Optimized Fragmentation Regime for Diazirine Photo-Cross-Linked
Peptides
Sven H. Giese,
†,‡
Adam Belsom,
‡
and Juri Rappsilber*
,†,‡
†
Chair of Bioanalytics, Institute of Biotechnology, Technische Universitat Berlin, 13355 Berlin, Germany
‡
Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
*
SSupporting Information
ABSTRACT: Cross-linking/mass spectrometry has evolved
into a robust technology that reveals structural insights into
proteins and protein complexes. We leverage a new tribrid
instrument with improved fragmentation capacities in a
systematic comparison to identify which fragmentation
method would be best for the identification of cross-linked
peptides. Specifically, we explored three fragmentation
methods and two combinations: collision-induced dissociation
(CID), beam-type CID (HCD), electron-transfer dissociation
(ETD), ETciD, and EThcD. Trypsin-digested, SDA-cross-
linked human serum albumin (HSA) served as a test sample,
yielding over all methods and in triplicate analysis in total 2602 matched PSMs and 1390 linked residue pairs at 5% false
discovery rate, as confirmed by the crystal structure. HCD wins in number of matched peptide-spectrum-matches (958 PSMs)
and identified links (446). CID is most complementary, increasing the number of identified links by 13% (58 links). HCD wins
together with EThcD in cross-link site calling precision, with approximately 62% of sites having adjacent backbone cleavages that
unambiguously locate the link in both peptides, without assuming any cross-linker preference for amino acids. Overall quality of
spectra, as judged by sequence coverage of both peptides, is best for EThcD for the majority of peptides. Sequence coverage
might be of particular importance for complex samples, for which we propose a data dependent decision tree, else HCD is the
method of choice. The mass spectrometric raw data has been deposited in PRIDE (PXD003737).
Current methods of structural biology have left a systematic
and large gap in our knowledge of protein structures.
1
Cross-linking/mass spectrometry (CLMS) is an emerging tool
that helps to gain structural information for challenging
proteins and protein complexes. In CLMS experiments, protein
complexes are chemically cross-linked, digested into peptides,
and then analyzed via mass spectrometry and bioinformatics.
2−5
Identifying a cross-linked peptide pair or the linked residues
within, defines their maximal distance in the folded protein.
The derived distance constraints can then be used to determine
the low-resolution arrangement of protein complexes
4,6,7
or
even the high-resolution structure of a protein by the help of
computational modeling.
8
To identify cross-linked peptides, fragmentation spectra have
to be matched with peptide sequences by database search. For
this purpose, a number of tools have been developed,
9,10
for
example, pLINK,
11
Protein Prospector,
12,13
StavroX,
14
xQuest,
15
Kojak,
16
Xi,
6,17
or even search engines
18
based on
linear peptide identification search paradigms such as Mascot.
19
One of the challenges in identifying cross-linked peptides is the
unequal fragmentation of the two linked peptides,
13,17
that is,
often one of the two peptides is better fragmented and thus also
better characterized by fragment ions. Under collision-induced
dissociation (CID) conditions this has been investigated in
more detail, revealing that the intensity of observed fragment
ions is also affected.
17
This is important for the scoring of cross-
linked peptides since in general the number of identified
fragment ions and their intensity is used for spectra matching.
Despite the obvious disadvantage of the unequal fragmentation,
scoring mechanisms managed to successfully exploit this fact:
To judge the complete cross-linked peptide-spectrum match
(PSM), the two individual peptide scores are weighted
differently.
13,16
However, this should only be an ad hoc
solution; ideally the experimental setup can be changed in such
a way that the sequence coverage for both peptides is increased.
It is plausible that one of the available fragmentation methods
performs better than the others, and a comparative analysis into
the behavior of cross-linked peptides might reveal options for a
refined acquisition strategy.
Throughout the manuscript we use CID for resonant
excitation CID in the linear ion trap and HCD as the
abbreviation for beam-type CID (HCD is also often referred to
as higher-energy collisional dissociation). CID is one of the
standard methods of fragmenting peptides in proteomics and
has been used in many CLMS studies.
