Tài liệu Báo cáo khoa học: Applications of diagonal chromatography for proteomewide characterization of protein modifications and activity-based analyses doc

MINIREVIEW
Applications of diagonal chromatography for proteome-
wide characterization of protein modifications and
activity-based analyses
Kris Gevaert
1,2
, Francis Impens
1,2
, Petra Van Damme
1,2
, Bart Ghesquie
`
re
1,2
, Xavier Hanoulle
3
and Joe
¨
l Vandekerckhove
1,2
1 Department of Medical Protein Research, VIB, Ghent, Belgium
2 Department of Biochemistry, Ghent University, Belgium
3 UMR 8576 CNRS ) University of Sciences and Technologies of Lille, Structural and Functional Glycobiology Unit, Villeneuve d’Ascq,
France
Introduction
Proteomics refers to a qualitative, differential and
quantitative estimation of a proteome. Proteomes can
be extremely complex, often encompassing more than
10 000 different components per cell. Two-dimensional
gel electrophoresis [1] followed by electroblotting and
microsequencing [2–4] or in-gel digestion combined

with Edman sequencing [5] of the generated peptides
or peptide mass fingerprinting [6–10] have been the
methods of choice to reproducibly separate and iden-
tify complex protein mixtures. Although large-scale 2D
gel electrophoresis separates thousands of proteins
[11,12], probably no more than a few hundred different
proteins have been identified from such gels. To obtain
better proteome coverage, alternative methods were
introduced. Groundbreaking methodologies became
available when high-throughput genome sequencing
started to cover the entire genetic information of
several species. This information is now available for a
Keywords
activity-based probe; ATP-binding proteins;
COFRADIC; diagonal chromatography;
N-terminal peptides; peptide sorting; protein
N-glycosylation; protein processing
Correspondence
K. Gevaert, Department of Biochemistry,
Faculty of Medicine and Health Sciences,
Ghent University, A. Baertsoenkaai 3,
B-9000 Ghent, Belgium
Fax: +32 92649496
Tel: +32 92649274
E-mail:
Website:
(Received 24 April 2007, revised 10 Septem-
ber 2007, accepted 17 October 2007)
doi:10.1111/j.1742-4658.2007.06149.x
Numerous gel-free proteomics techniques have been reported over the past

few years, introducing a move from proteins to peptides as bits of informa-
tion in qualitative and quantitative proteome studies. Many shotgun pro-
teomics techniques randomly sample thousands of peptides in a qualitative
and quantitative manner but overlook the vast majority of protein modifi-
cations that are often crucial for proper protein structure and function.
Peptide-based proteomic approaches have thus been developed to profile a
diverse set of modifications including, but not at all limited, to phosphory-
lation, glycosylation and ubiquitination. Typical here is that each modifica-
tion needs a specific, tailor-made analytical procedure. In this minireview,
we discuss how one technique ) diagonal reverse-phase chromatogra-
phy ) is applied to study two different types of protein modification: pro-
tein processing and protein N-glycosylation. Additionally, we discuss an
activity-based proteome study in which purine-binding proteins were pro-
filed by diagonal chromatography.
Abbreviations
ABP, activity-based probe; COFRADIC, combined fractional diagonal chromatography; FSBA, 5¢-p-fluorosulfonylbenzoyladenosine; FSBG,
5¢-p-fluorosulfonylbenzoylguanosine; iTRAQ, isobaric tags for relative and absolute quantification; MudPIT, multidimensional protein
identification technology; PNGaseF, peptide N-glycosidase F; SB, sulfobenzoyl; SILAC, stable isotope labeling by amino acids in cell culture;
Nbs
2
, 2,4,6-trinitrobenzenesulfonic acid.
FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS 6277
large number of species, and it now suffices to generate
partial protein sequence information with which to
access entire (predicted) protein sequences stored in
expressed sequence tag, gene and protein sequence
databases.
This brought the dawn of novel strategies for pro-
tein identification. Measured masses of peptides pro-
duced by cleaving a protein with a protease with

well-known specificity (e.g. trypsin) were searched
against a database of peptide masses calculated from
protein sequences derived from genome sequences [6–
10]. When these peptides are derived from a mixture
of proteins, they are subjected to MS ⁄ MS fragmenta-
tion for identification [13]. Recently, top-down pro-
tein sequencing combining ESI MS and highly
accurate FT MS [14] was shown to match proteins
larger than 200 kDa to sequences in databases [15].
Such strategies only became possible following the
availability of massive numbers of DNA sequences,
recent developments in MS, and bioinformatics tools
that link DNA and protein sequences to information
generated by different types of mass spectrometers
[16–18].
Recently, peptides have increasingly become the
center of analysis: protein mixtures, either partially
purified by prefractionation or as such, are digested
with trypsin, and the generated peptide mixture is
analyzed. When cell or tissue lysates, or even isolated
organelles, are analyzed, the number of peptides
becomes so high that mass spectrometers can no
longer analyze all of the peptides. This results in
poor sample coverage, generally referred to as ran-
dom sampling or undersampling [19], and it became
crucial to add peptide prefractionation before MS
analysis. Yates’ group introduced separation of pep-
tides based on two parameters [20] ) net charge and
hydrophobicity – and called their technique multi-
dimensional protein identification technology (MudPIT

