Synthesis of Potent Dishevelled PDZ Domain Inhibitors Guided by Virtual Screening and NMR Studies

Article abstract of DOI:10.1111/j.1747-0285.2011.01295.x

Dishevelled (Dvl) PDZ domains transduce Wnt signals from the membrane-bound receptor Frizzled to the downstream. As abnormal Wnt signaling has been implicated in tumorigenesis, the Dvl PDZ domain is a potential target for small-molecule inhibitors that block Wnt signaling at the Dvl level. We expanded our in silico search to examine the chemical space near previously developed PDZ binders and identified nine additional compounds bind to the Dvl PDZ. We then performed a quantitative structure-activity relationship (QSAR) analysis of these compounds and combined these results with structural studies of the PDZ domain in complex with the compounds to design and synthesize a group of new, further optimized compounds. Two rounds of synthesis and testing yielded a total of six compounds that have greatly improved binding affinity to the Dvl PDZ domain and most potent ones competitively displace Dapper peptide from the PDZ domain. In addition to providing more potent Dvl PDZ domain inhibitors, this study demonstrates that virtual screening and structural studies can be powerful tools in guiding the chemical synthesis hit-to-lead optimization stage during the drug discovery process.

Full text of DOI:10.1111/j.1747-0285.2011.01295.x

ª 2011 John Wiley & Sons A/S
doi: 10.1111/j.1747-0285.2011.01295.x
Chem Biol Drug Des 2012; 79: 376–383
Research Article
Synthesis of Potent Dishevelled PDZ Domain
Inhibitors Guided by Virtual Screening and NMR
Studies
Jufang Shan1,2, Xinxin Zhang1, Ju Bao1,
diseases (3). Dishevelled (Dvl) protein regulates Wnt signaling
pathways using its PDZ domain to interact with the Wnt recep-
Robert Cassell3 and Jie J. Zheng1,4,
*
tor Frizzled (Fz), thereby transducing Wnt signals downstream (4).
In the canonical Wnt signaling pathway, this interaction activates
the b-catenin ⁄ T cell factor (TCF) transcription pathway, which
regulates the transcription of many genes, including tumor-related
genes, such as Myc and Cyclin D1 (2). Dvl-Frizzled interaction
mainly relies on the interaction of Dvl PDZ domain with the C-
terminal intramolecular KTXXXW sequence, which has a moderate
binding affinity (4). Additional interaction involving Dvl DEP
domain with cell membrane may facilitate the formation of Dvl-
Frizzled complex (5). Transcriptional activation of Dapper, a native
Dvl PDZ inhibitor, was shown to strongly inhibit Wnt ⁄ b-catenin
signaling and induce dramatic apoptosis of colon cancer cells (6),
indicating the important role of Dvl in Wnt signaling and tumori-
genesis. Further, up-regulation of Dvl protein was observed in
Wnt-driven non-small cell lung cancer and malignant mesotheli-
oma, while down-regulation of Dvl through either RNA interfer-
ence or Dvl mutagenesis inhibited Wnt signaling and tumor
growth (7,8). Several small molecules or peptides have been
developed to target the Dvl PDZ domain and thereby regulate
the Wnt ⁄ b-catenin pathway (9,10). Such Wnt pathway inhibitors
can be useful not only in dissecting signaling mechanisms but
also in formulating rational approaches to the development of
potential pharmaceutical agents that block specific Wnt signaling
events that contribute to cancer (11).
