- Email: [email protected]
Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and e-pharmacophore based virtual screening and its biological evaluation
Accepted Manuscript Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and epharmacophore based virtual screening and its biological evaluation Sundaraj Rajamanikandan, Jeyaraman Jeyakanthan, Pappu Srinivasan PII:
S0882-4010(16)30156-5
DOI:
10.1016/j.micpath.2016.12.003
Reference:
YMPAT 2024
To appear in:
Microbial Pathogenesis
Received Date: 30 March 2016 Revised Date:
29 November 2016
Accepted Date: 5 December 2016
Please cite this article as: Rajamanikandan S, Jeyakanthan J, Srinivasan P, Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and e-pharmacophore based virtual screening and its biological evaluation, Microbial Pathogenesis (2017), doi: 10.1016/j.micpath.2016.12.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and e-pharmacophore based virtual screening and its biological evaluation Sundaraj Rajamanikandana, Jeyaraman Jeyakanthana, Pappu Srinivasanb* a
Department of Bioinformatics, Alagappa University, Karaikudi, Tamilnadu, India. Department of Animal Health and Management, Alagappa University, Karaikudi, Tamilnadu, India.
*Corresponding author Dr. Pappu Srinivasan
EP
Associate Professor
TE D
M AN U
SC
RI PT
b
Department of Animal Science and Management
AC C
Alagappa University, Karaikudi Tamilnadu-630 003, India Fax (off): +91-4564-2252
E-mail: [email protected] Tel: +91-4565-230725 Tel: +91-9444482814
ACCEPTED MANUSCRIPT Abstract Quorum sensing is widely recognized as an efficient mechanism in the regulation and production of several virulence factors, biofilm formation and stress responses. For this reason, quorum
RI PT
sensing circuit is emerging as a novel drug target for the development of anti-infective. Recently, cinnamaldehyde derivatives have been found to interfere with master quorum sensing transcriptional regulator and thereby decreasing the DNA binding ability of LuxR. However, the exact mode of
SC
cinnamaldehyde binding with LuxR and receptor interaction still remains inconclusive. In the current study, combined method of molecular docking and molecular dynamics simulations were performed to
M AN U
investigate the binding mode, dynamic and energy aspects of cinnamaldehyde derivatives into the binding site of LuxR. Based on the experimental and computational evidences, LuxR-3,4-dichlorocinnamaldehyde complex was chosen for the development of e-pharmacophore model. Further, shape and e-pharmacophore based virtual screening were performed against ChemBridge database to find potent and suitable ligands for LuxR. By comparing the results of shape and e-pharmacophore based
TE D
virtual screening; best 9 hit molecules were selected for further studies including ADMET prediction, molecular dynamics simulations and Prime MM-GBSA calculations. From the 9 hit molecules, the top most compound 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2-propen-1-one (ChemBridge-7364106)
EP
was selected for in vitro assays using Vibrio harveyi. The result revealed that ChemBridge-7364106
AC C
significantly reduced the bioluminescence production in a dose dependent manner. In addition, ChemBridge-7364106 showed a significant inhibition in biofilm formation and motility in V. harveyi. The results from the study suggest that ChemBridge-7364106 could serve as an anti-quorum sensing molecule for V. harveyi.
Keywords: Vibrio harveyi, quorum sensing, e-pharmacophore, shape based virtual screening, in vitro assays. Introduction The cell-cell communication in bacteria called quorum sensing (QS), is a process that allows
ACCEPTED MANUSCRIPT bacteria to synchronize the gene expression based on its population density [1]. Activities regulated by QS include virulence, antibiotic production, biofilm formation, symbiosis, motility and luminescence [2]. The bacterial population grows and once they reach a certain threshold, the auto-inducer (AI)
RI PT
signaling molecules bind and activate their cognate receptor, leading to the regulation of group behavior [3]. Vibrio harveyi uses AI-2 for interspecies cell-cell signaling and AI-1 for intra species communication [4].
SC
Many Gram negative bacteria use LuxI/LuxR QS system to control a wide range of cellular processes [5]. In this system, LuxI homolog is an AI synthase; it uses S-adenosylmethionine (SAM)
M AN U
and acyl carrier protein as a substrate for the production of freely diffusible N-acyl homoserine lactones (AHL) AI [6]. A high AI concentration results in the binding of AI to their cognate receptor (LuxR-like transcription factor) and the degradation of LuxR protein takes place in the absence of AI, that prevent bacteria from “short circuiting” their QS systems [7]. The process stabilizes the LuxR protein, allowing them to fold, bind DNA and activate the transcription of target genes [8].
TE D
In V. harveyi, two QS circuits function in parallel to regulate bioluminescence and a number of other target genes [9]. These networks are typically composed of two independent components. Among them, LuxM/LuxN system 1, which signals through the AHL molecule HAI-1 and system 2, comprises
EP
the LuxS dependent auto-inducer AI-2 and the AI-2 detector LuxPQ [10]. AIs HAI-1 and AI-2 are
AC C
synthesized by LuxM and LuxS and these two sensor transfers information to the phosphorelay protein called LuxU [11]. The histidine phosphotransferase LuxU, which transmits the signal to the response regulator LuxO that indirectly, represses the LuxR expression level [12]. Finally, LuxR directly or indirectly controls the expression of various virulence factors [13]. Although, few potent small molecules targeting LuxR has been identified so far. Among them, halogenated furanones and cinnamaldehyde derivatives were found to disrupt AHL and AI-2 based quorum sensing system in Vibrio spp. Unfortunately, the halogenated furanones are seemed to be toxic, which limits their use. In contrast, cinnamaldehyde is a non-toxic substance and also a well known anti-
ACCEPTED MANUSCRIPT QS inhibitor. Brackman et al., (2011) has synthesized forty one cinnamaldehyde derivatives and evaluated its anti-QS activity through in vitro and in vivo assays using V. harveyi. Among them, seven cinnamaldehyde derivatives showed AI-2 regulated bioluminescence inhibition in V. harveyi.
RI PT
Furthermore, the most pronounced effect were observed for 3,4-dichloro-cinnamaldehyde with an least IC50 value of 22 µM [14]. Therefore, in the present study, molecular docking was performed with seven potent cinnamaldehyde derivatives using LuxR to gain insights into their binding modes. Based on the
SC
experimental evidence, only 3, 4-dichloro-cinnamaldehyde is chosen for the e-pharmacophore and shape based virtual screening to identify potent ligands of LuxR. The expected binding mode of the
M AN U
protein-ligand complexes were reproduced in the molecular dynamics (MD) simulations. Prime MMGBSA (Molecular Mechanics, The Generalized Born Model and Solvent Accessibility) and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties were calculated for the identified hit molecules. Finally, the most promising hit molecule is validated through in vitro assays. Materials and Methods
In silico analysis Protein preparation
TE D
Fig. 1 summarizes the workflow of the methodology used in this study.
EP
The modelled LuxR structure reported in the literature was used for the computational studies
AC C
[15]. The structure was prepared using Protein Preparation Wizard (Protein Preparation Wizard, Schrödinger, LLC, New York, NY, 2014). It has two components, namely preparation and refinement. It corrects structural defects, adds hydrogen atoms and removes water molecules that are present 5 Å away from the binding cavity. Side chains that are not involved in the formation of salt bridges were neutralized. Energy minimization was carried out using OPLS-2005 (Optimized Potentials for Liquid Simulations) force field with implicit solvation. Optimization of the hydrogen bonding network, rotation of hydroxyl and thiol hydrogens, generation of appropriate protonation and tautomerization states of His, performing Chi 'flip' in Asn, Gln and His residues were selected by the protein assignment
ACCEPTED MANUSCRIPT script. The minimization was terminated until the root mean square deviation (RMSD) of the heavy atoms in the energy minimized structure relative to the starting X-ray structural co-ordinates exceeded 0.3 Å.
RI PT
Ligand preparation Cinnamalydehyde derivatives and hits retrieved from ChemBridge database were prepared using LigPrep module of Schrödinger (LigPrep, Schrödinger, LLC, New York, NY, 2014). LigPrep
SC
produce a number of structures from each input structure with various ionization states, tautomers, stereochemistries, ring conformations, and eliminate molecules using various criteria, including
M AN U
molecular weight or specified numbers and types of functional groups present. Missing hydrogen atoms were added and bond orders of the ligands were fixed. The ionization and tautomeric state of the ligands were generated between pH 6.8 to 7.2 using Epik module. Ligands were energy minimized using OPLS-2005 force field until the final RMSD of 1.8 Å was achieved [16]. Low energy ring
docking studies. Molecular docking
TE D
conformations for the ligands were generated and the optimized ligands were further used for the
Seven cinnamalydehyde derivatives were docked into the binding site of LuxR. The active site
EP
informations was obtained from the crystal structure of SmcR, transcriptional regulator from V.
AC C
vulnificus (PDB: 3KZ9) [17]. A grid was generated using receptor grid generation panel by setting the center of the grid map around the active site. The docking was carried out using Glide XP (extra precision) mode [18]. Glide searches for favorable interactions between one or more ligand molecules and a receptor. It also generates conformations internally for each ligand molecule and passes through a series of hierarchical filters that evaluate the ligand's interaction with the receptor. The initial filter test the spatial fit of the ligand to the defined active site and examine the complementarity of ligandreceptor interactions using a grid based method patterned after the empirical ChemScore function. The next filter stage involves a grid based force field evaluation and refinement of the docking solutions,
ACCEPTED MANUSCRIPT including torsional and rigid body movements of the ligand. The OPLS-2005 force field was used for intermediate docking. A small number of surviving docking solutions were subjected to monte carlo procedure to try and minimize the energy score. Finally, the Glide score multi-ligand scoring function
RI PT
was used to score the poses. In order to validate the receptor rigid docking (RRD), a mixed molecular docking/dynamic protocol called induced fit docking (IFD) was also performed. In IFD flexibility of both the receptor and ligand are considered as the main factor during the docking process, because both
SC
protein and ligand can change their spatial shape during the docking process. Molecular dynamics simulations
M AN U
MD simulations were performed using GROMACS v4.5.5 [19] with GROMOS96 43A1 force field [20, 21]. The molecular topology file and charge for the ligand atoms were generated using a PRODRG server [22]. The system was solvated in a cubic periodic box comprising the simple point charge (SPC-216) water environment. Five Na+ counter ions were added by replacing the water molecules to neutralize the whole system under the physiological conditions. The solvated system was
TE D
minimized using steepest descent algorithm. After energy minimization, the position restraint procedure was performed in association with NVT and NPT ensembles. The entire system was equilibrated under NVT ensemble for 100 ps using Berendsen coupling method (temperature constant
EP
at 300 K with a coupling time of 0.1 ps). Then in the NPT ensemble, the pressure was maintained with
AC C
a time constant of 0.5 ps and the reference pressure of 1 bar. All bonds involving hydrogen atoms were constrained with LINCS algorithm [23]. Particle-Mesh ewald (PME) method was employed to enlighten the electrostatic interactions in the system [24]. The geometry of the water molecules were constrained using SETTLE algorithm [25]. The van der Waals (vdw) force was treated with a cutoff of 12 Å and final MD simulations was carried out for 30 ns. Analysis such as potential energy, total energy, temperature, pressure, RMSD, root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg), number of intermolecular hydrogen bond (H-bond), hydrophobic contacts were analyzed using scripts included with the GROMACS distribution. The trajectories were
ACCEPTED MANUSCRIPT graphically visualized using OriginPro. e-Pharmacophore based virtual screening The e-pharmacophore model was generated from the docked complex using PHASE
RI PT
(Pharmacophore Alignment and Scoring Engine) module of Schrödinger [26]. Based on the Glide XP score the energetic terms were mapped onto atom centers. The pharmacophoric sites were automatically generated by PHASE module and Glide XP energies for each pharmacophoric site were
SC
summed. The final hypotheses were selected based on the ranking and quantification process. The common pharmacophoric features such as hydrogen bond acceptor (A), hydrogen bond donor (D),
M AN U
hydrophobic region (H), positive inoizable (P), negative inoizable (N) and aromatic ring (R) were utilized to rank the pharmacophoric hypotheses. Sites less than half of the heavy atoms contribute to the pharmacophoric features were excluded from the final hypotheses. The best e-pharmacophore model was further utilized to screen the ChemBridge database. The retrieved hits were ranked based on the fitness score, and a measure of how well the aligned ligand conformer matches the hypotheses
the molecular docking studies.
TE D
based on RMSD site and vector matching. The hits with the highest fitness score were shortlisted for
Shape based virtual screening
EP
Shape based molecular similarity approach has gained important and popular among virtual
AC C
screening techniques [27]. The approach is consistent with physical chemistry intuition, which utilize the shape and electrostatic properties of the query molecule to select new compounds that are likely to show similar binding mode in the active site of the protein. Shape screening is an effective tool for lead optimization with high potency and more selectivity, where rapid, flexible superposition of multiple similar molecules is essential to understand the structure activity relationship. It does not require a target protein structure or well developed structure activity relationship sets that might be necessary to create a reliable pharmacophore model. Only a single known active query compound is needed as an input. The shape screening can run in shape only mode, or it can incorporate atom type similarity when
ACCEPTED MANUSCRIPT aligning and scoring. In unique model, it describes each structure as a collection of pharmacophoric features rather than individual atoms. Prime MM-GBSA
RI PT
The prime MM-GBSA was used to predict the free energy of binding of a set of ligands to the receptor (Prime, Schrödinger, LLC, New York, NY, 2014). Prime uses OPLS-2005 force field and GBSA continuum solvent model to calculate the energies for each complex. The binding free energy
SC
(∆G) is estimated using the following equation. ∆Gbind = Gcomplex - (Gprotein + Gligand)
M AN U
G = EMM + GSGB + GNP
The Gcomplex represents complex energy, Gprotein is the receptor energy and Gligand is the unbound ligand energy. EMM represents molecular mechanics energies, GSGB is an SGB solvation model for polar solvation and GNP is a nonpolar solvation term. Interaction energy was also calculated from macro
Density functional theory
TE D
model script which calculates the component energies of interacting atoms between protein and ligand.
The density functional theory (DFT) calculations were performed using Jaguar (Jaguar, Schrödinger, LLC, New York, NY, 2014) module implemented in Schrödinger. Ligand molecules were
EP
optimized with hybrid DFT using Becke's three parameter exchange potential and Lee-Yang-Parr
AC C
correlation functional (B3LYP) gradient corrected exchange correlation functional in combination with 3-21+G* basis set [28, 29]. Energy calculations were performed using poisson boltzmann finite (PBF) solvation model. Highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO) and energy gaps were calculated. ADME and toxicity prediction QikProp is a quick, accurate, easy to use absorption, distribution, metabolism and excretion prediction program, which predicts physically significant descriptors and pharmaceutically relevant properties of the organic molecules (QikProp, Schrödinger, LLC, New York, NY, 2011). In addition,
ACCEPTED MANUSCRIPT QikProp provides ranges for comparing a particular molecule property with those of 95% of known drugs. It also flags 30 types of reactive functional groups that may cause false positives in high throughput screening assays. The hits retrieved from virtual screening were neutralized by QikProp.
RI PT
The neutralizing step is essential, as QikProp is unable to neutralize a structure and no properties will be generated. The physiochemical parameters such as Molecular Weight (MW), QPlogPo/w (octanol/water), QPlogS (aqueous solubility), QPlogKp (skin permeability), percentage of human oral
SC
absorption, stars (descriptor values that fall outside the 95% range of similar values for known drugs) were analyzed in detail. It also evaluates the acceptability of the hit molecules based on the Lipinski's
M AN U
rule of five, which is considered as an important factor in the pharmacokinetics and rational drug design. Finally, the toxicity profile of the hits were analyzed by PROTOX web server [30]. In vitro assays Bacterial strain
The aquatic bacterial pathogen V. harveyi (MTCC 3438) was kindly gifted by Dr. A.Veera Ravi,
TE D
Department of Biotechnology, Alagappa University, Karaikudi, India. The pathogenic V. harveyi was cultured in luria bertani (LB) media (pH 7.5±0.2) and incubated overnight at 30 ºC. For experimental purpose, the overnight culture was inoculated into fresh LB media to an initial OD600 of 0.5 and the
EP
culture was aerated on an orbital shaker at 300 rpm at 30 ºC.
AC C
Determination of minimum inhibitory concentration The 7364106)
compound
was
3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2-propen-1-one
purchased
from
ChemBridge
(Hit2Lead)
online
(ChemBridge-
chemical
store
(http://www.hit2lead.com). The stock was prepared by dissolving 5 mg of the compound in 188 µl of sterile dimethyl sulfoxide (DMSO) and stored in vials at -20 ºC. The working standard was freshly prepared on the day of the experiment by diluting the stock solution with DMSO and further used for in vitro assays. The minimal inhibitory concentration (MIC) of ChemBridge-7364106 was evaluated according to the method as described earlier [31]. The LB broth was added to the 24 well microtitre
ACCEPTED MANUSCRIPT plates. Then, the bacterial suspension (adjusted OD600 of 0.5) was added and supplemented with two fold serially diluted solution of ChemBridge-7364106 in order to give the final concentration of 0.5 µg/ml to 8 µg/ml. After treatment, the assay plates were incubated at 30 ºC for 24 hours. The lowest
RI PT
concentration of the compound that produced a complete suppression of visible growth was noted as MIC. Further investigations were performed with sub-MIC concentration of the compound. Bioluminescence inhibition assay
SC
The V. harveyi cells were enriched in the alkaline peptone water (APW) and treated in the presence (0.5-8 µg/ml) or absence of ChemBridge-7364106 for 12 hours at 30 ºC. The bioluminescence
M AN U
intensities were measured as relative light units (RLU) by luminometer. Positive control is the untreated culture of V. harveyi and negative control consist of sterile APW. Effect of ChemBridge-7364106 on biofilm development
V. harveyi biofilm formation was initially quantified using crystal violet staining as described earlier [32]. Briefly, the overnight culture of V. harveyi (OD adjusted to 0.4 at 600 nm) was treated with
TE D
the presence (0.5-8 µg/ml) or absence of ChemBridge-7364106 for 16 hours. After incubation, the plates were sequentially washed thrice with sterile deionized water to remove loosely associated bacteria. Subsequent to washes, the cells were stained with 200 µl of 0.4% (v/v) crystal violet solution
EP
for 15 minutes at room temperature. After staining, the plates were rinsed thrice with deionized water to
AC C
remove the excess stain and then 1 ml of 95% ethanol was added and incubated to extract the crystal violet solution. The absorbance was measured at 580 nm using UV-visible spectrophotometer (Hitachi U-2800, Japan).
Percentage inhibition of biofilm biomass = [Control OD -Test OD/Control OD] x 100 Disintegration of mature biofilm To evaluate the biofilm disintegration, the mature biofilm was incubated for 5 hours at 30 ºC in the presence or absence of ChemBridge-7364106. After incubation, the biofilm formed on the glass slides were stained with crystal violet staining. Then the slides were washed thrice with deionized
ACCEPTED MANUSCRIPT water and air dried at room temperature. The disintegration of biofilm on glass slides were finally observed under light microscope. Swimming and swarming assays
RI PT
The motility assays such as swimming and swarming were performed according to the procedure as described previously [33]. Swimming and swarming plates were prepared by adding 0.3%, 0.5% agar to the LB broth. 3 µl and 5 µl overnight cultures of V. harveyi (OD600 nm of 0.5) were
SC
inoculated at the center of the swimming and swarming agar plates and incubated for 16 hours at 30 ºC. Further, the reductions in swimming and swarming migration zones were observed.
M AN U
Growth curve and bioluminescence kinetics assay
The overnight culture of V. harveyi (OD600 nm of 0.5) was inoculated in 20 ml of LB broth supplemented with various concentrations (0.5-8 µg/ml) of ChemBridge-7364106 and was cultured on a rotary shaker under 180 rpm at 30 ºC. The cell density was measured using UV-visible spectrophotometer over 1 hour time intervals up to 16 hours. In the bioluminescence kinetic assay, the
TE D
overnight culture of V. harveyi (OD600 nm of 0.5) was inoculated in 20 ml of APW supplemented with various concentrations (0.5-8 µg/ml) of ChemBridge-7364106 and incubated by following the procedure as stated above. The bioluminescence intensity was calculated every 1 hour up to 16 hours.
