Discovery of potent and novel smoothened antagonists via structure-based virtual screening and biological assays

Article abstract of DOI:10.1016/j.ejmech.2018.05.035

The Hedgehog (Hh) signaling pathway plays a critical role in controlling patterning, growth and cell migration during embryonic development. Aberrant activation of Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell carcinoma (BCC) and medulloblastoma. As a key member of the Hh pathway, the Smoothened (Smo) receptor, a member of the G protein-coupled receptor (GPCR) family, has emerged as an attractive therapeutic target for the treatment and prevention of human cancers. The recent determination of several crystal structures of Smo in complex with different antagonists offers the possibility to perform structure-based virtual screening for discovering potent Smo antagonists with distinct chemical scaffolds. In this study, based on the two Smo crystal complexes with the best capacity to distinguish the known Smo antagonists from decoys, the molecular docking-based virtual screening was conducted to identify promising Smo antagonists from ChemDiv library. A total of 21 structurally novel and diverse compounds were selected for experimental testing, and six of them exhibited significant inhibitory activity against the Hh pathway activation (IC₅₀ < 10 μM) in a GRE (Gli-responsive element) reporter gene assay. Specifically, the most potent compound (compound 20: 47 nM) showed comparable Hh signaling inhibition to vismodegib (46 nM). Compound 20 was further confirmed to be a potent Smo antagonist in a fluorescence based competitive binding assay. Optimization using substructure searching method led to the discovery of 12 analogues of compound 20 with decent Hh pathway inhibition activity, including four compounds with IC₅₀ lower than 1 μM. The important residues uncovered by binding free energy calculation (MM/GBSA) and binding free energy decomposition were highlighted and discussed. These findings suggest that the novel scaffold afforded by compound 20 can be used as a good starting point for further modification/optimization and the clarified interaction patterns may also guide us to find more potent Smo antagonists.

Full text of DOI:10.1016/j.ejmech.2018.05.035

Accepted Manuscript
Discovery of potent and novel smoothened antagonists via structure-based virtual
screening and biological assays
Wenfeng Lu, Dihua Zhang, Haikuo Ma, Sheng Tian, Jiyue Zheng, Qin Wang, Lusong
Luo, Xiaohu Zhang
PII:
S0223-5234(18)30448-3
10.1016/j.ejmech.2018.05.035
EJMECH 10446
DOI:
Reference:
To appear in: European Journal of Medicinal Chemistry
Received Date: 26 February 2018
Revised Date: 19 May 2018
Accepted Date: 22 May 2018
Please cite this article as: W. Lu, D. Zhang, H. Ma, S. Tian, J. Zheng, Q. Wang, L. Luo, X. Zhang,
Discovery of potent and novel smoothened antagonists via structure-based virtual screening and
biological assays, European Journal of Medicinal Chemistry (2018), doi: 10.1016/j.ejmech.2018.05.035.
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Discovery of Potent and Novel Smoothened Antagonists via
Structure-Based Virtual Screening and Biological Assays
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Wenfeng Lua,1, Dihua Zhanga,1, Haikuo Maa,b,*, Sheng Tiana,d,*, Jiyue Zhenga, Qin Wangc,
Lusong Luoc, and Xiaohu Zhanga,*
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aJiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences,
Soochow University, Suzhou, Jiangsu 215123, P.R. China
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bCyrus Tang Hematology Center, Jiangsu Institute of Hematology and Collaborative Innovation Center
of Hematology, Soochow University, Suzhou 215123, P.R. China.
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cBeiGene (Beijing) Co., Ltd., No. 30 Science Park Road, Zhongguancun Life Science Park, Beijing
102206, P.R. China.
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dInstitute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu
215123, P. R. China
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1 these authors contributed equally to this paper
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Corresponding Authors：
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E-mail: xiaohuzhang@suda.edu.cn; stian@suda.edu.cn; mahaikuo123@163.com
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Keywords: smoothened receptor, antagonist, virtual screening, molecular docking
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Abstract
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The Hedgehog (Hh) signaling pathway plays a critical role in controlling patterning,
growth and cell migration during embryonic development. Aberrant activation of Hh
signaling has been linked to tumorigenesis in various cancers, such as basal cell
carcinoma (BCC) and medulloblastoma. As a key member of the Hh pathway, the
Smoothened (Smo) receptor, a member of the G protein-coupled receptor (GPCR)
family, has emerged as an attractive therapeutic target for the treatment and
prevention of human cancers. The recent determination of several crystal structures of
Smo in complex with different antagonists offers the possibility to perform
structure-based virtual screening for discovering potent Smo antagonists with distinct
chemical scaffolds. In this study, based on the two Smo crystal complexes with the
best capacity to distinguish the known Smo antagonists from decoys, the molecular
docking-based virtual screening was conducted to identify promising Smo antagonists
from ChemDiv library. A total of 21 structurally novel and diverse compounds were
selected for experimental testing, and six of them exhibited significant inhibitory
activity against the Hh pathway activation (IC50 < 10 µM) in a GRE (Gli-responsive
element) reporter gene assay. Specifically, the most potent compound (compound 20:
47 nM) showed comparable Hh signaling inhibition to vismodegib (46 nM).