1Department of Structural Biology, St. Jude Children's Research
Hospital, Memphis, TN 38105, USA
2Integrated Program in Biomedical Sciences, University of
Tennessee, Memphis, TN 38163, USA
3Hartwell Center for Bioinformatics and Biotechnology, St. Jude
Children's Research Hospital, Memphis, TN 38105, USA
4Department of Molecular Sciences, University of Tennessee,
Memphis, TN 38163, USA
*Corresponding author: Jie J. Zheng, jie.zheng@stjude.org
Dishevelled (Dvl) PDZ domains transduce Wnt
signals from the membrane-bound receptor Friz-
zled to the downstream. As abnormal Wnt signal-
ing has been implicated in tumorigenesis, the
Dvl PDZ domain is a potential target for small-
molecule inhibitors that block Wnt signaling at
the Dvl level. We expanded our in silico search
to examine the chemical space near previously
developed PDZ binders and identified nine addi-
tional compounds bind to the Dvl PDZ. We then
performed a quantitative structure-activity rela-
tionship (QSAR) analysis of these compounds
and combined these results with structural stud-
ies of the PDZ domain in complex with the com-
pounds to design and synthesize a group of new,
further optimized compounds. Two rounds of
synthesis and testing yielded a total of six com-
pounds that have greatly improved binding affin-
ity to the Dvl PDZ domain and most potent ones
competitively displace Dapper peptide from the
PDZ domain. In addition to providing more
potent Dvl PDZ domain inhibitors, this study
demonstrates that virtual screening and struc-
tural studies can be powerful tools in guiding
the chemical synthesis hit-to-lead optimization
stage during the drug discovery process.
We previously identified a PDZ domain antagonist (NSC668036)
through receptor-based virtual screening of the NCI small-mole-
cule library (9). Recently, after analyzing the complex structure of
PDZ bound to NSC668036, we proposed a pharmacophore model
and carried out ligand similarity screening based on the pharma-
cophore to identify additional PDZ antagonists (12). That study
identified 15 compounds that bind to the Dvl PDZ domain with
greater affinity than does NCS668036. In this study, based on
the structures of the 15 recently identified PDZ binders, we con-
ducted an additional round of pharmacophore-based ligand
similarity search and identified nine more compounds. A 3-dimen-
sional quantitative structure-activity relationship (3D-QSAR) analy-
sis of the nine new PDZ binders together with the earlier 15
compounds was consistent with our docking-based structural
examination of the Dvl PDZ in complex with the compounds.
Guided by the QSAR and structural studies, we designed and
synthesized several novel compounds that are much more potent
inhibitors of the Dvl PDZ domain.
Key words: dishevelled, PDZ domain, Wnt signaling pathway
Received 15 April 2011, revised 30 September 2011 and accepted for
publication 25 November 2011
Wnt signaling plays a crucial role in embryonic development and
regulation of cell growth (1,2). Inappropriate activation of Wnt
signaling has been implicated in cancers and other human
376

Inhibitors of the Dishevelled PDZ domain
Jolla, CA, USA), KD was normalized by dividing the experimentally
derived KD values by the difference between the KD values obtained
for NSC668036 by NMR and fluorescence methods, respectively
(12).
Experimental Procedures
Virtual screening
The UNITY module in the SYBYL software package (Tripos, Inc. St.
Louis, MO, USA) was used to screen the ChemDiv, ChemBridge and
NCI databases for potential PDZ domain inhibitors.
QSAR
Two 3D-QSAR CoMFA (comparative molecular-field analysis) (17)
models for the ligands of scaffold A and B were built by using
SYBYL 8.1 (Tripos Inc.). Compounds identified from the first round
virtual screening together with previously identified compounds
whose binding affinities to the Dvl PDZ domain were known
(12); a total of 25 compounds (nine in scaffold A and 16 scaf-
fold in B) were used to build the 3D-QSAR CoMFA models. The
NMR-derived complex structure of Val-Trp-Val peptide (VWV)
bound to Dvl PDZ was used to derive the structures of all com-
pounds (18). All compounds were sketched according to the Dvl
PDZ-bound conformation of VWV and minimized by 100 steps of
the steepest descendent method and 500 steps of the conjugate
gradient method with the presence of Dvl PDZ. All energy mini-
mizations converged before the maximum minimization steps and
all the key hydrogen bonds between the C-terminal carboxyl
groups of ligands and the 3 N–H groups (the backbone N–H
groups of Leu262, Gly263, and Ile264) of the Dvl PDZ domain
were preserved. The optimized compounds were later isolated
and superimposed on both scaffolds. The Gasteiger–Huckel
charges were assigned to each ligand, and the steric and elec-
trostatic energies were calculated for CoMFA modeling. The par-
tial least-squares (PLS) (19,20) regression was performed to
correlate the molecular fields and experimental binding affinity
data. Leave-one-out cross-validation was used to determine the
number of principal components, and PLS without cross-validation
was performed to build the CoMFA model. The coefficient of
determination defined as R2 = 1 - SSer ⁄ sstot, where SSer is the
sum of squares of residuals and SStot is the sum of total
squares, was calculated and used to evaluate the correlation
quality of each model.
