Exploring insights for virulent gene inhibition of multidrug resistant Salmonella typhi , Vibrio cholerae , and Staphylococcus areus by potential phytoligands via in silico screening

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Exploring insights for virulent gene inhibition ofmultidrug resistant Salmonella typhi, Vibrio cholerae,and Staphylococcus areus by potential phytoligands viain silico screeningSinosh Skariyachan a , Nisha Jayaprakash a , Navya Bharadwaj a & Rajeswari Narayanappa aa R&D Centre, Department of Biotechnology , Dayananda Sagar College of Engineering ,Bangalore , 560 078 , Karnataka , IndiaPublished online: 23 Jul 2013.

To cite this article: Journal of Biomolecular Structure and Dynamics (2013): Exploring insights for virulent gene inhibitionof multidrug resistant Salmonella typhi, Vibrio cholerae, and Staphylococcus areus by potential phytoligands via in silicoscreening, Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2013.819787

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Exploring insights for virulent gene inhibition of multidrug resistant Salmonella typhi, Vibriocholerae, and Staphylococcus areus by potential phytoligands via in silico screening

Sinosh Skariyachan, Nisha Jayaprakash, Navya Bharadwaj and Rajeswari Narayanappa*

R&D Centre, Department of Biotechnology, Dayananda Sagar College of Engineering, Bangalore 560 078, Karnataka, India

Communicated by Ramaswamy H. Sarma

(Received 22 April 2013; final version received 24 June 2013)

In our recent studies on prevalence of multidrug resistant pathogens in Byramangala reservoir, Karnataka, India, weidentified Salmonella typhi, Staphylococcus aureus, and Vibrio cholerae which had acquired multiple drug resistance(MDR) and emerged as superbugs. Hence, there is a pressing demand to identify alternative therapeutic remedies. Ourstudy focused on the screening of herbal leads by structure-based virtual screening. The virulent gene products of thesepathogens towards Kanamycin(aph), Trimethoprim(dfrA1), Methicillin (mecI), and Vancomycin (vanH) were identified asthe probable drug targets and their 3D structures were predicted by homology modeling. The predicted models showedgood stereochemical validity. By extensive literature survey, we selected 58 phytoligands and their drug likeliness andpharmacokinetic properties were computationally predicted. The inhibitory properties of these ligands against drug targetswere studied by molecular docking. Our studies revealed that Baicalein from S. baicalensis (baikal skullcap) and Luteolinfrom Taraxacum officinale (dandelion) were identified as potential inhibitors against aph of S. typhi. Resveratrol from Vitisvinifera (grape vine) and Wogonin from S. baicalensis were identified as potential inhibitors against dfrA1 of S. typhi.Herniarin from Herniaria glabra (rupture worts) and Pyrocide from Daucus carota (Carrot) were identified as the bestleads against dfrA1 of V. cholerae. Taraxacin of T. officinale (weber) and Luteolin were identified as potential inhibitorsagainst Mec1. Apigenin from Coffee arabica (coffee) and Luteolin were identified as the best leads against vanH ofS. aureus. Our findings pave crucial insights for exploring alternative therapeutics against MDR pathogens.

Keywords: multidrug resistant pathogens; virulent genes; homology modeling; virtual screening; potential inhibitors;herbal leads

1. Introduction

Antibiotics are the most common chemotherapeuticsubstances used against human illness, agricultural pests,and livestock (Bao, Maruya, Snyder, & Zeng, 2012;Hagedorn et al., 1999). Residues from many ecologicalniches may contain these drugs or drug resistance genesthat can contaminate natural ecosystems. One of themajor impacts of antibiotic release in natural environ-ments is the emergence of multidrug resistant (MDR)pathogens. Most of the bacteria acquired multipleresistances to routinely used antibiotics and emerged asMDR and extensively drug-resistant (XDR) pathogens.They not only require longer and more complextreatments, but are more exorbitant to diagnose and treat.The molecular mechanisms for antibiotics’ resistancesamong bacterial populations are quite complex anddiverse, which developed resistance to many convention-ally used antibiotics, including the strongest β- lactamgroups (Martínez, 2012; Walsh, Weeks, Livermore, &

Toleman, 2011). Hence, the therapeutic values of antimi-crobial drugs are constantly diminishing, which havesignificant global attention (Davies & Davies, 2010).

The mechanisms of drug resistance could be acquiredand transmitted horizontally through the conjugation of aplasmid or by acquisition of drug resistance genes andtransposable elements. Recently, new mechanisms ofresistances have resulted in the simultaneous developmentof resistance to several antibiotic classes creating verydangerous multidrug-resistant (MDR) pathogens knownas “superbugs” (Maclean, Hall, Perron, & Buckling,2010). The overuse and misuse of these chemotherapeuticagents in pharmaceutical and food industry are one ofthe main factors for drug resistance (Giedraitienė,Vitkauskienė, Naginienė, & Pavilonis, 2011).

By considering all these socioenvironmental issues,there is a pressing need for screening novel leadmolecules. The new approaches which have to be imple-mented include: identification of new molecular markers,

*Corresponding author. Email: [email protected]

Journal of Biomolecular Structure and Dynamics, 2013http://dx.doi.org/10.1080/07391102.2013.819787

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identification of novel lead molecules for antibioticdevelopment, identification of novel treatment methods,and identification of a sample bacteria and its susceptibil-ity to antibiotic treatment. There is also a need for adeeper understanding of the mechanisms by whichbacteria gain resistance to antibiotics, which will aidin identifying novel targets for drugs or treatment(Daniels, 2011).

