pubs.acs.org/jmc Published on Web 04/20/2010
r2010 American Chemical Society
3748J. Med. Chem. 2010, 53, 3748–3755
Structure-Based Discovery of A2AAdenosine Receptor Ligands
Jens Carlsson,†Lena Yoo,‡Zhan-Guo Gao,‡John J. Irwin,†Brian K. Shoichet,*,†and Kenneth A. Jacobson*,‡
†Department of Pharmaceutical Chemistry, University of California, 1700 4th Street, Box 2550, San Francisco, California 94158, and
‡Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases,
National Institutes of Health, Bethesda, Maryland 20892
Received February 23, 2010
The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these
targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may
be discovered for the A2Aadenosine receptor, based on complementarity to its recently determined
structure. The A2Aadenosine receptor signals in the periphery and the CNS, with agonists explored as
anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular
docking to screen a 1.4 million compound database against the X-ray structure computationally and
tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed sub-
stantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors,
over 50-fold specificity was observed for the A2Aversus the related A1and A3subtypes. These high hit
an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new
medically important target.
G-protein-coupled receptors (GPCRsa) are a large family
of transmembrane proteins that signal intracellularly after
topology, with seven transmembrane helices, but recognize a
intensely studied as pharmaceutical targets, and over 40% of
marketed drugs act through them.1Until recently, a missing
link to deeper understanding of GPCRs has been a lack of
atomic resolution structural information. With the recent
advent of several X-ray crystal structures of pharmacologi-
cally relevant GPCRs2-5it has for the first time become
possible to leverage high-resolution structures for ligand
discovery against these targets.6
Among the new GPCR structures is that of the A2Aadeno-
sine receptor (AR).5There are four subtypes of the AR (A1,
A2A, A2B, and A3), and they are activated by extracellular
adenosine in response to organ stress or tissue damage. The
A2AAR signals in both the periphery and the CNS, with
agonists explored as anti-inflammatory drugs and antagonists
explored for neurodegenerative diseases, e.g., Parkinson’s
the lack of structures has certainly not been an obstacle for
successful ligand discovery. For several decades, classical
ligand-based medicinal chemistry approaches have been used
to identify thousands of AR ligands. Almost all known AR
agonists are derivatives of the cognate ligand (1-3, Chart 1),
antagonists are xanthines, with members such as caffeine (4)
and theophylline (5), and adenine derivates such as 6
(ZM24138512), which is bound to the A2AAR binding site
in the crystallographic structure (Chart 1, Figure 1A).
Despite considerable medicinal chemistry efforts and the
wide range of possible therapeutic applications for AR
ligands, there are only a few approved drugs targeting this
receptor.8,11Consequently, there remains an ongoing need
for new subtype selective agonists and antagonists of this
Here, we wished to investigate whether we could find new
A2AAR ligand chemotypes by using structure-based mole-
of small molecules, looking for those that complement the
receptor structure. Docking evaluates the complementarity
of small molecules to a receptor binding site of known
structure13-18and can in principle discover new chemotypes,
site well. Such chemotypes might provide new routes for
modulation of this key target. Methodologically, we wanted
to explore what the hit rate of a structure-based (docking)
screen against the A2AAR might be. In docking screens
against the β2adrenergic GPCR, a hit rate of 24% had been
observed.19-23A docking “hit” isa molecule thatbindstothe
target at a relevant concentration, and a docking “hit rate” is
compounds experimentally tested. For the β2 adrenergic
*To whom correspondence should be addressed. For B.K.S.: phone,
415-514-4126; fax, 415-514-4260; e-mail, firstname.lastname@example.org. For
K.A.J.: phone, 301-496-9024; fax, 301-480-8422; e-mail, kajacobs@
aAbbreviations: GPCR, G-protein-coupled receptor;AR, adenosine
receptor; CNS, central nervous system; SEA, similarity ensemble ap-
proach; PDB, Protein Data Bank; WOMBAT, World of Molecular
Bioactivity; CHO, Chinese hamster ovary; DMEM, Dulbecco’s modi-
fied Eagle medium; PKA, protein kinase A; DLS, dynamic light
Article Journal of Medicinal Chemistry, 2010, Vol. 53, No. 93749
receptor, where the affinity of the best docking hit was 9 nM,
both were unusually high. We wished to understand whether
this would be true for this second GPCR and why this might
be so. To investigate this, we docked a library of 1.4 million
small molecules to the crystal structure of the A2AAR. From
the top-scoring molecules, 20 were selected on the basis of
their fit to the binding site and chemical diversity. Here, we
present the experimental evaluation of these molecules and
assess why GPCRs appear to be particularly suitable targets
for structure-based ligand discovery.
