Ligand-based pharmacophore modelling and screening of DNA minor groove binders targeting Staphylococcus aureus

Ligand-based pharmacophore modelling andscreening of DNA minor groove binderstargeting Staphylococcus aureusPeriyasamy Vijayalakshmia, Chandrabose Selvarajb,Raja Mohmed Beema Shafreenc, Sanjeev Kumar Singhb,Shunmugiah Karutha Pandianc and Pitchai Daisya*

The recognition of DNA by small molecules is of special importance in the design of new drugs. Many natural andsynthetic compounds have the ability to interact with the minor groove of DNA. In the present study, identificationof minor groove binding compounds was attained by the combined approach of pharmacophore modelling, virtualscreening and molecular dynamics approach. Experimentally reported 32 minor groove binding compounds wereused to develop the pharmacophore model. Based on the fitness score, best three pharmacophore hypotheses wereselected and used as template for screening the compounds from drug bank database. This pharmacophore-basedscreening provides many compounds with the same pharmacological properties. All these compounds weresubjected to four phases of docking protocols with combined Glide-quantum-polarized ligand docking approach.Molecular dynamics results indicated that selected compounds are more active and showed good interaction inthe binding site of DNA. Based on the scoring parameters and energy values, the best compounds were selected,and antibacterial activity of these compounds was identified using in vitro antimicrobial techniques. Copyright ©2014 John Wiley & Sons, Ltd.Additional supporting information may be found in the online version of this article at the publisher’s website.

Keywords: molecular dynamics; MIC; pharmacophore modelling; QM/MM docking; Staphylococcus aureus

INTRODUCTION

Staphylococcus aureus (S. aureus) is a notorious infectiouspathogenic bacterium causing many infections, and the diseasecontrol has become a serious issue worldwide (Kuehnert et al.,2005). S. aureus causes a wide range of diseases from soft-tissueinfections to life-threatening infections such as toxic shocksyndrome, necrotizing pneumonia, and endocarditis. There hasbeen an alarming increase in resistance of numerous Gram-positivebacterial strains to different classes of antibiotics such as beta-lactams, macrolides, and quinolones; more recently, resistancehas also been observed to the glycopeptide vancomycin and theoxazolidinone linezolid (Chu et al., 1996 and Jones et al., 2002).Bacterial DNA could potentially serve as an attractive and noveltarget for the development of new antibiotics.DNA, as carrier of genetic information, has the ability to inter-

fere with transcription (gene expression and protein synthesis)and DNA replication, a major step in cell growth and division.The latter is central for pathogenesis. The ligand binding toDNA will target the important sequences that could potentiallyinterfere with bacterial RNA transcription and/or DNA replica-tion and kill the bacterial organism (Collado-Vides et al., 1991and Straney and Crothers, 1987). In S. aureus, DNA is one ofthe successive drug targets, by targeting the major and minorgrooves that several inhibitors have been succeeded. Still,90% of the researches are done by choosing protein structureas the target. Blocking DNA replication will be more apt forblocking bacterial load, and here, we have chosen S. aureus

DNA as the drug target, considering the importance of minorgroove regions. The recognition of DNA by small molecules(Dervan, 2001) is of special importance in the design ofnew drugs. There are three specific modes of interaction ofsmall ligands with DNA, namely, intercalation, minor groovebinding, and major groove binding. (Palchaudhuri andHergenrother, 2007).

When compared with other types of binding, minor groovebinders are one of the most widely studied class of compounds;it was characterized by a high level of sequence specificity andcontains a wide range of biological activities (Baguley, 1982).These types of binding compounds usually have the greater

* Correspondence to: Pitchai Daisy, Head of the Department, Bioinformatics centre(BIF), PG and Research Department of Biotechnology and Bioinformatics, HolyCross College (Autonomous), Tiruchirappalli-620002, Tamil Nadu, India.E-mail: [email protected]

a P. Vijayalakshmi, P. DaisyBioinformatics Centre (BIF), PG and Research Department of Biotechnologyand Bioinformatics, Holy Cross College (Autonomous), Tiruchirappalli620002, Tamil Nadu, India

b C. Selvaraj, S. K. SinghComputer Aided Drug Design and Molecular Modelling Lab, Department ofBioinformatics, Alagappa University, Karaikudi 630004, Tamil Nadu, India

c R. M. B. Shafreen, S. K. PandianDepartment of Biotechnology, Alagappa University, Karaikudi 630004, TamilNadu, India

