Structure-based virtual screening and biological evaluation of LuxT inhibitors for targeting quorum sensing through an in vitro biofilm formation

Accepted Manuscript Structure-based virtual screening and biological evaluation of LuxT inhibitors for targeting quorum sensing through an in vitro biofilm formation Dakshinamurthy Sasikala, Jeyaraman Jeyakanthan, Pappu Srinivasan PII:

S0022-2860(16)30812-2

DOI:

10.1016/j.molstruc.2016.07.118

Reference:

MOLSTR 22821

To appear in:

Journal of Molecular Structure

Received Date: 9 May 2016 Revised Date:

28 July 2016

Accepted Date: 30 July 2016

Please cite this article as: D. Sasikala, J. Jeyakanthan, P. Srinivasan, Structure-based virtual screening and biological evaluation of LuxT inhibitors for targeting quorum sensing through an in vitro biofilm formation, Journal of Molecular Structure (2016), doi: 10.1016/j.molstruc.2016.07.118. 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.

ACCEPTED MANUSCRIPT

Structure-Based Virtual screening and biological evaluation of LuxT inhibitors for targeting Quorum sensing through an in vitro biofilm formation

M AN U

SC

RI PT

Dakshinamurthy Sasikalaa, Jeyaraman Jeyakanthana and Pappu Srinivasanb*

LuxT informs the readers of bacteriology as novel drug target and this study deals with specificity of potent lead compounds and justify the requirements of LuxT for inhibition of

TE D

Quorum sensing. This study is a combo of experimental and theoretical, which suggest that the screened compounds had better efficacy in biofilm destruction; as a consequence, there were

AC C

EP

significant effects on motility and EPS synthesis.

ACCEPTED MANUSCRIPT 1 Structure-Based Virtual screening and biological evaluation of LuxT inhibitors for targeting Quorum sensing through an in vitro biofilm formation Dakshinamurthy Sasikalaa, Jeyaraman Jeyakanthana and Pappu Srinivasanb* Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamilnadu,

RI PT

a

India b

Department of Animal Health and Management, Science Block, Alagappa University,

*Corresponding author:

EP

Dr. Pappu Srinivasan,

TE D

M AN U

SC

Karaikudi, Tamilnadu, India

Associate Professor

AC C

Department of Animal Health and Management Alagappa University, Karaikudi Tamil Nadu– 630 004, India Fax (off):+91- 4565- 2252

E-mail: [email protected] Tel: +91-4565-230725 Tel: +91-9444482814

1

ACCEPTED MANUSCRIPT 2 Abstract The LuxT regulator protein is a potential drug target for regulating quorum sensing (QS) in Vibrio alginolyticus. There is no substantiation of a 3D structure of LuxT in this particular

RI PT

species, thus the 3D model was constructed using molecular modeling and its stability was verified under solvation effect using molecular dynamics simulation. Further, exploration of the drug candidate against the LuxT binding site through structure-based approaches identified four

SC

Bitter compounds, viz., quercetin, cnicin, 5, 5ꞌ methylenedisalicyclic acid, and flufenamic acid with high docking score and optimum binding energy. Remarkably, these compounds established

M AN U

agreeable interactions with the amino acid residues Phe67, Asp63, Thr59, Gly64, Ile66, Phe99, Arg123, Asn119, and Gly120; which are found to play a vital role in the LuxT mechanistic inhibition. In addition to the scoring and energy parameters, MD simulation and ADME prediction suggested that the proposed compounds may be promising drug candidates against V.

TE D

alginolyticus. Therefore, the lead candidate quercetin was scrutinized to validate its potential in biofilm inhibition of V. alginolyticus. The results revealed that the compound had better efficacy in biofilm destruction; as a consequence, there was a significant effect on motility and EPS

EP

synthesis at an effective concentration of 100 µM, which was further confirmed by microscopic studies. Hence, the present study will provide an insight into the design of high potent drug

AC C

molecules to combat dreadful pathogenic diseases caused by V. alginolyticus

Keywords: LuxT, V. alginolyticus, Quorum sensing, Homology modeling, Molecular dynamics simulation, quercetin

2

ACCEPTED MANUSCRIPT 3 1. Introduction

Vibrio alginolyticus is a motile, rod-shaped, Gram-negative bacterium, naturally found in

RI PT

marine and estuarine environments. V. alginolyticus is both halophilic and mesophilic, and is recognized as a leading causative agent of vibriosis outbreak resulting in heavy economic damage to aquaculture worldwide [1-2]. V. alginolyticus is also an opportunistic pathogen that

SC

widely causes human infections such as endopthalmitis, otitis, and wound infections [3-6]. The fabrication of extracellular products (ECP) in V. alginoyticus has been recognized as the primary

M AN U

virulence in marine organisms, viz., fish, coral, sea grass, sea bream, groupers, zooplankton, and shellfish in its larval stage [4, 6-7]. Based on the cell adherence to the host surfaces, biofilm formation relies on the delivery of exotoxin such as proteases, hemolysin, and exopolysaccharide (EPS) [6, 8-10]. However, the mechanism of an immense range of gene expression involving biosynthesis of the above-mentioned toxin products is critical in regulation by the QS regulatory

TE D

circuit in response to the cell density [11-13].

Quorum sensing (QS) is a process of cell-to-cell communication using chemical signaling

EP

molecules that are highly conserved in Vibrio sp. The LuxS/AI-2 QS system plays an essential role in QS regulation of pathogenesis in Gram-negative bacteria, thereby influencing the

AC C

activation of numerous virulence factors such as siderophore, metalloprotease, and serine protease production [14-16]. The autoinducer (AI) molecule in QS cascade depends upon the cell density, which activates the LuxT expression through the LuxU phosphor-relaying protein [17]. At a high cell density, the regulation of LuxR activated through LuxO sensory protein progressively facilitates the activation of phosphatase enzyme and inactivates the LuxO, thereby resulting in a depressed expression of transcriptional regulator, which is critical for the virulence 3

ACCEPTED MANUSCRIPT 4 associated phenotypes such as type III secretion system, mobility, and ECP [4]. The LuxT enzyme activates the LuxO transcription directly by binding in its promoter region, and hence destabilizes the LuxR at its post-transcription level through small RNA

RI PT

synthesis. Alternatively, LuxT regulates the expression of LuxR through several unknown factors and subsequently alters the virulence phenotypes in V. alginolyticus. The mechanistic role of LuxT in V. alginolyticus is quite different from other Vibrio sp., which has high sequence

SC

similarity between them of about 85–93 % [7].

Nowadays, the impact of Vibrio sp. in the aquatic environment along with the emergence

M AN U

of antibiotic resistance necessitates the investigation of an alternative strategy for eradicating the disease outbreaks. Hence, QS is a novel pathway to control the virulence of bacteria and it can be addressed by identifying a suitable target for inhibition through in silico approaches. In this study, LuxT, a key regulator of V. alginolyticus, has been identified as a powerful drug target

TE D

having no experimentally solved structure, which prompts to construct a model structure using homology modeling. The proposed model was explored for finding potent inhibitors from the Bitter natural database. Further reassessment of the protein-ligand complexes by assigning

EP

energies can be accurate for favorable interactions in terms of the binding potential. Moreover, the best screened compound was investigated for its virulence potential to validate in silico

AC C

findings through in vitro analysis on V. alginolyticus. From the results of the combined approaches, it is evident that the screened compounds could have powerful drug-like properties and thus could proceed with the development of drug candidates in the control of pathogenesis.

4

ACCEPTED MANUSCRIPT 5 2. Materials and Methods 2.1. Homology modeling of LuxT regulator Homology modeling technique [18] was used to model the protein from its available

RI PT

sequence information. At present, the experimentally determined crystal structure of LuxT protein (length-132 amino acids) is not available in protein databank (PDB). Hence, the construction of a 3D model for LuxT protein was successfully done using functionally similar

SC

proteins from related species (V. parahaemolyticus) as template structure. The sequence of LuxT was retrieved from UNIPROT (Database no: FJ556583) (http://www.uniprot.org) and subjected

M AN U

to BLASTp analysis (http:/blast.ncbi.nlm.nih.gov/), which revealed the desired template of highly similar sequences. The maximum identity, query coverage, and E value proclaimed that the crystal structure of PDB ID: 3LJL as the most appropriate template to generate a model for the LuxT protein. The LuxT homology model was predicted using MODELLER 9.10 acquired

TE D

by optimally fulfilling spatial restraint between the template and the target sequence [19-20]. The generated models were sorted out based on the discrete optimized protein energy (DOPE) scoring function of the lowest value. Further, the protein structure was refined using protein

EP

preparation wizard (Protein Preparation Wizard, 2015), which incorporates with assigning of bond orders and addition of hydrogen atoms by default sampling. The network of hydrogen

AC C

atoms in the protein model was optimized at a neutral pH and the heavy atoms were restrained through subjection of energy minimization using optimized potential for liquid simulations (OPLS) force field 2005. Finally, the complete optimized protein structure was attained at an average root mean square deviation (RMSD) of non-hydrogen atoms reaching 0.30 Å. 2.2. Model validation The predicted LuxT model was further ensured for its reliability through different protein 5

ACCEPTED MANUSCRIPT 6 structure validation servers. Initially, the target and the template structure were verified for its equivalence of secondary element using ESPript, describing the similarity of the predicted LuxT protein. The overall structural compatibility between the target and the template was computed

RI PT

by means of root mean square deviation (RMSD) using UCSF chimera that determines the most identical and similar secondary structures among them [21]. In addition, the predicted LuxT model was assessed for its overall stereochemical property using Ramachandran plot [22-24].

SC

Validations such as verify 3D and ERRAT describe the structural aspects of the LuxT model and its quality was further evaluated using QMEAN, ANOLEA, and GROMOS analysis [25-27]. The

M AN U

error in the structural conformation expressed in Z-score was calculated using ProSA server for understanding the structural integrity of the LuxT protein 2.3. Active site prediction

The active site was predicted for LuxT from V. alginolyticus using SiteMap (SiteMap,

TE D

version 3.4, Schrödinger, LLC, New York, NY, 2015) implemented in the Maestro interface. Additionally, the binding site information was manually predicted by separating the crystal structure complex with a cofactor so as to obtain the significant residues involved in the potential

EP

binding region. Further, druggability sites are located through clustering the favorable regions by means of van der Waals charges on the protein surface using SiteMap. It implies the OPLS-2005

AC C

force field parameters to estimate the interaction energies of probes placed at all the points on 3D grids that encompass the entire protein. The cofactor location was exactly matched and confirmed by both the methods. The binding site residues were assigned for the theoretical model to perform docking studies.