Preparation of the Molecular Docking Screen. All dock-
ing calculations were carried out with the program DOCK
3.5.5416,17,24using a 2.6 A˚ crystallographic structure of the
A2Aadenosine receptor in complex with an antagonist (6)12
(PDB accession code 3EML5). The receptor structure was
prepared by removing all non-protein atoms and the intracellu-
lar T4-lysozyme insertion. The protonation states of ionizable
The flexible-ligand sampling algorithm in DOCK3.5.54
superimposes atoms of the docked molecule onto binding site
matching spheres, which indicate putative ligand atom posi-
tions.16,17In the case of the A2AAR, 45 matching spheres were
used, and these were either based on the atoms of the crystallo-
graphic ligand or positioned manually. The spheres were also
labeled for chemical matching based on the local receptor
environment.25The degree of ligand sampling is determined
by the bin size, bin size overlap, and distance tolerance. These
three parameters were set to 0.4, 0.3, and 1.5 A˚, respectively, for
both the binding site matching spheres and the docked mole-
cules. For ligand conformations passing an initial steric filter, a
physics-based scoring function is used to evaluate the fit to the
receptor binding site. For the best scoring conformation of each
docked molecule, 100 steps of rigid-body minimization are
carried out. The score for each conformation is calculated as
the sum of the receptor-ligand electrostatic and van der Waals
terms are evaluated from precalculated grids. The three-dimen-
sional map of the electrostatic potential in the binding site was
charges from the united atom AMBER force field27were used
Chart 1. Structures of Known Agonists (1-3) and Antagonists (4-6) of the A2AAdenosine Receptor
Figure 1. Bindingmodeofthecocrystallizedligand6(A)andthepredictedbindingmodesofthesevenligandsdiscoveredinthedockingscreen
is shown using orange carbon atoms. In (B-H), the crystallographic ligand is shown using blue lines and the docking poses for the ligands are
depicted with orange carbon atoms. Black dotted lines indicate hydrogen bonds. The compounds are (B) 7, (C) 8, (D) 9, (E) 10, (F) 11, (G) 12,
and (H) 13.
3750Journal of Medicinal Chemistry, 2010, Vol. 53, No. 9Carlsson et al.
which the dipole moment was increased to favor hydrogen
bonding to this residue (we have adopted this technique of
increasing local dipoles on a few polar residues in the active site
without changing their formal charges extensively in past
studies).28,29The program CHEMGRID was used to generate
the AMBER force field.30The desolvation penalty for a ligand
conformation is estimated from a precalculated transfer free
energy of the molecule between solvents of dielectrics 78 and 2.
energy with a scaling factor that reflects the degree of burial of
the ligand in the receptor binding site.31,32
ofcommerciallyavailablecompoundsusingthecriteria logP <
3.5, molecular weight of <350, and number of rotatable bonds
of e7.33Each molecule has been prepared for docking by
pregenerating up to 1000 conformations using the program
OMEGA.34Partial atomic charges and transfer free energies
have been calculated using AMSOL,35,36and van der Waals
parameters have been derived from an all-atom AMBER
Similarity and Library Bias Calculations. Similarity calcula-
tions were carried out with the program Pipeline Pilot38using
the Tanimoto coefficient and ECFP4 fingerprints. For each of
the docking-discovered ligands, the Tanimoto similarity to all
annotated A1, A2A, A2B, and A3AR ligands with Kie 10 μM in
the World of Molecular Bioactivity (WOMBAT 2006.2)39and
ChEMBL (a StARlite 2009 prerelease version)40databases was
calculated. The number of molecules in the ZINC leadlike
database that are similar to known ligands of the ARs, adre-
nergic receptors, adenylyl cyclases, and AmpC β-lactamase was
predicted with the similarity ensemble approach (SEA) using
ECFP4 fingerprints.41Ligands (Kie 10 μM) that are annotated
to ARs, adrenergic receptors, and adenylyl cyclases were ex-
tracted from the WOMBAT database. Ligands for AmpC
β-lactamase were extracted from refs 28, 29, and 42. For each
SEA P value better than 10-10was calculated. The predicted
compounds were then postfiltered for molecules that match the
molecular weight and formal charge ranges of the known
A2A AR Receptor Binding and Functional Assay. Binding
assays at three hAR subtypes were carried out using standard
radioligands43-45and membrane preparations from Chinese
hamster ovary (CHO) cells (A1and A3) or human embryonic
kidney (HEK293) cells (A2A) stably expressing a hAR sub-
type.46,47Afunctional assay attheA2AAR consisted ofstimula-
tion of cAMP production48,49in A2AAR-expressing HEK293
cells. [3H]R-N6-(2-phenylisopropyl)adenosine ([3H]R-PIA, 42.6
Ci/mmol) was obtained from Moravek Biochemicals (Brea,
carboxamidoadenosine) ([3H]CGS21680, 40.5 Ci/mmol) and
mide ([125I]I-AB-MECA, 2200 Ci/mmol) were purchased from
Perkin-Elmer Life and Analytical Science (Boston, MA). Test
compounds were prepared as 5 mM stock solutions in DMSO
and stored frozen at -20 ?C.
