Fragment-based drug design and drug repositioning using multiple ligand simultaneous docking (MLSD): Identifying celecoxib and template compounds as novel inhibitors of signal transducer and activator of transcription 3 (STAT3)

Article abstract of DOI:10.1021/jm101330h

We describe a novel method of drug discovery using MLSD and drug repositioning, with cancer target STAT3 being used as a test case. Multiple drug scaffolds were simultaneously docked into hot spots of STAT3 by MLSD, followed by tethering to generate virtual template compounds. Similarity search of virtual hits on drug database identified celecoxib as a novel inhibitor of STAT3. Furthermore, we designed two novel lead inhibitors based on one of the lead templates and celecoxib.

Full text of DOI:10.1021/jm101330h

BRIEF ARTICLE
pubs.acs.org/jmc
Fragment-Based Drug Design and Drug Repositioning Using Multiple
Ligand Simultaneous Docking (MLSD): Identifying Celecoxib and
Template Compounds as Novel Inhibitors of Signal Transducer and
Activator of Transcription 3 (STAT3)
||
Huameng Li,† Aiguo Liu,§, Zhenjiang Zhao,^ Yufang Xu,^ Jiayuh Lin,§ David Jou,§ and Chenglong Li*,†,‡
†Biophysics Graduate Program, The Ohio State University, Columbus, Ohio 43210, United States
‡Division of Medicinal Chemistry & Pharmacognosy, College of Pharmacy, The Ohio State University, Columbus, Ohio 43210, United States
§Center for Childhood Cancer, Department of Pediatrics, College of Medicine, The Research Institute at Nationwide Children’s
Hospital, Columbus, Ohio 43205, United States
Department of Pediatrics, Tongji Hospital, Huazhong University of Science & Technology, Wuhan 430030, China
^State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of Chemical Biology, School of Pharmacy,
East China University of Science and Technology, Shanghai 200237, China
S Supporting Information
b
ABSTRACT: We describe a novel method of drug discovery using MLSD and drug repositioning, with cancer target STAT3 being
used as a test case. Multiple drug scaffolds were simultaneously docked into hot spots of STAT3 by MLSD, followed by tethering to
generate virtual template compounds. Similarity search of virtual hits on drug database identified celecoxib as a novel inhibitor of
STAT3. Furthermore, we designed two novel lead inhibitors based on one of the lead templates and celecoxib.
’ INTRODUCTION
drug scaffolds would help to generate lead compounds with
improved ADMET properties.
New drug development still presents grand challenges. Con-
ventional high throughput screening (HTS) drug discovery
approach identifies many hits, but few of them can be developed
into drugs. Currently, there are less than 1500 FDA-approved
drugs. Poor efficacy and safety of hits are the major attritions of
drug development. It is suggested that poor drug space, low
structural diversity, and poor drug ADMET properties of com-
pounds in HTS libraries may contribute to false positives and
negatives. Over the past decade, fragment-based drug design
(FBDD) has emerged as a successful alternative to drug dis-
covery using biophysical methods like NMR and X-ray crystal-
lography. For computational FBDD, conventional single fragment
docking has problems of nonspecific binding and poor ranking
power due to weak binding of small fragments. Recently, we have
developed multiple ligand simultaneous docking (MLSD) to
simulate the interplay of multiple molecules binding to the
protein binding site(s).1 In a test case, MLSD identified the
correct binding modes of multiple fragments of drug lead 4-[4-
[(40-chloro[1,10-biphenyl]-2-yl)methyl]-1-piperazinyl]-N-[[4-
[[(1R)-3-(dimethylamino)-1-[(phenylthio)methyl]propyl]amino]-
3-nitrophenyl]sulfonyl]benzamide (ABT-737)1 in the respective
subpockets of the binding groove of cancer target Bcl-xL, whereas
single-fragment docking failed to do so because of energetic and
dynamic coupling among the fragments.2 The results suggest
potential applications of MLSD to improve fragment-based docking
screening. On the other hand, to reuse existing drugs for new targets,
a drug repositioning concept has been proposed recently.3
Previous analysis revealed that more than 30% of drugs share
building blocks.4 We hypothesize that FBDD using privileged
Tomeet the challenge of drug discovery, we present here a novel
approach for drug lead discovery using MLSD, drug scaffolds, and
drug repositioning. Cancer target signal transducer and activator of
transcription 3 (STAT3), an oncogene being constitutively acti-
vated in numerous cancers, was used as a test case in our study.5ꢀ7
Currently there is no report of an approved drug to target STAT3,
although a number of small molecule inhibitors of STAT3 have
been discovered via HTS and virtual docking.8ꢀ15 Figure 1 shows
our drug discovery methodology. It proceeds as follows: (1) A
small library of drug scaffolds is identified for the binding hot spots
of STAT3 SH2 domain. (2) MLSD screening of the privileged drug
scaffolds is then performed to identify optimal fragment combination-
(s). (3) Linking of the fragment hits generates possible hit
compounds as templates. (4) Similarity search of template
compounds in drug databases identifies existing drugs as possible
inhibitors of the protein target of interest.
