Molecular Docking, Molecular Dynamics Simulations ...

Molecular Docking, Molecular Dynamics Simulations, Computational Screening to Design Quorum Sensing Inhibitors Targeting LuxP of Vibrio harveyi and Its Biological Evaluation Sundaraj Rajamanikandan, Jeyaraman Jeyakanthan & Pappu Srinivasan

Applied Biochemistry and Biotechnology Part A: Enzyme Engineering and Biotechnology ISSN 0273-2289 Appl Biochem Biotechnol DOI 10.1007/s12010-016-2207-4

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Author's personal copy Appl Biochem Biotechnol DOI 10.1007/s12010-016-2207-4

Molecular Docking, Molecular Dynamics Simulations, Computational Screening to Design Quorum Sensing Inhibitors Targeting LuxP of Vibrio harveyi and Its Biological Evaluation Sundaraj Rajamanikandan 1 & Jeyaraman Jeyakanthan 1 & Pappu Srinivasan 1,2

Received: 3 March 2016 / Accepted: 5 August 2016 # Springer Science+Business Media New York 2016

Abstract Quorum sensing (QS) plays an important role in the biofilm formation, production of virulence factors and stress responses in Vibrio harveyi. Therefore, interrupting QS is a possible approach to modulate bacterial behavior. In the present study, three docking protocols, such as Rigid Receptor Docking (RRD), Induced Fit Docking (IFD), and Quantum Polarized Ligand Docking (QPLD) were used to elucidate the binding mode of boronic acid derivatives into the binding pocket of LuxP protein in V. harveyi. Among the three docking protocols, IFD accurately predicted the correct binding mode of the studied inhibitors. Molecular dynamics (MD) simulations of the protein-ligand complexes indicates that the inter-molecular hydrogen bonds formed between the protein and ligand complex remains stable during the simulation time. Pharmacophore and shape-based virtual screening were performed to find selective and potent compounds from ChemBridge database. Five hit compounds were selected and subjected to IFD and MD simulations to validate the binding mode. In addition, enrichment calculation was performed to discriminate and separate active compounds from the inactive compounds. Based on the computational studies, the potent Bicyclo [2.2.1] hept-5-ene-2,3dicarboxylic acid-2,6-dimethylpyridine 1-oxide (ChemBridge_5144368) was selected for in vitro assays. The compound exhibited dose dependent inhibition in bioluminescence and also inhibits biofilm formation in V. harveyi to the level of 64.25 %. The result from the study suggests that ChemBridge_5144368 could serve as an anti-quorum sensing molecule for V. harveyi. Electronic supplementary material The online version of this article (doi:10.1007/s12010-016-2207-4) contains supplementary material, which is available to authorized users.

* Pappu Srinivasan [email protected]

1

Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India

2

Department of Animal Health and Management, Science Campus, Alagappa University, Karaikudi, Tamil Nadu 630003, India

Author's personal copy Appl Biochem Biotechnol

Keywords Quorum sensing . Boronic acid derivatives . Molecular docking . Molecular dynamics simulations . In vitro assays

Introduction Quorum sensing (QS) is a cell-cell communication mechanism used by bacteria to coordinate group behaviors in a cell density-dependent manner [1, 2]. Bacterial cell constantly produce and release chemical substances called autoinducers (AIs) [3]. The bacterial population density increases concordantly with an increase in AIs in the extracellular environment [4, 5]. The bacteria monitor the concentration of AIs to track changes in their cell number and coordinately alter the expression of a large set of genes [6]. The systems not only regulate the expression of genes encoding the virulence factor, but also other proteins, which are involved in the basic metabolic process. A significant portion of 4–10 % of bacterial genome and more than 20 % of bacterial proteome are influenced by QS [7]. The most diverse processes including biofilm formation, secretion of virulence factors, competence, sporulation, motility, bioluminescence, and antibiotic production are controlled by QS [8]. Therefore, it has been suggested that QS signaling system represents an obvious target for the development of novel antimicrobial drugs. The development of resistance to anti-QS drugs would be minimized, because they target virulence mechanism without interfering the growth of the microorganisms [9]. Gram-negative bacteria use acylated homoserine lactone (AHL) as AIs, while Grampositive bacteria use autoinducing peptide (AIP) to communicate between bacteria [10]. LuxS protein is the AI-2 synthase in the biosynthetic pathway that is responsible for the production of AIs [11]. Periplasmic binding protein (LuxP) binds to AI-2 by clamping it between two domains. The AI-2 bound LuxP activates the inner membrane protein LuxQ [12]. At a low cell density, LuxQ act as autophosphorylating kinase that subsequently phosphorylate the cytoplasmic protein LuxU and DNA-binding response regulator protein LuxO [13]. The phosphorylated LuxO repress QS response by repressing the production of known master QS transcription factor LuxR. However, at high density, AIs enter the periplasmic space and it is detected by LuxPQ complex [14]. The LuxPQ receptor appears to switch from kinase state (at low AI-2 concentrations) to the phosphatase state (at high AI-2 concentrations), resulting in the removal of phosphate groups from LuxU. Since LuxU act as kinase and it is not able to dephosphorylate the LuxO. Finally, autoinducer-1 (AI-1) serves as a species-specific QS signal and regulates the levels of LuxO phosphate, but through a distinct two component sensor kinase, LuxO [15]. In the present study, molecular docking (Rigid Receptor Docking (RRD), Induced Fit Docking (IFD), and Quantum Polarized Ligand Docking (QPLD)); Prime MM-GBSA (Molecular Mechanics, The Generalized Born Model and Solvent Accessibility) and MD simulations were performed to understand the binding mode of boronic acid derivatives into the binding site of LuxP. The IFD protocol has shown improvement in predicting the favorable binding mode of the boronic acid derivatives. Five protein-ligand complexes were subjected to MD simulations to determine the stability of the predicted conformations. Both pharmacophore and shape-based virtual screening were applied to screen compounds from ChemBridge database. The pharmacokinetics properties of the identified hits are within the acceptable range. Enrichment calculation was performed to discriminate active compounds from the inactive compounds. Finally, the potent hit obtained from virtual screening was validated using in vitro assay.


Materials and Methods Protein Preparation The x-ray crystal structure of LuxP in complex with AI-2 (PDB: 1JX6) was retrieved from the Protein Data Bank. The structure was prepared using Protein Preparation Wizard from Schrödinger, LLC, New York, NY, USA [16]. Water molecules and other chemical components present in the crystal structure were omitted; right bond orders and atom types were assigned and hydrogen atoms were added to the parent carbon atoms. The protein was energy minimized using Optimized Potentials for Liquid Simulations (OPLS)-2005 force field. Reorienting hydroxyl, thiol group, protonation states, tautomers of His residues, and Chi Bflip^ assignments for Asn, Gln, and His residues were selected using protein assignment script. The minimization was terminated when the root mean square deviation (RMSD) of heavy atoms in the minimized structure relative to the starting x-ray structural coordinates exceeded 0.3 Å.