Compound 20 was further confirmed to be a potent Smo antagonist in a fluorescence
based competitive binding assay. Optimization using substructure searching method
led to the discovery of 12 analogues of compound 20 with decent Hh pathway
inhibition activity, including four compounds with IC50 lower than 1 µM. The
important residues uncovered by binding free energy calculation (MM/GBSA) and
binding free energy decomposition were highlighted and discussed. These findings
suggest that the novel scaffold afforded by compound 20 can be used as a good
starting point for further modification/optimization and the clarified interaction
patterns may also guide us to find more potent Smo antagonists.
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The Hedgehog (Hh) signaling cascade plays a critical role in controlling patterning,
growth and cell migration during embryonic development and inhibition of the Hh
pathway at this stage has been shown to cause cyclopia and other developmental
defects.[1-4] In adult organisms, the Hh pathway is down-regulated significantly and
contributes to the maintenance and regeneration of certain tissues such as skin and
bone. In vertebrates, there are three Hh homologues including Sonic Hedgehog (Shh),
Desert Hedgehog (Dhh) and Indian Hedgehog (Ihh). Typically, the Hh signaling can
be activated when the Hh ligands bind to their receptor Patched (Ptch) directly,
alleviating the inhibition effect of Ptch on Smoothened (Smo), a class F receptor of
the G protein-coupled receptor (GPCR) family. The activated Smo then translocate
from intracellular to the cell membrane, leading to the activation of Hh signaling
transcription factors of the Gli family, which regulates cell proliferation,
differentiation and survival. It was reported that deregulation or hyperactivation of the
Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell
carcinoma (BCC), medulloblastoma, leukemia, rhabdomyosarcoma, lung, breast and
prostate cancers.[2, 5-9] Therefore, inhibition of the aberrant Hh signaling has
emerged as an attractive approach for the treatment and prevention of human cancers.
The first reported Hh signaling pathway inhibitor was cyclopamine (Figure 1),
which was isolated from Veratrum californicum because of its teratogenicity in sheep.
It was later identified as a Smo antagonist.[10, 11] More efforts were made to develop
Smo antagonists and a number of Smo antagonists have entered to advanced clinical
trials successfully.[12-15] Encouragingly, vismodegib (GDC-0449, Figure 1)[16, 17]
developed by Roche/Genentech was approved by the FDA in January 2012 for the
treatment of locally advanced or inoperable metastatic BCC. Moreover, in July 2015,
sonidegib (NVP-LDE225, Figure 1)[18] from Novartis also received FDA approval
for use in treating locally advanced BCC. These approvals suggest that Smo receptor
is an ideal therapeutic target and boosted interest in finding/designing potent and
novel Smo antagonists for treating Hh signaling pathway related diseases. In spite of
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their therapeutic effectiveness, side effects including diarrhea, muscle spasms, weight
loss and tiredness occurred in many patients with the clinical treatment of vismodegib
or sonidegib. Moreover, drug resistance due to Smo mutations or downstream
ligand-independent pathway activation has also been reported by treating with
vismodegib.[19-21] Consequently, there remains ongoing need to explore potent Smo
antagonists with novel scaffolds.
In pursuit of potent Smo antagonists with novel chemical scaffolds, several
optimization strategies, such as “scaffold hopping”, were proposed by our group and a
number of novel chemical scaffolds, including tetrahydroimidazo[1,2-a]pyrazine,[22]
tetrahydrothiazolo[5,4-c]-pyridine,[23] and tetrahydropyrido[4,3-d]pyrimidine,[24]
with satisfactory binding affinities against the Smo receptor were rationally designed.
Other promising Smo antagonists through chemical modifications/optimizations had
also been reported in the past few years (Figure 1).[25-31]
As an important complementary approach to high-throughput screening (HTS),
virtual screening (VS) has received increasing attentions and been widely used for hit
identifications in drug discovery.[32, 33] In 2010, Manetti and co-workers generated
and applied a pharmacophore model based on a set of Smo antagonists with known
antagonistic activities for carrying out ligand-based virtual screening (LBVS) of
commercial libraries. An acylthiourea (MRT-10) was identified and validated as a
potent Smo antagonist with binding affinity in the micromolar range (IC50 = 0.65
µM).[34] Subsequent optimizations led to the identification of more promising Smo
antagonists with increased inhibition potency, MRT-14 (IC50 = 0.16 µM)[34] or novel
scaffold, MRT-83 (IC50 = ~ 0.01 µM).[35, 36] Besides, with the rapid development of
structural biology, several 3D crystal structures of Smo in complex with different
antagonists were resolved successfully by X-ray diffraction in recent years. It has
opened up new avenues for Smo antagonists screening/designing.[37] Based on the
precise knowledge and explicit interaction patterns afforded by the available crystal
structures of Smo in complex with different antagonists, the structure-based virtual
screening (SBVS), especially molecular docking-based VS, can be employed to
obtain potent Smo antagonists. In 2016, based on the crystal complex of the Smo
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receptor (PDB ID: 4JKV), Lacroix et al. identified four novel Smo antagonists with
IC50 values better than 50 µM from the clean lead-like library of ZINC through
DOCK3.6-based virtual screening. One of the most active Smo antagonists was
resilient to the resistance-conferring mutation D473H, from which vismodegib
suffered in patients.[38] Besides, it should be noted that the predictions from
molecular docking based on the different complexes for the same target may differ
greatly because the binding patterns characterized by these different complexes are
varied in the previous studies.[39-45] Comparing the prediction capacities of
docking-based VS by applying different crystal complexes in molecular docking and
selecting the most reliable complexes to screen commercial libraries seems to be a
more reasonable way to identify promising active compounds for a specified target.