Chemicals and reagents
Compounds 19 and 20 were acquired from the Drug Synthesis and
Chemistry Branch, Developmental Therapeutics Program, Division of
Cancer Treatment and Diagnosis, National Cancer Institute (http://
129.43.27.140/ncidb2/). Compounds 16 to 24 except 19 and 20
were purchased from Chemical Diversity Inc. (San Diego, CA, USA).
Fmoc-protected amino acids and HBTU were purchased from Ana-
spec (San Jose, CA, USA), resins and HATU from Applied Biosys-
tems (Foster City, CA, USA), Fmoc-protected 4-methylphenylalanine
from Advanced ChemTech (Louisville, KY, USA), and N-(9-fluorenylm-
ethyloxycarbonyloxy) succinimide from Novabiochem (Gibbstown,
NJ, USA). All other chemicals were purchased from Sigma-Aldrich
(Milwaukee, WI, USA).
Expression and purification of the mouse Dvl
PDZ domain
The 15N-labeled mouse Dvl1 PDZ domain (residues 247–341 of
mDvl1) was prepared as described previously (4,9,13) by the pro-
tein production facility at St. Jude Children's Research Hospital.
CYS338, a residue located outside the ligand binding site, was
mutated to alanine in the expression construct to increase the
solubility of the protein.
NMR studies
15N-HSQC experiments were performed using a Varian Inova
600 MHz NMR spectrometer at 25 ꢀC. Samples consisted of mouse
Dvl1 PDZ domain (0.2–0.3 mM) in 100 mM potassium phosphate buf-
fer (pH 7.5), 10% D2O, and 0.5 mM EDTA. Compounds were dis-
solved in the same buffer but with 5% DMSO, which did not
change the spectra of the PDZ domain (data not shown). NMR
spectra were processed with NMRpipe (14) and analyzed using the
SPARKY program (15). The dissociation constants (KD) of PDZ ligands
were calculated from HSQC spectra as previously reported (16). The
mean chemical shift perturbation changes caused by ligand binding
Synthesis
All synthesis was performed on a Symphony 12-channel peptide syn-
thesizer (Protein Technologies, Inc; Tucson, AZ) using standard solid-
phase Fmoc peptide chemistry. All compounds were synthesized from
the C-terminus to the N-terminus, starting from Fmoc-protected Leu
attached to resin, de-protected with 20% piperadine in N-Methylpyr-
rolidone (NMP) for 15 min at room temperature, and coupled by using
Fmoc-Leu (10 eq), HBTU (9 eq), and DIEA (10 eq) in anhydrous NMP
for 2 h. The second residues of J01- and J02- series compounds
were Fmoc-protected 4-methylphenylalanine and phenylalanine,
respectively. The last segments added to J01-007, J01-012, J01-
015, J01-016, J01-019, and J01-017a were 3-fluorobenzoic
acid, 3-cyanobenzoic acid, 3-methylbenzoic acid, 3-(aminomethyl)ben-
zoic acid, 3-(phenylthio)benzoic acid and 3,4-difluoro-5-methylbenzoic
acid, respectively. The last residues added to J02-001 and J02-
002 were 3-fluorobenzoic acid and 4-fluorobenzoic acid, respectively.
All modified benzoic acids used as the last residues were activated
with HATU. Products were cleaved from the resin with 90% trifluoro-
acetic acid (TFA), 5% water, and 5% TIS for 2 h at room temperature.
were calculated as follows:
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
. ꢀ
ꢁ
2
Ddavg
¼
1
2 ðDdN=5Þ þDdH2
KD was then calculated from
.