[21]). MudPIT has since then been used in several
studies and has demonstrated its value, but it still
suffers from undersampling [19].
Selecting a lower number of peptides representative
of each protein originally present in the mixture may
alleviate this problem. These so-called signature pep-
tides [22] are then the only analyzed components, and
in this way a less complex peptide mixture is presented
to the mass spectrometer. The first reports using this
strategy were selective for cysteinyl peptides, allowed
quantification (differential analysis), and used biotin
tagging for consecutive capture by immobilized avidin
[23]. Later on, affinity selection was used to isolate, for
instance, phosphopeptides [24], N-glycosylated peptides
[25], ubiquitinated peptides [26], and N-terminal pep-
tides [27].
COFRADIC as a peptide-sorting tool
Our peptide-centric proteome approach [28,29] sorts
signature peptides and selects the part of a proteome
containing the information of biological interest. Our
technique is based on diagonal chromatography [30,31]
consisting of two repeated, identical peptide separa-
tions with a specific modification reaction (sorting
step) in between. Peptides that remain unchanged elute
at the same position in the two chromatographic runs,
whereas peptides that acquire a modification segregate
from the unchanged peptides either in earlier or in
later fractions. To reduce the number of repetitive
chromatographic runs, several fractions from the pri-
mary run can be combined and subjected to the sort-

ing reaction (Fig. 1). For this reason, we call this
adapted version of diagonal chromatography com-
bined fractional diagonal chromatography (COFRAD-
IC [32]).
It should be clear from the peptide-sorting principle
that any chemical or enzymatic modification that is
highly specific, is quantitative and produces a suffi-
ciently large chromatographic shift can be imple-
mented in COFRADIC. This is illustrated by
applications in which selection for methionyl or cyste-
inyl peptides was carried out in tryptic digests of total
cellular lysates [33,34], or where the N-terminal pep-
tides of the proteins present in the mixture were
selected [33–36]. Similarly, as we can select peptides
based on the specific chemical nature of their amino
acid side chains, we can also select peptides carrying
post-translational modifications, either by removing
this modification (e.g. by dephosphorylation of phos-
phopeptides [37]), or by converting it into a moiety
with altered properties (e.g. by reducing nitrotyrosine to
aminotyrosine [38]). An overview of the COFRADIC
sorting protocols that have been developed is given in
Table 1.
We here concentrate on applications of COFRADIC
in studying selected post-translational modifica-
tions ) protein processing [34,36] and N-glycosylation
[39] – and describe the use of COFRADIC for study-
ing interactions between small molecules and proteins.
The latter is a particular application of ‘post-transla-
tional COFRADIC’, by which a small molecule is

covalently linked to a target protein and the corre-
sponding modified tryptic peptide is then sorted using
the principles of diagonal chromatography. The exam-
ple given here is a global activity-based proteome
COFRADIC and protein modifications K. Gevaert et al.
6278 FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS
analysis of purine-binding proteins in a total lysate of
human Jurkat T-cells [40].
COFRADIC analysis of protein
processing ) protease degradomics
Protein processing introduces novel protein fragments
that may be visualized on 2D polyacrylamide gels. For
example, Canals et al. used the fluorescent 2D differ-
ence gel electrophoresis technique [41] to catalog quan-
titative differences in the protein composition of
conditioned media of cells either expressing the metal-
loproteinase ADAMTS1 at physiological levels or
overexpressing it [42]. The latter scenario led to an
increase of fragments of proteins shed by ADAMTS1
into the medium that were picked by difference gel
electrophoresis and identified by MS. In fact, this
study led to the identification of five potential ADAM-
TS1 substrates, two of which (nidogens 1 and 2) were
further validated. Gel-free proteomic approaches have
been introduced for ‘degradomics’ [43] research as well.
The group of Overall used isotope-coded affinity tag
[23] combined with LC-MS ⁄ MS to quantify the levels
of secreted extracellular matrix proteins in breast carcin-
oma cell cultures overexpressing a membrane type 1
matrix metalloproteinase [44] and, more recently, they

