Development of an activity-based probe and in silico design reveal highly selective inhibitors for diacylglycerol lipase-α in brain

Article abstract of DOI:10.1002/anie.201306295

A model method: A strategy that combines a knowledge-based insilico design approach and the development of novel activity-based probes (ABPs) for the detection of endogenous diacylglycerol lipase-α (DAGL-α) is presented. This approach resulted in the rapi

Full text of DOI:10.1002/anie.201306295

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DOI: 10.1002/anie.201306295
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Development of an Activity-Based Probe and In Silico Design Reveal
Highly Selective Inhibitors for Diacylglycerol Lipase-a in Brain**
Marc P. Baggelaar, Freek J. Janssen, Annelot C. M. van Esbroeck, Hans den Dulk,
Marco Allarꢀ, Sascha Hoogendoorn, Ross McGuire, Bogdan I. Florea, Nico Meeuwenoord,
Hans van den Elst, Gijsbert A. van der Marel, Jaap Brouwer, Vincenzo Di Marzo,
Herman S. Overkleeft, and Mario van der Stelt*
Diacylglycerol lipase-a (DAGL-a) is an intracellular, multi-
domain protein responsible for the formation of the endo-
cannabinoid 2-arachidonoylglycerol (2-AG) in the central
nervous system.[1] 2-AG is an endogenous signaling lipid that
interacts with the cannabinoid CB1 and CB2 receptors.[2]
Little is known about the regulation of its biosynthetic
pathway and it is largely unclear to what extent 2-AG is
responsible for distinct cannabinoid CB1 receptor mediated
biological processes. Selective inhibitors of DAGL-a may
contribute to a more fundamental understanding of the
physiological role of 2-AG and may serve as potential drug
candidates for the treatment of obesity and neurodegener-
ative diseases.[3] Currently, there are no selective inhibitors
and activity-based probes available for the study of
DAGL-a.[4]
various physiological functions.[5] Fluorophosphonate (FP)-
based probes are routinely employed in competitive activity-
based protein profiling (ABPP) experiments to determine the
selectivity of serine hydrolase inhibitors in complex pro-
teomes. DAGL-a, however, does not react with these activity-
based probes.[6] Therefore, a new probe that can label native
DAGL-a would be of value for studying the potency and
selectivity of novel DAGL-a inhibitors in brain proteomes.
Here we present a strategy that combines a knowledge-based
in silico design approach and the development of a novel
activity-based probe (ABP), based on the nonselective
DAGL-a inhibitor tetrahydrolipstatin (THL; also known as
Orlistat, a drug used for the treatment of obesity). This
strategy resulted in the rapid identification of DAGL-a
inhibitors with a new chemotype and high selectivity in the
brain proteome.
To identify novel DAGL-a inhibitors, we built a pharma-
cophore model based on THL using Discovery Studio
Software Suite from Accelrys. Since THL can assume many
different conformations, we searched the protein crystallo-
graphic database for crystal structures with a bioactive
conformation for THL. A cocrystal structure of THL with
fatty acid synthase (pdb-code: 2PX6) was identified (Fig-
ure 1A)[7] that contains the same Ser-His-Asp catalytic triad
and typical a/b hydrolase fold motif as DAGL-a. In this
cocrystal structure, the nucleophilic Ser of the enzyme is
covalently attached to the carbonyl moiety of the lactone. We
reconstituted the ester to form the b-lactone to recover the
active warhead of THL. After optimization of the geometry
of the lactone, the resulting conformation was used to
generate two pharmacophore models (Figure 1B,C).
The essential features of both models are 1) a hydrogen-
bond acceptor mimicking the carbonyl from the b-lactone;
2) hydrophobic hot spots corresponding to the lipophilic tails
of THL; 3) a hydrogen-bond acceptor positioned at the sn-2
ester functionality; and 4) exclusion volumes representing the
space occupied by the nucleophilic Ser and the backbone
oxyanion hole residues in the active site of DAGL-a. Model 2
contained an additional hydrogen-bond donor feature
derived from the leucinyl formamide moiety of THL. Using
these models, we virtually screened a set of commercially
available lipase inhibitors, which were mainly selected for
their reactivity towards enzymes involved in endocannabi-
noid signaling (Table S1 in the Supporting Information).
Analysis of the docking results revealed that two compounds
ranked in the top five of both models, LEI103 (1) and LEI104
The identification of selective DAGL-a inhibitors is
hampered by a lack of structural knowledge of the target,
and
a lack of assays that make use of endogenous
DAGL-a activity in proteomes. No crystal structures are
available and no homology models have been reported to aid
hit identification and to guide optimization of the inhibitors.