To our knowledge, this is the first case to evaluate the prediction capacity of
docking-based virtual screening comprehensively for discovering promising Smo
antagonists. Based on four available Smo crystal complexes, the performances of
Glide docking-based VS were compared using two well-prepared validation datasets
(VD1 and VD2). Two Smo crystal complexes with the best discrimination power were
verified as most reliable docking structures and used to screen the ChemDiv library.
Following by drug-likeness and ADME/T predictions, REOS filtering and structural
clustering, 21 compounds were selected and purchased for experimental testing. Six
compounds exhibited significant inhibitory activity against Hh pathway activation
(IC50 < 10 µM) and the most potent hit (compound 20: 47 nM) showed comparable
inhibitory activity to the positive control compound (vismodegib: 46 nM) in a GRE
(Gli-responsive element) reporter gene assay. Compound 20 was further confirmed to
be a potent Smo antagonist in a fluorescence based competitive binding assay. Then,
based on the scaffold architecture of compound 20, the substructure searching method
was employed to find more promising antagonists of Smo receptor and 12 analogues
of compound 20 were chosen and synthesized for biological testing. All analogues
showed quite acceptable antagonistic activity of Smo receptor (IC50 < 10 µM)
including four most potent analogues with IC50 below 1 µM. Subsequently, the
Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free
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energy calculation and binding free energy decomposition were applied to detect the
differences of antagonistic activity against the Smo receptor for compound 20 and 12
analogues. The favorable and unfavorable residues for ligand binding were clearly
uncovered and the structure-activity relationships (SARs) of 12 analogues of
compound 20 were also discussed.
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2.1. Docking-based virtual screening pipeline
Prior to virtual screening campaign, the performances of molecular docking of four
Smo crystal complexes were evaluated and compared by using SP and XP scoring
modes of Glide. Firstly, the "docking power", which is an important index of docking
reliability for reproducing the experimental binding mode/conformation of
co-crystallized ligand was examined. For each Smo crystal complex, the native
antagonist was extracted from the crystal complex and re-docked into the respective
binding site/pocket. The root-mean-squared-deviation (RMSD) between the docking
pose and experimental conformation was calculated. Generally speaking, reliable
molecular docking can be achieved when the RMSD ≤ 2.0 Å. As shown in Table 1,
the Smo crystal complex (PDB ID: 4N4W) can satisfy the requirement of RMSD and
the RMSD values of co-crystallized antagonists for the remaining Smo crystal
complexes were slight higher than 2.0 Å by using SP or XP scoring functions of Glide.
We aligned the co-crystallized antagonist with the predicted binding conformations
using the SP and XP scoring modes of Glide for 4JKV and 4QIM. The results
demonstrated that the near native co-crystallized antagonists and the most important
interaction patterns of the two Smo crystal complexes could be well reproduced by
Glide (Figure S1 in the Supporting Information). Then, compared with "docking
power", the "discrimination power" of molecular docking, which is the capacity of
distinguishing the known antagonists from presumed non-antagonists of Smo is a
more practical index used in docking-based VS campaign. The significance of the
difference between the means of docking score distributions of known antagonists and
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non-antagonists in VD1 and VD2 was assessed by applying student's t test. For VD1
(Table 1 and Figure 2), the known antagonists and non-antagonists of Smo can be
well distinguished from each other, indicating by quite lower P-values (< 10-50) using
SP or XP modes of Glide. The most reliable "discrimination power" can be obtained
by using SP scoring function based on the 4QIM crystal structure (P-values = 0).
Similarly, the Glide docking can also achieve reliable prediction capacity for VD2 in
terms of the P-values (Table 1 and Figure 3). By applying 4JKV as the docking
complex, the most reliable "discrimination power" can be achieved (P = 2.21×10-162
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by using SP mode of Glide.
According to the performances of docking-based VS based on four Smo crystal
complexes, 4JKV and 4QIM were chosen in the following VS campaign. The scoring
functions including high-throughput virtual screening (HTVS), SP and XP of Glide
docking were employed to carry out the sequential VS strategy. Briefly, the 100000
top-ranked compounds of the prepared ChemDiv library scored by HTVS were saved
and set to Glide docking by using SP mode. Then, the 5000 top-ranked compounds
obtained by using SP scoring function were re-docked and scored using the XP mode.
Finally, 1000 top-ranked compounds for each Smo crystal complex were retained for
the following analysis. By removing duplicates, the remaining compounds from
docking-based VS against 4JKV and 4QIM were evaluated by "Rule-of Five"
proposed by Lipinski[46] and drug-likeness models developed in our group.[47, 48]
The molecules with toxic, reactive, or undesirable functional groups were also
removed by applying rapid elimination of swill (REOS).[49] Then, by filtering the
compounds with more than two chiral centers, the remaining compounds were
structural clustered based on the similarity index (Tanimoto coefficient) calculated
using MACCS structural keys. By setting the cutoff value of Tanimoto coefficient to
0.7, the compounds with the lowest docking scores were selected in each cluster.
Finally, 21 compounds were chosen and purchased from ChemDiv database for
biological testing.