multiplexed their analyses using isobaric tags for
relative and absolute quantification (iTRAQ) reagents
for the identification of matrix metalloproteinase-2
substrates in fibroblasts [45].
Clearly, both gel-based and gel-free approaches
point to potential protease substrates; however, at this
stage it is important to note that the characterization
of the actual protein cleavage site has typically
remained elusive. Nonetheless, the latter information is
highly valuable, as it can lead to more rational design
of protease inhibitors [46], it is vital for constructing
precise algorithms that predict protease substrates [47],
and, after all, protein processing is a post-translational
modification that should preferably be characterized
before any assumption concerning the protease sub-
strate potential is made. Protein processing produces a
novel C-terminal peptide (from the N-terminal frag-
ment of a substrate) and a novel N-terminal peptide
(from the C-terminal fragment). Hence, identifying
either one of these ‘reporter peptides’ directly points to
the actual processing site. As recently reviewed [29], in
a whole proteomic background, C-terminal peptides
are only poorly isolated. On the other hand, N-termi-
nal COFRADIC [33], and the more or less ‘single-step’
isolations of N-terminal peptides by protein sequence
tags [48] and positional proteomics [27] were shown to
isolate N-terminal peptides from complex mixtures.
min
20 30 40 50 60 70 80
mAU

0
200
400
600
800
1000
1200
1400
primary separation
combine primary fractions
COFRADIC sorting reaction
LC-MS/MS analysis
min
20 30 40 50 60 70 80
mAU
0
100
200
300
400
500
600
700
secondary separation
Fig. 1. The COFRADIC peptide sorting scheme. A peptide mixture
is first separated by RP-HPLC (the primary COFRADIC separation).
Here, the UV absorbance profile at 214 nm of a tryptic digest of a
proteome preparation from human Jurkat T-cells is shown. Primary
fractions (indicated in light gray boxes) are combined ) here, four
primary fractions (each 1 min wide) that are separated by a 13 min

window – and undergo a chemical or enzymatic reaction (the
COFRADIC sorting reaction). In this particular case, the side chains
of methionines were oxidized by hydrogen peroxide, leading to the
formation of methione-sulfoxide. During a second, identical separa-
tion (the secondary COFRADIC separation), such oxidized methionyl
peptides undergo a hydrophilic shift and segregate away from the
bulk of nonmethionyl peptides. Methionyl peptides are thus col-
lected (dark gray boxes) and analyzed by LC-MS ⁄ MS.
K. Gevaert et al. COFRADIC and protein modifications
FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS 6279
However, only the N-terminal COFRADIC approach
has thus far been applied to protease degradomics
research [36,38] and is discussed here (potential draw-
backs of the two affinity-based peptide isolation proto-
cols are discussed in Conclusions).
In essence, N-terminal COFRADIC segregates pep-
tides containing the protein N-termini from internal
peptides. This is achieved following an initial acetyla-
tion or trideutero-acetylation reaction on a complete
proteome prior to trypsin digestion. This blocks all
free a-amines and e-amines and, further down, distin-
guishes between in vivo blocked (acetylated) and in vivo
free (trideutero-acetylated) protein N-termini. Trypsin
no longer recognizes acetylated lysines, and, conse-
quently, upon digestion, Arg-C type peptides are
generated. In fact, two types of peptides are now
apparent: N-terminal peptides with a blocked, acety-
lated ⁄ trideutero-acetylated a-amine, and internal pep-
tides carrying a free a-amine. This peptide mixture is
first separated by RP-HPLC and collected in a small

number of primary fractions. Then, internal peptides
present in each fraction are reacted with 2,4,6-trinitro-
benzenesulfonic acid, which is known to efficiently and
quantitatively modify primary amines [49]. Internal
peptides thereby acquire a trinitrophenyl group at their
N-terminus and thus become very hydrophobic. Run-
ning such TNBS-modified primary fractions a second
time on the same column and under identical chro-
matographic conditions will now segregate TNBS-non-
reactive N-terminal peptides (all their amino groups
were already blocked) from TNBS-reacted internal
peptides, which underwent a very strong hydrophobic
shift (Table 1). Following metabolic or postmetabolic
labeling, N-terminal peptides of two (or more) proteo-
mes can be weighed against each other and, impor-
tantly, neo-N-termini originating from protein
processing are readily distinguished [34,36].
The characterization of protease substrates by such
a differential N-terminal COFRADIC approach is
illustrated in Fig. 2. In an ongoing project, host cell
substrates of the HIV-1 protease are catalogued in
human Jurkat T-cells grown in stable isotope labeling
by amino acids in cell culture (SILAC) medium supple-
mented with either natural, light
12
C
6
-arginine or
heavy
13