Ligand Preparation Five boronic acid derivatives and hits retrieved from virtual screening were prepared using LigPrep module of Schrödinger [17]. Preparation of ligand includes 2D-3D structure conversion, generation of stereoisomers and determination of most probable ionization state at pH 7.2 ± 0.2. Hydrogen atoms were added and bond orders and formal charges were assigned to the ligands. Epik module was used to generate the ligand protonation states in the pH range of 5.0 to 9.0. The molecules were optimized using OPLS-2005 force field, which produce the lowest energy conformer of the ligands.

Molecular Docking Three docking protocols such as RRD, IFD, and QPLD were used to predict the binding mode of boronic acid derivatives into the binding site of LuxP [18–20]. In order to validate the docking protocols, re-docking was carried out with the co-crystal ligand (furanosyl borate diester) into the binding site of LuxP and compared with the experimental co-crystal pose. Normally, the success rate of docking is judged using the RMSD value lesser than 2.0 Å. Five boronic acid derivatives were docked into the binding site of LuxP using RRD. A grid box was generated at the centroid of the active site. In RRD, the internal geometry of the receptor is fixed while the ligands are flexible. Then, in order to take the flexibility of receptor and ligand into consideration, the IFD protocol was used. Initially, glide docking of each ligand was carried out using a softened potential (van der Waals radii scaling). Maximum of 20 poses for each ligand is retained and by default, poses to be retained must have a coulomb-vdW score less than 100 and hydrogen bond (H-bond) score less than −0.05. Prime minimization was performed for each protein-ligand complex. The receptor structure in each pose now reflects an induced fit to the ligand structure. Glide re-docking was performed for each protein-ligand complex within a specified energy of the lowest energy structure (default 30 kcal/mol). Finally, the ligands are docked into the induced fit receptor using glide with default settings. QPLD protocol aims to improve the partial charges on the ligand atoms by replacing them with charges derived from quantum mechanical calculations [21]. The ligands are first docked through standard precision (SP) followed by refinement using extra precision (XP). Five best docked poses were submitted to QM-ESP charge calculation using Density Functional Theory


(DFT) and Lee-Yang-Parr correlation functional (B3LYP) with basis set 6-31G* level. Finally, improved docking accuracy was predicted using re-docking of ligands with updated charges of XP docking. The resulting glide score was used to predict the binding affinity and ranking of the ligands.

MD Simulations MD simulations were performed for the selected five protein-ligand complexes using Desmond module, which uses a Bneutral territory method^ [22]. The OPLS-2005 force field was used to model all amino acid interactions in the protein [23]. Using system builder, a 10 Å orthorhombic box with periodic boundary condition was constructed with 4-Point Transferable Inter-molecular Potential (TIP4P) water model [24]. A shortenergy minimization was performed via steepest descent method followed by limited memory variation of Broyden Fletcher Goldfarb Shanno (LBFGS) algorithm. Salt concentrations of 0.15 M of Na+ or Cl− molecules were added to balance the net charge of the system. Before continuing with the production phase of MD simulations, the system was minimized with the default parameter set. The covalent bonds involving hydrogen atoms were constrained using SHAKE algorithm and Particle Mesh Ewald (PME) method for electrostatics [25]. The temperature was maintained at 300 K using Nose-Hoover coupling algorithm and pressure of 1 bar was maintained through Martyna-Tobias Klein method. Under equilibrium state, energy minimization was performed for the solvated system with solute restrained. Again, the proteinligand complexes were submitted to minimization with restraints by using hybrid method of steepest descent and LBFGS algorithm. Two short simulations, i.e., the system was heated to 10 K in NPT and NVT ensembles using Berendsen and Barostats for a time period of 12 and 24 ps by keeping non-hydrogen solute atoms restrained. In the final stage of relaxation protocol, simulations were carried out using 24 ps in NPT ensemble with Berendsen thermostats and Barostats with no atom restrained. In production MD, all protein-ligand complexes were simulated for a 10ns time period. Energy and atomic coordinate trajectories were recorded every 4.8 ps. The RMSD, root mean square fluctuation (RMSF), and protein-ligand contacts in each trajectory was analyzed with respect to time scale. Plots were graphically analyzed using OriginPro.

Common Pharmacophore Generation Ligand-based pharmacophore modeling obviously demonstrates many successive chronicles in the field of medicinal chemistry. The pharmacophore modeling was carried out using Pharmacophore Alignment and Scoring Engine (PHASE) module of Schrödinger [26]. The 3-D conversion and energy minimization of ligands were performed using LigPrep module. Monte Carlo algorithm was applied to generate conformers for the ligands using OPLS-2005 force field [27]. However, subsequent minimization was performed with Truncated Newton Conjugate Gradient (TNCG) method. The pharmacophore features, namely hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic group (H), negatively ionizable (N), positively ionizable (P), and aromatic ring (R) were defined by a set of chemical structure patterns as SMART queries [28]. Common pharmacophore hypotheses (CPHs) were identified


from a set of variants: a set of feature type that defines a possible pharmacophore using tree-based partitioning algorithm. The CPHs scoring was achieved by setting the RMSD value below 1.0, vector score value to 0.5 and weighing to include consideration of the alignment of inactive compounds using default parameters.

Virtual Screening The CPHs and structure of furanosyl borate diester was used to screen the compounds from ChemBridge database. Hits retrieved from virtual screening were subjected to filtration technique for the elimination of unwanted compounds. Further, docking was performed by utilizing the three levels of accuracy; hits passing the high throughput virtual screening (HTVS) step were subsequently analyzed in SP and finally, in XP. The final hits were subjected to IFD and absorption, distribution, metabolism and excretion (ADME) prediction [29]. Finally, the successive hit was subjected for toxicity prediction using PROTOX web server.

DFT DFT calculations were performed using Jaguar module implemented in Schrödinger [30]. Complete geometry optimization was carried out using Hybrid DFT model with Becke’s three parameter exchange potential and Lee-Yang-Parr correlation functional (B3LYP) method [31, 32] and the basis set 6-31G* [33]. Energy calculation was performed with Poisson-Boltzmann Solvers in an aqueous environment. highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and energy gap was calculated.

Prime MM-GBSA Prime MM-GBSA (Molecular Mechanics, The Generalized Born Model and Solvent Accessibility) was used to calculate the ligand binding energies and ligand strain energies for a set of ligand molecules and a receptor molecule [34]. The free energy change upon binding was calculated using the following expressions. ΔGbind ¼ Gcomplex − Gprotein þ Gligand G ¼ EMM þ GSGB þ GNP:

The Gcomplex is the complex energy, Gprotein is the receptor energy, and Gligand is the unbound ligand energy. EMM represents moleculer mechanics energies, GSGB is an SGB solvation model for polar solvation, and GNP is a nonpolar solvation term. Interaction energy was also calculated from MacroModel script which calculates the component energies of interacting atoms between the protein and ligand.