Our previous studies on characterization ofpathogenic bacteria from water samples collected fromByramangala reservoir, a major source of drinking waterfor urban and rural residents in Bangalore District,Karnataka, India showed that Salmonella typhi, Staphylo-coccus aureus, and Vibrio cholerae acquired highdrug resistance against conventional therapeutic drugs.S. aureus showed resistance to conventionally used drugssuch as Vancomycin, Methicillin, Oxacillin, Tetracycline,etc. Similarly, Salmonella sps showed resistance toTrimethoprim, Kanamycin, Cefoperazone, Cefotaxime,Cefixime, Moxifloxacin, Piperacillin/Tazobactam, Co-trimoxazole, Levofloxacin, Trimethoprim, and Ceftazi-dime (Skariyachan et al., 2012). These pathogens areresponsible for adverse health implications in humansand animals. The drug resistance seems mainly due tomutations, acquisition of drug resistance genes, transferof drug-resistant plasmids, and horizontal gene transfer(Lupo, Coyne, & Berendonk, 2012; Nazir, Abraham, &Islam, 2012). Hence, there is a necessity for alternativetherapeutic strategies against such kind of pathogens. Apromising approach is to inhibit the expression ofdrug-resistant genes by potential inhibitors. The inhibi-tions of virulent gene products by natural remedies haveprofound scope in health care management. Structure-based virtual screening offers a significant application toscreen such kind of natural substances, especially theactive compounds present in many medicinal herbs. Thevirulent functions of drug-resistant genes could beprevented by such kind of lead molecules. This couldgive an alternative therapeutic insight against MDR andXDR pathogens when most conventional therapeuticsubstance seems to have failed. Hence, we aimed toevaluate the inhibitory properties of various herbal leadmolecules against virulent gene products of these threepathogenic bacteria by structure-based virtual screening.The new lead molecules were chosen based ontraditional knowledge of medicine as their use is wellestablished and widely acknowledged to be safe andeffective.

2. Materials and methods

2.1. Identification of virulent gene products for drugresistance

The genes responsible for multidrug resistances ofS. typhi, S. aureus, and V. cholerae toward present

generation drugs such as Kanamycin, Trimethoprim,Methicillin, and Vancomycin were retrieved from EntrezGene database (Maglott, Ostell, Pruitt, & Tatusova,2011). The amino acid sequences of all gene productswere retrieved from Uniprot database (Apweiler et al.,2004). The proteins responsible for drug resistancetowards Kanamycin (UniProt: E2D0Y8), Trimethoprim(UniProt: A7DY50, G7TU76), Methicillin (UniProt:P68261), and Vancomycin (UniProt: 7BWD8) wereretrieved in FASTA format.

2.2. Computer aided molecular modeling andvalidation

The three-dimensional structure of the selected drugtargets is essential for structure-based virtual screeningand drug designing. The structure of Methicillin-resistantprotein is available in its native form (PDB: 1OKR)(García-Castellanos et al., 2003). However, the three-dimensional structures of drug-resistant gene productsfor Kanamycin, Trimethoprim, and Vancomycin are notavailable in their native form. Hence, the structures ofthese virulent proteins were predicted by computer-aidedhomology modeling.

The amino acid sequences of the virulent proteins forKanamycin of S. typhi, Trimethoprim of S. typhi andV. cholerae, and Vancomycin of Staphylococcus areuswere retrieved from UniProt database. The best templatestowards homology modeling of all selected genes wereretrieved by PSI-BLAST (Altschul et al., 1997). The besthomologous templates were selected based on thepercentage of identity, similarity, alignment scores, andE-value. The three-dimensional structural coordinates ofall the best templates were retrieved from Protein DataBank (Berman, Henrick, Nakamura, & Markley, 2007).The homologous relationship between the targets andtemplates were further confirmed by multiple sequencealignment and phylogenetic analysis (Supplementarymaterials, Figure 1) by T-COFFEE (Notredame, Higgins,& Heringa, 2000).

The three-dimensional structures of Kanamycin-,Trimethoprim-, and Vancomycin-resistant proteins werepredicted by comparative modeling by Modeller 9v11(Eswar, Eramian, Webb, Shen, & Sali, 2008). Thestructural alignment between the target and templatewere performed and the initial model was created. Themodeled proteins were visualized using UCSF-Chimera(Pettersen et al., 2004) and refined by various computa-tional biology tools. The stereochemistries of modeledproteins were validated by ProCheck (Laskowski,Rullmann, MacArthur, Kaptein, & Thornton, 1996). Theoverall quality factors of nonbonded interactions betweendifferent atoms were calculated by ERRAT (Colovos &Yeates, 1993). The backbone fragments of the modeledprotein were threaded against the template chains byDaliLite (Holm, Kääriäinen, Rosenström, & Schenkel,

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2008) and root mean square deviation (RMSD) wascalculated. The hypothetical model was deposited to Pro-tein Model Data Base (Castrignanò, De Meo, Cozzetto,Talamo, & Tramontano, 2006). These modeled proteinswere used as probable drug targets for further analysis inour study.

2.3. Screening and selection of phytoligands

We had identified about 58 phytoligands after extensiveliterature survey and they were screened based on druglikeliness and pharmacokinetics prediction by PreAD-MET tool (Veber et al., 2002). The selected phytoligandsare shown in supplementary materials, Table 1. Thethree-dimensional structures of these phytoligands wereretrieved from ChemSpider (Hettne et al., 2010) data-base. The drug likeness properties of the phytoligandswere computationally predicted by Lipinski’s rule of five(Giménez, Santos, Ferrarini, & Fernandes, 2010),Comprehensive Medicinal Chemistry (CMC)-like rule(Ajay, Walters, & Murcko, 1998), MDDR (MDL DrugData Report)-like rule (Frimurer, Bywater, Naerum,Lauritsen, & Brunak, 2000), Lead-like Rule (Opreaet al., 2007) and World drug index (WDI)-like rule(Wagener & van Geerestein, 2000) available in PreAD-MET package. Those lead molecules that qualified therules were selected for docking studies. Subsequently,pharmacokinetic features such as human intestinalabsorption, Caco2 (heterogeneous human epithelial colo-rectal adenocarcinoma) cell permeability, Madin-Darbycanine kidney (MDCK) cell permeability, skin perme-ability, and blood brain barrier penetration were alsopredicted by PreADMET (Veber et al., 2000). In additionto ADME (adsorption, distribution, metabolism, andexcretion) prediction, toxicity was also predicted toanalyze carcinogenicity in mouse and rat models bycomputer-aided approach. Similarly, the mutagenecity ofthe selected lead molecules was predicted using Amestest by PreADMET.

2.4. Structure-based drug design

The study of receptor–ligand interactions is the mainconcept behind structure-based drug designing (Zhang &Lai, 2011). The inhibitory properties of the selectedligands against probable drug targets were studied bymolecular docking by AutoDock Vina (Trott & Olson,2010). This program was executed to simulate the realtime molecular interactions of the receptor and ligands.The active site of the target was identified based on thestructural and chemical properties by Lamarckian geneticalgorithm and AutoGrid program. Rigid body dockingstudies were carried out in which the function scoring,energy calculations, and ranking of the best conforma-tions were carried out. The electrostatic interactions weredetermined by interpolating the values of the electrostaticpotential and multiplying by the charge on the atom.