6,20−24
The details of
CID of cross-linked peptides have recently been systematically
Received: May 27, 2016
Accepted: July 25, 2016
Published: July 25, 2016
Article
pubs.acs.org/ac
© 2016 American Chemical Society 8239 DOI: 10.1021/acs.analchem.6b02082
Anal. Chem. 2016, 88, 8239−8247
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assessed,
17
but a systematic comparison to other fragmentation
methods such as HCD is lacking. HCD has also been used in
many CLMS studies.
11,13,25,26
Neither a systematic analysis of
cross-linked peptides under HCD exists nor under electron-
transfer dissociation (ETD). ETD-based fragmentation, that is,
ETD with and without supplemental activation of CID
(ETciD) or HCD (EThcD)
27
has neither routinely been
applied to cross-linked peptides nor investigated in much detail.
A sequential fragmentation scheme of CID and ETD is
reported to increase the identification and confidence levels of
cross-linked peptides.
28
Another study acquired sequential CID
and ETD fragmentation spectra as an optimized method for
CID cleavable cross-linkers with signature peaks. Both spectra
are then matched with their appropriate ion types and scored
together, yielding an improved sequence coverage compared to
CID alone.
29
Search strategies for noncleavable cross-linkers,
however, do not rely on the detection of signature peaks, and
thus, the time for the reisolation of the precursor can be saved
by simply using ETD with supplemental activation. It was also
shown that ETD alone can generate good ion coverage for both
peptides using a novel cross-linker,
30
albeit the effect on
peptides cross-linked with another cross-linker remains to be
investigated. In contrast, ETD has been used frequently for
complete proteins
31
or to characterize post-translational
modifications (since it leaves the often labile peptide
modifications intact
32
). Earlier studies stated that ETD
fragment peptides with charge states higher than two, more
extensively than CID.
33
However, the underlying effect seems
to correlate with the mass-to-charge ratio (m/z) of the
precursors.
34
For cross-linked peptides, we expect highly
charged precursors
6,15,18
and, thus, potentially well-suited
targets for ETD.
High-sequence coverage is important to ensure selectivity
during database search when trying to identify the two cross-
linked peptides from the large choice of alternatives offered by
the database. Good backbone fragmentation should also be
beneficial to pinpoint the exact location of the cross-link.
Despite being the intuitive expectation, sequence coverage and
site calling precision do not necessarily have to be linked
directly. Properties of the linkage site might direct fragmenta-
tion toward neighboring backbone bonds or away from them.
Also, for amine-reactive cross-linkers, pinpointing the exact
position of the cross-link is assisted by the restricted chemical
reactivity toward lysine, serine, threonine, tyrosine, or the
protein N-terminus. Hence, depending on the peptide
sequence there might only be a single amino acid amenable
to the cross-linker reaction. For highly reactive cross-linkers
such as succinimidyl 4,4-azipentanoate (SDA) each residue in a
peptide needs to be considered when locating the linkage site.
Therefore, pinpointing the cross-link sites potentially requires
more complete backbone fragmentation than for more specific
cross-linkers.
In this study we compared three different fragmentation
techniques and two combined fragmentation schemes available
on a novel tribrid mass spectrometer (Orbitrap Fusion Lumos,
Thermo Fisher Scientific), CID, HCD, ETD, ETciD, and
EThcD, on cross-linked peptides obtained by tryptic cleavage of
SDA-cross-linked human serum albumin (HSA). The three-
dimensional structure of HSA has been resolved by X-ray
crystallography
35
and is used as ground-truth to evaluate the
identification results. The right choice of fragmentation method
allows the number of identified linkage sites to be increased;
increasing the sequence coverage of both linked peptides
boosts the confidence of the matches and also the correct
localization of the cross-link site.
■METHODS
Sample Preparation. Purified HSA (Sigma-Aldrich, St.
Louis, MO) was cross-linked using different cross-linker-to-
protein, weight-to-weight (w/w) ratios: 0.152:1, 0.203:1,
0.303:1, 0.406:1, 0.606:1, 0.811:1, 1.21:1, and 1.62:1. Aliquots
of purified HSA (15 μg, 0.75 mg/mL) in cross-linking buffer
(20 mM HEPES−OH, 20 mM NaCl, 5 mM MgCl2, pH 7.8)
were mixed with sulfo-SDA (Thermo ScientificPierce,
Rockford, IL) to initiate incomplete reaction of the protein
with the sulfo-NHS ester component of the cross-linker.