Cell Culture and Membrane Preparation. CHO cells stably
expressing therecombinanthA1andhA3Rs, andHEK-293 cells
stably expressing the hA2AAR were cultured in Dulbecco’s
modified Eagle medium (DMEM) and F12 (1:1) supplemented
with 10% fetal bovine serum, 100 units/mL penicillin, 100 μg/
mL streptomycin, and 2 μmol/mL glutamine. In addition,
800 μg/mL Geneticin was added to the A2A media, while
500 μg/mL hygromycin was added to the A1and A3media.
was resuspended in 50 mM Tris-HCl buffer (pH 7.5) containing
10 mM MgCl2. The suspension was homogenized and was then
ultracentrifuged at 14330g for 30 min at 4 ?C. The resultant
pellets were resuspended in Tris buffer and incubated with
adenosine deaminase (3 units/mL) for 30 min at 37 ?C. The
suspension was homogenized with an electric homogenizer for
was measured using the BCA protein assay kit from Thermo
Scientific Pierce Protein Research Products (Rockford, IL).50
Binding Assays. The tested compounds were purchased from
Pharmeks, and Asinex). The vendors had verified that each
compound had g95% purity by liquid chromatography-mass
spectrometry (LC-MS) or nuclear magnetic resonance (NMR)
experiments. Into each tube in the binding assay was added 50 μL
of increasing concentrations of the test ligand in Tris-HCl buffer
(50 mM, pH 7.5) containing 10 mM MgCl2, 50 μL of the
appropriate agonist radioligand, and finally 100 μL of mem-
brane suspension. For the A1AR (22 μg of protein/tube)
the radioligand used was [3H]R-PIA (final concentration of
3.5 nM). For the A2AAR (20 μg/tube) the radioligand used
was [3H]CGS21680 (10 nM). For the A3AR (21 μg/tube) the
radioligand used was [125I]I-AB-MECA (0.34 nM). Nonspecific
binding was determined using a final concentration of 10 μM
unlabeled 50-N-ethylcarboxamidoadenosine (NECA, 2) diluted
in a shaking water bath. Binding reactions were terminated by
using a M-24 cell harvester (Brandel, Gaithersburg, MD).
Filters were washed three times with 3 mL of 50 mM ice-cold
Tris-HCl buffer (pH 7.5). Filters for A1and A2AAR binding
were placed in scintillation vials containing 5 mL of Hydrofluor
scintillation buffer and counted using a Perkin Elmer liquid
scintillation analyzer (Tri-Carb 2810TR). Filters for A3AR
binding were counted using a Packard Cobra II γ-counter.
The Kivalues were determined using GraphPad Prism for all
Cyclic AMP Accumulation Assay. Intracellular cyclic AMP
(cAMP) levels were measured with a competitive protein bind-
ing method.48,49CHO293 cells that expressed the recombinant
human A2AAR were harvested by trypsinization. After centri-
fugation and resuspension in medium, cells were planted in
24-well plates in 1.0 mL of medium. After 24 h, the medium was
containing 50 mM HEPES, pH 7.4. Cells were then treated with
the test compound in the presence of rolipram (10 μM) and
adenosine deaminase (3 units/mL), and incubation was contin-
ued for an additional 1 h. The reaction was terminated by
removing the supernatant, and cells were lysed upon the addi-
tion of 200 μL of 0.1 M ice-cold HCl. The cell lysate was
resuspended and stored at -20 ?C. For determination of cyclic
AMP production, protein kinase A (PKA) was incubated with
[3H]cyclic AMP (2 nM) in K2HPO4/EDTA buffer (K2HPO4,
150 mM; EDTA, 10 mM), 20 μL of the cell lysate, and 30 μL of
0.1 M HCl or 50 μL of cyclic AMP solution (0-16 pmol/200 μL
filtration through Whatman GF/C filters and washed once with
cold buffer. Bound radioactivity was measured by liquid scin-
Counterscreen for Colloidal Inhibition. To control for artifac-
tual inhibition by colloidal aggregation, we looked for particle
formation by Dynamic Light Scattering (DLS) and by inhibi-
tion of two counterscreen enzymes, cruzain and AmpC
β-lactamase. Concentrated DMSO stocks of compounds were
diluted with filtered 50 mM KPi, pH 7.0. Measurements were
made using a DynaPro MS/X (Wyatt Technology) with a
55 mW laser at 826.6 nm. The laser power was 100%, and the
detector angle was 90?. Cruzain assays were performed in
100 mM sodium acetate, pH 5.5, containing 5 mM DTT with
and without 0.1% Triton X-100. Compounds were incubated
with 0.8 nM cruzain for 5 min, and reactions were initiated by
Article Journal of Medicinal Chemistry, 2010, Vol. 53, No. 93751
adding the fluorogenic substrate Z-Phe-Arg-aminomethylcou-
cruzain at 0.4 nM and ZF-R-AMC at 2.5 μM. Final DMSO
concentrations were 0.5%. To measure enzyme inhibition, the
increase in fluorescence (excitation wavelength of 355 nm,
emission wavelength of 460 nm) was recorded for 5 min in a
microtiter plate spectrofluorimeter (Molecular Devices, Flex-
Station). Assays were performed in duplicate in 96-well plates,
with controls measuring enzyme activity in the presence of
DMSO. Activity was measured for seven different concentra-
tions for each compound. Inhibition of AmpC β-lactamase was
measured for the two best compounds identified here, com-
pounds 9 and 11, to complement the cruzain assay results.