Around 2500,000 binding poses were identified by 10iterations and the best binding poses were selected basedon the lowest energy calculations and the number ofhydrogen bonds stabilizes the interaction.

3. Results and discussion

Our previous pilot studies on characterization ofpathogenic bacteria from water samples collected fromByramangala reservoir revealed that most isolates werefound to be drug resistances to conventionally used anti-biotics (Skariyachan et al., 2013). From our studies, itwas evident that S. typhi, S. aureus, and V. cholerae haveshown maximum drug resistances. Hence, we haveselected these organisms for present studies. S. typhi wasfound to be resistant against the conventionally used anti-biotics such as Trimethoprim and Kanamycin. V. choleraealso showed drug resistance to Trimethoprim. S. aureusshowed multidrug resistance to present generationdrugs such as Methicillin and Vancomycin (Skariyachanet al., 2012). Our previous results clearly indicate thatthese bacteria became resistant to conventionally useddrugs and emerged as superbugs (multidrug-resistantorganisms). Recent studies reported that many presentgeneration drugs seem to have failed against superbugswhich have created major health implications worldwide(Nazir, Abraham, & Islam, 2012). Hence, in the presentstudy, we have focused to inhibit the virulent functionsof the drug-resistant genes by novel phytoligands. Ourprevious studies proved the utility of computer-aidedvirtual screening toward many multidrug-resistantpathogens (Skariyachan et al., 2012). Several herbal sub-stances are known to have vital inhibitory propertiesagainst various drug targets of many gastrointestinalpathogens (Eumkeb & Chukrathok 2013; Eumkeb,Siriwong, & Thumanu, 2012; Rúa et al., 2012; Schrader,2010).

The genes responsible for multidrug properties of thementioned organisms were screened from Entrez Genedatabase and the protein sequences of these geneswere retrieved from Uniprot database. These geneswere screened based on literature studies, where the genesequence coding for the MDR properties of thesame strains were already reported (Chen et al., 2010;Hiramatsu, Asada, Suzuki, Okonogi, & Yokota, 1992;Martínez et al., 2007; Reimer et al., 2011; Weigel et al.,2003). Aminoglycoside phosphotransferase (aph; UniprotID: E2D0Y8) of 271 amino acids length, virulent proteinfor Kanamycin resistance (Chen et al., 2010), anddihydrofolate reductase (dhfr; Uniprot ID: A7DY50) ofsequence length 152 amino acids, responsible factor forTrimethoprim resistance of S. typhi (Martínez et al.,2007), were selected. Similarly, dihydrofolate reductasetype I (dfrA1; Uniprot ID: G7TU76), virulent factor forTrimethoprim resistance, of 157 amino acids from

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V. cholerae (Reimer et al., 2011) were selected. Methicil-lin-resistant gene (Mec1; Uniprot ID: P68261) of 123amino acids (Hiramatsu et al., 1992) and Vancomycin-resistant gene (VanH; Uniprot ID: Q7BWD8) of 322amino acids from S. aureus (Weigel et al., 2003) were alsoselected. The three-dimensional structure of aminoglyco-side phosphotransferase, dihydrofolate reductase, andvancomycin-resistant proteins are not reported in any ofthe structural database. Hence, homology modeling wascarried out to predict the three-dimensional structure ofthese proteins by Modeller 9v11. The tool PSI-BLAST

was used to identify the best templates for modeling.Further, multiple sequence analysis was performed byT-COFFEE and the phylogenetic characterization wascarried out by NJ plot. This step is important to identifythe best templates for modeling as the templates sharedcommon evolutionary origin with the targets in a phyloge-netic tree. The features of the best homologous templatesare shown in Table 1.

The structural alignments between each of the targetwith their template showed that the qualities of allmodels are reliable. The structure-annotated pairwise

Table 1. Identification of the best homologous templates for homology modeling of virulent proteins.

TargetUniprotID Organism Template Organism

PDB IDandChain Length

Percentageof Identity

E-value

Secondarystructure

Resolutionfactor ofthe crystalstructure

(Å)

aph E2D0Y8 S. typhi Aminoglycosidephosphotransferase

Acinetobacterbaumannii

4EJ7_A 292 99 0 61%helices

2.29

11% sheetdhfr A7DY50 S. typhi Dihydrofolate

reductaseStreptococcuspneumoniae

3IX9_A 190 31 1e-13

20%helices

1.95

30% sheetvanH Q7BWD8 S. aureus Lactate

dehydrogenaseLactobacillushelveticus

2DLD_A 337 35 1.5e-48

38%helices

2.7

16% sheetdfrA1 G7TU76 V.

choleraeDihydrofolatereductase

S. aureus 3IX9_A 190 82 1e-21

20%helices

1.8

30% sheet

Figure 1. (a) Three-dimensional model of Aminoglycoside 3'-phosphotransferase type I (aph) of S. typhi. (b) Ramachandran plot of themodel showed that 94.9% residues are in most favored region [A, B, L] and 5.1% residues are in additional allowed region [a, b, l, p].

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sequence identity between targets and best templatesgenerated by DaliLite revealed the good quality of themodel. (Supplementary materials, Figures 1–4). The rootmean square deviation (RMSD) values of structuralsuperposition between models, aminoglycoside phospho-transferase, dihydrofolate reductase (S. typhi), vancomy-cin resistance protein, and dihidrofolate reductase type I(V. cholerae) were calculated and the best templates werefound to be 2, 1.6, 1.6, and 1.1Å, respectively. RMSDstatistics are generally used to monitor the degree ofsimilarity between two optimally superposed protein

three-dimensional structures (Bennett, 2008) and assessthe quality of the model. The Z scores of each modelwere determined by DaliLite (Holm et al., 2008) werefound to be acceptable (Z score was 41.9 for aph, 44.7for dhfr, 44.7 for vanH and 27.7 for dfrA1) in compari-sons with experimental structures, which implies goodquality models. The Z scores of our models are reliable,which reveals the accuracy and native folds of proteins(Carugo, 2007).

The hypothetical models were further refined by vari-ous stereochemical analyses. The Ramachandran plot of

Figure 2. (a) Three-dimensional model of dihidrofolate reductase (dhfr) of S. typhi. (b) Ramachandrn plot of the model showed that91.7% residues are in most favored region [A, B, L] and 8.3% residues are in additional allowed region [a, b, l, p].