AC C
In silico analysis
EP
Results and Discussion
Molecular dynamics simulations of apo protein The conformational stability, intrinsic flexibility and the dynamic patterns hidden in the protein and protein-ligand complexes are well understood by performing the MD simulation [34]. RMSD and RMSF are the most commonly used criterion for the validation of an MD simulations. RMSD is considered as an average displacement of backbone atoms at an instant of the reference structure, whereas reference structure is taken from the first frame of the simulation. The RMSD is calculated on the C-α atoms of protein against time dependent function (Fig. 2a). The variation values were
ACCEPTED MANUSCRIPT calculated by means of the standard deviation (SD). The inspection of RMSD plot indicates the stability of LuxR throughout the simulation of 50 ns with an RMSD value of 0.2 nm. The lower RMSD value indicates the system is well equilibrated and remained stable throughout the simulation. From the
RI PT
result, we observed that RMSD values deviated around 2 Å and it does not show any sign of divergence from the initial structure. The residual fluctuations in the protein were monitored and the results are shown in Fig. 2b. The protein showed an average RMSF value of 0.12 with the SD value of
SC
0.07 nm. Amino acid residues showing higher fluctuations includes Met1 (0.4 nm), Arg202 (0.4 nm), Glu203 (0.5 nm) and His204 (0.7 nm). As expected, an increase in fluctuation was observed in the loop
M AN U
region between the N- and C-terminal domains. The amino acid residues at the active site region in the protein have shown low mobility and, thereby it supports the correct relative positioning of the active site residues. The Rg refers to the mass weighted root mean square distance of group of atoms from their common centre of mass. An observation into a global dimension of protein can be monitored in Rg. The average Rg value was found to be 1.9 nm with an SD of 0.01 nm. From the results, there was
TE D
no major fluctuation observed in LuxR protein between a time periods of 0 to 50 ns. The least Rg value indicates the compactness of the LuxR protein. Apart from these analyses, the stability of protein was measured using parameters including potential energy, total energy, temperature and pressure. In this
EP
study, all these parameters were calculated and found that the protein assemblies remain constant at the
AC C
average temperature of 299.61 K and a pressure of 1 bar. The potential energy (-1.40815 KJ mol-1) and total energy (-1.044772 KJ mol-1) of the system was found stable during the simulation time. The MD simulation confirms the stability of the predicted model. Hence, the LuxR model was used for the molecular docking studies. Molecular docking In computer aided drug design, molecular docking was frequently used to predict the binding orientation of small molecule for its optimal binding to a receptor or an enzyme active site. In the present study, two docking protocols such as RRD and IFD were used to predict the binding mode for
ACCEPTED MANUSCRIPT the studied inhibitors. The overall fold of LuxR was very similar to other members of the TetR super family, including Escherichia coli tetracycline repressor TetR [35, 36], Staphylococcus aureus multidrug binding transcription regulator QacR [37, 38] and Mycobacterium tuberculosis EthA
RI PT
repressor EthR [39, 40]. The two LuxR homologous from Vibrio species, such as the master quorum sensing regulator SmcR of V. vulnificus and HapR of V. cholerae have been recently crystallized [17, 41]. Both the proteins have shown conserved stretches in the putative ligand binding domain. The
SC
binding site is occupied by polar amino acid residues in one side of the pocket and hydrophobic residues on the other side. The active site informations obtained from SmcR has been used for docking
M AN U
studies. Seven cinnamaldehyde derivatives (Cinnamaldehyde (compound 1), 2-nitro-cinnamaldehyde (compound 2), 3,4-dichloro-cinnamaldehyde (compound 3), ((E)-4-phenyl-3-buten-2-one (compound 4), ((E)-3-decen-2-one (compound 5), (E)-2-pentenal (compound 6) and ((E)-2-nonenal (compound 7)) were docked into the active site region of the LuxR. The Glide score and Glide energy obtained from RRD do not correlate well with the IFD results, while both the protocols have shown similar interaction
TE D
profiles (Table 1). In RRD, Glide score of protein-ligand complexes ranges from -2.60 kcal/mol to 6.06 kcal/mol, in case of IFD, an improvement on ligand binding poses was observed (Glide score ranges from -3.36 kcal/mol to -8.17 kcal/mol). The docking results showed that IFD was able to
EP
prospectively predict the favourable binding orientation of cinnamaldehyde derivatives in the binding
AC C
site of LuxR. Therefore, IFD results are discussed in further pursuing sections. Based on the experimental evidence, cinnamaldehyde derivatives (compounds 3, 6 & 7) exhibit potent inhibitory activity in the nanomolar range than other cinnamaldeyde derivatives (compound 1, 2, 4, & 5) against V. harveyi [14]. The binding mode of three cinnamaldehyde (compound 3, 6 & 7) derivatives at the active site of the protein is shown in Supplementary Fig. 1Sa-1Sd. Structurally, the dock poses obtained from RRD are very close to the dock poses generated by IFD. The receptor-ligand interactions are observed through the formation of hydrogen bonds. The oxygen atom of the carboxyl group of compound 3 interacts with the amide group of Arg133 with the distance of 1.95 Å. Additionally, two π-
ACCEPTED MANUSCRIPT π stacking interactions were observed with the amino acid residues Phe166 and Trp114. A similar type of interaction patterns were observed for all other cinnamaldehyde derivatives, but there is a drastic difference in their hydrogen bonding distance. The hydrogen bond distance observed between the
RI PT
carboxyl group of cinnamaldehyde derivatives (compound 1 & 2, compound 4-7) with the amide group of Asn133 are 2.06 Å, 2.01 Å, 2.24 Å, 2.09 Å, 2.39 Å & 2.00 Å, respectively. Overall, the docking
to their stability. Molecular dynamics simulations of protein-ligand complex
M AN U
Root mean square deviation
SC
results confirms the key role of Asn133 in ligand binding and π-π stacking interactions that contribute
To gain insights on ligand induced protein conformation, the RMSD of protein backbone were analyzed from the starting structure over the course of the simulation. Fig. 3a illustrates the graphical depiction of the RMSD profile of the protein-ligand complexes over a period of 30 ns of MD simulations. The LuxR-2-nitro-cinnamaldehyde (complex 2) and LuxR-((E)-4-phenyl-3-buten-2-one
TE D
(complex 4) has shown the lower and higher RMSD values, among other protein-ligand complexes. These two complexes have shown an average RMSD of 0.24 nm and 0.42 nm, respectively. LuxRcinnamaldehyde (complex 1) has shown initial fluctuations from 0 to 10 ns and then reached a stable
EP
state with an average RMSD and SD value of 0.30 nm and 0.03 nm. Complex 2 (LuxR-2-nitro-
AC C
cinnamaldehyde) do not show much larger deviation during the simulation time, and constant stability was observed with the RMSD and SD value of 0.24 nm and 0.03 nm, respectively. LuxR-3,4-dichlorocinnamaldehyde (complex 3) exhibits a much larger degree of fluctuation during initial 3 ns of simulations and then reached a stable RMSD value of 0.23 nm after 5 ns with an SD of 0.03 nm. Complex 4 (LuxR-(E)-4-phenyl-3-buten-2-one) has shown higher RMSD deviation with an SD value of 0.08 nm. LuxR-(E)-3-decen-2-one (complex 5) deviated slightly during initial 6 ns of simulations and then the RMSD settled around 0.25 nm with an SD of 0.03 nm. Large scale conformational changes in the LuxR-(E)-2-pentenal (complex 6) were observed up to 15 ns and once the complex is
ACCEPTED MANUSCRIPT well equilibrated, it remained stable for the remaining 15 ns with an RMSD of 0.38 nm. The SD value of 0.03 nm was maintained for the complex 6. It was noted that the LuxR-(E)-2-nonenal (complex 7) does not deviate much and remained highly stable with the RMSD of 0.36 nm with an SD of 0.03 nm.
RI PT
Results from RMSD analysis confirmed the binding mode and stability of the protein-ligand complexes. Root mean square fluctuation
SC
The RMSF variations were calculated to evaluate the fluctuation of each residue in the proteinligand complexes during the MD simulation (Fig. 3b). The plot clearly displayed that none of the
M AN U
residues present in the protein shown an RMSF value more than 0.93 nm. The overall fluctuations of complex 1 have shown the fluctuation rate of 0.14 nm with an SD of 0.06 nm. The amino acid residues showing higher fluctuations includes Met1 (0.60 nm), Arg11 (0.51 nm), Arg202 (0.61 nm), Glu203 (0.57 nm), His204 (0.77 nm) and His 205 (0.96 nm). The same kind of fluctuations was observed in complex 2. Complex 3 have shown RMSF value of 0.16 nm and SD of 0.07 nm fluctuations throughout
TE D
the simulation. Higher fluctuations were observed in the amino acid residues, which includes Met1 (0.55 nm), Gly2 (0.32 nm), Ser3 (0.30 nm), Arg9 (0.36 nm), Thr10 (0.35 nm), Arg11 (0.45 nm), Arg17 (0.30 nm), Ile34 (0.31 nm), Arg36 (0.34 nm), Arg202 (0.38 nm), Glu203 (0.33 nm), His204 (0.36 nm)
EP
and His205 (0.51 nm). These residues are insignificant for the study since the major dynamic changes
AC C
were found absent in the active site amino acid residues of the protein. Complex 4 & complex 5 have shown the fluctuation value of 0.17 nm, 0.17 nm with an SD of 0.07 nm and 0.11 nm, respectively. The amino acid residues showing higher fluctuations in complex 4 includes Met1 (0.59 nm), Glu203 (0.47 nm), His204 (0.62 nm) and His205 (0.55 nm). Higher mobility was observed in the amino acid residues in complex 5 includes Arg11 (0.46 nm), Gly35 (0.44 nm), Arg36 (0.59 nm), His204 (0.50 nm) and His205 (0.63 nm). The major co-operative fluctuations associated with the amino acid residue or loop region of complex 6, includes Met1 (0.72 nm), Gly2 (0.44 nm), Ser3 (0.39 nm), Arg9 (0.36 nm), Thr10 (0.34 nm), Arg11 (0.50 nm), Arg32 (0.41 nm), Gly35 (0.38 nm), Arg36 (0.50 nm), Arg150 (0.32 nm),
ACCEPTED MANUSCRIPT Arg202 (0.30 nm), Glu203 (0.47 nm), His204 (0.61 nm) and His205 (0.62 nm). The RMSF with an SD value was found to be 0.17 nm and 0.09 nm, respectively. The RMSF value of 0.77 nm with an SD of 0.09 nm was observed in complex 7. However, few amino acid residues exhibiting high mobility in
RI PT
complex 7 includes Met1 (0.48 nm), Arg11 (0.37 nm), Arg36 (0.31 nm), Arg122 (0.31 nm), Gln156 (0.33 nm), Lys181 (0.30 nm), Arg202 (0.50 nm), Glu203 (0.56 nm), His204 (0.70 nm) and His205 (0.89 nm). The interacting amino acid residue Asn133 has shown slight conformational changes in the
SC
protein-ligand complexes, which includes 0.17 nm (complex 1 & complex 2), 0.12 nm (complex 3), 0.19 nm (complex 4), 0.12 nm (complex 5), 0.15 nm (complex 6) and 0.16 nm (complex 7). The
M AN U
catalytic residues present in the active site of the protein are unchanged upon binding of the compounds. The result clearly indicates that higher fluctuations coincide with the loop region of the protein (N- and C-terminal domains). It was also concluded that these fluctuations does not affect the overall structural stability of the protein at the active site. Solvent accessible surface area
TE D
SASA were used to predict the ligand induced conformational changes in the protein structure. Fig. 3c shows the solvation free energy (∆Gsolv) of the protein-ligand complexes. Average solvation free energy value of the complexes (complex 1-complex 7) are found to be 308.81 nm, 320.78 nm,
EP
312.51 nm, 314.20 nm, 310.17 nm, 320.17 nm, 313.96 nm with the SD of 12.14 nm, 16.00 nm, 12.12
AC C
nm, 13.20 nm, 14.18 nm, 12.42 nm and 12.08 nm, respectively. Low SASA value was observed for five complexes (1, 3, 4, 5, & 7) compared with other two complexes (2 & 6). The Low SASA value reflects higher thermodynamic stability. The result indicates that the protein attains higher stability against the binding of cinnamaldehyde derivatives in the binding site of LuxR. SASA results are more comparable with the RMSF plot. Hydrophobic contact The contribution of hydrophobic interactions between protein-ligand complexes were analyzed to understand the effect of packing (Fig. 3d). In addition, the protein-ligand complexes have shown
ACCEPTED MANUSCRIPT very similar hydrophobic contacts during the MD simulation. The average hydrophobic value of the (complex 1-complex 7) complexes were found to be 57.81 nm, 60.18 nm, 58.48 nm, 59.30 nm, 58.31 nm, 59.97 nm and 59.37 nm with the SD of 1.86 nm, 2.32 nm, 1.63 nm, 2.02 nm, 2.07 nm, 1.69 nm and
RI PT
1.73 nm, respectively. The protein-ligand complexes are stabilized through these hydrophobic interactions. Radius of gyration
SC
The simulations were analyzed in terms of Rg over 30 ns. The average Rg value of the (complex 1-complex 7) complexes (Fig. 3e) are found to be 1.84 nm, 1.86 nm, 1.85 nm, 1.91 nm, 1.90
M AN U
nm, 1.89 nm, 1.86 nm with the SD value of 0.02 nm, 0.03 nm, 0.02 nm, 0.01 nm, 0.01 nm, 0.01 nm and 0.02 nm, respectively. The low Rg value with minimal SD indicates that the protein undergo least conformational change upon ligand binding and also indicates the compactness of the structural network. Hydrogen bond analysis
TE D
The strength of hydrogen bond interactions between protein-ligand complexes were investigated during the simulation time and it is schematically represented in Fig. 3f. Hydrogen bond plays a major factor in controlling the steady conformation of protein. The hydrogen bond interaction
EP
between the carboxyl group of Asn133 and an amide group of cinnamaldehyde derivatives were found
AC C
stable throughout the whole simulation of 30 ns in all the protein-ligand complexes. From the figure, it was clear that a maximum of 2, 4, 3, 3, 3, 3, and 2 inter molecular hydrogen bond interactions were observed in the protein-ligand (complex 1-complex 7) complexes, respectively. The maximum number of H-bond interactions observed makes the complexes more rigid. Moreover, there is no significant change in the binding pattern of cinnamaldehyde derivatives within the binding site of the LuxR protein. e-pharmacophore based virtual screening The e-pharmacophore method uses spatial information of the target protein for a topological
ACCEPTED MANUSCRIPT description of the ligand-receptor interactions. Based on the experimental and computational evidence, the docked complex (LuxR-3,4-dichloro-cinnamaldehyde) was used as a basis to generate the epharmacophore model. The predicted model consists of four pharmacophoric sites, including one
RI PT
hydrogen bond acceptor (A), one aromatic ring (R) and two hydrophobic regions (H) and their distance are shown in Fig. 4. The acceptor A1 lies in the C=O group, while two chlorine atoms at 3 & 4 position of the basic structure occupies the hydrophobic region (H2 & H3) and the aromatic ring is occupied in
SC
the spacer region. The best pharmacophore hypotheses were used as a 3D query for screening ChemBridge database. Compounds retrieved from the database were subject to screening protocol
M AN U
based on the drug-like prediction which includes Lipinski's rule of five and the number of rotatable bond ≤ 7. In total, 943 compounds passing AMDE filters were then subjected to virtual screening. These compounds have fitness score ranging from 2.43 to 2.78. The first level of docking (HTVS) screens 95 compounds and these hits were further refined and filtered using SP (standard precision) mode. About 19 compounds from SP docking were re-docked into the more precise mode of Glide XP.
TE D
Finally, 9 compounds were identified as potent hits against LuxR. All the four pharmacophoric sites were found in the identified hits. The chemical name of the top two hits with their ChemBridge database identity number are 2-(4-chloro-3-methylphenoxy)-N-4-pyridinylacetamide (ChemBridge-
EP
6575543) and 3,4-dichlorobenzyl N-benzoylglycinate (ChemBridge-7477948). These hits contain
AC C
chlorine atom and carboxyl functional group, mainly responsible for the formation of hydrogen bonding interactions with the LuxR protein. Shape based virtual screening The structure of 3,4-dichloro-cinnamaldehyde was used as a 3D query for shape based screening. Shape screening is ideally suited for use in the early stages of lead discovery. In shape based screening, the molecules are ranked on the basis of their similarity to a known active molecule in three dimensional shape spaces. Different generated conformers are aligned to the target molecules in a various way and the shape similarity is computed based on the hard sphere atom volume overlaps.
ACCEPTED MANUSCRIPT Alignment between each conformer yields the similarity for each molecule. Compounds retrieved from ChemBridge database were filtered based on phase-sim-score. About 5869 compounds with the highest sim-score value in the range of 0.77 to 0.50 were selected for further studies. Screening protocol
RI PT
constraints like Lipinski's rule of five and the number of rotatable bond ≤ 7 were applied to eliminate the unwanted compounds. Compounds passing the filters were subjected to HTVS mode. About 558 compounds retained from HTVS were passed to SP docking and compound survive here were taken to
SC
the precise XP docking. Seven compounds with remarkable high binding affinities towards LuxR were finally chosen for binding mode analysis. The systematic name of the top seven compounds with their
M AN U
identity number are 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2yl)-2-propen-1-one (ChemBridge-7364186), N-(4-chlorobenzyl)-N-methyl-N'-phenylurea (ChemBridge-9192049), 1-[(4-chlorobenzyl)sulfinyl]-3methoxyl-2-propanol
(ChemBridge-5350755),
2-(4-chlorophenyl)-N-(-4-nitrobenzyl)ethanamine
(ChemBridge-5521550), N-benzyl-N'(4-chlorobenzylidene) formic hydrazide (ChemBridge-5671025), 3-(4-chlorophenyl)-N-(3-phenylpropyl)acrylamide
(ChemBridge-7243765)
and
2-(4-chloro-3-
Binding mode analysis
TE D
methylphenoxy)-N-(3-nitrophenyl) acetamide (ChemBridge-6347339).