’ RESULTS AND DISCUSSION
Identifying Privileged Drug Scaffolds for STAT3. It has
been reported that the STAT3 pathway is activated upon the
phosphorylation of tyrosine 705, followed by dimerization, nuclear
translocation, and DNA binding. The druggable binding cleft of
the STAT3 SH2 domain (PDB code 1BG1) consists of three
subpockets: pTyr705 (pY705) binding site, Leu706 binding site
(L706), and a side pocket (Ile597, Leu607, Thr622, and Ile634).
Received: October 14, 2010
Published: June 16, 2011
r
2011 American Chemical Society
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Journal of Medicinal Chemistry
BRIEF ARTICLE
Figure 1. Scheme of drug discovery using MLSD and drug repositioning.
Figure 3. Scheme of MLSD screening of drug scaffolds for binding hot
spots of STAT3 SH2. (A) In hit 1 (H1), fragments f1 (sulfonamide), f2
(1-phenylethanol), and f3 (2-phenylpropane) occupied the three hot
spots (pY705, L706, and side pocket) of STAT3 with a binding energy of
ꢀ12.5 kcal/mol. (B) In hit 2 (H2), fragments f1 (sulfonamide), f4 (1,4-
dimethylbenzene), and f5 (1-bromo-4-methylbenzene) docked into
three hot spots with a binding energy of ꢀ12.1 kcal/mol. Scaffolds are
shown in stick-ball and colored by atom type.
pY705 binding pocket formed by surrounding residues Arg609,
Lys591, Glu612, and Ser613. In hit H1, fragment f2 (1-phenyle-
thanol) occupied the L706 subpocket and f3 (2-phenylpropane)
docked to the side pocket. In hit H2, f4 and f5 bound to the L706
and subpocket, respectively.
Interestingly, predicted binding energies of hit H1 and H2
are very close. Further binding mode cluster analysis revealed that
with similar binding energies, the binding modes of f2, f3, f4, f5 in the
L706 and side pocket could be different and dynamic (Figure S2 of
Supporting Information). The binding affinity of aromatic frag-
ments f2 and f3 was similar to that of f4 and f5. Docking simulation
results suggest that the polar pY705 site is essential and sulfonamide
f1 moiety is a key fragment for binding. The docked fragments and
their binding modes could be used as a blueprint to design possible
inhibitors for STAT3. Our results show for the first time three
fragments docked into the three hot spots of STAT3 SH2.
Linking Fragments for Hits and Virtual Template Com-
pounds Design. To generate possible lead candidates, we linked
the three fragments in hits H1 and H2 using different chemical
tethers such as amide, amine, ether, and olefin. The structures of
linked compounds and their docking energies are listed in Table
S1 of Supporting Information. The virtual templates had docking
binding energies in the range ꢀ8.2 to ꢀ12.0 kcal/mol. All 15
virtual compounds had relatively good binding to all three hot
spots of STAT SH2 in docking modeling. Figure 4A shows the
docking models of template compounds T1 and T2. The top hit
T1 (green) demonstrated binding modes and binding energy
(ΔG = ꢀ12.0 kcal/mol) very close to that of the docked f1, f2,
and f3 in hit H1 (ΔG = ꢀ12.5 kcal/mol). Compound T2 (red
color) also bound relatively well to the three hot spots of STAT3,
with binding modes similar to that of fragments f1, f4, and f5 in
H2. The linked molecule T2 gave a lower binding affinity
compared to the three docked fragments (f1, f4, and f5) possibly
because of the short linker between f1 and f5 in T2, which could
lead to the tilt of f1 and shallow binding of f5. Also, the strain and
single bond entropic penalty of linker upon binding compared to
the free fragments could result in a weaker binding energy. We
Figure 2. Privileged drug scaffolds for STAT3 SH2. Pool 1 is for pY705
site, and pool 2 is for L706 site or side pocket.