Figure 3. (a) Three-dimensional model of Dihydrofolate reductase type 1(dfrA1) of V. cholerae. (b) Ramachandrn plot of the modelshowed that 92.7% residues are in most favored region [A, B, L] and 7.3% residues are in additional allowed region [a, b, l, p].

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aminoglycoside phosphotransferase (aph) of S. typhirevealed that 94.9% of residues in the most favoredregion and 5.1% residues are additional allowed regions(Figure 1). The overall quality factor of aph identified byERRAT was found to be 87.79%. The hypotheticalmodel of dihydrofolate reductase (dhfr) of S. typhi alsohas good stereochemical quality, which indicates 91.7%

of residues in the most allowed region and 8.3% residuesare additional allowed regions (Figure 2). However, wehave observed errors in ERRAT plot and the patterns ofnonbonded atomic interactions were found to be 40.97%.Similarly, Ramachandran plot of dihydrofolate reductasetype I (dfrA1), drug target of V. cholerae, indicated that92.7% of residues are in the most favored region and

Figure 4. (a) Three-dimensional model of Vancomycin resistance protein (vanH) of S. aureus. (b) Ramachandrn plot of the modelrevealed that 91.0% residues are in most favored region [A,B,L] and 8.3% residues are in additional allowed region [a, b, l, p].

Figure 5. Inhibitory properties of potential phytoligands toward aminoglycoside 3'-phosphotransferase type I (aph) of S. typhiidentified by molecular docking. Ligands and interacting residues are shown in stick figures (a) Phytoligand Baicalien interacted withmodeled aph protein at Thr 99 and Thr 98 by the formation of two hydrogen bonds (binding energy �6.39 Kcal/mol). (b) Interactionof Luteolin with aph is stabilized by two hydrogen bonds, which are shown in the figure. The interacting amino acids are found to beSer 10 and Asn 88 (binding energy �6.42 Kcal/ mol).

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Table 2. Docking interactions between aminoglycoside phosphotransferase (aph), virulent factor for Kanamycin resistance, anddihydrofolate reductase (dhfr), virulent factor for Trimethoprim resistance, of S. typhi, and the selected lead molecules. The bindingenergy (Kcal/mol) and interacting residues are shown in the table. Baicalin and Luteolin against aph and Resveratrol and Wogoninagainst dhfr were identified as the best inhibitors as these liagnds showed minimum binding energy and more number of hydrogenbonds compared to other lead molecules. The best ligands are highlighted.

Ligand

Interaction with Aminoglycoside phosphotransferase(aph) Interaction with dihydrofolate reductase (dhfr)

BindingEnergy (Kcal/

mol)

Number ofhydrogenbonds

Amino acidinteracting withligand

Binding energy(Kcal/mol)

Number ofhydrogenbonds

Amino acidinteracting withligand

Allicin �6.17 2 Ile101, Thr98 –3.43 1 Gly94Apigenin �5.14 2 Thr98, Thr99 –6.43 1 Glu23Baicalein �6.39 2 Thr 98, Thr 99 –5.29 2 Ser92, Ala3Borneol �5.1 1 Asp216 –4.34 2 Ser92, Tyr98Deoxyhexose �5.41 0 – –5.43 1 Glu23Diosphenol �3.49 0 � �4.29 2 Ser92; Asn68Esculetin �4.36 1 Gly218 �5.34 2 Ser92;Tyr98Eugenol �6.12 0 � �1.64 1 Glu67Fraxetin �4.57 0 � �0.87 1 Gly94Gallic Acid �3.15 2 Asp67, Asp216, �5.63 1 Ser92Geraniol �4.64 1 Arg148 �4.25 2 Tyr98, Ser92Geranyl Acetate �4.95 2 Asp198, Asp216 �5.46 1 Asn68Herniarin �5.07 1 Gly218 �5.07 1 Glu23Isohamnetin �4.7 1 Arg148 �4.77 0 0Kaempferol �5.54 1 Gln35 �5.08 1 Met1Ledol �2.88 2 Asp67, Arg148 �3.48 1 Met 46Limonene �4.25 1 Arg148 �3.51 2 His109, Val89Linalool �4.6 2 Asp67, Phe271 �4.01 2 Ser92, Ser92Linalyl Acetate �4.77 2 Asp198, Asp198 �5.48 1 Ile10Lutein �4.67 2 Asp216 �5.55 1 Asp106Luteolin �6.42 2 Asn 88, Ser 10 �5.03 2 Ala3, Gly94Melatonin �4.61 2 Asp67,Gln35 �5.79 1 Ser92Menthol �4.68 1 Arg71 �5.64 1 Ser92Methyl Benzoate �4.36 1 Asp67 �4.88 1 Ala3Methylanisole �4.69 1 Asp198 �5.74 1 Val69Methylisoeugenol �5.18 0 � �5.24 1 Tyr98Methylpyrrolidine �5.25 1 Arg148 �5.62 0 0Myrcene �6.2 1 Asp216 �3.58 1 Glu 23Myrtenol �5.05 1 Lys55 �5.25 1 Tyr98Neocnidilide �4.49 1 Arg219 �3.9 1 Glu23Neryl Acetate �3.46 1 Arg67 �3.37 1 Arg52Niacin �6.39 0 � �5.67 0 0Ocimene �5.3 1 Arg148 �3.48 1 Gly94Oleuropein �4.93 0 0 0 0 0Osthol �3.28 1 Arg148 �4.14 1 Val69Palmatine �5.21 1 Arg67 �1.44 1 Arg219Phellandrene �4.3 1 Arg71 �0.27 1 Arg71Pinene �4.76 2 Arg219, Arg219 �3.43 1 Glu23Pinocarvone �3.78 1 Asp216 �4.21 0 �Pulegone �3.49 0 � �3.46 1 Ser92Pyrocide �2.46 0 � �3.07 0 �Resveratrol �4.39 0 � �7.58 2 Glu 23, Ser 92Sabenene �5.3 0 � �2.08 0 �Scopoletin �4.93 0 � �5.43 0 �Senkyunolide �1.28 0 � �3.51 0 �Sotolon �4.21 0 � �1.01 0 �Taraxacin �4.3 0 � �4.46 1 Phe 271Terpinolene �4.66 1 Gln35 �4.55 0 �Thujone �3.38 0 � �3.03 0 �Thymol �4.09 0 � �4.69 1 Arg148Trans�Jasmone �4.3 0 � �3.64 1 Pro 82