2.4. Preparation of lead molecules A set of 1560 compounds from the Bitter database (http://bitterdb.agri.huji.ac.il/) were 6

ACCEPTED MANUSCRIPT 7 adopted for docking against the binding cavity of the receptor, as of bound in the complex [28]. The druggability site based on the top ranked site score in SiteMap was prearranged for Glide input files and the receptor grid was generated. LigPrep module (LigPrep, Version 3.3,

RI PT

Schrödinger, LLC, New York, NY, 2015) implemented in the Maestro interface was used to optimize the ligands for binding analysis. A total of 32 different conformers per ligand structure were generated using the default energy ring conformation. The treatment of Epik in each

SC

structure to achieve protonation and the tautomeric state at pH 7.0±2.0 is followed by subsequent neutralization of the charged groups of compounds. The resulting geometries were finally

M AN U

optimized using the OPLS-All Atom (AA) force field. 2.5. Virtual screening of lead compounds

Virtual screening has emerged as a cost-effective computational approach to achieve success in drug designing. The optimized large set of compounds were allowed to dock toward

TE D

the LuxT druggability site using virtual screening workflow, which comprises three different screening protocols, viz., high throughput virtual screening (HTVS), standard precision (SP) and extra precision (XP) [23, 29]. For this study, the receptor was kept rigid and the optimized

EP

ligands were set to be flexible in docking. Prior to docking, a grid was generated at the centroid of the desirable active site of the LuxT with a dimension of x: 46.4425, y: 5.0994, and z:

AC C

88.7721, which is enough room for the ligands to dock. Here the docking simulation was performed using Glide docking algorithm with default settings. Glide produces the exact orientation of ligands after several conformations of 1000 poses per run by applying Monte Carlo method [30]. Subsequently, the final energy was evaluated and a single best pose was generated as output for a particular ligand with the help of the following equation. Gscore = a*vdW + b*Coul + Lipo + Hbond + Metal + BuryP + RotB + Site, 7

ACCEPTED MANUSCRIPT 8 where vdW = van der Waals energy; Coul = Coulomb energy; Lipo = lipophilic contact term; H Bond = Hydrogen-bonding term; Metal = metal-binding term; BuryP = penalty for buried polar group; RotB = penalty for freezing rotable bonds; Site = polar interaction at active

RI PT

site; and the coefficients of vdW and Coul are: a = 0.065 and b = 0.130.

Thus the final accurate pose of ligands with the highest binding affinity and the lowest binding free energy was evaluated through Glide scoring function designed with the OPLS-AA

SC

force field. The module computes scores for each best fit to the potential target, which is termed as Glide score. The best binding mode obtained was analyzed and visualized by Glide pose

M AN U

viewer.

2.6. Quantum Polarized ligand docking

Quantum polarized ligand docking (QPLD) was employed to provide accurate partial charges of ligand for each unique orientation pose toward the binding region of the chosen target.

TE D

In this workflow, we performed the combined theory of Glide docking with the accuracy of QSite (Semiemprical method/Mulliken charges) that incorporates the QM/MM software for enhancing the accuracy of ligand docking poses in docked complexes (QSite, version 6.6,

EP

Schrödinger, LLC, New York, NY, 2015). This algorithm works with the initial docking of ligands in Glide XP mode, resulting in several geometries of complexes. Second, the partial

AC C

atomic charges of ligands induced by the macromolecular target are calculated by QSite according to the single-point energy calculation of each complex [32]. The set of the best ligand poses were redocked and the final pose was ranked through Glide scoring function. The hydrogen bond (H bond) interactions existed between the complexes were analyzed based on the cutoff of the maximum H bond acceptor distance of 2 Å and the minimum donor angle 120° and acceptor 90° using Maestro in Schrödinger. 8

ACCEPTED MANUSCRIPT 9 2.7. Binding free energy calculation The best docked poses of each top screened ligand were rescored for computing its binding free energy using prime MM/GBSA approach based on the fastest force field method.

RI PT

GB/SA continuum model is distinctively used for free energy calculations by MM method for the electrostatic region of solvation energy, whereas the solvent accessible surface is for the nonpolar

Gbind = ∆E + ∆Gsolv + ∆GSA

SC

region of solvation energy [33-34]. Thus, the binding energy is expressed as

∆E = Ecomplex – Eprotein – Eligand

protein, and ligand, respectively.

M AN U

where Ecomplex, Eprotein, and Eligand are the minimized energies of the protein–ligand complex,

∆Gsolv = Gsolv (complex) – Gsolv (protein) – Gsolv (ligand) where Gsolv (complex), Gsolv (protein), and Gsolv (ligand) are the solvation free energies of the

TE D

complex, protein, and inhibitor, respectively.

∆GSA = GSA (complex) – GSA (protein) – GSA (ligand) where GSA (complex), GSA (protein) and GSA (ligand) are the surface area energies for the

EP

complex, protein, and ligand, respectively.

2.8. Molecular dynamics simulation studies

AC C

All MD simulations were adopted in Desmond (Desmond, version 4.1, Schrödinger,

LLC, New York, NY, 2015) implemented in Maestro graphical user interface to assess the dynamic aspects of the biomolecules such as structural stability and flexibility, conformational changes, and allosteric transition on the diverse time limit. MD simulation combined with efficient parallelization helps to provide insights into the internal mechanism of the receptorligand complex by obtaining a dynamic process through simulating their internal coordinates of 9

ACCEPTED MANUSCRIPT 10 atoms and molecules. MD simulation was carried out in the aqueous biological system embedded with the OPLS-AA force field 2005. The apo and complex structure was neutralized by adding 0.15 M Na+/Cl-, and subsequently filled with TIP3P water molecules at a marginal radius of 10 Å

RI PT

in an orthorhombic box with the default boundary conditions [35]. Further, the entire system was energy minimized using the steepest descent method in a maximum of 3000 steps followed by stabilization with a coupled parameter of temperature and pressure using Nose-Hoover

SC

thermostat and Martina-Tobias-Klein barostat, respectively [36-37]. Further, smooth particles Mesh-Ewald method was used for constant long-range interactions and van der Waals truncated

M AN U

at 9.0 Å for short-range interactions. The final system consists of the desired atoms at the end of preliminary minimization and simulations. This can be achieved by applying a series of restrained minimization steps that are programmed as default in the Desmond interface. Furthermore the minimized system was allowed to simulate for 2 fs time steps, thus saving all

TE D

assessing configurations in 2 ps time interval. The final equilibrated system of apo protein and the protein ligand complexes were simulated for 30 ns using the parameter described above. The result of the final data was collected from Desmond and MD trajectory analysis was visualized

EP

using the VMD software package.

2.9. Pharmacokinetic prediction of screened lead molecules

AC C

The lead molecules were filtered based on their pharmacokinetic property using QikProp

module in Schrödinger suite [38-39]. A total of 44 physically significant descriptors with other pharmaceutically relevant properties were investigated, which includes stars (range of properties that exceeds the 95 % range of known drugs), QPlogPw (prediction of water/gas partition coefficient), QPlogPo/w (prediction of octanol/water partition coefficient), QPlogHERG, QPPMDCK (prediction of apparent MDCK cell permeability), and QPlogKp (prediction of skin 10

ACCEPTED MANUSCRIPT 11 permeability), which determines the absolute bioavailability of drugs in humans. Further, the drugs were inspected for drug likeness using Lipinski rule of five index [40]. 2.10. In vitro methods

RI PT

2.11. Bacterial strains and culture conditions

V. alginolyticus (Accession number: KP698604) identified previously was utilized for this study. The bacterial strain was cultured in nutrient broth (NB) medium (Hi-Media Pvt Ltd,

SC

India) supplemented with 3 % NaCl and incubated at 30 ºC for overnight. The stock culture was maintained for the strain at -80 ºC in 10 % (vol/vol) glycerol for further use. Quercetin as the top

vitro inhibition assay on V. alginolyticus.

M AN U

hit compound from the docking study was purchased from Sigma-Aldrich (India) to perform in

2.12. Inhibitory activity of quercetin against V. alginolyticus

In vitro antibacterial activity of quercetin was tested against V. alginolyticus using agar

TE D

well diffusion assay and broth microdilution method [41]. The bacterial strain was cultured in NB medium (pH 7.5 ± 0.2) to obtain OD 0.5 (107 colony forming unit (CFU) mL-1) for determining the effect of inhibitory activity of quercetin using Muller Hinton Agar (MHA)

EP

medium. An aliquot of 100 µL bacterial culture was swabbed over the surface of MHA medium. After drying the excessive moisture on the plates, 10−30 mM of the quercetin dissolved in

AC C

DMSO (100 %) was loaded in each well and drug-free DMSO served as the control. The plate was kept for overnight incubation at 30 ºC and the zone of inhibition was recorded. The minimum inhibitory concentration (MIC) of quercetin was estimated according to the Clinical and Laboratory Standards Institute (CLSI) 2006 guidelines [42]. For the broth dilution method, the bacterial cells (OD600 = 0.5) were aliquot into the well of a 96-well microtiter plate which was prefilled with a serially diluted quercetin to give a final concentration ranging from 0.001 to 11

ACCEPTED MANUSCRIPT 12 10 mM and incubated at 30 ºC overnight. The lowest concentration of the compound at which no visible growth of bacteria after incubation was assigned as MIC [43]. 2.13. In vitro anti-biofilm activity of quercetin

RI PT

In vitro anti-biofilm assay was carried out to evaluate the ability of biofilm disruption by quercetin using 24-well microtiter plates [43]. The bacterial biofilms was constructed by inoculating 1 % log phase culture of V. alginolyticus (OD600 = 0.5) in fresh sterile NB media with

SC

the presence and absence of quercetin at varying concentrations of 20–100 µM. This experiment was conducted in triplicate and the plates were incubated for biofilm formation at 30 ºC

M AN U

overnight. After incubation, the planktonic cells from each well were discarded and washed twice with 200 µl of phosphate buffer saline (PBS) at pH 7.0. Adherent biofilm cells on the walls were stained with 0.4 % crystal violet (CV) for 10 min and subsequently rinsed with PBS (200 µL) four times to extract the unbound cells. The stained biofilms were decolorized with 95 %

TE D

ethanol and measured at 650 nm using a microtiter plate reader. The reduction in bacterial biofilms was compared with the control to determine the percentage inhibition of biofilm by quercetin. The biofilm inhibitory concentration (BIC) of quercetin was evaluated from the

EP

particular treated well at which very less number of bacteria (CFU mL-1) was encountered compared with the control biofilm.