Determination of the selectivity of the inhibitors in native
tissues is important because DAGL-a belongs to the serine
hydrolase family, which contains more than 200 members with
[*] M. Sc. M. P. Baggelaar, M. Sc. F. J. Janssen, A. C. M. van Esbroeck,
M. Sc. S. Hoogendoorn, Dr. B. I. Florea, Ing. N. Meeuwenoord,
Ing. H. van den Elst, Prof. Dr. G. A. van der Marel,
Prof. Dr. H. S. Overkleeft, Dr. M. van der Stelt
Dept. of Bio-organic Synthesis, Leiden University
Einsteinweg 55, 2333 CC Leiden (The Netherlands)
E-mail: m.van.der.stelt@chem.leidenuniv.nl
M. Sc. M. Allarꢀ, Prof. Dr. V. Di Marzo
Endocannabinoid Research Group
Institute of Biomolecular Chemistry, C.N.R.
Via Campi Flegrei 34, 80078, Pozzuoli (Italy)
Ing. H. den Dulk, Prof. Dr. J. Brouwer
Dept. of Molecular Genetics, Leiden University
Einsteinweg 55, 2333 CC Leiden (The Netherlands)
Dr. R. McGuire
Bioaxis Research
Bergse Heihoek 56, Berghem (The Netherlands)
[**] We would like to thank Prof. Dr. P. Doherty (King’s College London
(UK)) for his kind gift of wild type and DAGL-a knockout mouse
brains.
Supporting information for this article is available on the WWW
under http://dx.doi.org/10.1002/anie.201306295.
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Figure 1. Identification of novel inhibitors for DAGL-a. A) Bioactive conformation of THL as retrieved from its known fatty acid synthase cocrystal
structure (PDB code 2PX6). B) Pharmacophore model 1. C) Pharmacophore model 2. D) Docking results in pharmacophore model 1: highest
ranked binding pose of LEI103 (4 features) and LEI104 (5 features). E) Screen of the targeted library using the colorimetric biochemical assay.
^
Normalized residual activity was measured against hDAGL-a in HEK-293T cell membranes. F) Dose–response curves of LEI103 (black ) and
LEI104 (gray ) against hDAGL-a as determined with the colorimetric assay. LEI103: IC50 =3.8Æ0.4 mm; LEI104: IC50 =37Æ5 nm (ÆSEM, n=4).
K
G) Structures of LEI103 and LEI104.
(5; Figure 1D; Table S2 in the Supporting Information),
whereas no binding mode was identified for compounds 3, 4,
7–11, 13, 14, and 16 in either one or both models. This result
demonstrates that the pharmacophore models were capable
of discriminating between related structures of lipase inhib-
itors. Compound LEI103 is an oxadiazolone known to inhibit
hormone-sensitive lipase,[8] whereas LEI104 is an a-ketohe-
terocycle that has been reported to inhibit fatty acid amide
hydrolase (FAAH).[9] LEI104 has been shown to be active in
in vivo models of antinociception through inhibiting
FAAH.[10] Both hits represent new chemotypes that have
not previously been shown to display DAGL-a inhibitory
activity.
To validate our in silico hits, we used a colorimetric
biochemical DAGL-a activity assay, which makes use of the
hydrolysis of para-nitrophenylbutyrate by membrane prepa-
rations from HEK293T cells transiently transfected with
human DAGL-a (hDAGL-a; Figure S2A–D in the Support-
ing Information).[11] Screening of the compound library
against hDAGL-a (Figure 1E) confirmed LEI103 (1) and
LEI104 (5) as the only compounds to inhibit hDAGL-a
enzymatic activity by over 50% at 10 mm. Determination of
the concentration–response dependency resulted in an IC50 of
37 Æ 5 nm (n = 4) for LEI104, thus making it a hundred-fold
more potent than LEI103 (IC50 = 3.8 Æ 0.4 mm; n = 4; Fig-
ure 1F). Of note, the reported DAGL-a inhibitor
RHC80267[1a] (16) showed no inhibitory activity at 10 mm in
our biochemical assay. In addition, we determined the activity
of the hits in a radiometric assay using 1-[14C]oleoyl-2-
arachidonoylglycerol (1.0 mCimmolÀ1, 25 mm][1a] as natural-
like substrate. This assay confirmed that LEI104 is a more
potent inhibitor of DAGL-a (IC50 = 2.9 Æ 0.1 mm) than
LEI103 (37% inhibition at 10 mm).