(Continued)

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7.3% residues are in additionally allowed region(Figure 3). The quality of the model was estimated as58.38%. The stereochemical analysis of vancomycinresistance protein (vanH), potential drug target ofS. aureus, showed that 91.2% of residues are in the mostfavored region (Figure 4) and 8.3% residues are inadditionally allowed region. The quality of the modelwas found to be 67. 84% by protein structure verificationalgorithm, ERRAT (Colovos & Yeates, 1993), whichevaluates the progress of crystallographic model buildingand refinement. This parogram analyzes the statistics ofnonbonded interactions between different atom types.From our results, it is evident that all the four predictedmodels have good stereochemical validity and highaccuracy. Our models satisfy most of the criteria andregulations required for judgment of good-quality models(Hollingsworth & Karplus, 2010; Hooft, Sander, &

Vriend, 1997; Kolaskar & Sawant, 1996; Zhang &Skolnick, 1998). The hypothetical models of all thefour drug targets were submitted to PMDB database(Castrignanò et al., 2006) and can be downloaded bytheir unique accession numbers; PM0078109 (aph),PM0078110 (dfrA1, S. typhi), PM0078111 (vanH), andPM0078112 (dfrA1, V. cholerae).

The drug likeness features of various phytoligandsare shown in Table 3. Most of the herbal ligands selectedwere obeying Lipinski’s rule of five, however, showedsmall variations in CMC-like rule and Lead-like rule.Similarly, most of the molecules are in the 90% cut-offrange for WDI-like rule and showed midstructure forMDDR-like rule. The predicted drug likenesses revealedthat some of the molecules have shown better druglikeliness and those qualified most of the rules werescreened for molecular docking studies. Similarly, the

Table 2. (Continued)

Ligand

Interaction with Aminoglycoside phosphotransferase(aph) Interaction with dihydrofolate reductase (dhfr)

BindingEnergy (Kcal/

mol)

Number ofhydrogenbonds

Amino acidinteracting withligand

Binding energy(Kcal/mol)

Number ofhydrogenbonds

Amino acidinteracting withligand

Trigonelline �4.93 0 � �2.81 0 �Umbelliferone �2.28 0 � �3.55 1 Met1Valeric Acid �4.21 1 Asp216 �2.03 0 �Verbenene �3.3 1 Asp67 �3.19 1Violaxanthin �3.76 1 Arg148 �4.64 0 �Wogonin �3.78 1 Asp216 �7.28 2 Ala 3, Gly 93Zingerone �3.49 0 Thr98 �2.98 0 �

Figure 6. Molecular interaction between dihydrofolate reductase (dhfr) of S. typhi and the best inhibitors are shown the figure asstick figures (a) Herbal ligand Resveratrol binding with the receptor at Glu 23 and Ser 92 by two hydrogen bonds (binding energy -7.58 Kcal/ mol). (b) Interaction between Wogonin and dfrA1 is stabilized by two hydrogen bonds and the interacting amino acids areAla 3 and Gly 94 (binding energy be �7.28 Kcal/mol).

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pharmacokinetic features were also predicted (Supple-mentary materials, Table 3). From our results, it isevident that few molecules are suitable in terms of theirpharmacokinetic features such as human intestinalabsorption, Caco2 cell permeability, MDCK cell perme-ability, skin permeability, and blood barrier penetration.

Skin permeability rate is a crucial parameter for thetransdermal delivery of drugs and for the risk assessmentof all chemicals that come into contact with the skin

either accidentally or by design (Singh & Singh, 1993).PreADMET predicts in vitro skin permeability and theresults explained in terms of logKpKp (cm/h) is definedas

Kp ¼ km�D=h

where Km is the distribution coefficient between stratumcorneum and vehicle, and D is the average diffusioncoefficient (cm2/h), and h is the thickness of skin (cm)

Figure 7. Docked structures of dihydrofolate reductase type 1(dfrA1) of V. cholerae and the best inhibitors (a) Ligand Herniarinbinding with the receptor at Ser 97 and Gly 98 by two hydrogen bonds (binding energy -8.06 kcal/mol). (b) Interaction betweenPyrocide and dfrA1 is stabilized by a hydrogen bond formed at Tyr 103 (binding energy �8.93 kcal/mol).

Figure 8. Docked pose of methicillin resistance protein (mecI) of S. aureus and the best inhibitors (a) Taraxacin was found to bebest inhibitor, the ligand binds at Lys 89 and Trp with two hydrogen bonds (binding energy -7.28 Kcal/mol). (b) Luteolin identifiedas another best inhibitor which binds at Ala 101 and Tyr 102 by two hydrogen bonds (binding energy �7.58 Kcal/mol).

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(Singh & Singh, 1993). The threshold value <1 indicateslow skin permeability, but the values between one andtwo, and >2 revealed middle and high transdemal perme-ability, respectively. In the present study, except resvera-trol, the skin permeability threshold values of the mostselected molecules were found to be low. The predictedtoxicity results of selected molecules revealed that mostof them are noncarcinogens in mouse models, however,predicted as mutagens. Similarly, the carcinogenicitypredictions for most of the molecules in rat models werepredicted as positive results (Supplementary material,Table 4). Most of the lead molecules described in thepresent study are naturally derived compounds; hence,spontaneous mutations are more prevalent. There arereports that revealed that examination of the mutagenic-ity of naturally occurring leads may indicate theirrelevance of a test compound's mutagenicity. However,many life-threatening or severe debilitating diseases mayjustify treatment with mutagenic or even carcinogenictherapeutic agents (Clive, 1985).

Out of the 58 molecules docked against the fiveproteins, Baicalein (5, 6, 7-trihydroxyflavone), a type offlavonoid, commonly present in the root of Scutellariabaicalensis, and Luteolin (2- (3, 4- Dihydroxyphenyl)-5,7-dihydroxy-4-chromenone), another flavanoid presentin Terminalia chebula showed the best interactions withaminoglycoside phosphotransferase (aph, virulent factorfor Kanamycin resistance) of S. typhi. These ligandsqualified all the rules for drug likeness (Supplementarymaterials, Table 2) and showed better pharmacokineticfeatures (Supplementary materials, Tables 3 and 4).Hence, these can be screened as promising drugcandidates. The binding energy of aph- Baicalein dockedcomplex was estimated to be �6.39 kcal/mol and the

interactions were stabilized by two hydrogen bonds (Thr98, Thr 99). The interacting residues present in thebinding cavity are Phe 56, Pro 82, Thr 98, Thr 99, Thr105, Ile 205, and Ile 215 (Figure 5(a)). Similarly, thebinding energy of aph- Luteolin was estimated to be-6.42 kcal/ mol and the interactions were stabilized bytwo hydrogen bonds (Asn 88 and Ser 10). The mainresidues associated with the interactions are Asn 88, Glu14, Ser 10, Lys 89, and Trp 13 (Figure 5(b)). From ourstudies, it is evident that these phytoligands have signifi-cant binding and inhibitory properties toward kanamy-cin-resistant protein (Table 2). Recent studies revealedthat these phytoligands have good inhibitory propertiestoward various MDR pathogens and their probable drugtargets (Cushnie & Lamb, 2005; Eumkeb et al., 2012).