ꢀ
ꢁ
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Ddbinding
¼
1
2Ddmax A ꢀ A2 ꢀ 4R
and
A
¼
1 þ R þ ðPR þ CÞKD=ðPCÞ
by applying a one-site binding model corrected for dilution, where
R was the ligand ⁄ protein molar ratio, P was the protein concentra-
tion before titration, C was the ligand stock concentration, and KD
was the dissociation constant. After two-parameter non-linear
least-squares fitting with the program PRISM (GraphPad Software, La
Chem Biol Drug Des 2012; 79: 376–383
377

Shan et al.
The resulting solutions were filtered, precipitated with cold diethyl
ether, centrifuged, suspended in distilled water, and lyophilized. HPLC
analysis showed compounds in library J01 to be more than 90%
pure. All synthesized compounds were verified by mass spectroscopy
(Table S1).
phore model generated in that study in combination with ligand-
based screening to identify 15 potent PDZ domain potential inhibi-
tors in the ChemDiv database (12). These 15 compounds comprise
two scaffolds: scaffold A (compounds 1–3) and scaffold B (the
remaining compounds) (Figure 1A). To further understand the molec-
ular mechanisms of PDZ ligand binding and to further improve PDZ
ligand affinity, in this study, we continued virtual screening to
search for compounds with similar scaffold but with more diversity
in substituents. The new searches were based on the pharmaco-
phore models derived from previously identified compounds in scaf-
folds A and B. The pharmacophore models indicated that three
hydrogen bond donors and one hydrogen bond acceptor are impor-
tant for ligand binding; in addition, at least three independent
hydrophobic interactions (R1, R2, and R3, Figure 1A) are preserved in
the binding model of compound 1 (Chemdiv 5435-0027) to the PDZ
domain. We designed 2D and 3D similarity queries based on the
above pharmacophore model to search several databases, including
ChemDiv, NCI and ChemBridge, with the UNITY program in SYBYL
(Tripos, Inc.). The searches returned 38 and 18 hits based on the A
and B scaffolds, respectively. After docking the hits to the PDZ
domain with program GLIDE (Schrçdinger, Inc., New York, NY, USA),
we visually inspected the docking complex structures and selected
five scaffold A molecules and four scaffold B molecules for experi-
mental validation. As in previous studies (12), NMR chemical shift
perturbation experiments were used to examine the interactions
between the Dvl PDZ domain and the nine selected molecules. All
nine compounds perturbed the PDZ domain at the same sites as
previously identified inhibitors, suggesting that all of them bind to
the binding pocket where native PDZ ligands bind, which is consis-
tent with our docking studies. As an example, Figure 1B,C show the
Glide-docked structure of compound 17 in complex with the Dvl
PDZ domain. Although all the newly identified recognized the PDZ
domain, four of them, in particular, 18 and 20, bound to the PDZ
domain with slightly better affinities than 1, the best inhibitor iden-
tified in the previous studies (Table 1).
Fluorescence spectroscopy
A Fluorolog-3 spectrofluorometer (Jobin-Yvon Inc., Edison, NJ, USA)
with a 10 · 4 mm quartz cell (Hellma Inc., Plainview, NY, USA) was
used for competitive binding experiments. The binding affinity of
Rox-DprC (ROX-SGSLKLMTTVCOOH) (4) with Dvl1 PDZ was obtained
by monitoring the fluorescence polarization of 1.4 mL of 50-nM Rox-
DprC in 0.1 M potassium phosphate buffer (pH = 7.5) at 20 ꢀC. The
equation 1=DmP ¼ a=½sꢁ þ b was used to fit the double-reci-
procal plot of the fluorescence data, where DmP and [S] are
change in fluorescence polarization and the concentration of unla-
beled Dvl PDZ domain, respectively. KD was calculated as
KD ¼ a=b, where a and b are the fitted values from above and it
was 7.7 lM Rox-DprC, which is close to the affinity between PDZ
and the Fz7 peptide (4). Considering Rox-DprC binds to the same
region as Frizzled peptide does, this peptide is representative of the
PDZ-Fz interaction as the PDZ-Fz interaction is mainly mediated by
a binding grove formed by aA helix and bB sheet of the PDZ
domain and an internal peptide of Fz. Therefore, we used this pep-
tide as a probe to test PDZ ligand binding. For competition assay,
each compound was incubated with 100 nM Rox-DprC for 10 min
before titrating with PDZ protein. The KI values of 16, J01-007,
J01-015,
and
J01-017a
were
determined
from
KDapp ¼ KD ꢂ ð1 þ ½Iꢁ=KIÞ, where Kapp is the apparent KD of
D
PDZ binding to Rox-DprC, and [I] is the compound's concentration.