2.2. In vitro biological activity of virtual screening compounds
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To evaluate the Hh signaling inhibition activity of the 21 candidate compounds
predicted by the docking-based VS based on two Smo crystal complexes (4JKV and
4QIM), we used NIH3T3-GRE-Luc reporter gene assay as described in the
experimental protocols section as a screening assay. The results were summarized in
Table 2, we found that 6 compounds (14, 15, 17, 18, 19 and 20) exhibited decent Hh
signaling pathway inhibitory activity, with IC50 < 10 µM. Among them, compound 14
exhibited good inhibitory activity at 950 nM, while compound 20 demonstrated
excellent Hh inhibition activity at IC50 = 47 nM (Figure 4). The IC50 curves for other
five compounds (14, 15, 17, 18 and 19) were depicted in Figure S2 in the Supporting
Information. As a hit from VS, compound 20 was remarkably equally potent at
inhibiting Hh signaling compared with the marketed drug vismodegib (compound 22,
IC50 = 46 nM). As a precaution, we synthesized compound 20 in our laboratory and
tested the synthetic compound in the same screening assay. The results were the same
as the commercial compound, thus validating the results of the VS hit. The compound
20 was further confirmed to be a potent Smo antagonist in the fluorescence based
BODIPY-Cyclopamine competitive binding assay as described in the experimental
protocols section and vismodegib was used as a reference (Figure 5).
The structures for the 6 ligands of the Smo receptor (IC50 < 10 µM) from the VS
are shown in Figure 6 and the remaining studied compounds can be found in Figure
S3 in the Supporting Information. Then, the structures of 6 identified Smo ligands
were compared with the known antagonists of Smo receptor from Binding DB[50] by
using default setting of Find Similar Molecules by Fingerprints mode in DS3.1.[51]
The results illustrated that the 6 identified Smo ligands did not share high similarity
with any known Smo antagonists (Table 2). For the two most potent Smo ligands,
compounds 14 and 20, the pairwise similarities (Tanimoto coefficient) were only 0.36
and 0.29, respectively. In addition, it should be noted that the two most potent Smo
ligands were obtained by applying different Smo crystal complexes in Glide docking
(compound 14 from 4QIM and compound 20 from 4JKV), indicating that evaluating
and comparing the prediction capacity of different crystal structures prior to VS
pipeline is quite necessary. The schematic representation of the predicted interaction
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patterns derived from Glide docking between the Smo and compounds 14 and 20 are
depicted in Figure 7.
2.3. Hit confirmation and Structural-Activity-Relationship discussions.
Based on the scaffold architecture (Murcko framework) of the most potent Smo
antagonist (compound 20) identified from docking-based VS, substructure searching
was applied to screen the whole ChemDiv library. The pairwise similarities (Tanimoto
coefficient) between compound 20 and each compound in the ChemDiv library were
calculated based on the MACCS Structural Keys (Bit packed) fingerprints in MOE.
The pairwise similarities over 85% were saved. Then, according to knowledge-based
experiences, 12 representative analogues of compound 20 were selected and
synthesized for ultimately experimental testing. As can be seen from Table 3, all
analogues showed quite acceptable inhibitory activity (IC50 < 10 µM) against Hh
pathway signaling and four of them with IC50 below 1 µM. Nevertheless, the most
potent analogue (compound 20-2: 58 nM) showed no improved binding affinity
compared with the parent compound (compound 20). Then, for detecting the
differences of antagonistic activities of compound 20 and 12 analogues, all
compounds were docked into the binding pocket of the best Smo crystal complex
(PDB ID: 4JKV) using SP scoring function of Glide docking. As shown in Figure 8a,
the correlation coefficient (r2) between the experimental pIC50 and the docking scores
was only 0.346. The results demonstrated that the predicted docking scores have poor
capacity for ranking the actual experimental antagonistic activity. Thus, the binding
free energy calculation and binding free energy decomposition were utilized to
analyze the interaction patterns between the studied compounds and Smo receptor.
The predicted conformations for compound 20 and 12 analogues interacting with Smo
receptor (PDB ID: 4JKV) from Glide docking were optimized and rescored by using
the MM/GBSA approach. The detailed protocols for the molecular dynamics (MD)
simulation and MM/GBSA binding free energy calculations/decompositions was
described in the Supporting Information. Obviously, the correlation coefficient
between the antagonistic activities and the binding free energies calculated by the
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MM/GBSA can achieved quite satisfied prediction accuracy (r2 = 0.733) (Figure 8b).
Compared with the Glide docking, the MM/GBSA rescoring has better capability to
rank the bioactivities for this series of analogues represented by compound 20.
In order to reveal the key residues for Smo antagonist binding, the total binding
free energies predicted by the MM/GBSA (εin = 1) of compound 20 and three
representative analogues (compounds 20-3, 20-5 and 20-12) were decomposed
quantitatively into individual residue contributions.[52-54] The identified key
residues (favorable or unfavorable for ligand binding) and the comparison of the
antagonist-residues spectra of four compounds were depicted in Figure 9. As shown in
Figure 9a, the most favorable residues for compound 20 interacting with Smo receptor
were Asn219, Val386, Ser387, Tyr394, Arg400 and Phe484, and their contributions to
predicted total binding free energies (∆Gpred) are all lower than -2.0 kcal/mol.
