Life beyond kinases: Structure-based discovery of sorafenib as nanomolar antagonist of 5-HT receptors

Article abstract of DOI:10.1021/jm300338m

Of great interest in recent years has been computationally predicting the novel polypharmacology of drug molecules. Here, we applied an "induced-fita€ protocol to improve the homology models of 5-HT_2A receptor, and we assessed the quality of these models in retrospective virtual screening. Subsequently, we computationally screened the FDA approved drug molecules against the best induced-fit 5-HT_2A models and chose six top scoring hits for experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib, has shown unexpected promiscuous 5-HTRs binding affinities, K_i = 1959, 56, and 417 nM against 5-HT _2A, 5-HT_2B, and 5-HT_2C, respectively. Our preliminary SAR exploration supports the predicted binding mode and further suggests sorafenib to be a novel lead compound for 5HTR ligand discovery. Although it has been well-known that sorafenib produces anticancer effects through targeting multiple kinases, carefully designed experimental studies are desirable to fully understand whether its "off-targeta€ 5-HTR binding activities contribute to its therapeutic efficacy or otherwise undesirable side effects.

Full text of DOI:10.1021/jm300338m

Article
pubs.acs.org/jmc
Life Beyond Kinases: Structure-Based Discovery of Sorafenib as
Nanomolar Antagonist of 5-HT Receptors
Xingyu Lin,†,‡ Xi-Ping Huang,§ Gang Chen,∥ Ryan Whaley,§ Shiming Peng,† Yanli Wang,†
Guoliang Zhang,∥ Simon X. Wang,⊥ Shaohui Wang,∥ Bryan L. Roth,§ and Niu Huang*,†
†National Institute of Biological Sciences, Beijing, No. 7 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
‡College of Life Sciences, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
§Department of Pharmacology and Division of Medicinal Chemistry and Natural Products, The University of North Carolina, Chapel
Hill, North Carolina 27759, United States
∥BeiGene (Beijing) Co., Ltd., No. 30 Science Park Road, Zhongguancun Life Science Park, Beijing 102206, China
⊥Department of Pharmaceutical Sciences, Howard University, Washington, D.C. 20059, United States
S
* Supporting Information
ABSTRACT: Of great interest in recent years has been computationally
predicting the novel polypharmacology of drug molecules. Here, we applied an
“induced-fit” protocol to improve the homology models of 5-HT2A receptor, and
we assessed the quality of these models in retrospective virtual screening.
Subsequently, we computationally screened the FDA approved drug molecules
against the best induced-fit 5-HT2A models and chose six top scoring hits for
experimental assays. Surprisingly, one well-known kinase inhibitor, sorafenib, has
shown unexpected promiscuous 5-HTRs binding affinities, Ki = 1959, 56, and 417
nM against 5-HT2A, 5-HT2B, and 5-HT2C, respectively. Our preliminary SAR
exploration supports the predicted binding mode and further suggests sorafenib to
be a novel lead compound for 5HTR ligand discovery. Although it has been well-
known that sorafenib produces anticancer effects through targeting multiple
kinases, carefully designed experimental studies are desirable to fully understand
whether its “off-target” 5-HTR binding activities contribute to its therapeutic
efficacy or otherwise undesirable side effects.
broad target binding profile.1,10 Similarly, the first “magic
bullet” approved by the FDA for the treatment of chronic
1. INTRODUCTION
Currently, target-based drug discovery is typically defined as
myeloid leukemia, gleevec, was initially developed to specifically
“one compound−one target−one disease” with the idea that
inhibit the abnormal tyrosine kinase BCR-ABL. However, it
deliberately designed single-target drugs may hold the promise
was shown subsequently to target several other kinases
to specifically bind their target with reduced side effects due to
off-target actions. However, single-target drugs often turn out
to be less effective in treating complicated diseases such as
simultaneously, including c-KIT and PDGFR.4,5 Historically,
GPCR ligands and kinase inhibitors have been developed in
quite distinct chemical spaces,11 and the selectivity panel
cancers, metabolic disorders, and central nervous system
(CNS) diseases.1,2 Furthermore, many compounds designated
screening campaign has generally been limited to within the
same protein family members. Thus, as far as we are aware, the
as target-specific drugs are in fact not that selective and
ligand cross-reactivity between GPCR orthosteric ligands and
subsequently have been discovered to bind to other targets with
similar binding affinities.3−5 To better and more efficiently
identify effective compounds that work through either known
or undiscovered mechanisms, of great interest in recent years
kinase inhibitors has not been previously reported.
The 5-hydroxytryptamine receptors (5-HTRs) are composed
of 14 GPCRs in five families (5-HT1, 2, 4, 5, 6, and 7) and one
ligand-gated ion channel (5-HT3). Among 5-HTRs, the 5-HT2A
has been the development of computational methods to predict
the promiscuous binding propensities of drug molecules.6−9
receptor is one of the most studied serotonergic receptors, and
its inhibition is generally associated with antipsychotic and
G protein-coupled receptors (GPCRs) and kinases are two of
antidepressive effects.12 In addition, the 5-HT2A receptor also
the most important drug target families. Many of their ligands
plays a role in thermoregulation, sleep, cardiovascular function,
are well-known to have promiscuous binding propensities
and muscle contraction.13−17 A typical 5-HT2A antagonist
within their own protein families. For example, as one of the
most efficacious atypical antipsychotic drugs discovered half a
century ago, clozapine binds to dozens of GPCRs with nM
Received: January 15, 2012
Published: June 13, 2012
affinity10 and its clinical efficacy is certainly associated with its
© 2012 American Chemical Society
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Figure 1. Chemical structures of ketanserin, cyproheptadine, and sorafenib with activity data of their known primary targets.