Human blood serum from a healthy donor (20 μg, 1.0 mg/mL)
was cross-linked in a similar manner, using cross-linker-to-
protein ratios (w/w) of 0.5:1, 1:1, 2:1, and 4:1. Total reaction
volume in each case was 30 μL. For the second step of the
cross-linking procedure, photoactivation of the diazirine group
was carried out using UV irradiation from a UVP CL-1000 UV
Cross-linker (UVP Inc.). Samples were irradiated for either 25
or 50 min for purified HSA samples, and either 10, 20, 40, or 60
min in the case of blood serum samples and separated using gel
electrophoresis. Bands corresponding to monomeric HSA were
excised from gels and the proteins reduced with DTT, alkylated
using IAA, and digested using trypsin following standard
protocols.
18
Peptides were loaded onto self-made C18
StageTips
36
and eluted using 80% acetonitrile and 20%, 0.1%
TFA in water. The eluates from blood serum HSA and purified
HSA digests were mixed 0.33:1 as a master mix to be used
throughout this study. The two samples originally used in our
structural analysis of HSA
8
were mixed here to gain enough
material to perform the experiments of this study in triplicates.
Data Acquisition. Peptides were loaded directly (2% B,
500 nL/min) onto a spray emitter analytical column (75 μm
inner diameter, 8 μm opening, 250 mm length; New
Objectives) packed with C18 material (ReproSil-Pur C18-AQ
3μm; Dr Maisch GmbH, Ammerbuch-Entringen, Germany)
using an air pressure pump (Proxeon Biosystems).
37
The 0.1%
formic acid served as mobile phase A and 0.1% formic acid/
80% acetonitrile as mobile phase B. Peptides were eluted (200
nL/min, linear gradient of 2−40% B over 139 min) directly
into an Orbitrap Fusion Lumos Tribrid mass spectrometer
(Thermo Fisher Scientific, San Jose, CA). Survey spectra were
recorded in the Orbitrap at 120000 resolution. Spectra for all
fragmentation methods were acquired using a scan range of
300−1700 m/z. Precursor ion isolation was performed with the
quadrupole and an m/zwindow of 1.6 Th. The precursor
automatic gain control (AGC) target value was 4 ×105,
maximum injection time 50 ms. For CID only, CID collision
energy was set to 30%. For HCD only, HCD collision energy
was set to 35%. For ETD only, the option to inject ions for all
available parallelizable time was selected (anion AGC 5 ×104,
60 ms maximum injection time). Supplemental activation (SA)
collision energy was set to 10% for ETciD, and 25% for EThcD.
Data Analysis. Raw files were preprocessed with MaxQuant
(v. 1.5.2.8) with “Top MS/MS peaks per 100 Da”set to 100.
38
Resulting peak files (APL format) were subjected to Xi (ERI
Edinburgh, v. 1.5.584) and searched with the following settings:
MS accuracy, 6 ppm; MS/MS accuracy, 20 ppm; enzyme,
trypsin; max. missed cleavages, 4; max. number of modifica-
tions, 3; fixed modification, none; variable modifications,
carbamidomethylation on cysteine; oxidation on methionine;
cross-linker, SDA (mass modification: 109.0396 Da). In
Analytical Chemistry Article
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Anal. Chem. 2016, 88, 8239−8247
8240
addition, variable modifications by the hydrolyzed cross-linker
(“SDA-hyd”, mass modification: 82.0413 Da) and loop-links
(“SDA-loop”, mass modification: 83.0491 Da) were allowed.
SDA cross-link reactions were assumed to connect lysine,
serine, threonine, tyrosine, or the protein N-terminus on the
one end of the spacer with any other amino acid on the other
end. FDR was estimated using XiFDR (v. 1.0.6.14)
39
on a 5%
peptide spectrum match (PSM) level and 5% link-level only
including unique PSMs. The reference database consisted of a
single entry with the protein sequence of HSA (Uniprot:
P02768). For further analysis, PSM information (precursor m/
z, annotated fragments, score, peptide sequences, etc.) were
extracted from a local PostgreSQL database. The annotated
spectra are available in the Supporting Information (Figures
S4−S8).