AC C

2.14. Effect of quercetin on V. alginolyticus biofilm formation The efficacy of quercetin on V. alginolyticus biofilm was developed and evaluated using

static ring assay with a few modifications as described [44]. The experiment described above was conducted in glass test tubes and incubated overnight at 30 ºC under microaerobic condition. The cell suspension after incubation was removed and rinsed with PBS to detach the loosely bound sessile bacteria. The adherent bacteria on walls were stained with CV (0.4 %) and dried to 12

ACCEPTED MANUSCRIPT 13 observe the ring occurrence. 2.15. Microscopic imaging techniques on biofilm inhibition of quercetin A 24-well microtiter plate assay was performed to determine the antibiofilm effect of

RI PT

quercetin on test strains as described [45] with a few modifications. The bacterial biofilm was allowed to grow on the glass pieces (1 x 1 cm) in the presence and absence of quercetin at varying concentrations of 60, 80, and 100 µM, respectively, and incubated at 30 ºC overnight.

SC

The bacteria were removed and the biofilm was stained with CV (0.4 %) for 10 min and washed with PBS to remove the unbound cells. The dispersal of the biofilm structure on glass slides

M AN U

treated in relative to the control was observed under oil immersion light microscope at 40x magnification. The biofilm established on glass surfaces was fixed with glutaraldehyde (0.1 %) and subsequently dehydrated in ethanol gradient (20, 50, and 70 %) for 10 min. Specimens were sputtered with gold palladium and viewed through a scanning electron microscope (SEM).

TE D

2.16. Motility inhibition assay

The motility assay was carried out in V. alginolyticus according to the method described with minor modifications [46]. A log phase culture of V. alginolyticus (OD adjusted to 0.5 at 600

EP

nm) was exposed to treatment with quercetin (100 µM BIC) in NB medium overnight at 30 ºC. After incubation, the treated and control (without addition of quercetin) samples were spotted at

AC C

the center onto the solidified NA assay plate containing 0.5 % of agar. The assay plates were incubated and mobility was determined. 2.17. Effects of quercetin in EPS synthesis of V. alginolyticus on biofilm formation The phenol sulfuric acid method was performed to estimate the effect of quercetin in the

synthesis of exopolysaccharide of V. alginolyticus [47]. In brief, the test strain (0.4 OD at 600 nm) was allowed to grow in the absence and presence of quercetin at their various BIC 13

ACCEPTED MANUSCRIPT 14 concentrations (20, 40, 60, 80, and 100 µM) on glass slides rinsed in 24-well microtiter plates and incubated overnight at 30 ºC. After incubation, the cells were scrapped from the glass slides and suspended in PBS in test tubes. Next, the cell suspension was added with an equal proportion

RI PT

of phenol (5–7 %) and mixed with 5 times the volume of H2SO4 containing hydrazine sulfate (0.2 %). This mixture was incubated in a water bath (70 ºC) until color formation and the supernatant (10,000 rpm for 10 min) collected for each sample. The change in the color intensity

SC

of all the samples was measured at 490 nm using a spectrophotometer.

2.18. Quantification of slime production on congo red agar (CRA) assay

M AN U

The potent ability of quercetin against a biofilm profile of V. alginolyticus was evaluated on congo red agar (CRA) plates [48]. The test strain treated with BIC (100 µM) of quercetin and untreated (control) was inoculated on Brain Heart Infusion (BHI) agar media supplemented with 5 % sucrose and congo red stains. The plates were incubated at 30 ºC overnight and the results

TE D

were observed. The slime producers are biofilm positive bacteria and appeared as black dry crystalline colonies, while the nonproducers of slime appeared as brown or pink noncrystalline or crystalline colonies.

EP

2.19. FT-IR analysis on impacts of V. alginoyticus biofilms by quercetin FT-IR was employed to access the direct impact of quercetin on each component of the

AC C

bacterial cells. After treatment with various BIC concentrations and devoid of quercetin used as control, the aliquots of bacterial cell suspension in each treated and control sample were centrifuged at 10,000 rpm for 10 min. The medium free cell pellets were collected and pelletized with dried potassium bromide (KBr) under vacuum. The essential FT-IR spectra were recorded for each KBr sample pellets using an infrared spectrometer. A total of 64 scans were obtained with a resolution of 4 cm−1 in the range of 400–4000 cm−1 [49]. The FT-IR spectral features of all 14

ACCEPTED MANUSCRIPT 15 the samples were acquired and analyzed using OPUS TM software. 2.20. Statistical analysis All the experiments were carried out independently three times and the values were

RI PT

reported as standard error of the mean (SEM). The data were analyzed by one way analysis of variance using GRAPH PAD PRISM software 5.01 (Graph Pad software Inc., LA Jolla, CA). P value of 0.001 is considered as statistically significant.

SC

3. Results and Discussion 3.1. Generation of LuxT Protein structure

M AN U

The 3D structure of LuxT (V. alginolyticus) is not evident in the literature so far. Thus, the lack of structural knowledge provoked us to construct a model for understanding the structural features of the protein. Based on the BLASTp search against PDB with LuxT sequence resulted in LuxT (PDB ID: 3LJL) from V. parahaemolyticus at a resolution of 3.2 Å as a suitable

TE D

template. This template has maximum query coverage of 99 % and 91 % identity at an E value cutoff of 4e-85. Further, sequence alignment was carried out between LuxT and the template using UCSF Chimera, which is crucial in finding the highly conserved region of the structural

EP

elements, as shown in Supplementary Fig. 1. This outcome confirmed that both the sequences are highly conserved and belong to the same TetR family in the genus of Vibrio. Henceforth, the

AC C

LuxT of V. parahaemolyticus was considered as a suitable template to build the model (Fig. 1a) using MODELLER 9.10 software. Based on the lowest DOPE score value (-16417.43), the best model was selected and its structural compatibility was verified using several validation servers. 3.2. Structure validation

The LuxT protein was validated to qualify the stereochemical geometric structure using PROCHECK program. The statistics of the Ramachandran plot illustrated that the protein 15

ACCEPTED MANUSCRIPT 16 residues of 96.6, 2.5, 0.8, and 0.0 % are located in the favorable, additional allowed, generous allowed, and disallowed region, respectively. The plot generated by PROCHECK is shown in Fig. 1b. The result of the plot demonstrated that the model was found to have fairly consistent

RI PT

residues in the distribution of the Phi and Psi torsion angle [50]. In addition, the structural divergence was evident by superimposing the template and LuxT protein and the backbone RMSD was computed to be 0.181 Å using Chimera (Fig. 1c), which described the refined model

SC

as quite good and reliable. The secondary structure of the LuxT model was predicted using PDBSUM (Supplementary Fig. 3), showing that the model consists of a nine helical structure.

M AN U

The Z-score calculated for the template and LuxT model as -5.04 and -6.52, respectively, indicated that the overall geometrical quality of the model are within the acceptable range in comparison with the solved experimental structure (Fig. 1d) [51]. The overall quality of the LuxT model validated by ERRAT score is 89.916, which is very close to the range of the high-quality

TE D

protein structure [52]. The assessment of ANOLEA was performed to exemplify the allocation of a nonlocal environment (NLE) [53]. Each atomic coordinates of the LuxT protein revealed that the majority of the residues are located well within the favored region. QMEAN and GROMOS

Fig. 2).

EP

exhibited that the model is quite satisfactory to explore in docking of inhibitors (Supplementary

AC C

3.3. Binding of LuxT inhibitors using Glide and QPLD In this study, the most successful and efficient method of Glide from Schrödinger was

employed for insight into potent compounds against the binding site of the LuxT model. Although the sequence shares 91 % identity with the template LuxT, which make direct H-bond contact with the ligand through Phe67 and are conserved in the LuxT target. Thus, the corresponding active residues located in the region around 4 Å were precisely chosen for 16

ACCEPTED MANUSCRIPT 17 assigning the binding pocket in addition to the predicted active site using SiteMap. Prior to docking simulation, the natural compounds from Bitter database were optimized by assigning with default parameters of possible tautomers, ring conformation, and ionization at the pH

RI PT

7.0±2.0 using Epik state. Further, execution of lead molecules was filtered on the basis of QikProp properties and thus was subsequently screened through a series of hierarchical filters initially with high throughput virtual screening (HTVS). The top assigned 10% compounds from

SC

HTVS were subjected to SP and XP docking to enhance the accuracy of binding thereby eliminating false positives through extensive sampling. A total of 21 compounds from XP

M AN U

docking were visualized for its key interactions with amino acid residues that are expected to inhibit the function of the LuxT regulator. The top four compounds with high Glide score and Glide energy indicate the potential affinity of lead molecules into the binding site of LuxT target. The relative binding orientation of the top four selected compounds into the active site of LuxT

TE D

regulator protein was investigated through subsequent redocking using QPLD. The docking results clearly show that the least Glide score with efficient docking of screened compounds into putative binding site in comparison with Glide XP score, as given in Table 1. This is primarily

EP

due to assigning partial atomic charges for each orientation of ligands. Consequently, the significant variation in the polarization effect by QM/MM fields relies on impacts in the potential

AC C

conformation of protein-ligand complexes. However, the result of docking simulation demonstrated that all the screened lead

molecules had good binding pose, high Glide score, Glide energy, QPLD score, and other pharmacokinetic and physiochemical properties as given in Tables 1, 3, and 4. Interestingly, the binding pose of the hit molecules from XP docking and QPLD aided in the network of H bond with similar solvent accessible surface area of LuxT protein composed of Asp63, Thr59, Ile99, 17

ACCEPTED MANUSCRIPT 18 Glu115, Ala60, Ile66, Asn119, Gly64, Ile99, Gly120, Phe67, and Arg123 as given in Table 1. 3.4. Predicted binding free energy for lead compounds After successful evaluation of ligand docking using Glide, the top discerning compounds

RI PT

were analyzed for their optimal binding ability within the LuxT binding cavity. According to the energy prediction, the accurate binding pose obtained by the lead molecules upon binding affinity was ranked by applying the composite scoring function. In addition, the energy acquired

SC

for specific interaction by the atomic residues roughly indicates the accuracy of the proteinligand complexity. Thus, the energy favored for the protein-ligand complex was calculated by

M AN U

assigning the mean value. Comparative docking model established by Glide scoring of the screened lead molecules such as quercetin, cnicin, 5, 5' methylenedisalicyclic acid, and flufenamic acid, which were analyzed for its efficient binding by calculating the interaction energy and Glide energy, are summarized in Table 1. The protein structure was geometrically

TE D

optimized using Prime energy minimization in GBSA continuum solvation model. Further, the bound complexes rendering binding energies are computed using OPLS-AA force field. Herein, docking analysis reported quercetin as the first hit compound which has already been reported as

EP

flavonoids. Currently, the derivatives of quercetin are used as antioxidant, antitumor, and anticancer drugs [54] and showed an efficient binding model with the lowest binding energy of

AC C

-85.97kcal/mol along with favorable interactions with Asp63, Thr59, and Gly120 residues (Table 1). Secondly, cnicin has also been accounted with multiple biological properties such as antibiotic, antifungal, phytotoxic, and antiherbivore [55]. This study revealed that cnicin was relatively strong in attracting the LuxT target when complexes through specific residual interactions with H bonds of Glu115, Ala60, Arg123, Asn119, Ile66, Gly64, and Ile99. The estimated binding-free energy of cnicin was found to be -61.86kcal/mol. Further, in attempting 18

ACCEPTED MANUSCRIPT 19 docking studies of the compound includes 5, 5' methylenedisalicyclic acid and flufenamic acid showed good affinity in the binding complex in terms of binding energy, which was found to be -

residues Ile99, Thr59, Asp63, Ile66, and Phe67.