Assays were performed in 50 mM potassium phosphate, pH
7.0. Compounds were incubated with 1 nM β-lactamase for
5 min, and reactions were initiated by adding the substrate
CENTA to a final concentration 92 μM. The final reaction
absorbance at 405 nm was recorded for 5 min in a UV-vis
spectrophotometer (Agilent). Assays were performed in dupli-
cate in 1 mL cuvettes, with controls measuring enzyme activity
in the presence of DMSO. Activity was measured at 10 μM,
Results and Discussion
Molecular Docking Screen and Compound Selection. The
program DOCK3.5.5416,17was used to screen 1.4 million
database against the orthosteric site of the A2AAR. On
average, each molecule was sampled in 5000 orientations
and, for each that fit, an average of 16500 conformations;
the receptor was held rigid. Each molecule was scored for
ligand desolvation. Molecules typically overlapped with the
geometry of the crystallized antagonist, making a mixture of
polar and hydrophobic interactions, packing deeply into the
site (Figure 1). The 500 top-ranking molecules (Table S1,
Supporting Information), 0.035% of the docking prioritized
library, were analyzed visually for features that are not taken
into account in the docking calculation. This is a standard
procedure for all our docking screens in which each of the
molecules is inspected for novelty, physical properties, and
binding energy contributions that are not included in the
docking scoring function. For example, compound 9 (Table 1)
was chosen both because it complemented the site well and
because there were several molecules with the same scaffold in
the top 500 list of compounds (e.g., the molecules ranked 7, 31,
59, 104, 115, 135, 137, 172, 184, 199, and 278 in Table S1,
Supporting Information).From this evaluation, 20 compounds
ranking molecules) were prioritized for experimental testing.
Radioligand Displacement Assays and Docking Hit Rate.
The 20 compounds selected from the docking screen were
Table 1. Ligand Structures and Experimental Data for the Seven Hits from the Docking Screen against the A1, A2A, and A3ARs
aRank in the docking screen.bMeasured in three independent experiments.cThe most similar compound annotated to ARs in WOMBAT39and
ChEMBL.40 dTanimoto similarity coefficient to the closest annotated adenosine receptor ligand from ECFP4 fingerprints.
3752Journal of Medicinal Chemistry, 2010, Vol. 53, No. 9 Carlsson et al.
of these molecules inhibited binding by g40% at 20 μM,
corresponding to a “hit rate” of 35%. Subsequent dose-
response curves were well-behaved, with Kivalues varying
from 200 nM to 8.8 μM (Table 1 and Figure 2). Four of the
ligands, 9, 10, 11 and 13, were counterscreened for colloidal
aggregation, a common mechanism of artifactual inhibi-
tion.51No colloidal particles were observed at 10 μM, by
dynamic light scattering, for 9 and 13, nor did they inhibit
cruzain at the same concentration. For compounds 10 and
11, particles were observed at 10 μM, but for 10 these
appeared to be precipitant rather than colloids and this
compound did not inhibit cruzain up to 10 μM. For com-
pound 11 particles were observed at 10 μM, as was enzyme
inhibition, but this inhibition was not reversible by deter-
gent, inconsistent with colloidal aggregation. Further-
more, no inhibition of AmpC β-lactamase was observed up
to10 μMfor 9 and11.Taken togetherwiththe well-behaved
dose-response curves (Figure 2), these results indicate
that the molecules are well behaved, classical binding
All seven docking hits are specific for the A2AAR versus
the related A1and A3subtypes (Table 1). Notably, our most
potent ligand, compound 11, is also the most specific with
over 50-fold higher affinity at the A2AAR. We therefore
of docking that also fit well into the site (compounds 14-18,
Table 2). Four of the analogues were found to bind to the
A2AAR with submicromolar affinities, and these molecules
also had an improved A2A/A1subtype selectivity. From
these results, the prospects of identifying specific high-affi-
nity A2Aantagonists in this new class of compounds appear
To put the results from this docking screen in perspective,
our laboratory considers a high-throughput docking screen
tobesuccessfulifahitrate of5% withligand affinitiesinthe
micromolar range can be achieved. For example, we tested
56 compounds from a docking screen against AmpC
β-lactamase and found one compound with a Kivalue better
than 100 μM, corresponding to a hit rate of 2%;29this
inhibitor had a Kivalue of 26 μM (Table 3). In the case
of the A2AAR we observe 10-fold higher hit rate and the
affinities of the hits are 10- to 100-fold better. Intriguingly,
similar results were obtained in two docking screens against
the other pharmaceutically relevant GPCR for which a
crystallographic structure has been solved, the β2adrenergic
receptor.19,20Kolb et al. identified six previously unknown
ligands of the β2adrenergic receptor, a 24% hit rate, with
affinities as high as 9 nM.19It may be that GPCRs are
particularly well-suited for structure-based docking screens,
a point to which we will return.