To derive a decision tree for an optimized fragmentation
scheme for cross-linked peptides we divided the acquisition
range into a grid of m/zbins of size 200 for each charge state
from 3 to 7. After sorting all PSMs into this theoretical grid we
assigned each cell the best performing and second best
performing fragmentation method. The performance was
assessed through the median achieved sequence coverage of
the complete cross-linked peptide. Note, sequence coverage
does not depend on the possible fragment ions but rather on
the actual evidence (fragment ions) for specific n-terminal or c-
terminal sequences. To decide whether or not a fragmentation
method is favorable over another we conducted a simple, one-
sided permutation test
40
with label swaps and 10.000 iterations.
Pvalues lower than 0.05 were regarded as significant.
Permutation tests were only performed if more than 15
observations were in the best performing class. If the best and
second best were too similar to give significant results the best
performing method was also compared to all other methods.
All raw files are available via the PRIDE repository
41
(PDX:
PXD003737) along with PSM results and the reference
FASTA.
■RESULTS AND DISCUSSION
We investigated the impact of five fragmentation techniques
(CID, HCD, ETD, EThcD, ETciD) on the analysis of cross-
linked peptides using a latest generation Orbitrap mass
spectrometer (Orbitrap Fusion Lumos, Thermo Fisher
Scientific). HSA was used as a model protein with a known
crystal structure. Cross-linking experiments suffer under CID
conditions from the underrepresentation of fragment ions from
one of the two peptides.
13,17
Here we define the peptide with
more intense ions among the ten most intense fragment ions as
the α-peptide and the remaining peptide as the β-peptide.
17
Note that the nomenclature for the two peptides in a cross-link
is not standardized; other definitions using the achieved search
score
13
or the peptide’s chain length or mass
4
are used. We
hypothesized that the usage of other fragmentation techniques
has an impact on the fragmentation pattern of cross-linked
peptides and subsequently on the success rate of identification.
In our analysis we applied two different FDR-levels according
to the descriptive features that we evaluated.
39
For the
evaluation of identification results on the crystal structure, a
link-level FDR is used. For the evaluation of PSM properties
(e.g., sequence coverage), a regular PSM FDR is used. An
overview is available in Table S1.
HCD Fragmentation Gives the Highest Number of
Identified Cross-Links. We compared the number of
identified cross-links that passed a 5% link-level FDR and a
5% PSM-level FDR to assess which fragmentation approach
leads to the highest identification success. The results,
accumulating the three technical replicates for all fragmentation
techniques, show that HCD (958 PSMs) gives the highest
number of identifications followed by CID (604 PSMs, Figure
1A). ETciD fragmentation achieves the lowest number of
identified cross-links with 296 PSMs. This order is closely
related to the number of acquired spectra in all replicates.
While HCD is the fastest acquisition technique producing
∼109000 MS2 spectra ETciD and ETD only produce ∼80000
spectra (Table S1). While the number of PSMs is only a proxy
for the success of CLMS experiments, the true value of CLMS
data comes from the corresponding distance constraints.
Therefore, for the comparison of cross-linking data it makes
sense to compare the results on the link-level. For the
comparison on the link-level only unique links are regarded for
further analysis. A unique link is defined by the combination of
residues involved in a cross-link, that is, a unique residue pair.
As is the case for PSMs, HCD fragmentation also returns the
highest number of identified links (Figure 1A). In total 1390
links (972 unique) were identified with the various methods:
Of the unique links HCD observed 446 links (46%), CID 297
links (31%), EThCD 240 links (25%), ETciD 205 links (21%),
and ETD 202 (21%). Note, the comparison of the links is not
straightforward if the cross-link site is ambiguous. We applied a
simple heuristic that assigns the linkage site to the c-terminal
Figure 1. Number of SDA-induced cross-links identified in HSA using
different fragmentation techniques. (A) Identified PSMs and links
were computed for 5% FDR-level on the respective category. (B)
Evaluation of the identified cross-links against the crystal structure of
HSA. The light gray distribution reflects the distance measurement
between identified residues in a cross-link mapped to the crystal
structure (the median is shown above the vertical line). The dark gray
distribution reflects all pairwise combinations of cross-linkable residues
in the crystal structure. The black vertical line at 25 Å is used to classify
cross-links as long distance or not.