incorporate knowledge of “induced-fit” effects associated with varying
5-HT2A antagonists’ scaffolds.36,37 Although 5-HT2A ligands are
structurally quite diverse, the majority of 5-HT2A antagonists belong
to class I and class II antagonists. Specifically, we chose ketanserin as
the representative ligand of the class I antagonist and cyproheptadine
as the class II antagonist, and we applied an induced-fit protocol
(Figure 2) to sample the receptor conformational changes upon
binding ketanserin and cyproheptadine, respectively.
consists of two aryl rings and a positively charged nitrogen
atom (Figure 1) and generally can be divided into class I
antagonists characterized by a basic nitrogen atom in the center
of the molecule and in linear disposition with the aryl rings
(e.g., ketanserin) or class II antagonists with a triangular
arrangement of aryl rings and a basic nitrogen (e.g.,
cyproheptadine).18 Currently, very few 5-HTR ligands are
subtype-selective, and the development of novel 5-HT
antagonists with better specificity is highly desirable. However,
this has been compromised by the lack of experimental
structures of 5-HTRs.
High-resolution crystal structures of GPCRs have been
published in recent years,19 in addition to the pioneering
structures of rhodopsin,20 which greatly facilitates the GPCR
structure−function study and drug discovery.19,21 Much
research is now engaged in using the available structural
information for homology modeling the 3D structures of
GPCRs and subsequently for docking screening and lead
compound optimization purposes.22−29
Here we combine homology modeling, molecular docking,
and molecular dynamics simulation methods to predict the
potential 5-HT2A off-target activity of FDA approved drugs,
which has not been investigated previously. We employed an
“induced-fit” protocol to simulate the receptor conformational
changes upon binding with two representative 5-HT2A
antagonists. We asked whether such induced-fit models can
be used to enrich known ligands from decoy molecules in
retrospective virtual screening. We then asked whether we
could discover potential novel polypharmacology in a
prospective docking screening of FDA drug molecules.
Figure 2. Our step-by-step induced-fit protocol to improve the 5-HT2A
homology model for bound ligands.
The ligand was docked into the modeled 5-HT2A binding-site using
the DOCK 3.5.54 program, a flexible-ligand method that uses a force-
field-based scoring function.38,39 The ligand binding-site residues were
defined as in a consensus aminergic binding-site residue set, which
includes 12 residues on TM3 (3.32, 3.33, 3.37, and 3.40), TM5 (5.42,
5.43, 5.46, and 5.47), TM6 (6.51 and 6.52), and TM7 (7.42 and
7.43).40 We adopted the default parameter settings from an automated
docking platform as described previously41−43 in which all tasks
including sphere generation, scoring grid, and docking calculations are
driven automatically and the same docking protocol was used in the
subsequent docking screenings. At this step, we saved all the docking
poses for further structural analysis.
2. METHODS
5-HT2A Comparative Modeling. The crystal structure (PDB
code: 3D4S)30 of the inverse agonist bound β2-adrenoceptor (β2-AR)
was chosen as template to model the inactive 5-HT2A structure using
the comparative modeling program MODELLER (version 9v7).31 The
β2 receptor has higher homology with the 5-HT2A receptor than with
rhodopsin and has been suggested as a better template for homology
modeling.25 The sequence alignment was retrieved from GPCRDB32
and CDD database33 with the extracellular loop 2 (ECL2) included
and the conserved disulfide bond patched, while four residues at the N
terminal and five residues at the C terminal of the third intracellular
loop were treated as gaps in sequence alignment to avoid the
generation of linkage between transmembrane helix (TM) 5 and TM6
during structure prediction (Figure S1 in Supporting Information).
The best quality model was identified with most residues located in
the favored regions assessed by Ramachandran plot using Maestro
Docking poses of ketanserin and cyproheptadine were filtered by
the 5 Å distance criteria between the positively charged nitrogen atom
of the ligand and negatively charged carboxylate oxygen atom of D3.32
.
The resulting poses were clustered into dissimilar structural groups
using the DBSCAN algorithm44 where the minimum spanning number
was set to 5 or 10 points and a rmsd cutoff value of 1.5 or 2 Å for
cyproheptadine and ketanserin, respectively, was applied individually.
One single representative docking pose was identified from each
structural cluster by choosing the most highly ranked pose that
exhibits a reasonable binding mode in the binding site. Finally, 12
diverse docking poses were selected for ketanserin and four for
cyproheptadine.