Fluorescence polarization data were analyzed using the PRISM pro-
gram (GraphPad Software, Inc.).
Results and Discussion
Pharmacophore-based virtual screening
Structure-activity relationships and structural
analysis
The above virtual screening designed based on previous SAR studies
allowed us to develop nine more PDZ binders with increased diver-
sity of the molecules in both scaffold groups. Together with the PDZ
By combining structure-based virtual screening with NMR binding
studies, we initially identified an organic molecule (NSC668036) in
the National Cancer Institute (NCI) small-molecule library (9) that
can bind to the Dvl PDZ domain. We recently used the pharmaco-
A
B
C
Figure 1: Complex structure-based pharmacophore model. (A) 2D structures of scaffolds A and B. (B) Complex structure of Dvl PDZ domain
bound to scaffold A compound 17. (C) Protein surface plotted in color to represent PDZ domain pockets P1 (orange), P2 (gray), and P3 (blue)
that interact with R1, R2, and R3 fragments of 17 in the complex.
378
Chem Biol Drug Des 2012; 79: 376–383

Inhibitors of the Dishevelled PDZ domain
Table 1: Structures and binding affinities of the PDZ binders identified by virtual screening
Compound No.
ID
Scaffold
R1
R2
R3
KD' (lM)a
16
17
18
19
20
21
22
23
24
5435-0028
5435-0029
3865-0112
NSC350589
NSC334018
3865-0014
3865-0016
5613-0123
5613-0128
A
A
A
A
A
B
B
B
B
–ph-4–CH3
–ph-4–CH3
–ph
–ph
–ph
–ph
–ph
–ph
–ph
–ph
–ph
–ph
–i–Bu
–Bn
–Bn
–i–Bu
–i–Bu
–i–Bu
–Bn
–CH3
–i–Bu
6.1
6.9
4.7
19.3
2.8
14.1
20.7
187.5
18.7
–CH2NHCOCH2NH2
–O–Bn
–ph
–ph
–ph-4–CH3
–ph-4–CH3
aNormalized KD values obtained by dividing the NMR-derived KD values by the difference between the KD values of NSC668036 measured by NMR and
fluorescence methods (12).
binders identified previously, there were 8 molecules in scaffold A
group and 16 molecules in scaffold B group. To aid the design of
more potent PDZ inhibitors, we carried out 3D-QSAR analyses of the
molecules in both groups. Comparative molecular-field analysis
(CoMFA) (17) models were built for scaffolds A and B using SYBYL 8.1
(Tripos, Inc.). The molecular fields and experimental binding data
were highly correlated in both models; R2 values were 0.934 and
0.836 for scaffolds A and B, respectively (Table 2), indicating that
both CoMFA models are highly accurate and predictive (17).
at R3 while the relative low-binding affinity of 16 was possibly due
to the ortho-methyl group on the benzyl at R1.
The CoMFA contour map for scaffold A was highly consistent with
the Glide-docked complex structure of the Dvl PDZ domain bound to
16. The steric map showed a small number of few unfavorable
areas (yellow) in R1, indicating that the binding affinity may not
benefit from increasing the molecular sizes of these two positions,
consistent with the above SAR analysis. There is a large sterically
favorable area at the R2 position, which points toward the hydro-
phobic site composed by the side chains of Ile266, Ile278, and
Val318 (Figure 3). In addition, the R2 position also possesses both
positively (blue) and negatively (red) charged favorable areas, which
correspond to the electrostatic interactions with the backbone car-
boxyl group of Ile266 and the distant charge effects from the side-
chain of Arg322, respectively (Figure 3).