To characterize the activity and selectivity of our hits on
native DAGL-a in brain proteome, we employed competitive
activity-based protein profiling (ABPP), which is an attractive
and powerful chemical biology technique. It integrates
organic chemistry, pharmacology, and chemical proteomics
into the early stages of hit identification in the drug discovery
process. It is unique in its ability to rapidly identify inhibitor
activity and selectivity over large protein family classes in
tissue samples.[5a] For this purpose, we designed MB064 as
a novel activity-based probe (ABP) for DAGL-a based on the
THL-analogue 31 (Scheme 1). An important consideration
for choosing a THL-like compound as an ABP was that our
pharmacophore models were also based on THL, thereby
potentially leading to the identification of hits with the same
off-target activities as THL. ATHL-based ABP will enable us
to detect these off-target activities as early as possible and to
select the optimal hit. Compound 31 contains an alkyne for
conjugation to a fluorophore and relies on a b-lactone
warhead that forms a covalent bond with the catalytic
nucleophilic serine. This principle has been used previously
with bacterial, plant, and mammalian enzymes that share an
a,b-hydrolase fold motif.[12] MB064 was synthesized following
an established strategy.[13] The lactone ring was constructed
using a tandem Mukaiyama aldol lactonization, yielding
a (10:1 anti/syn) mixture of diasteromers, with a total
selectivity for the trans-b-lactone. After removal of the TBS
group, the diastereomers could be separated by column
chromatography over silica gel. N-Formyl-l-leucine was
introduced by a Mitsunobu reaction, inverting the d-stereo-
center towards the desired configuration. Coupling with
a fluorophore (azido bodipy acid)[14] by using a copper
catalyzed click reaction resulted in MB064, the first DAGL-
2
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ished the labeling of the proteins with MB064, thereby
excluding enzymatic activity in the biochemical assay, and no
band at around 120 kD was observed. hDAGL-a protein
expression was not altered by the mutagenesis as determined
with the FLAG-tag antibody (Figure 2A,B). Finally, we made
a C-terminal deletion construct of hDAGL-a that was missing
amino acids 688–1042. This mutant enzyme was still active as
confirmed by our biochemical assay and was also labeled by
MB064. Together, these data demonstrate that MB064 can
efficiently label active hDAGL-a by forming a covalent bond
with Ser472.
To determine whether our probe was able to react with
native DAGL-a, we incubated MB064 with mouse membrane
proteomes from different tissues (brain, heart, lung, testes,
kidney, and liver). The highest DAGL-a activity was found in
brain proteome, with negligible activity in the other tissues.
MB064 labeled at least eight different proteins in brain
(Figures 2C and 3A), and this labeling was prevented (or
reduced to a large extent) by preincubation with THL (20 mm;
Figure 3A). A distinct fluorescent band at approximately
120 kD was observed; this band could not be observed when
the brain membrane proteome was incubated with TAMRA-
FP. Of note, MB064 showed a much more restricted labeling
profile than TAMRA-FP. In brain membrane proteomes from
DAGL-a knockout mice, no specific band at around 120 kD
was found (Figure 2C). Incubation of mouse brain lysate
proteomes with MB064 led to the labeling of several proteins,
but no specific signal at around 120 kD was detected (Fig-
ure S3 in the Supporting Information). This result is in line
with the fact that DAGL-a resides in membranes. Finally, we
detected mouse DAGL-a protein and eleven other proteins
(Table 1) in a pull down experiment, using streptavidin beads
and a biotinylated THL derivative, MB077, followed by
tryptic digestion and mass spectrometric analysis of the
isolated peptides. These proteins were not identified when the
brain membrane proteome was preincubated with THL. Our
Scheme 1. Reagents and conditions: a) ZnCl2, CH2Cl2, d.r. 1:10, 50%;
b) HF, CH3CN, 08C, 91%; c) 2,6-lutidine/acetone/H2O (0.1:1:1),
AgNO3, 79%; d) N-formyl-l-leucine, PPh3, DIAD, THF, 30%; e) sodium
ascorbate, CuSO4, H2O, CH2Cl2, 40%. DIAD=Diisopropyl azodicarbox-
ylate. TBS=tert-butyldimethylsilyl, TMS=trimethylsilyl.
a sensitive ABP, (Scheme 1), which gave an IC50 of 6.0 Æ
1.0 nm (n = 4) in the colorimetric assay.
To validate MB064 as a DAGL-a sensitive ABP, different
hDAGL-a constructs were transiently transfected into
HEK293T cells (Figure 2A). Incubation of the membrane
fractions of hDAGL-a-FLAG transfected cells with MB064,
followed by SDS-PAGE and fluorescence scanning revealed
seven fluorescence signals (Figure 2B). The signal at approx-
imately 120 kDa, which corresponded to the molecular mass
of hDAGL-a, overlapped with a band visualized by the
FLAG-tag antibody and was absent in mock-transfected cells.