We submitted the selected dissimilar docking poses to a MM-GB/
SA refinement and rescoring procedure,45−50 where the side chain of
binding-site residues were sampled along with the docked ligand using
Protein Local Optimization Program (PLOP).51−53 Note that in our
(Schrodinger LLC, New York NY). The ECL2 loop and the third
̈
intracellular loop were deleted after the generation of the homology
model to avoid interference from the less accurately modeled loops to
the subsequent molecular docking and MD simulation. The same
strategy has been applied in other GPCR modeling projects.34,35
Binding-Site Refinement. Instead of docking to the comparative
model directly, we deliberately modified the receptor structure to
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previously published works, the protein was kept rigid during
minimization of the ligand−protein complex; here, we attempted to
sample the side chain conformational changes with the presence of the
docked ligand.28,36,50 The docked complex structure was minimized
first, followed by the side chain prediction of the binding-site residues
within 5 Å of the ligand, and then the ligand was minimized with the
fixed protein structure. The binding-site “induced-fit” complex
structure was utilized as the starting point for further global structure
refinement via molecular dynamics (MD) simulation including explicit
lipid membrane and water environment.
bkslab.org/) server to avoid the selection of structurally similar
compounds of known GPCR ligands.7,63 SEA makes use of the
chemical fingerprints of annotated ligands, calculates the similarity
score between each set of ligands, and ranks the significance of the
similarity scores using a rigorous statistical model.
Experimental Assays. The detailed description of experimental
assays is included in the Supporting Information. Briefly, the
experimental binding assays were performed by the National Institute
of Mental Health’s Psychoactive Drug Screening Program (PDSP)
following the standard protocol. The radiolabeled reference com-
pounds ([3H]8-OH-DPAT for 5-HT1A; [3H]GR127543 for 5-HT1B
and 5-HT1D; [3H]5-HT for 5-HT1E; [3H]Ketanserin for 5-HT2A;
[3H]LSD for 5-HT2B and 5-HT2C, 5-HT5a, 5-HT6, and 5-HT7;
[3H]LY278584 for 5-HT3) are used in Ki determination. The PDSP
online data entry and analysis system calculates the variance of the
quadruplicate determinations (for the total, nonspecific, and test
compound binding values) and variances greater than 20% are flagged
for further inspection and assays are repeated if necessary.
Global “Induced-Fit” via MD Simulation. All molecular
dynamic simulations were performed using the Desmond software
package54 and the OPLS-AA 2005 force field.55 Using the default
Schrodinger protein membrane building protocol, a 10 Å buffered
̈
orthorhombic boundary system was built with a POPC lipid
membrane and SPC water and then neutralized by ions. The default
Schrodinger protein membrane equilibration protocol was applied
̈
before production run. Briefly, each system was minimized using 2000
steps of steepest descent algorithm, followed by L-BGFS algorithm.
Temperature was gradually increased from 0 to 300 K, while 50
kcal·mol−1 ·Å −2 harmonic position restraints were applied to all heavy
atoms of the protein and ligand during system equilibration. The
restraints were gradually removed and the production run was
performed in MTK-NPT (1 bar, 300 K) ensemble for 20 ns. The M-
SHAKE algorithm56 was applied to constrain all bonds involving
hydrogen atoms with a time step of 2 fs. The short-range electrostatic
and Lennard-Jones interactions were cut off at 9 Å. Long-range
electrostatic interactions were computed by the particle mesh Ewald
(PME) method57 using a 64 × 64 × 64 grid with σ equal to 2.18 Å .
Analysis of the MD simulations focused on structural and energetic
properties averaged over the 10 ns production simulation. Structural
analysis was performed using the UCSF Chimera58 and VMD59
programs, including standard root-mean-square differences (RMSDs),
atom contacts, and hydrogen bonding analysis.
3. RESULTS AND DISCUSSION
5-HT2A Induced-Fit Models upon Binding with
Ketanserin and Cyproheptadine. Previous computational
studies have demonstrated that incorporating ligand informa-
tion, binding-site residue mutation data, and molecular
dynamics simulations improves the quality of GPCR structure
prediction and ligand docking.26 It is likely that the receptor
undergoes conformational changes to accommodate different
ligands, and rigid docking against one particular receptor
conformation may be of limited utility in identifying a diverse
set of ligands. The majority of 5-HT2A antagonists belong to
class I and class II antagonists, therefore, we choose ketanserin
and cyproheptadine to represent the typical 5-HT2A antago-
nists. We systematically improved the homology model in the
context of these two ligands, and we assessed the extent of such
ligand-induced conformational differences and revealed further
details of ligand binding. We expect that the binding modes of
class I and class II antagonists are similar to ketanserin and
cyproheptadine, respectively.
Although the position of the orthosteric ligand binding site is
conserved in the aminergic GPCRs, the detailed atomic
interactions with binding site residues vary quite considerably.40
It has been suggested that 5-HT2A ligands may bind into two
different sites.64,65 Site 1 is bordered by TM3, 4, 5, and 6, and
site 2 is flanked by TM1, 2, 3, and 7. The shared region
between site 1 and site 2 includes residues D3.32 and S3.36 on
TM3, and W6.48 and F6.51 on TM6.65 In aminergic GPCRs, the
conserved D3.32 forms a salt bridge with the tertiary amine of
the ligand, which is critical for ligand binding.66 Therefore, we
generated a wide range of docking poses at the initial docking
stage, followed by eliminating the misdocked poses using the
conserved salt-bridge interaction as a criterion. Further
structural clustering significantly reduced the redundant
docking poses and eventually led to 12 dissimilar poses for
ketanserin and four poses for cyproheptadine. Consistent with
previous suggestions, our docking results indicate that
ketanserin adopts extended conformations that allow binding
in both sites, while cyproheptadine mainly binds in site 1
(Figure 3). The binding site refinement procedure did not
introduce large structural perturbation; however, the side chain
prediction within the binding pocket along with the docked
ligand is an effective approach to maximize the interactions
between binding-site residues and docked ligand and thus
provides a physically reasonable complex structure for
subsequent molecular dynamics simulation.