Finally, 9 hits (two from e-pharmacophore and seven from shape-based screening) based on the
EP
scoring functions were selected for binding mode analysis. The binding mode of the identified hits was
AC C
also validated using IFD. The identified hits have shown stable interaction with the binding site amino acid residues of LuxR and are shown in Fig. 5a-5h. Moreover, the hits showed docking score ranges from -7.62 kcal/mol to -8.26 kcal/mol (Table 2). Inspection of the docking results revealed that the amino acid residue Asn133, Gln137, His167, Phe166 and Phe75 plays a critical role in ligand binding. The carboxyl and amide group of ChemBridge-7364106 forms hydrogen bond interactions with the side chain amino acid residues Asn133 (C=O...H2N, bond length=2.43 Å) and Gln137 (NH...O=C, bond length=1.95 Å). Additionally, four π-π stacking interactions were observed with the amino acid residues His167, Trp114, Phe166 and Phe66. ChemBridge-5350755 interact with Asn133 (C-O...H2N,
ACCEPTED MANUSCRIPT bond length=1.99 Å, OH...O=C, bond length=1.98 Å), Gln137 (OH...O=C, bond length=1.98 Å) forming a total of three hydrogen bond interactions. Meanwhile, two π-π stacking interactions were observed with the residues Phe166 and Phe75. It was observed that the amide group of ChemBridge-
RI PT
5521550 has shown hydrogen bond interaction with side chain amino acid residue Gln137 (NH...O=C, bond length=1.85 Å). Amino acid residues including His167, Phe166 and Phe75 have shown π-π stacking interactions with the basic aromatic ring of ChemBridge-5521550. The amide group of all
SC
other hits (ChemBridge-9192049, ChemBridge-5671025, ChemBridge-7243765, ChemBridge6347339, ChemBridge-6575543 & ChemBridge-7477973) has shown hydrogen bond interaction with
M AN U
side chain amino acid residue Asn133, but differs in their bonding length. The observed hydrogen bond distance of these hits was found to be 1.97 Å, 1.98 Å, 2.24 Å, 1.99 Å, 2.00 Å & 2.19 Å, respectively. Furthermore, the aromatic ring of these hits showed π-π stacking interactions with the amino acid residues His167, Phe166 and Phe75. Molecular dynamics simulations
TE D
In order to confirm the binding mode and stability of the identified hit molecules in the binding site of LuxR, MD simulation of the protein-ligand complexes were performed. Root mean square deviation
EP
The stability of protein-ligand (LuxR-7364106, LuxR-9192049, LuxR-5350755, LuxR-
AC C
5521550, LuxR-5671025, LuxR-7243765, and LuxR-6347339 & LuxR-6575543) complexes was studied using MD simulation. Each system was subjected to position restraint simulation for 10 ns and the RMSD of backbone atoms were calculated over the entire MD simulation trajectories (Supplementary Fig. 2Sa). The nine protein-ligand complexes have shown the RMSD values of 0.30 nm, 0.19 nm, 0.24 nm, 0.31 nm, 0.27 nm, 0.23 nm, 0.25 nm, 0.22 nm & 0.24 nm with the SD of 0.04 nm, 0.03 nm, 0.04 nm, 0.08 nm, 0.04 nm, 0.03 nm, 0.05 nm, 0.02 nm & 0.02 nm, respectively. All these complexes have shown stable and low RMSD value, indicating the stability of the each complex during the simulation period. From the results, it was also observed that all the complexes have shown
ACCEPTED MANUSCRIPT very less deviation from the initial structure. Root mean square fluctuation The RMSF of the C-α atom of each complex was plotted to evaluate the flexibility of each
RI PT
residue during the simulation time (Supplementary Fig. 2Sb). The overall fluctuation rate of each complex has shown an average of 0.16 nm, 0.15 nm, 0.15 nm, 0.18 nm, 0.17 nm, 0.16 nm, 0.17 nm, 0.16 nm, 0.17 nm with the SD of 0.10 nm, 0.08 nm, 0.09 nm, 0.10 nm, 0.08 nm, 0.09 nm, 0.10 nm,
SC
0.07 nm & 0.06 nm, respectively. In LuxR-7364106, the major fluctuation occurs in the C-terminal region, which includes Arg202 (0.62 nm), Glu203 (0.60 nm) and His204 (0.93 nm). Observations on
M AN U
LuxR-9192049 have shown slight deviation in the residues such as Met1 (0.55 nm), His204 (0.66 nm) and His205 (0.63 nm). The C-terminal region in the LuxR-5350755 have shown higher fluctuation of 0.54 nm for Glu203, 0.74 nm for His204 and 0.89 nm for His205. Few amino acid residues in the LuxR-5521550 exhibited greater mobility which includes Met1 (0.54 nm), Glu203 (0.69 nm) and His204 (0.79 nm). In case of LuxR-5671025 and LuxR-7243765 complexes, the amino acid residue
TE D
Glu203 has shown a fluctuation rate of 0.50 nm, His204 residue has shown the average fluctuation of 0.66 nm, whereas the residue His205 has shown the fluctuation of 0.72 nm. The LuxR-6347339 complex induced a conformational change in the C-terminal region, which includes Arg202 (0.53 nm),
EP
Glu203 (0.60 nm) and His204 (0.88 nm). However, two amino acid residues such as Met1 (0.54 nm)
AC C
and His205 (0.59 nm) have shown higher fluctuation in the LuxR-6575543 complex. These residues are insignificant for the study since the major dynamic changes are found absent in the active site of the protein. From the RMSF plot, it was observed that most of the residue in the protein was found stable and the residues lie in the loop regions had shown larger fluctuations. Solvent accessible surface area The SASA were calculated for the protein-ligand complexes and are shown in Supplementary Fig. 2Sc. The complexes (complex 1-complex 9) have shown an average free energy of solvation (∆G) of 363.09 nm 378.95 nm, 357.84 nm, 378.06 nm, 378.62 nm, 383.64 nm, 374.38 nm, 373.77 nm,
ACCEPTED MANUSCRIPT 376.48 nm with the SD of 14.10 nm, 11.52 nm, 15.48 nm, 12.53 nm, 10.89 nm, 11.76 nm, 11.66 nm, 13.12 nm, 12.96 nm, respectively during the simulation time. The result indicates that the protein attain higher stability upon ligand binding. Low variation in the SASA plot indicates higher thermodynamic
RI PT
stability. Hydrophobic contacts
The hydrophobic contacts between protein-ligand complexes were analyzed and are shown in
SC
Supplementary Fig. 2Sd. From the result, the LuxR-5671025 complex has shown higher hydrophobic contacts, while LuxR-5350755 complex has shown lower hydrophobic contact. The average
M AN U
hydrophobic contacts for the protein-ligand complexes (complex 1-complex 9) are 62.79 nm, 63.37 nm, 61.75 nm, 64.07 nm, 64.78 nm, 64.60 nm, 63.62 nm, 63.57 nm, 62.54 nm with the SD of 1.67 nm, 1.52 nm, 1.92 nm, 1.53 nm, 1.37 nm, 1.50 nm, 1.55 nm, 1.67 nm and 1.81 nm, respectively. The hydrophobic contacts observed between protein-ligand complexes remained stable throughout the simulation.
TE D
Radius of gyration
Supplementary Fig. 2Se shows the calculated Rg values of the protein-ligand complexes (complex 1-complex 9). Results showed that the average Rg values of 1.98 nm, 1.93 nm, 1.94 nm, 1.98
EP
nm, 1.98 nm, 1.97 nm, 1.94 nm, 1.93 nm, 1.95 nm with the SD of 0.02 nm, 0.01 nm, 0.01 nm, 0.01 nm,
AC C
0.01 nm, 0.02 nm, 0.01 nm, 0.02 nm and 0.01 nm were observed for the respective protein-ligand complexes. The least Rg value indicates the compactness of the protein-ligand complexes. Hydrogen bond analysis
The number of hydrogen bonds that mediate protein-ligand complexes were calculated for the entire 10 ns of MD simulation and the results are shown in Supplementary Fig. 2Sf. All the complexes have shown stable hydrogen bond interactions with the conserved amino acid residues Asn133 and Gln137. From the figure, it was clear that a maximum of 2, 3, 4, 3, 1, 3, 3, 2 & 4 inter molecular hydrogen bonds were observed for the respective protein-ligand complexes (complex 1-complex 9).
ACCEPTED MANUSCRIPT From the whole MD analysis, it was confirmed that the protein-ligand complexes were found stable during the whole 10 ns of simulation time. Density functional theory
RI PT
Electronic molecular features such as electron density, frontier molecular orbital density fields such as LUMO, HOMO and molecular electrostatic map have been reported to be significant in QSAR studies to explain the biological activity and molecular properties [42]. HOMO/LUMO is critical for
SC
charge transfer in the chemical reactions. The cinnamaldehyde derivatives have shown high HOMO values in the range of -0.23 eV to -0.26 eV (Table 3). The higher HOMO values indicate the tendency
M AN U
of strong binding to the LuxR. Lower LUMO values are related to the electron affinity and both HOMO/LUMO are indicators of the possible electrophilic and nucleophilic attack sites in the molecules. The smaller energy gap between the HOMO and their corresponding LUMO explains the chemical reactivity of the molecules. The energy gap of the cinnamaldehyde derivatives and identified hits were found to be in the range of -0.15 eV to 0.20 eV and 0.13 eV to 0.21 eV, respectively, which
TE D
implies high reactivity, high polarizability and low stability of the hits (Table 3). Lower the energy gap favors the electron to be excited from HOMO to LUMO, which consequently leads to the strong
Prime MM-GBSA
EP
binding affinity to the LuxR protein.
AC C
To investigate prime MM-GBSA re-scoring, the cinnamaldehyde derivatives and identified hits from virtual screening were re-ranked and tabulated in Table 1 & Table 2. The calculated binding free energies (∆G) of the LuxR-cinnamaldehyde derivatives (complex 1-complex 7) were found to be -24.04 kcal/mol, -27.37 kcal/mol, -27.25 kcal/mol, -36.36 kcal/mol, -57.28 kcal/mol and -57.12 kcal/mol, respectively. According to the absolute binding free energy calculation, complex 6 and complex 7 bound strongly to LuxR than other cinnamaldehyde derivatives. Experimental evidence revealed that compound 3 exhibits strong and consistent inhibitory activity, but the compound showed lower binding free energy. The observable free energy of binding does not correlate well with the
ACCEPTED MANUSCRIPT experimental data. The calculated binding free energy for the identified hit molecules ranges from -35.38 kcal/mol to -62.30 kcal/mol. ADME and toxicity prediction
RI PT
The ADMET properties are the primary cause for drug failure in the development phase and the resultant mounting cost of bringing a new drug to the market [43]. Thus, we tried to evaluate the ADMET properties of the identified hits using QikProp program and the computed results are tabulated
SC
in Table 4. The molecular weight of the identified hits ranges from 262.75 kDa to 338.19 kDa. The partition co-efficient (QPlogPo/w) and water solubility (QPlogS) are critical for the absorption and
M AN U
distribution of drugs within the body and the computed results ranges from 1.42 to 4.95 and 0.29 to 5.96, respectively. QPlogKp predicted skin permeability ranges from -0.49 to -4.18. QPPCaco that governs the drug metabolism and its access to biological membrane ranges from 179.88 to 3121.84. QPPMDCK are considered to be a good mimic cell for the blood brain barrier and the predicted apparent MDCK cell permeability ranges from 211.55 to 6456.33. The human oral absorption for the hits ranges from
TE D
69% to 100%. According to the ADMET prediction results, the pharmacokinetic properties of the hits are within the acceptable range. Therefore the hits were found suitable for further drug development process. PROTOX server revealed no toxicity related fragments present in the identified hit molecule.
EP
On the basis of pharmacokinetics and toxicity properties the hit ChemBridge-7364186 was taken for in
AC C
vitro assays. The 2-D structure of ChemBridge-7364186 is shown in Fig. 6. In vitro assays
Determination of minimal inhibitory concentration ChemBridge-7364186 inhibited the growth of V. harveyi at the least concentration of 16 µg/ml. Therefore, all further experiments were performed at mean sub-MIC concentration (0.5, 1, 2, 4, 8 µg/ml) of ChemBridge-7364106. DMSO is used as negative control in all the bioassays does not show any inhibition effect on the test organism.
ACCEPTED MANUSCRIPT Bioluminescence inhibition assay In V. harveyi, QS circuits control the expression of bioluminescence and virulence factors [44]. Therefore, the anti-quorum sensing properties of ChemBridge-7364106 were assessed using V. harveyi.
RI PT
The effect of ChemBridge-7364106 on bioluminescence of V. harveyi is shown in Fig. 7. As expected, ChemBridge-7364106 exhibited concentration dependent inhibition in bioluminescence without inhibiting the growth of V. harveyi. Our results clearly indicate that the enzyme of V. harveyi involved
SC
in bioluminescence was inhibited by ChemBridge-7364106. Our result falls in line with the report, [45] in which, the extracts of two Pseudo alteromonas isolates exhibited significant reduction in V. harveyi
M AN U
bioluminescence by 98% without altering the growth of the bacteria. Effect of ChemBridge-7364106 on biofilm development
As discussed in multiple reports, QS controls biofilm formation through AI-2 mediated cell signaling [46]. Therefore, it was hypothesized that ChemBridge-7364106 disrupt bioluminescence may have the inhibition effect on biofilm formation. The effect of ChemBridge-7364106 on biofilm
TE D
formation of V. harveyi was investigated and shown in Supplementary Fig. 3S. ChemBridge-7364106 reduced biofilm formation in a dose dependent manner. Moreover, at 8 µg/ml concentration the ChemBridge-7364106 has shown a maximum of 72% biofilm inhibition without affecting the bacterial
EP
growth rate. The findings of the present study consistent with the previous report [47], wherein the
AC C
synthetic cationic peptide 1037 had shown significant biofilm inhibition in Gram negative and Gram positive bacteria such as Pseudomonas aeruginosa wilde type strains PA14, PAO1, Burkholderia cenocepacia 4813 and Cisteria monocytogenes. Our result also falls in line with the report [48], wherein the A66 extract is documented to inhibit biofilm formation of V. harveyi without affecting the bacterial growth rate. Disintegration of mature biofilm The biofilm growing bacteria can be up to 1000 fold more resistant to antibiotics, host immune defense system than that of planktonic bacteria [49]. Therefore, biofilm disintegration plays a crucial
ACCEPTED MANUSCRIPT role to overcome infections that are highly resistant to antibiotics. The ChemBridge-7364106 treated cover glasses have shown a thin and disintegrated matrix of biofilm, whereas at control cover glasses continuous matrix was observed. The dispersal of biofilm on glass slides was observed under oil
RI PT
immersion light microscope using 40 X magnification. The light microscopic analysis showed in Fig. 8 explains the ability of ChemBridge-7364106 to disintegrate the mature biofilms of V. harveyi. Swimming and swarming assays
SC
The swimming and swarming motility play a key role in promoting the initial stages of biofilm formation in the Vibrios [33]. Therefore inhibiting bacterial motility represents an important strategy to
M AN U
control biofilm formation and bacterial colonization. The anti QS activity of ChemBridge-7364106 was assessed using swimming and swarming motility assays. The obtained result clearly indicates a great reduction in swimming and swarming motility after exposure to 8 µg/ml concentration of ChemBridge7364106. The result of the current findings consistent with the report [50], the curcumin was found to inhibit the mobility of the reporter strain V. harveyi MTCC 3438. Fig. 9a, 9b and Fig. 9c, 9d display
ChemBridge-7364106.
TE D
the reduction in swimming and swarming motility of V. harveyi at various concentrations of
Growth curve and bioluminescence kinetics assay
EP
In order to determine the antibacterial activity of ChemBridge-7364106, the growth curve assay
AC C
was determined by V. harveyi. The curve depicted in Fig. 10a shows the antibacterial activity of ChemBridge-7364106 at different concentrations. No differences in cell densities were observed in both treated (0.5-8 µg/ml) and untreated control after 16 hour incubation. In bioluminescence kinetic assay evidenced reduction in bioluminescence of V. harveyi by ChemBridge-7364106. Fig. 10b & 10c represents the reduction of bioluminescence of V. harveyi by the treatment of ChemBridge-7364106 at various concentrations. Conclusion We have applied RRD and IFD to determine the binding mode of cinnamaldehyde derivatives
ACCEPTED MANUSCRIPT in the binding site of LuxR. The study demonstrates that there was a significant hydrogen bond interaction between cinnamaldehyde derivatives with the polar amino acid residue Asn133 in the binding pocket of LuxR. Two π-π stacking interactions were observed with the residues Phe166 and
RI PT
Trp114. Thus the observed Asn133 interaction may play an important role in protein-ligand recognition process and π-π stacking interactions help better stabilization of protein-ligand complexes. Furthermore, the MD simulation analysis, confirmed the binding mode and stability of the complexes.
SC
Shape and e-pharmacophore based virtual screening were utilized to identify novel inhibitors for LuxR. The ADMET properties of the identified hits are within the acceptable range. The performed in vitro
M AN U
assays explained the potency of screened compound 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2propen-1-one with anti-QS properties. The overall result provides new insights for the development of novel drugs against LuxR of V. harveyi. The anti-QS activity of 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2yl)-2-propen-1-one is reported here for the first time. Acknowledgement
TE D
The authors thank the University Grants Commission (F. No. 40-169/2011 (SR), dated 05/07/2011), Government of India for financial support provided for this study. PS gratefully acknowledges UGC for the fellowship. The authors also thank the Department of Bioinformatics,
EP
Alagappa University and UGC Innovative Scheme (No. F. 14-13/2013 (Inno/ASIST) for the
AC C
computational facilities provided for this work. References
1. T. Defoirdt, N. Boon, P. Sorgeloss, W. Verstraete, P. Bossier, Quorum sensing and quorum quenching in Vibrio harveyi: lessons learned from in vivo work, ISME J. 2 (2008) 19-26. 2. M. Asif, M. Acharya, Quorum sensing; A novel target for antibacterial agents, Avicenna. J. Med. 2 (2012) 97-99. 3. N.A. Whitehead, A.M. Barnard, H. Slater, N.J. Simpson, G.P. Salmond, Quorum-sensing in gram negative bacteria, FEMS Microbiol. Rev. 25 (2001) 365-404. 4. S.W. Teng, Y. Wang, K.C. Tu, T. Long, P. Mehta, N.S. Wingreen, B.L. Bassler, N.P. Ong,
ACCEPTED MANUSCRIPT Measurement of the copy number of the master quorum sensing regulator of a bacterial cell. Biophys J. 98 (2010) 2024-2031. 5. M.J. Federle, B.L. Bassler, Interspecies communication in bacteria, J. Clin. Invest. 112 (2003) 1219-1299.
RI PT
6. S.T. Rutherford, B.L. Bassler, 2012. Bacterial quorum sensing: its role in virulence and possibilities for its control, Cold. Spring. Harb. Perspect. Med. 2 (2012) 1-25.
7. A. Pai, Y. Tanouchi, C.H. Collins, L. You, Engineering multicellular systems by cell-cell communication, Curr. Opin. Biotechnol. 20 (2009) 461-470.
8. L.R. Swem, D.L. Swem, C.T. O'Loughlin, R. Gatmaitan, B. Zhao, S.M. Ulrich, B.L. Bassler, A
SC
quorum sensing antagonist targets both membrane bound and cytoplasmic receptors and controls bacterial pathogenicity, Mol. Cell. 35 (2009) 143-153.
M AN U
9. M.E. Taga, B.L. Bassler, Chemical communication among bacteria. Proc. Natl. Acad. Sci. USA, 2 (2003)14549-14554.
10. J.M. Henke, B.L. Bassler, Three parallel quorum sensing systems regulate gene expression in Vibrio harveyi, J. Bacteriol. 186 (2004) 6902-6914.
11. B.N. Lilley, B.L. Bassler, Regulation of quorum sensing in Vibrio harveyi by LuxO and sigma54, Mol. Microbiol. 36 (2000) 940-954.
TE D
12. D.H. Lenz, K.C. Mok, B.N. Lilley, R.V. Kulkarni, N.S. Wingreen, B.L. Bassler, The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae, Cell. 118 (2004) 69-82.
13. A. Tala, D. Delle Side, G. Buccolieri, S.M. Tredici, L. Velardi, F. Paladini, M. De Stefano, V.
EP
Nassisi, P. Alifano, Exposure to static magnetic field stimulates quorum sensing circuit in luminescent Vibrio strains of the Harveyi clade, PLoS One. 9 (2014) 1-12.
AC C
14. G. Brackman, S. Celen, U. Hillaert, S. Van Calenbergh, P. Cos, L. Maes, H.J. Nelis, T. Coenye, Structure-activity relationship of cinnamaldehyde analogs as inhibitors of AI-2 based quorum sensing and their effect on virulence of Vibrio spp, PLoS One. 6 (2011) 1-10. 15. S. Rajamanikandan, J. Jeyakanthan, P. Srinivasan, Binding mode exploration of LuxRthiazolidinedione analogues, e-pharmacophore based virtual screening in the designing of LuxR inhibitors and its biological evaluation, J. Biomol. Struct. Dyn. 3 (2016) 1-20. 16. W.L. Jorgensen, D.S. Maxwell, J. Tirado-Rives, Development and testing of all OPLS all-atom force field on conformation energetics and properties of organic liquids, J. Am. Chem. Soc. 118 (1996) 11225-11236. 17. Y. Kim, B.S. Kim, Y.J. Park, W.C. Choi, J. Hwang, B.S. Kang, T.K. Oh, S.H. Choi, M.H. Kim,
ACCEPTED MANUSCRIPT Crystal structure of SmcR, a quorum sensing master regulator of Vibrio vulnificus, provides insight into its regulation of transcription, J. Biol. Chem. 285 (2010) 14020-14030. 18. Glide, Version, 6.2, Schrödinger, LLC, New York, 2014. 19. B. Hess, C. Kutzner, D. van der Spoel, E. Lindahl, GROMACS 4: Algorithms for highly
RI PT
efficient, load balanced and scalable molecular simulation, J. Chem. Theory. Comput. 4 (2008) 435-447.
20. C. Ooestenbrink, A.Villa, A.E. Mark, W.F. van Gunsteren, A biomolecular force field based on the free enthalpy of hydrogen and solvation: the GROMOS force field parameter sets 53A5 and 53A6, J. Comput. Chem. 25 (2004) 1656-1676.
SC
21. C. Oostenbrink, T. Soares, N.F. van der Vegt, W.F. van Gunsteren, Validation of the 53A6 GROMOS force field, Eur. Biophys. J. 34 (2005) 273-284.
M AN U
22. A.W. Schuettelkopf, D.M. van Aalten, PRODRG: a tool for high throughput crystallography of protein-ligand complexes, Acta Crystallogr D Biol Crystallogr. 60 (2004) 1355-1363. 23. B. Hess, H. Bekker, H.J.C. Berendsen, G.E.M. Fraaije, LINCS: A linear constraint solver for molecular simulations, J. Comput. Chem. 18 (1997) 1463-1472. 24. D. Tom, Y. Darrin, P. Lee, Particle mesh Ewald: An N.log (N) method for Ewald sums in large systems, J. Chem. Phys. 98 (1993) 10089-10092.