The CoMFA contour maps for scaffolds A and B are illustrated in
Figure 2, with 16 used as the reference for scaffold A (Figure 2A)
and 21 as the reference for scaffold B (Figure 2B). The contour
map of scaffold B, unlike that of scaffold A, shows large sterically
unfavorable areas (yellow) in all three positions, with a sterically
favorable area (green) only at the R1 position. This marked differ-
ence between the two contour maps is probably caused by the
rigidity and steric effects arising from the sp2 carbons at the R1
position of scaffold B, which make this scaffold less promising for
further optimization. We, therefore, focused on the scaffold A mole-
cules. For the first model, compound 16 was selected as a refer-
ence because it was the compound, which we wanted to use as a
starting point to synthesize more PDZ inhibitors. Although compound
20 has a higher binding affinity, it was not chosen because its –
O–Bn group might be more susceptible to water hydrolysis and syn-
thesis conditions. In addition, compound 18 was not chosen
because comparing 16 and 17 suggesting that –Bn is not favored
Design and synthesis of additional compounds
To optimize the hits obtained from the virtual screens and verified
by NMR experiments, we decided to use QSAR and structural anal-
yses to guide the synthesis of additional scaffold A compounds that
could not be found in the existing small-molecule libraries. To
reduce the burden of the synthesis, we first virtually generated all
of the potential compounds that could be synthesized in a combina-
torial fashion.
As the QSAR model based on 16 (Figure 2) indicated that R2 is the
hot spot for modification, we first generated a group of compounds
using CombiLibMaker in SYBYL (Tripos, Inc.) with the following altera-
tions on 16: (i) the electronegative atoms ⁄ groups F, Cl, Br, I, –CH2F,
–CH2Cl, –CH2Br, –CH2I, –CH2NO2 or –CH2OH were attached at posi-
tion 2; (ii) the electronegative atoms ⁄ groups F, Cl, Br, I, –CN, –OH
or –NO2 were placed at position 3 to maximize the likelihood of
favorable electrostatic interactions with Arg322; (iii) a methyl group
was added at position 4 and (iv) methyl, ethyl, –S–ph or –O–ph
groups were added at position 5 for favorable hydrophobic interac-
tions with the residues in pocket P2. We termed this set of com-
pounds the J01 group. As the QSAR analysis also indicated that
the methyl group at the 4 position on the benzyl ring of R1 might
not be ideal for modification, we also generated a second set of
molecules, the J02 group, using the same method with the excep-
tion that the R1 was a phenyl ring instead of a –ph-4–CH3.
Table 2: PLS statistics of CoMFA 3D-QSAR models for com-
pounds in Scaffold A and B
PLS statistics (LOO)
Scaffold A
Scaffold B
q2(cross-validated correlation coefficient)
Number of components
r2 (correlation coefficient)
SEE (standard error of estimation)
F [ratio of r2 explained to
unexplained = r2 ⁄ (1)r2)]
Pr2 = 0
0.513
5
0.998
0.037
259.754
0.243
4
0.874
0.134
12.469
0.000
0.001
Contribution
Steric
Electrostatic
0.642
0.358
0.899
0.101
Chem Biol Drug Des 2012; 79: 376–383
379

Shan et al.
A
B
Figure 2: CoMFA contour maps of (A) scaffold A and (B) scaffold B binders. Greater affinity in electrostatic contours is related with a
more positive charge near blue and a more negative charge near red. Greater affinity in steric contours is correlated with more bulky groups
near green and less bulky groups near yellow.