Similarly, Resveratrol (3, 5, 4′-trihydroxystilbene), anatural phytoalexin commonly found in Vitis vinifera(grape) and Wogonin, an oxy-methylated flavone foundin S. baicalensis (baikal skullcap) showed significantinhibitory activities against dihydrofolate reductase (dhfr,gene product responsible for Trimethoprim resistance) ofS. typhi. From our preliminary screening, it is evidentthat these molecules are potential inhibitors as both thesewere well qualified for druggish and ADME features(Supplementary materials, Table 3). The binding energyof Resveratrol towards dfrA1 was identified as�7.58 kcal/ mol and the interaction was stabilized bytwo hydrogen bonds (Glu 23, Ser 92). The main interact-ing residues present in the binding cavity are Met 1, Val2, Glu 23, Leu 26, Phe 27, Ile 30, Ser 92, His 109, Ile110, Ser 111, and Tyr 146 (Figure 6(a)). The bindingenergy of Wogonin against dhfr was estimated to be�7.28 kcal/ mol. The interactions were stabilized by twohydrogen bonds (Ala 3, Gly 93) and the main residues

Figure 9. Inhibitory activity of herbal ligands against vancomycin resistance protein (vanH) of S. aureus (a) The interaction betweenApigenin and VanH protein is stabilized by two hydrogen bonds formed at Tyr 102, Leu 200 (binding energy �6.07 Kcal/mol). (b)Luteolin binds with the receptor at Gln 35, Asp 198, and Asp 64 by three hydrogen bonds. The binding energy of the interaction wasfound to be 6.32Kcal/mol.

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Table 3. Docking interaction between Vancomycin-resistant (vanH) protein, virulent factor for Vancomycin resistance, andMethicillin-resistant protein (mecI), virulent factor for methicillin resistance, of S. aureus, and the selected lead molecules. Apigeninand Luteolin against vanH, and Taraxacin and Luteolin against mecI were identified as the best inhibitors based on better bindingefficiency. The best ligands are highlighted.

Ligand

Vancomycin-resistant protein (vanH) Methicillin-resistant protein (mecI)

BindingEnergy (Kcal/

mol)

Number ofhydrogenbonds

Amino acidinteracting withligand

Bindingenergy (Kcal/

mol)

Number ofHydrogenbonds

Amino acidinteracting withligand

Allicin �2.61 1 Asn101 �4.07 1 Tyr102Apigenin �6.07 2 Tyr 102, Leu 200 �5.59 0 0Baicalein �4.58 0 0 �2.65 1 Leu200Borneol �5.63 1 Tyr6 �4.99 0 0Deoxyhexose �4.98 1 Thr82 �4.15 1 Tyr102Diosphenol �4.32 1 Asn88 �4.34 2 Val202, Arg 231Esculetin �4.97 1 Lys89 �3.81 1 Tyr102Eugenol �4.89 1 Asn85 �1.68 1 Val202Fraxetin �3.87 1 Lys89 �4.85 1 Leu200Gallic acid �4.49 1 Asn85 �3.75 1 Arg231Geraniol �3.85 1 Lys89 �4.62 1 Leu200Geranyl acetate �4.37 0 0 �6.58 0 0Herniarin �3.68 2 Trp13,Ile8 �4.65 0 0Isohamnetin �4.41 1 Lys89 �2.32 1 Thr 229Kaempferol �4.43 1 Ser10 �3.11 1 Arg 231Ledol �4.46 1 Lys89 �4.75 1 Asp 255Limonene �4.49 1 Lys89 �4.35 1 Ala 294Linalool �3.74 1 Glu7 �4.17 1 His 201Linalyl acetate �4.76 2 Thr82, Phe86 �3.03 1 Tyr 102Lutein �5.26 1 Glu104 �3.74 1 His 201Luteolin �6.32 3 Gln 35, Asp 198,

Asp 64�7.58 2 Ala 101, Tyr 102

Melatonin �3.96 1 Thr82 �5.91 2 Tyr102, His201Menthol �4.60 0 � �3.69 1 Asp255Methyl benzoate �4.69 2 Thr82,Thr82 �4.67 1 Tyr102Methylanisole �4.34 2 Lys89,Ile8 �4.97 0 �Methylisoeugenol �4.80 1 Ile8 �5.46 2 His201, His292Methylpyrrolidine �3.64 0 – �4.41 1 Leu200Myrcene �4.15 1 Ile8 �4.59 1 Tyr102Myrtenol �1.31 1 Lys79 �4.43 1 Asp255Neocnidilide �5.93 0 0 �0.76 1 His201Neryl Acetate �4.71 1 Lys89 �4.94 1 Tyr102Niacin �7.24 1 Asn17 �4.78 1 Val202Ocimene �4.1 1 Asn85 �1.17 1 His201Oleuropein �4.28 0 � �5.71 2 Tyr102, Leu200Osthol �3.84 0 � �5.36 1 His292,Tyr102Palmatine �3.56 1 Ser10 �4.03 1 Asp255Phellandrene �5.57 2 Trp13, Lys89 �4.65 1 HIS201Pinene �3.72 1 Lys89 �4.39 0 �Pinocarvone �4.33 2 Lys89,Lys89 �3.66 1 Gly94Pulegone �4.32 1 Ile8 �3.63 1 Ser92Pyrocide �2.18 1 � �1.97 0 �Resveratrol �1.64 0 � �4.42 1 Val69Sabenene �4.17 1 Ser10 �3.43 1 Tyr98Scopoletin �1.33 1 Asp216 �4.19 1 Ile 157Senkyunolide �4.90 0 � �4.73 2 Thr 229, Gly 230Sotolon �3.71 1 Asn88 �3.76 2 Trp13, Ile8Taraxacin �4.14 1 Asp67 �7.28 2 Trp 13, Lys 89Terpinolene �2.11 1 Arg148 �4.78 0 �Thujone �3.28 0 – �2.17 1 Thr 229Thymol �3.04 0 � �5.71 1 His292,Tyr102Trans-jasmone �3.76 1 Glu7 �5.06 1 Asp255,Tyr295Trigonelline �3.17 0 � �2.03 1 His201