Meanwhile, the residue Glu518 takes the vast majority of the negative contributions
for the compound 20 binding (2.88 kcal/mol). Similar phenomenon can also be
observed for compounds 20-3, 20-5 and 20-12. Next, in order to understand the
effects of different substituents on the antagonistic activity of Smo receptor, the
antagonist-residues spectra of four investigated compounds were compared. By
replacing 2-methylcyclohexan-1-amine with ethylamine in the R3 position, the
antagonistic activity of compound 20-3 (IC50 = 5200 nM) was about 100 times lower
than compound 20 (IC50 = 47 nM). According to Figures 9b and 9c, we found that the
compound 20 and 20-3 share quite similar interactions with Smo receptor. The most
significant differences are mainly caused by the interactions with residues Asn219 and
Phe484. The energy contributions of Asn219 and Phe484 for compound 20 were -2.90
and -2.55 kcal/mol, and those for compound 20-3 were only -2.20 and -1.77 kcal/mol,
respectively. Subsequently, the replacements of 2-methylcyclohexan-1-amine by
cyclopropylamine (20-5) and pyrrolidin-3-ol (20-12) at R3 group of compound 20
decrease the binding affinity significantly. As shown in the antagonist-residues
interaction spectra (Figures 9b, 9d and 9e), the energy contributions of Asn219 for
compounds 20-5 and 20-12 were only -0.96 and –0.67 kcal/mol, playing a dominating
role in the antagonistic activity difference. Based on these observations, keeping
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stable and strong interactions with uncovered favorable residues (Asn219, Val386,
Ser387, Tyr394, Arg400 and Phe484) and avoiding the unfavorable interactions
primary caused by residue Glu518 are the requirements of Smo antagonists for
improving binding affinities. This finding will guide rational-design of more potent
antagonists of Smo receptor.
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In summary, we evaluated and compared the prediction capacities of four available
Smo crystal complexes in Glide docking-based VS for consideration of the inherent
protein flexibility of GPCR targets. Two Smo crystal complexes with the best
discrimination power were selected to screen the ChemDiv database. 21 potential hits
with novel scaffold were submitted to biological activity testing, and six of them
revealed significant inhibitory activity towards Hh signaling pathway activation,
including two compounds with IC50 values below 1 µM (compound 14: 950 nM and
compound 20: 47 nM). The compound 20 was further confirmed to be a potent Smo
antagonist in a fluorescence based competitive binding assay. The VS strategy
presented here may be applied in the drug discovery for targets of interest, especially
for GPCR targets. The novel scaffold afforded by compound 20 can also be used as a
good starting point for developing promising Smo antagonists.
331 4. Virtual Screening Pipeline
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4.1. Preparation of crystal complexes and validation datasets for docking-based
VS
Only five Smo crystal complexes, including 4JKV,[55] 4N4W,[56] 4O9R,[57]
4QIM[56] and 4QIN,[56] have been crystallized and released. The crystal structures
of Smo in complex with different antagonists were retrieved from the RCSB Protein
Data Bank (PDB).[58] Recently, more Smo crystal complexes were reported with the
technology development of structural biology.[59, 60] 4QIN is the crystal complex of
an agonist, SAG1.5, interacting with Smo and thus not considered in this work. The
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aligned structures and detailed interaction patterns of four Smo crystal complexes
were depicted in Figure 10. For each Smo complex, the docking-based VS was
carried out using Glide in Schrodinger 9.0.[61] The Protein Preparation Wizard
module of Schrodinger 9.0 was utilized to remove all crystallographic water
molecules, add missing side chains and hydrogen atoms, assign protonation states and
partial charges with the OPLS2005 force field, and then the minimize procedure of
the whole Smo crystal complex terminated until the RMSD of the non-hydrogen
atoms reached a maximum default value of 0.3 Å.
Similar to our previous reported study,[51] two independent validation datasets
were well-prepared and applied to evaluate the actual prediction capacity of the Glide
docking-based VS of four Smo crystal complexes. The known antagonists of Smo
were retrieved from the BindingDB database.[50] The known antagonists with weak
biological binding affinities (IC50 or Ki > 2 µM) were removed. Considering the
accuracy and efficiency of VS campaign, 300 diverse known antagonists of Smo were
randomly chosen based on the 2D similarity (Tanimoto Coefficient) of the FCFP_6
fingerprints by using the Find Diverse Molecules module in Discovery Studio 3.1.[62]
As reported by Kruger and co-workers, it is an effective way to represent the
compound space of a decoy set by using commercial database, especially for the VS
database is the source of decoy set.[63] Thus, based on the 2D similarity of the
FCFP_6 fingerprints, the presumed non-antagonists of the validation dataset 1 (VD1)
were selected randomly from the ChemDiv database using the Find Diverse
Molecules module in Discovery Studio 3.1.[51] To mimic the unbalanced nature of
the known antagonists versus the non-antagonists of Smo, the ratio of non-antagonists
versus antagonists was set to 100 in VD1. Then, the validation dataset 2 (VD2), which
conforms the rules defined by Cereto-Massague et al., was generated directly by using
DecoyFinder 1.1.[64] For each selected Smo antagonist, 36 decoys were chosen from
the ChemDiv database ensuring the similarity of five physical descriptors (molecular
weight, number of rotatable bonds, total hydrogen bond donors/acceptors and the
octanol-water partition coefficient (logP) but structural dissimilarity evaluated by
MACCS fingerprints (Tanimoto coefficient < 0.75). Finally, the VD1 with 300 known
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antagonists and 30000 non-antagonists and VD2 with 300 known antagonists and
10800 decoys were prepared for the following analysis.