The main pTyr705 binding site is polar and basic, while the
Leu706 and side pocket are hydrophobic. We built a small library
of feature fragments from a collection of small molecule inhibitors
of STAT3 SH2 in previous reports.8ꢀ15 To avoid fragments with
undesired drug ADMET properties, drug scaffolds structurally or
chemically similar to the obtained feature fragments were identi-
fied by similarity search on a drug scaffold database. Figure 2 lists a
small library of drug scaffolds identified, which were grouped into
two pools: polar and nonpolar. The polar scaffolds in pool 1 favor
binding to the polar and basic pY705 site, and the relatively
nonpolar scaffolds in pool 2 are for the L706 site or side pocket.
Simultaneous Docking of Three Fragments to Binding Hot
Spots of STAT3 SH2. So far, there has been no report of a fragment-
based design approach to identify inhibitors of STAT3. Docking
modeling showed that previously reported inhibitors bound to two
of the three subpockets of the STAT3 SH2 domain. To improve
binding affinity, we applied MLSD to dock multiple drug scaffolds in
a concerted way to the three binding hot spots of STAT3, like fitting
the right piece into the right place in jigsaw puzzle (Figure 3).
Briefly, three drug fragments, one from pool 1 and the other two
from pool 2, were used as inputs for the MLSD docking screening.
The combination of drug scaffolds in the two pools generated a
diverse set. Figure 3 shows that hits H1 (f1, f2, and f3) and H2 (f1,
f4, and f5) docked to the hot spots of STAT3 SH2, with a predicted
binding energy of ꢀ12.5 and ꢀ12.1 kcal/mol, respectively. In both
hits, the polar fragment f1 (phenylsulfonamide) occupied the main
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BRIEF ARTICLE
Figure 6. Selective inhibition of STAT3 by celecoxib, T2, and T3. (A)
Celecoxib, T2, and T3 inhibited STAT3 phosphorylation (Y705).
Compounds T2 and T3 were more potent than celecoxib at inhibiting
P-STAT3. (B) Celecoxib showed inhibition of STAT3 phosphorylation
induced by IL-6 but not STAT1 phosphorylation by interferon-γ.
Figure 4. (A, top) Linking the docked fragments to obtain virtual
template compounds. Tethering f1, f2, and f3 in hit H1 gave T1 (green
stick-ball), and linking f1, f4, and f5 of H2 generated T2 (red stick-ball).
Docked fragments of f1, f2, and f3 are represented by the thin green line,
and f1, f4, and f5 are represented by the thin red line. Redocking of T1
and T2 to STAT3 resulted in binding energies of ꢀ12.0 and ꢀ8.6 kcal/
mol, respectively. (B, bottom) Drug xelecoxib was identified as a novel
inhibitor of STAT3 SH2 by similarity searching for T1, T2, and other
virtual compounds in DrugBank. Docking of celecoxib (atomic coloring
stick-ball) to STAT3 SH2 showed that phenylsulfonamide and phenyl-
methyl bound to the pY705 site and side pocket, respectively.
compounds identified celecoxib as a top hit (Tanimoto similarity
coefficient of g0.6). Both T1 and T2 matched celecoxib as a top hit in
DrugBank. Docking celecoxib to STAT3 SH2 resulted in a major
binding cluster (80% probability), where the polar phenylsulfonamide
occupied the pY705 site and nonpolar phenylmethyl occupied the
side pocket (Figure 4B). In addition, docking modeling of celecoxib
to STAT3 indicated that the binding modes and binding energy were
comparable to most of the known inhibitors, which bound to the
main pY705 site and only one of the L706 and side-pocket hot
spots.4ꢀ11 Docking celecoxib gave a lower binding affinity (ꢀ6.9
kcal/mol) than that of T1 (ꢀ12.0 kcal/mol) and T2 (ꢀ8.6 kcal/
mol). Comparing binding modes of celecoxib with that of T1 and T2
indicates that the lower binding affinity is likely due to the missing
third fragment in celecoxib for the L706 binding subpocket.