Enrichment Calculation The Schrödinger decoy set was used to validate the hits retrieved from the virtual screening. About 1000 drug-like compounds with an average molecular weight of 400 KDa were downloaded as 3D files from Schrödinger website. Compounds retrieved as hits were mixed


with decoy molecules and docking was performed using Glide XP protocol. The method would discriminate and separate active compounds from the inactive compounds.

In Vitro Assays Bacterial Strain The aquatic bacterial pathogen Vibrio harveyi (MTCC 3438) was kindly gifted by Dr. A. Veera Ravi, Department of Biotechnology, Alagappa University, Karaikudi, India. The monoculture of pathogenic V. harveyi was cultured in Luria Bertani (LB) media (pH 7.5 ± 0.2) and incubated overnight at 30 °C. For experimental purpose, the overnight culture was inoculated into fresh LB liquid medium to an initial optical density at 600 (OD600) of 0.5 and the culture was aerated on an orbital shaker at 100 rpm at 30 °C.

Minimum Inhibitory Concentration The potent hit (ChemBridge_5144368) was purchased from ChemBridge online chemical store (http://www.hit2lead.com) and tested against V. harveyi. The stock solution was prepared by diluting 5 mg of compound in 164 μl of dimethyl sulfoxide (DMSO). Working standard was prepared on the day of the experiment by diluting 20 μl of stock solution with 80 μl of DMSO. The antibacterial activity of the test compound was determined by a broth microdilution technique using 24-well microtiter plates. The well of each row was filled with 1 ml of LB broth supplemented with 1 % of the bacterial suspension (adjusted to OD600 of 0.5 ). Sequentially, the twofold serially diluted compound at different concentrations (0.5, 1, 2, 4, and 8 μg/ml) were added into the wells. The plate was incubated at 30 °C for 24 h. The turbidity was monitored using optical density at 600 nm with UV-Vis spectrophotometer. The lowest concentration required for the compound to inhibit the bacterial growth was recorded as minimum inhibitory concentration (MIC). Further investigation was performed with sub-MIC concentration of the compound.

Bioluminescence Inhibition Assay The V. harveyi cells were grown overnight in LB broth at 30 °C with aeration. The overnight culture was diluted to an OD600 of 0.5 in sterile LB medium and 1 % of the bacterial suspension was delivered into a 24-well microtiter plate. Different concentration (0.5, 1, 2, 4 μg/ml) of the test compound was then added into the wells. The plate was incubated for 12 h at 30 °C and the bioluminescence intensity was measured in relative light units (RLU) using luminometer. The untreated culture of V. harveyi serve as positive control and sterile LB medium was used as negative control.

Biofilm Inhibition Assay Biofilm formation was quantified by crystal violet staining as described previously [35]. In brief, the overnight culture of V. harveyi was diluted to an OD600 of 0.5 in sterile LB broth and 1 ml of the liquid broth medium was delivered into a 24-well microtiter plate. Further, the culture was treated with the presence (0.5, 1, 2, 4 μg/ml) or absence of the test compound. The plates were incubated


for 24 h at 30 °C without agitation. After incubation, the media was decanted from the wells and the plate was sequentially washed thrice with sterile deionized water to remove the loosely associated bacteria. The biofilms were stained with 200 μl of 0.4 % crystal violet solution for 15 min. Excess stain was removed by washing thrice with deionized water. Further, the plates were air dried and 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. Percentage inhibition of biofilm biomass ¼ ½Control OD–Test OD=Control OD 100

Disintegration of Mature Biofilm Disrupting the architecture of mature biofilm was confirmed through the presence or absence of the test compound. The slides were incubated at 30 °C for 5 h and stained with 200 μl of 0.4 % crystal violet solution. Excess stain was removed by washing the slides with distilled water and then air dried. The biofilm disruption by the test compound was observed under light microscope.

Swimming and Swarming To test the motility, swimming and swarming assays were performed according to the procedure described [36]. The swimming and swarming plates were performed in LB plate with 0.3 and 0.5 % of Bacto agar. About 3 and 5 μl overnight culture of V. harveyi (OD600 nanometers of 0.05) was inoculated with sterile needle at the center of the swimming and swarming plates. Further, the plates were incubated for 16 h at 30 °C to observe the reduction in swimming and swarming migration zones.

Growth Curve and Bioluminescence Kinetic Assay The overnight culture of V. harveyi was diluted with LB broth to an OD600 nanometers of 0.5. About 250 μl of diluted culture was inoculated in 25 ml of LB broth supplemented with various concentrations (0.5, 1, 2, 4 μg/ml) of the test compound on a rotary shaker under 180 rpm at 30 °C. The optimal density was recorded at 1 h time intervals up to 16 h. LB broth was used as a negative control and bacteria without compound served as a positive control. In bioluminescence kinetic assay, culture was inoculated in 25 ml of alkaline peptone water (APW) supplemented with various concentrations (0.5, 1, 2, 4 μg/ml) of the test compound and incubated by following the procedure as stated earlier. The bioluminescence intensity was measured at 1 h time interval up to 16 h. APW was used as negative control and bacteria without compound served as a positive control.

Results and Discussion Molecular Docking In the present study, three different docking protocols including RRD, IFD, and QPLD were used to determine the binding mode of the studied inhibitors. Literature survey showed boronic


acid derivatives (IC50 value range from 9 to 4 μm) as potent anti-QS compounds targeting LuxP of V. harveyi [37]. Hence, the pharmacophoric feature of these boronic acid derivatives was utilized to screen the potent compounds from ChemBridge database. Before screening, the binding mode and stability of the protein-ligand complexes was confirmed through docking and MD simulation. Docking accuracy is assessed by re-docking the co-crystal ligand (furanosyl borate diester) into the binding site of LuxP. Moreover, comparison between results obtained by three docking protocols revealed that RRD and QPLD achieves better performance to enrich the native conformation of furanosyl borate diester in the receptor binding site with an RMSD of 0.08 and 0.06 Å. The low RMSD value strongly suggests that the protein undergoes slight conformational changes during docking of RRD and QPLD (Fig. 1a, b). We observed a strong correlation in the interaction profile between the docked complexes and the experimental structure of LuxP bound with furanosyl borate diester (PDB: 1JX6). In contrast, IFD protocol yielded an average heavy atom RMSD of 0.28 Å with the same ligand-receptor pairs. The result suggests that IFD does not perform well for this system. Five boronic acid derivatives were docked into the binding site of LuxP through RRD, IFD, and QPLD protocols and their results are summarized in Table 1. A docking performance was evaluated based on the binding affinity, glide score and experimental bioactivities. The above-mentioned result highlights that IFD protocol accurately ranked the bioactivities of the studied inhibitors and also successfully predicted the accurate binding orientation of the protein-ligand complexes. It also indicates that IFD yields good performance than RRD and QPLD for the boronic acid

Fig. 1 The superimposition of Glide XP docked conformation to the crystallographic complex. The carbon atoms of both docked and crystal conformation are colored in blue and magenta, respectively. a Superposition of QPLD docked conformation and co-crystalized structure. b Superposition of RRD docked conformation and cocrystalized structure

Author's personal copy Appl Biochem Biotechnol Table 1 Experimental pIC50, docking scores predicted by three docking protocols, Prime MM/GBSA re-scoring of the boronic acid derivatives S. no

a

IFD QPLD score score (kcal/ (kcal/ mol) mol) -6.92 -5.02

ΔGbind (kcal/ mol)

1.