TE D
25. S. Miyamoto, P.A. Kollman, Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models, J. Comput. Chem. 13 (1992) 952-962. 26. N.K. Salam, R. Nuti, W. Sherman, Novel method for generating structure based pharmacophore using energetic analysis, J. Chem. Inf. Model. 49 (2009) 2356-2368.
EP
27. G.M. Sastry, S.L. Dixon, W. Sherman, Rapid shape based ligand alignment and virtual screening method based on atom/feature pair similarities and volume overlap scoring, J. Chem.
AC C
Inf. Model. 51 (2011) 2455-2466. 28. C. Lee, W. Yang, R.G. Parr, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density, Phys. Rev. B. Condens. Matter. 37 (1998) 785-789. 29. A.D. Becke, Density functional thermochemistry III. The role of exact exchange, J. Chem. Phys. 98 (1993) 5648-5652. 30. M.N. Drwal, P. Banerjee, M. Dunkel, M.R. Wettig, R. Preissner, ProTox: a web server for in silico prediction of rodent oral toxicity, Nucleic. Acids. Res. 42 (2014) W53-58. 31. C. Nithya, S.K. Pandian, Isolation of heterotrophic bacteria from Palk Bay sediments showing heavy metal tolerance and antibiotic production, Microbiol. Res. 165 (2010) 578-593. 32. K.S. Musthafa, V. Saroja, S.K. Pandian, A.V. Ravi, Antipathogenic potential of marine Bacillus
ACCEPTED MANUSCRIPT sp. SS4 on N-acyl-homoserine-lactone-mediated virulence factors production in Pseudomonas aeruginosa (PAO1), J. Biosci. 36 (2011) 55-67. 33. P.R. Jesudhasan, M.L. Cepeda, K. Widmer, S.E. Dowd, K.A. Soni, M.E. Hume, J. Zhu, S.D. Pillai, Transcriptome analysis of genes controlled by luxS/autoinducer-2 in Salmonella enterica
RI PT
serovar Typhimurium, Foodborne. Pathog. Dis. 7 (2010) 399-410. 34. A. Cardone, S.A. Hassan, R.W. Albers, R.D. Sriram, H.C. Pant, Structural and dynamic determinants of ligand binding and regulation of cyclin dependent kinase 5 by pathological activator p25 and inhibitory peptide CIP, J. Mol. Biol. 401 (2010) 478-492.
35. W. Hinrichs, C. Kisker, M. Duvel, A. Muller, K. Tovar, W. Hillen, W. Saenger, Structure of the
SC
Tet repressor-tetracycline complex and regulation of antibiotic resistance, Science 264 (1994) 418-420.
M AN U
36. P. Orth, D. Schnappinger, W. Hillen, W. Saenger, W. Hinrichs, Structure basis of gene regulation by the tetracycline inducible Tet repressor-operator system, Nat. Struct. Biol. 7 (2000) 215-219. 37. M.A. Schumacher, M.C. Miller, S. Grkovic, M.H. Brown, R.A. Skurray, R.G. Brennan, Structural mechanisms of QacR induction and multidrug recognition, Science. 294 (2001) 2158-2163.
38. M.A. Schumacher, M.C. Miller, S. Grkovic, M.H. Brown, R.A. Skurray, R.G. Brennan,
TE D
Structural basis for cooperative DNA binding by two dimers of the multidrug-binding protein QacR, EMBO J. 21 (2002) 1210-1218.
39. L.G. Dover, P.E. Corsino, I.R. Daniels, S.L. Cocklin, V. Tatituri, G.S. Besra, K. Futterer, Crystal structure of the TetR/CamR family repressor Mycobacterium tuberculosis EthR implicated in
EP
ethionamide resistance, J. Mol. Biol. 340 (2004) 1095-1105. 40. F. Frenois, J. Engohang-Ndong, C. Locht, A.R. Baulard, V. Villeret, Structure of EthR in a
AC C
ligand bound conformation reveals therapeutic perspectives against tuberculosis, Mol. Cell. 16 (2004) 301-307.
41. R.S. De Silva, G. Kovacikova, W. Lin, R.K. Taylor, K. Skorupski, F.J. Kull, Crystal structure of the Vibrio cholerae quorum sensing regulatory protein HapR, J. Bacteriol. 189 (2007) 56835691.
42. M. Arooj, S. Thangapandian, S. John, S. Hwang, J.K. Park, K.W. Lee, 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors, Int. J. Mol. Sci. 12 (2011) 9236-9264. 43. M. Zheng, X. Liu, Y. Xu, H. Li, C. Luo, H. Jiang, Computational methods for drug design and discovery: focus on china, Trends. Pharmacol. Sci. 34 (2013) 549-559.
ACCEPTED MANUSCRIPT 44. C. Anetzberger, U. Schell, K. Jung, Single cell analysis of Vibrio harveyi uncovers functional heterogeneity in response to quorum sensing signals, BMC Microbiol. 12 (2012) 1-10. 45. J.S. Linthorne, B.J. Chang, G.R. Flematti, E.L. Ghisalberti, D.C. Sutton, A direct pre-screen for marine bacteria producing compounds inhibiting quorum sensing reveals diverse planktonic
RI PT
bacteria that are bioactive, Mar. Biotechnol. 17 (2015) 33-42. 46. J.A. Thompson, R.A. Oliveira, A. Djukovic, C. Ubeda, K.B. Xavier, Manipulation of the quorum sensing signal AI-2 affects the antibiotic treated gut microbiota, Cell. Rep. 10 (2015) 1861-1871.
47. C. de la Fuente-Nunez, V. Korolik, M. Bains, U. Nguyen, E.B. Breidenstein, S. Horsman, S.
SC
Lewenza, L. Burrows, R.E. Hancock, Inhibition of bacterial biofilm formation and swarming motility by a small synthetic cationic peptide, Antimicrob. Agents. Chemother. 56 (2012) 2696-
M AN U
2704.
48. J. You, X. Xue, L. Cao, X. Lu, J. Wang, L. Zhang, S. Zhou, Inhibition of Vibrio biofilm formation by a marine actinomycete strain A66, Appl. Microbiol. Biotechnol. 76 (2007) 11371144.
49. F. Sun, F. Qu, Y. Ling, P. Mao, P. Xia, H. Chen, D. Zhou, Biofilm associated infections: antibiotic resistance and novel therapeutic strategies, Future. Microbiol. 8 (2013) 877-886.
TE D
50. I.A. Packiavathy, S. Priya, S.K. Pandian, A.V. Ravi, Inhibition of biofilm development of uropathogens by curcumin - an anti-quorum sensing agent from Curcuma longa. Food. Chem.
Table legends
EP
148 (2014) 453-460.
AC C
Table 1 Docking scores, interaction profiles and predicted binding free energy estimates (kcal/mol) of the cinnamaldehyde derivatives. Table 2 Docking scores, binding free energy and hydrogen bond interaction between LuxR and 9 hits identified from ChemBridge database. Table 3 HOMO, LUMO and energy gap of cinnamaldehyde derivatives and hit molecules. Table 4 Physico-chemical properties of hits obtained from Qikprop of Schrödinger.
ACCEPTED MANUSCRIPT Figure legends Fig. 1. Pictorial representation of workflow followed in this study. Fig. 2. (a) RMSD of backbone atoms plotted against the time dependent function of the MD
RI PT
simulation, (b) RMSF of C-α atom of the individual residues in the protein analyzed during the simulation time.
Fig. 3. (a) Graphical representation of RMSD of C-α atoms of the protein-ligand complexes, (b) RMSF
SC
of C-α atoms in co-ordinates for each residue of the protein-ligand complexes during the simulation time, (c) Total SASA are calculated, showing the total surface area occupied in the protein-ligand
M AN U
complexes during the simulation period, (d) Hydrophobic contact of the protein-ligand complexes, (e) Radius of gyration as a function of time, showing the structural compactness of the protein-ligand complexes during the simulation time, (f) Total number of inter-molecular hydrogen bond interactions observed between the cinnamaldehyde derivatives and LuxR during the simulation time. Frames are written out every 2 ps. All the plots are well correlated.
TE D
Fig. 4. Common pharmacophore hypotheses (AHHR) and their distance between the pharmacophoric sites. All the distance is in the Å units. Red sphere represents acceptor feature, green hydrophobic feature and orange aromatic ring feature.
EP
Fig. 5. 2D representations of the protein-ligand interactions for the hits identified from the virtual
AC C
screening. Hydrogen bond interactions are shown in pink dashed line and π-π stacking interactions are in green solid line. (a) LuxR-7364106, (b) LuxR-9192049, (c) LuxR-5350755, (d) LuxR-5521550, (e) LuxR-5671025, (f) LuxR-7245765, (g) LuxR-6347339, (h) LuxR-6575543. Fig. 6. 2D structure of ChemBridge-7364106 Fig. 7. Inhibition of V. harveyi bioluminescence by ChemBridge-7364106 at various concentrations. Bioluminescence is measured as relative light units using a luminometer. Fig. 8. Light Microscope image, (a) control, (b) ChemBridge-7364106 treated (8 µg/ml) culture of V. harveyi.
ACCEPTED MANUSCRIPT Fig. 9. Swimming behavior of V. harveyi, (a) untreated control, (b) ChemBridge-7364106 treated (8 µg/ml) culture of V. harveyi. Swamming behavior of V. harveyi (c) untreated control, (d) ChemBridge7364106 treated (8 µg/ml) of V. harveyi.
RI PT
Fig. 10. (a) Effect of ChemBridge-7364106 at various concentrations on growth of V. harveyi strain. Cell density is quantified by absorbance measurement at 600nm, (b) Effect of bioluminescence in V. harveyi in the presence or absence of the ChemBridge-7364106. Bioluminescence measurements are
SC
performed 4h after the addition of the compound. Supplementary Figures
M AN U
Fig. 1S. Stable conformation of three potent cinnamaldehyde derivatives in the binding site of LuxR. Compound 3, 6, 7 (a, b, c) forming 1Hbond interaction with Asn133. (d) Superimposition of all three cinnamaldehyde derivatives in the protein binding site.
Fig. 2S. (a) The RMSD plot showing the stability of hits in the bound state with LuxR for the time scale of 10 ns, (b) RMSF values of atomic positions calculated for the backbone atoms for the LuxR-hit
TE D
complexes as a function of residue number, (c) the average SASA of the LuxR-hits complexes, the xaxis represents the distance (nm), and the y-axis is time (ps), (d) Hydrophobic contacts between LuxR and hits complexes, (e) Rg of C-α atoms of the LuxR-hit complexes, (f) Average number of protein-hit
EP
intermolecular hydrogen bond obtained during the simulation time.
AC C
Fig. 3S. Percentage inhibition of biofilm formation of V. harveyi at various concentrations of ChemBridge-7364106.
ACCEPTED MANUSCRIPT
Table 1 Docking scores, interaction profiles and predicted binding free energy estimates (kcal/mol) of the cinnamaldehyde derivatives
2.
2-Nitro Cinnamaldehyde 3,4-Dichloro Cinnamaldehyde (E)-4-Phenyl-3Buten-2-One (E)-2-Pentenol
3. 4. 5. 6. 7.
a
(E)-2-Nonenol (E)-3-Decen-2One
Glide Emodeld kcal/mol -32.33
Glide energye kcal/mol -23.21
-2.60
-6.38
-29.29
-26.39
-5.67
-8.46
-40.00
-30.49
RI PT
Cinnamaldehyde
Glide scorec kcal/mol -7.80
-6.06
-7.85
-20.93
-22.04
-5.19
-6.18
-16.63
-2.69 -5.17
-5.76 -6.52
-25.09 -33.01
H-bondh
π-π stackingi Phe75a, Phe166ab Phe75a, Phe166ab Phe75a, Phe166a -
-461.84
-24.04
-460.23
-27.78
Asn133ab, Trp114b Asn133ab
-464.98
-12.37
Asn133ab
-462.27
-27.25
Asn133ab
-25.38
-457.17
-36.36
-27.55 -25.36
-460.64 -463.29
-57.28 -52.62
Asn133a, Trap114b Asn133ab Asn133ab
SC
1.
IFD scoref
∆Gbindg kcal/mol
Glide scoreb kcal/mol -5.78
M AN U
Compound namea
TE D
S. no
-
AC C
EP
Name of the compounds Glide score: Scoring of ligand through Glide XP algorithm C Glide score: Scoring of ligand through Induced fit docking algorithm d Glide Emodel: (induced fit docking) is a specific combination of Glide score, the non-bonded interaction energy between the ligand and the receptor and the international torsion energy of the ligand conformer e Glide energy: Scoring of ligand through an induced fit docking algorithm f IFD score: Induced fit docking score g ∆G bind: Energy utilized from induced fit docking pose with XP charges h Amino acid that interacts with the ligand molecule, ainteraction observed in XP docking, binteraction observed in an induced fit docking, ab interaction observed in both XP and induced fit docking i Amino acid that mediate π-π stacking with ligand molecule, aπ-π stacking observed in XP docking, bπ-π stacking observed in an induced fit docking, abπ-π stacking observed in both XP and induced fit docking b
ACCEPTED MANUSCRIPT
π-π stackingj
-43.720
Asn133ab, Gln137ab
0.51
-42.947
Asn133ab
-467.64
0.52
-38.828
-466.31
0.51
-50.494
Asn133a, Gln137ab, Ala163b Gln137a
Hie167ab, Trp114ab, Phe166a, Phe75ab Hie167ab, Phe166a, Phe75ab Phe166a, Phe75a
-43.081
-464.94
0.53
-35.383
Asn133ab
-68.79
-47.535
-468.36
0.50
-58.319
Asn133ab
-73.22
-51.041
-465.76
0.50
-49.770
Asn133ab
e-pharmacophore based screening hits -76.17 -45.183 -463.27 2.45
-49.432
Asn133ab
-62.300
Asn133ab, Gln137b
Chem Bridge IDa
Glide scoreb kcal/mol
Glide scorec kcal/mol
1.
7364106
-8.26
-10.31
2.
9192049
-8.03
-9.94
-73.41
-48.827
3.
5350755
-8.01
-10.48
-47.06
-43.846
4.
5521550
-7.97
-10.14
-78.81
-50.208
5.
5671025
-7.92
-9.08
-66.98
6.
7243765
-7.80
-9.95
7.
6347339
-7.78
-9.52
8.
6575543
-7.63
9.
7477973
-7.62
-11.36
∆G bindh kcal/mol
0.50
-81.49
-467.38
M AN U
TE D
EP
AC C -10.11
Sim/ Fitness scoreg
Glide Glide IFD Emodeld energye scoref kcal/mol kcal/mol Shape based screening hits -53.55 -35.031 -466.85
RI PT
H-bondi
S. no
SC
Table 2 Docking scores, binding free energy and hydrogen bond interaction between LuxR and 9 hits identified from ChemBridge database
-55.508
-470.54
2.43
Hie167a, Phe166ab, Phe75a, Trp114b Hie167ab, Phe166a, Phe75ab Hie167ab, Phe166a, Phe75ab Hie167ab, Phe166a, Phe75a Hie167ab, Phe166a, Phe75a Hie167a, Phe166ab,
ACCEPTED MANUSCRIPT
Phe75ab a
Compound IDs are from ChemBridge database Glide score: Scoring of ligand through Glide XP algorithm C Glide score: Scoring of ligand through Induced fit docking algorithm d Glide Emodel: (induced fit docking) is a specific combination of Glide score, the non-bonded interaction energy between the ligand and the receptor and the international torsion energy of the ligand conformer e Glide energy: Scoring of ligand through an induced fit docking algorithm f IFD score: Induced fit docking score g Sim score/Fitness score: Shape similarity score, scoring parameter of new compound matched with predicted pharmacophore model of known compounds h ∆G bind: Energy utilized from induced fit docking pose with XP charges i Amino acid that interacts with the ligand molecule, ainteraction observed in XP docking, binteraction observed in an induced fit docking, ab interaction observed in both XP and induced fit docking j Amino acid that mediate π-π stacking with ligand molecule, aπ-π stacking observed in XP docking, b π-π stacking observed in an induced fit docking, abπ-π stacking observed in both XP and induced fit docking
AC C
EP
TE D
M AN U
SC
RI PT
b
ACCEPTED MANUSCRIPT Table 3 HOMO, LUMO and energy gap of cinnamaldehyde derivatives and hits.
EP AC C
Energy gap 0.16 0.15 0.15 0.16 0.20 0.20 0.19 0.15 0.19 0.18 0.13 0.16 0.18 0.16 0.21 0.21
RI PT
LUMO -0.08 -0.10 -0.08 -0.07 -0.06 -0.06 -0.05 -0.07 -0.02 -0.02 -0.08 -0.06 -0.05 -0.08 -0.03 -0.03
SC
HOMO -0.24 -0.25 -0.24 -0.23 -0.26 -0.26 -0.25 -0.22 -0.21 -0.20 -0.21 -0.22 -0.23 -0.24 -0.24 -0.24
M AN U
Cinnamaldehyde derivatives Cinnamaldehyde 2-Nitro Cinnamaldehyde 3,4-Dichloro Cinnamaldehyde (E)-4-Phenyl-3-Buten-2-One (E)-2-Pentenol (E)-2-Nonenol (E)-3-Decen-2-One ChemBridge-7364106 ChemBridge-9192049 ChemBridge-5350755 ChemBridge-5521550 ChemBridge-5671025 ChemBridge-7243765 ChemBridge-6347339 ChemBridge-6575543 ChemBridge-7477948
TE D
S. NO 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
ACCEPTED MANUSCRIPT
Table 4 Physico-chemical properties of hits obtained from Qikprop in Schrödinger QP logPo/wc 3.69 3.97 1.42 3.40 4.04 4.95 3.18 2.98 4.50
QP logSd -4.26 -4.28 0.29 -3.47 -4.51 -5.62 -4.93 -4.16 -5.96
a
QP logKpe -1.63 -0.68 -1.63 -4.18 -1.05 -0.49 -2.86 -1.61 -1.74
%HOAf 100 100 69 87 100 100 91.74 100 100
QPP MDCKg 4683.21 6456.36 3253.65 211.553 3036.31 4182.07 374.26 1848.41 2979.17
QP logHERGh -5.01 -4.12 -4.01 -6.51 -5.64 -6.57 -5.83 -5.80 -6.23
RI PT
7364106 9192049 5350755 5521550 5671025 7243765 6347339 6575543 7477943
Molecular weightb 266.12 274.74 262.75 290.74 272.73 299.79 320.73 276.72 338.19
SC
IDa
M AN U
S. No 1. 2. 3. 4. 5. 6. 7. 8. 9.
QPP Caco
Starsj
i
1865.92 2974.94 85.68 179.884 2322.10 3121.84 378.61 1659.30 1159.80
1 0 0 0 0 0 0 0 0
Ligands IDs are from ChemBridge database Molecular weight of the molecule (130.0 to 725.0) c Predicted octanol/water partition co-efficient (-2.0 to 6.5) d Predicted aqueous solubility, logS. S in moldm-3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (-6.5 to 0.5) e Predicted skin permeability (-8.0 to 1.0) f Predicted human oral absorption on 0 to 100% scale. The prediction is based on a quantitative multiple linear regression model. This property usually correlates well with human oral absorption, as both measures the same property (< 25 % is poor, >80 % is high) g Predicted apparent MDCK cell permeability in nm/sec. MDCK cells are considered to be a good mimic for blood brain barrier. Qikprop predictions are for non active transport (<25 is poor, > 500 is high) h Predicted IC50 value for blockage of HERG K+ channels (concern below -6) i Predicted Caco-2 cell permeability in nm/sec. Caco-2 cells are a model for the gut blood barrier. Qikprop predictions are for non active transport (<25 is poor, >500 is high) j Number of property or descriptor values that fall outside the 95% range of similar values for known drugs (0-5 acceptable)
AC C
EP
TE D
b
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Highlights •
E-pharmacophore model was developed using LuxR-3,4-dichloro-cinnamaldehyde complex.
•
E-pharmacophore- and shape-based virtual screening was carried out with ChemBridge
•
RI PT
database.
Top 9 hits molecules were selected based on favorable binding interaction, high docking score and ADMET prediction.
The top most retrieved hit is biological evaluated and it’s showed bioluminescence
EP
TE D
M AN U
SC
inhibition, biofilm inhibition and motility reduction in Vibrio harveyi.