Virtual Hit Compounds T2 and T3 as More Potent Inhibitors
ofSTAT3. To investigate whether binding to all three subpockets of
STAT3 could improve inhibition on STAT3, template compounds
were selected for synthesis based on ease of synthetic procedure
and predicted binding modes and energies. The synthesis of top hit
compound T1 is challenging and the effort to synthesize it is
ongoing. T2 was synthesized. T3 is an optimization of celecoxib to
strengthen side pocket binding and to add L706 binding. The
docking modeling of T2 and T3 shows that T2 and T3 bind
relatively well to all three subpockets of STAT3 and can effectively
compete with native phosphotyrosine peptide binding (Figure 5).
Compared to celecoxib, T2 and T3 had better binding modes and
an improved binding energy of ꢀ8.6 and ꢀ9.2 kcal/mol, respec-
tively. The stronger binding is likely due to an extra fragment in T2
and T3 for binding to the L706 subpocket. The modeling would
suggest that binding the L706 subpocket helps increase inhibition
of STAT3. Cancer cell (HCT-116) assays also demonstrated that
T2 and T3 are more potent inhibitors of STAT3 than celecoxib
(Figure 6A and Table 1). The modeling results are consistent with
the inhibition activities of celecoxib, T2, and T3.
Figure 5. Docking modeling of celecoxib (yellow thin line), T2 (green
stick-ball), and T3 (purple stick-ball) to STAT3. Native pTyr peptide
(pYLK) binding is shown as a thin white line. Both compounds T2 and
T3 have an extra hydrophobic group binding to the L706 site, which
results in better binding energies of ꢀ8.6 and ꢀ9.2 kcal/mol, respec-
tively, compared to celecoxib (ꢀ6.9 kcal/mol). The predicted binding
modes and energies show good correlations with inhibition activities of
celecoxib, T2, and T3 on STAT3 in HCT-116 cell based assays.
found it is very difficult for a linked compound to exactly match
binding modes and orientations of the docked fragments. One
possible reason is that the docking simulation gives a cluster of
binding modes with very similar binding energies. Fragment
binding in the L706 and side pocket is dynamic to some degree as
shown in Figure S2 of Supporting Information.
Drug Repositioning. The docked fragments define the blueprint
and main pharmacophore of possible binders. This would enable us to
search for template compounds on a drug database to match
compounds with a similar binding pharmacophore. We performed
a similarity search of all 15 template compounds on an FDA approved
drug set in DrugBank.17 Thirteen out of the 15 (87%) virtual
Cancer Cell Line Assays: Celecoxib and T2 and T3 Down-
Regulate STAT3 Phosphorylation in a Dose-Dependent Man-
ner. The drug celecoxib is commonly known as a cyclooxygenase-2
(COX-2) inhibitor. To exclude interference of COX-2, human
colon cancer cell line (HCT-116) that expresses constitutively
activated STAT3 but not COX-2 was used in our study.18ꢀ21 To
examine the inhibition of STAT3 phosphorylation, a Western blot
was performed to detect the amount of phosphorylated STAT3
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Table 1. Docked Binding Energies and IC50 of Celecoxib, T2, and T3 in HCT-116 Cell Viability Assays
compd
docking ΔG (kcal/mol)
IC50 (μM)
binding mode
MW
CLogP
PSA (Å2)
celecoxib
T2
ꢀ6.9
ꢀ8.6
ꢀ9.2
43.3
9.7
pY705 and side pocket
381.4
482.4
501.6
3.6
5.5
3.2
78.0
78.0
pY705, L706, and side pocket
pY705, L706, and side pocket
T3
10.1
110.3
(P-STAT3) after HCT-116 cells were treated with celecoxib, T2,
and T3 (10ꢀ50 μM). As shown in Figure 6, the amount of
P-STAT3 decreased in HCT-116 cells with increasing doses of
celecoxib, T2, and T3. STAT3 is phosphorylated through the gp130
cytoplasmic pTyr loop binding to the STAT3 SH2 domain; thus,
blocking of STAT3 SH2 by inhibitors stops STAT3 phosphoryla-
tion. The expression of total STAT3 remained constant, which
indicates that the decrease of P-STAT3 was not due to a constitu-
tional decrease of total STAT3 protein. The results also show that
T2 and T3 have more potent inhibitory effects on P-STAT3 than
celecoxib (Figure 6A). This is consistent with the docking
prediction.
