complementarity between the HCD
and the secondary scans by adding the annotated fragments
from the secondary MS2 scan to its primary scan and
recalculating the sequence coverage. In Figure 3 the gain in
sequence coverage for each method is plotted. Note that the
combination of two consecutive HCD scans on the same
precursor already resulted in an average ∼4% gain and should
therefore be considered to be a baseline. UVPD with excitation
times >20 ms showed sequence coverage gains above this
baseline, but the gain seemed to level offat 40 ms around 7%.
Both ETD and EThcD were favorable over UVPD in terms of
sequence coverage complementarity to HCD.
Figure 4. Influence of (A) charge and (B) charge density on the sequence coverage in UVPD experiments with varying laser excitation times.
Linear regression model fit of all analyzed PSM data with the 0.95 confidence interval shown as translucent bands around lines. Precursor charge
states 6+ and 7+ were excluded for panel A due to their low number (<2%). (C−F) Example fragmentation spectra of peptide
QEPERNECcmFLQHKDDNPNLPR (Ccm stands for carbamidomethylated cysteine) subjected to 20 ms of UVPD in charge states 2+, 3+, 4+,
and 5+. Spectra are zoomed in on the yaxis to 300k intensity for visibility, and the remaining unfragmented precursor peaks are cut off.
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Influence of the Charge State on UVPD. In search of
precursor properties that influenced UVPD fragmentation, we
first looked at the precursor charge state. We split our PSMs by
the charge state and evaluated the correlation to the sequence
coverage by linear regression fitting (Figure 4A). We excluded
charge states 6+ and 7+ for the regression analysis due to their
low number (<2%, Figure S3). In contrast to ETD,
22
a higher
charge state showed negative correlation with sequence
coverage over all tested laser excitation times. The charge
density (precursor charge divided by the peptide amino acid
length), on the other hand, seemed to be an important
influence on UVPD fragmentation. A high charge density was
correlated with high sequence coverage independent of
excitation time (Figure 4B). The influence of the charge
density became even more apparent when comparing spectra
of the same peptide that were acquired in multiple charge
states. Figure 4 panels C−F show spectra of the peptide
QEPERNECcmFLQHKDDNPNLPR fragmented in charge
states 2+, 3+, 4+, and 5+. In the spectrum with the lowest
charge state, only four sequence fragments are visible (i.e., 10%
sequence coverage). The sequence coverage increased to
17.5% for charge state 3+ (7 sequence fragments) and charge
state 4+ already showed 27 sequence fragments, resulting in
42.5% sequence coverage. Finally, the highest detected charge
state for this peptide (5+) presented a rich fragmentation
spectrum with high sequence coverage (67.5%), albeit the
fragmentation efficiency remains relatively low.
A possible interpretation of the charge density dependence
of UVPD could be incomplete dissociation through non-
covalent interactions between newly formed fragments, a
phenomenon which has been previously described for
electron-based fragmentation methods.
21,22
In the case of
ETD, several supplemental activation techniques have been
developed to address this drawback using collision-based
activation (ETciD/ETcaD
23
and EThcD
19
) or infrared
excitation.
24
Recently, Halim et al. have reported increased
sequence coverage in a top-down proteomics experiment
through simultaneous and consecutive irradiation using UV
(213 nm) and IR (10.6 μm) lasers, coined HiLoPD.
25
Unfortunately, however, thus far there is no commercial
instrument available that contains both a UV and an IR laser
source. Furthermore, the combination of UVPD and collision-
based activation is not possible with the current version of the
instrument control software.
Influence of Amino Acid Composition on UVPD. The
availability of chromophores is crucial for absorbing the energy
provided by photons, without which UVPD will not take place.
While for shorter wavelengths such as 157 nm almost all bonds
can act as chromophores, 213 nm is already at the absorption
threshold for smaller chromophores such as amide bonds.
10
Talbert and Julian found that 213 nm can give rise to both
bond specific as well as nonspecific dissociation depending on
the laser power and excitation time as well as the molecular
composition. The molecular composition and structure of the
peptides is defined by their amino acid composition. We
analyzed the cleavage propensity on UVPD for all backbone
ion types. The heat maps in Figure 5 show the relative
likelihood for the detection of a backbone cleavage product
Figure 5. Cleavage propensities of backbone fragment ion types for UVPD. Heat maps show the ratio of detected versus theoretical cleavage events
for all amino acid combinations that can occur at the N- and C-terminals of the fragmentation site. Values were normalized by the mean for each
ion type and then converted to the base 2 logarithm. White represents the average cleavage propensity for this ion type, blue represents a lower-
than-average occurrence, and red represents a higher-than-average occurrence.
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between two amino acids. Propensities were calculated by
dividing the observed occurrences of a fragmentation site by
the theoretical possible values for all combinations of amino
acid N- and C-terminals of the site. For ease of comparison,
ratios were normalized by the mean for each product ion type
and then converted to the logarithm (base 2). Amino acid pairs
between which cleavage occurs preferentially, differed between
the different ion types, but overall the aromatic amino acids
and proline showed hot spots for increased cleavage
propensity. Similar to what has been described by Oh et
al.
26
for 266 nm and by Fornelli et al.