RI PT

57.20kcal/mol and -55.81kcal/mol, respectively, through assembly of H bond linked with

Table 2 which summarizes the calculated binding free energies using Prime MM-GBSA suggests that the screened hits are occupied sufficiently with more or less a similar binding

SC

pattern and greater binding affinity to the LuxT protein. The binding free energy (∆Gbind) of the lowest range between -85.97 and -55.81kcal/mol showed that the screened hits bind LuxT with

M AN U

the highest affinity. This is because the more the negative value of the binding free energy, the stronger the binding of the complex. Moreover, the contribution of other energy components in the binding-free energy such as coulomb (∆Gcoulomb) and van der Waals (∆Gvdw), also favors considerable ligand binding into the binding pocket of LuxT (Table 2). Likely, the other

TE D

components of solvation energy (∆Gsolv) also attributed favorably to achieve more stable interactions with the compounds. Henceforth, the proposed compounds can be examined for their biological activity using experimental studies for further drug development.

EP

3.5. Binding pose of best hit compounds

Molecular docking approaches were carried out to accurately examine the compounds to

AC C

serve as potential inhibitor for the LuxT therapeutic target. The binding pose of the top four identified prominent lead molecules are shown in Fig 2. As mentioned in Table 1, the proteinligand complexes were ranked for its better orientation using a composite scoring function in Glide module. The scrutinized top lead molecules from docking simulation had a scoring function in the range of Glide score -9.65 to -11.32kcal/mol, Glide energy -39.42 to -53.62kcal/mol and Glide Emodel -56.73 to -70.70kcal/mol, respectively. Furthermore, the 19

ACCEPTED MANUSCRIPT 20 docking analysis revealed the top lead compounds having a good binding energy that is likely to share a similar binding pocket of the LuxT receptor chosen for further analysis. The interaction profile of the ligands in respect of the LuxT binding site is shown in Supplementary Fig. 4. The

RI PT

active region of the LuxT comprising of amino acid residues within 4 Å from the ligands are enclosed in the figure of each docked complex. The distinct H bond interactions in each complex were discriminately evaluated for its prominent binding orientation of ligands and are discussed

SC

below.

Among the top four compounds, quercetin was found to have more constructive

M AN U

interactions with the highest Glide score of -11.30 kcal/mol and Glide energy of -53.62kcal/mol. The core conformation of quercetin exhibited better orientation inside the putative binding mode through three efficient intermolecular hydrogen bonding with polar and charged residues. As shown in Fig. 2a, the hydroxyl group of quercetin aided the strong network of H bond to the

TE D

residue Thr59 and Glycine residue 120, and also to the carbonyl backbone of Asp63 with a distance of 1.87 Å. A π-cationic interaction also existed with the residue Arg123. In addition, protein residues such as Ala60, Ala61, Gly64, Arg65, Leu62, Ile66, Phe70, Ile99, leu100,

EP

Leu102, Leu103, Leu124, Ile121, Phe67, phe116, Asn119 were also involved in van der Waals contact with the lead compounds, thus modifying the complex strong enough to be actively

AC C

bound in the hydrophobic region.

The binding pose of the second ranked compound cnicin was found to reside exclusively

deep with putative binding residues by forming 7 stable H bond interactions. Further, the evolution of cnicin was interacted through highly attracting polar, hydrophobic, and charged residues of the LuxT protein. Majorly, the ligand containing carboxyl group possesses a network of H bonds toward the side chain amine group of Arg123 and Asn119, and also to Ile66 and 20

ACCEPTED MANUSCRIPT 21 Gly64 of the main chain atom, respectively. Moreover, other favorable interactions also possibly occurred with residues such as Ile99 and Ala60 of backbone and side chain of Glu115 by the hydroxyl group of compound as shown in Fig. 2b.

RI PT

In the core complex of 5, 5' methylenedisalicyclic acid, 3 H bonds were established between ligands and the active site of residues of Thr59, Ile99, and Asp63. On the other hand, the docking conformation of 5, 5' methylenedisalicyclic acid exhibited significant contact of π-π

SC

stacking and π-cationic with Arg123 and Phe116, respectively. The carboxyl group of flufenamic acid as a fourth ranked compound was observed with linkages of H bond by two different atoms

M AN U

in the amide group of Ile66 and Thr67 hydrophobic residues in docked complex. Additional interactions mediated through π-π stacking with Arg123 and Phe116, and π-cationic with Arg123 by the phenyl ring of compound was observed. Interestingly, all the predicted conformations were found to have high Glide score of -10.26 and -9.65kcal/mol and binding energy -46.28 and

TE D

-41.66kcal/mol with greater potential activity on the therapeutic target as listed in Table 1. Overall, docking analysis suggested that the hydrophobic residues, viz., Gly115, Asn119, and Arg123 in side chain and Thr59, Ala60, Asp63, Gly64, Ile66, phe67 Ile99, and Gly120 in

EP

backbone plays a key interaction in the docking of lead compounds (Fig. 2) [56]. 3.6. Pharmacokinetic prediction of screened hit compounds

AC C

In this study, QikProp module was assessed for the potential inhibitors to evaluate its

druggable properties. The Lipinski's 'rule of five' consisting of H bond donor, acceptor, lipophilicity and molecular weight were found to be in a desirable range all the screened compounds as summarized in Table 3. The lead molecules with pharmacokinetic properties still remained as prominent challenge in drug designing. Therefore, the significant properties of adsorption, desorption, metabolism and excretion, and physiochemical parameters that includes 21

ACCEPTED MANUSCRIPT 22 stars (the estimated values of the number of properties and physical descriptors falls outside 95 % of the known drugs) were analyzed using QikProp [38, 39]. The results demonstrated that the lead molecules were found to be fairly consistent in accordance with these expected properties.

RI PT

For the screened compounds, the octanol/water (QPlogPo/w) partition coefficient that measures the hydrophobicity of lead molecules affecting the bioavailability, drug absorption, and metabolism ranged from 0.377 to 5.24, which were within an agreeable range of -2.0–6.5. The

SC

partition coefficient of water/gas (QplogPw) was also found to be more favorable for all the compounds, ranging from 4.37 to 12.98. The aqueous solubility (QplogS) is a significant

M AN U

property for a drug as its therapeutic response also satisfies for all compounds in the range of 2.6 to -4.4 [57]. The percentage of human oral adsorption verified for the screened compounds were also found with a very good range from 42 to 87 %. Thus, the screened hits comparable with the known drugs can serve as an efficient antipathogenic drug candidate against Vibrio

TE D

infections. 3.7. Molecular simulation analysis

Molecular dynamics simulation was carried out to investigate the dynamic behavior and

EP

conformational changes of native protein and protein-ligand complexes, which facilitate the ultimate detail about the aspects of the individual motion of atoms as a function of time. The

AC C

dynamic behavior of the native protein structure was ascertained using Desmond software in Schrödinger suite for a timescale of 30 ns. Subsequently, the simulation results were analyzed by adopting simulation interaction diagram revealing the backbone RMSD, which were stable with minor fluctuations seen initially at 2.5 ns and then suddenly elevated between 1.5 and 2.5 Å throughout the event of 30 ns simulation. The RMSD of side chain within the range of 2.5–3.5 Å remained constant during the entire time frame in the model generated using MODELLER and is 22

ACCEPTED MANUSCRIPT 23 shown in Fig. 3a. 3.8. RMSD analysis of complex structure in MD simulations The process of MD simulation for protein-ligand complex facilitates to confirm the

RI PT

stability of different atomic interactions as well as the contribution of interacting residues in the ligand binding pose at 30 ns MD run. From the investigation, the RMSD of all the four proteinligand complexes are shown in Fig. 3b. For the LuxT-quercetin complex, the RMSD showed

SC

relatively stable in its conformation over the time period of 30 ns. The RMSD curve for ligand fit on the protein displayed constant with no obvious fluctuations in the whole process of

M AN U

simulations. By obtaining this plot, the heavy atoms of the ligand were calculated successfully after the alignment of the complex to the backbone of the protein structure coordinate. Overall, the RMSD plot exhibited consistent stability in the ligand binding pose inside the LuxT druggability pocket.

TE D

Similarly, the RMSD of the LuxT-cnicin complex exhibited the least deviation in its structural conformation during the simulation time frame. The RMSD of the Cα backbone atoms, side chain, and ligand fit on protein in this complex exposed less deviation in curve at 1 to 6 ns

EP

initially, followed by a moderate fluctuation throughout 30 ns simulation. The ligand RMSD graph demonstrated that the ligand could coexist well with the protein structure coordinates. As a

AC C

consequence, the ligand RMSD plot (y axis) acquired an equivalent stability in respect of the protein structure over the simulation period. As can be seen from Fig. 3b, the RMSD of complex LuxT-5, 5' methylenedisalicyclic

acid revealed that the complex was relatively constant during 4 ns at about 2.5 to 3.0 Å, and subsequently a slight divergence occurred in its conformation due to flexibility till the end of MD simulation. However, ligand fit on the protein plot of RMSD remained constant up to 4 ns, 23

ACCEPTED MANUSCRIPT 24 similar to the Cα backbone and a gradual decrease in deviation in the curve existed throughout the run of simulations. On the other hand, the side chain plot exhibited a higher divergence as compared with the Cα backbone over the time period.