Model Assessment by Retrospective Docking Screening. We
next investigated the ability of our induced-fit models to enrich known
ligands of the 5-HT2A receptor. Forty-three structurally diverse 5-HT2A
antagonists were collected from references, and molecules were
prepared for docking using the latest version of the ZINC protocol.60
Twenty decoy compounds were selected for each ligand from an in-
house screening compound library (170000 compounds) based on the
DUD protocol,41 leading to a total of 774 nonredundant decoys that
were physically similar but topologically dissimilar to the 43 annotated
ligands (both ligands and decoy molecules are available at http://
www.huanglab.org.cn/5-HT2A). Our automatic docking screening
protocol was applied in default setting for each modeled receptor
structure. Enrichment performance represents the prioritization of
ligands among the top ranks of a docking-ordered library. We assessed
the quality of the 20 induced-fit models by the early enrichment of
annotated ligands from a background of decoy molecules..
Prospective Virtual Screening of FDA Drugs. We compiled a
FDA drug library by merging the drug molecules from DrugBank
(version 2.0)61 and ZINC FDA drug subset (version 2005)60 with
excluding the molecules with molecular weight larger than 600 or
smaller than 100 Da. A total of 1430 unique molecules were screened
against two receptor models by applying our automatic docking and
MM-GB/SA rescoring protocol, individually.47−49 Note that only a
single docking pose with the best total docking energy score was
rescored for each molecule entry to reduce the computation cost;
ultimately, we will test the docking screening capacity to rescore
multiple docking poses by including the receptor binding-site
flexibility. We saved the top 200 hits from MM-GB/SA scoring
method for further structural analysis and visual check.
To simulate the stringent scenario with which to discover potential
novel polypharmacology, we excluded the top scoring molecules with
any potential GPCR-related activities such as ligands of monoamine
GPCRs, monoamine transporters, and opioid receptors. All the activity
data was retrieved from publicly available resources, including
ChEMBL database62 and DrugBank.61 In addition, we also submitted
identified hits to the Similarity Ensemble Approach (SEA, http://sea.
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energetic calculation results while the rest of the systems do not
agree with at least one of the descriptors (Figure 4); the
detailed analysis are also summarized in the Supporting
Information (Table S1).
Figure 3. Overlapped diverse representative docking poses of
ketanserin and cyproheptadine in the binding site of the initial
homology model of 5-HT2A. Cyproheptadine (carbon atoms colored
cyan) mainly binds in site 1, mainly bordered by TM3, 5, and 6, while
ketanserin (colored green) adopts extended conformations that allow
binding both sites.
Figure 4. The structural descriptors used to assess the simulation
quality of ketanserin complex systems. The Y axis is the measured
probability of forming a specific interaction during 10 ns production
simulation. The formation of D3.32−ligand salt-bridge interaction is
defined as the distance between carboxylate oxygen atom of D3.32 and
the piperidine nitrogen atom of ligand less than 3.5 Å, the hydrogen
bond between ligand and hydroxyl of residue S3.36 is defined by the
distance of donor and acceptor less than 3.5 Å and angle greater than
120° (same for D3.32−Y7.43 hydrogen bond), the ratio of F6.52−
ligand contacts is defined by the number of atom pairs within vdW
contact distance between ligand and F6.52 divided by the number of
atom pairs between ligand and F6.51 (same for W6.48−ligand
contacts), and the R3.50−E6.30 ionic lock is defined by distance less
than 4 Å between CZ atom of R3.50 and CD atom of E6.30.
On the basis of docking and refinement results alone, it is
difficult to determine the correct binding orientation for
ketanserin. The anchoring interaction is the salt bridge between
the piperidine basic nitrogen of the ligand with carboxylate
group of D3.32; however, it appears reasonable that either the p-
fluorobenzoyl ring or quinazolinedione moiety binds in site 1
(Figure 3). Therefore, this limitation stimulated the develop-
ment of the improved protein−ligand complex models in a
more realistic treatment using the unbiased molecular dynamics
simulation approach. We expect that multiple independent
simulations can significantly increase the sampling of the
complex structure, and the near-native system will be the stable
system with favorable interactions between ligand and receptor
and satisfies the experimental evidence like site-directed
mutagenesis data.
In the Ket-6 system (Figure 5A), the p-fluorobenzoyl moiety
binds in site 1, forming favorable hydrophobic interaction with
F6.51 but relatively fewer contacts with W6.48 and F6.52. The
quinazolinedione group binds in site 2, establishing aromatic
stacking interaction with W3.28 without forming any stable
hydrogen bonds in the binding site; the positively charged
piperidine nitrogen forms a strong salt-bridge interaction with
D3.32 throughout the entire simulation, and S3.36 only interacts
transiently with the carbonyl group of the p-fluorobenzoyl ring.
Nevertheless, the last 10 ns simulation trajectory in the Ket-6
simulation was clustered, a representative structure from each
of the 10 largest conformational ensembles was selected, and
resulted in a total of 10 representative model structures for
docking evaluation.