C
6
-arginine [50]. Arginine is clearly the essen-
tial amino acid of choice, as all N-terminal peptides
isolated by COFRADIC, by the nature of the process,
will end on an arginine residue. This metabolic labeling
introduces a mass spacing of 6 Da between light and
heavy N-terminal peptides.
Cells are typically lysed by repeated freeze–thawing,
and the lysate is either incubated with recombinant
HIV-1 protease or left untreated (control). Following
protease incubation, both proteomes are S-alkylated
and acetylated, and equal amounts are then mixed and
subjected to N-terminal COFRADIC. In the setup
depicted in Fig. 2, neo-N-termini generated by the ret-
roviral protease are expected in the ‘light proteome’
and will only be present in the light
12
C
6
-arginine
form. Almost identical numbers of pre-existing N-ter-
mini (i.e. the N-termini of intact proteins), on the
other hand, should appear as couples of light and
heavy labeled peptides in ratios close to 1 : 1. This is
illustrated by taking b-actin as an example: its acety-
lated N-terminal peptide is present in a near 1 : 1 ratio
(Fig. 2B), whereas a second, now trideutero-acetylated
peptide is only present in the light proteome (Fig. 2C).
Following MS ⁄ MS analysis (Fig. 2D), the latter

peptide is identified as TEAPLNPKANR(106-116)
and constitutes a neo-N-terminus indicative of HIV-1-
mediated protein processing. Processing of b-actin by
the HIV-1 protease between Leu105 and Thr106 was
already identified in previous studies [51], thereby vali-
dating our findings.
Table 1. Overview of the different COFRADIC procedures that have been developed. The type of peptide, the sorting agent used in
between the two consecutive RP-HPLC separation steps and the type of evoked shift are indicated. References to our original papers, in
which full technical details can be found, are given.
Sorted peptide Sorting agent RP shift Reference
Methionyl peptide Hydrogen peroxide Hydrophilic [32]
Cysteinyl peptide Reduction of nitrothiobenzoic
acid-modified cysteine
Hydrophilic [80]
N-terminal peptide TNBS reaction on internal,
free a-amine peptides
Hydrophobic
(internal peptides)
[33]
Phosphorylated peptide Cocktail of phosphatases Hydrophobic [37]
N-glycosylated peptide PNGaseF Hydrophilic or
hydrophobic
[39]
ATP-binding peptide NaOH treatment Hydrophilic [40]
3-Nitrotyrosinyl peptide Reduction of NO group Hydrophilic [38]
COFRADIC and protein modifications K. Gevaert et al.
6280 FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS
A similar approach but now with postmetabolic,
trypsin-mediated
18

O-labeling [52] was used to charac-
terize in vivo protein processing in Fas-induced apopto-
tic Jurkat cells [34]. In this study, 93 cleavage sites in
71 different proteins were characterized in a ‘proteomic
background’ of more than 1800 proteins. At the time
of reporting these results, the overall majority of the
identified cleavage sites were uncharacterized. An anal-
ogous setup was used for an in vitro analysis of the
substrates of the HrtA2 ⁄ Omi protease [36]. In that
human Jurkat T-cells
SILAC medium
12
C
6
-arginine
human Jurkat T-cells
SILAC medium
13
C
6
-arginine
freeze-thaw
lysate
recombinant
HIV-1 protease
A
B
D
C
control

combine
N-terminal COFRADIC analysis
651.39
651.88
652.37
650.91
650 651 652 653 m/z
947.49
948.06
948.29
949.50
949.80
950.06
950.39
947 948 949 950 m/z
947.71
630.43; y5
744.46; y6
954.66; y8
200 400 600 800 1000 m/z
347.24; b3
444.41; b4
557.42; b5
671.37; b6
1012.66; b9
300 500 700 900 1100
Fig. 2. HIV-1 protease processes b-actin in vitro at Leu105. The experimental route is sketched in (A). Following N-terminal COFRADIC, two
different peptides from b-actin were identified. Its N-terminal peptide, DDDIAALVVDNGSGMCKAGFAGDDAPR(2–28) (N-terminus acetylated,
lysine trideuteroacetylated, methionine oxidized and cysteine carbamidomethylated) was present in both proteome digests [ion trap MS
spectrum of triply charged precursor in (B)], whereas a second peptide was only present in the proteome treated with the HIV-1 protease

[ion trap MS spectrum of doubly charged precursor in (C)]. Following MS ⁄ MS analysis [(D), b and y fragment ions indicated), this peptide
was identified as TEAPLNPKANR(106–116) (N-terminus and lysine were both trideuteroacetylated), pointing to a previously characterized
cleavage site of the HIV-1 protease in b-actin].
K. Gevaert et al. COFRADIC and protein modifications
FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS 6281
study, we identified 50 different cleavage events in
15 human proteins, and further validated these Omi
substrates by nonproteomic methods. Finally, as our
method directly points to the actual site of proteolytic
cleavage, data interpretation and the design of follow-
up analyses are straightforward.
COFRADIC-based sorting of
N-glycosylated peptides
Glycosylation of asparagines in the Asn-Xaa-Ser ⁄ Thr
acceptor motif [53] is a widespread protein modifica-
tion: a survey in the UniProtKB ⁄ Swiss-Prot database
(release 52.2, 3 April 2007) indicates that 3694 human
protein entries (i.e. about 23% of all human protein
entries) have at least one feature key pointing to an
N-glycosylation event.
Different methods have been used to isolate and
identify N-glycosylated proteins and characterize their
glycosylation sites. In general, glycosylated proteins
and peptides are affinity-isolated or chemically trapped
prior to further analysis. Affinity-based isolation of
N-glycosylated proteins is rather simple and is based
on lectin-affinity chromatography. Lectins are proteins
or glycoproteins that recognize oligosaccharides but
generally favor certain classes of oligosaccharides [54].
Thus, to increase the overall coverage of N-glycosylat-