3-fluoro-4-methylphenylboronic acida

5.04

RRD score (kcal/ mol) -4.81

2.

4-(methyoxycarbonyl) phenylboronic acidb

5.30

-4.40

-7.63

-4.05

-63.03

3.

4-(benzyloxyl) phenylboronic acidc

5.39

-5.01

-9.44

-1.63

-67.59

4.

2-fluoro-4-methyl phenylboronic acidd

5.39

-4.90

-7.02

-5.12

-40.20

5.

4-cyanophenylboronic acid

5.22

-4.40

-7.61

-3.96

-43.76

Compound name

Compound structure

pIC50

-45.08

Compound 1

b

Compound 2

c

Compound 3

d

Compound 4

e

Compound 5

derivatives. Prime/MM-GBSA method was used to rank the binding energies in relation to their biological activity. The result suggests that binding free-energy calculation did not translate into an accurate prediction of binding affinity to rank the biological activities for the boronic acid derivatives. In case of boronic acid derivatives (compounds 1–5), the dock poses obtained from RRD, IFD and QPLD protocols showed similar type of hydrogen bond interactions with the active site residues of LuxP, but differs in their hydrogen bond distance. RRD and QPLD results of the boronic acid derivatives exhibited glide score in the range from −4.40 to −5.01 and −1.63 to −5.02 kcal/mol, respectively. Comparing the docking scores suggests that RRD and QPLD do not perform well for this system. Interestingly, IFD protocol has shown the highest glide score ranging from −6.92 to −9.44 kcal/mol and it correlates well with the biological data [37]. Pearson correlation coefficient was used to examine the relationship that exists between the observed experimental values (IC50, pIC50 or EC50) against the predicted docking scores of the protein-ligand complexes. The Pearson correlation coefficient of r2 = 0.25, 0.52, and 0.46 was observed for RRD, IFD, and QPLD, respectively. The value close to +1 suggests a high degree of positive correlation exists between the two variables. IFD showed the highest correlation coefficient of 0.52 indicating a strong positive correlation between the experimental pIC50 values and the predicted docking score. The results led us to hypothesize that IFD seems more accurate than RRD and QPLD. However, IFD protocol performs much better than regular


docking (RRD) in some cases [38]. Based on the docking results, the discussion was mainly focused on IFD of the ligands. The selected boronic acid derivatives interact with LuxP through hydrogen bond, π-π stacking, π-cation, salt bridge, and hydrophobic interactions. In compound 1 (3-fluoro-4-methylphenylboronic acid) and compound 2 (4-(methyoxycarbonyl) phenylboronic acid), the groups at R3 and R4 positions are different. The methyl moiety at fourth position in compound 1 was replaced by a 4-methyoxycarbonyl moiety in compound 2. Experimental evidence have shown that compound 2 possesses better activity than compound 1, suggesting that the additional methoxycarbonyl group in compound 1 is favorable for achieving the better potency [37]. The docking score of boronic acid derivatives ranges from −6.92 to −9.44 kcal/mol. As shown in Fig. 2a, compound 1 forms hydrogen bond interactions with the amino acid residues such as Trp82 (OH...NH, bond length = 1.72 Å), Ser79 (OH...O=C, bond length = 1.88 Å), Thr266 (OH...O=C, bond length = 2.01 Å), and Arg310 (OH...NH, bond length = 2.10 Å). In particular, the benzyl ring of compound 1 forms π-π stacking and π-cation interactions with Arg215. As shown in Fig. 2b, compound 2 forms two hydrogen bond interactions with Thr266 (OH...O=C, bond length = 1.95 Å) and Arg310 (OH...NH, bond length = 2.45 Å). At the same time, the benzyl ring of compound 2 forms three π-π stacking with the amino acid residues Arg215, Arg310, Tyr81, and one π-cation interaction with Arg215. The removal of fluorine atom at R3 position in compound 1 with an addition of the carboxyl group at R4 for compound 3 (4-(benzyloxyl) phenylboronic acid) leads to the substantial differences in the ligand potency, the basic phenylboronic group in compound 3 forms three hydrogen bond interactions with Thr266 (OH ...O=C, bond length = 1.98 Å) and Arg215 (OH...NH, bond length = 1.88, 1.91 Å). Meanwhile, π-π stacking and π-cation interactions were observed with the residues such as Trp82 and Arg310 as displayed in Fig. 2c. The simple replacement of fluorine atom at R3 position in compound 1 to R6 position for compound 4 (2-fluoro-4-methyl phenylboronic acid) increased the activity. However, one atom difference leads to the differences in the binding affinity. As shown in Fig. 2d, the binding pose of compound 4 shows two hydrogen bond interactions, one with Thr266 (OH...NH, bond length = 2.55 Å) and another one with water molecule. It also forms three π-π stacking interactions with positive and hydrophobic residues such as Arg215, Phe206, and Trp82. In compound 5 (4-cyanophenylboronic acid), the deletion of fluorine at R3 position with a replacement of cyano group at R4 position increased the activity. Therefore, the addition of the cyano group in the basic phenylboronic acid derivative enhanced the binding affinity of the compound. Figure 2e, the hydroxy group of compound 5 forms hydrogen bond interactions with the amino acid residues such as Arg310 (OH...NH, bond length = 1.83 Å), Thr266 (OH...O=C, bond length = 1.88 Å), and Ser79 (OH...O=C, bond length = 1.70 Å) and also shown π-π stacking and π-cation interaction with the amino acid residue Arg215. Five boronic acid derivatives exhibits π-π stacking and π-cation interactions with the receptor molecule imply their role in the protein-ligand recognition mechanism. It was reported that the positively charged side chain amino acid residues such as Arg215 and Arg310 play a significant role in stabilizing the protein-ligand complex [39] through ionic interactions. The two residues important for protein-ligand stability were observed in the five protein-ligand Fig. 2 Binding pattern of boronic acid derivatives in the binding site of LuxP. a 3-fluoro-4-methylphenyboronic acid, b 4-(methoxycarbonyl) phenylboronic acid, c 4-(benzyloxyl) phenylboronic acid, d 2-fluoro-4methylphenylboronic acid, and e 4-cyanophenylboronic acid. For clarity, only a few of the important amino acid residues are shown. The binding modes of the compounds are shown in balls and sticks. Hydrogen bond interactions are shown in pink dashed lines with the distance between donor and acceptor atoms indicated by Angstrom



complexes. Overall, the interactions with Arg215 and Arg310 should be taken into consideration for designing novel inhibitors targeting LuxP of V. harveyi.