RI PT

The result of RMSD produced by the complex LuxT-flufenamic acid showed the Cα atoms in complex to be nearly constant up to 30 ns between 2.0 and 3.0 Å after 3 ns, suggesting the structural stability of complex. Although the ligand fit on the protein complex of RMSD

SC

remained constant between 0.8 and 1.2 Å for 2.8 ns, it raised its position suddenly followed by equivalent stability in the entire simulation as shown in Fig. 3b, which is hardly surprising owing

M AN U

to the physically feasible ligand stability with its residual interactions. Thus, the plot of side chain RMSD displayed stable conformation with minimal fluctuation during the period of 30 ns MD run.

3.9. Average RMSD values in MD simulations

TE D

The estimated RMSD from the trajectory analysis for all the four protein-ligand complexes were encountered, as shown in Fig. 3b. The scrutinized average RMSD value for the apo protein is shown in Fig. 3a and shows about 4.836±0.34. As regards the nature of constancy

EP

between the protein-ligand complexes, the calculated average RMSD value was 2.078±0.25, 2.364±0.30, 3.641±0.64, and 2.387±0.31. From this analysis, the RMSD with the lowest values

AC C

was exhibited by all the four complexes and this concludes that all the hits could produce relatively steady and constant residual interactions toward LuxT for 30 ns simulations. 3.10. RMSF analysis

RMSF analysis helps in measuring the fluctuations in the structural elements of protein

structures including apo and protein-ligand complexes. The lower RMSF of native apo LuxT protein exhibited mild deviations in the atomic level of structural conformation between 1.5 and 24

ACCEPTED MANUSCRIPT 25 3.0 Å. The RMSF assigned for the Cα backbone of LuxT showed that the maximum number of residues underwent fluctuations, which were falling in the loop regions as shown in Fig. 3c. Notably, the RMSF Cα atoms of the binding residues of complex executes very few variations

RI PT

within the bounded region by forming small peaks for each residual position, suggesting the profound region found to be a loop that may be prone to fluctuations (Fig. 3d). Overall, the fluctuation was noticed only with a few residues that were not involved in the ligand interactions.

SC

The resulting plot suggested that the interacting residues are highly potential and strong in attracting ligands to the binding site in the whole simulation process as shown in Fig. 3d. The

M AN U

RMSF plot obtained from VMD persisted at 70 % over the entire trajectory. The remaining residues shown by the plot in all the complexes underwent less fluctuation between 1.8 and 2.8 Å in 30 ns.

3.11. Docking analysis of compounds through MD simulations

TE D

The MD simulation trajectory analysis was carried out to refine the understanding of the binding nature of protein-ligand complexes for the time course of 30 ns. The stability of interacting residues was monitored from the trajectory report. The potential LuxT-quercetin

EP

complex could establish contact through different types of bonding to the ligands. A total of 7 intermolecular H bonds were formed between the residues Thr59, Ala60, Leu62, Asp63, Glu115,

AC C

Gly120, and Arg123. Among these H bonding, only three H bonds were retained by the Asp63 and Arg123 of backbone and side chain residues. The remaining amino acids were less stable with quercetin due to the high flexibility, thereby losing contact over a period of a few nanoseconds. Interestingly, a few hydrophobic and water bridges also existed in bonding with the ligands, which are crucial for taut binding affinity. Unfortunately, the destruction of all these contacts within a few picoseconds was encountered except the amino acid Arg123. The protein 25

ACCEPTED MANUSCRIPT 26 residue of Arg123 makes the complex more stable by network via hydrophobic and ionic bonding that originated after 1.8 ns and sustained up to 5 ns. In the complex of LuxT-cnicin, the network of H bonds were produced initially by

RI PT

residues, viz., Thr59, Ile66, Phe67, Ile99, Glu115, Arg123, and Asn119. Later, stabilized H bonds were found only with Thr59 at 2.5 ns, while a few of the existing residues found with lost stability were Phe67, Gly115, and Asn119 in 0.3, 0.8 and 0.8 ns, respectively. The compound had

SC

a highly consistent H bond network with Arg123 (32 % of time period) up to 3 ns, whereas binding with Ile66 and Ile99 was unstable in 1 to 2 ns. Interestingly, this compound also has the

M AN U

capability of formation of water-mediated bridges with several atoms and lost its stability immediately within a few seconds of simulations. In contrast, origination of water bridges with Arg123 at 2 ns was stable up to 4 ns. Both the ionic and hydrophobic contacts were also maintained by the residues Arg123 and Phe119 for a few nanoseconds, indicating the stable

TE D

complex with a high potential binding affinity as shown in Fig. 3e.

For 5, 5' methylenedisalicyclic acid, the complex exhibited 4 H bonds with amino acids Ile99, Phe116, Asn119, and Arg123 in its binding site. During 1 ns of simulation, the H bond

EP

with Asn119 was not stable and the rest of them bound well stabilized with ligand through amino acid residues Ile99 (71 % of time), whereas Phe116 (84 % of time) in the specified trajectory.

AC C

Among all, Asp123 is an important residue in contributing contacts over the period of simulation by hydrophobic, ionic, and water-mediated H bonds, which accomplished these interactions more stably with the optimized ligand. The hydrophobic and water bridges had occurred initially and were subsequently destructed as a result of the lack of stability in the simulation system. Finally, the Ile99 and phe116 residues were actively involved in interactions with the respective ligand, revealing the prominent complex structure coordinate over the simulation process. 26

ACCEPTED MANUSCRIPT 27 The result of MD analysis revealed that flufenamic acid was bound with LuxT initially through 5 H bonds, 7 hydrophobic and 3 water bridges. The residues such as Leu62, Gly64, and Phe67 were destructed due to the arrangement of conformations during initial simulations, where

RI PT

in other bonding through hydrogen atoms were stable with the ligand by the residue Thr59 (56 % of time) and Ile66 (63 % of time) in its oxygen atom. Hence, the existing intermolecular attraction of ligand to the residues Thr59 and Ile66 were solely supporting the LuxT-flufenamic

SC

acid complex with the optimum bound conformation in the course of the simulation time event. 3.12. In vitro validation of lead molecule against pathogenic V. alginolyticus

M AN U

3.13. Antibacterial activity of quercetin

Exemplification of the lead compounds screened from in silico was validated using established in vitro assays on V. alginolyticus. Initially, the top screened quercetin was accessed to investigate their effect of antibacterial activity on V. alginolyticus using agar well diffusion

TE D

assay. As a result, the cell death was estimated by measuring the inhibitory zone around the lawn of V. alginolyticus. Among various concentrations of drug upon bacterial inhibition, 30 mM exhibited a maximum clearance of 1 mm, whereas 10 and 20 mM displayed near inhibition of

EP

0.8 to 1 mm in diameter, respectively (Supplementary Fig. 5). This clearly suggests that quercetin is less susceptible in the bactericidal activity of V. alginolyticus. Further, the compound

AC C

was evaluated to determine its minimum inhibitory effect of the respective strain using spectrophotometric assay. The result showed that the lead molecule is unable to possess a significant effect on eradicating bacteria compared with untreated bacteria observed under 600 nm (Fig. 4A).

3.14. In vitro quantification of biofilm detachment of V. alginolyticus By the spectrophotometric method, the infectivity of cell growth intensity and the 27

ACCEPTED MANUSCRIPT 28 architecture of biofilms was examined and recorded with aliquots of diluted compound ranging from 20 to 100 µM comparatively with drug untreated control. As shown in Fig. 4A, the compound did not show any bacterial inhibition after 4–5 h incubation, which could display the

RI PT

antibiofilm effect on the respective strain after 24 h treatment. This is primarily caused by the excessive production of exopolysaccharide that helps in the formation of biofilms that prevent the penetration of antimicrobials into the bacterial surfaces [49]. Interestingly, the previous report

SC

of Durai et al. [58] also documented the implication of the antipathogenic effect on V. alginolyticus virulence rather than the potential impact on cell growth. Henceforth, such an

M AN U

identification of drugs is more requisite to prevent the adaptability of pathogenic drug resistance. By monitoring the differences in CV stained cell density read at OD650 nm, the effective BIC concentration of quercetin was determined after the treatment that caused a collapse in the biofilm architecture than that of control, as shown in Fig. 4C. Likewise, the therapeutic effect of

TE D

quercetin on the eradication of biofilms was compared with control on glass surface as shown in Fig. 4B, which clearly shows that the apparent reduction in the biofilm ring structure proved to be superior in the antibiofilm effect of quercetin.

EP

As shown in Fig. 5B, larger variations in the phenomenon of mucoid biofilms leads to differences in the colony morphotypes of infected bacteria grown on the CRA medium on

AC C

treatment with quercetin. Noticeably, the color turns from black to pinkish red indicating the poor slime synthesis that could be observed at a concentration of 100 µM, whereas visible pink colored colony occurred in the concentration of 60 and 80 µM, respectively (data not shown), after treatment represented the increased ability in the dispersal of biofilm deformation. Thus, the result of the study suggested that quercetin had the potent ability in the disruption of biofilm architecture rapidly, and therefore, it may widely assist in altering the infection phenomenon of 28

ACCEPTED MANUSCRIPT 29 V. alginolyticus. 3.15. Determination of BIC concentration of quercetin Before examining the effect of antibiofilm, the top screened quercetin was initially tested

RI PT

for its antibacterial activity of test strain. Using 24-well plates, the compound did not show any appreciable inhibition on the growth of V. alginolyticus, but showed efficient biofilm inhibition at the same concentrations (Fig. 4C). The prominent inhibition in biofilm formation at a very low

SC

range concentration was determined. Fascinatingly, a significant reduction in the dispersal of V. alginolyticus biofilms of about 77 % was achieved at a concentration of 100 µM and thus was

M AN U

declared as BIC of compound. Similarly a larger reduction (P< 0.001) in absorbance of treated samples in dispersal of biofilms was attained at about 12, 35, 60, and 69 % at the concentration less than BIC at 20, 40, 60, and 80 µM, respectively (Fig. 4C) and is elucidated for further biofilms morphological characteristics.