Predicted Binding Modes, Novelty, and Efficacy of the
Discovered A2AAR Ligands. All seven of the new ligands
are predicted to interact with the key recognition residue
Asn253 in transmembrane helix 6 and many also hydrogen-
bond with the carboxylate of Glu169 in extracellular loop 2,
both in the orthosteric site of the receptor (Figure 1). The
importance of interactions with Asn253 was identified early
in our docking screens. We found that increasing the dipole
moment of the Asn253 side chain amide, a technique we
known A2AAR ligands among a database of decoys in
Figure 2. Representative dose-response curves for displacement
of binding of the radiolabeled A2AAR agonist 3 by compounds 9,
10, and 11.
Table 2. Binding Affinities and Structures of Five Analogues to Compound 11 in Radioligand Binding Assays at A1, A2A, and A3ARs
aMeasured in three independent experiments.
ArticleJournal of Medicinal Chemistry, 2010, Vol. 53, No. 9 3753
control calculations. Asn253 is conserved in all four AR
receptor subtypes and has also been found to be a crucial
interaction partner for both agonists and antagonists in
Whereas all the seven ligands are previously uncharacter-
ized for the A2AAR, some of them bear known chemotypes.
To quantify their novelty, or lack of it, we calculated the
similarity of each molecule to 7500 known AR ligands from
the WOMBAT and ChEMBL databases using pairwise
Tanimoto coefficients (Tc, ECFP4 fingerprints) (Table 1).
Ligand 12 resembles members of the xanthine class of
antagonists, while compound 7 resembles certain quinazo-
line53ligands. Conversely, whereas compounds 10, 11, and
13 do conserve several moieties with known ligands, they
also differ substantially from them, with Tcvalues of 0.3 to
the closest annotatedligand. Nevertheless, they complement
the site well both sterically and electrostatically (Figure 1).
The potency of these molecules suggests that they may
merit further study as new lead families for antagonists of
To determine the efficacy of the compounds, their ability
to inhibit intracellular cAMP production induced by
agonist 3 was tested. No stimulation of cAMP production
was detected for any of the molecules, while a clear displace-
ment of agonist function was observed for the two most
potent compounds, 9 and 11 (Figure 3). All seven of the new
ligands are thus almost certainly A2Aantagonists, as is the
cocrystallized ligand. Intriguingly, this efficacy bias was also
observed in the docking screens against the β2adrenergic
receptor, where only inverse agonists were found against the
structure crystallized with the inverse agonist carazolol. In
may bias the screen toward molecules with the same efficacy
as the cocrystallized ligand. This represents a challenge to
our ability to exploit these structures for mechanisms of
action, such as agonism, not represented in the experimental
of commercially available molecules screened against the
A2AAR binding site. Whereas multiple known antagonists
would have been ranked among the top 500 molecules in the
docking screen, these two agonists were ranked 951057 and
Is There Library Bias toward GPCR Chemotypes in Che-
mical Libraries? Returning to one of the questions that
motivated this study, the structure-based screen against the
A2AAR returned a diverse set of ligands dissimilar to those
previously characterized, as well as several similar to known
ligands, and did so with a hit rate of 35%. Not only is this hit
rate much higher than we have come to expect for enzyme
targetsscreened withthesameapproach,but thenewantago-
nists were also close to 100-fold more potent than we have
come to expect for our docking “hits”. Furthermore, these
results are strikingly similar to those observed in docking
screens against the β2adrenergic GPCR.19,20To what may
these unusually high hit rates and affinities be attributed?
Family A GPCRs like the A2AAR are the targets for a
substantial fraction of marketed drugs, and this partly
reflects the quality of their sites for specific recognition of
do so with a mixture of nonpolar and polar interactions.
Consequently, a large and sustained medicinal chemistry
effort has focused on these targets, and by now even puta-
tively unbiased libraries, like ZINC, have become populated
with molecules bearing “GPCR-like” chemotypes. This also
reflects a bias toward naturally occurring molecules in our
screening libraries.54Indeed, Kolb et al. estimated that there
were 3-12 times as many small molecules that were similar
common drug targets such as kinases, proteases, and ligand-
and relevant to the adenosine receptor versus other docking
targets that we ourselves have worked, we investigated the
library bias in ZINC for the adrenergic and adenosine
receptors together with two other targets for which we have
observed much lower hit rates and affinities19,29,55(Table 3).