Because of its relatively rigid structure, determination of the
binding mode of cyproheptadine is less uncertain. We still
assess the simulation quality using the conserved salt-bridge
interaction between the positively charged nitrogen of the
ligand and D3.32, as well as the ionic lock between R3.50 and E6.30
and the hydrogen bond between D3.32 and Y7.43. Only one
simulation system (designated as Cyp-4) satisfies the structural
requirements (data not shown) and additionally exhibits more
favorable hydrophobic interaction between the bound ligand
and binding site residues (Figure 5B). Cyproheptadine mainly
binds in site 1 deeply, forming strong hydrophobic interaction
with V3.33, F5.38, W6.48, F6.51, and F6.52 while maintaining its
critical ionic interaction with D3.32. Similarly, we clustered the
last 10 ns simulation trajectory in the Cyp-4 simulation and
In addition to the critical residue D3.32, the binding site
residues important for ketanserin binding have been extensively
studied by mutagenesis experiments. The F6.51L mutant
decreases ketanserin binding by at least 800-fold, and the
W6.48A mutant decreases only 7-fold, while S3.36A/C and F6.52L
mutations were found to have almost no effect on ketanserin
binding affinity.67−70 Structure−activity relationship (SAR)
studies on ketanserin analogues have been shown that the
hydrogen bonding capability of the quinazolinedione ring has
only minor contributes to the binding affinity.71 Furthermore, it
is suggested that an ionic lock (R3.50 And E6.30) forms in
aminergic GPCRs to stabilize the receptor in an inactive
conformation.20 Also, conserved residue Y7.43 forms a stable
hydrogen bond with D3.32 in all known GPCR crystal
structures. Therefore, we defined six structural descriptors to
assess the simulation quality of each ketanserin system during
the last 10 ns simulation, including the formation of salt-bridge
interaction between ternary amine of ligand and conserved
residue D3.32, the absence of hydrogen bond between ligand
and hydroxyl of residue S3.36, larger ratio of vdw contacts
between ligand and Phe6.51 in comparison to Trp6.48 and Phe6.52
,
the formation of conserved ionic lock between R3.50 and E6.30
residues, and the presence of a stable hydrogen bond between
Y7.43 and D3.32. We also compared the average MM-GBSA
binding energies of different systems. A single simulation
system (designated as Ket-6) was eventually chosen on the
basis of satisfying all these available experimental evidence and
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Figure 5. Conformational changes upon binding ligands ketanserin (A) and cyproheptadine (B). Induced-fit model (stick, carbon atoms colored
yellow) is superimposed on the initial homology model (thin line, carbon atoms colored gray), highlighting the binding-site conformational changes
in molecular dynamics simulation. The transmembrane helixes in the initial homology model are shown in ribbon representation and are omitted in
the induced-fit models for clarity purpose. Carbon atoms of ligands are colored in green. The salt-bridge interaction between the tertiary amine of the
ligand and the conserved D3.32 is illustrated with orange lines. Molecular images were generated with UCSF Chimera.
Table 1. Enrichments of the 43 5-HT2A Ligands among a Background of 774 “DUD” Style Decoy Molecules by Docking against
a
Ketanserin and Cyproheptadine Induced-Fit Models
Ket-models
1
2
3
4
5
6
7
8
9
10
EF1
EF5
2.3
0.5
0.0
0.5
0.0
0.5
0.0
0.0
0.0
0.0
4.6
1.4
4.6
2.3
0.0
0.9
2.3
0.9
0.0
0.9
Cyp-models
1
2
3
4
5
6
7
8
9
10
EF1
EF5
2.3
1.4
0.0
0.9
4.6
1.9
4.6
3.3
1.0
1.0
2.3
2.3
0.0
2.3
2.3
3.3
4.6
2.3
2.3
1.9
a
EF1 (enrichment factor at 1% of the ranked database) and EF5 (enrichment factor at 5% of the database) present the early enrichment performance.
Figure 6. The enrichment profile of percentage of ligands found (y-axis) plotted as a function of the percentage of the ranked docked database (x-
axis in logarithmic scale).
selected a representative structure from each of the 10 largest
clusters for docking evaluation.
indicating that different receptor conformations may be
required for extensive virtual screening studies, which is exactly
the case in our study. Thus, we further extracted two subsets of
antagonists as ketanserin-like set and cyproheptadine-like set on
the basis of structural similarity (Table S2 in Supporting
Information), and we expected that the ketanserin-like ligands
should be better enriched by the corresponding ketanserin
induced-fit models, and similarly in cyproheptadine cases.
Indeed, the early enrichment was significantly improved for the
same group of ligands against the corresponding induced-fit
models (Figure 6). Thus, 16.7% and 25% of the ketanserin-like
ligands can be found in the top 1% and 5% of the ranked
database by docking against Ket-6−7 model, respectively,
corresponding to enrichment factors of 16.7 and 5. A
significantly better enrichment occurs when docking against
Assessing Induced-Fit Models by Retrospective
Docking Screening. We next investigate the docking
enrichment performance of a total of 20 5-HT2A induced-fit
models. The early enrichment results are presented using EF1
(enrichment factor at 1% of the ranked database) and EF5
(enrichment factor at 5% of the ranked database) (Table 1).
The best early enrichment performances are achieved for the
seventh representative structure from the Ket-6 simulation
(designated as Ket-6−7) with EF1 of 4.6 and EF5 of 2.3, and for
the fourth representative structure from the Cyp-4 simulation
(designated as Cyp-4−4) with EF1 of 4.6 and EF5 of 3.3,
respectively. During the docking screening, it was frequently
observed that one class of ligand was favored over others,
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Cyp-4−4 model, as 25% and 58.3% of the cyproheptadine-like
ligands are found in the top 1% and 5% of the docking ranked
database, corresponding to enrichment factors of 25 and 11.7.