TE D

3.16. Impact of motility on V. alginolyticus

An ultimate control of motility in V. alginolyticus by quercetin was monitored under specific incubation after inoculation on agar plates. As a consequence of biofilm inhibition, when

EP

treated with varying concentrations of compound limited the swarming ability of V. alginolyticus on agar surface compared with the control (Fig. 5A). From this, we suspect that the ability of

AC C

cells in the formation of biofilm might vanish as with the known fact of bacterial motility that helps in surface attachment [49]. The result was comparable with the findings of Sandhakumari et al. [59], who reported that the extract of Q-25 displayed progressive reduction in the swimming and swarming ability of V. harveyi and V. vulnificus. Concurrently, the report of Packiavathy et al. [46] evaluated the dose-dependent reduction in the swimming and swarming motility of Vibrio sp. upon treatment with curcumin. 29

ACCEPTED MANUSCRIPT 30 3.17. Effect of EPS synthesis in V alginolyticus biofilms Although the synthesis of extracellular polysaccharide is increasingly due to the biofilm forming cells, the activated metabolic modification of bacteria induced by quercetin was

RI PT

tested under the graded series of concentrations. The result clearly demonstrated that the statistically significant reduction (P< 0.001) in the synthesis of EPS was detected to be about 22, 39, 58, 66, and 75 % when treated with quercetin at the concentration of 20, 40, 60, 80, and 100

SC

µM, respectively. This indicates the maximum efficacy of quercetin at a very less concentration in comparison with untreated biofilms control with increased production of EPS and is shown in

M AN U

Fig. 5C. From the obtained result, it is clear that the test strain was capable of producing intact biofilm structure on the surface, which tends to cause pathogenicity. This work was evaluated to find the therapeutic lead molecule having the potential of diminishing the cell membrane made by EPS. However, EPS is known to protrude the resistivity of cells to other antipathogens and

TE D

antibiotics. This can be recalcitrant through the efficacy of the aforementioned screened compounds from the natural database as an alternative strategy using combined in silico and in vitro assay [47].

EP

3.18. Effect of quercetin on V. alginolyticus biofilms using microscopic studies Furthermore, the antibiofilm effect was confirmed by revealing the microscopic

AC C

experiments on glass slides. The antibiofilm effect of Quercetin was tested against the pathogenic biofilm of V. alginolyticus under direct visualization condition of both light and SEM. The drug at which specific concentration displayed better inhibitory was accurately determined from the aforementioned spectrophotometric assay according to the diminished thickness in the production of a slimy layer and was subsequently used in this assay. The image obtained from light microscope revealed the appearance of thick biofilm architecture on the glass slides of 30

ACCEPTED MANUSCRIPT 31 untreated samples. In contrast, the biofilm of V. alginolyticus treated samples exhibited a significant inhibitory effect on the formation of biofilm, which results in the ruptured architecture of biofilms on glass slides and also improved infection in the growth of the adherent

RI PT

bacteria (Supplementary Fig. 6). Observing the SEM image also strongly evidenced the inhibitory effect of quercetin on biofilm producing V. alginolyticus in a dose-dependent manner. Interestingly, the therapeutic potency of quercetin when testing against pathogen showed

SC

effective inhibition of biofilm formation to a considerable extent on the bacterial cell lysis at their BIC (100 µM) concentration, as is dramatically shown in Fig. 6. There was a potentially

M AN U

significant reduction in the development of biofilm at 60 and 80 µM of drug concentration. Hence, by in vitro assay, the compound proved its efficacy at a very low concentration of 100 µM, and thus suggests its virulence therapeutics on V. alginolyticus. The results of this work is consistent with the study of Vibrio sp. by Brackman et al. [60], in which the treatment of

TE D

cinnamaldehyde and its derivatives progressively exhibited concentrations-dependent reduction in the dispersal of biofilm architecture. This proved that Vibrio sp. had the potent ability in the production of biofilm biomass, which is accountable for the severe outbreak of diseases,

EP

especially to aquatic and marine organisms and thereby acquired by humans in association with such contaminations. In this study, we report for the first time that quercetin has the potent ability

AC C

and sufficient pharmacological properties of targeting V. alginolyticus as per the thorough mechanistic inhibition acquired over in silico methods and validated effects on in vitro studies. 3.19. FT-IR spectral analysis The spectral features derived from FT-IR spectroscopy revealed the abrupt deviations

between the drugs treated and untreated bacterial fingerprints. The graphical representation in Supplementary Fig. 7 shows that the vibrational signatures designed as peak representing the 31

ACCEPTED MANUSCRIPT 32 various components include hydration of bacterial cells, fatty acid in the bacterial cell membrane, and amide linkages of protein and peptides. As shown in Supplementary Fig. 7, there was a considerable deviation in the existing peak at 3500–3100 cm–1, indicating the apparent

RI PT

variation in the hydration of bacterial cells upon treatment with quercetin. A large peak was displayed at 1643 cm-1, which attributes the C=O stretching in carboxyl or amide group (protein and peptides) and is relatively less in the treated biofilm cells. A weak peak at 1090 cm-1 was

SC

attributed to be C–N stretching, which corresponds to aliphatic amines quite different from the control biofilms. The fatty acid profile existed in a range of 3000–2800 cm-1 found to be less

at a symmetric

M AN U

distributed in drug treated biofilms [61]. Likewise, a few absolute fluctuations could be observed C–O stretch of COO- which is assigned by a narrow peak at 1400 cm-1 rather

than control biofilms. Thus, the apparent variations in the vibrational stretching due to the intensities of cellular components were significantly less in agreement with the control biofilm

TE D

without drug treatment, indicating the substantial effect of quercetin on the bacterial cell membrane subsequent to the ruination of biofilm development. Although there was a decrease in absorbance, there were no remarkable variations in the stretching wave number on exposure of

EP

quercetin compared with that of control biofilms. This study result falls in line with the finding of Gowrishankar et al. [41] against Staphylococcus aureus upon treatment with CAB extract that

AC C

exhibited apparent variations in the cellular components within biofilms. The FT-IR result validated that the quercetin intercalating activity on bacteria cellular components lead to a change of virulence in the associated gene metabolism. 4. Conclusion

This study deals with the screening of novel inhibitors targeting against the quorum sensing (QS) mechanism enzyme LuxT regulator of V. alginolyticus. In contrast, we proposed the 32

ACCEPTED MANUSCRIPT 33 LuxT model from the homologous protein for potential searching of lead molecules through bitter taste compounds from Bitter database. Furthermore, the determination of steric potential binding sites for the optimized model was identified, which improved the selection of ligands. As

RI PT

a result, the four ligands were identified as the best leads having a good Glide score and Glide energy. Further, validation of ligand attraction was outperformed in QPLD docking, revealing the similar mode of binding with the amino acid residues and improved network of hydrogen

SC

bonding and scoring. MD simulation and ADME prediction support the efficiency of lead molecules toward the LuxT target. Based on the in silico evidence, the best compound quercetin

M AN U

was evaluated experimentally through in vitro antibiofilm activity on established V. alginolyticus biofilms. Evidently, the compound exhibited a potent synergistic effect at a very low concentration of 100 µM, and thus substantiated themselves as a prominent therapeutic on V. alginolyticus. The experimental validation of this study provides valuable insights into the proper

TE D

molecular understanding of drug pathogenic interactions and thus hopefully the information assists in discovering the highly efficient antibiofilm and antipathogenic drugs against Vibrio sp.

AC C

EP

as well as other pathogenic microbes.

33

ACCEPTED MANUSCRIPT 34 Acknowledgement The authors gratefully acknowledge the Department of Bioinformatics, Alagappa University, Karaikudi for providing infrastructure facilities to carry out this work. DS gratefully UGC

RGNF

(RGNF

Ref:

F1-17.1/RGNF-2012-13-SC-TAM-28029),

RI PT

acknowledge

Government of India, for financial assistance. We are also would like to thank Mr. K. T Loganathan, Assistant Professor, Department of Chemistry, Alagappa Chettiar College of

SC

Engineering and Technology, Karaikudi for SEM instrumentation facility.

M AN U

Declaration of Interest:

AC C

EP

TE D

The authors declare no conflicts of interest concerning this article

34

ACCEPTED MANUSCRIPT 35 References [1]

K.K. Lee, S.R. Yu, P.C. Liu, Alkaline serine protease is an exotoxin of Vibrio alginolyticus in kuruma prawn, Penaeus japonicas, Curr. Microbiol. 34(2) (1997) 110–117. B. Austin, Vibrios as causal agents of zoonoses, Vet. Microbiol. 140(3–4) (2010) 310–317.

[3]

G. Brackman, S. Celen, K. Baruah, P. Bossier, S. Van Calenbergh, H.J. Nelis, T. Coenye, AI-2

RI PT

[2]

quorum-sensing inhibitors affect the starvation response and reduce virulence in several Vibrio

[4]

SC

species, most likely by interfering with LuxPQ, Microbiology. 155(Pt 12) (2009) 4114–4122. Z. Zhao, C. Chen, C.Q. Hu, C.H. Ren, J.J. Zhao, L.P. Zhang, X. Jiang, P. Luo, Q.B. Wang, The

M AN U

type III secretion system of Vibrio alginolyticus induces rapid apoptosis, cell rounding and osmotic lysis of fish cells, Microbiology 156(Pt) (2010) 2864–2872. [5]

L. Sheng, Y. Lv, Q. Liu, Q. Wang, Y. Zhang, Connecting type VI secretion, quorum sensing, and c-di-GMP production in fish pathogen Vibrio alginolyticus through phosphatase PppA, Vet.

[6]

TE D

Microbiol. 162(2–4) (2013) 652–662.

L. Sheng, D. Gu, Q. Wang, Q. Liu, Y. Zhang, Quorum sensing and alternative sigma factor RpoN regulate type VI secretion system I (T6SSVA1) in fish pathogen Vibrio alginolyticus, Arch.

[7]

EP

Microbiol. 194(5) (2012) 379–390.

H. He, Q. Wang, L. Sheng, Q. Liu, Y. Zhang, Functional characterization of Vibrio alginolyticus

AC C

twin-arginine translocation system: its roles in biofilm formation extracellular protease activity, and virulence towards fish, Curr. Microbiol. 62(4) (2011) 1193–1199. [8]

Y. Tian, Q. Wang, Q. Liu, Y. Ma, X. Cao, L. Guan, Y. Zhang, Involvement of LuxS in the regulation of motility and flagella biogenesis in Vibrio alginolyticus, Biosci. Biotechnol. Biochem. 72(4) (2008) 1063–1071.

[9]

X. Cao, Q. Wang, Q. Liu, H. Rui, H. Liu, Y. Zhang, Identification of a luxO- regulated 35

ACCEPTED MANUSCRIPT 36 extracellular protein Pep and its roles in motility in Vibrio alginolyticus, Microb. Pathog. 50(2) (2011) 123– 131. [10]

J. Ye, Y. Ma, Q. Liu, D.L. Zhao, Q.Y. Wang, Y.K. Zhang, Regulation of Vibrio alginolyticus

[11]

RI PT

virulence by the LuxS quorum-sensing system, J. Fish. Dis. 31(3) (2008) 161–169.

W. Wang, T. Morohoshi, T. Ikeda, L. Chen, Inhibition of Lux quorum-sensing system by synthetic N-acyl-L-homoserine lactone analogous, Acta. Biochim. Biophys. Sin (Shanghai).