The number of molecules in the ZINC leadlike set that are
similar to the ligands of these targets was estimated using
SEA,54insisting on a P value of 10-10or better; acceptable
physical properties (see Methods). Over 4000 small mole-
cules resemble ligands annotated to the ARs and adrenergic
receptors in the WOMBAT39database, almost 10-fold more
than found for the enzymes adenylyl cyclase and AmpC
β-lactamase, against which, correspondingly, our docking
hit rates andaffinitieshavebeen 10- to100-foldlower. Thus,
it is the convolution of the high “ligand-ability” of the
orthosteric sites and the many GPCR-like chemotypes in
Table 3. Target Library Bias and Docking Hit Rates
representative DOCK screen
targetno. of ZINC molecules similar to known ligandsa
hit rateb(%) best potencyc(nM)
aZINC leadlike molecules with at least 10-10P values to annotated target ligands in WOMBAT using the similarity ensemble approach (SEA).
b(Number of true ligands)/(number of predictions tested experimentally).cThe affinity of the ligand with the best potency from the docking screen.
Figure 3. Functional assay based on measuring the production of
cAMP for 3 (control), a potent A2AAR agonist, with or without
10 μM 9 or 11. The dose-response curve is shifted for both
compounds, as expected in the case of competitive antagonistic
inhibition.The%activationrefers toproductionofcAMP normal-
ized to the effect of 3 at 100 μM.
3754 Journal of Medicinal Chemistry, 2010, Vol. 53, No. 9Carlsson et al.
our libraries that makes the adrenergic and adenosine re-
ceptors so fruitful for structure-based techniques.
Note Added after Initial Review of This Paper. After this
paper was submitted for review, a paper by Abagyan,
Stevens, and colleagues appeared that also targeted the
docking screen.56As here, Abagyan et al. also observed a
very substantial hit rate with high affinities (indeed, both the
affinities and hit rates were slightly better than those we
observe). Whereas the compounds discovered in the two
screens were substantially different, the observation of the
high hit rates and, by screening standards, high affinity
“hits” is consistent with the high “ligand-ability” of the class
A GPCRs and the fortuitous library bias toward them, and
this is among the important conclusions of this study.
Acknowledgment. This work is supported by NIH Grant
GM59957 (to B.K.S.), NIDDK Intramural Research Pro-
gram (to K.A.J.), and a fellowship from the Knut and
Alice Wallenberg Foundation (to J.C.). We thank A. Doak
for aggregation assays and members of the Shoichet lab for
docking “hit-list” evaluation. We thank Tudor Oprea for
access to the WOMBAT database and John Overington
for a prerelease version of the ChEMBL database.
Supporting Information Available: Table S1 of structures of
the 500 top-ranking molecules from the docking screen. This
material is available free of charge via the Internet at http://
targets are there? Nat. Rev. Drug Discovery 2006, 5, 993–996.
(2) Cherezov, V.; Rosenbaum, D. M.; Hanson, M. A.; Rasmussen,
S. G. F.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Kuhn, P.; Weis,
W. I.; Kobilka, B. K.; Stevens, R. C. High-resolution crystal
structure of an engineered human beta(2)-adrenergic G protein-
coupled receptor. Science 2007, 318, 1258–1265.
(3) Rosenbaum, D. M.; Cherezov, V.; Hanson, M. A.; Rasmussen,
S. G. F.; Thian, F. S.; Kobilka, T. S.; Choi, H. J.; Yao, X. J.; Weis,
W. I.; Stevens, R. C.; Kobilka, B. K. GPCR engineering yields
high-resolution structural insights intobeta(2)-adrenergicreceptor
function. Science 2007, 318, 1266–1273.
(4) Warne, T.; Serrano-Vega, M. J.; Baker, J. G.; Moukhametzianov,
R.; Edwards, P. C.; Henderson, R.; Leslie, A. G. W.; Tate, C. G.;
Schertler, G. F. X. Structure of a beta(1)-adrenergic G-protein-
coupled receptor. Nature 2008, 454, 486–491.
(5) Jaakola, V. P.; Griffith, M. T.; Hanson, M. A.; Cherezov, V.;
angstrom crystal structure of a human A(2A) adenosine receptor
bound to an antagonist. Science 2008, 322, 1211–1217.
(6) Congreve, M.; Marshall, F. The impact of GPCR structures on
pharmacology and structure-based drug design. Br. J. Pharmacol.
2010, 159, 986–996.
pursuit of therapeutic adenosine receptor antagonists. Med. Res.
Rev. 2006, 26, 131–159.
(8) Jacobson, K. A.; Gao, Z. G. Adenosine receptors as therapeutic
targets. Nat. Rev. Drug Discovery 2006, 5, 247–264.
(9) Sebastiao, A. M.; Ribeiro, J. A. Adenosine receptors and the
central nervous system. Handb. Exp. Pharmacol. 2009, 471–534.
(10) Blackburn, M. R.; Vance, C. O.; Morschl, E.; Wilson, C. N.
Adenosine receptors and inflammation. Handb. Exp. Pharmacol.