We were also interested in comparing the docking
enrichment performance of two induced-fit models to the
initial comparative model and to a previously published 5-HT2A
induced-fit model structure.28 Clearly, enrichments are much
better in docking screening against our induced-fit models than
the original homology model and one published model using
exactly the same group of ligands and decoy molecules (Figure
6). In addition, we visually checked the docking poses of these
ligands based on the assumption that the binding modes of
class I and class II antagonists shall be similar to ketanserin and
cyproheptadine, respectively. Our results (Supporting Informa-
tion Table S2) demonstrate that the ligand docked to its
corresponding induced-fit model typically superimposes well
with its reference molecule (91% success rate for cyprohepta-
dine-like ligands and 73% for ketanserin-like ligands), while the
binding orientation by docking to the initial homology model is
frequently incorrect (only 45% and 36% of success rate,
correspondingly). It was encouraging that our induced-fit
models are reliable for typical 5-HT2A antagonist binding
geometry prediction and enrichment studies, and we are
confident that the same induced-fit protocol can be applied to
model 5-HT2A atypical antagonist bound conformations.
Nevertheless, the Ket-6−7 and Cyp-4−4 models, each
corresponding to one class of 5-HT2A ligands, were chosen
for subsequent docking screening of FDA drug molecules. The
structural coordinates of both induced-fit models are freely
available online (http://www.huanglab.org.cn/5-HT2A).
Prospective Virtual Screening of FDA Drugs and
Experimental Validation. We then docked FDA drug
molecules against both modeled 5-HT2A structures and checked
the top 200 compounds based on MM-GB/SA energy scores.
For the present study, we mainly focused on analyzing and
testing the docking results from Cyp-4−4 model due to its
better enrichment performance and pose fidelity prediction in
our retrospective virtual screening. First, we filtered out
compounds without forming favorable hydrogen bonding
interaction with residue Asp3.32, resulting in 99 remaining
molecules. Unsurprisingly, 73 molecules among these 99
compounds belong to annotated ligands of monoaminergic
GPCRs, opioid GPCRs, or their corresponding membrane
transporters (Supporting Information Table S3) such as
phentolamine (ranking 10, α adrenergic receptor blocker),
mesoridazine (ranking 14, 5-HT2A and D2 receptor antagonist),
and epinastine (ranking 27, histamine receptor antagonist). The
aminergic GPCRs share the same or similar cognate ligands
such as serotonin, dopamine, and epinephrine, and drugs
targeting these receptors display broad cross-reactivity. There-
fore, we eliminated all these 73 monoamine drugs related to
GPCR receptors, and we were interested in discovering
unexpected cross-reactivity between completely unrelated
protein targets regarding the sequence, functional, and
structural similarity. Finally, six drugs (Supporting Information
Table S4) were selected based on commercial availability and
submitted to radiolabel competitive binding assay. Among
them, one kinase drug, sorafenib (Figure 1), ranks 85 in the
original score list, 37 after structural filtering and 9 after activity
annotation check.
vendors had verified the compound purity >95% by liquid
chromatography−mass spectrometry (LC-MS) or nuclear
magnetic resonance (NMR) experiments. The 1H NMR
spectrum and LC-MS data for sorafenib are included in
Supporting Information Figure S2 to further validate its
structure and purity. The primary screening results indicate
that two compounds show a radiolabeled ligand replacement
ratio larger than 20% at 10 μM concentration and sorafenib
exhibits 88% inhibition. Subsequent secondary dose−response
experiments indicate that sorafenib binds to 5-HT2A with Ki
value of 1959 nM (Figure 7). The cellular functional assay
Figure 7. Radioligand competition binding assays of sorafenib. Ki value
is calculated as: Ki = IC50/(1 + L/Kd) (Cheng−Prusoff equation), in
which [L] = the radioligand concentration used in the binding assay
and Kd is the affinity of radioligand at corresponding receptor. [3H]-
Ketanserin was used for 5-HT2A binding; [3H]-LSD for 5-HT2B and 5-
HT2C binding. For 5-HT2A, [L] = 3.54 nM and Kd = 2.2 nM; for 5-
HT2B, [L] = 2 nM and Kd = 0.5 nM; for 5-HT2C, [L] = 2 nM and Kd =
0.6 nM.
validated sorafenib as a 5-HT2A antagonist with 93.3 1.4% of
inhibition activity at 50 μM concentration. Remarkably, further
5-HTR profiling results suggest that sorafenib is a promiscuous
5-HTR ligand (Table 2), strongly binds to 5-HT2B and 5-HT2C
a
Table 2. The 5-HTRs Binding Profile of Sorafenib
receptor
5-
HT1A
5-
HT2A
5-
HT2B
5-
HT2C
5-
HT5a 5-HT6 5-HT7
Ki (nM)
1181
1959
56.0
417.0
3296
6213
7071
a
Note that data represent Ki (nM) values obtained from nonlinear
regression of radioligand competition binding isotherms. Ki values
were calculated from best fit IC50 values using the Cheng−Prusoff
equation.
with Ki values of 56 and 417 nM (Figure 7), and weakly binds
to other five 5-HTRs including 5-HT1A, 5-HT2A, 5-HT5a, 5-
HT6, and 5-HT7, while it does not bind to 5-HT1B, 5-HT1E, 5-
HF1F, and 5-HT3. Although at the current stage, it is not clearly
whether sorafenib binds to other monoaminergic GPCRs, but it
is highly likely to do so considering the ligand promiscuity
among the monoaminergic GPCR family.