[12]

SC

40(12), (2008) 1023–1028.

H. Liu, Q. Wang, Q. Liu, X. Cao, C. Shi, Y. Zhang, Roles of Hfq in the stress adaptation and

M AN U

virulence in fish pathogen Vibrio alginolyticus and its potential application as a target for live attenuated vaccine, Appl. Microbiol. Biotechnol. 91(2) (2011) 353–364. [13]

C. Garcia-Aljaro, G.J. Vargas-Cespedes,

A.R. Blanch, Detection of acylated homoserine

lactones produced by Vibrio spp. and related species isolated from water and aquatic organisms,

[14]

TE D

Appl. Microbiol. 112(2) (2012) 383–389.

H. Liu, S. Srinivas, X. He, G. Gong, C. Dai, Y. Feng, X. Chen, S. Wang, Quorum Sensing in Vibrio and its Relevance to Bacterial Virulence, J. Bacteriol. Parasitol. 4 (2013) 172. Q. Yang, T. Defoirdt, Quorum sensing positively regulates flagellar motility in pathogenic Vibrio

EP

[15]

harveyi, Environ. Microbiol. 17(4) (2015) 960–968. H. Rui, Q. Liu, Y. Ma, Q. Wang, Y. Zhang, Roles of LuxR in extracellular alkaline serine

AC C

[16]

protease A expression, extracellular polysaccharide production and mobility of Vibrio alginolyticus, FEMS Microbiol. Lett. 285(2) (2008) 155–162. [17]

H. Liu, D. Gu, X. Cao, Q. Liu, Q. Wang, Y. Zhang, Characterization of a new quorum sensing regulator LuxT and its roles in the extracellular protease production, motility, and virulence in fish pathogen Vibrio alginolyticus, Arch. Microbiol. 194(6) 2012 439–452. 36

ACCEPTED MANUSCRIPT 37 [18]

S.H. Tousi, M.R. Saberi, J. Chamani, Comparing the interaction of cyclophosphamide monohydrate to human serum albumin as opposed to holo-transferrin by spectroscopic and molecular modeling methods: evidence for allocating the binding site, Protein Pept. Lett. 17(12)

[19]

RI PT

2010 1524-35.

E. Rahnama, M. Mahmoodian-Moghaddam, S. Khorsand-Ahmadi, M.R. Saberi, J. Chamani, Binding site identification of metformin to human serum albumin and glycated human serum

SC

albumin by spectroscopic and molecular modeling techniques: a comparison study, J. Biomol. Struct. Dyn. 33(3) (2015) 513-33.

M. Kesheri, S. Kanchan, Richa, R.P. Sinha, Isolation and in silico analysis of Fe-superoxide

M AN U

[20]

dismutase in the cyanobacterium Nostoc commune, Gene. 553(2) (2014) 117–125. [21]

R.A. Laskowski, M.W. MacArthur, D.S. Moss, J.M. Thornton, PROCHECK a program to check the stereo chemical quality of protein structure. J. Appl. Cryst. 26 (1993) 283–291. E.F. Pettersen, T.D. Goddard, C.C. Huang, G.S. Couch, D.M. Greenblatt, E.C. Meng, and T.E.

TE D

[22]

Ferrin, UCSF Chimera–a visualization system for exploratory research and analysis, J. Comput. Chem. 25(13) (2004) 1605–1012.

R.K. Kar, M.Y. Ansari, P. Suryadevara, B.R. Sahoo, G.C. Sahoo, M.R. Dikhit, P. Das,

EP

[23]

Computational elucidation of structural basis for ligand binding with Leishmania donovani

[24]

AC C

adenosine kinase, Biomed. Res. Int. (2013) 609289. M.Y. Ansari, M.R. Dikhit, G.C. Sahoo, P. Das, Comparative modeling of HGPRT enzyme of L. donovani and binding affinities of different analogs of GMP, Int. J. Biol. Macromol. 50(3) (2012) 637-49. [25]

P. Lu, D.R. Bevan, S.N. Lewis, R. Hontecillas, J. Bassaganya-Riera, Molecular modeling of lanthionine synthetase component C-like protein 2: a potential target for the discovery of novel 37

ACCEPTED MANUSCRIPT 38 type 2 diabetes prophylactics and therapeutics, J. Mol. Model. 17(3) (2011) 543–553. [26]

M. Wiederstein, M.J. Sippl, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins, Nucleic. Acids. Res. 35 (2007) W407–W410. G. Anupriya, K. Roopa, S. Basappa, Y.S. Chong, L. Annamalai, Homology modeling and in

RI PT

[27]

silico screening of inhibitors for the substrate binding domain of human Siah2: implications for hypoxia-induced cancers, J. Mol. Model. 17(12) (2011) 3325–3332.

Wiener, M. Shudler, A. Levit, M.Y. Niv, BitterDB: a database of bitter compounds. Nucleic.

SC

[28]

Acids. Res. 40 (2012) D413–D419.

R.A. Friesner, J.L. Banks, R.B. Murphy, T.A. Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky,

M AN U

[29]

E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P. Francis, P.S. Shenkin, Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy, J. Med. Chem. 47(7) (2004) 1739–1749.

D. Sengupta, D. Verma, P. K. Naik, Docking mode of delvardine and its analogues into the p66

TE D

[30]

domain of HIV-1 reverse transcriptase: screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties, J. Biosci.

[31]

EP

32(7) (2007) 1307–1316.

Glide version 6.4, Schrödinger, LLC, New York, NY, 2014; Jaguar version 8.5, Schrödinger,

[32]

AC C

LLC, New York, NY, 2014; QSite version 6.4, Schrödinger, LLC, New York, NY, 2014. E. Cho, V. Guallar, B.J. Berne, R. Friesner, Importance of Accurate Charges in Molecular Docking: Quantum Mechanical/Molecular Mechanical (QM/MM) Approach, J. Comput. Chem. 26(9) (2005) 915–931. [33]

C. Selvaraj, S.K. Singh, S.K. Tripathi, K.K. Reddy, M. Rama, In silico screening of indinavirbased compounds targeting proteolytic activity in HIV PR: binding pocket fit approach, Med. 38

ACCEPTED MANUSCRIPT 39 Chem. Res. 21 (2012) 4060–4068. [34]

J. Du, H. Sun, L. Xi, J. Li, Y. Yang, H. Liu, X. Yao, Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM-GBSA calculation, J. Comput.

[35]

RI PT

Chem. 32(13) (2011) 2800–2809.

N. Jatana, S. Jangid, G. Khare, A.K. Tyagi, N. Latha, Molecular modeling studies of Fatty acylCoA synthetase (FadD13) from Mycobacterium tuberculosis--a potential target for the

[36]

SC

development of antitubercular drugs, J. Mol. Model. 17(2) (2011) 301–313.

S. Vilar, J. Karpiak, B. Berk, S. Costanzi, In silico analysis of the binding of agonists and

[37]

M AN U

blockers to the β2-adrenergic receptor, J. Mol. Graph. Model. 29(6) (2011) 809–817. K.K. Reddy, P. Singh, S.K. Singh, Blocking the interaction between HIV-1 integrase and human LEDGF/p75: mutational studies, virtual screening and molecular dynamics simulations, Mol. Biosyst. 10(3) (2014) 526–536.

QikProp, version 4.1, Schrödinger, LLC, New York, NY, 2014.

[39]

P. Antony, R. Vijayan, Determination of LMF Binding Site on a HSA-PPIX Complex in the

TE D

[38]

Presence of Human Holo Transferrin from the Viewpoint of Drug Loading on Proteins, PLoS

[40]

EP

ONE 10(9) (2015) e013818.

C.A. Lipinski, F. Lombardo, B.W. Dominy, P.J. Feeney, Experimental and computational

AC C

approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug. Deliv. Rev. 46(1-3) (2001) 3–26. [41]

S. Gowrishankar, N. Duncun Mosioma, S. Karutha Pandian, Coral-Associated Bacteria as a Promising Antibiofilm Agent against Methicillin-Resistant and -Susceptible Staphylococcus aureus Biofilms, Evid Based Complement Alternat. Med. (2012) 862374.

[42]

Clinical and Laboratory Standards Institute (2006) Methods for dilution antimicrobial 39

ACCEPTED MANUSCRIPT 40 susceptibility tests for bacteria that grow aerobically; Approved standard, 7th edn. Clinical and Laboratory Standards Institute document M7-A7. Clinical and Laboratory Standards Institute, Wayne H. Mohammad, A.S. Mayhoub, M. Cushman, M.N. Seleem, Anti-biofilm activity and synergism

RI PT

[43]

of novel thiazole compounds with glycopeptide antibiotics against multidrug-resistant staphylococci, J. Antibiot. (Tokyo) 68(4) (2015) 259-66

P. Kumar, S.S. Selvi, M. Govindaraju, In vitro anti-biofilm and anti-bacterial activity of

SC

[44]

Junceella juncea for its biomedical application, Asian Pac. J. Trop. Biomed. 2(12) (2012) 930–

[45]

M AN U

935.

K.S. Musthafa, V. Saroja, S.K. Pandian, A.V. Ravi, Antipathogenic potential of marine Bacillus sp. SS4 on N-acyl-homoserine-lactone-mediated virulence factors production in Pseudomonas aeruginosa (PAO1), J. Biosci. 36(1) (2011) 55–67.

R. Thenmozhi, P. Nithyanand, J. Rathna, S.K. Pandian, Antibiofilm activity of coral-associated

TE D

[46]

bacteria against different clinical M serotypes of Streptococcus pyogenes. FEMS. Immunol. Med. Microbiol, 57(3) (2009) 284–294.

I. Packiavathy, P. Sasikumar, S.K. Pandian, A. Veera Ravi, Prevention of quorum-sensing-

EP

[47]

mediated biofilm development and virulence factors production in Vibrio spp. by curcumin,

[48]

AC C

Appl. Microbiol. Biotechnol. 97(23) (2013) 10177-87. C. Selvaraj, J. Sivakamavalli, B. Vaseeharan, P. Singh, S.K. Singh, Examine the characterization of biofilm formation and inhibition by targeting SrtA mechanism in Bacillus subtilis: a combined experimental and theoretical study, J. Mol. Model. 20(8) (2014) 2364. [49]

T.D. Kaiser, E.M. Pereira, K.R Dos Santos, E.L. Maciel, R.P. Schuenck, A.P. Nunes, Diagn. Microbio. Modification of the congo red agar method to detect biofilm production by 40

ACCEPTED MANUSCRIPT 41 Staphylococcus epidermis, Infect. Dis. 75(3) (2013) 235–239. [50]

P.K. Singh, P. Shukla, Molecular modeling and docking of microbial inulinases towards perceptive enzyme-substrate interactions, Indian J. Microbiol. 52(3) (2012) 373–380. Z.J. Hu, L. Bai, Y. Tizabi, W. Southerland, Computational modeling study of human nicotinic

RI PT

[51]

acetylcholine receptor for developing new drugs in the treatment of alcoholism, Interdiscip. Sci. 1(4) (2009) 254–262.