As compounds in libraries J01 and J02 were derivatives of 16
with small substitutions, it was likely that their binding characteris-
tics would be similar to those of 16. Indeed, when they were
superimposed on the docked 16, they showed no major impediment
to binding with the PDZ domain. After energy minimization of these
compounds at the binding site, the binding potency of these mole-
cules with the Dvl PDZ domain was predicted by using the Cscore
Subset module in SYBYL (Tripos, Inc.). Scored docking poses were
then extracted and ranked as previously described (9,12). On the
basis of this ranking, seven compounds (Table S1) were selected
and synthesized by using a relative standard solid-phase synthesis
protocol (Scheme S1). Owing to solubility issues with two com-
pounds, five of the synthesized compounds were tested by NMR
chemical shift perturbation experiments. All of the five tested com-
pounds perturbed the same binding sites as previously identified
inhibitors, suggesting that they all bind to the same binding pocket.
Binding affinities (Table 3) were determined by monitoring chemical
shift perturbations as previously reported (9,12). Four compounds
(J01-007, -012 and -015, J02-002) bound to the Dvl PDZ
domain with greater affinity than 16 (Table 3), indicating that the
proposed substitutions interact favorably with the P2 pocket of the
PDZ domain.
Figure 3: Schematic of model for new compound design based
on docking structure of 16 bound to Dvl PDZ domain and QSAR.
Positions on R1 and R2 for modification are labeled.
To take full advantage of this group (R2) within the compound scaf-
fold, we carried out a second round of synthesis and generated
J01-017a (Tables 3 and 4), which combined three components
shown to enhance binding in compounds J01-007, J01-015 and
J02-002 (comparing J01-007 and J01-015 to 16, as well as
J02-002 to 21). Docking studies of J01-017a suggested that it
would interact with both the hydrophobic groove and the positively
Table 3: Structures and binding affinities of synthesized
compounds
Compound
R1
R2
R3
KD' (lM)a
J01-007
J01-012
J01-015
J02-001
J02-002
J01-017a
–ph-4–CH3
–ph-4–CH3
–ph-4–CH3
–ph
–ph
–ph-4–CH3
–ph-3–F
–i–Bu
–i–Bu
–i–Bu
–i–Bu
–i–Bu
–i–Bu
2.27
1.83
2.96
7.67
3.49
Table 4: Fluorescence polarization competitive binding assay
–ph-3–CN
–ph-5–CH3
–ph-3–F
–ph-4–F
–ph-3,4-2F-5–CH3
Compound
R1
R2
R3
KI (lM)
J01-017a
J01-015
J01-007
16
–ph-4–CH3
–ph-4–CH3
–ph-4–CH3
–ph-4–CH3
–ph-3,4-2F-5–CH3
–ph-5–CH3
–ph-3–F
–i–Bu
–i–Bu
–i–Bu
–i–Bu
1.5 € 0.52
3.5 € 0.21
4.2 € 0.46
7.1 € 2.8
b
–
aKD values obtained directly by NMR titration (12).
–ph
bKD could not be obtained by NMR titration because of slow exchange.
380
Chem Biol Drug Des 2012; 79: 376–383

Inhibitors of the Dishevelled PDZ domain
charged Arg322 residue around pocket P2 (Figure 1). Indeed, NMR
titration generated 1H-15N-HSQC spectra different from those of
previously identified compounds. In particular, peak Arg319 that
locates right at the binding site on helix aA shifted continuously
when the PDZ domain was titrated with increasing concentrations
of compound J01-007 (Figure 4A). However, in sharp contrast,
when titrated with J01-017a (Figure 4B), the peak did not shift
but instead the intensity decreased (see decreased magenta con-
tours) when the ligand to protein ratio went up to one; when the
ratio reached three, not only the intensity further decreased but
also the peak appeared at a new position; and the intensity further
increased with increasing concentrations of ligand until saturated.
This suggests J01-017a is in slow exchange with PDZ domain on
the NMR timescale (Figure 4), which are the classical behavior of
compounds with submicromolar binding affinities (21). In addition to
Arg319, many other residues on the binding site also show slow
exchange, for example, Arg322, Val318 on the aA helix and Ile266
on the bB sheet as well. We choose to show Arg319 simply for
clarity because it is well isolated from other signals.
Figure 5: Competitive binding of structurally related compounds
to the PDZ domain. Binding affinity was measured by fluorescence
polarization.