7
for 213 nm top-down
proteomics, we found an increased frequency of fragmentation
events adjacent to aromatic amino acids. Type a and x ions and
to a lesser extent b, y, and z ions showed a preference for the
N-terminal cleavage of phenylalanine. Interestingly, c- and z-
type cleavage occurred with a largely increased frequency of N-
terminal tryptophan. The most prominent ion types (a, b, and
y) all occur with increased frequency with a proline C-terminal
fragmentation site. This is in line with findings from Girod et
al.,
8
who have shown C−C and C−N bond activation close to
that of proline residues following 213 nm excitation of the
proline-containing peptides, which was also seen by Fornelli et
al.
7
in top-down analysis.
To systematically check the influence of the amino acid
composition on the fragmentation of the whole peptide, we
compared the mean amino acid composition of those peptides
that fragmented well under UVPD with those that showed
poor fragmentation over all acquired excitation times. We
looked at the sequence coverage as well as the sequence
fragment intensity ratio and divided our data into terciles for
both of those metrics. We calculated the average frequency for
each amino acid in the upper and lower terciles and compared
the fold-change. This showed which amino acids were
disproportionately prevalent in UVPD-susceptible peptides
(Figure 6A). Peptides with high sequence coverage had a
significantly increased frequency of tryptophan and phenyl-
alanine. Looking at the sequence fragments’intensity ratio,
peptides in the upper tercile exhibited increased ratios of
aromatic amino acids (most prominently again tryptophan).
Interestingly, while a higher proline ratio seemed to translate
into intense sequence fragments, it did not promote high
sequence coverage, suggesting that proline leads only to the
efficient cleavage of specific bonds (adjacent, see Figure 5).
Figure 6 panels B and C show example spectra of two peptides
at the same charge state and with similar m/zvalues both
subjected to 20 and 100 ms UVPD. The peptide in panel B
contains both a tryptophan and a phenylalanine and already
shows almost complete sequence coverage at 20 ms but still an
intense remaining precursor peak. At 100 ms, the precursor is
almost completely fragmented. The peptide in panel C, on the
other hand, contains no aromatic amino acids or proline.
There is a complete absence of fragment peaks at 20 ms, and
only very small low-intensity fragment peaks are present in the
100 ms spectrum.
■CONCLUSIONS
In this study, we evaluated the fragmentation behavior of
tryptic peptides in 213 nm UVPD. We see fragmentation on
time-scales feasible for large-scale bottom-up experiments;
however, the fragmentation efficiency is comparably low.
Fragmentation seemed to be highly dependent on precursor
properties, namely, the charge density and the presence of
aromatic amino acids (and proline). We see a possibility in the
development of supplemental activation techniques using
collision-based methods to help improve fragmentation. Also,
the use of an aromatic tag could increase photon absorption
and possibly lead to increased spectral quality.
Figure 6. Influence of amino acid composition on peptide backbone
fragmentation for UVPD. (A) The box plots show the log 2-fold
change of the mean amino acid composition of PSMs in the upper
tercile (T1) against the lower tercile (T3) in terms of sequence
coverage and sequence fragment intensity ratio, respectively. Data
from the E. coli data set was considered for the box plots. The spread
of the respective HCD data is shown in gray as a reference. (B, C)
Example spectra of two peptides with similar m/zvalues and the same
charge state, both subjected to 20 and 100 ms UVPD. (B)
DDNKVEDIWSFLSK containing both tryptophan and phenylalanine
and (C) KCcmNNLRDLTQQER without any aromatic amino acids
or proline.
Journal of the American Society for Mass Spectrometry pubs.acs.org/jasms Research Article
https://dx.doi.org/10.1021/jasms.0c00106
J. Am. Soc. Mass Spectrom. 2020, 31, 1282−1290
1288
■ASSOCIATED CONTENT
*
sıSupporting Information
The Supporting Information is available free of charge at
https://pubs.acs.org/doi/10.1021/jasms.0c00106.
Overview of PSMs found in dual MS2 data sets,
fragmentation analysis of the 4 PM data set, and
precursor charge state distribution (PDF)
List of all analyzed m/zspecies (peptide sequence and
charge state combination) (XLS)
■AUTHOR INFORMATION
Corresponding Author
Juri Rappsilber −Bioanalytics, Institute of Biotechnology,
Technische Universitat Berlin, 13355 Berlin, Germany;
Wellcome Centre for Cell Biology, School of Biological Sciences,
University of Edinburgh, Edinburgh EH9 3BF, United
Kingdom; orcid.org/0000-0001-5999-1310;
Email: [email protected]
Authors
Lars Kolbowski −Bioanalytics, Institute of Biotechnology,
Technische Universitat Berlin, 13355 Berlin, Germany;
Wellcome Centre for Cell Biology, School of Biological Sciences,
University of Edinburgh, Edinburgh EH9 3BF, United
Kingdom
Adam Belsom −Bioanalytics, Institute of Biotechnology,
Technische Universitat Berlin, 13355 Berlin, Germany;
Wellcome Centre for Cell Biology, School of Biological Sciences,
University of Edinburgh, Edinburgh EH9 3BF, United
Kingdom
Complete contact information is available at:
https://pubs.acs.org/10.1021/jasms.0c00106
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
This work was supported by the Wellcome Trust (103139 and
108504) and the Deutsche Forschungsgemeinschaft (DFG,
German Research Foundation, 329673113 and 426290502).
The Wellcome Centre for Cell Biology is supported by core
funding from the Wellcome Trust (203149).
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