4.2. Molecular docking-based VS procedure
The docking-based VS were carried out by using Glide of Schrodinger 9.0.[51] For
the four Smo crystal complexes, the performances of docking-based VS were
investigated systematically. Firstly, all compounds including known Smo antagonists
and decoys in VD1 and VD2 were processed by using the LigPrep module in
Schrodinger 9.0. The ionized states and tautomers/stereoisomers were generated using
Epik at pH = 7.0 ± 2.0. For the known antagonists from BindingDB with 3D structural
information, the original chiralities were reserved. Considering only 2D structural
information available for the decoys selected from the ChemDiv database, different
combinations of chiralities were generated, and the maximum number of
stereoisomers for each decoy was set to 32. Finally, the number of prepared Smo
antagonists was 300, and the numbers of prepared decoys for VD1 and VD2 were
53408 and 22455, respectively.
Then, by applying the Receptor Grid Generation component of Glide in
Schrodinger 9.0, the binding pocket with the size of 10 Å × 10 Å × 10 Å was detected
and centered on the mass center of the co-crystallized antagonist for each Smo crystal
complex. The other parameters in grid generation were kept as default setting. All
compounds of VD1 and VD2 were docked into the four Smo crystal complexes and
scored by using two scoring functions (SP: Standard Precision and XP: Extra
Precision) embedded in Glide. During the initial phase of the Glide docking
calculation, 5000 poses per compound were generated. Then, the best 400 poses were
selected for the following energy minimization using 100 steps of conjugate-gradient
minimization process with a dielectric constant of 2.0. Finally, the performances of
the Glide docking-based VS based on SP and XP modes of four Smo crystal
complexes were evaluated and compared.
The ChemDiv database comprising more than 1 million compounds was used as
the screening library and all compounds in the ChemDiv database were also
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preprocessed by using the LigPrep mode in Glide. The ionized states and tautomers
were generated at pH = 7.0 ± 2.0 by using Epik. Then, the different combinations of
chiralities were generated and the maximum number of stereoisomers for each
compound was set to 32. The prepared ChemDiv library including more than 2.65
million chemical structures was submitted to the docking-based VS campaign.
4.3. Substructure searching
In order to get more promising antagonists of Smo receptor, the substructure
searching method in Molecular Operating Environment (MOE)[65] was utilized to
find the analogues sharing the similar scaffold architecture (Murcko framework)[66]
of the most potent Smo antagonist identified from Glide docking-based VS. For
compound 20, 12 representative analogues with different functional groups
substitution were selected and synthesized for biological testing.
413
414 5. Experimental protocols
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5.1. Synthesis procedure of the targeted compound 20 to 20-12
The synthesis and characterization of the intermediates and final compounds can be
found in the Supporting Information.
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5.2. NIH3T3-GRE-Luc reporter gene assay
The detailed experimental procedures had been reported before.[23, 24] Briefly,
NIH3T3 cells (CRL-1658, ATCC) were maintained in DMEM (Gibico) supplemented
with 10% FBS (Hyclone). GRE-Luc plasmid was generated by inserting 8x Gli-1
responsive element (GRE) into the multiple cloning site of pGL4.26 vector (Promega).
NIH3T3-GRE-Luc reporter cell line was established by hygromycin (Invitrogen)
selection after transfected with GRE-Luc luciferase reporter plasmid. Single clones
were validated by the induction of luciferase by recombinant sonic hedgehog (sHh)
protein or small molecule agonist SAG (ABIN629346). Selected clone was used to
monitor the Hh signaling.
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The NIH3T3-GRE-Luc cells were maintained in complete culture medium
(DMEM with 4 mM L-Gln, 1.5 g/L sodium bicarbonate and 4.5 g/L glucose
supplemented with 100 µg/mL hygromycin and 10% FBS). When confluent, the cells
were trypsinized and re-suspended in assay medium (0.5% serum-containing DMEM).
After 100 µL/well of cells suspension was added to the 96-well-plate (Final cell
concentration is 15,000 cells/well), cells were cultured for additional 48 hours before
adding the compounds.
Compounds were serially diluted in DMSO and further diluted with assay
medium. In an embodiment, 10 nM SAG was added in assay medium as the agonist
of Hh signaling. After the compounds and agonist were prepared, the medium was
removed carefully. 100 µL of assay medium containing compound and agonist was
added to the cell with care. Cell plates were incubated at 37 °C for additional 48 hours.
Following the 48 hours incubation, 40 µL /well of luciferase media (Brigh-Glo,
Promega) was added to the cells. The plate was incubated at room temperature for 5
minutes under gentle shaking. Luminescence signal was measured with plate reader
(PHERAstar FS, BMG). The IC50 of compounds was calculated based on the
inhibition of luminescence signaling.
5.3. Fluorescence based BODIPY-Cyclopamine competitive binding assay
The detailed experimental procedures had been reported before.[23, 24] Briefly,
U2OS-Smo stable clones were established by puromycin (1 µg/mL, Invitrogen)
selection after transfection with human Smo-HA-pLVX. Plasmid U2OS-Smo cells
were maintained in complete culture medium (DMEM with 4 mM L-Gln, 1.5 g/L
sodium bicarbonate and 4.5 g/L glucose supplemented with 100 ng/mL puromycin
and 10% FBS). The expression of human Smo was validated with western blot and
cell immunofluorescence. BODIPY-Cyclopamine was purchased from Toronto
Research Chemicals and dissolved in methanol.