Celecoxib Inhibits Interleukin-6 (IL-6) Induced STAT3
Phosphorylation but Not STAT1. We investigated whether
celecoxib could inhibit IL-6 induced STAT3 phosphorylation in
PANC-1 cancer cells. PANC-1 cells were cultured in serum free
medium for 24 h and were pretreated with 25 or 50 μM celecoxib
for 2 h. Then the cells were treated with 50 ng/mL IL-6 or
interferon-γ for 30 min. We observed that celecoxib inhibited
IL-6 induced STAT3 phosphorylation but had little effect on
STAT1 phosphorylation induced by IFN-γ (Figure 6B).
Cell Viability Assay. Cell viability assay was used to measure
inhibitory effects of celecoxib, T2, and T3 on human colon
cancer cells (HCT-116). The results are listed in Table 1. IC50
values are asfollows:celecoxib, 43.3μM;T2, 9.7μM;T3, 10.1 μM.
T2 and T3 showed more potent inhibition of P-STAT3 than
celecoxib. The results are consistent with the modeling prediction.
fragments on drug scaffold database. The privileged drug scaffold library
for STAT3 was used for multifragment docking screening.
Multifragment Docking Screening and Drug Repositioning.
The crystal structure of STAT3 SH2 domain (PDB code 1BG1) was used
as the receptor for docking. MLSD program was employed for multi-
fragment docking.1,16 Privileged drug scaffolds were used as fragments for
MLSD screening. In multifragment docking, two or three fragments from
the two pools of the drug scaffolds were used to probe binding subpockets
of STAT3 SH2. Systematic multifragment docking screening with the
combinations of drug scaffolds was ranked by the predicted binding energy.
The docked fragments with a predicted binding energy of less than ꢀ8.2
kcal/mol (dissociation constant Kd in submicromolar range) were con-
sidered for further visual inspection of binding modes and selected as hits.
Previously reported procedure and parameter settings were used for
multifragment docking.1 Lamarckian genetic algorithm (LGA) and particle
swarm optimization (PSO) were used as a searching method depending on
the dimensionality of search space. PSO, a searching method inspired by
bird flocking, was used for MLSD with relatively high dimensions of
conformational space. Virtual template compounds were obtained by
linking docked fragments using various types of tethers. The candi-
dates were optimized for drug properties: octanolꢀwater partition
coefficient (log P) and polar surface area (PSA). Drug properties were
calculated using Molinspiration molecular property service (http://
www.molinspiration.com). The linked compounds were redocked to
STAT3 SH2 and ranked by binding energies and binding modes to
generate hits. To apply the drug repositioning concept, similarity
searches for template compounds in DrugBank (http://www.drug-
bank.ca/) were performed to identify potential drug analogues of hit
compounds.17 SMILES encoding and Tanimoto similarity coefficient
cutoff of 0.5 were used for similarity search. The identified hit
compounds were verified by computational redocking before selection
for synthesis or purchase to perform cell line assays.
’ CONCLUSION
Our results show that combinations of three drug fragments
found through MLSD bind to the three subpockets of STAT3 SH2.