Research Article

Received: 19 December 2013, Revised: 20 January 2014, Accepted: 21 January 2014, Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/jmr.2363

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binding affinity and high sequence specificity. Minor groovebinding compounds have been demonstrated for neutral,monocharged and multicharged ligands (Bailly and Chaires,1998). Minor groove binding drug–DNA interactions include acombination of hydrogen bond, hydrophobic and van derWaals contacts, and electrostatic interactions (Gallmeier andKönig, 2003).

The natural products distamycin and netropsin, which areoligoamides of pyrrole amino acids that bind to the minorgroove of DNA, have been known for 50 years to haveantibacterial and antiviral activity (Dervan, 2001). DNA-bindingmodel of these compounds give us the support to searches fornew compounds with similar interaction to DNA. In the presentstudy, we aimed to search the DNA minor groove bindingcompounds using ligand-based pharmacophore modelling.Pharmacophore studies are more cost-effective than experi-mental chemical screening of large databases. Already reported32 minor groove binding ligands were used for pharmacophore-based screening. These 32 compounds were designed based onthe distamycin A (Kaizerman et al., 2003).

MATERIALS AND METHODS

System configuration

All computational analyses were carried out on a Red Hat Linuxplatform running on an IBM-System K3200 M2 series with IntelXeon processor and 2GB of random-access memory.

Ligand preparation

Kaizerman et al., 2003, has reported 130 novel compounds,which have a tendency to bind with minor groove region ofDNA. DNA-binding capacity of these compounds were provedexperimentally using DNase I footprint method and in vivostudies. In our previous study (Vijayalakshmi et al., 2013), wechecked the activity of these 130 compounds against S. aureusDNA. From that results based on the docking score, the best32 compounds were selected for this study. Considering this,we have chosen the highest active profile compounds for thisresearch work. The 2D structures of the selected ligands weredrawn in a MarvinSketch programme (Marvin Draw 5.1.5), andthe 3D conversion and minimization were performed usingLigPrep 2.5 [optimized potentials for liquid simulations (OPLS)force field] incorporated in PHASE. Conformers were gener-ated using a rapid torsion angle search approach followed byminimization of each generated structure using the OPLS-2005 force field, with an implicit Generalized Born/SolventAccessibility (GB/SA) solvent model. A maximum of 1000 con-formers were generated for each structure using preprocessminimization of 100 steps and postprocess minimization of50 steps (Dixon et al., 2006a, 2006b). Each minimized con-former was filtered through a relative energy window of10 kcal/mol and a minimum atom deviation of 1.00 Å. Thisvalue (10 kcal/mol) sets an energy threshold relative to thelowest-energy conformer. Conformers having higher energythan the threshold were discarded. All distances between pairsof corresponding heavy atoms must be below 1.00 Å for thetwo conformers to be considered identical. This criterion isapplied only after the energy difference threshold and only iftwo conformers are within 1 kcal/mol (Reddy et al., 2012).

DNA preparation

The crystal structure of DNA gyrase in complex with DNA of S.aureus [Protein Data Bank (PDB) ID: 2XCS] was retrieved fromthe PDB. From that, complex DNA was separated and preparedusing MacroModel for optimization and minimization (Maestro9.2). Here, bond order of the residues was assigned, addition ofhydrogen atoms, and also, hydrogen bonding network isoptimized. The optimized model structure was minimized untilthe average root-mean-square deviation (RMSD) of the non-hydrogen atoms reached 0.3Å. OPLS-all-atom (AA) force fieldswere utilized for obtaining the optimized and minimized confor-mation of DNA (Jacobson et al., 2002).