(Continued)

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present in the binding cavities are Val 2, Ala 3, Ile 10,Phe 27, Tyr 42, Met 46, Ser 92, Gly 93, Gly 94, andTyr 98 (Figure 6(b), Table 2). The antimicrobial effectsof Resveratrol and Wogonin against various bacterialpathogens and their toxins have been studied (Chan,2002; Docherty, Fu, & Tsai, 2001; Schrader, 2010),which revealed that these phytochemicals have signifi-cant inhibitory properties toward virulent factors of manyMDR pathogens.

Herniarin (7-O-methylumbelliferone), a coumarinfound in Herniaria glabra (smooth rupturewort) andPyrocide [(3-Allyl-2-methyl-4-oxo-2-cyclopenten-1-yl 2,2-dimethyl-3-(2-methyl-1-propen-1-yl) cyclopropanecarb-oxylate)], a common flavone present in Daucus carota(carrot) showed the best binding activity towardsdihydrofolate reductase (dfrA1, Trimethoprim-resistantprotein) of V. cholerae. From our preliminary screening,it is evident that Herniarin and Pyrocide showed betterdrug-like features and pharmacokinetic properties(Supplementary materials, Tables 3 and 4). The dockedcomplex of dfrA1-Herniarin was stabilized by twohydrogen bonds (Ser 97, Gly 98) with binding energy of�8.06 kcal/mol. The main residues present in the bindingcavity of dfrA1 were Met 6, Val 7, Glu 23, Phe 32, Thr47, Met 52, Gly 95, Gly 96, Ser 97, Tyr 103, and Glu128 (Figure 7(a)). Studies on the antifungal and antibac-terial activities of various Herniarin derivatives(Céspedes et al., 2006) revealed that these phytoligandsshowed good inhibitory activities against various entericbacterial pathogens. Similarly, the interaction betweenPyrocide and dfrA1 was stabilized by a hydrogen bond(Tyr 103) with binding energy of -8.93 kcal/mol. Val 7,Ile 15, Pro 19, Asp 20, Glu 28, Phe 32, Ile 51, Met 52,Thr 47, Gly 99, and Tyr 103 are the main residues pres-ent in the binding cavity (Figure 7(b)) (Table 4). ThoughPyrocide exhibits better binding energy (8.93 kcal/40mol), only Tyr 103 is interacting with the receptor.Hence, better simulation studies are necessary to screenthis ligand and the present data pave significant insightfor such studies.

Luteolin and Taraxacin (3,5,8-Trimethyl-9,9a-dihydroazuleno[6,5-b]furan-2,7-dione), a sesquiterpene

guaianolide present in Taraxacum officinale (weber),showed better binding properties toward mecI protein(gene code for Methicillin resistance) of S. aureus. Ourin silico studies revealed that Luteolin and Taraxacinshowed good drug likeliness and pharmacokineticsfeatures (Supplementary materials, Tables 3 and 4).Computer-aided simulation studies revealed that thedocked complex of mecI and Luteolin were stabilized bytwo hydrogen bonds (Ala 101, Tyr 102) with bindingenergy of �7.58 kcal/mol. The main residues present inthe active sites were found to be Ile 80, Ala 101, Tyr102, Ile 157, His 201, Thr 229, and Gly 230 (Figure 8(a)). Similarly, Taraxacin binds with mecI by the forma-tion of two hydrogen bonds (Trp 13, Lys 89) with thebinding energy of -7.28 kcal/mol. The main residuespresent in the binding cavity were identified as Ile 8, Lys89, Trp13, Thr 82, and Asn 85 (Figure 8(b), Table 3).We have noticed that Gly, Lys, His, and Thr are the con-served residues that play a major role in receptor–ligandinteraction as reported recently (Kahlon, Darokar, &Sharma, 2012). However, Taraxacin was not qualified forCMC-like rule and Lead-like rule. CMC-like rule defineddrug-like character for the CMC database, whichremoved several classes of compounds such as diagnos-tic imaging agents, solvents, and pharmaceutical aids(Oprea, Davis, Teague, & Leeson, 2001). Similarly, lead-like rule defined to consider designing libraries withdrug-like physicochemical properties such as high-affinity leads, lead-like leads, and drug-like leads (Ghose,Viswanadhan, & Wendoloski, 1999). From our studies, itis evident that these phytochemicals have significantinhibitory properties toward probable drug targets ofMDR pathogens. Many studies revealed the inhibitoryproperties of Taraxacin and Luteolin (Ahmad et al.,2000) against various pathogenic microorganisms bydifferent mechanisms.

Apigenin (4′, 5, 7-trihydroxyflavone), a flavonefound in Coffee arabica (coffee), and Luteolin werefound to be interacted against vanH (gene for Vancomy-cin resistance) protein. In silico prediction of druglikeliness and ADMET features were found to besuitable for Apigenin and Luteolin (Supplementary

Table 3. (Continued)

Ligand

Vancomycin-resistant protein (vanH) Methicillin-resistant protein (mecI)

BindingEnergy (Kcal/

mol)

Number ofhydrogenbonds

Amino acidinteracting withligand

Bindingenergy (Kcal/

mol)

Number ofHydrogenbonds

Amino acidinteracting withligand

Umbelliferone �1.72 1 Gln35 �3.65 �Valeric acid �1.35 0 � �4.68 2 Leu200, Arg231Verbenene �4.33 1 Glu23 �4.45 2 Val202, Leu200Violaxanthin �3.82 0 � �4.15 1 Tyr102Wogonin �3.33 0 � �3.62 0 �Zingerone �1.12 0 � �2.03 1 Tyr102

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Table 4. Docking interaction between dihydrofolate reductase (dfrA1), virulent factor for Trimethoprim resistance of V. cholerae andselected inhibitors. Herniarin and Pyrocide were identified as the best inhibitors based on the binding energy. The best ligands arehighlighted.