U2OS-Smo cells were plated in 96-well-plate (#3340, Corning), the final cell
concentration is 10,000 cells/well in 100 µL 10% serum-containing DMEM. The
plates were incubated in 37 °C for additional 48 hours.
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U2OS-Smo cells were fixed with 4% paraformaldehyde (PFA) for 20 minutes at
room temperature. After removing the PFA buffer, the cells were incubated with DAPI
(5 µg/mL) for 10 minutes, followed by twice wash with PBS. After wash, cells were
incubated for 2 h at room temperature in PBS containing 100 nM
BODIPY-cyclopamine and serial diluted compounds for competitive binding. After
incubation, the cells were washed for 3 times with the PBST (PBS buffer supplied
with 0.05% Tween-20). The fluorescence images were automatically captured and
analyzed by a high content fluorescence imaging system (Arrayscan VTI, Thermo).
GDC-0449 was used as reference compound to normalize the data. IC50 values were
calculated with GraphPad Prism software using the sigmoidal dose-response function.
The Ki was calculated following the Cheng-Prusoff equation, as Ki = IC50/[l +
[BODIPY-cyclopamine]/Kd)]. The Kd of BODIPY-cyclopamine for WT-Smo is 255
± 57 nM in our experiments.
473 Supporting Information
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1. Figure S1. (a) The aligned structure of the co-crystallized antagonist with the
predicted binding conformations using the SP and XP scoring modes of Glide for
4JKV; The interaction patterns of (b) crystal structure, (c) the predicted complex
using the SP scoring and (d) the predicted complex using the XP scoring for 4JKV.
1. Figure S2. The IC50 curves of five promising compounds (14, 15, 17, 18 and 19)
with decent Hh signaling pathway inhibitory activity (IC50 < 10 µM).
2. Figure S3. The structures of 15 identified VS hits with IC50 above 10 µM of Smo
receptor.
3. The detailed protocols for the molecular dynamics (MD) simulation and
MM/GBSA binding free energy calculations/decompositions.
4. Synthesis and characterization data.
486 Author Information
487 Corresponding Authors
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*X. Z. E-mail: xiaohuzhang@suda.edu.cn.
*S. T. E-mail: stian@suda.edu.cn
*H. M. E-mail: mahaikuo123@163.com.
491 ORCID
492
Xiaohu Zhang: 0000-0002-0579-1636
493 Author Contributions
494
1W. L. and D. Z. contributed equally to this paper.
495 Notes
496
497
The authors declare no competing financial interest.
498 Acknowledgements
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505
This study was supported by the National Natural Science Foundation of China
(81473090, 81773561, and 81502982), China Postdoctoral Science Foundation
(2016M601884), the Priority Academic Program Development of the Jiangsu Higher
Education Institutes (PAPD) and the Jiangsu Key Laboratory of Translational
Research for Neuropsychiatric Diseases (BM2013003), Natural Science
Foundation of Jiangsu Province (BK20140313).
506 References:
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704
705
706
707
708
709
710
711
712
713
714
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716
717
718
719
720
721
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723
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Figure 1. Representative smoothened antagonists in advanced development.
Figure 2. Distributions of the docking scores of VD1 for the four available Smo
crystal complexes by using SP and XP scoring modes of Glide docking.
Figure 3. Distributions of the docking scores of VD2 for the four available Smo
crystal complexes by using SP and XP scoring modes of Glide docking.
Figure 4. Hh signaling pathway inhibitory activity (IC50) of compound 20 using
NIH3T3-GRE-Luc reporter gene assay.
Figure 5. Inhibition of BODIPY-cyclopamine fluorescence signaling in the
competitive displacement experiment. (a) BODIPY-cyclopamine competition with
vismodegib analysis tested by fluorescent microscope at different concentrations. (b)
BODIPY-cyclopamine competition with compound 20 analysis tested by fluorescent
microscope at different concentrations.
Figure 6. Chemical structures of identified Smo antagonists with IC50 under 10 µM
using docking-based virtual screening.
Figure 7. The predicted conformations and interaction patterns of (a) compound 14 in
the binding pocket applying 4QIM as docking structure and (b) compound 20 in the
binding pocket applying 4JKV as docking structure.
Figure 8. The correlation coefficient (r2) between the biological activities (pIC50) of
13 Smo antagonists (compound 20 and 12 analogues) and (a) the docking scores
predicted using SP mode of Glide docking, (b) the total binding free energies
predicted by the MM/GBSA based on the solute dielectric constant of 1.
Figure 9. (a) The binding pose of compound 20 derived from the MM/GBSA
minimization stage (the favorable and unfavorable residues for antagonist binding are
colored in blue and red, respectively), antagonist-residues interaction spectra of four
representative Smo antagonists: (b) compound 20, (c) compound 20-3, (d) compound
20-5 and (e) compound 20-12.
Figure 10. The detailed interaction patterns of four Smo crystal complexes. The
classical and non-classical hydrogen bonds are colored in green and gray, respectively.