(11) Cristalli, G.; Muller, C. E.; Volpini, R. Recent developments in
adenosine A2A receptor ligands. Handb. Exp. Pharmacol. 2009,
(12) Poucher, S. M.; Keddie, J. R.; Singh, P.; Stoggall, S. M.; Caulkett,
P. W. R.; Jones, G.; Collis, M. G. The in-vitro pharmacology of
Zm-241385, a potent, nonxanthine, a(2a) selective adenosine re-
ceptor antagonist. Br. J. Pharmacol. 1995, 115, 1096–1102.
(13) Degen, J.; Rarey, M. FlexNovo: structure-based searching in large
fragment spaces. ChemMedChem 2006, 1, 854–868.
(14) Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R.
Development and validation of a genetic algorithm for flexible
docking. J. Mol. Biol. 1997, 267, 727–748.
(15) Kairys, V.; Fernandes, M. X.; Gilson, M. K. Screening drug-like
compounds by docking to homology models: a systematic study.
J. Chem. Inf. Model. 2006, 46, 365–379.
(16) Lorber, D. M.; Shoichet, B. K. Flexible ligand docking using
conformational ensembles. Protein Sci. 1998, 7, 938–950.
(17) Lorber, D. M.; Shoichet, B. K. Hierarchical docking of databases of
(18) Zavodszky, M. I.; Kuhn, L. A. Side-chain flexibility in pro-
tein-ligand binding: the minimal rotation hypothesis. Protein
Sci. 2005, 14, 1104–1114.
(19) Kolb, P.; Rosenbaum, D. M.; Irwin, J. J.; Fung, J. J.; Kobilka,
B. K.; Shoichet, B. K. Structure-based discovery of beta(2)-adre-
nergic receptor ligands. Proc. Natl. Acad. Sci. U.S.A. 2009, 106,
(20) Sabio, M.; Jones, K.; Topiol, S. Use of the X-ray structure of the
beta(2)-adrenergic receptor for drug discovery. Part 2: Identifica-
tion of active compounds. Bioorg. Med. Chem. Lett. 2008, 18,
(21) de Graaf, C.; Rognan, D. Selective structure-based virtual screen-
ing for full and partial agonists of the beta 2 adrenergic receptor.
J. Med. Chem. 2008, 51, 4978–4985.
brane helix V in agonist-specific conformational changes. J. Mol.
Recognit. 2009, 22, 307–318.
(23) Reynolds, K. A.; Katritch, V.; Abagyan, R. Identifying conforma-
tional changes of the beta(2) adrenoceptor that enable accurate
predictionof ligand/receptorinteractionsand screeningfor GPCR
modulators. J. Comput.-Aided Mol. Des. 2009, 23, 273–288.
(24) Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin,
T. E. A geometric approach to macromolecule-ligand interac-
tions. J. Mol. Biol. 1982, 161, 269–288.
(25) Shoichet, B. K.; Kuntz, I. D. Matching chemistry and shape in
molecular docking. Protein Eng. 1993, 6, 723–732.
(26) Nicholls, A.; Honig, B. A rapid finite-difference algorithm, utiliz-
ing successive over-relaxation to solve the Poisson-Boltzmann
equation. J. Comput. Chem. 1991, 12, 435–445.
(27) Weiner, S. J.; Kollman, P. A.; Case, D. A.; Singh, U. C.; Ghio, C.;
Alagona, G.; Profeta, S.; Weiner, P. A new force-field for mole-
cular mechanical simulation of nucleic-acids and proteins. J. Am.
Chem. Soc. 1984, 106, 765–784.
(28) Babaoglu, K.; Simeonov, A.; Lrwin, J. J.; Nelson, M. E.; Feng, B.;
Thomas, C. J.; Cancian, L.; Costi, M. P.; Maltby, D. A.; Jadhav,
A.; Inglese, J.; Austin, C. P.; Shoichet, B. K. Comprehensive
mechanistic analysis of hits from high-throughput and docking
(29) Powers, R. A.; Morandi, F.; Shoichet, B. K. Structure-based
Structure 2002, 10, 1013–1023.
(31) Shoichet, B. K.; Leach, A. R.; Kuntz, I. D. Ligand solvation in
molecular docking.Proteins: Struct., Funct., Genet. 1999, 34, 4–16.
(32) Wei, B. Q. Q.; Baase, W. A.; Weaver, L. H.; Matthews, B. W.;
molecular docking. J. Mol. Biol. 2002, 322, 339–355.
available compounds for virtual screening. J. Chem. Inf. Model.
2005, 45, 177–182.
(34) Bostrom, J.; Greenwood, J. R.; Gottfries, J. Assessing the perfor-
mance of OMEGA with respect to retrieving bioactive conforma-
tions. J. Mol. Graphics Modell. 2003, 21, 449–462.
(35) Chambers, C. C.; Hawkins, G. D.; Cramer, C. J.; Truhlar, D. G.
Model for aqueoussolvationbasedon classIV atomic chargesand
first solvation shell effects. J. Phys. Chem. 1996, 100, 16385–16398.