AC C
•
S0882-4010(16)30156-5
DOI:
10.1016/j.micpath.2016.12.003
Reference:
YMPAT 2024
To appear in:
Microbial Pathogenesis
Received Date: 30 March 2016 Revised Date:
29 November 2016
Accepted Date: 5 December 2016
Please cite this article as: Rajamanikandan S, Jeyakanthan J, Srinivasan P, Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and e-pharmacophore based virtual screening and its biological evaluation, Microbial Pathogenesis (2017), doi: 10.1016/j.micpath.2016.12.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT Discovery of potent inhibitors targeting Vibrio harveyi LuxR through shape and e-pharmacophore based virtual screening and its biological evaluation Sundaraj Rajamanikandana, Jeyaraman Jeyakanthana, Pappu Srinivasanb* a
Department of Bioinformatics, Alagappa University, Karaikudi, Tamilnadu, India. Department of Animal Health and Management, Alagappa University, Karaikudi, Tamilnadu, India.
*Corresponding author Dr. Pappu Srinivasan
EP
Associate Professor
TE D
M AN U
SC
RI PT
b
Department of Animal Science and Management
AC C
Alagappa University, Karaikudi Tamilnadu-630 003, India Fax (off): +91-4564-2252
E-mail: [email protected] Tel: +91-4565-230725 Tel: +91-9444482814
ACCEPTED MANUSCRIPT Abstract Quorum sensing is widely recognized as an efficient mechanism in the regulation and production of several virulence factors, biofilm formation and stress responses. For this reason, quorum
RI PT
sensing circuit is emerging as a novel drug target for the development of anti-infective. Recently, cinnamaldehyde derivatives have been found to interfere with master quorum sensing transcriptional regulator and thereby decreasing the DNA binding ability of LuxR. However, the exact mode of
SC
cinnamaldehyde binding with LuxR and receptor interaction still remains inconclusive. In the current study, combined method of molecular docking and molecular dynamics simulations were performed to
M AN U
investigate the binding mode, dynamic and energy aspects of cinnamaldehyde derivatives into the binding site of LuxR. Based on the experimental and computational evidences, LuxR-3,4-dichlorocinnamaldehyde complex was chosen for the development of e-pharmacophore model. Further, shape and e-pharmacophore based virtual screening were performed against ChemBridge database to find potent and suitable ligands for LuxR. By comparing the results of shape and e-pharmacophore based
TE D
virtual screening; best 9 hit molecules were selected for further studies including ADMET prediction, molecular dynamics simulations and Prime MM-GBSA calculations. From the 9 hit molecules, the top most compound 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2-propen-1-one (ChemBridge-7364106)
EP
was selected for in vitro assays using Vibrio harveyi. The result revealed that ChemBridge-7364106
AC C
significantly reduced the bioluminescence production in a dose dependent manner. In addition, ChemBridge-7364106 showed a significant inhibition in biofilm formation and motility in V. harveyi. The results from the study suggest that ChemBridge-7364106 could serve as an anti-quorum sensing molecule for V. harveyi.
Keywords: Vibrio harveyi, quorum sensing, e-pharmacophore, shape based virtual screening, in vitro assays. Introduction The cell-cell communication in bacteria called quorum sensing (QS), is a process that allows
ACCEPTED MANUSCRIPT bacteria to synchronize the gene expression based on its population density [1]. Activities regulated by QS include virulence, antibiotic production, biofilm formation, symbiosis, motility and luminescence [2]. The bacterial population grows and once they reach a certain threshold, the auto-inducer (AI)
RI PT
signaling molecules bind and activate their cognate receptor, leading to the regulation of group behavior [3]. Vibrio harveyi uses AI-2 for interspecies cell-cell signaling and AI-1 for intra species communication [4].
SC
Many Gram negative bacteria use LuxI/LuxR QS system to control a wide range of cellular processes [5]. In this system, LuxI homolog is an AI synthase; it uses S-adenosylmethionine (SAM)
M AN U
and acyl carrier protein as a substrate for the production of freely diffusible N-acyl homoserine lactones (AHL) AI [6]. A high AI concentration results in the binding of AI to their cognate receptor (LuxR-like transcription factor) and the degradation of LuxR protein takes place in the absence of AI, that prevent bacteria from “short circuiting” their QS systems [7]. The process stabilizes the LuxR protein, allowing them to fold, bind DNA and activate the transcription of target genes [8].
TE D
In V. harveyi, two QS circuits function in parallel to regulate bioluminescence and a number of other target genes [9]. These networks are typically composed of two independent components. Among them, LuxM/LuxN system 1, which signals through the AHL molecule HAI-1 and system 2, comprises
EP
the LuxS dependent auto-inducer AI-2 and the AI-2 detector LuxPQ [10]. AIs HAI-1 and AI-2 are
AC C
synthesized by LuxM and LuxS and these two sensor transfers information to the phosphorelay protein called LuxU [11]. The histidine phosphotransferase LuxU, which transmits the signal to the response regulator LuxO that indirectly, represses the LuxR expression level [12]. Finally, LuxR directly or indirectly controls the expression of various virulence factors [13]. Although, few potent small molecules targeting LuxR has been identified so far. Among them, halogenated furanones and cinnamaldehyde derivatives were found to disrupt AHL and AI-2 based quorum sensing system in Vibrio spp. Unfortunately, the halogenated furanones are seemed to be toxic, which limits their use. In contrast, cinnamaldehyde is a non-toxic substance and also a well known anti-
ACCEPTED MANUSCRIPT QS inhibitor. Brackman et al., (2011) has synthesized forty one cinnamaldehyde derivatives and evaluated its anti-QS activity through in vitro and in vivo assays using V. harveyi. Among them, seven cinnamaldehyde derivatives showed AI-2 regulated bioluminescence inhibition in V. harveyi.
RI PT
Furthermore, the most pronounced effect were observed for 3,4-dichloro-cinnamaldehyde with an least IC50 value of 22 µM [14]. Therefore, in the present study, molecular docking was performed with seven potent cinnamaldehyde derivatives using LuxR to gain insights into their binding modes. Based on the
SC
experimental evidence, only 3, 4-dichloro-cinnamaldehyde is chosen for the e-pharmacophore and shape based virtual screening to identify potent ligands of LuxR. The expected binding mode of the
M AN U
protein-ligand complexes were reproduced in the molecular dynamics (MD) simulations. Prime MMGBSA (Molecular Mechanics, The Generalized Born Model and Solvent Accessibility) and ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties were calculated for the identified hit molecules. Finally, the most promising hit molecule is validated through in vitro assays. Materials and Methods
In silico analysis Protein preparation
TE D
Fig. 1 summarizes the workflow of the methodology used in this study.
EP
The modelled LuxR structure reported in the literature was used for the computational studies
AC C
[15]. The structure was prepared using Protein Preparation Wizard (Protein Preparation Wizard, Schrödinger, LLC, New York, NY, 2014). It has two components, namely preparation and refinement. It corrects structural defects, adds hydrogen atoms and removes water molecules that are present 5 Å away from the binding cavity. Side chains that are not involved in the formation of salt bridges were neutralized. Energy minimization was carried out using OPLS-2005 (Optimized Potentials for Liquid Simulations) force field with implicit solvation. Optimization of the hydrogen bonding network, rotation of hydroxyl and thiol hydrogens, generation of appropriate protonation and tautomerization states of His, performing Chi 'flip' in Asn, Gln and His residues were selected by the protein assignment
ACCEPTED MANUSCRIPT script. The minimization was terminated until the root mean square deviation (RMSD) of the heavy atoms in the energy minimized structure relative to the starting X-ray structural co-ordinates exceeded 0.3 Å.
RI PT
Ligand preparation Cinnamalydehyde derivatives and hits retrieved from ChemBridge database were prepared using LigPrep module of Schrödinger (LigPrep, Schrödinger, LLC, New York, NY, 2014). LigPrep
SC
produce a number of structures from each input structure with various ionization states, tautomers, stereochemistries, ring conformations, and eliminate molecules using various criteria, including
M AN U
molecular weight or specified numbers and types of functional groups present. Missing hydrogen atoms were added and bond orders of the ligands were fixed. The ionization and tautomeric state of the ligands were generated between pH 6.8 to 7.2 using Epik module. Ligands were energy minimized using OPLS-2005 force field until the final RMSD of 1.8 Å was achieved [16]. Low energy ring
docking studies. Molecular docking
TE D
conformations for the ligands were generated and the optimized ligands were further used for the
Seven cinnamalydehyde derivatives were docked into the binding site of LuxR. The active site
EP
informations was obtained from the crystal structure of SmcR, transcriptional regulator from V.
AC C
vulnificus (PDB: 3KZ9) [17]. A grid was generated using receptor grid generation panel by setting the center of the grid map around the active site. The docking was carried out using Glide XP (extra precision) mode [18]. Glide searches for favorable interactions between one or more ligand molecules and a receptor. It also generates conformations internally for each ligand molecule and passes through a series of hierarchical filters that evaluate the ligand's interaction with the receptor. The initial filter test the spatial fit of the ligand to the defined active site and examine the complementarity of ligandreceptor interactions using a grid based method patterned after the empirical ChemScore function. The next filter stage involves a grid based force field evaluation and refinement of the docking solutions,
ACCEPTED MANUSCRIPT including torsional and rigid body movements of the ligand. The OPLS-2005 force field was used for intermediate docking. A small number of surviving docking solutions were subjected to monte carlo procedure to try and minimize the energy score. Finally, the Glide score multi-ligand scoring function
RI PT
was used to score the poses. In order to validate the receptor rigid docking (RRD), a mixed molecular docking/dynamic protocol called induced fit docking (IFD) was also performed. In IFD flexibility of both the receptor and ligand are considered as the main factor during the docking process, because both
SC
protein and ligand can change their spatial shape during the docking process. Molecular dynamics simulations
M AN U
MD simulations were performed using GROMACS v4.5.5 [19] with GROMOS96 43A1 force field [20, 21]. The molecular topology file and charge for the ligand atoms were generated using a PRODRG server [22]. The system was solvated in a cubic periodic box comprising the simple point charge (SPC-216) water environment. Five Na+ counter ions were added by replacing the water molecules to neutralize the whole system under the physiological conditions. The solvated system was
TE D
minimized using steepest descent algorithm. After energy minimization, the position restraint procedure was performed in association with NVT and NPT ensembles. The entire system was equilibrated under NVT ensemble for 100 ps using Berendsen coupling method (temperature constant
EP
at 300 K with a coupling time of 0.1 ps). Then in the NPT ensemble, the pressure was maintained with
AC C
a time constant of 0.5 ps and the reference pressure of 1 bar. All bonds involving hydrogen atoms were constrained with LINCS algorithm [23]. Particle-Mesh ewald (PME) method was employed to enlighten the electrostatic interactions in the system [24]. The geometry of the water molecules were constrained using SETTLE algorithm [25]. The van der Waals (vdw) force was treated with a cutoff of 12 Å and final MD simulations was carried out for 30 ns. Analysis such as potential energy, total energy, temperature, pressure, RMSD, root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg), number of intermolecular hydrogen bond (H-bond), hydrophobic contacts were analyzed using scripts included with the GROMACS distribution. The trajectories were
ACCEPTED MANUSCRIPT graphically visualized using OriginPro. e-Pharmacophore based virtual screening The e-pharmacophore model was generated from the docked complex using PHASE
RI PT
(Pharmacophore Alignment and Scoring Engine) module of Schrödinger [26]. Based on the Glide XP score the energetic terms were mapped onto atom centers. The pharmacophoric sites were automatically generated by PHASE module and Glide XP energies for each pharmacophoric site were
SC
summed. The final hypotheses were selected based on the ranking and quantification process. The common pharmacophoric features such as hydrogen bond acceptor (A), hydrogen bond donor (D),
M AN U
hydrophobic region (H), positive inoizable (P), negative inoizable (N) and aromatic ring (R) were utilized to rank the pharmacophoric hypotheses. Sites less than half of the heavy atoms contribute to the pharmacophoric features were excluded from the final hypotheses. The best e-pharmacophore model was further utilized to screen the ChemBridge database. The retrieved hits were ranked based on the fitness score, and a measure of how well the aligned ligand conformer matches the hypotheses
the molecular docking studies.
TE D
based on RMSD site and vector matching. The hits with the highest fitness score were shortlisted for
Shape based virtual screening
EP
Shape based molecular similarity approach has gained important and popular among virtual
AC C
screening techniques [27]. The approach is consistent with physical chemistry intuition, which utilize the shape and electrostatic properties of the query molecule to select new compounds that are likely to show similar binding mode in the active site of the protein. Shape screening is an effective tool for lead optimization with high potency and more selectivity, where rapid, flexible superposition of multiple similar molecules is essential to understand the structure activity relationship. It does not require a target protein structure or well developed structure activity relationship sets that might be necessary to create a reliable pharmacophore model. Only a single known active query compound is needed as an input. The shape screening can run in shape only mode, or it can incorporate atom type similarity when
ACCEPTED MANUSCRIPT aligning and scoring. In unique model, it describes each structure as a collection of pharmacophoric features rather than individual atoms. Prime MM-GBSA
RI PT
The prime MM-GBSA was used to predict the free energy of binding of a set of ligands to the receptor (Prime, Schrödinger, LLC, New York, NY, 2014). Prime uses OPLS-2005 force field and GBSA continuum solvent model to calculate the energies for each complex. The binding free energy
SC
(∆G) is estimated using the following equation. ∆Gbind = Gcomplex - (Gprotein + Gligand)
M AN U
G = EMM + GSGB + GNP
The Gcomplex represents complex energy, Gprotein is the receptor energy and Gligand is the unbound ligand energy. EMM represents molecular mechanics energies, GSGB is an SGB solvation model for polar solvation and GNP is a nonpolar solvation term. Interaction energy was also calculated from macro
Density functional theory
TE D
model script which calculates the component energies of interacting atoms between protein and ligand.
The density functional theory (DFT) calculations were performed using Jaguar (Jaguar, Schrödinger, LLC, New York, NY, 2014) module implemented in Schrödinger. Ligand molecules were
EP
optimized with hybrid DFT using Becke's three parameter exchange potential and Lee-Yang-Parr
AC C
correlation functional (B3LYP) gradient corrected exchange correlation functional in combination with 3-21+G* basis set [28, 29]. Energy calculations were performed using poisson boltzmann finite (PBF) solvation model. Highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO) and energy gaps were calculated. ADME and toxicity prediction QikProp is a quick, accurate, easy to use absorption, distribution, metabolism and excretion prediction program, which predicts physically significant descriptors and pharmaceutically relevant properties of the organic molecules (QikProp, Schrödinger, LLC, New York, NY, 2011). In addition,
ACCEPTED MANUSCRIPT QikProp provides ranges for comparing a particular molecule property with those of 95% of known drugs. It also flags 30 types of reactive functional groups that may cause false positives in high throughput screening assays. The hits retrieved from virtual screening were neutralized by QikProp.
RI PT
The neutralizing step is essential, as QikProp is unable to neutralize a structure and no properties will be generated. The physiochemical parameters such as Molecular Weight (MW), QPlogPo/w (octanol/water), QPlogS (aqueous solubility), QPlogKp (skin permeability), percentage of human oral
SC
absorption, stars (descriptor values that fall outside the 95% range of similar values for known drugs) were analyzed in detail. It also evaluates the acceptability of the hit molecules based on the Lipinski's
M AN U
rule of five, which is considered as an important factor in the pharmacokinetics and rational drug design. Finally, the toxicity profile of the hits were analyzed by PROTOX web server [30]. In vitro assays Bacterial strain
The aquatic bacterial pathogen V. harveyi (MTCC 3438) was kindly gifted by Dr. A.Veera Ravi,
TE D
Department of Biotechnology, Alagappa University, Karaikudi, India. The pathogenic V. harveyi was cultured in luria bertani (LB) media (pH 7.5±0.2) and incubated overnight at 30 ºC. For experimental purpose, the overnight culture was inoculated into fresh LB media to an initial OD600 of 0.5 and the
EP
culture was aerated on an orbital shaker at 300 rpm at 30 ºC.
AC C
Determination of minimum inhibitory concentration The 7364106)
compound
was
3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2-propen-1-one
purchased
from
ChemBridge
(Hit2Lead)
online
(ChemBridge-
chemical
store
(http://www.hit2lead.com). The stock was prepared by dissolving 5 mg of the compound in 188 µl of sterile dimethyl sulfoxide (DMSO) and stored in vials at -20 ºC. The working standard was freshly prepared on the day of the experiment by diluting the stock solution with DMSO and further used for in vitro assays. The minimal inhibitory concentration (MIC) of ChemBridge-7364106 was evaluated according to the method as described earlier [31]. The LB broth was added to the 24 well microtitre
ACCEPTED MANUSCRIPT plates. Then, the bacterial suspension (adjusted OD600 of 0.5) was added and supplemented with two fold serially diluted solution of ChemBridge-7364106 in order to give the final concentration of 0.5 µg/ml to 8 µg/ml. After treatment, the assay plates were incubated at 30 ºC for 24 hours. The lowest
RI PT
concentration of the compound that produced a complete suppression of visible growth was noted as MIC. Further investigations were performed with sub-MIC concentration of the compound. Bioluminescence inhibition assay
SC
The V. harveyi cells were enriched in the alkaline peptone water (APW) and treated in the presence (0.5-8 µg/ml) or absence of ChemBridge-7364106 for 12 hours at 30 ºC. The bioluminescence
M AN U
intensities were measured as relative light units (RLU) by luminometer. Positive control is the untreated culture of V. harveyi and negative control consist of sterile APW. Effect of ChemBridge-7364106 on biofilm development
V. harveyi biofilm formation was initially quantified using crystal violet staining as described earlier [32]. Briefly, the overnight culture of V. harveyi (OD adjusted to 0.4 at 600 nm) was treated with
TE D
the presence (0.5-8 µg/ml) or absence of ChemBridge-7364106 for 16 hours. After incubation, the plates were sequentially washed thrice with sterile deionized water to remove loosely associated bacteria. Subsequent to washes, the cells were stained with 200 µl of 0.4% (v/v) crystal violet solution
EP
for 15 minutes at room temperature. After staining, the plates were rinsed thrice with deionized water to
AC C
remove the excess stain and then 1 ml of 95% ethanol was added and incubated to extract the crystal violet solution. The absorbance was measured at 580 nm using UV-visible spectrophotometer (Hitachi U-2800, Japan).
Percentage inhibition of biofilm biomass = [Control OD -Test OD/Control OD] x 100 Disintegration of mature biofilm To evaluate the biofilm disintegration, the mature biofilm was incubated for 5 hours at 30 ºC in the presence or absence of ChemBridge-7364106. After incubation, the biofilm formed on the glass slides were stained with crystal violet staining. Then the slides were washed thrice with deionized
ACCEPTED MANUSCRIPT water and air dried at room temperature. The disintegration of biofilm on glass slides were finally observed under light microscope. Swimming and swarming assays
RI PT
The motility assays such as swimming and swarming were performed according to the procedure as described previously [33]. Swimming and swarming plates were prepared by adding 0.3%, 0.5% agar to the LB broth. 3 µl and 5 µl overnight cultures of V. harveyi (OD600 nm of 0.5) were
SC
inoculated at the center of the swimming and swarming agar plates and incubated for 16 hours at 30 ºC. Further, the reductions in swimming and swarming migration zones were observed.
M AN U
Growth curve and bioluminescence kinetics assay
The overnight culture of V. harveyi (OD600 nm of 0.5) was inoculated in 20 ml of LB broth supplemented with various concentrations (0.5-8 µg/ml) of ChemBridge-7364106 and was cultured on a rotary shaker under 180 rpm at 30 ºC. The cell density was measured using UV-visible spectrophotometer over 1 hour time intervals up to 16 hours. In the bioluminescence kinetic assay, the
TE D
overnight culture of V. harveyi (OD600 nm of 0.5) was inoculated in 20 ml of APW supplemented with various concentrations (0.5-8 µg/ml) of ChemBridge-7364106 and incubated by following the procedure as stated above. The bioluminescence intensity was calculated every 1 hour up to 16 hours.