D. Chakraborty, P. Saravanan, S. Patra, V.K. Dubey, Studies on ornithine decarboxylase of

SC

[52]

466–478. [53]

M AN U

Leishmania donovani: structure modeling and inhibitor docking. Med. Chem. Res. 22 (2013)

E. Sala, L. Guasch, J. Iwaszkiewicz, M. Mulero, M.J. Salvadó, M. Pinent, V. Zoete, A. Grosdidier, S. Garcia-Vallvé, O. Michielin, G. Pujadas, Identification of humanIKK-2 inhibitors of natural origin (part I): modeling of the IKK-2 kinase domain, virtual screening and activity

[54]

TE D

assays. PLoS One 6(2) (2011) e16903.

M. K. Gadhwal,S. Patil, P. D'Mello, U. Joshi, R. Sinha, G. Govil, Synthesis, characterisation and antitumour activity of some quercetin analogues. Indian. J. Pharm. Sci. 75(2) (2013) 233–237. V. Tesevic, S. Milosavljevic, V. Vajs, P. Janackovic, I. Dordevicand, M. Jadranin, I. Vuckovic,

EP

[55]

Quantitative analysis of sesquiterpene lactone cnicin in seven Centaurea species wild-growing in

1280. [56]

AC C

Serbia and Montenegro using 1H-NMR spectroscopy. J. Serb. Chem. Soc. 72(12) (2007) 1275–

Z. Sattar, M.R. Saberi, J. Chamani, Determination of LMF Binding Site on a HSA-PPIX Complex in the Presence of Human Holo Transferrin from the Viewpoint of Drug Loading on Proteins. PLoS ONE 9(1) (2014) e84045.

41

ACCEPTED MANUSCRIPT 42 [57]

K.T. Savjani, A.K. Gajjar, J.K. Savjani, Drug solubility: importance and enhancement techniques. ISRN Pharm. (2012) 195727.

[58]

S. Durai, L. Vigneshwari, K. Balamurugan, Caenorhabditis elegans-based in vivo screening of

RI PT

bioactives from marine sponge-associated bacteria against Vibrio alginolyticus. J. Appl. Microbiol. 115(6) (2013) 1329-1342. [59]

S. Santhakumari, A. Kannappan, S.K. Pandian, A.V. Ravi, N. Thajuddin, R.B.

Rajendran,

SC

Inhibitory effect of marine cyanobacterial extract on biofilm formation and virulence factor production of bacterial pathogens causing vibriosis in aquaculture. J. Appl. Phycol. 28 (2016)

[60]

M AN U

313-324 DOI 10.1007/s10811-015-0554-0

G. Brackman, T. Defoirdt, C. Miyamoto, P. Bossier, S.V. Calenbergh, H. Nelis, T. Coenye, Cinnamaldehyde and cinnamaldehyde derivatives reduce virulence in Vibrio spp. by decreasing the DNA-binding activity of the quorum sensing response regulator LuxR. BMC Microbiol.

X. Lu, B.A. Rasco, J.M. Jabal, D.E. Aston, M. Lin, M.E. Konkel, Investigating antibacterial effects of garlic (Allium sativum) concentrate and garlic-derived organosulfur compounds on

EP

Campylobacter jejuni by using Fourier transform infrared spectroscopy, Raman spectroscopy, and electron microscopy. Appl. Environ. Microbiol. 77(15) (2011) 5257-5269.

AC C

[61]

TE D

(2008) 8:149

42

ACCEPTED MANUSCRIPT 43 Tables Table 1. Binding results for the best lead compounds against LuxT regulator protein using Glide module from Schrödinger suite

Bitter ligands

1

Quercetin

XP Gscore (kcal/ mol) -10.718

Glide Glide Emodel energy (kcal/ (kcal/ mol) mol) -70.707 -41.805

QPLD Score (kcal/ mol) -11.323

Glide energy (kcal/ mol) -53.627

Residual Interactions H-bond (D-A)

π-π & π-cationic

(ASP63)C-O…NH

Arg123(4.821)

RI PT

S. No

SC

OH…C=O(THR59) OH...C=O(ILE99

Cnicin

-10.617

-64.268 -40.129

-11.303

-47.916

3

5,5‘

-10.223

Methylenedisalicylic acid

acid

-9.582

-56.731 -38.869 -10.035

EP

Flufenamic

-60.323 -44.454 -10.265

AC C

4

TE D

M AN U

2

43

-44.185

OH...C-O(GLU115) OH...C=O(ALA60)

(ARG123)NH...C=O (ASN119)NH...C=O (ILE66) NH...C=O (GLY64)NH…C=O OH...C=O(ILE99) OH...C=O(ILE99)

Arg123(4.232)

OH...C=O(THR59)

Phe116(5.401)

(ASP63)NH…C-O -39.420

(ILE66)NH…C=O

Arg123(4.492)

(PHE67)NH...C=O

Phe116(3.840)

ACCEPTED MANUSCRIPT 44 Table 2. Binding free energy of docked complex using Prime MM/GBSA in kcal/mol

S. No

Bitter compounds

∆Gcoulomba

∆Gvdwb

∆Gcovalentc

∆GSolvd

∆Gbinde

30.760

-85.978

Quercetin

-40.096

-39.266

10.586

2

Cnicin

-23.804

-21.358

5.366

26.454

-61.864

3

5, 5‘ Methylenedisalicyclic acid

-5.182

-40.776

3.496

-14.093

-57.209

4

Flufenamic acid

-11.343

-35.187

8.565

13.591

-55.813

SC

a

RI PT

1

Contribution of coulomb energy to calculate MM/GBSA binding free energy

Contribution of covalent binding energy to calculate MM/GBSA binding free energy

d

TE D

Free energy for binding

EP

e

Contribution of solvation energy to calculate MM/GBSA binding free energy

AC C

c

M AN U

bContribution of van der Waals energy to calculate MM/GBSA binding free energy

44

ACCEPTED MANUSCRIPT 45 Table 3. ADME properties of the screened lead molecules using QikProp in Schrödinger Bitter compounds

Molecular weight 378.416

HB donors 3

HB acceptors 7

Rotatable bonds 6

6

4

7

1

3

2

1

Quercetin

2

Cnicin

288.252

4

3

5, 5‘ Methylenedisalicyclic

302.236

5

249.264

1

acid

EP

TE D

M AN U

SC

Flufenamic acid

AC C

4

RI PT

S. No

45

ACCEPTED MANUSCRIPT 46

Table 4. Qikprop properties of the predicted drug like candidates from bitter database using Schrödinger QPlogSb

1

Quercetin

-2.673

QPlog PWc 12.988

2

Cnicin

-2.816

3

5, 5‘ Methylenedisalicylic acid Flufenamic

4

QPlogPo/wd HOAe % HOAf QPlogHERGg Starsh 1.137

2

68.43

4.37

0.377

2

52.549

-4.993

0

-3.303

9.241

2.359

1

42.367

-1.129

0

-4.481

5.841

5.24

3

87.298

-3.821

1

Compound derived from Bitter database

b

c

Predicted aqueous solubility S in mol/L (Desirable range is -6.5 to 0.5)

Predicted water/gas partition coefficient (Desirable range is 4.0 to 45.0)

d

e

M AN U

a

SC

acid

-3.886

RI PT

S. No Compounda

Predicted octanal/water partition coefficient logP (Desirable range is -2.0 to 6.5)

Predicted qualitative human oral absorption: 1, 2 and 3 is for low medium and high respectively

f

Percentage of human oral absorption (<25% is poor and >80% is high)

Predicted IC50 value for blockage of HERG K+ channels (acceptable range: below -5.0)

h

Number of property or descriptor values that fall outside the 95% range of similar values for known drugs (0-5). A

TE D

g

AC C

EP

molecule with large number of stars is less drug-like than molecules with few stars

46

1

ACCEPTED MANUSCRIPT 47 Figure Captions Fig 1. a) Modeled structure of LuxT regulator protein from V. alginolyticus. The polypeptide ribbon

RI PT

representation of structure consists of N terminal end coded with red and C terminal end in blue; b) Ramachandran plot; c) Superimposition of template (3LJL) and LuxT therapeutic model colored with red and pink, respectively; d) Energy plot of the template and modeled LuxT structure by ProSA

SC

analysis.

Fig 2. Binding orientation of the best lead molecules against the binding site of LuxT regulator.

M AN U

Hydrogen bonds are shown by hot pink dashed line. The ligands are colored in yellow and the interacting residues are colored in green. a) Quercetin; b) Cnicin; c) 5, 5‘ Methylenedisalicylic acid; d) Flufenamic acid.

Fig 3. MD simulation over 30 ns time period. a) RMSD plot of LuxT regulator protein; b) RMSD plots

TE D

of docked complexes; c) RMSF plot of individual residues in LuxT regulator protein; d) Fluctuations of protein residues of each docked complex; e) H bonds of protein-ligand complexes

EP

Fig 4. Therapeutic efficacy of Quercetin on V. alginolyticus. A) Growth inhibition assay; B) Static biofilm ring assay; C) Percentage of biofilm disruption under treatment of varying BIC concentration.

AC C

Mean values estimated for triplicate independent experiments and SE are given. Fig 5. Influence of Quercetin on biofilm eradication. A) Bacteria motility assay [a) control and b) 100 µM]; B) Modification of EPS synthesis in CRA medium [a) control and b) 100 µM]; C) EPS quantification assay. Mean values of triplicate independent experiments and SE are shown. Fig 6. Inspection of V. alginolyticus biofilms on glass surface using SEM. a) 60 µM; b) 80 µM; C) 100 µM; and d) control (without treatment). 47

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 •

Homology model was constructed for LuxT as a quorum sensing target from V. alginolyticus. High scoring potent ligands with good ADME properties were ascertained against LuxT.

•

Binding free energy and MD simulations validated the efficiency of potential hits.

•

The top hit was validated in in vitro assays and revealed antibiofilm potency.

•

These findings influence the designing of potent drugs in the future.

AC C

EP

TE D

M AN U

SC

RI PT

•