ed proteins, several lectins were combined in multilec-
tin affinity chromatography [55,56]. Alternatively, the
lectins’ glycan bias was exploited in a serial lectin
approach separating N-glycosylated (concanavalin A)
from O-glycosylated peptides (Jacalin) [57]. Chemical
trapping and release of N-glycosylated peptides was
introduced by the group of Aebersold in 2003 [25]. In
their approach, aldehydes are first introduced into the
glycan by periodate oxidation. These aldehydes then
covalently bind to immobilized hydrazide groups by
which glycosylated proteins are retained and all non-
glycosylated proteins are removed. Immobilized gly-
cosylated proteins are then further trimmed by trypsin
such that only tryptic peptides carrying glycans
remained fixed. Such peptides are finally recovered by
peptide N-glycosidase F (PNGaseF), which efficiently
removes N-glycans from conjugated asparagines while
converting these to aspartic acids [58]. The potential of
this chemical trapping approach is evident from recent
studies [59–62]; however, it requires several chemical
and enzymatic modification steps, and it is therefore
more complex than lectin-affinity methods; this could
potentially obstruct its widespread introduction in
proteomics laboratories.
We recently showed that N-glycosylated peptides can
be isolated by diagonal chromatography [39]. In our
approach, a protein mixture containing N-glycosylated
proteins is digested with trypsin, and the resulting pep-
tide mixture is separated by RP-HPLC. N-glycosylated
peptides are then specifically targeted by PNGaseF and

thus deglycosylated (COFRADIC sorting step). When
separated a second time by RP-HPLC, deglycosylated
peptides shift out of the primary interval of nongly-
cosylated peptides and are thereby isolated. Impor-
tantly, the shift evoked in this way can be both
hydrophilic and hydrophobic, reflecting the nature of
the glycan. Indeed, N-glycans can contain negatively
charged sugars such sialic acid [63] and sulfated carbo-
hydrates [64], and removing such glycans with PNG-
aseF evokes a hydrophobic shift analogous to that
observed for dephosphorylated peptides [37]. Following
MS ⁄ MS analysis, former N-glycosylated asparagines in
the Asn-Xaa-Ser ⁄ Thr motif are deamidated to aspartic
acids. This mass signature in the consensus N-glycosyl-
ation motif is used to distinguish deglycosylated pep-
tides from artificially deamidated peptides, especially in
Asn-Gly and Asn-Ser motifs [65], undergoing small
hydrophilic shifts [39].
Our COFRADIC procedure was applied to a trypsin
digest of 10 lL of mouse serum depleted for its three
most abundant proteins (albumin, IgGs and transfer-
rin), and resulted in the characterization of 127 differ-
ent N-glycosylation sites (comprising 10 novel sites) in
82 proteins estimated to span a concentration range of
at least five orders of magnitude [39]. Several N-glyco-
sylation sites of the large subunit of mouse carboxy-
peptidase N (UniProtKB ⁄ Swiss-Prot entry Q9DBB9)
were identified in this study (Table 2). This protein
binds to the catalytic subunit of carboxypeptidase N,
which functions in protecting organisms from circulat-

ing vasoactive and inflammatory peptides containing
C-terminal arginine or lysine [66]. The large subunit of
this complex binds and stabilizes the catalytic subunit
and thereby keeps the complex in circulation. In silico
predictions indicate that this protein potentially has
nine different targets for N-glycosylation, six of which
were identified in our study: the asparagines at posi-
tions 74, 111, 119, 348, 359 and 367 (Table 2). Three
other asparagines at positions 266, 311 and 520 were
missed and, as is evident from the annotations in the
UniProtKB ⁄ Swiss-Prot database, have hitherto not
been experimentally characterized. A closer look at the
sequences of the tryptic peptides harboring the poten-
tial glycosylation sites at positions 266 and 311 clearly
indicates that these peptides are very large (66 and 36
amino acids long, respectively). Therefore, they could
have been missed either because they are insoluble or
because our mass spectrometers, which have an empiri-
cal upper mass limit close to 3000 Da for producing
COFRADIC and protein modifications K. Gevaert et al.
6282 FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS
MS ⁄ MS spectra that are unambiguously identified by
mascot [67], could not detect them. One obvious way
to overcome this is by using proteases with nontryptic
specificities such as proteinase K that generally pro-
duce smaller peptides [68] and thus increase the chance
that more glycopeptides will be finally identified.
However, such protease digests sharply augment the
complexity of the analyte mixture.
Activity-based proteome-wide profiling