AC C
In silico analysis
EP
Results and Discussion
Molecular dynamics simulations of apo protein The conformational stability, intrinsic flexibility and the dynamic patterns hidden in the protein and protein-ligand complexes are well understood by performing the MD simulation [34]. RMSD and RMSF are the most commonly used criterion for the validation of an MD simulations. RMSD is considered as an average displacement of backbone atoms at an instant of the reference structure, whereas reference structure is taken from the first frame of the simulation. The RMSD is calculated on the C-α atoms of protein against time dependent function (Fig. 2a). The variation values were
ACCEPTED MANUSCRIPT calculated by means of the standard deviation (SD). The inspection of RMSD plot indicates the stability of LuxR throughout the simulation of 50 ns with an RMSD value of 0.2 nm. The lower RMSD value indicates the system is well equilibrated and remained stable throughout the simulation. From the
RI PT
result, we observed that RMSD values deviated around 2 Å and it does not show any sign of divergence from the initial structure. The residual fluctuations in the protein were monitored and the results are shown in Fig. 2b. The protein showed an average RMSF value of 0.12 with the SD value of
SC
0.07 nm. Amino acid residues showing higher fluctuations includes Met1 (0.4 nm), Arg202 (0.4 nm), Glu203 (0.5 nm) and His204 (0.7 nm). As expected, an increase in fluctuation was observed in the loop
M AN U
region between the N- and C-terminal domains. The amino acid residues at the active site region in the protein have shown low mobility and, thereby it supports the correct relative positioning of the active site residues. The Rg refers to the mass weighted root mean square distance of group of atoms from their common centre of mass. An observation into a global dimension of protein can be monitored in Rg. The average Rg value was found to be 1.9 nm with an SD of 0.01 nm. From the results, there was
TE D
no major fluctuation observed in LuxR protein between a time periods of 0 to 50 ns. The least Rg value indicates the compactness of the LuxR protein. Apart from these analyses, the stability of protein was measured using parameters including potential energy, total energy, temperature and pressure. In this
EP
study, all these parameters were calculated and found that the protein assemblies remain constant at the
AC C
average temperature of 299.61 K and a pressure of 1 bar. The potential energy (-1.40815 KJ mol-1) and total energy (-1.044772 KJ mol-1) of the system was found stable during the simulation time. The MD simulation confirms the stability of the predicted model. Hence, the LuxR model was used for the molecular docking studies. Molecular docking In computer aided drug design, molecular docking was frequently used to predict the binding orientation of small molecule for its optimal binding to a receptor or an enzyme active site. In the present study, two docking protocols such as RRD and IFD were used to predict the binding mode for
ACCEPTED MANUSCRIPT the studied inhibitors. The overall fold of LuxR was very similar to other members of the TetR super family, including Escherichia coli tetracycline repressor TetR [35, 36], Staphylococcus aureus multidrug binding transcription regulator QacR [37, 38] and Mycobacterium tuberculosis EthA
RI PT
repressor EthR [39, 40]. The two LuxR homologous from Vibrio species, such as the master quorum sensing regulator SmcR of V. vulnificus and HapR of V. cholerae have been recently crystallized [17, 41]. Both the proteins have shown conserved stretches in the putative ligand binding domain. The
SC
binding site is occupied by polar amino acid residues in one side of the pocket and hydrophobic residues on the other side. The active site informations obtained from SmcR has been used for docking
M AN U
studies. Seven cinnamaldehyde derivatives (Cinnamaldehyde (compound 1), 2-nitro-cinnamaldehyde (compound 2), 3,4-dichloro-cinnamaldehyde (compound 3), ((E)-4-phenyl-3-buten-2-one (compound 4), ((E)-3-decen-2-one (compound 5), (E)-2-pentenal (compound 6) and ((E)-2-nonenal (compound 7)) were docked into the active site region of the LuxR. The Glide score and Glide energy obtained from RRD do not correlate well with the IFD results, while both the protocols have shown similar interaction
TE D
profiles (Table 1). In RRD, Glide score of protein-ligand complexes ranges from -2.60 kcal/mol to 6.06 kcal/mol, in case of IFD, an improvement on ligand binding poses was observed (Glide score ranges from -3.36 kcal/mol to -8.17 kcal/mol). The docking results showed that IFD was able to
EP
prospectively predict the favourable binding orientation of cinnamaldehyde derivatives in the binding
AC C
site of LuxR. Therefore, IFD results are discussed in further pursuing sections. Based on the experimental evidence, cinnamaldehyde derivatives (compounds 3, 6 & 7) exhibit potent inhibitory activity in the nanomolar range than other cinnamaldeyde derivatives (compound 1, 2, 4, & 5) against V. harveyi [14]. The binding mode of three cinnamaldehyde (compound 3, 6 & 7) derivatives at the active site of the protein is shown in Supplementary Fig. 1Sa-1Sd. Structurally, the dock poses obtained from RRD are very close to the dock poses generated by IFD. The receptor-ligand interactions are observed through the formation of hydrogen bonds. The oxygen atom of the carboxyl group of compound 3 interacts with the amide group of Arg133 with the distance of 1.95 Å. Additionally, two π-
ACCEPTED MANUSCRIPT π stacking interactions were observed with the amino acid residues Phe166 and Trp114. A similar type of interaction patterns were observed for all other cinnamaldehyde derivatives, but there is a drastic difference in their hydrogen bonding distance. The hydrogen bond distance observed between the
RI PT
carboxyl group of cinnamaldehyde derivatives (compound 1 & 2, compound 4-7) with the amide group of Asn133 are 2.06 Å, 2.01 Å, 2.24 Å, 2.09 Å, 2.39 Å & 2.00 Å, respectively. Overall, the docking
to their stability. Molecular dynamics simulations of protein-ligand complex
M AN U
Root mean square deviation
SC
results confirms the key role of Asn133 in ligand binding and π-π stacking interactions that contribute
To gain insights on ligand induced protein conformation, the RMSD of protein backbone were analyzed from the starting structure over the course of the simulation. Fig. 3a illustrates the graphical depiction of the RMSD profile of the protein-ligand complexes over a period of 30 ns of MD simulations. The LuxR-2-nitro-cinnamaldehyde (complex 2) and LuxR-((E)-4-phenyl-3-buten-2-one
TE D
(complex 4) has shown the lower and higher RMSD values, among other protein-ligand complexes. These two complexes have shown an average RMSD of 0.24 nm and 0.42 nm, respectively. LuxRcinnamaldehyde (complex 1) has shown initial fluctuations from 0 to 10 ns and then reached a stable
EP
state with an average RMSD and SD value of 0.30 nm and 0.03 nm. Complex 2 (LuxR-2-nitro-
AC C
cinnamaldehyde) do not show much larger deviation during the simulation time, and constant stability was observed with the RMSD and SD value of 0.24 nm and 0.03 nm, respectively. LuxR-3,4-dichlorocinnamaldehyde (complex 3) exhibits a much larger degree of fluctuation during initial 3 ns of simulations and then reached a stable RMSD value of 0.23 nm after 5 ns with an SD of 0.03 nm. Complex 4 (LuxR-(E)-4-phenyl-3-buten-2-one) has shown higher RMSD deviation with an SD value of 0.08 nm. LuxR-(E)-3-decen-2-one (complex 5) deviated slightly during initial 6 ns of simulations and then the RMSD settled around 0.25 nm with an SD of 0.03 nm. Large scale conformational changes in the LuxR-(E)-2-pentenal (complex 6) were observed up to 15 ns and once the complex is
ACCEPTED MANUSCRIPT well equilibrated, it remained stable for the remaining 15 ns with an RMSD of 0.38 nm. The SD value of 0.03 nm was maintained for the complex 6. It was noted that the LuxR-(E)-2-nonenal (complex 7) does not deviate much and remained highly stable with the RMSD of 0.36 nm with an SD of 0.03 nm.
RI PT
Results from RMSD analysis confirmed the binding mode and stability of the protein-ligand complexes. Root mean square fluctuation
SC
The RMSF variations were calculated to evaluate the fluctuation of each residue in the proteinligand complexes during the MD simulation (Fig. 3b). The plot clearly displayed that none of the
M AN U
residues present in the protein shown an RMSF value more than 0.93 nm. The overall fluctuations of complex 1 have shown the fluctuation rate of 0.14 nm with an SD of 0.06 nm. The amino acid residues showing higher fluctuations includes Met1 (0.60 nm), Arg11 (0.51 nm), Arg202 (0.61 nm), Glu203 (0.57 nm), His204 (0.77 nm) and His 205 (0.96 nm). The same kind of fluctuations was observed in complex 2. Complex 3 have shown RMSF value of 0.16 nm and SD of 0.07 nm fluctuations throughout
TE D
the simulation. Higher fluctuations were observed in the amino acid residues, which includes Met1 (0.55 nm), Gly2 (0.32 nm), Ser3 (0.30 nm), Arg9 (0.36 nm), Thr10 (0.35 nm), Arg11 (0.45 nm), Arg17 (0.30 nm), Ile34 (0.31 nm), Arg36 (0.34 nm), Arg202 (0.38 nm), Glu203 (0.33 nm), His204 (0.36 nm)
EP
and His205 (0.51 nm). These residues are insignificant for the study since the major dynamic changes
AC C
were found absent in the active site amino acid residues of the protein. Complex 4 & complex 5 have shown the fluctuation value of 0.17 nm, 0.17 nm with an SD of 0.07 nm and 0.11 nm, respectively. The amino acid residues showing higher fluctuations in complex 4 includes Met1 (0.59 nm), Glu203 (0.47 nm), His204 (0.62 nm) and His205 (0.55 nm). Higher mobility was observed in the amino acid residues in complex 5 includes Arg11 (0.46 nm), Gly35 (0.44 nm), Arg36 (0.59 nm), His204 (0.50 nm) and His205 (0.63 nm). The major co-operative fluctuations associated with the amino acid residue or loop region of complex 6, includes Met1 (0.72 nm), Gly2 (0.44 nm), Ser3 (0.39 nm), Arg9 (0.36 nm), Thr10 (0.34 nm), Arg11 (0.50 nm), Arg32 (0.41 nm), Gly35 (0.38 nm), Arg36 (0.50 nm), Arg150 (0.32 nm),
ACCEPTED MANUSCRIPT Arg202 (0.30 nm), Glu203 (0.47 nm), His204 (0.61 nm) and His205 (0.62 nm). The RMSF with an SD value was found to be 0.17 nm and 0.09 nm, respectively. The RMSF value of 0.77 nm with an SD of 0.09 nm was observed in complex 7. However, few amino acid residues exhibiting high mobility in
RI PT
complex 7 includes Met1 (0.48 nm), Arg11 (0.37 nm), Arg36 (0.31 nm), Arg122 (0.31 nm), Gln156 (0.33 nm), Lys181 (0.30 nm), Arg202 (0.50 nm), Glu203 (0.56 nm), His204 (0.70 nm) and His205 (0.89 nm). The interacting amino acid residue Asn133 has shown slight conformational changes in the
SC
protein-ligand complexes, which includes 0.17 nm (complex 1 & complex 2), 0.12 nm (complex 3), 0.19 nm (complex 4), 0.12 nm (complex 5), 0.15 nm (complex 6) and 0.16 nm (complex 7). The
M AN U
catalytic residues present in the active site of the protein are unchanged upon binding of the compounds. The result clearly indicates that higher fluctuations coincide with the loop region of the protein (N- and C-terminal domains). It was also concluded that these fluctuations does not affect the overall structural stability of the protein at the active site. Solvent accessible surface area
TE D
SASA were used to predict the ligand induced conformational changes in the protein structure. Fig. 3c shows the solvation free energy (∆Gsolv) of the protein-ligand complexes. Average solvation free energy value of the complexes (complex 1-complex 7) are found to be 308.81 nm, 320.78 nm,
EP
312.51 nm, 314.20 nm, 310.17 nm, 320.17 nm, 313.96 nm with the SD of 12.14 nm, 16.00 nm, 12.12
AC C
nm, 13.20 nm, 14.18 nm, 12.42 nm and 12.08 nm, respectively. Low SASA value was observed for five complexes (1, 3, 4, 5, & 7) compared with other two complexes (2 & 6). The Low SASA value reflects higher thermodynamic stability. The result indicates that the protein attains higher stability against the binding of cinnamaldehyde derivatives in the binding site of LuxR. SASA results are more comparable with the RMSF plot. Hydrophobic contact The contribution of hydrophobic interactions between protein-ligand complexes were analyzed to understand the effect of packing (Fig. 3d). In addition, the protein-ligand complexes have shown
ACCEPTED MANUSCRIPT very similar hydrophobic contacts during the MD simulation. The average hydrophobic value of the (complex 1-complex 7) complexes were found to be 57.81 nm, 60.18 nm, 58.48 nm, 59.30 nm, 58.31 nm, 59.97 nm and 59.37 nm with the SD of 1.86 nm, 2.32 nm, 1.63 nm, 2.02 nm, 2.07 nm, 1.69 nm and
RI PT
1.73 nm, respectively. The protein-ligand complexes are stabilized through these hydrophobic interactions. Radius of gyration
SC
The simulations were analyzed in terms of Rg over 30 ns. The average Rg value of the (complex 1-complex 7) complexes (Fig. 3e) are found to be 1.84 nm, 1.86 nm, 1.85 nm, 1.91 nm, 1.90
M AN U
nm, 1.89 nm, 1.86 nm with the SD value of 0.02 nm, 0.03 nm, 0.02 nm, 0.01 nm, 0.01 nm, 0.01 nm and 0.02 nm, respectively. The low Rg value with minimal SD indicates that the protein undergo least conformational change upon ligand binding and also indicates the compactness of the structural network. Hydrogen bond analysis
TE D
The strength of hydrogen bond interactions between protein-ligand complexes were investigated during the simulation time and it is schematically represented in Fig. 3f. Hydrogen bond plays a major factor in controlling the steady conformation of protein. The hydrogen bond interaction
EP
between the carboxyl group of Asn133 and an amide group of cinnamaldehyde derivatives were found
AC C
stable throughout the whole simulation of 30 ns in all the protein-ligand complexes. From the figure, it was clear that a maximum of 2, 4, 3, 3, 3, 3, and 2 inter molecular hydrogen bond interactions were observed in the protein-ligand (complex 1-complex 7) complexes, respectively. The maximum number of H-bond interactions observed makes the complexes more rigid. Moreover, there is no significant change in the binding pattern of cinnamaldehyde derivatives within the binding site of the LuxR protein. e-pharmacophore based virtual screening The e-pharmacophore method uses spatial information of the target protein for a topological
ACCEPTED MANUSCRIPT description of the ligand-receptor interactions. Based on the experimental and computational evidence, the docked complex (LuxR-3,4-dichloro-cinnamaldehyde) was used as a basis to generate the epharmacophore model. The predicted model consists of four pharmacophoric sites, including one
RI PT
hydrogen bond acceptor (A), one aromatic ring (R) and two hydrophobic regions (H) and their distance are shown in Fig. 4. The acceptor A1 lies in the C=O group, while two chlorine atoms at 3 & 4 position of the basic structure occupies the hydrophobic region (H2 & H3) and the aromatic ring is occupied in
SC
the spacer region. The best pharmacophore hypotheses were used as a 3D query for screening ChemBridge database. Compounds retrieved from the database were subject to screening protocol
M AN U
based on the drug-like prediction which includes Lipinski's rule of five and the number of rotatable bond ≤ 7. In total, 943 compounds passing AMDE filters were then subjected to virtual screening. These compounds have fitness score ranging from 2.43 to 2.78. The first level of docking (HTVS) screens 95 compounds and these hits were further refined and filtered using SP (standard precision) mode. About 19 compounds from SP docking were re-docked into the more precise mode of Glide XP.
TE D
Finally, 9 compounds were identified as potent hits against LuxR. All the four pharmacophoric sites were found in the identified hits. The chemical name of the top two hits with their ChemBridge database identity number are 2-(4-chloro-3-methylphenoxy)-N-4-pyridinylacetamide (ChemBridge-
EP
6575543) and 3,4-dichlorobenzyl N-benzoylglycinate (ChemBridge-7477948). These hits contain
AC C
chlorine atom and carboxyl functional group, mainly responsible for the formation of hydrogen bonding interactions with the LuxR protein. Shape based virtual screening The structure of 3,4-dichloro-cinnamaldehyde was used as a 3D query for shape based screening. Shape screening is ideally suited for use in the early stages of lead discovery. In shape based screening, the molecules are ranked on the basis of their similarity to a known active molecule in three dimensional shape spaces. Different generated conformers are aligned to the target molecules in a various way and the shape similarity is computed based on the hard sphere atom volume overlaps.
ACCEPTED MANUSCRIPT Alignment between each conformer yields the similarity for each molecule. Compounds retrieved from ChemBridge database were filtered based on phase-sim-score. About 5869 compounds with the highest sim-score value in the range of 0.77 to 0.50 were selected for further studies. Screening protocol
RI PT
constraints like Lipinski's rule of five and the number of rotatable bond ≤ 7 were applied to eliminate the unwanted compounds. Compounds passing the filters were subjected to HTVS mode. About 558 compounds retained from HTVS were passed to SP docking and compound survive here were taken to
SC
the precise XP docking. Seven compounds with remarkable high binding affinities towards LuxR were finally chosen for binding mode analysis. The systematic name of the top seven compounds with their
M AN U
identity number are 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2yl)-2-propen-1-one (ChemBridge-7364186), N-(4-chlorobenzyl)-N-methyl-N'-phenylurea (ChemBridge-9192049), 1-[(4-chlorobenzyl)sulfinyl]-3methoxyl-2-propanol
(ChemBridge-5350755),
2-(4-chlorophenyl)-N-(-4-nitrobenzyl)ethanamine
(ChemBridge-5521550), N-benzyl-N'(4-chlorobenzylidene) formic hydrazide (ChemBridge-5671025), 3-(4-chlorophenyl)-N-(3-phenylpropyl)acrylamide
(ChemBridge-7243765)
and
2-(4-chloro-3-
Binding mode analysis
TE D
methylphenoxy)-N-(3-nitrophenyl) acetamide (ChemBridge-6347339).