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residue in ambiguous linkage windows. As HSA’s three-
dimensional structure has been resolved, it is possible to utilize
it as ground truth and further evaluate the quality of the
identified links. We used 25 Å here for SDA as the maximal α-
carbon distance of two linkable amino acids in the three-
dimensional structure. This provides a clear distinction between
true positive and false positive identifications. Each identified
link that lies within 25 Å in the crystal structure is plausibly a
true positive. Accordingly, every link that is further than 25 Å
apart is plausibly a false positive. This is a simplified approach,
as links shorter than 25 Å will also contain false positives as a
result of random matching, and conversely, longer links may be
true and result from protein structural flexibility. Comparing
the link information from all five fragmentation techniques
shows that the overall quality of the results is comparable across
all fragmentation modes and distinctly different from random
results. The derived distance distributions have a median of
12−13 Å and are very distinct from the random distance
distribution (Figure 1B). In addition, the results are comparable
in meeting the approximated 5% FDR. FDR analysis for the
HCD data and the ETciD data slightly underestimates the
number of false positives by 1% and 2.5%, respectively (Figure
S1). These can be partially explained by the definition of the
FDR itself, which only gives an approximation of the true false
discovery rate. Furthermore, the hard cutoffthat was used has a
large impact on the computed FDR. For example, the ETciD
distances showed a larger peak just to the right of the desired
distance cutoff, indicating that a small increase in the maximal
allowed distance would give an FDR closer to the desired 5%.
The HCD distance distribution looks similar to a small
enrichment of false positives just outside the maximal allowed
distance. Thus, accounting for more flexibility would change
the FDR and suggests that the different methods lead to data of
comparable quality but different quantity.
Having a preranking of the individual fragmentation
techniques in terms of number of PSMs and unique cross-
links is desirable to maximize the information content in a
single run. Depending on the peptide properties, some
fragmentation methods might be more suited for a certain
group of peptides, and thus, using two (or more) orthogonal
fragmentation techniques may increase the overall yield in
peptide identifications and thus distance constraints. Disregard-
ing the link information to focus first on the identified peptide
pairs shows that HCD fragmentation also yields the largest
number of unique peptide pairs (Figure 2A). A total of 43%
(201 peptide pairs) are shared between at least two
fragmentation techniques. The remaining 57% (269 peptide
pairs) are unique to one of the five fragmentation techniques.
To maximize the information content, HCD should be
combined with CID fragmentation to increase the number of
unique links by 58 (Figure 2B). Interestingly, ETD
fragmentation can maximally increase the number of unique
links by 41 by using EThcD. We suspect that the difference in
the number of acquired spectra and actually identified PSMs is
the main driver for this effect. We define the identification rate,
IR, as
=IR
N
N
id
acq
, where Nid is the number of identified unique
PSMs and Nacq is the total number of acquired MS2 spectra
(Table S1). The IR reveals that HCD not only acquires most
spectra, but also has the highest success rate of 0.88% compared
to CID (0.61%), EThcD (0.52%), and ETD/ETciD (0.38%). If
speed and reliability of ETD-based fragmentation should
change in the future, this order of complementarity may
change. In comparison with linear peptide identifications,
where the IR reaches up to 54%
42
(depending on the
instrumentation), the success rate of cross-link identification
is much lower. A contributing factor will be the generally low
abundance of cross-linked peptides when compared to linear
peptides, which will reduce their frequency of selection for
MS2, especially in competition with the linear peptides also
present. Other factors will include poorer database matching
due to often lower intensity, but also more complex spectra and
a larger search space.
ETD-Aided Fragmentation Improves the Coverage of
the Second Peptide. The identification of cross-linked
peptides poses two challenges: First, finding the correct peptide
pair, and second, assigning the correct cross-link site. High
peptide sequence coverage for both individual peptides should
be beneficial to assigning the correct site. Site calling will be
Figure 2. Pairwise result overlaps of fragmentation techniques. (A)
Overlap of identified peptide pairs (disregarding link-site positions)
between fragmentation techniques (Venn diagram generated with
Jvenn49). (B) Set difference matrix shows the number of uniquely
identified peptide pairs (disregarding link-site positions) by one
fragmentation technique (y-axis) when compared to another one (x-
axis).
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especially challenging when considering cross-linkers such as
SDA, where the number of cross-link target sites is large.