To determine the binding affinity between the synthesized com-
pounds and the PDZ domain, we carried out competitive PDZ bind-
ing assays of structurally related compounds, using fluorescence
polarization as previously described (22) (Figure 5). J01-017a had
the highest binding affinity, it competed with the binding of Dapper
peptide, a Dvl PDZ domain binding peptide (22), with inhibition con-
stant 1.5 € 0.2 lM, as compared to 7.1 € 0.5 lM for 16. In the
same binding assay study, the precursors of J01-017a (J01-007,
J01-015) also showed slightly greater affinity than 16, coinciding
with the NMR titration analysis that J01-017a is the first PDZ
inhibitor entered the slow exchange regime. Therefore, J01-017a
offers much better binding affinity than its parent compounds.
guide the synthesis of additional, more optimal compounds. We
constructed two series of new compounds with similar scaffolds
based on the QSAR derived from virtually identified PDZ binders
and the complex structure of these compounds with PDZ. After
docking and scoring the compounds in the first series, we synthe-
sized seven compounds and confirmed five of them to be more
potent inhibitors than the template, 16. We then further optimized
the leads and synthesized compound J01-017a, whose R2 group
has several hydrophobic contacts with the P2 pocket residues on
the surface of the Dvl PDZ domain that are more favorable than
those of 16. In addition, the fluoride on position 3 of J01-017a is
able to form a polar interaction with the side chain of Arg322 (Fig-
ure 6). As the result, the binding affinity of J01-07a to the Dvl
PDZ domain is about five times lower that of 16.
Conclusions
We have extended our previous studies to identify additional, more
potent PDZ domain inhibitors. By exhaustive virtual screening of
libraries for the derivatives of PDZ binders and close inspection of
their docking structures, we identified compounds that can not only
provide scaffolds for subsequent synthesis but also be used to
To our knowledge, J01-017a is the most potent inhibitor of the
Dvl PDZ domain that has been produced. Testing of its biological
effects is currently under way. Because its binding affinity is much
higher than the native ligands of the Dvl PDZ domain (4), we
believe that the compound could be a very useful tool for various
A
B
Figure 4: NMR binding studies. NMR titration spectra for J01-007 (A) and J01-017a (B). Spectra of free PDZ domain are shown in
black; spectra of PDZ domain with increasing concentration of compounds (ligand: protein ratio = 1, 3, 5, 7, and 11) are shown in magenta,
blue, green, yellow and red, respectively.
Chem Biol Drug Des 2012; 79: 376–383
381

Shan et al.
A
B
Figure 6: The Dvl PDZ domain in complex with 16 (A) and J01-017a (B). Carbon atoms in 16 and J01-017a are shown in magenta.
Surface of PDZ protein is shown in brown. PDZ domain residues composing the P2 pocket are labeled and the side chains are shown.
biological studies. We are also testing the selectivity of this com-
pound against other PDZ domains. Although there are many copies
of PDZ domain in human, it is possible to achieve selectivity. One
of our previously identified compound NSC668036 is selective to
the mDvl PDZ domain against two other PDZ domains, a class I
PDZ domain and a class II PDZ domain (8). Our studies also demon-
strate that virtual screening has an additional important use beyond
identifying potential lead compounds from existing compound
libraries for drug discovery. When combined with other experimen-
tal methods to validate compound potency, it can be used for struc-
ture-activity relationship studies that lead to the design and
synthesis of feasible compounds with potentially higher binding
potency.
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Acknowledgments
We thank Dr. Weixing Zhang for assistance with NMR experiments,
Dr. Charles Ross and Mr. Scott Malone for computer support, and
Sharon Naron for editing the manuscript. This work was supported
by grants CA21765 and GM061739 from the National Institutes of
Health, by the American Lebanese Syrian Associated Charities (AL-
SAC), and by an American Heart Association Predoctoral Fellowship
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Scheme S1. Reagents and conditions: (i) de-protection: NMP,
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Table S1. Verification of identity and purity of synthesized com-
pounds
Please note: Wiley-Blackwell is not responsible for the content or
functionality of any supporting materials supplied by the authors.
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Supporting Information
Additional Supporting Information may be found in the online ver-
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