Preincubation with THL (10 mm) blocked enzymatic activity
and nonspecific labeling. Site-directed mutagenesis of the
catalytic DAGL-a nucleophile Ser472 into an alanine abol-
proteomics
confirmed
experiment
that THL
reacts with several struc-
tural proteins (such as
tubulin), glyceraldehyde-
3-phosphate dehydrogen-
ase, and lipid metabolizing
enzymes (for example
ABHD16a and platelet-
activating factor acetylhy-
drolase), as reported.[6,13]
We conclude that MB064
is able to visualize and
detect DAGL-a in the
mouse brain membrane
proteome, and confirm
that THL is a nonselective
inhibitor of DAGL-a in
mouse brain.
Figure 2. Validation of MB064 as a DAGL-a ABP. A) Activity, as measured with the colorimetric assay, of
different hDAGL-a constructs transiently transfected into HEK-293T cells. Mock membranes and different
constructs measured at the same protein concentration (1 mgmLÀ1), uncorrected activity shown (ÆSEM,
n=4). B) ABPP using MB064 (250 nm) with different hDAGL-a constructs at the same protein concentration
(2 mgmLÀ1), and Western blot of the ABPP gel using an anti-FLAG antibody. C) ABPP using MB064 (250 nm) in
brain membrane proteomes of wild type and DAGL-a knockout (KO) mice.
To determine their
activity and selectivity pro-
files in the brain, we per-
formed
a
competitive
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Based on its activity and selectivity profile, we decided to
test LEI104 in intact cells. We incubated LEI104 (20 mm) or
vehicle with intact SHSY5Y cells, stimulated them with
ionomycin (3 mm), and measured 2-AG levels after 20 min.[1a]
The 2-AG levels were significantly lower (À46%) in the
treated cells compared with the control cells. This demon-
strates that LEI104 is cell permeable and able to inhibit
stimulus-induced 2-AG formation by DAGL-a in living cells.
In conclusion, LEI104 represents a novel potent and selective
chemotype for DAGL-a inhibition.
We performed a preliminary structure–activity relation-
ship study of LEI104 with hDAGL-a using the colorimetric
assay. Replacement of the isoxazolepyridine heterocycle with
a benzoxazole led to a 100-fold loss in activity, thus indicating
that the pyridine nitrogen could form a potentially important
interaction with the active site of the enzyme (see the
Supporting Information for synthesis).[9] Reduction of the a-
keto group to the alcohol abolished all activity, a result in line
with the assumption that this group functions as an electro-
philic trap for the catalytic Ser472.
Figure 3. Selectivity profile and activity of LEI103 and LEI104 in the
mouse brain membrane proteome. A) Competitive ABPP with the
DAGL-a inhibitors THL, LEI103, and LEI104 (20 mm) using ABPs
MB064 and carboxytetramethylrhodamine fluorophosphonate (TAMRA-
FP) in mouse brain membrane proteome. B) Dose–response curves
^
for DAGL-a inhibition by LEI103 (black a a) and LEI104 (gray
*
c c) as measured by competitive ABPP with ABP MB064
(ÆSEM, n=3). C) Concentration dependent inhibition of DAGL-a in
the mouse brain membrane proteome.
To understand the interaction of LEI104 with
hDAGL-a at a molecular level, we developed a homology
model of DAGL-a (see the Supporting Information). The
model represents the typical a,b-hydrolase fold and has the
catalytic triad (Ser472, His650, and Asp524) appropriately
aligned in the binding cavity (Figure 4). The tetrahedral
transition state of LEI104, which is formed through the
nucleophilic attack of Ser472 on the a-carbonyl, was mini-
mized and subjected to a short molecular dynamics refine-
ment. According to our model, the oxyanion intermediate is
stabilized by the backbone N–H of the residue adjacent to the
catalytic serine, Leu473. In addition, both the side chain O–H
and the backbone N–H of Thr400 are observed to make
hydrogen bonds with the oxyanion. The oxazole nitrogen of
LEI104 formed hydrogen-bond interactions with His650 and
the pyridine nitrogen showed hydrogen-bond interactions
with His471, both of which could further stabilize the
transition state, while the hydrophobic pocket lined with
Table 1: Proteins identified with MB077.