Under HCD conditions the coverage distribution for the α-
peptide is the lowest, with a mean coverage around 50%
(Figure 3A). The other four fragmentation techniques perform
very similar to only small improvements in the coverage of the
α-peptide with CID or EThcD fragmentation. Interestingly,
ETD involving fragmentation schemes do not increase the
fragmentation efficiency (measured by the peptide coverage)
much for the α-peptide. In fact, the highest coverage values for
the α-peptide were observed with CID fragmentation. In
contrast, the sequence coverage for the beta peptide largely
depends on the fragmentation method (Figure 3B). ETD,
ETciD, EThcD, and HCD show a much better fragmentation
compared to CID. Previously, ETD was reported to improve
the sequence coverage compared to CID.
32,43
We observe here
that for cross-linked peptides this effect is very pronounced for
the β-peptide, but not for the α-peptide.
In general, in cross-linked peptides, one peptide matches
more and with higher intense fragment ions than the other. All
fragmentation methods yield at least an average coverage of
around 50% for the α-peptide. For the β-peptide, the average
coverage lies between 39% and 50%. CID would be the method
of choice for high α-peptide coverage. However, CID is
systematically disadvantaging the β-peptide. For the β-peptide,
the other fragmentation methods perform much better: EThcD
and HCD almost reach the same fragmentation efficiency as for
the α-peptide. In numbers, the largest discrepancy between α-
and β-peptide coverage was observed with CID, with a mean
coverage difference (MCD) of 19%. EThCD and HCD show
the lowest MCD of 8%. The overall best coverage is observed
with EThcD fragmentation (Figure 3C). ETciD seems to be
less effective, presumably as ETD in the first stage leads to
charge reduction, and CID then fragments a single precursor,
while HCD fragments all. Nevertheless, ETciD greatly
improves the coverage of the second peptide when compared
to CID.
To compare the fragmentation efficiency on both peptides in
a cross-link more systematically, we define the symmetry factor
(SF) as
=| − |
αβ
S
Fcovcov
(1)
where covαand covβrefer to the sequence coverage of the α-
and β-beta peptide, respectively. For convenience, we use the
negation SF′of SF defined as
′= −
S
F1S
F
(2)
A large SF′means that α- and β-peptide coverage are very
similar and vice versa. CID shows the smallest among the five
fragmentation methods of ∼0.8. The other four methods
perform better than CID, with a median of ∼0.9 (Figure 3D).
In addition, ETD, ETciD, EThcD, and HCD have a smaller
spread than CID. In summary, CID exasperates the second
peptide problem. Nevertheless, CID still slightly outperforms
HCD in overall cross-linked peptide sequence coverage. In
order to maximize overall cross-linked peptide coverage ETD,
ETciD, and EThcD are recommended, based on median
coverage of the complete cross-linked peptide.
Precursor m/zHas a Large Effect on the Efficiency of
the Fragmentation. To follow-up on the different
fragmentation behavior of cross-linked peptides we investigated
how the precursor properties influence the fragmentation
efficiency. We first divided the m/zacquisition range into bins
of m/z150 (starting from m/z550). For each bin we then
collected the peptide identifications of all different fragmenta-
tion methods and investigated the sequence coverage based on
the m/zof the precursor.
ETD and EThcD lead to the highest sequence coverage
between m/z500−800 (Figure 4A,B).However,ETD
Figure 3. Achieved sequence coverage comparison. Coverage
distribution of the α-peptide (A; more matches among the 10 most
intense fragment ions) and the β-peptide (B). The vertical line in
(A)−(C) reflects a reference value of 50% sequence coverage, meaning
fragments (b, c, y, or z) match to half of the backbone links between
residues along the sequence of the peptide. (C) Coverage distribution
for the complete cross-linked peptide. (D) Symmetry (absolute
coverage difference between alpha and beta peptide) distributions for
the different fragmentation techniques. The data in (A)−(D) were
analyzed using a 5% PSM FDR.
Analytical Chemistry Article
DOI: 10.1021/acs.analchem.6b02082
Anal. Chem. 2016, 88, 8239−8247
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efficiency decreases steeply with higher m/z, making HCD and
CID the better choice for precursors larger than m/z1000. The
same trend is observed for all ETD-based methods. These
differences are more pronounced on the individual α- and β-
peptides. When the complete peptide coverage is compared
(Figure 4C), all methods stick more closely together but
EThcD and ETD still outperform all other methods for
precursors smaller than m/z850. In higher m/zareas, only CID
and HCD are able to still produce enough peptide
identifications (data in the figure was limited to only include
m/zbins with at least five observations).