Creation of pharmacophoric sites

The next step in developing a pharmacophore model is to use aset of pharmacophoric features to create pharmacophoric sites(site points) for all the 32 compounds. Selected ligand structuresare represented by a set of points in 3D space, which coincidewith various chemical features that may facilitate non-covalentbinding between the ligand and its target receptor (Korb et al.,2010). PHASE (version 3.4, Schrödinger, LLC, New York, NY) pro-vides a built-in set of six pharmacophoric features, hydrogenbond acceptor (A), hydrogen bond donor (D), hydrophobicgroup (H), negatively ionizable (N), positively ionizable (P), andaromatic ring (R). The rules that are applied to map the positionsof pharmacophoric sites are known as feature definitions, andthey are represented internally by a set of SMiles ARbitraryTarget Specification (SMARTS) patterns (SMARTS—language fordescribing molecular patterns). Each pharmacophoric feature isdefined by a set of chemical structure patterns.All user-defined patterns are specified as SMARTS queries and

assigned one of the three possible geometries, which definephysical characteristic of the site like point vector and group. Adefault setting having A, D, H, N, P, and R was used to createpharmacophoric sites (Bharate et al., 2013).

Finding a common pharmacophore and scoring hypothesis

Common pharmacophoric features were identified from a set ofvariants—a set of features that define a possible pharmacophoreusing a tree-based partitioning algorithm. The terminal size ofthe box was 1Å, which governs the tolerance on matching—the more closely related pharmacophores having smaller boxsizes. Common pharmacophoric hypotheses were generated byvarying the number of sites (nsites) and the number of matchingactive compounds (nact). We used nact = nact_tot initially (nact_tot isthe total number of active compounds in the training set); nsiteswas varied from seven to three until at least one hypothesiswas found and scored successfully (Tawari et al., 2008 andSteindl and Langer, 2004). The common pharmacophorichypotheses were scored by setting the RMSD value below 1.0,the vector score value to 0.5 and weighing to includeconsideration of the alignment of inactive compounds usingdefault parameters.

Pharmacophore-based database screening

Several common pharmacophoric hypotheses were obtained,from that, three different pharmacophoric hypotheses wereselected based on the fitness score. Selected hypotheses wereused as a template for pharmacophore-based screening

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(Sakkiah and Lee, 2012). The selected three pharmacophoreswere imported in find matches to hypothesis and screenedwith drug bank database. Similar pharmacophoric hypotheseswere screened from this database. The screening criteriainclude the distance matching at 2.00 Å, and fitness valuesshould not be less than 2.0. Using these criteria, thecompounds were screened from drug bank database (http://www.drugbank.ca/).One hundred compounds from eachpharmacophore were selected from drug bank database.Three hundred hit compounds that have common pharma-

cophoric features were docked into the DNA. The active site ofDNA was identified in our previous study (Vijayalakshmi et al.,2013). These sites were used in a grid generation step througha receptor grid generation programme from Glide (version 5.6)(Friesner et al., 2004). This receptor grid file was utilized forscreening of compounds from pharmacophoric hypothesis. Asthey contain large number of compounds, funnel-shapedfiltering with Glide [high-throughput virtual screening (HTVS),standard precision (SP) and extra precision (XP) docking] wasperformed in order to obtain the best compounds. We used 3different pharmacophores for screening the compounds, and100 compounds from each pharmacophore screening wereselected. Three hundred compounds from pharmacophorescreening were subjected to HTVS screening. From HTVSscreening, the top 50 compounds from each pharmacophorewere again redocked with SP docking. The redocking protocolwas repeated for the top 50 compounds from XP docking, andthe best 10 compounds from 3 pharmacophores were selectedfor further studies.

QM/MM methodology

For quantum mechanics/molecular mechanics (QM/MM) calcula-tions, we employed the QSite programme (QSite 2000) that wasconstructed through a tight coupling of Jaguar Suite (Jaguar,2000) for QM region and the IMPACT molecular modelling codefor MM region. Initially, docking was performed with Glide;conformations obtained from Glide were subjected to QPLD.Because DNA was charged, obtaining the docking conformationsusing charge-based docking algorithm (Jaguar 7.8) was foundmore significant. The Glide outputs of grid file and ligand con-formations were imported in QPLD. The selection of QM levelfor charge calculation is a trade-off between speed (fast) thatuses the 3–21G basis set, Becke-Lee-Yang-Parr (BLYP) functional,and ‘quick’ SCF accuracy level and accuracy that uses the 6–31G/LACVP⁄ basis set, B3LYP density functional, and ‘ultrafine’ Self-Consistent Field (SCF) accuracy level (iacc = 1 and iacscf = 2)(QPLD, 2013). The small molecules (ligands) were treated quan-tum mechanically, and the remainder of the system (DNA) wastreated as MM. This method applies Glide algorithm to gener-ate the best candidate poses for which ligand-docking polariza-tion was carried out with Jaguar.