of purine-binding proteins
In order to assign functions to the many uncharacter-
ized (hypothetical) proteins that genome sequencing
projects provide, several small compounds occupying
and modifying active sites of enzymes have recently
found their way into functional proteomics [69,70].
Quite a lot of these so-called activity-based probes
(ABPs) are natural protein-reactive products or syn-
thetic analogs [71]. ABPs in functional proteome stud-
ies generally consist of four parts: a reactive group
targeting amino acids within the enzyme’s binding
pocket, a structural moiety that is recognized by this
binding pocket, a linker, and a tag for visualization
and ⁄ or isolation of modified proteins [72]. Different
classes of enzymes have already been studied using
activity-based proteomics. Examples include biotinyl-
ated fluorophosphonates for monitoring serine
hydrolases [73], and biotinylated a-bromobenzyl-
phosphonates for detecting protein tyrosine phosphata-
ses [74].
ATP, ADP and AMP are important sensors for the
energy status of cells, interacting with and thereby reg-
ulating the activities of key enzymes in cellular metab-
olism. In addition, ATP and, to a lesser extent, GTP
are known as carriers of high-energy phosphoryl
groups that can be covalently linked to proteins and
metabolites. Here, kinases play a pivotal role and
transiently interact with triphospho derivatives before
the b–c phosphodiester bond is cleaved. To character-
ize ATP-binding and GTP-binding proteins in cells,

we profiled purine-binding proteins on a proteome
scale [40]. For this purpose, we used 5¢- p-fluoro-
sulfonylbenzoyladenosine (FSBA) (Fig. 3B), a known
reactive homolog of ATP (Fig. 3A) that binds proteins
in their nucleotide-binding region and then covalently
modifies nucleophilic amino acids (especially tyrosine
and lysine) in its proximity [75]. In the past, FSBA
was mainly used to profile the ATP-binding features of
selected, individual proteins. However, in 2004, Moore
et al. published a study in which FSBA and 5¢-p-fluoro-
sulfonylbenzoylguanosine (FSBG) were used to profile
ATP-binding and GTP-binding proteins, respectively,
in the proteomes of different lymphoid cells [76]. In
their approach, proteins were labeled with FSBA or
FSBG in cell extracts, separated by 2D PAGE and
electrotransferred onto a poly(vinylidene difuoride)
membrane. Subsequent treatment of sulfobenzoyl
adenosine ⁄ sulfobenzoyl guanosine (SBA ⁄ SBG)-labeled
proteins with NaOH hydrolyzed the ester bond
between the adenosine or guanosine and the sulfo-
benzoyl (SB) group and exposed the latter. Antibodies
to SB were then used to immunodetect FSBA-targeted
or FSBG-targeted proteins. Overlaying an image of
the immunoblot with the 2D pattern of silver-stained
proteins pointed to candidate ATP-binding or GTP-
binding proteins that were selected from the 2D gel
and identified by MS. In this way, 12 different proteins
could be identified as FSBA-labeled proteins.
Given the fact that a mild alkaline treatment as used
by Moore et al. [76] hydrolyzes the rather unstable

benzoate ester bond between the adenosine and the SB
group, we recently developed a COFRADIC protocol
sorting for SBA-labeled peptides [40]. The central sort-
ing reaction is shown in Fig. 3C and consists of a
25 min incubation of SBA-labeled peptides in 50 mm
Table 2. N-glycosylation sites in the large subunit of mouse carboxypeptidase N. Both N-glycosylation sites characterized in our study [39]
and those that were missed are given. Known or potential glycosylation sites are in bold type. [M +H]
+
, mass of the singly protonated
peptide ion.
Position Tryptic peptide sequence [M +H]
+
Characterized N-glycosylation sites
74 AFSGSPNLTK(68–77) 1021.53
111, 119 LQDLEITGSPVSNLSAHIFSNLSSLEK(99–125) 2899.50
348, 359 LSLDSNNLTALHPALFHNLSR(342–362) 2333.24
367 LQLLNLSR(363–370) 956.59
Unidentified N-glycosylation sites
266 LPEGALGSLSSLQELFLDGNAITELSPHLFSQLFSLEMLWLQHNAICHLPVSLFSSLHNLTFLSLK(208–273) 7316.82
311 TLPEGLFAHNQGLLHLSLSYNQLETIPEGAFTNLSR(279–314) 3981.05
520 DGSDSAAMVYNSSQEWGLR(510–528) 2072.90
K. Gevaert et al. COFRADIC and protein modifications
FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS 6283
NaOH. We first tested our protocol on a tryptic digest
of SBA-labeled recombinant Chinese hamster a-protein
kinase A, and noted that removing the adenosine group
in between the two COFRADIC separations resulted
in a strong hydrophilic shift of only one peptide that
was identified as HKETGNHYAMK*ILDK(62–76)
(K* indicates the SB-labeled lysine). This peptide har-