MD Simulation MD simulations provide detailed information about the conformational changes of a system. It also explores protein flexibility and uncovers dynamic patterns hidden in the complex dynamics [40, 41]. Dynamic motions of proteins achieved through MD simulations are directly associated with their function [42]. MD simulation of each protein-ligand complex was carried out for a simulation period of 10 ns. The parameters such as potential energy, total energy, temperature, and pressure were used to stabilize the system extremely well. Potential energy is described as the sum of bonded and non-bonded terms, a simple way of measuring system stability [43]. The average potential energies of the five protein-ligand complexes are found to be 25,880.75; 25,882.91; 25,863.27; 25,854.44; and 25,872.45 kcal/mol with the standard deviation (SD) of 91.14, 101.96, 98.01, 95.46, and 101.39, respectively. Total energy of the systems was analyzed to evaluate the equilibrium state at a given temperature of 300 K and a pressure of 1 bar. The total energies and SD values of the five protein-ligand complexes are found to be 1901.90, 2034.47, 1841.47, 1833.73, and 1783.89 kcal/mol and 240.06, 38.84, 165.66, 147.56 and 151.40, respectively. The results showed that the protein-ligand complexes reach thermal equilibrium at a given temperature of 300 K. The trajectories were found stable during the whole production part of 10 ns.

RMSD The conformational changes in the protein-ligand complexes were calculated to analyze the backbone RMSD from the starting structure over the course of the trajectory. Figure 3 shows the RMSD values of the protein-ligand complexes. From the figure, it was clear that there was an initial rise in RMSD of complex 1, which might be due to the absence of restraining the production phase of MD simulations. The average RMSD of complex 1 was found to be

Fig. 3 The RMSD values of the protein-ligand complexes over the simulation time. X-axis represents time scale in picosecond and Y-axis represent the RMSD in Angstrom. Simulation was carried out in a TIP4P water environment with a time step of 10 ns


2.36 Å with an SD of 0.25 Å. It was observed that 8 ns were sufficient to reach an equilibrium state and the stability of the system was observed during later stages. In complex 2, the RMSD curve initially showed largest deviation with an RMSD of 1.60 Å over the first 5 ns and then shifted back slightly to 1.20 Å to attain its stability with an SD of 0.17 Å. In complex 3, the RMSD curve showed stability after 4 ns with 2.1 Å deviations with an SD of 0.28 Å. In complex 4 and 5, constant RMSD was observed throughout the whole simulation with an RMSD of 1.2 and 1.3 Å and SD value of 0.17 and 0.20 Å, respectively. Different conformational changes were used to stabilize the protein-ligand complexes.

RMSF RMSF was calculated to characterize the local changes in the protein residues upon ligand binding. Figure 4 shows the residue-wise RMSF profile of the protein-ligand complexes. The binding of compound 1 to LuxP does not induce conformational changes in the protein structure. Complex 1 showed an average fluctuation of 0.74 Å with an SD of 0.27 Å. However, few amino acid residues exhibits higher fluctuation, which includes Ile113 (1.55 Å), Arg139 (1.65 Å), Asp149 (1.50 Å), Asn152 (1.75 Å), Lys169 (1.72 Å), and Arg225 (1.58 Å). The residues exhibiting large fluctuations correspond to the loop and beta sheet regions. In case of complex 2, average fluctuation of 0.76 Å with the SD of 0.20 Å was observed in the protein structure. Two amino acid residues such as Ser150 (1.52 Å) and Asp364 (1.52 Å) has shown higher fluctuation in the protein. The amino acid residues, which has shown higher fluctuation in complex 3 are Ser293 (2.07 Å), Ala294 (2.27 Å), Asp297 (2.06 Å), Gln300 (2.07 Å), Lys301 (2.34 Å), Gly302 (1.91 Å), Asp303 (1.85 Å), and Asp364 (1.95 Å). The complex 3 showed an average fluctuation of 0.80 Å with an SD of 0.32 Å. Few amino acid residues that includes His221 (1.76 Å), Gln222 (1.83 Å), and Asp364 (1.77 Å) has shown higher fluctuation value in case of complex 4 with an average and SD value of 0.64 and 0.21 Å. Complex 5 has shown higher mobility rate in the amino acid residues such as His221 (1.76 Å), Gln22 (1.83 Å), and Asp364 (1.77 Å) with an average and SD of 0.66 and 0.24 Å. The active site residues have shown less conformational changes in the protein structure.

Fig. 4 RMSF of C-α atom in coordinates of each residue averaged over the duration of the MD simulation


H-Bond Analysis The H-bond interaction profiles of the five protein-ligand complexes were monitored throughout the simulation period. Consideration of hydrogen bonding properties in drug design is important because of their strong influence on drug specificity, metabolization, and adsorption [44]. The amino acid residues such as Arg215 and Arg310, which makes hydrogen bond interactions with all five protein-ligand complexes, were found stable throughout the simulation period. Other amino acid residues found to have hydrogen bond interactions with the boronic acid derivatives include Gln77, Ser79, Tyr81, Tyr82, Asn159, Phe178, Phe206, Ile211, and Trp289. From Fig. 5, it was clear that a maximum of 16 and 12 hydrogen bond interactions were observed in the five protein-ligand complexes. It was concluded that the protein-ligand complexes are relatively stronger during the MD simulations.