5-HT2A−Sorafenib Binding Mode. Here we examine in
more depth the docked complex structure of sorafenib, focusing
on its chemical composition and binding mode (Figure 8A).
Compared to the predicted ketanserin and cyproheptadine
Sorafenib was purchased from LC Laboratories (Woburn,
MA), and the remaining five drug compounds were purchased
from Sigma-Aldrich (Supporting Information Table S4). The
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Figure 8. The binding mode of sorafenib in the modeled 5-HT2A−sorafenib complex structure and the BRAF−sorafenib crystal complex structure.
Carbon atoms of sorafenib are colored in yellow. The hydrogen bond interactions between the urea group of the sorafenib and the conserved D3.32
in 5-HT2A (A) or the catalytic residue E500 in BRAF (B) are illustrated with orange lines. Molecular images were generated with UCSF Chimera.
Figure 9. Chemical structures of designed sorafenib analogues (A) and their corresponding radioligand competition binding assay results (B). Note
that the measured Ki value of sorafenib in this binding assay is 115.1 nM, different from our reported value of 56 nM. It is due to a new batch of 5-
HT2B pellets used in this new assay, where the Kd value of radioligand [3H]-LSD is 0.97 nM, and 0.5 nM for the previously used batch. Thus, the
measured sorafenib binding affinity values are consistent in both experiments.
binding modes, sorafenib has very different binding character-
istics regarding its numerous polar interactions with binding
site residues. Its hydrophobic trifluoromethylphenyl unit is
buried in the hydrophobic site 1 and might contribute largely to
ligand binding. Remarkably, it does not contain a positively
charged nitrogen atom; instead, it forms strong hydrogen
bonds between amide nitrogen atoms of its urea moiety to the
carboxylate group of D3.32. Other polar interactions include
hydrogen bonds between its methyl amide oxygen atom to the
hydroxyl group of T2.64 and amide nitrogen atom of Asn7.36, also
its methyl amide nitrogen donates a hydrogen bond to the main
chain carbonyl of L7.35. It is interesting that the amide nitrogen
atoms of the urea moiety form exactly the same interaction with
the carboxylate group of the residue E500 in sorafenib−kinase
crystal complex structures.72 The binding details of sorafenib
with BRAF kinase (PDB code: 1UWH) are illustrated in Figure
8B. It is not likely that the lack of a positively charged group
largely reduces the binding affinity between sorafenib and 5-
HT2A, as its binding to 5-HT2B is at low nanomolar range,
where this selectivity may be caused by the other variable
binding-site residues like the 5.46 position residue (a serine in
5-HT2A, while an alanine in 5-HT2B and 5-HT2C) just as the
ergoline compounds.70 This is consistent with a recent report
where Ladduwahetty and co-workers discovered novel 5-HT2A
receptor antagonists without containing any positively charged
groups.73 Nevertheless, it is likely that sorafenib can be used as
a novel 5-HT2A lead compound for further structural
optimization with maintaining the biaryl urea structural moiety.
As the ligand similarity-based SEA method has been
successfully applied in identifying GPCR related off-targets,
the receptor structure-based docking method may become a
complementary approach for GPCR drug off-target discovery
when the receptor structure is available or can be reliably
modeled. Nevertheless, well designed experimental mutagenesis
studies and the development of more accurate 5-HT2A−
sorafenib structure models are desirable for further inves-
tigation of the binding details at the atomic level.
As a proof-of-concept study, we designed series of sorafenib
analogues to assess the predicted binding mode; two of them
(Figure 9A) have been synthesized and evaluated against 5-
HT2B receptor (Figure 9B). The chemical synthesis route and
analysis data are reported in Supporting Information. The
replacement of aromatic nitrogen atom to carbon atom in
compound HN01, leads to slight improvement of binding,
which indicates that the aromatic nitro atom does not form
direct polar interaction with receptor. The addition of methyl
group on amide nitrogen atom in compound HN02 removes its
potential hydrogen bond to the main chain carbonyl of L7.35
and leads to 8-fold loss of binding. Both modifications strongly
support the predicted binding mode of sorafenib. The complete
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SAR exploration on sorafenib will be pursued and published at
somewhere else.
binding affinity to 5-HT2A, and subsequent 5-HTR profiling
results indicate its promiscuous 5-HTRs inhibition activities.
Whether or not the off-target inhibition of 5-HTRs by sorafenib
has any clinical relevance has yet to be determined. However, it
is desirable to dissect the contributions of kinase inhibitions
and 5-HTR antagonist activities in sorafenib-produced
anticancer effects. Ultimately, we can also envision a strategy
to virtual screening GPCR ligands against therapeutically
relevant kinases, and ask whether we could discover known
GPCR ligands with unexpected kinase activities.
Interestingly, the structural characteristics of sorafenib are
distinct to classic 5-HT2A antagonists, especially considering the
lack of the tertiary amine to form the salt-bridge interaction
with the critical binding-site residue D3.32. Instead, sorafenib
may form strong hydrogen bonds between amide nitrogen
atoms of its urea moiety to carboxylate group of D3.32, and it
may also form additional hydrogen bonds between its methyl
amide with binding site residues. Nevertheless, the biaryl urea
moiety may suggest new direction for developing novel 5-HTR
ligands.