bors the site known to be involved in the catalytic
transfer of c-phosphate from ATP to protein kinase A
substrates [77].
When this sorting procedure was applied to a whole
proteome ) here, a human Jurkat T-cell proteome
depleted of small compounds like ATP and GTP,
which could compete with FSBA ) 185 sites in 132
proteins were identified. Clearly, this is a significantly
higher number of proteins than were detected in
the previous gel-based study [76]. Therefore, our
COFRADIC technique allows the functional interpre-
tation of a larger part of a sampled proteome. More
importantly, our approach directly points to the actual
site that was modified and might thereby aid in inter-
preting structural features of ATP-binding proteins.
As expected, the majority of FSBA-labeled Jurkat
proteins were known binders of small nucleotides,
cofactors, or DNA and RNA molecules. However,
several proteins and sites were not readily explained by
the known affinity of FSBA for purine-binding pock-
ets. Closer inspection revealed that at least 23 of such
unexplainable sites were previously characterized as
tyrosine phosphorylation sites. Therefore, we assume
that when FSBA is recognized by an ATP-binding site,
there are two options for SBA labeling: either the
fluorosulfonyl group reacts with a target side chain
located on the protein carrying the ATP-binding site
(homo-reaction), or, through lack of a suitable reac-
tion partner, it may react with a side chain present on
proteins that interact with the protein carrying the

actual ATP-binding site (crossover reaction). An illus-
tration of the second case is observed for kinases that
can transfer the SBA group onto their substrate pro-
teins by a pseudocatalytic mechanism. In this way, the
SBA group can be linked to proteins that have no
ATP-binding site. We have verified this assumption by
incubating a Src substrate peptide with FSBA in the
presence or absence of Src; it was shown that Src
‘catalyzed’ the labeling of the substrate peptides by a
factor of more than 20 [40]. Hence, we concluded that
care must be taken when interpreting the results of
activity-based proteome studies, as not all identified
proteins will actually carry out the function that was
assessed by the used ABP.
Conclusions
As compared to other gel-free proteomics techniques
[72], COFRADIC has a number of unique properties.
As COFRADIC is essentially a peptide-sorting tech-
nology by which only a set of peptides representative
of the proteomic problem is withdrawn from the com-
plex analyte mixture, the sample-to-sample reproduc-
ibility is much higher than in shotgun approaches [78].
For instance, although MudPIT uses a powerful
chromatographic technology combining two basic sep-
aration principles (peptide net charge and hydro-
phobicity), peptide separation still takes place on the
entire, complex mixture. COFRADIC eliminates a
O
HO OH
O

O
S
O
O
PEPTIDE
A
C
B
N
N
N
N
NH
2
OH
O
S
O
O
PEPTIDE
N
N
N
N
NH
2
O
HO OH
O
P

O
P
O
P
O O O O O O
O
N
N
O
HO OH
O
O
S
O
O
F
N
N
NH
2
50 mM NaOH
25 min @ 25°C
Fig. 3. FSBA COFRADIC. The structures of
ATP and its reactive homolog FSBA are
shown in (A) and (B), respectively. The
COFRADIC reaction sorting for SBA-labeled
peptides is shown in (C).
COFRADIC and protein modifications K. Gevaert et al.
6284 FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS
large number of peptides that are irrelevant to the

biological problem under consideration, thereby reduc-
ing the complexity of the problem without losing
much information. Unlike targeted peptide-centric
approaches such as isotope-coded affinity tag [23],
COFRADIC is not based on affinity procedures,
which are limited at two levels: first, the chemistries
used to convert sets of peptides into affinity probes;
and second, the limitations of mass transfer that are
inherently to liquid–solid state chemistries [79]. At the
first level, COFRADIC has a fundamental advantage
because its chemistries do not need to create different
affinity labels. For instance, an affinity tag specific for
methionyl peptides is extremely difficult to establish; in
contrast, a simple oxidation step by hydrogen peroxide
will specifically produce methionyl-sulfoxide derivatives
showing significant hydrophilic shifts in diagonal
reverse-phase chromatography [32]. At the second
level, affinity-based experiments [23,27,48] have limita-
tions either at the level of incomplete or variable incor-
poration of the tag (for example, linking a biotinyl
group to a specific set of peptides can be incomplete
and partly unspecific) or at the level of interactions of
tagged peptides with the affinity resin, where the high-
est affinities and avidities are not always reached. In
contrast, using COFRADIC, we select subsets of pep-
tides related to the biological question under investiga-
tion. For instance, for the study of the oxidation of
protein methionines during oxidative stress, cells can
be differentially labeled with [
13