Ligand

Interaction with dihydrofolate reductase (dfrA1)

Binding energy (Kcal/mol) Number of hydrogen bonds Amino acid interacting with ligand

Allicin �5.97 2 Ala8, Asp20Apigenin �4.08 2 Glu28, Glu28Baicalein �5.71 1 Glu28Borneol �4.19 1 Ser97Deoxyhexose �5.28 1 Tyr103Diosphenol �5.67 0 0Esculetin �3.12 2 Tyr103,Tyr103Eugenol �3.57 1 Glu28Fraxetin �4.78 0 0Gallic Acid �4.61 0 0Geraniol �5.22 0 0Geranyl Acetate �3.51 2 Ala8, Gly99Herniarin �8.06 2 Ser 97, Gly 98Isohamnetin �5.2 1 Ser97Kaempferol �5.58 0 0Ledol �5.14 1 Glu28Limonene �5.48 1 Tyr103Linalool �5.09 1 Ser97Linalyl Acetate �6.06 2 Gly99, Ser97Lutein �5.61 2 Ser97, Gln29Luteolin �4.54 1 Ser97Melatonin �5.17 0 0Menthol �4.53 2 Glu28, Ile15Methyl Benzoate �5.09 1 Tyr103Methylanisole �5.41 1 Ser97Methylisoeugenol �4.06 0 0Methylpyrrolidine �5.22 1 Ala8Myrcene �5.83 1 Gln29Myrtenol �4.64 0 –Neocnidilide �5.13 2 Tyr103, Tyr103Neryl Acetate �6.13 1 Glu28Niacin �4.93 0 0Ocimene �5.81 1 Ala8Oleuropein �3.92 1 Ala8Osthol �5.73 0 0Palmatine �3.53 1 Gln29Phellandrene �5.59 1 Ser116Pinene �4.27 0 �Pinocarvone �5.16 1 Ser 97Pulegone �5.68 1 Ile15Pyrocide �8.93 1 Tyr 103Resveratrol �4.63 1 Tyr 6Sabenene �5.06 0 �Scopoletin �4.32 1 Asn 88Senkyunolide �4.19 1 Lys 89Sotolon �4.89 1 Asn 85Taraxacin �3.87 1 Lys 89Terpinolene �4.43 1 Asn 85Thujone �3.85 1 Lys 89Thymol �4.13 0 �Trans�Jasmone �4.98 1 Thr 82Trigonelline �4.83 1 Thr 47Umbelliferone �2.93 0 �Valeric Acid �1.81 1 Ala 101Verbenene �3.62 0 �Violaxanthin �4.75 1 Ile 157Wogonin �4.03 0 �Zingerone �5.52 0 �

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materials, Tables 3 and 4). The docked complex ofvanH–Apigenin was stabilized by two hydrogen bonds(Tyr 102, Leu 200; binding energy �6.07 kcal/mol). Thebinding site contains Ala 101, Thr 102, Val 107, Ile 157,Leu 200, His 201, Val 202, Thr 229, and Gly 230(Figure 9(a)). Similarly, Luteolin interacted with vanHby three hydrogen bonds (Gln 35, Asp 198, and Asp 64;binding energy �6.32 kcal/mol) and the main residuespresent in the active sites are Gln 35, Ser 36, Asp 64,Asp 67, Asp 198, Asp 216, and Arg 219 (Figure 9(b),Table 3). As mentioned in the present study, recentreports revealed that Gly, His, and Asp are the main con-served residues present in the active site of Vancomycin-resistant protein, they have a vital role in receptor–ligandinteraction (Kahlon et al., 2012). The antimicrobialactivities of all these lead molecules are well studied. Asignificant inhibitory property of Apigenin toward drug-resistant Enterobacter cloacae was recently reported incomparison with the known chemotherapeutic agent,Ceftazidime (Eumkeb & Chukrathok, 2013). Fromour simulation studies, we have identified various phytol-igands which showed effective binding and conforma-tional changes in drug targets. The structural featuresand pharmacophoric properties of all these ligands areshown in supplementary materials, Table 6. The bindingefficiency of these phytoligands toward various drug-resistant proteins paves better understanding of theinhibitory mechanism of herbal leads and such studieshave high relevance in clinical and preclinical trials.

Since many bacteria showed high drug resistanceagainst currently prescribed chemotherapeutics, designingof alternative remedies against the drug targets are essen-tial to block the virulent functions. Thus, there is a needto discover new leads and deeper understanding of themechanisms by which bacteria gain resistance to antibi-otics which will aid in identifying novel targets fordrugs. Computer-aided studies serve as ideal platforms toscreen novel inhibitors against these drug targets and thepresent study provides remarkable insights for furtherin vitro and in vivo studies.

4. Conclusion

Most of the notorious pathogens developed resistance toconventional chemotherapeutic agents and emerged asmultidrug-resistant (MDR) and extensive drug-resistant(XDR) strains, “the superbugs”. The emergence of suchpathogenic bacteria has become serious healthcareissue worldwide. Our previous studies on multidrug-resistant pathogens from water samples collected fromByramangala reservoir indicated that S. typhi, Staphyloc-cus aureus, and V. cholerae showed resistance to manyconventionally used drugs such as Trimethoprim,Kanamycin, Vancomycin, and Methicillin. Hence, there isa necessity to address this issue and screen alternative

therapeutic substances. Bacterial drug resistance seems tobe mainly due to virulent genes; inhibition of such geneproducts by appropriate ligands may prevent the virulentfunctions. The three-dimensional structures of most of thevirulent proteins are not available in their native form.Hence, we employed homology modeling to predict thethree-dimensional folding of virulent proteins toward theseantibiotics. We have identified many herbal inhibitorswhich are beneficial in providing a remedy to block theactivity of virulent proteins by structure-based virtualscreening. The present study suggests that active com-pounds from herbal sources exhibit promising inhibitoryroles against these MDR and XDR pathogens. However,further experimental analysis is essential for the conforma-tion of the selected lead molecules.

Acknowledgments

The authors thankfully acknowledge Dr. G S Jagannatha Rao,senior professor and head, Department of Biotechnology,Dayananda Sagar College of Engineering, Bangalore, and Dr.G A Ravishankar, vice president, (R & D) in Life Sciences,Dayananda Sagar Institutions, Bangalore for their constantsupport and encouragement throughout the study.

Supplementary material

The supplementary material for this paper is availableonline at http://dx.doi.10.1080/07391102.2013.819787.

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