Pharmacophore and Shape-Based Virtual Screening In this study, common pharmacophoric features derived from the boronic acid derivatives and structure of furanosyl borate diester were used to identify potent hits against LuxP. Shapebased method for aligning and scoring ligands from a database has been proven to be valuable in the field of computer aided drug design [45]. In shape-based screening, the molecules are ranked on the basis of their similarity to a known active molecule in 3D shape spaces. Each conformer from a given molecule is aligned to the query in various ways, and a similarity is computed based on overlapping hard-sphere volumes. The conformer and alignment yield the highest similarity for each molecule. Additionally, they have other successful applications such as scaffold hopping, bioisostere replacement, virtual library design, and flexible ligand superposition. The furanosyl borate diester was used as the query structure for shape screening against ChemBridge database. The compounds retrieved from the database were selected based on the similarity score (sim-score) and the compounds were then sorted by applying drug-like properties based on Lipinski’s rule of 5 and number of rotatable bonds (≤7). Pharmacophore model was developed using five boronic acid derivatives. The best

Fig. 5 Total number of intermolecular hydrogen bond interactions between LuxP and boronic acid derivatives


pharmacophore hypotheses, showing the distance and angles between their pharmacophoric sites are depicted in Fig. 6a, b. Six different 3-point pharmacophore hypothesis were generated from the list of variants. Based on the common pharmacophore hypothesis (AAR), screening was conducted against ChemBridge database. The identified compounds from the database were subjected to various filtering methods such as fitness score, Lipinski’s filter and the rotatable bonds (≤7). Compounds with the best fitness score of above 2.0 was taken for further analysis. Compounds retrieved from both pharmacophore and shape-based screening were taken for molecular docking studies. All three stages of docking process were exploited to rank the compounds. First phase executes HTVS, which is computationally least intended for rapid screening of ligands. Approximately 10 % of the ligands were passed onto the next stage of SP. Finally, five compounds were identified as selective inhibitors of LuxP using selection criteria based on XP scoring function and visual examination. Five compounds were selected as the best hits from glide XP docking and the docking scores ranging from −9.07 to −10.34 kcal/mol. Binding mode analysis revealed that all the five compounds forms hydrogen bond interaction with the highly conserved amino acid residues. In order to recognize the accurate docking results, all the compounds were subjected to IFD. From the comparative study, it was found that IFD protocol dramatically changes the results of the glide XP docking. In IFD, docking scores of the five hits ranging from −14.64 to −10.57 kcal/mol and the results are tabulated in Table 2. The compounds ChemBridge_5144368, ChemBridge_5182298, ChemBridge_5163227, ChemBridge_9018586, and ChemBridge_5140233 established 6, 8, 7, 6, and 5 hydrogen bond interactions, respectively. The 2D interaction profiles of the hits are shown in Fig. 7a–e. Two compounds (ChemBridge_5163227 and ChemBridge_5140233) are predicted to form salt bridges with two amino acid residues such as Arg215 and Arg310. The observed salt bridge in two hits contributes to the overall stability of the protein. The hit identified from pharmacophore-based screening have mapped the entire features of the CPHs. It was observed that the two arginine residues such as Arg215 and Arg310 are capable of forming interactions with the identified hits. The calculated binding free energies of the protein-ligand complexes are ranges from −16.48 to −59.66 kcal/mol. The result indicates strong interactions between the protein and ligands. The pharmacokinetic properties of the identified hits are in the

Fig. 6 Common pharmacophore hypothesis (AAR) showing distance and angles between their pharmacophoric sites. a Sphere with vectors A1 and A2 represent acceptor features and R7 is the aromatic ring feature. b Alignment of boronic acid derivatives with the common pharmacophore hypothesis

5182298

5163227

9018586

5140233

2.

3.

4.

5.

Glide energy (kcal/mol)

−45.28

−45.23

−46.13

−53.08

−31.30

Glide score (kcal/mol)

−14.64

−14.17

−13.36

−11.11

−10.57

−39.96

−68.77

−69.10

−58.26

−59.15

Emodel score (kcal/mol)

2997.76

2973.55

2994.52

2996.83

2997.33

IFD score (kcal/mol)

Interacting residues: a forms two hydrogen bond interactions with same amino acid residues

5144368

ChemBridge ID

1.

S. no

−16.48

−68.11

−47.04

−43.29

−43.91

ΔGbind (kcal/mol)

Table 2 Induced fit docking scores, prime MM/GBSA re-scoring of the hits identified from virtual screening

5

6

7

8

6

No. of Hbonds

Trp82, Arg215, Arg310a, Thr266

Ser79, Arg215, Arg310a, Asn159, HOH

Thr266, Trp289, Ser79, Arg310a, Asn159, Arg215

Arg215a, Asn159, Ser265, Thr266, Arg310a

Ser79, Thr266, Arg310a, Arg215, Trp289

Interacting residues



Fig. 7 2D interaction profile of hits identified from virtual screening. a ChemBridge_5144368, b ChemBridge_5182298, c ChemBridge_5163227, d ChemBridge_9018586, and e ChemBridge_5140233. Hydrogen bond interactions with side chain amino acid residues are represented in a pink dashed line, while the hydrogen bond interaction with backbone amino acid residues are presented in a pink continuous line. Both side chain and backbone interactions are shown with an arrow head directed toward the electron donor. Salt bridges are represented in dark red and blue continuous lines

acceptable range and are shown in Table 3. In order to validate the computational results, the top most compound retrieved from virtual screening was tested with experimental assays. PROTOX server revealed no toxicity related fragments present in the hit molecule.

DFT Calculation HOMO, LUMO, and their energy gap imply the chemical stability of the molecule [46]. These orbitals determine the charge transfers occur within the molecule. The donor and acceptor characteristics of the molecule are stated by the frontier orbital electron densities [47, 48]. The energy of the HOMO is directly related to the ionization potential and characterizes the susceptibility of molecule toward attack by electrophiles, whereas LUMO is related to electron affinity and the susceptibility of molecule toward attack by nucleophiles. Both HOMO and LUMO energies are important in radical reaction. HOMO and LUMO energy values of the five boronic acid derivatives ranging from −0.31 to −0.25 and −0.26 to −0.23 eV. Two boronic acid derivatives, 4-(methyoxycarbonyl) phenylboronic (compound 3) and 2-fluro-4methylphenylboronic acid (compound 4) with high inhibitory activity (pIC50 greater than 5.3) possess low HOMO-LUMO energy gap (on average −0.02 eV) on comparison with other boronic acid derivatives. The calculated HOMO and LUMO values are tabulated in Table 4.

Author's personal copy Appl Biochem Biotechnol Table 3 ADME or pharmacokinetic properties of the hits identified from virtual screening QP logPo/wb

Molecular weighta

1.

5144368

182.17

1.17

−1.16

0.99

0

2.

5182298

200.14

−0.21

−0.50

2.07

0

3.

5163227

184.19

1.36

−1.74

0.56

0

4. 5.

9018586 5140233

307.74 198.09

2.21 1.19

−3.43 −1.52

−2.50 1.05

0 0

a

Molecular weight of the molecule (130.0 to 725.0)

b

Predicted octanol/water partition coefficient (−2.0 to 6.5)

QP logSc

QP logHERGd

ChemBridge ID

S. no

Rule of fivee

c

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)

d

Predicted IC50 value for blockage of HERG K+ channels (concern below −5)

e

Number of violation of Lipinski’s rule of five (0–4)

The result implies that the modification of the functional group influence the HOMO-LUMO energy gap that possesses a direct impact on the inhibitory effect of the compounds. Top five identified hits showed the HOMO and LUMO energies ranging from −0.30 to −0.19 and −0.27 to −0.03 eV indicates the fragile nature of the bound electrons. The energy gap varies from 0.028 to 0.024 eV indicates high chemical reactivity of the hit molecules at the active site of the protein. Among all the compounds, ChemBridge_5140233 and 4-(methoxycarbonyl) phenylboronic acid with high HOMO and LUMO values indicates the hard nucleophile and electrophile index.