(36) Li, J. B.; Zhu, T. H.; Cramer, C. J.; Truhlar, D. G. New class IV
charge model for extracting accurate partial charges from wave
functions. J. Phys. Chem. A 1998, 102, 1820–1831.
(37) Weiner, S. J.; Kollman, P. A.; Nguyen, D. T.; Case, D. A. An all
atom force-field for simulations of proteins and nucleic-acids.
J. Comput. Chem. 1986, 7, 230–252.
N.; Olah, I.; Banda, M.; Simon, Z.; Mracec, M.; Oprea, T. I.
WOMBAT: World of Molecular Bioactivity. In Chemoinformatics
in Drug Discovery; Oprea, T. I., Ed.; Wiley-VCH: Weinheim, Germany,
2005; pp 221-239.
ArticleJournal of Medicinal Chemistry, 2010, Vol. 53, No. 93755 Download full-text
J. J.; Shoichet, B. K. Relating protein pharmacology by ligand
chemistry. Nat. Biotechnol. 2007, 25, 197–206.
(42) Tondi, D.; Morandi, F.; Bonnet, R.; Costi, M. P.; Shoichet, B. K.
Structure-based optimization of a non-beta-lactam lead results in
inhibitorsthat donotup-regulatebeta-lactamase expressionin cell
culture. J. Am. Chem. Soc. 2005, 127, 4632–4639.
(43) Jarvis, M. F.; Schulz, R.; Hutchison, A. J.; Do, U. H.; Sills, M. A.;
Williams, M. [H-3] Cgs-21680, a selective A2 adenosine receptor
agonist directly labels A2-receptors in rat-brain. J. Pharmacol.
Exp. Ther. 1989, 251, 888–893.
(44) Klotz, K. N.; Lohse, M. J.; Schwabe, U.; Cristalli, G.; Vittori, S.;
a high-affinity agonist radioligand for A1 adenosine receptors.
Naunyn-Schmiedeberg’s Arch. Pharmacol. 1989, 340, 679–683.
(45) Olah, M. E.; Gallorodriguez, C.; Jacobson, K. A.; Stiles, G. L.
I-125 4-aminobenzyl-50-N-methylcarboxamidoadenosine, a high-
affinity radioligand for the rat a(3) adenosine receptor. Mol.
Pharmacol. 1994, 45, 978–982.
transfected human A(3) adenosine receptors in CHO cells. Bio-
chem. Pharmacol. 2002, 64, 61–65.
(47) Jacobson, K. A.; Park, K. S.; Jiang, J. L.; Kim, Y. C.; Olah, M. E.;
Stiles, G. L.; Ji, X. D. Pharmacological characterization of novel
1997, 36, 1157–1165.
(48) Nordstedt, C.; Fredholm, B. B. A modification of a protein-
binding method for rapid quantification of camp in cell-culture
supernatants and body-fluid. Anal. Biochem. 1990, 189, 231–234.
(49) Post, S. R.; Ostrom, R. S.; Insel, P. A. Biochemical methods for
detection and measurement of cyclic AMP and adenylyl cyclase
activity. Methods Mol. Biol. 2000, 126, 363–374.
(50) Bradford, M. M. Rapid and sensitive method for quantitation of
microgram quantities of protein utilizing principle of protein-dye
binding. Anal. Biochem. 1976, 72, 248–254.
(51) McGovern, S. L.; Helfand, B. T.; Feng, B.; Shoichet, B. K. A
specific mechanism of nonspecific inhibition. J. Med. Chem. 2003,
(52) Kim, J. H.; Wess, J.; Vanrhee, A. M.; Schoneberg, T.; Jacobson,
K. A. Site-directed mutagenesis identifies residues involved in
ligand recognition in the human a(2a) adenosine receptor. J. Biol.
Chem. 1995, 270, 13987–13997.
(53) Webb, T. R.; Lvovskiy, D.; Kim, S. A.; Ji, X. D.; Melman, N.;
Linden, J.; Jacobson, K. A. Quinazolines as adenosine receptor
Chem. 2003, 11, 77–85.
(54) Hert, J.; Irwin, J. J.; Laggner, C.; Keiser, M. J.; Shoichet, B. K.
Quantifying biogenic bias in screening libraries. Nat. Chem. Biol.
2009, 5, 479–483.
(55) Soelaiman, S.; Wei, B. Q.; Bergson, P.; Lee, Y. S.; Shen, Y.;
Mrksich, M.; Shoichet, B. K.; Tang, W. J. Structure-based inhi-
bitor discovery against adenylyl cyclase toxins from pathogenic
bacteria that cause anthrax and whooping cough. J. Biol. Chem.
2003, 278, 25990–25997.
(56) Katritch, V.; Jaakola, V. P.; Lane, J. R.; Lin, J.; Ijzerman, A. P.;
Yeager, M.; Kufareva, I.; Stevens, R. C.; Abagyan, R. Structure-
antagonists. J. Med. Chem. 2010, 53, 1799–1809.