Linking of the docked fragments gave virtual hit compounds with
potentially improved potency and ADMET properties. Similarity
searching of virtual compounds in DrugBank identified celecoxib as
a novel inhibitor of STAT3. Also, two novel hit compounds were
designed to bind all three subpockets, which demonstrated more
potent inhibition of STAT3 with IC50 in the low micromolar range
in HCT-116 cancer cell line assays. The proposed computational
method, which uses privileged drug fragments, MLSD, and drug
repositioning, can potentially be applied for drug discovery and
fragment-based drug design for other targets.
Cell Lines and Cell Culture. Human colon cancer cell line (HCT-
116) and human pancreatic cancer cellline(PANC-1) werepurchasedfrom
the American Type Culture Collection (ATCC). Cancer cell lines were
cultured in DMEM medium supplemented with 10% FBS and 100 U/mL
penicillin/streptomycin B, in a humidified 37 ꢀC incubator with 5% CO2.
Western Blot Analysis. HCT-116 cells were treated with Ccelecox-
ib, T2, and T3 (10ꢀ50 μM) or DMSO control at 60ꢀ80% confluence in
the presence of 10% fetal bovine serum (FBS) for 24 h and lysed in cold
RIPA lysis buffer containing a cocktail of protease inhibitors to prepare
whole-cell extracts.11 Lysates were then centrifuged at 14 000 rpm for 10
min to remove insoluble materials. The 30ꢀ100 protein samples were
separated by SDSꢀPAGE, transferred onto a PVDF membrane. After
being blocked with 5% nonfat milk, the proteins were immunoblotted
overnight at 4 ꢀC with 1:1000 dilution of primary antibodies (Cell
Signaling Technology) against phospho-STAT3 (pTyr705), STAT3,
and GAPDH and 1:10000 dilution of HRP conjugated secondary
antibody for 1 h at room temperature. The target proteins were
visualized by chemiluminescence (Cell Signaling Technology).
Cell Viability Assay. HCT-116 cells were seeded in 96-well plates at a
density of 3000 cells per well. Escalating concentrations (5ꢀ160 μM)
of celecoxib and (1ꢀ75 μM) of T2 and T3 were added in triplicate to
the plates in the presence of 10% FBS. The cells were incubated at 37 ꢀC for
72 h. 3-(4,5-Dimethylthiazolyl)-2,5-diphenyltetrazolium bromide (MTT)
viability assay was done according to the manufacturer’s protocol (Roche
’ EXPERIMENTAL SECTION
Privileged Drug Scaffolds Preparation for STAT3. The 436
drug scaffolds from FDA approved drugs (AD) as reported by Wang
et al. were used for a drug scaffold database.4 The top 50 AD scaffolds in
the database covered 52.6% of all FDA approved drugs. To prepare the
drug scaffold library for STAT3, a collection of known inhibitors of STAT3
in previous reports were used to generate a set of feature fragments for
the binding hot spots of STAT3 SH2. These known inhibitors were
fragmented by a retrosynthetic approach. Drug scaffolds for STAT3
were then identified by similarity search of the obtained feature
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BRIEF ARTICLE
Diagnostics, Mannheim, Germany). The absorbance was read at 595 nm.
Half-maximal inhibitory concentrations (IC50) were determined using
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’ ASSOCIATED CONTENT
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Supporting Information. Virtual docking results of tem-
b
plate compounds, binding mode cluster of docked fragments,
and synthesis of T2 and T3. This material is available free of
charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION
Corresponding Author
*Phone: (614) 247-8786. Fax: (614) 292-2435. E-mail: Li.728@
osu.edu.
’ ACKNOWLEDGMENT
The work was partially supported by a NIH R21 grant (Grant
1R21 CA 133652-01A1) to J.L. and C.L. and by the Ohio
Supercomputer Center Glenn cluster computing resources.
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’ ABBREVIATIONS USED
FBDD, fragment-based drug design; HTS, high throughput
screening; MLSD, multiple ligand simultaneous docking; DAPI,
40,6-diamidino-2-phenylindole; IL-6, interleukin 6; DMSO, di-
methylsulfoxide; log P, octanolꢀwater partition coefficient; PSA,
polar surface area; STAT3, signal transducer and activator of
transcription 3 (STAT3); ADMET, absorption, distribution,
metabolism, excretion, and toxicity
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