Finally, 9 hits (two from e-pharmacophore and seven from shape-based screening) based on the
EP
scoring functions were selected for binding mode analysis. The binding mode of the identified hits was
AC C
also validated using IFD. The identified hits have shown stable interaction with the binding site amino acid residues of LuxR and are shown in Fig. 5a-5h. Moreover, the hits showed docking score ranges from -7.62 kcal/mol to -8.26 kcal/mol (Table 2). Inspection of the docking results revealed that the amino acid residue Asn133, Gln137, His167, Phe166 and Phe75 plays a critical role in ligand binding. The carboxyl and amide group of ChemBridge-7364106 forms hydrogen bond interactions with the side chain amino acid residues Asn133 (C=O...H2N, bond length=2.43 Å) and Gln137 (NH...O=C, bond length=1.95 Å). Additionally, four π-π stacking interactions were observed with the amino acid residues His167, Trp114, Phe166 and Phe66. ChemBridge-5350755 interact with Asn133 (C-O...H2N,
ACCEPTED MANUSCRIPT bond length=1.99 Å, OH...O=C, bond length=1.98 Å), Gln137 (OH...O=C, bond length=1.98 Å) forming a total of three hydrogen bond interactions. Meanwhile, two π-π stacking interactions were observed with the residues Phe166 and Phe75. It was observed that the amide group of ChemBridge-
RI PT
5521550 has shown hydrogen bond interaction with side chain amino acid residue Gln137 (NH...O=C, bond length=1.85 Å). Amino acid residues including His167, Phe166 and Phe75 have shown π-π stacking interactions with the basic aromatic ring of ChemBridge-5521550. The amide group of all
SC
other hits (ChemBridge-9192049, ChemBridge-5671025, ChemBridge-7243765, ChemBridge6347339, ChemBridge-6575543 & ChemBridge-7477973) has shown hydrogen bond interaction with
M AN U
side chain amino acid residue Asn133, but differs in their bonding length. The observed hydrogen bond distance of these hits was found to be 1.97 Å, 1.98 Å, 2.24 Å, 1.99 Å, 2.00 Å & 2.19 Å, respectively. Furthermore, the aromatic ring of these hits showed π-π stacking interactions with the amino acid residues His167, Phe166 and Phe75. Molecular dynamics simulations
TE D
In order to confirm the binding mode and stability of the identified hit molecules in the binding site of LuxR, MD simulation of the protein-ligand complexes were performed. Root mean square deviation
EP
The stability of protein-ligand (LuxR-7364106, LuxR-9192049, LuxR-5350755, LuxR-
AC C
5521550, LuxR-5671025, LuxR-7243765, and LuxR-6347339 & LuxR-6575543) complexes was studied using MD simulation. Each system was subjected to position restraint simulation for 10 ns and the RMSD of backbone atoms were calculated over the entire MD simulation trajectories (Supplementary Fig. 2Sa). The nine protein-ligand complexes have shown the RMSD values of 0.30 nm, 0.19 nm, 0.24 nm, 0.31 nm, 0.27 nm, 0.23 nm, 0.25 nm, 0.22 nm & 0.24 nm with the SD of 0.04 nm, 0.03 nm, 0.04 nm, 0.08 nm, 0.04 nm, 0.03 nm, 0.05 nm, 0.02 nm & 0.02 nm, respectively. All these complexes have shown stable and low RMSD value, indicating the stability of the each complex during the simulation period. From the results, it was also observed that all the complexes have shown
ACCEPTED MANUSCRIPT very less deviation from the initial structure. Root mean square fluctuation The RMSF of the C-α atom of each complex was plotted to evaluate the flexibility of each
RI PT
residue during the simulation time (Supplementary Fig. 2Sb). The overall fluctuation rate of each complex has shown an average of 0.16 nm, 0.15 nm, 0.15 nm, 0.18 nm, 0.17 nm, 0.16 nm, 0.17 nm, 0.16 nm, 0.17 nm with the SD of 0.10 nm, 0.08 nm, 0.09 nm, 0.10 nm, 0.08 nm, 0.09 nm, 0.10 nm,
SC
0.07 nm & 0.06 nm, respectively. In LuxR-7364106, the major fluctuation occurs in the C-terminal region, which includes Arg202 (0.62 nm), Glu203 (0.60 nm) and His204 (0.93 nm). Observations on
M AN U
LuxR-9192049 have shown slight deviation in the residues such as Met1 (0.55 nm), His204 (0.66 nm) and His205 (0.63 nm). The C-terminal region in the LuxR-5350755 have shown higher fluctuation of 0.54 nm for Glu203, 0.74 nm for His204 and 0.89 nm for His205. Few amino acid residues in the LuxR-5521550 exhibited greater mobility which includes Met1 (0.54 nm), Glu203 (0.69 nm) and His204 (0.79 nm). In case of LuxR-5671025 and LuxR-7243765 complexes, the amino acid residue
TE D
Glu203 has shown a fluctuation rate of 0.50 nm, His204 residue has shown the average fluctuation of 0.66 nm, whereas the residue His205 has shown the fluctuation of 0.72 nm. The LuxR-6347339 complex induced a conformational change in the C-terminal region, which includes Arg202 (0.53 nm),
EP
Glu203 (0.60 nm) and His204 (0.88 nm). However, two amino acid residues such as Met1 (0.54 nm)
AC C
and His205 (0.59 nm) have shown higher fluctuation in the LuxR-6575543 complex. These residues are insignificant for the study since the major dynamic changes are found absent in the active site of the protein. From the RMSF plot, it was observed that most of the residue in the protein was found stable and the residues lie in the loop regions had shown larger fluctuations. Solvent accessible surface area The SASA were calculated for the protein-ligand complexes and are shown in Supplementary Fig. 2Sc. The complexes (complex 1-complex 9) have shown an average free energy of solvation (∆G) of 363.09 nm 378.95 nm, 357.84 nm, 378.06 nm, 378.62 nm, 383.64 nm, 374.38 nm, 373.77 nm,
ACCEPTED MANUSCRIPT 376.48 nm with the SD of 14.10 nm, 11.52 nm, 15.48 nm, 12.53 nm, 10.89 nm, 11.76 nm, 11.66 nm, 13.12 nm, 12.96 nm, respectively during the simulation time. The result indicates that the protein attain higher stability upon ligand binding. Low variation in the SASA plot indicates higher thermodynamic
RI PT
stability. Hydrophobic contacts
The hydrophobic contacts between protein-ligand complexes were analyzed and are shown in
SC
Supplementary Fig. 2Sd. From the result, the LuxR-5671025 complex has shown higher hydrophobic contacts, while LuxR-5350755 complex has shown lower hydrophobic contact. The average
M AN U
hydrophobic contacts for the protein-ligand complexes (complex 1-complex 9) are 62.79 nm, 63.37 nm, 61.75 nm, 64.07 nm, 64.78 nm, 64.60 nm, 63.62 nm, 63.57 nm, 62.54 nm with the SD of 1.67 nm, 1.52 nm, 1.92 nm, 1.53 nm, 1.37 nm, 1.50 nm, 1.55 nm, 1.67 nm and 1.81 nm, respectively. The hydrophobic contacts observed between protein-ligand complexes remained stable throughout the simulation.
TE D
Radius of gyration
Supplementary Fig. 2Se shows the calculated Rg values of the protein-ligand complexes (complex 1-complex 9). Results showed that the average Rg values of 1.98 nm, 1.93 nm, 1.94 nm, 1.98
EP
nm, 1.98 nm, 1.97 nm, 1.94 nm, 1.93 nm, 1.95 nm with the SD of 0.02 nm, 0.01 nm, 0.01 nm, 0.01 nm,
AC C
0.01 nm, 0.02 nm, 0.01 nm, 0.02 nm and 0.01 nm were observed for the respective protein-ligand complexes. The least Rg value indicates the compactness of the protein-ligand complexes. Hydrogen bond analysis
The number of hydrogen bonds that mediate protein-ligand complexes were calculated for the entire 10 ns of MD simulation and the results are shown in Supplementary Fig. 2Sf. All the complexes have shown stable hydrogen bond interactions with the conserved amino acid residues Asn133 and Gln137. From the figure, it was clear that a maximum of 2, 3, 4, 3, 1, 3, 3, 2 & 4 inter molecular hydrogen bonds were observed for the respective protein-ligand complexes (complex 1-complex 9).
ACCEPTED MANUSCRIPT From the whole MD analysis, it was confirmed that the protein-ligand complexes were found stable during the whole 10 ns of simulation time. Density functional theory
RI PT
Electronic molecular features such as electron density, frontier molecular orbital density fields such as LUMO, HOMO and molecular electrostatic map have been reported to be significant in QSAR studies to explain the biological activity and molecular properties [42]. HOMO/LUMO is critical for
SC
charge transfer in the chemical reactions. The cinnamaldehyde derivatives have shown high HOMO values in the range of -0.23 eV to -0.26 eV (Table 3). The higher HOMO values indicate the tendency
M AN U
of strong binding to the LuxR. Lower LUMO values are related to the electron affinity and both HOMO/LUMO are indicators of the possible electrophilic and nucleophilic attack sites in the molecules. The smaller energy gap between the HOMO and their corresponding LUMO explains the chemical reactivity of the molecules. The energy gap of the cinnamaldehyde derivatives and identified hits were found to be in the range of -0.15 eV to 0.20 eV and 0.13 eV to 0.21 eV, respectively, which
TE D
implies high reactivity, high polarizability and low stability of the hits (Table 3). Lower the energy gap favors the electron to be excited from HOMO to LUMO, which consequently leads to the strong
Prime MM-GBSA
EP
binding affinity to the LuxR protein.
AC C
To investigate prime MM-GBSA re-scoring, the cinnamaldehyde derivatives and identified hits from virtual screening were re-ranked and tabulated in Table 1 & Table 2. The calculated binding free energies (∆G) of the LuxR-cinnamaldehyde derivatives (complex 1-complex 7) were found to be -24.04 kcal/mol, -27.37 kcal/mol, -27.25 kcal/mol, -36.36 kcal/mol, -57.28 kcal/mol and -57.12 kcal/mol, respectively. According to the absolute binding free energy calculation, complex 6 and complex 7 bound strongly to LuxR than other cinnamaldehyde derivatives. Experimental evidence revealed that compound 3 exhibits strong and consistent inhibitory activity, but the compound showed lower binding free energy. The observable free energy of binding does not correlate well with the
ACCEPTED MANUSCRIPT experimental data. The calculated binding free energy for the identified hit molecules ranges from -35.38 kcal/mol to -62.30 kcal/mol. ADME and toxicity prediction
RI PT
The ADMET properties are the primary cause for drug failure in the development phase and the resultant mounting cost of bringing a new drug to the market [43]. Thus, we tried to evaluate the ADMET properties of the identified hits using QikProp program and the computed results are tabulated
SC
in Table 4. The molecular weight of the identified hits ranges from 262.75 kDa to 338.19 kDa. The partition co-efficient (QPlogPo/w) and water solubility (QPlogS) are critical for the absorption and
M AN U
distribution of drugs within the body and the computed results ranges from 1.42 to 4.95 and 0.29 to 5.96, respectively. QPlogKp predicted skin permeability ranges from -0.49 to -4.18. QPPCaco that governs the drug metabolism and its access to biological membrane ranges from 179.88 to 3121.84. QPPMDCK are considered to be a good mimic cell for the blood brain barrier and the predicted apparent MDCK cell permeability ranges from 211.55 to 6456.33. The human oral absorption for the hits ranges from
TE D
69% to 100%. According to the ADMET prediction results, the pharmacokinetic properties of the hits are within the acceptable range. Therefore the hits were found suitable for further drug development process. PROTOX server revealed no toxicity related fragments present in the identified hit molecule.
EP
On the basis of pharmacokinetics and toxicity properties the hit ChemBridge-7364186 was taken for in
AC C
vitro assays. The 2-D structure of ChemBridge-7364186 is shown in Fig. 6. In vitro assays
Determination of minimal inhibitory concentration ChemBridge-7364186 inhibited the growth of V. harveyi at the least concentration of 16 µg/ml. Therefore, all further experiments were performed at mean sub-MIC concentration (0.5, 1, 2, 4, 8 µg/ml) of ChemBridge-7364106. DMSO is used as negative control in all the bioassays does not show any inhibition effect on the test organism.
ACCEPTED MANUSCRIPT Bioluminescence inhibition assay In V. harveyi, QS circuits control the expression of bioluminescence and virulence factors [44]. Therefore, the anti-quorum sensing properties of ChemBridge-7364106 were assessed using V. harveyi.
RI PT
The effect of ChemBridge-7364106 on bioluminescence of V. harveyi is shown in Fig. 7. As expected, ChemBridge-7364106 exhibited concentration dependent inhibition in bioluminescence without inhibiting the growth of V. harveyi. Our results clearly indicate that the enzyme of V. harveyi involved
SC
in bioluminescence was inhibited by ChemBridge-7364106. Our result falls in line with the report, [45] in which, the extracts of two Pseudo alteromonas isolates exhibited significant reduction in V. harveyi
M AN U
bioluminescence by 98% without altering the growth of the bacteria. Effect of ChemBridge-7364106 on biofilm development
As discussed in multiple reports, QS controls biofilm formation through AI-2 mediated cell signaling [46]. Therefore, it was hypothesized that ChemBridge-7364106 disrupt bioluminescence may have the inhibition effect on biofilm formation. The effect of ChemBridge-7364106 on biofilm
TE D
formation of V. harveyi was investigated and shown in Supplementary Fig. 3S. ChemBridge-7364106 reduced biofilm formation in a dose dependent manner. Moreover, at 8 µg/ml concentration the ChemBridge-7364106 has shown a maximum of 72% biofilm inhibition without affecting the bacterial
EP
growth rate. The findings of the present study consistent with the previous report [47], wherein the
AC C
synthetic cationic peptide 1037 had shown significant biofilm inhibition in Gram negative and Gram positive bacteria such as Pseudomonas aeruginosa wilde type strains PA14, PAO1, Burkholderia cenocepacia 4813 and Cisteria monocytogenes. Our result also falls in line with the report [48], wherein the A66 extract is documented to inhibit biofilm formation of V. harveyi without affecting the bacterial growth rate. Disintegration of mature biofilm The biofilm growing bacteria can be up to 1000 fold more resistant to antibiotics, host immune defense system than that of planktonic bacteria [49]. Therefore, biofilm disintegration plays a crucial
ACCEPTED MANUSCRIPT role to overcome infections that are highly resistant to antibiotics. The ChemBridge-7364106 treated cover glasses have shown a thin and disintegrated matrix of biofilm, whereas at control cover glasses continuous matrix was observed. The dispersal of biofilm on glass slides was observed under oil
RI PT
immersion light microscope using 40 X magnification. The light microscopic analysis showed in Fig. 8 explains the ability of ChemBridge-7364106 to disintegrate the mature biofilms of V. harveyi. Swimming and swarming assays
SC
The swimming and swarming motility play a key role in promoting the initial stages of biofilm formation in the Vibrios [33]. Therefore inhibiting bacterial motility represents an important strategy to
M AN U
control biofilm formation and bacterial colonization. The anti QS activity of ChemBridge-7364106 was assessed using swimming and swarming motility assays. The obtained result clearly indicates a great reduction in swimming and swarming motility after exposure to 8 µg/ml concentration of ChemBridge7364106. The result of the current findings consistent with the report [50], the curcumin was found to inhibit the mobility of the reporter strain V. harveyi MTCC 3438. Fig. 9a, 9b and Fig. 9c, 9d display
ChemBridge-7364106.
TE D
the reduction in swimming and swarming motility of V. harveyi at various concentrations of
Growth curve and bioluminescence kinetics assay
EP
In order to determine the antibacterial activity of ChemBridge-7364106, the growth curve assay
AC C
was determined by V. harveyi. The curve depicted in Fig. 10a shows the antibacterial activity of ChemBridge-7364106 at different concentrations. No differences in cell densities were observed in both treated (0.5-8 µg/ml) and untreated control after 16 hour incubation. In bioluminescence kinetic assay evidenced reduction in bioluminescence of V. harveyi by ChemBridge-7364106. Fig. 10b & 10c represents the reduction of bioluminescence of V. harveyi by the treatment of ChemBridge-7364106 at various concentrations. Conclusion We have applied RRD and IFD to determine the binding mode of cinnamaldehyde derivatives
ACCEPTED MANUSCRIPT in the binding site of LuxR. The study demonstrates that there was a significant hydrogen bond interaction between cinnamaldehyde derivatives with the polar amino acid residue Asn133 in the binding pocket of LuxR. Two π-π stacking interactions were observed with the residues Phe166 and
RI PT
Trp114. Thus the observed Asn133 interaction may play an important role in protein-ligand recognition process and π-π stacking interactions help better stabilization of protein-ligand complexes. Furthermore, the MD simulation analysis, confirmed the binding mode and stability of the complexes.
SC
Shape and e-pharmacophore based virtual screening were utilized to identify novel inhibitors for LuxR. The ADMET properties of the identified hits are within the acceptable range. The performed in vitro
M AN U
assays explained the potency of screened compound 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2-yl)-2propen-1-one with anti-QS properties. The overall result provides new insights for the development of novel drugs against LuxR of V. harveyi. The anti-QS activity of 3-(2,4-dichlorophenyl)-1-(1H-pyrrol-2yl)-2-propen-1-one is reported here for the first time. Acknowledgement
TE D
The authors thank the University Grants Commission (F. No. 40-169/2011 (SR), dated 05/07/2011), Government of India for financial support provided for this study. PS gratefully acknowledges UGC for the fellowship. The authors also thank the Department of Bioinformatics,
EP
Alagappa University and UGC Innovative Scheme (No. F. 14-13/2013 (Inno/ASIST) for the
AC C
computational facilities provided for this work. References
1. T. Defoirdt, N. Boon, P. Sorgeloss, W. Verstraete, P. Bossier, Quorum sensing and quorum quenching in Vibrio harveyi: lessons learned from in vivo work, ISME J. 2 (2008) 19-26. 2. M. Asif, M. Acharya, Quorum sensing; A novel target for antibacterial agents, Avicenna. J. Med. 2 (2012) 97-99. 3. N.A. Whitehead, A.M. Barnard, H. Slater, N.J. Simpson, G.P. Salmond, Quorum-sensing in gram negative bacteria, FEMS Microbiol. Rev. 25 (2001) 365-404. 4. S.W. Teng, Y. Wang, K.C. Tu, T. Long, P. Mehta, N.S. Wingreen, B.L. Bassler, N.P. Ong,
ACCEPTED MANUSCRIPT Measurement of the copy number of the master quorum sensing regulator of a bacterial cell. Biophys J. 98 (2010) 2024-2031. 5. M.J. Federle, B.L. Bassler, Interspecies communication in bacteria, J. Clin. Invest. 112 (2003) 1219-1299.
RI PT
6. S.T. Rutherford, B.L. Bassler, 2012. Bacterial quorum sensing: its role in virulence and possibilities for its control, Cold. Spring. Harb. Perspect. Med. 2 (2012) 1-25.
7. A. Pai, Y. Tanouchi, C.H. Collins, L. You, Engineering multicellular systems by cell-cell communication, Curr. Opin. Biotechnol. 20 (2009) 461-470.
8. L.R. Swem, D.L. Swem, C.T. O'Loughlin, R. Gatmaitan, B. Zhao, S.M. Ulrich, B.L. Bassler, A
SC
quorum sensing antagonist targets both membrane bound and cytoplasmic receptors and controls bacterial pathogenicity, Mol. Cell. 35 (2009) 143-153.
M AN U
9. M.E. Taga, B.L. Bassler, Chemical communication among bacteria. Proc. Natl. Acad. Sci. USA, 2 (2003)14549-14554.
10. J.M. Henke, B.L. Bassler, Three parallel quorum sensing systems regulate gene expression in Vibrio harveyi, J. Bacteriol. 186 (2004) 6902-6914.
11. B.N. Lilley, B.L. Bassler, Regulation of quorum sensing in Vibrio harveyi by LuxO and sigma54, Mol. Microbiol. 36 (2000) 940-954.
TE D
12. D.H. Lenz, K.C. Mok, B.N. Lilley, R.V. Kulkarni, N.S. Wingreen, B.L. Bassler, The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae, Cell. 118 (2004) 69-82.
13. A. Tala, D. Delle Side, G. Buccolieri, S.M. Tredici, L. Velardi, F. Paladini, M. De Stefano, V.
EP
Nassisi, P. Alifano, Exposure to static magnetic field stimulates quorum sensing circuit in luminescent Vibrio strains of the Harveyi clade, PLoS One. 9 (2014) 1-12.
AC C
14. G. Brackman, S. Celen, U. Hillaert, S. Van Calenbergh, P. Cos, L. Maes, H.J. Nelis, T. Coenye, Structure-activity relationship of cinnamaldehyde analogs as inhibitors of AI-2 based quorum sensing and their effect on virulence of Vibrio spp, PLoS One. 6 (2011) 1-10. 15. S. Rajamanikandan, J. Jeyakanthan, P. Srinivasan, Binding mode exploration of LuxRthiazolidinedione analogues, e-pharmacophore based virtual screening in the designing of LuxR inhibitors and its biological evaluation, J. Biomol. Struct. Dyn. 3 (2016) 1-20. 16. W.L. Jorgensen, D.S. Maxwell, J. Tirado-Rives, Development and testing of all OPLS all-atom force field on conformation energetics and properties of organic liquids, J. Am. Chem. Soc. 118 (1996) 11225-11236. 17. Y. Kim, B.S. Kim, Y.J. Park, W.C. Choi, J. Hwang, B.S. Kang, T.K. Oh, S.H. Choi, M.H. Kim,
ACCEPTED MANUSCRIPT Crystal structure of SmcR, a quorum sensing master regulator of Vibrio vulnificus, provides insight into its regulation of transcription, J. Biol. Chem. 285 (2010) 14020-14030. 18. Glide, Version, 6.2, Schrödinger, LLC, New York, 2014. 19. B. Hess, C. Kutzner, D. van der Spoel, E. Lindahl, GROMACS 4: Algorithms for highly
RI PT
efficient, load balanced and scalable molecular simulation, J. Chem. Theory. Comput. 4 (2008) 435-447.
20. C. Ooestenbrink, A.Villa, A.E. Mark, W.F. van Gunsteren, A biomolecular force field based on the free enthalpy of hydrogen and solvation: the GROMOS force field parameter sets 53A5 and 53A6, J. Comput. Chem. 25 (2004) 1656-1676.
SC
21. C. Oostenbrink, T. Soares, N.F. van der Vegt, W.F. van Gunsteren, Validation of the 53A6 GROMOS force field, Eur. Biophys. J. 34 (2005) 273-284.
M AN U
22. A.W. Schuettelkopf, D.M. van Aalten, PRODRG: a tool for high throughput crystallography of protein-ligand complexes, Acta Crystallogr D Biol Crystallogr. 60 (2004) 1355-1363. 23. B. Hess, H. Bekker, H.J.C. Berendsen, G.E.M. Fraaije, LINCS: A linear constraint solver for molecular simulations, J. Comput. Chem. 18 (1997) 1463-1472. 24. D. Tom, Y. Darrin, P. Lee, Particle mesh Ewald: An N.log (N) method for Ewald sums in large systems, J. Chem. Phys. 98 (1993) 10089-10092.