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Table1. The docking power and discrimination power of the Glide docking for the
four Smo-antagonist crystal complexes of VD1 and VD2
Docking power
(RMSD/Å)
Discrimination power(P-value)
VD1 VD2
PDB ID
Ligand
SP
XP
SP
XP
SP
XP
4JKV
4N4W
4O9R
4QIM
LY2940680
SANT1
2.26
1.77
4.11
2.24
2.30
1.93
0.61
2.25
2.13×10-237
1.40×10-84
4.34×10-57
0
1.98×10-128
2.16×10-10
5.77×10-22
6.56×10-70
2.21×10-162
1.02×10-51
1.72×10-40
5.78×10-123
1.16×10-92
5.03×10-24
4.66×10-18
1.58×10-64
Cyclopamine
Anta XV
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
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Table 2. Biological activities, representative molecular properties and key parameters
identified in docking-based VS of the 21 purchased compounds from ChemDiv
database
Compd
ID_numbera
N-G-L IC50
(nM) ± SEMb
>10000
docking
scorec
-12.09
-11.20
-11.19
-10.55
-10.57
-11.03
-11.34
-11.16
-11.17
-10.92
-10.62
-11.01
-11.35
-11.29
-11.03
-12.00
-11.43
-11.48
-11.51
-11.82
-10.77
MWd
logPe logSf similarityg structureh
1
2
3
4
5
6
7
8
C326-0256
8009-2945
5182-3585
K400-10138
C075-0142
K892-0135
F443-0633
V023-8072
K781-9640
G802-0671
G795-0588
C241-2115
8139-0324
C522-1924
G435-0188
K784-7096
V029-7360
F550-3944
V015-8739
C794-1677
V004-1819
Vismodegibi
516.57
485.99
530.00
506.64
485.52
492.52
479.90
473.58
499.58
447.56
470.55
490.58
437.42
465.02
453.59
552.09
457.48
471.56
451.47
492.69
470.50
421.30
4.60
5.97
2.91
4.64
3.87
4.16
5.79
6.05
4.27
4.35
4.93
2.86
3.03
4.82
3.69
3.22
3.88
2.34
4.86
6.08
4.02
4.00
-6.28
-8.85
-7.33
-8.16
-7.42
-6.93
-7.57
-7.82
-7.23
-7.02
-6.37
-5.45
-4.32
-7.08
-6.11
-6.06
-4.82
-5.07
-5.97
-6.36
-7.20
-6.10
0.34
0.52
0.25
0.28
0.26
0.37
0.41
0.27
0.50
0.39
0.38
0.35
0.37
0.36
0.32
0.32
0.45
0.29
0.38
0.29
0.34
4JKV
4JKV
4QIM
4QIM
4QIM
4JKV
4JKV
4JKV
4JKV
4JKV
4QIM
4QIM
4QIM
4QIM
4QIM
4QIM
4JKV
4JKV
4JKV
4JKV
4QIM
>10000
>10000
>10000
>10000
>10000
>10000
>10000
>10000
>10000
>10000
>10000
>10000
9
10
11
12
13
14
15
16
17
18
19
20
21
22
950 ± 450
4600 ± 2800
>10000
2400 ± 1000
3000 ± 600
1200 ± 57
47 ± 15
>10000
46 ± 22
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
aThe compound number labeled in the ChemDiv database. According to the purity
statements, the purity of all compounds purchased from the ChemDiv database is
higher than 95%. bInhibition of luminescence signaling in NIH3T3-GRE-Luc reporter
gene assay (N-G-L) with 10 nM SAG as the Hh pathway agonist. Data are expressed
as geometric mean values of at least two runs ± the standard error measurement
(SEM).cThe predicted binding affinity evaluated by docking score for each compound
employing XP scoring function based on 4JKV or 4QIM crystal complex. dMolecular
e
f
weight. The predicted octanol/water partition coefficient. The predicted aqueous
solubility. gPairwise similarity (Tanimoto coefficient) based on the FCFP_4
fingerprints for each identified antagonist with the most similar known Smo
h
antagonist. Crystal complex of Smo receptor applied in the docking-based VS.
iVismodegib was run as standard in each assay. Data are expressed as geometric mean
values of six runs ± the standard error measurement (SEM).
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Table 3. Biological activities against Smo receptor and chemical structures for the 12
analogues of compound 20
c
Compd
R1
C
R2
C
R3
N-G-L IC50
pIC50 docking ∆Gpred
(nM) ± SEMa
scoreb
47 ± 15
7.33
6.62
-11.82
-9.40
-71.28
20
20-1
N
C
240 ± 69
-68.95
20-2
20-3
20-4
C
C
C
N
C
C
58 ± 3.4
5200 ± 220
1200 ± 22
7.24
5.28
5.92
-11.16
-10.48
-10.62
-71.76
-59.75
-61.71
C
C
C
C
C
C
C
C
C
C
C
C
4200 ± 46
1100 ± 8.5
1700 ± 370
530 ± 47
5.38
5.96
5.77
6.28
6.18
5.37
-10.51
-10.89
-10.78
-10.36
-10.82
-10.05
-57.72
-64.48
-64.80
-68.57
-68.15
20-5
20-6
20-7
20-8
20-9
20-10
660 ± 300
4300 ± 890
-60.71
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C
C
C
C
4300 ± 2200
5.37
5.12
-9.95
-8.95
-65.63
-51.80
7600 ± 49
20-12
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
aInhibition of luminescence signaling in NIH3T3-GRE-Luc reporter gene assay
(N-G-L) with 10 nM SAG as the Hh pathway agonist. Data are expressed as
geometric mean values of at least two runs ± the standard error measurement (SEM).
bThe predicted binding affinity evaluated by docking score for each compound by
employing SP scoring function based on 4JKV crystal complex. The predicted total
binding free energies between each compound and Smo receptor (PDB ID: 4JKV, εin
c
= 1.0)
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Figure 2
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