New Clinical Implication of Sorafenib. Sorafenib is well-
known to produce anticancer effects through targeting multiple
kinases. Sorafenib was originally developed as a RAF-kinase
inhibitor (52 nM) but subsequently has been shown to be a
multikinase inhibitor that also inhibits PDGFRβ (37 nM),
VEGFR2 (59 nM), VEGFR3 (16 nM), c-Kit (31 nM), and
FLT1 (31 nM).74 5-HT2B is highly expressed in the liver,
kidneys, stomach, and gut.12 Considering that the 5-HT2B
binding affinity of sorafenib is in the same therapeutic window
as its kinase inhibition activities, one may hypothesize that the
5-HT2B inhibition might directly contribute to the anticancer
effect of sorafenib; in this regard, we have previously suggested
that 5-HT2B antagonists might be of special benefit for
carcinoid tumors and sorafenib might represent a novel
treatment for this disorder.75 Nevertheless, recent studies
have suggested that 5-HT receptors may be involved in the cell
viability and cell cycle progression in certain cancers, especially
for liver cancer and carcinoid-like tumors.76−78 Sorafenib was
approved to treat advanced renal cell carcinoma (RCC) and
hepatocellular carcinoma (HCC), and intriguingly, 5-HT was
suggested to promote cell survival and growth of HCC cells by
activation of the 5-HT2B receptor.78 However, we cannot
exclude the possibility that the 5-HTR activities of sorafenib
might also cause side effects instead of bringing clinical benefits
in certain circumstances. Although it is well beyond the scope
of our current study, it is desirable to dissect the contributions
of kinase inhibitions and 5-HTR antagonist activities in
sorafenib-produced anticancer effect; as such information may
facilitate clinical usage of sorafenib as well as designing new
drugs with better anticancer efficacy and fewer side effects.
ASSOCIATED CONTENT
* Supporting Information
■
S
The sequence alignment between 5-HT2A and its template β-2
adrenoceptor, the structural descriptors used to evaluate the
ketanserin complex system, the chemical structure and docking
pose prediction assessment of ketanserin-like and cyprohepta-
dine-like ligands, the ranks and annotated activities of top
scored docking hits, the structures and experimental binding
data of six FDA approved drugs, the detailed experimental assay
protocols, and the chemical synthesis route for sorafenib
analogues and their corresponding analysis data. This material
is available free of charge via the Internet at http://pubs.acs.org.
4. CONCLUSION
AUTHOR INFORMATION
Corresponding Author
*Phone: 86-10-80720645. Fax: 86-10-80720813. E-mail:
huangniu@nibs.ac.cn.
Drug profiling campaigns have revealed novel polypharmacol-
ogy of existing drugs. It is critical to fully understand the target
binding profile of a drug molecule, as its potential off-target
binding properties may lead to better clinical efficacy in certain
circumstances while causing side effects in other cases. GPCRs
and kinases are two of the most important drug target families,
and many of their ligands have been discovered to have
promiscuous binding propensities within their own protein
families. However, as far as we are aware, the ligand cross-
reactivity between GPCR orthosteric ligand and kinase
inhibitor has not been previously reported.
To predict novel polypharmacology, we computationally
screened the FDA approved drug molecules against the
induced-fit models of the 5-HT2A receptor. We employed a
comprehensive “induced-fit” protocol to simulate the receptor
conformational changes upon binding with two representative
5-HT2A antagonists, where different computational techniques
were integrated systematically, including homology modeling,
molecular docking, side chain prediction, and molecular
dynamics in explicit membrane and solvent conditions. The
multiple independent simulations with the presence of different
ligand docking poses lead to the best quality structural models
which satisfy the available experimental evidence and achieve
the best docking performance by enriching the known ligands
from decoy molecules in retrospective virtual screening. Such
identified induced-fit models were used in docking screening of
FDA drug molecules, with a total of six drug molecules chosen
for experimental binding assay. Surprisingly, a well-known
multikinase inhibitor, sorafenib has shown relatively strong
■
Notes
The authors declare no competing financial interest.
ACKNOWLEDGMENTS
■
We thank the reviewers for their constructive comments for
improving the manuscript. Financial support from the Chinese
Ministry of Science and Technology “973” grant
2011CB812402 (to N.H.) is gratefully acknowledged. B.L.R.,
R.W., and X-P.H. were supported by RO1MH82441 and the
NIMH Psychoactive Drug Screening Program (PDSP).
Computational support was provided by the Supercomputing
Center of Chinese Academy of Sciences (SCCAS) and the
Beijing Computing Center (BCC). We also thank Dr. Pearl
Huang at BeiGene LTD for reading the manuscript and advice
and Dr. Andrew Christofferson for proofreading.
ABBREVIATIONS USED
■
CNS, central nervous system; GPCRs, G protein-coupled
receptors; 5-HTRs, 5-hydroxytryptamine receptors; β2-AR, β2-
adrenoceptor; ECL2, extracellular loop 2; TM, transmembrane
helix; PLOP, Protein Local Optimization Program; MD,
molecular dynamics; PME, particle mesh Ewald; RMSDs,
root-mean-square differences; SEA, similarity ensemble ap-
proach; PDSP, Psychoactive Drug Screening Program; SAR,
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structure−activity relationship; EF, enrichment factor; LC-MS,
liquid chromatography−mass spectrometry; NMR, nuclear
magnetic resonance; RCC, advanced renal cell carcinoma;
HCC, hepatocellular carcinoma
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