TE D
25. S. Miyamoto, P.A. Kollman, Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models, J. Comput. Chem. 13 (1992) 952-962. 26. N.K. Salam, R. Nuti, W. Sherman, Novel method for generating structure based pharmacophore using energetic analysis, J. Chem. Inf. Model. 49 (2009) 2356-2368.
EP
27. G.M. Sastry, S.L. Dixon, W. Sherman, Rapid shape based ligand alignment and virtual screening method based on atom/feature pair similarities and volume overlap scoring, J. Chem.
AC C
Inf. Model. 51 (2011) 2455-2466. 28. C. Lee, W. Yang, R.G. Parr, Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density, Phys. Rev. B. Condens. Matter. 37 (1998) 785-789. 29. A.D. Becke, Density functional thermochemistry III. The role of exact exchange, J. Chem. Phys. 98 (1993) 5648-5652. 30. M.N. Drwal, P. Banerjee, M. Dunkel, M.R. Wettig, R. Preissner, ProTox: a web server for in silico prediction of rodent oral toxicity, Nucleic. Acids. Res. 42 (2014) W53-58. 31. C. Nithya, S.K. Pandian, Isolation of heterotrophic bacteria from Palk Bay sediments showing heavy metal tolerance and antibiotic production, Microbiol. Res. 165 (2010) 578-593. 32. K.S. Musthafa, V. Saroja, S.K. Pandian, A.V. Ravi, Antipathogenic potential of marine Bacillus
ACCEPTED MANUSCRIPT sp. SS4 on N-acyl-homoserine-lactone-mediated virulence factors production in Pseudomonas aeruginosa (PAO1), J. Biosci. 36 (2011) 55-67. 33. P.R. Jesudhasan, M.L. Cepeda, K. Widmer, S.E. Dowd, K.A. Soni, M.E. Hume, J. Zhu, S.D. Pillai, Transcriptome analysis of genes controlled by luxS/autoinducer-2 in Salmonella enterica
RI PT
serovar Typhimurium, Foodborne. Pathog. Dis. 7 (2010) 399-410. 34. A. Cardone, S.A. Hassan, R.W. Albers, R.D. Sriram, H.C. Pant, Structural and dynamic determinants of ligand binding and regulation of cyclin dependent kinase 5 by pathological activator p25 and inhibitory peptide CIP, J. Mol. Biol. 401 (2010) 478-492.
35. W. Hinrichs, C. Kisker, M. Duvel, A. Muller, K. Tovar, W. Hillen, W. Saenger, Structure of the
SC
Tet repressor-tetracycline complex and regulation of antibiotic resistance, Science 264 (1994) 418-420.
M AN U
36. P. Orth, D. Schnappinger, W. Hillen, W. Saenger, W. Hinrichs, Structure basis of gene regulation by the tetracycline inducible Tet repressor-operator system, Nat. Struct. Biol. 7 (2000) 215-219. 37. M.A. Schumacher, M.C. Miller, S. Grkovic, M.H. Brown, R.A. Skurray, R.G. Brennan, Structural mechanisms of QacR induction and multidrug recognition, Science. 294 (2001) 2158-2163.
38. M.A. Schumacher, M.C. Miller, S. Grkovic, M.H. Brown, R.A. Skurray, R.G. Brennan,
TE D
Structural basis for cooperative DNA binding by two dimers of the multidrug-binding protein QacR, EMBO J. 21 (2002) 1210-1218.
39. L.G. Dover, P.E. Corsino, I.R. Daniels, S.L. Cocklin, V. Tatituri, G.S. Besra, K. Futterer, Crystal structure of the TetR/CamR family repressor Mycobacterium tuberculosis EthR implicated in
EP
ethionamide resistance, J. Mol. Biol. 340 (2004) 1095-1105. 40. F. Frenois, J. Engohang-Ndong, C. Locht, A.R. Baulard, V. Villeret, Structure of EthR in a
AC C
ligand bound conformation reveals therapeutic perspectives against tuberculosis, Mol. Cell. 16 (2004) 301-307.
41. R.S. De Silva, G. Kovacikova, W. Lin, R.K. Taylor, K. Skorupski, F.J. Kull, Crystal structure of the Vibrio cholerae quorum sensing regulatory protein HapR, J. Bacteriol. 189 (2007) 56835691.
42. M. Arooj, S. Thangapandian, S. John, S. Hwang, J.K. Park, K.W. Lee, 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors, Int. J. Mol. Sci. 12 (2011) 9236-9264. 43. M. Zheng, X. Liu, Y. Xu, H. Li, C. Luo, H. Jiang, Computational methods for drug design and discovery: focus on china, Trends. Pharmacol. Sci. 34 (2013) 549-559.
ACCEPTED MANUSCRIPT 44. C. Anetzberger, U. Schell, K. Jung, Single cell analysis of Vibrio harveyi uncovers functional heterogeneity in response to quorum sensing signals, BMC Microbiol. 12 (2012) 1-10. 45. J.S. Linthorne, B.J. Chang, G.R. Flematti, E.L. Ghisalberti, D.C. Sutton, A direct pre-screen for marine bacteria producing compounds inhibiting quorum sensing reveals diverse planktonic
RI PT
bacteria that are bioactive, Mar. Biotechnol. 17 (2015) 33-42. 46. J.A. Thompson, R.A. Oliveira, A. Djukovic, C. Ubeda, K.B. Xavier, Manipulation of the quorum sensing signal AI-2 affects the antibiotic treated gut microbiota, Cell. Rep. 10 (2015) 1861-1871.
47. C. de la Fuente-Nunez, V. Korolik, M. Bains, U. Nguyen, E.B. Breidenstein, S. Horsman, S.
SC
Lewenza, L. Burrows, R.E. Hancock, Inhibition of bacterial biofilm formation and swarming motility by a small synthetic cationic peptide, Antimicrob. Agents. Chemother. 56 (2012) 2696-
M AN U
2704.
48. J. You, X. Xue, L. Cao, X. Lu, J. Wang, L. Zhang, S. Zhou, Inhibition of Vibrio biofilm formation by a marine actinomycete strain A66, Appl. Microbiol. Biotechnol. 76 (2007) 11371144.
49. F. Sun, F. Qu, Y. Ling, P. Mao, P. Xia, H. Chen, D. Zhou, Biofilm associated infections: antibiotic resistance and novel therapeutic strategies, Future. Microbiol. 8 (2013) 877-886.
TE D
50. I.A. Packiavathy, S. Priya, S.K. Pandian, A.V. Ravi, Inhibition of biofilm development of uropathogens by curcumin - an anti-quorum sensing agent from Curcuma longa. Food. Chem.
Table legends
EP
148 (2014) 453-460.
AC C
Table 1 Docking scores, interaction profiles and predicted binding free energy estimates (kcal/mol) of the cinnamaldehyde derivatives. Table 2 Docking scores, binding free energy and hydrogen bond interaction between LuxR and 9 hits identified from ChemBridge database. Table 3 HOMO, LUMO and energy gap of cinnamaldehyde derivatives and hit molecules. Table 4 Physico-chemical properties of hits obtained from Qikprop of Schrödinger.
ACCEPTED MANUSCRIPT Figure legends Fig. 1. Pictorial representation of workflow followed in this study. Fig. 2. (a) RMSD of backbone atoms plotted against the time dependent function of the MD
RI PT
simulation, (b) RMSF of C-α atom of the individual residues in the protein analyzed during the simulation time.
Fig. 3. (a) Graphical representation of RMSD of C-α atoms of the protein-ligand complexes, (b) RMSF
SC
of C-α atoms in co-ordinates for each residue of the protein-ligand complexes during the simulation time, (c) Total SASA are calculated, showing the total surface area occupied in the protein-ligand
M AN U
complexes during the simulation period, (d) Hydrophobic contact of the protein-ligand complexes, (e) Radius of gyration as a function of time, showing the structural compactness of the protein-ligand complexes during the simulation time, (f) Total number of inter-molecular hydrogen bond interactions observed between the cinnamaldehyde derivatives and LuxR during the simulation time. Frames are written out every 2 ps. All the plots are well correlated.
TE D
Fig. 4. Common pharmacophore hypotheses (AHHR) and their distance between the pharmacophoric sites. All the distance is in the Å units. Red sphere represents acceptor feature, green hydrophobic feature and orange aromatic ring feature.
EP
Fig. 5. 2D representations of the protein-ligand interactions for the hits identified from the virtual
AC C
screening. Hydrogen bond interactions are shown in pink dashed line and π-π stacking interactions are in green solid line. (a) LuxR-7364106, (b) LuxR-9192049, (c) LuxR-5350755, (d) LuxR-5521550, (e) LuxR-5671025, (f) LuxR-7245765, (g) LuxR-6347339, (h) LuxR-6575543. Fig. 6. 2D structure of ChemBridge-7364106 Fig. 7. Inhibition of V. harveyi bioluminescence by ChemBridge-7364106 at various concentrations. Bioluminescence is measured as relative light units using a luminometer. Fig. 8. Light Microscope image, (a) control, (b) ChemBridge-7364106 treated (8 µg/ml) culture of V. harveyi.
ACCEPTED MANUSCRIPT Fig. 9. Swimming behavior of V. harveyi, (a) untreated control, (b) ChemBridge-7364106 treated (8 µg/ml) culture of V. harveyi. Swamming behavior of V. harveyi (c) untreated control, (d) ChemBridge7364106 treated (8 µg/ml) of V. harveyi.
RI PT
Fig. 10. (a) Effect of ChemBridge-7364106 at various concentrations on growth of V. harveyi strain. Cell density is quantified by absorbance measurement at 600nm, (b) Effect of bioluminescence in V. harveyi in the presence or absence of the ChemBridge-7364106. Bioluminescence measurements are
SC
performed 4h after the addition of the compound. Supplementary Figures
M AN U
Fig. 1S. Stable conformation of three potent cinnamaldehyde derivatives in the binding site of LuxR. Compound 3, 6, 7 (a, b, c) forming 1Hbond interaction with Asn133. (d) Superimposition of all three cinnamaldehyde derivatives in the protein binding site.
Fig. 2S. (a) The RMSD plot showing the stability of hits in the bound state with LuxR for the time scale of 10 ns, (b) RMSF values of atomic positions calculated for the backbone atoms for the LuxR-hit
TE D
complexes as a function of residue number, (c) the average SASA of the LuxR-hits complexes, the xaxis represents the distance (nm), and the y-axis is time (ps), (d) Hydrophobic contacts between LuxR and hits complexes, (e) Rg of C-α atoms of the LuxR-hit complexes, (f) Average number of protein-hit
EP
intermolecular hydrogen bond obtained during the simulation time.
AC C
Fig. 3S. Percentage inhibition of biofilm formation of V. harveyi at various concentrations of ChemBridge-7364106.
ACCEPTED MANUSCRIPT
Table 1 Docking scores, interaction profiles and predicted binding free energy estimates (kcal/mol) of the cinnamaldehyde derivatives
2.
2-Nitro Cinnamaldehyde 3,4-Dichloro Cinnamaldehyde (E)-4-Phenyl-3Buten-2-One (E)-2-Pentenol
3. 4. 5. 6. 7.
a
(E)-2-Nonenol (E)-3-Decen-2One
Glide Emodeld kcal/mol -32.33
Glide energye kcal/mol -23.21
-2.60
-6.38
-29.29
-26.39
-5.67
-8.46
-40.00
-30.49
RI PT
Cinnamaldehyde
Glide scorec kcal/mol -7.80
-6.06
-7.85
-20.93
-22.04
-5.19
-6.18
-16.63
-2.69 -5.17
-5.76 -6.52
-25.09 -33.01
H-bondh
π-π stackingi Phe75a, Phe166ab Phe75a, Phe166ab Phe75a, Phe166a -
-461.84
-24.04
-460.23
-27.78
Asn133ab, Trp114b Asn133ab
-464.98
-12.37
Asn133ab
-462.27
-27.25
Asn133ab
-25.38
-457.17
-36.36
-27.55 -25.36
-460.64 -463.29
-57.28 -52.62
Asn133a, Trap114b Asn133ab Asn133ab
SC
1.
IFD scoref
∆Gbindg kcal/mol
Glide scoreb kcal/mol -5.78
M AN U
Compound namea
TE D
S. no
-
AC C
EP
Name of the compounds Glide score: Scoring of ligand through Glide XP algorithm C Glide score: Scoring of ligand through Induced fit docking algorithm d Glide Emodel: (induced fit docking) is a specific combination of Glide score, the non-bonded interaction energy between the ligand and the receptor and the international torsion energy of the ligand conformer e Glide energy: Scoring of ligand through an induced fit docking algorithm f IFD score: Induced fit docking score g ∆G bind: Energy utilized from induced fit docking pose with XP charges h Amino acid that interacts with the ligand molecule, ainteraction observed in XP docking, binteraction observed in an induced fit docking, ab interaction observed in both XP and induced fit docking i Amino acid that mediate π-π stacking with ligand molecule, aπ-π stacking observed in XP docking, bπ-π stacking observed in an induced fit docking, abπ-π stacking observed in both XP and induced fit docking b
ACCEPTED MANUSCRIPT
π-π stackingj
-43.720
Asn133ab, Gln137ab
0.51
-42.947
Asn133ab
-467.64
0.52
-38.828
-466.31
0.51
-50.494
Asn133a, Gln137ab, Ala163b Gln137a
Hie167ab, Trp114ab, Phe166a, Phe75ab Hie167ab, Phe166a, Phe75ab Phe166a, Phe75a
-43.081
-464.94
0.53
-35.383
Asn133ab
-68.79
-47.535
-468.36
0.50
-58.319
Asn133ab
-73.22
-51.041
-465.76
0.50
-49.770
Asn133ab
e-pharmacophore based screening hits -76.17 -45.183 -463.27 2.45
-49.432
Asn133ab
-62.300
Asn133ab, Gln137b
Chem Bridge IDa
Glide scoreb kcal/mol
Glide scorec kcal/mol
1.
7364106
-8.26
-10.31
2.
9192049
-8.03
-9.94
-73.41
-48.827
3.
5350755
-8.01
-10.48
-47.06
-43.846
4.
5521550
-7.97
-10.14
-78.81
-50.208
5.
5671025
-7.92
-9.08
-66.98
6.
7243765
-7.80
-9.95
7.
6347339
-7.78
-9.52
8.
6575543
-7.63
9.
7477973
-7.62
-11.36
∆G bindh kcal/mol
0.50
-81.49
-467.38
M AN U
TE D
EP
AC C -10.11
Sim/ Fitness scoreg
Glide Glide IFD Emodeld energye scoref kcal/mol kcal/mol Shape based screening hits -53.55 -35.031 -466.85
RI PT
H-bondi
S. no
SC
Table 2 Docking scores, binding free energy and hydrogen bond interaction between LuxR and 9 hits identified from ChemBridge database
-55.508
-470.54
2.43
Hie167a, Phe166ab, Phe75a, Trp114b Hie167ab, Phe166a, Phe75ab Hie167ab, Phe166a, Phe75ab Hie167ab, Phe166a, Phe75a Hie167ab, Phe166a, Phe75a Hie167a, Phe166ab,
ACCEPTED MANUSCRIPT
Phe75ab a
Compound IDs are from ChemBridge database Glide score: Scoring of ligand through Glide XP algorithm C Glide score: Scoring of ligand through Induced fit docking algorithm d Glide Emodel: (induced fit docking) is a specific combination of Glide score, the non-bonded interaction energy between the ligand and the receptor and the international torsion energy of the ligand conformer e Glide energy: Scoring of ligand through an induced fit docking algorithm f IFD score: Induced fit docking score g Sim score/Fitness score: Shape similarity score, scoring parameter of new compound matched with predicted pharmacophore model of known compounds h ∆G bind: Energy utilized from induced fit docking pose with XP charges i Amino acid that interacts with the ligand molecule, ainteraction observed in XP docking, binteraction observed in an induced fit docking, ab interaction observed in both XP and induced fit docking j Amino acid that mediate π-π stacking with ligand molecule, aπ-π stacking observed in XP docking, b π-π stacking observed in an induced fit docking, abπ-π stacking observed in both XP and induced fit docking
AC C
EP
TE D
M AN U
SC
RI PT
b
ACCEPTED MANUSCRIPT Table 3 HOMO, LUMO and energy gap of cinnamaldehyde derivatives and hits.
EP AC C
Energy gap 0.16 0.15 0.15 0.16 0.20 0.20 0.19 0.15 0.19 0.18 0.13 0.16 0.18 0.16 0.21 0.21
RI PT
LUMO -0.08 -0.10 -0.08 -0.07 -0.06 -0.06 -0.05 -0.07 -0.02 -0.02 -0.08 -0.06 -0.05 -0.08 -0.03 -0.03
SC
HOMO -0.24 -0.25 -0.24 -0.23 -0.26 -0.26 -0.25 -0.22 -0.21 -0.20 -0.21 -0.22 -0.23 -0.24 -0.24 -0.24
M AN U
Cinnamaldehyde derivatives Cinnamaldehyde 2-Nitro Cinnamaldehyde 3,4-Dichloro Cinnamaldehyde (E)-4-Phenyl-3-Buten-2-One (E)-2-Pentenol (E)-2-Nonenol (E)-3-Decen-2-One ChemBridge-7364106 ChemBridge-9192049 ChemBridge-5350755 ChemBridge-5521550 ChemBridge-5671025 ChemBridge-7243765 ChemBridge-6347339 ChemBridge-6575543 ChemBridge-7477948
TE D
S. NO 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
ACCEPTED MANUSCRIPT
Table 4 Physico-chemical properties of hits obtained from Qikprop in Schrödinger QP logPo/wc 3.69 3.97 1.42 3.40 4.04 4.95 3.18 2.98 4.50
QP logSd -4.26 -4.28 0.29 -3.47 -4.51 -5.62 -4.93 -4.16 -5.96
a
QP logKpe -1.63 -0.68 -1.63 -4.18 -1.05 -0.49 -2.86 -1.61 -1.74
%HOAf 100 100 69 87 100 100 91.74 100 100
QPP MDCKg 4683.21 6456.36 3253.65 211.553 3036.31 4182.07 374.26 1848.41 2979.17
QP logHERGh -5.01 -4.12 -4.01 -6.51 -5.64 -6.57 -5.83 -5.80 -6.23
RI PT
7364106 9192049 5350755 5521550 5671025 7243765 6347339 6575543 7477943
Molecular weightb 266.12 274.74 262.75 290.74 272.73 299.79 320.73 276.72 338.19
SC
IDa
M AN U
S. No 1. 2. 3. 4. 5. 6. 7. 8. 9.
QPP Caco
Starsj
i
1865.92 2974.94 85.68 179.884 2322.10 3121.84 378.61 1659.30 1159.80
1 0 0 0 0 0 0 0 0
Ligands IDs are from ChemBridge database Molecular weight of the molecule (130.0 to 725.0) c Predicted octanol/water partition co-efficient (-2.0 to 6.5) d Predicted aqueous solubility, logS. S in moldm-3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid (-6.5 to 0.5) e Predicted skin permeability (-8.0 to 1.0) f Predicted human oral absorption on 0 to 100% scale. The prediction is based on a quantitative multiple linear regression model. This property usually correlates well with human oral absorption, as both measures the same property (< 25 % is poor, >80 % is high) g Predicted apparent MDCK cell permeability in nm/sec. MDCK cells are considered to be a good mimic for blood brain barrier. Qikprop predictions are for non active transport (<25 is poor, > 500 is high) h Predicted IC50 value for blockage of HERG K+ channels (concern below -6) i Predicted Caco-2 cell permeability in nm/sec. Caco-2 cells are a model for the gut blood barrier. Qikprop predictions are for non active transport (<25 is poor, >500 is high) j Number of property or descriptor values that fall outside the 95% range of similar values for known drugs (0-5 acceptable)
AC C
EP
TE D
b
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Highlights •
E-pharmacophore model was developed using LuxR-3,4-dichloro-cinnamaldehyde complex.
•
E-pharmacophore- and shape-based virtual screening was carried out with ChemBridge
•
RI PT
database.
Top 9 hits molecules were selected based on favorable binding interaction, high docking score and ADMET prediction.
The top most retrieved hit is biological evaluated and it’s showed bioluminescence
EP
TE D
M AN U
SC
inhibition, biofilm inhibition and motility reduction in Vibrio harveyi.
AC C
•