Molecular dynamics study

Simulation studies were carried out for understanding thebehaviour of DNA minor groove binding ligands’ interaction indynamic movement against staphylococcal DNA using an AAforce field (OPLS-2005). Our purpose of simulation was toanalyse the ligand conformational changes in staphylococcalDNA and stability of ligand interactions in a dynamic environ-ment. We computed simulation studies in SPC model system,

constructed for DNA–ligand complexes. The water volume wasfit within orthorhombic box (covers the rectangular dimensionsof DNA and ligands) along with 0.15 Na+Cl in system as neu-tralizing components (Zhang et al., 2010). Simulations werecarried out through Desmond molecular dynamics (MD) package(Kevin et al., 2006). The DNA–ligand complexes were solvated inan orthorhombic simulation box with timescale of 5 ns.Simulation ensemble properties, viz, number of atoms, pressure,area, and timescale were considered, and so, NPAT was chosen.For maintaining the constant volume throughout the simulation,it was ensured that the density and pressure were correct duringthese simulations (Ikeguchi, 2004). The box size was fixed at theend of constant pressure equilibration to ensure the correctatom count (density of whole system). The distance betweenthe complex and wall of the box includes the spaces of 0.9-Ådistance. Temperature scale was maintained at 300 K for thewhole simulation using Nose–Hoover thermostats (Kleinermanet al., 2008), and for maintaining stable pressure, Martina–Tobias–Klein barostat method was used. The equations ofmotion were integrated using the multistep RESPA integratorwith an inner time step up of 2.0 fs.

In vitro antimicrobial activity

The selected compounds from in silico analysis were evaluatedagainst S. aureus (ATCC 33591) for their antimicrobial activity.The well-diffusion susceptibility test was carried out in Mueller–Hinton agar (MHA) (Himedia Laboratories, India) according tothe standard recommendations of the Clinical and LaboratoryStandards Institute (2006). From the overnight culture ofS. aureus, sub-culture was prepared in Todd–Hewitt broth (THB)until a turbidity of 0.5 McFarland (1 × 106 CFU/ml) was reached.The culture was uniformly swabbed over the surface of theMHA plates. Absorption of excess moisture was allowed to occurfor 10min. After absorption, the plates were punched to makethe well of 6-mm diameter with the help of sterile cork borer.Then, varying concentration of the compounds ranging from2 to 2000μg was loaded onto the wells. Further, the MHA plateswere incubated at 37°C, and the zone of inhibition wasmeasured after 24 h (Shafreen et al., 2011).

Determination of minimum inhibitory concentration (MIC)

The MICs of the compounds were determined based on amicrodilution method in 96-well microtiter plates as describedpreviously (Al-Bayati, 2008). The inoculums of bacterial strainswere prepared from 18-h broth culture at 37°C; culture sus-pensions were adjusted to a final density of 106 CFU/ml.Compounds were dissolved in Milli-Q and then in nutrientbroth to reach a final concentration of 500mg/ml. Serial two-fold dilutions were made in a concentration ranging from 1 to2000 μg/ml. The assay mixture was prepared by dispensing1% (5 μl) of the bacterial inoculum and 95 μl of THB supple-mented with varying concentration of the compounds intoeach well of the 96-well plate. The well with bacterial inoculum(1%) and THB without any compounds was determined as thecontrol well. The 96-well plate was incubated at 37ºC for 18 h,and the optical density was recorded using spectrometer(SpectraMax M3) at 600 nm. The lowest concentration thatformed complete inhibition of visible growth was recorded asMIC (Srinivasan et al., 2010).