C]methionine or
[
12
C]methionine. With COFRADIC, we sort for methi-
onine-containing peptides only: thus, we select out of
the mixture only those peptides containing the differ-
ential information, while all other peptides, which are
of no relevance, are discarded.
All kinds of peptide selections can be done with-
out, each time, modifying or adapting the sorting
apparatus itself. The latter is, in principle, an auto-
mated HPLC apparatus equipped with an auto-
sampler, and can be purchased from a variety of
companies; and, at least in our hands, HPLC solvent
gradients and flow rates can nowadays be controlled
such that the overall reproducibility of HPLC runs
is very high, allowing efficient peptide sorting by
COFRADIC. The only parameter that needs chang-
ing is the nature of the COFRADIC sorting reaction,
which can be chemical or enzymatic, but should
under all circumstances be highly specific and prefer-
entially quantitative. Together with a sufficiently large
chromatographic shift (to segregate altered and unal-
tered peptides to the highest degree), the specificity
and quantitative nature of the COFRADIC sorting
reaction are clearly crucial for efficient peptide sort-
ing. Unspecific sorting reactions and only slight alter-
ations in peptide column retention will yield ‘impure’
sorted peptides, whereas nonquantitative sorting reac-
tions will lead to irreproducible peptide sorting. In

Table 1, the chemical or enzymatic modification
reactions that have been used successfully in a
COFRADIC-based approach are listed. They cover
sorting methods varying from modifications to spe-
cific side chains, such as cysteinyl [80] and methionyl
[32] moieties, to post-translational modifications by
phosphatase [37] and PNGaseF [39] treatments. In
another application, peptides located at the N-termini
of proteins or of their fragments are sorted [33]. In
this way, we have successfully analyzed protein pro-
cessing in highly complex proteomes by the target
proteins and identified the exact cleavage site(s), cre-
ating the basis for fundamental protease degradomics
[34,36].
As mentioned above, it is also possible to set up spe-
cific covalent interactions between proteins and small
molecules such as drugs or mimetic molecules of natu-
ral metabolites such that chromatographic shifts can
be evoked, thus allowing sorting of the conjugated
peptides by COFRADIC. The example shown relates
to a study with an ATP analog [40]; however, it can,
in principle, be extended to drugs that covalently inter-
act with their target protein, either directly or after
being metabolized in the tissue or organism to form
reactive products.
One of the drawbacks of COFRADIC relates to the
segmentation of the peptide separation flow during the
primary run: many peptides may end up in two con-
secutive fractions for their secondary analyses. When
the same separation is repeated a second time or fur-

ther times, peptides eluting at the boundaries of the
primary selected time intervals may show a slight drift,
thus ending up in different secondary fractions. By the
same effect, peptides that are differentially labeled by
isotopes, such as hydrogen and deuterium, the deriva-
tives of which display slightly different chromato-
graphic properties, may be artificially enriched in
certain fractions. Such situations could impose a hin-
drance on any type of quantitative differential analyses
performed with COFRADIC. In addition, if eluting
peptide peaks are cut, the remaining material is
diluted, imposing limitations on the overall sensitivity.
This sensitivity issue remains one of the limitations of
the technology; however, it is very well compensated
by the efficiency of the other steps in the system: lim-
ited losses during consecutive chromatographic separa-
tions, more freedom in the selection of efficient
reaction systems and better reaction conditions, which
are performed in a homogeneous phase, and, finally,
K. Gevaert et al. COFRADIC and protein modifications
FEBS Journal 274 (2007) 6277–6289 ª 2007 The Authors Journal compilation ª 2007 FEBS 6285
the fact that at the end only a selection of peptides is
presented for analysis to mass spectrometers. The sta-
bility of the peptide elution profile, particularly in the
primary runs of COFRADIC approaches, has not
been found to be a big problem as long as the buffer
conditions in which the sample has been prepared are
kept constant.
One general drawback of signature peptides for
characterizing protein modifications is the fact that

only those peptides that can be separated by RP-
HPLC, ionize well in mass spectrometers and yield
informative MS ⁄ MS spectra can be identified. An
interesting, recent development is top-down protein
sequencing, which enables researchers to focus on an
increasing set of protein modifications [81,82]. Such
top-down techniques focus on complete proteins, allow
detection of normally labile protein modifications, and
avoid several problems associated with signature pep-
tides (see above). However, proteins of interest need to
be rather pure (the number of contaminating proteins
should be low), which may currently hinder the routine
applicability of such approaches.
This review has shown that the COFRADIC tech-
nology is extremely versatile and flexible and provides
profound insights into biological questions, often
much more than what could be obtained by alterna-
tive proteomics procedures such as 2D gels or shotgun
proteomics. Its strong point is its high flexibility in
selecting specific chemistries or enzymatic modifica-
tions oriented towards the biological question(s) under
consideration. It should be clear from the supporting
concepts that the repertoire of applications can only
be expected to grow in the future through the develop-
ment of specific chemical or enzymatic sorting reac-
tions that alter the chemical nature of a predetermined
set of peptides.
Acknowledgements
F. Impens is a research assistant of the Fund for
Scientific Research ) Flanders (Belgium). The work in

this paper was supported by research grants from the
Fund for Scientific Research ) Flanders (Belgium)
(project number G.0280.07) and the Inter University
Attraction Poles (IAP-Phase VI).
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