MD Simulation RMSD Five independent MD simulations were carried out using the docked poses obtained from the docking protocol. An RMSD was calculated to evaluate the stability and flexibility of the protein-ligand complexes. The five protein-ligand complexes showed an RMSD value ranges Table 4 Frontier orbital energies of boronic acid derivatives and hits identified from virtual screening S. no

Compound name/ChemBridge_ID

HOMO (−eV)

LUMO (−eV)

Energy Gap (−eV)

1.

Compound 1

−0.25

−0.23

0.02

2.

Compound 2

−0.31

−0.26

0.05

3. 4.

Compound 3 Compound 4

−0.29 −0.25

−0.26 −0.23

0.03 0.01

5.

Compound 5

−0.28

−0.24

0.03

6.

ChemBridge_5144368

−0.25

−0.23

0.02

7.

ChemBridge_5182298

−0.27

−0.25

0.02

8.

ChemBridge_5163227

−0.26

−0.23

0.02

9.

ChemBridge_9018586

−0.19

−0.03

0.16

10.

ChemBridge_5140233

−0.30

−0.27

0.02


from 1.2 to 1.7 Å and are shown in Supplementary Fig. 1. From the figure, it was observed that all the five protein-ligand complexes stabilized at 10 ns of simulation. The LuxP_9018586 complex exhibits low RMSD value of 0.9 Å with the SD of 0.09 Å on comparison with other protein-ligand complexes. In case of LuxP_5144368 complex, the RMSD showed large conformational drift in the first 5 ns of simulation period, after that the protein-ligand complex attains stable conformation with an average RMSD and SD of 1.34 and 0.18 Å, respectively. The LuxP_5163227 and LuxP_5140233 complexes clearly showed that there is no much variation in the RMSD values during the simulation time and the system maintained stability around 1.75 and 1.4 Å, respectively. The average RMSD of 1.61 and 1.26 Å with the SD of 0.29 and 0.13 Å was observed for the LuxP_5163227 and LuxP_5140233, respectively. The LuxP_5182298 exhibit several drifts throughout the whole simulation period and the stability of the system were maintained with an average RMSD of 1.19 Å and the SD of 0.13 Å.

RMSF The RMSF values represent the atomic fluctuation of each residue during the simulation period. The RMSF profile is shown in Supplementary Fig. 2. The LuxP_5144368 complex exhibits high fluctuation in the amino acid residues such as Asp149 (1.77 Å), Ser150 (1.84 Å), Asp226 (2.01 Å), and Asn227 (1.54 Å). The amino acid residues shown higher fluctuation in the case of LuxP_5163227 are Pro62 (1.62 Å), Thr63 (1.68 Å), Ser150 (1.80 Å), Thr151 (1.95 Å), Asp226 (1.62 Å), and Asp364 (1.62 Å). Major conformational changes were observed in the amino acid residues such as Ser60 (1.3 Å), Pro62 (1.41 Å), and Lys169 (1.41 Å) in the LuxP_5140233 complex. Several amino acid residues exhibit higher mobility in the LuxP_5182298 complex, which includes Lys61 (1.50 Å), Arg139 (1.62 Å), Asp149 (2.30 Å), Ser150 (2.0 Å), Thr151 (1.71 Å), Asp168 (1.51 Å), and Asp364 (1.54 Å). In case of LuxP_9018586, major fluctuations were observed in the residues including Arg139 (1.78 Å), Lys142 (1.54 Å), Asp149 (1.62 Å), Ser150 (1.62 Å), Thr151 (1.67 Å), and Asn152 (1.56 Å).

H-Bond Analysis The H-bond interactions between protein-ligand complexes monitored during the simulation time are shown in Supplementary Fig. 3. The figure clearly states that a maximum of 15, 16, 14, 18, and 17 H-bond interactions were observed in the protein-ligand complexes (LuxP_ChemBridge_5144368, 5163227, 5140233, 5182298, and 9018586), respectively. All five protein-ligand complexes showed constant H-bond interactions over a whole simulation. H-bond interactions predicted in the docking studies were retained in the MD simulations. The dynamics studies showed that these compounds can steadily anchor LuxP to exert an inhibition effect.

Enrichment Analysis The ability to select active ligands from a large number of decoys depend on the quality of sampling and re-scoring of ligands, as well as the ability to accommodate receptor side chain flexibility in the binding site. The Schrödinger decoy set with an average molecular weight of 400 KDa was used to validate the hits retrieved from the pharmacophore and shape-based virtual screening. The method could discriminate the active compounds from the inactive compounds. For validation, a data set of 10 (pharmacophore) and 11 (shape-based) active


compounds were mixed with 1000 decoy compounds making a total of 1021 compounds. These compounds were further subjected to Glide XP docking. The obtained results clearly showed that all 21 active compounds were discriminated from the inactive compounds. The resultant Glide XP identified 9 out of 11 from shape-based screening and 10 actives from pharmacophore as the top scoring compounds, while few drug-like molecules from the decoy set has shown docking score ranging from −10.34 to −0.11 kcal/mol. The parameters such as total active hits (Ht), number of active hits (Ha), percentage yield of active, percentage ratio of active, enrichment factor, false negatives, false positives, and goodness of the hits (GH) were used to analyze the results. The compounds with a minimal docking score of −0.1 to −4.0 kcal/ mol were eliminated for the enrichment studies. The statistical parameters predicted for both pharmacophore and shape-based screening are given in Table 5. From the results, the observed GH values of 0.88 for shape-based screening and 0.52 for pharmacophore based screening indicate the quality of the model is acceptable.

In Vitro Assays Minimal Inhibitory Concentration of the Compound In recent years, significant research has been directed towards the development of small molecules that has the ability to interfere with various components of the QS in V. harveyi [49]. The MIC of ChemBridge_5144368 was found to be 8 μg/ml against V. harveyi. Therefore, all further assays were performed with the sub-MIC concentration (0.5, 1, 2, 4 μg/ml) of ChemBridge_5144368. DMSO which was used as negative control in all the bio assays does not show any inhibition effect in the test organism.

Bioluminescence Inhibition Assay In the bioluminescent bacterium V. harveyi, light production is directly proportional to the metabolic activity of the bacterial population and any inhibition of enzymatic activity causes a Table 5 Statistical parameters of decoy set validation of the hits identified from pharmacophore and shape-based virtual screening S. no

Parameters

Pharmacophore results

Shape results

1.