IDENTIFICATION OF NEW MINOR GROOVE BINDER USING PHARMACOPHORE APPROACH

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http://www.drugbank.ca/

http://www.drugbank.ca/

RESULTS AND DISCUSSION

DNA is essential for several biological processes. Transcriptionand replication are the vital processes essential for the survivalof the living system. Transcription is responsible for the construc-tion of proteins, while replication produces copies of DNA. Thesevital processes can be targeted with the help of small-moleculeligands that bind with DNA (Chaudhauri and Hargenrother,2007). The investigation of interactions between DNA andDNA-binding agents is crucial to a deeper understanding of suchimportant biochemical processes as replication, repair, recombi-nation, and expression of genes. In the present study, we identi-fied the compounds that will interact with DNA usingpharmacophoric analysis, and their interaction was also studiedusing molecular docking studies.

Generation of common pharmacophore

The pharmacophore method is one of the major computer-aided drug discovery method; it has been widely used inthe virtual screening for retrieving hit compounds. Experi-mentally reported 32 compounds that had the high potencyto bind to the minor groove of S. aureus DNA were selectedto generate the common pharmacophoric hypothesis. Struc-tures of these 32 compounds were shown in SupplementaryFigure 1. Identification of pharmacophore model using exper-imentally reported compounds will provide on valued model,and using this model for screening can raise a potentialactive lead molecules.

Standard set of six pharmacophoric features, A, D, H, N, P, andR, was used to generate the pharmacophore model. Tree-basedpartition algorithm was used to identify the commonpharmacophore; these algorithm groups identified the similarpharmacophores according to their intersite distance. Commonpharmacophoric sites were selected from a set of variants andwith the option of create sites. Hypothesis generation was doneby find option in find pharmacophore model. Scoring function

was used to rank the identified five-featured pharmacophorichypothesis based on the alignment of site points and vectors,volume overlap, number of ligands matched, and relative confor-mational energy. In order to identify which pharmacophoricfeature compounds show more activity, we selected the ADHHR,ADHPR, and ADHRR (Figures 1, 2 and 3) hypotheses based on thescore value. Selected hypotheses alignments of active com-pounds were shown in Supplementary Figure 2. These hypothe-ses models have the common chemical group that is used tounderstand the special arrangement of structural features thatis used to inhibit the replication of DNA in S. aureus.

Pharmacophore-based virtual screening

Pharmacophore-based virtual screening was used to identify thenew lead compounds that have the same pharmacologicalproperties. Selected three different pharmacophore modelswere used as a template for screening of compounds from drugbank database. The criteria of fitness score >2 were helped toidentify the new compounds suitable for selected model.Although many compounds have the same pharmacologicalproperty, but having less fitness score will not be consideredfor further study. One hundred compounds were selected fromeach pharmacophore model; totally, 300 compounds wereselected for further HTVS screening studies.The virtual screening docking phase carries four different

phases (varying precisions and computational intensities), andin each phase, the best compounds were chosen for next phasebased on the scoring parameters. Compounds selected frompharmacophore screening were docked using the HTVS dockingprotocol. One hundred compounds that have good scoring andinteraction with DNA were selected from HTVS screening. Forfurther refinement, we placed these compounds for Glide SPdocking. Fifty compounds were filtered out from Glide SPdocking and further preceded for more precise Glide XP dockingstudy that was one of the most powerful and discriminativeprocedure, which took longer time to run than SP docking.

Figure 1. Pharmacophoric hypothesis ADHHR and distance between pharmacophoric sites (A°).



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Finally, we identified 24 ligands (Table 1) (11 compoundsfrom ADHHR pharmacophore, 4 compounds from ADHPRpharmacophore, and 9 compounds from ADHRR phar-macophore) that showed very good interaction to the activesites of DNA and had good scoring parameters. All thepharmacophoric hypotheses sites were present in our identifiedcompounds. Based on the docking score, energy, H-bond inter-action, and best five compounds were selected. Results ofdocking studies evaluated the binding mechanism of newlyidentified ligands with DNA, and interestingly, involvement ofguanine-cytosine content was rich in these interactions. Fromthis DNA–drug interaction, we exposed that DC and DG mainlyinteract with N, H, and O atom of ligands (Figure 4). In order to

identify the charge involvement in scoring function and alsoimprove the accuracy of the docking, the selected compoundswere passed to QPLD docking.