As demonstrated in the sections above, there are differences
in the efficiency of the fragmentation of cross-linked peptides.
In a more detailed comparison, we divided the acquisition
range into a grid made of charge bins of size one and m/zbins
of size 200. In each of these cells we then tested how well the
five different fragmentation methods performed. The perform-
ance was evaluated on the cross-linked peptide sequence
coverage. For the majority of peptides, EThcD achieved
significantly higher sequence coverage (Figure 4D) than the
second best method between 600 and 800 m/z(precursor
charge 3−6). In addition, the m/zcells 400−600 (z= 4) and
800−1000 (z= 5) are also favored by EThcD fragmentation.
Since the majority of cross-linked peptides (71%) lie within
600−1000 m/z, the most important area is dominated by
EThcD fragmentation. However, evaluated by pure numbers of
identifications, EThcD is not the best performing method. On
average, ∼35 PSMs are missed if EThcD is chosen over the
method that achieves the highest number of identifications. If
the evaluation metric is changed to the highest number of
identifications, HCD is outperforming the other fragmentation
methods for all m/zbins (Figure S3). Therefore, HCD was
selected as default method for regions where no significant
improvement could be observed by any of the other methods
(Figure 4D, HCD written in gray).
HCD, EThcD, and ETD fragmentation define the cross-
link site most unambiguously. The overall sequence
coverage is a valuable feature to assess the quality of peptide
identifications. However, for cross-linked peptides those
fragments flanking the cross-linked residues are important to
define the linkage site. This resembles the localization of post-
translational modifications such as phosphorylation, which
greatly benefited from the usage of combined fragmentation
methods.
44
Limited information about the cross-link site is
available when none of the fragments next to a cross-linked
residue are observed; the cross-link site can then only be
assigned by prior assumptions or to larger sequence windows,
which becomes problematic if the site call is offby ±5 residues
(at least in HSA and using current ab initio structure
computation).
8
Given the information from correct fragment
identifications, a combination of one c-terminal and one n-
terminal ion is enough to locate the cross-link site
unambiguously. Utilizing the high-resolution/accurate mass
measurement in our experimental design, we thus assumed that
each assigned fragment is correct for peptides passing the
specified FDR.
The cross-link site in α-peptides could be assigned to a single
residue in ∼65% of all PSMs identified with EThcD or HCD
(Figure 5A). The second best performing method was ETD,
with approximately 60% of PSMs where the cross-link could be
assigned to a single residue. CID and ETciD PSMs show the
lowest number of accurate site localizations to a single residue
(below 50% of all PSMs). All methods placed the cross-link site
on average within the critical 5 residue window for 97.2% ±
1.17 (α-peptides) and 95.6% ±1.3 (β-peptides) of all PSMs.
For the β-peptide, this looks very similar; EThcD and HCD
show the best fragmentation behavior to localize the cross-link
Figure 4. Sequence coverage depending on precursor m/zand charge.
The average coverage values from (A) α-peptides, (B) β-peptides, and
(C) the complete cross-linked peptide are plotted vs the precursor m/
z. Each dot represents the median of all identified peptides in a
window of m/z150. Error bars show the standard deviation. (D)
Decision surface to optimize the sequence coverage of cross-linked
peptide. The acquisition range was divided into bins of 200 m/zper
charge state. In each bin the best performing fragmentation method
(judged by median achieved sequence coverage) is used to color that
particular bin. The “*”denotes a significant improvement in sequence
coverage by using the best performing fragmentation method over the
second best. Areas with less than 15 observations are colored in light
red, falling back to HCD as standard fragmentation technique. Gray
annotations show areas where no significant improvement could be
obtained by choosing one method over the others.