Proteins
tubulin beta
visinin-like protein 1
40S ribosomal protein S6
actin
glyceraldehyde-3-phosphate dehydrogenase
myelin basic protein
histone H2B type 1-M
ATP synthase subunit a
Sn1-specific diacylglycerol hydrolase a
abhydrolase domain containing protein 16A
monoacylglycerol hydrolase ABHD6
platelet-activating factor acetylhydrolase
ABPP experiment with LEI103 and LEI104 at 20 mm using
mouse brain membrane proteome (Figure 3A). LEI103
prevented the labeling of six proteins by MB064, whereas
LEI104 was much more selective and completely blocked the
labeling of DAGL-a. This indicates that LEI103 has a similar
off-target profile to that of THL. Other hits from the library
screen did not inhibit DAGL-a (Figure S4 in the Supporting
Information). LEI104 inhibited DAGL-a labeling with an
IC50 of 450 Æ 104 nm (n = 3), thereby making it approximately
40-fold more potent than LEI103 (IC50 = 18 Æ 5 mm; n = 3;
Figure 3B). In addition, we performed a comparative ABPP
experiment with TAMRA-FP to obtain a selectivity profile
for our hits in the serine hydrolase family (Figure 3A). We
observed that LEI103 inhibited several proteins, whereas
LEI104 abolished only one signal at approximately 64 kD,
which corresponded to FAAH.[10d] These results demonstrate
that an ABP based on a pharmacophore model is highly
useful for selecting and prioritizing hits found with the model,
because it can rapidly detect potential off-target activities of
these hits in the proteome that share a similar pharmaco-
phore.
Figure 4. Binding pose of LEI104 (cyan) in a homology model of
hDAGL-a.
4
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Mahadevan, P. Urbani, A. Minassi, G. Appendino, C. Saturnino,
aliphatic amino acids accommodated the flexible acyl chain of
LEI104. This proposed binding mode is consistent with our
observed structure–activity relationships. This model also
provides a clear view of the opportunities to improve the
potency and selectivity over FAAH. Since FAAH is the main
enzyme responsible for the degradation of another important
endocannabinoid, anandamide (AEA), its inhibition will lead
to an upregulation of AEA levels.
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To conclude, we have demonstrated the power of combin-
ing a pharmacophore-based in silico screening approach with
ABPP-based chemoproteomics to identify and profile a novel
chemotype for DAGL-a inhibition. Using an existing drug
(THL) as a common starting point for the generation of both
a pharmacophore model and an activity-based probe, we were
able to rapidly identify the a-ketoheterocycle LEI104 as
a highly selective DAGL-a inhibitor. It is anticipated that the
a-ketoheterocycle class will provide an excellent lead series
for dissecting 2-AG- and AEA-mediated cannabinoid CB1
signaling and for the development of in vivo active and
selective DAGL-a inhibitors, because these compounds
1) have a clearly defined scaffold with excellent physico-
chemical properties; 2) are not based on the natural substrate
and do not contain known toxicophores (for example
fluorophosponate);[4b] 3) are plasma membrane permeable;
4) are highly selective; 5) do not form irreversible covalent
bonds,[10] which could lead to problems with immunogenicity;
and 6) have been shown to be bioavailable and active in
animal models.[10] Finally, our probe and the a-ketohetero-
cycles, together with structural insights provided by the
DAGL-a homology model, may serve as a basis for the
development of new therapeutics that can be used to study
and treat diseases such as obesity and neurodegeneration.
These studies are currently in progress.
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Received: July 19, 2013
Revised: September 11, 2013
Published online: && &&, &&&&
Keywords: activity-based probe · drug discovery ·
.
endocannabinoid · hydrolases · proteomics
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Angew. Chem. Int. Ed. 2013, 52, 1 – 6
ꢀ 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim
www.angewandte.org
5
These are not the final page numbers!

.
Angewandte
Communications
Communications
Enzyme Inhibition
M. P. Baggelaar, F. J. Janssen,
A. C. M. van Esbroeck, H. den Dulk,
M. Allarꢁ, S. Hoogendoorn, R. McGuire,
B. I. Florea, N. Meeuwenoord,
H. van den Elst, G. A. van der Marel,
J. Brouwer, V. Di Marzo, H. S. Overkleeft,
M. van der Stelt*
&&&& — &&&&
A model method: A strategy that com-
bines a knowledge-based in silico design
approach and the development of novel
activity-based probes (ABPs) for the
detection of endogenous diacylglycerol
lipase-a (DAGL-a) is presented. This
approach resulted in the rapid identifica-
tion of new DAGL-a inhibitors with high
selectivity in the brain proteome. ABPP=
activity-based protein profiling.
Development of an Activity-Based Probe
and In Silico Design Reveal Highly
Selective Inhibitors for Diacylglycerol
Lipase-a in Brain
6
www.angewandte.org
ꢀ 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim
Angew. Chem. Int. Ed. 2013, 52, 1 – 6
These are not the final page numbers!