Total molecules in database (D)

1010.00

1011.00

2. 3.

Total number of actives in the database (A) Total hits (Ht)

10.00 26.00

11.00 13.00

4.

Active hits (Ha)

10.00

11.00

5.

% yield of active [(Ha/A) × 100

38.46

84.61

6.

% ratio of actives [(Ha/A) × 100

100.00

100.00

7.

Enrichment factor (Et) [Ha × D]/(Ht × A)]

38.84

77.76

8.

False negative (A − Ha)

0.00

0.00

9.

False positive (Ht − Ha)

16.00

2.00

10.

Goodness of hit (GH)a

0.52

0.88

a

[Ha/4HtA) (3 A + Ht) × 1 − (Ht − Ha)/(D − A)]: 0.6–0.9 range of GH score indicates a good model


corresponding decrease in bioluminescence [50, 51]. Therefore, the bioluminescence inhibitory activity of the test compound was assessed using V. harveyi. As shown in Fig. 8, the compound (ChemBridge_5144368) showed significant reduction in the bioluminescence values ranging from 5,055,522 to 3,138,693 (0.5 to 4 μg/ml) as compared with control (15,307,388). In the presence of increasing concentrations of the test compound, the bioluminescence value decreased in a dose-dependent manner. It was found that the test compound decreased bioluminescence thereby inhibiting the enzymes involved in the QS based biofilm formation in V. harveyi. The findings correlate with the report [52] where the isolimonic acid showed 99.23 % of AI-2-mediated bioluminescence at the concentration of 100 μg ml−1. The results also fall in line [53], where the furanone was shown to block bioluminescence of the V. harveyi in a concentration-dependent way.

Biofilm Inhibition Assay Cell-cell communication between bacteria has shown to regulate virulence factors in V. harveyi such as biofilm formation and toxins. Also, AI-2 plays a promotive role in the formation and maturation of biofilm [54]. Disrupting the signaling process is used to prevent bacterial biofilm formation, thereby preventing infectious disease [55]. Therefore, the anti-biofilm efficacy of the compound (ChemBridge_5144368) was assessed using V. harveyi. Inhibition of biofilm formation by test compound was found to be in a dose-dependent manner. The compound (ChemBridge_5144368) significantly decreased the biofilm formation up to 64.25 % at a concentration of 4 μg/ml and showed no apparent effect on bacterial growth rate (Supplementary Fig. 4). In previous reports, it was found that halogenated furanone shown to possess anti-biofilm efficacy in Escherichia coli, Bacillus subtilis, Salmonella enteric serovar Typhimurium, Streptococcus spp., and Vibrio spp. [56–59]. The findings of the present study was consistent with the previous report [60], wherein the flavonoid compounds such as quercetin and naringenin have been shown to possess potent anti-biofilm efficacy in V. harveyi BB886 and E. coli 0157:H7.

Fig. 8 Measurement of effect on bioluminescence in V. harveyi at various concentration of ChemBridge_5144368. Bioluminescence is measured as relative light units using luminometer


Disintegration of Mature Biofilm At the current time, biofilm-forming bacteria are resistant to a wide variety of currently available antibiotics [61]. Therefore, biofilm disintegration plays a crucial role to overcome infections that are highly resistant to antibiotics. The result of the biofilm inhibition was confirmed using light microscope. Control plate without compound treatment has shown highest biofilm formation with a continuous dense matrix with membranous structure and the V. harveyi cell attached to the solid surface. Decrease in biofilm formation was observed after treatment with the compound. The light microscopic analysis has explained the ability of compound to disintegrate the mature biofilms of V. harveyi (Fig. 9).

Swimming and Swarming Assay Cell motility of twitching, swimming, and swarming plays important roles in the biofilm formation are well-studied with the previous report [62]. Also, flagella promote the initial stages of biofilm formation by Vibrio spp. [63]. Therefore, inhibition of the bacterial biofilm formation and motility by a small molecule represent an important strategy to control bacterial colonization. Swimming and swarming plates showed reduction in the bacterial motility compared with the control. The swimming and swarming motility was reduced at the concentration of 4 μg/ml (Fig. 10a–d). The results agree well with the previous report [64], the motility of Pseudomonas aeruginosa is blocked by cranberry proanthocyanidins and pomegranate punicalagin in a concentration-dependent manner.

Growth Curve and Bioluminescence Kinetic Assay The test compound showed no significant growth inhibition in both treated and untreated groups up to 16 h of treatment. Figure 11 shows the effect of various concentrations (1–4 μg/ ml) of compound on the growth of V. harveyi. The results of the present study clearly demonstrated that the test compound does not exhibit any antimicrobial effect in V. harveyi. In bioluminescence kinetic assay, the test compound reduced the bioluminescence intensity of V. harveyi in the dose-dependent manner. Figure 12 represents the reduction of bioluminescence of V. harveyi by the treatment to the test compound.

Fig. 9 Light microscope image, a control, b V. harveyi cells treated with 4 μg/ml of ChemBridge_5144368


Fig. 10 Swimming of V. harveyi, a untreated control, b treatment with 4 μg/ml of ChemBridge_5144368. Swarming of V. harveyi, c untreated control, d treatment with 4 μg/ml of ChemBridge_5144368. Incubation period of 24 h at 30 °C

Fig. 11 Effect of various concentrations of ChemBridge_5144368 on the growth curve of V. harveyi. Cell density was measured by absorbance at 600 nm


Fig. 12 Bioluminescence effect on V. harveyi in the presence or absence of the ChemBridge_5144368. Bioluminescence measurements were performed 4 h after the addition of the compound

Conclusion Different docking approaches such as RRD, IFD, and QPLD were applied to determine the binding mode of boronic acid derivatives in the binding site of LuxP. Moreover, docking results highlight that IFD protocol accurately ranked the bioactivities of the studied inhibitors. Interaction profile such as hydrogen bonding network, hydrophobic, π-π stacking, π-cation, and salt bridges helps to stabilize the protein-ligand complex. The pharmacophore (AAR) and shape-based screening were successfully applied to screen compounds from the ChemBridge database. The identified hits were screened based on Lipinski’s filter, number of rotatable bonds, fitness score, sim score, and three stages of docking precision. In addition, the pharmacokinetic properties of the hits are in the acceptable range. The stability and dynamic property of protein-ligand complexes were investigated with MD simulations. The MD results were supported by the hydrogen bond interaction analysis. The identified hits were validated using enrichment calculations. Furthermore, evaluation of the biological activity of ligands would help in specifying drugs against QS in V. harveyi. The identified potent hit molecule was validated using in vitro assays. The results can offer useful references for designing potent inhibitors targeting quorum sensing system of V. harveyi.

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