QPLD docking

The spectacular reason behind using the QPLD for docking wasmost of algorithm will not support the DNA docking, and so,QPLD performs with polarization of ligand charges (QM) andminimization of DNA molecules (MM). The partial charges onthe atoms of the ligand were then replaced with charges derivedfrom QM calculations on the ligand in the field of the receptorfor each ligand–receptor complex, and Glide redocked each of

Figure 2. Pharmacophoric hypothesis ADHPR and distance between pharmacophoric sites (A°).

Figure 3. Pharmacophoric hypothesis ADHRR and distance between pharmacophoric sites (A°).



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the ligands with updated atom charges and returned the mostenergetically favourable pose, and so, DNA docking was possiblein its predicted binding pockets.

Analysis of QPLD results showed that selected five compoundshad high docking score, energy and H-bond interaction thanGlide docking. When investigated the reason behind this differ-ence in scoring and interaction, we have noticed that XP dockingalgorithm was not fully utilizing the atomic charges whereasQPLD generated the atomic partial charges accurately usingDFT calculations and performs the interactions.

Results of the docking studies indicated that more negativevalues of G-score indicate that good binding of ligand withreceptor. Selected best five compounds showed a good interac-tion in terms of G-score. Compounds that have the G-score morethan �7 K/cal were selected as the top compounds. H-bondinteraction was also higher than Glide docking. The involvementof H-bond interaction helped the complex to achieve theestablished conformation of the complex structure. Selectedcompounds had more than two H-bond interaction with DNA;it is indicated that selected compounds have high affinitytowards the DNA. In order to find out the atomic interaction,the selected compounds complexes were analysed, which indi-cates most of the compounds correctly bound to the DNA minorgroove region [DG 9 (OH), DG 10 (NH), and DC 11 (NH)].

Best compounds were selected from three differentpharmacophoric hypotheses (sparfloxacin from ADHPR hypothe-sis, fluconazole from ADHRR hypothesis, and tamsulosin andrifaximin from ADHHR hypothesis). Sparfloxacin from ADHPR

hypothesis was already reported to treat the bacterial infections.It also has the capacity to inhibit the DNA gyrase, a bacterialtopoisomerase. DNA gyrase is an essential enzyme that controlsDNA topology and assists in DNA replication, repair, deactiva-tion, and transcription. Our theoretical results also indicate thatsparfloxacin has good antibacterial activity. Cyclopropyl moietyof sparfloxacin is responsible for the activity against S. aureus.This indicates that our theoretical prediction was close to theexperimental results.Fluconazole from ADHRR hypothesis is a triazole and also

prescribed as an antifungal drug. In the present study, we iden-tified that it also has an antibacterial activity against S. aureus.

Table 1. Selected compounds from three differentpharmacophoric hypotheses

ADHHR pharmacophore

Serial Number Compound ID Drug name

1a Domperidone1b Alfuzosin1c Metoclopramide1d Aripiprazole1e Bicalutamide1f Glipizide1g Rifaximin1h Atorvastatin1i Tamsulosin1j Trichlormethiazide1k Dutasteride

ADHPR pharmacophore2a Amisulpride2b Methotrexate2c Doxycycline2d Sparfloxacin

ADHRR pharmacophore3a Indapamide3b Olanzapine3c Bosentan3d Torasemide3e Fluconazole3f Lansoprazole3g Rabeprazole2h Riboflavin3a Indapamide

Figure 4. Molecular interactions of DNA and top-ranked compoundsthrough H-bond interactions.



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Tamsulosin fromADHHRhypothesis is a sulfamoylphenethylamine-derivative alpha-adrenoceptor blocker with enhanced specificityfor the alpha-adrenoceptors of the prostate, which is commonlyused to treat benign prostatic hyperplasia. The results of the cur-rent study showed that tamsulosin also possesses the antibacterialactivity against S. aureus.Rifaximin from ADHHR hypothesis is a structural analogue of

rifampin and a non-systemic gastrointestinal site-specific antibi-otic. It is used to treat diarrhea caused by E. coli.From these results, we noticed that compounds belong to the

ADHPR hypothesis showed good interaction compared withother compounds. Tables 2 and 3 represent the docking score,docking energy and QPLD score, QPLD energy and their atomicinteraction of the top five compounds. Structures of selectedcompounds were shown in Figure 5.