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Anal. Chem. 2016, 88, 8239−8247
8244
site (Figure 5B). With approximately 50% of precisely localized
cross-links in the β-peptide, the link-localization is less well for
the β-peptide than for the α-peptide. However, this is not as
pronounced as would be expected from the sequence coverage
asymmetry. This is counterintuitive since the coverage
distributions for HCD is among the lowest of all five
fragmentation techniques for the β-peptide. For EThcD, the
results for the determination of the cross-link site are more in
line with the observed coverage distributions. Still, the large
difference in the coverage distribution of the α- and β-peptides
seems not to be as pronounced for the distribution of correct
localizations of the cross-link site. One of the possible reasons is
that the cleavage of the peptides before and after the cross-link
site is preferred. For CID a statistical trend was reported that
cross-linked fragments outnumber linear fragments and tend to
have a higher intensity.
17
We encounter the opposite for HCD,
linear fragments visibly outnumber cross-linked fragments
(Figure S8).
Data-Dependent Decision Tree for Optimized Acquis-
ition of Cross-Linked Peptides. CLMS studies vary in the
degree of complexity: single proteins, multiple protein
complexes or complete proteomes can be analyzed to generate
protein−protein interaction information or the three-dimen-
sional structure. Depending on the specific case we propose
two different acquisition strategies (Figure 6A): First, for single
proteins or small protein complexes, we recommend HCD as
the method of choice. Since the complexity of the sample is not
very high, cross-linked peptides can often be matched by
precursor mass alone. In addition, HCD fragmentation
generates enough fragments to precisely localize the cross-
link site in the majority of cases. For the second case, that is,
complex samples with many proteins not only the search space
becomes an issue but also the associated random matches. A
fragmentation scheme that generates highly discriminative
scores for target and decoy peptides will identify more peptides
under the same FDR threshold. The optimal fragmentation
scheme for such an experiment is shown in Figure 6B. Earlier
studies on the development of data dependent decision trees
(DDDT) for the acquisition of linear peptides mainly support
our conclusions: HCD gives the highest number of
identifications, but ETD gives higher search engine scores
45
or, as in our case, higher sequence coverage. Compared to a
DDDT for linear peptides our results are slightly different but
still comparable. For example, linear DDDTs precursors with
charge state 3+ have been analyzed with ETD up to 750 m/z
46
or 650 m/z,
45
we only use ETD from 600−800 m/z.In
addition, instead of using ETD alone for 4+, 5+ precursors
below 1000 m/zand 800 m/z, respectively, EThcD is used. In
this study we investigated SDA-cross-linked, tryptic peptides.
Other cross-linkers or enzymes may lead to peptide populations
Figure 5. Cross-link site localization precision. (A) Cumulative
precision curve for the α-peptide. (B) Cumulative precision curve for
the β-peptide. With a precision value of one the cross-link site is
unambiguously located by adjacent backbone fragments (b, c, y, or z)
in the peptide. A value of two limits the cross-link site to two eligible
residues.
Figure 6. Acquisition strategy for cross-linked peptides. (A)
Recommended acquisition scheme for cross-linking samples. (B)
Data-dependent decision tree (DDDT) for cross-linked peptides.
Depending on the precursor charge state (3+, 4+, 5+, 6+, and other)
and the m/z, the appropriate fragmentation technique is selected.
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Anal. Chem. 2016, 88, 8239−8247
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with distinct fragmentation behavior due to differences in size
or amino acid composition. Note, however, that the proposed
fragmentation scheme is similar to the decision tree for linear
peptides
45,46
and may therefore be of more general value.
■CONCLUSION
For the majority of the peptides EThcD is the method of
choice to achieve the highest sequence coverage. HCD is an
important alternative because of its superior speed, with only
somewhat reduced peptide sequence coverage. CID, ETD, and
ETciD only play minor roles. We advise to adjust the
acquisition scheme to follow the experimental setup: simple
protein samples should be analyzed using only HCD to
maximize number of observed links, which starts having value
in protein structure determination.
8,47
For complex samples, we
propose a decision tree that is mainly based on EThcD and
HCD to maximize search specificity.
■ASSOCIATED CONTENT
*
SSupporting Information
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.anal-
chem.6b02082.
Quality control results, details regarding the decision tree
areas, the relative performance of the individual
fragmentation techniques, and annotated spectra of all
PSMs (PDF).
■AUTHOR INFORMATION
Corresponding Author
*E-mail: [email protected].
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
The Wellcome Trust generously funded this work through a
Senior Research Fellowship to J.R. (103139), a Centre Core
Grant (092076), and an Instrument Grant (108504).
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