Molecular dynamics studies on DNA–compounds complex

The dynamic behaviour of selected five compound complexeswere analysed using Desmond MD simulation. MD study wasconducted with the aim to find the interaction betweenStaphylococcus DNA and sparfloxacin, tamsulosin, rifaximin,flucanazole and doxycycline molecules. The RMSD graph plottedin Figure 6 indicated the result of dynamic behaviour of selectedligands from the initial position to final position throughout thesimulation time. The stability of the complex was compared byusing the mean variation plot for sparfloxacin, tamsulosin,rifaximin, flucanazole and doxycycline as 1.66, 2.07, 1.95, 1.65and 1.83 Å, respectively. Overall, the mean variations (Figure 7)

Table 2. Molecular docking results via XP docking and QM/MM (G-score, Glide energy and number of hydrogen bonds)

SerialNumber

Compoundname

Glide score(kcal/mol)

Glide energy(kcal/mol)

QPLD score(kcal/mol)

QPLD energy(kcal/mol)

1 Sparfloxacin �6.368641 �50.279786 �11.9244231 �62.823902092 Flucanazole �6.490426 �50.38759 �11.3173409 �62.524307113 Tamsulosin �6.639568 �31.81849 �10.39236146 �52.144133454 Rifaximin �5.430071 �40.647536 �9.830969207 �55.848534195 Doxycycline �5.68239 �48.541645 �8.701516368 �50.29156782

Table 3. Interacting atom notifications of H-bond donor andacceptor in QM/MM docking

Compoundname

Interactionbehaviour

H-bonddonor

H-bondacceptor

Rifaximin DC 4 (O)–NH Li (NH)–O DNA (O)DC 11 (H)–O DNA (H)–O Li (O)

Tamsulosin DG 10 (O)–NH Li (NH)–O DNA (O)DC 11 (O)–NH Li (NH)–O DNA (O)

Flucanazole DG 9 (H)–N DNA (H)–N Li (N)DG 10 (H–N DNA (H)–N Li (N)DC 11 (O)–H Li (H)–O DNA (O)

Sparfloxacin DG 9 (O)–H Li (H)–O DNA (O)Doxycycline DG 8 (O)–NH Li (H)–O DNA (O)

DG 9 (H)–O DNA (H)–O Li (O)DC 12 (H)–O DNA (H)–O Li (O)

Figure 5. Structure of selected five compounds.

Figure 6. RMSD values of ligand binding with DNA on the result ofpharmacophore screening.



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showed that all the compounds lie within 2.1-Å deviations fromits original position. These five compounds showed active move-ment, good interaction and good poses according to the DNAdynamic changes, and so, modification of hydrogen bonds wasviable throughout the simulation. The average hydrogen bondinvolvement in total simulation was represented in Figure 8.The hydrogen bond involvement in each nanosecond of simula-tion was indicated in Supplementary Table S1. The mean varia-tion plot of hydrogen bond interactions provides sparfloxacin,tamsulosin, rifaximin, flucanazole and doxycycline values of 4.9,4.6, 5.7, 3.1 and 3.9, respectively. This clearly showed that allthe screened compounds have unique strong bonds for DNA–ligand interactions.

In vitro antimicrobial activity

Antimicrobial activity test was performed for 24 compounds(Table 1) that were selected from 3 different pharmacophores(ADHHR, ADHPR, and ADHRR); out of 24 compounds, only 12compounds (1e–1j, 2a–2d, 3e and 3h) showed significantantibacterial activity against S. aureus. The zone of inhibition(mm) measured for each compound against S. aureus wasrepresented in Table 4. The maximum inhibition zones wereobserved between 10 and 36mm for the compounds usedagainst S. aureus. Comparative analysis revealed that thecompounds under the ‘ADHPR’ showed maximum inhibitionwhen compared with the ‘ADHRR’ and ‘ADHHR’ pharmacophoricgroups. Sparfloxacin (2d) from ADHPR group was recorded withthe highest inhibitory effect of 36-mm (200mg/ml) zone ofclearance against S. aureus. ADHHR pharmacophoric featuredcompounds show high activity when compared with the‘ADHRR’. Thus, comparative analysis of the pharmacophoricgroups demonstrated that among the three pharmacophorichypotheses, ADHPR shows good antimicrobial activity, andcompounds under the ADHRR showed the least inhibitory effectagainst S. aureus.

Determination of minimum inhibitory concentration

Antimicrobial susceptibility test was carried out for the four (twofrom ADHHR, one from ADHPR, and one from ADHRR) bestcompounds that had a maximum zone of clearance comparedwith the other compounds. The MIC of the compounds variedfrom 1 to 20-mg/ml concentration (Table 5). Among them,compound 2d (sparfloxacin) showed very strong activity againstS. aureus even at 1-μg/ml concentration that is in very strongbiological activity range <10μg/ml (O’Donnell et al., 2010). Thecompounds flucanazole, tamsulosin and rifaximin showedstrong (15μg/ml), moderate (200μg/ml) and no activity(2000μg/ml), respectively. Among the tested compounds, thetwo compounds 2d and 3e were identified with promisingantimicrobial activity against S. aureus. The results were similarto the currently used antibiotics oxacillin, vancomycin for thetreatment of methicillin-resistant S. aureus (Lee et al., 2003).The development of drug against S. aureus is one of the mostimportant to prevent the serious infection caused by this bacte-rium, so the present findings concluded that the compoundssparfloxacin and fluconazole can be explored further as a thera-peutic alternative for S. aureus-related infections.

CONCLUSION

Gram-positive and Gram-negative bacteria are common causesof serious diseases such as pneumonia and complicated skininfections. Most available antibiotics are resistant to the bacteria.There is an urgent need to search compounds with novelmechanism of action.DNA is a key target for the delivery of drugs because it affects

the replication, which is necessary for the growth of bacteria. Inthe present study, we successfully employed pharmacophore

Figure 7. Mean variation plot of RMSD of ligand bound DNA simulation.

Figure 8. Mean variation plot of average hydrogen bonds in ligand–DNA interaction simulation.

Table 4. Antibacterial activity of selected compounds

Compound Name ofcompounds

MRSA diameter of inhibitionzone (mm) (200mg/ml)

1e Bicalutamide 201f Glipizide 181g Rifaximin 331h Atorvastatin 221k Dutasteride 191j Tamsulosin 322a Amisulpride 202b Methotrexate 152c Doxycycline 252d Sparfloxacin 363e Fluconazole 183h Riboflavin 10

Table 5. Minimum inhibitory concentration for best fourcompounds

Compound MIC (μg/ml)

1g (Rifaximin) 20001i (Tamsulosin) 2002d (Sparfloxacin) 13e (Flucanazole) 15



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model for identification of new compounds that interact withDNA. Three different pharmacophoric hypotheses (ADHHR,ADHPR, and ADHRR) were used to screen the compounds fromdrug bank database. Based on the fitness score, compoundswere selected and subjected to HTVS screening. Finally, 24compounds were selected and subjected to QM/MM dockingstudies. The docking study was used to identify the bindingmechanism of the compounds. Antibacterial activity and MICof selected compounds were carried out against S. aureusbacteria. Results of in silico and in vitro showed that thecompounds contained the ADHPR hypothesis showed verygood activity against S. aureus compared with otherpharmacophoric hypotheses.

Future perspectives

Further studies will be carried out for the compounds selectedthrough in silico and in vitro analysis for their DNA-bindingstudies using in vitro analysis.

Acknowledgements

The authors PV and PD gratefully acknowledge the BiotechnologyInformation System, Department of Biotechnology, Ministry ofScience and Technology, Government of India, for research fundingand fellowship. The authors CS and SKS thankfully acknowledgethe Council of Scientific Industrial Research for research fundingand fellowship grants [ref. no: 37(1491)/11/EMR-II].

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Ligand-based pharmacophore modelling and screening of DNA minor groove binders targeting Staphylococcus aureus