Computational Modeling of the Staphylococcal Enterotoxins and Their Interaction with Natural Antitoxin Compounds

International Journal of Molecular Sciences

Article Computational Modeling of the Staphylococcal Enterotoxins and Their Interaction with Natural Antitoxin Compounds

Mahantesh Kurjogi 1, Praveen Satapute 1, Sudisha Jogaiah 1,*, Mostafa Abdelrahman 2,3, Jayasimha Rayalu Daddam 4, Venkatesh Ramu 5 and Lam-Son Phan Tran 6,7,*

1 Plant Healthcare and Diagnostic Center, Department of Studies in Biotechnology and Microbiology, Karnatak University, Dharwad 580003, India; [email protected] (M.K); [email protected] (P.S.) 2 Department of Botany, Faculty of Sciences, Aswan University, Aswan 81528, Egypt; [email protected] 3 Graduate School of Life Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan 4 Department of Biotechnology, Jawaharlal Nehru Technology University, Anantapur 515002, India; [email protected] 5 Department of Biochemistry, Indian Institute of Science, Bengaluru 560012, India; [email protected] 6 Institute of Research and Development, Duy Tan University, 03 Quang Trung, Da Nang 550000, Vietnam 7 Signaling Pathway Research Unit, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurmi-ku, Yokohama 230-0045, Japan * Correspondence: [email protected] (S.J.); [email protected] (L.-S.P.T.); Tel.: +91-0836-277-9533 (S.J.); +81-45-503-9593 (L.-S.P.T.); Fax: +91-0836-274-7884 (S.J.); +81-45-503-9591 (L.-S.P.T.)

Received: 22 October 2017; Accepted: 27 December 2017; Published: 3 January 2018

Abstract: Staphylococcus aureus is an opportunistic bacterium that produces various types of toxins, resulting in serious food poisoning. Staphylococcal enterotoxins (SEs) are heat-stable and resistant to hydrolysis by digestive enzymes, representing a potential hazard for consumers worldwide. In the present study, we used amino-acid sequences encoding SEA and SEB-like to identify their respective template structure and build the three-dimensional (3-D) models using homology modeling method. Two natural compounds, Betulin and 28-Norolean-12-en-3-one, were selected for docking study on the basis of the criteria that they satisﬁed the Lipinski’s Rule-of-Five. A total of 14 and 13 amino-acid residues were present in the best binding site predicted in the SEA and SEB-like, respectively, using the Computer Atlas of Surface Topology of Proteins (CASTp). Among these residues, the docking study with natural compounds Betulin and 28-Norolean-12-en-3-one revealed that GLN43 and GLY227 in the binding site of the SEA, each formed a hydrogen-bond interaction with 28-Norolean-12-en-3-one; while GLY227 residue established a hydrogen bond with Betulin. In the case of SEB-like, the docking study demonstrated that ASN87 and TYR88 residues in its binding site formed hydrogen bonds with Betulin; whereas HIS59 in the binding site formed a hydrogen-bond interaction with 28-Norolean-12-en-3-one. Our results demonstrate that the toxic effects of these two SEs can be effectively treated with antitoxins like Betulin and 28-Norolean-12-en-3-one, which could provide an effective drug therapy for this pathogen.

Keywords: Staphylococcus aureus; enterotoxin; food poisoning; in silico; Betulin; 28-Norolean-12-en-3-one; 3-D structure; amino-acid residues; docking

1. Introduction Staphylococcus aureus is among the most common microﬂora present on the skin and mucous membrane of humans and cattle [1]. Despite its generally benign nature, S. aureus can turn opportunistic

Int. J. Mol. Sci. 2018, 19, 133; doi:10.3390/ijms19010133 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2018, 19, 133 2 of 13 and become pathogenic. The pathogenic nature of S. aureus has been widely reported in cattle, which was found to be the predominant cause of bovine mastitis [2]. The induction of intramammary infection by S. aureus requires the production of a variety of virulence factors secreted by the pathogen needed for adherence, colonization and invasion of epithelial cells of the mammary glands of the cows [3]. In addition to its opportunistic pathogenicity, S. aureus is the most prevalent enterotoxin producing microbe that causes food-borne diseases among Staphylococcus species worldwide [4]. The possession of virulence factors and survival of S. aureus in the host environment are attributed to the self-defense mechanism expressed by S. aureus that helps in protection against defense molecules produced by the host [5,6]. Staphylococcus spp. secret many potential virulence factors, or exotoxins, such as α- and β-toxins, toxic shock syndrome toxin and enterotoxins [7]. Both α- and β-toxins cause lysis of erythrocytes by pore formation or disrupting normal cellular metabolism [8,9]. Staphylococcal enterotoxins (SEs) are responsible for major food poisoning outbreak with symptoms, such as violent vomiting, nausea and abdominal cramping with or without diarrhea [10–13]. In growth curve, these toxins are synthesized during the transition from logarithmic phase to the stationary phase, highly active even at low concentrations and resistant to heat as well as proteolytic enzymes [14,15]. Therefore, enterotoxins are active even in the presence of host digestive enzymes [16–18]. In addition to their gastrointestinal effects, enterotoxins also cause pyrogenic immune suppression and non-speciﬁc T-cell proliferation, and are thus aptly referred to as superantigens [19]. Staphylococcal food poisoning is highly under-reported because of misdiagnosis, minor outbreaks, improper sample collections and false positive lab reporting. In addition, the poor personal hygiene is considered to be one of the critical elements, which causes S. aureus infection, making disease control process more complicated. Although there are more than 20 distinct SEs, only a few of them have been examined in depth [4,20]. The SEA and SEB are the most common toxins involved in Staphylococcus-related food poisoning followed by SED [20]. However, in spite of the available knowledge on the enterotoxins produced by S. aureus, an effective control strategy is still lacking. New and cheaper drugs that target the active sites of the toxins should be designed in order to increase the affordability of drugs against the highly evolving S. aureus. In our previous study, we have identiﬁed a gene encoding SEB-like in S. aureus isolated from milk samples [21]. In this context, the focus of the present study was to construct the three-dimensional (3-D) model structures of our SEB-like and the SEA of S. aureus Newman isolated previously from human infection [22], which will serve as useful targets for structure-based drug designing. We have also carried out docking studies with the designated binding sites of the examined SEA and SEB-like using potent natural molecules Betulin and 28-Norolean-12-en-3-one.

2. Results and Discussion

2.1. Homology Modeling of SEA and SEB-Like Proteins The present study extends our previous work to develop the 3-D model structure of the SEB-like that we identiﬁed from milk samples [21], and that of the SEA previously isolated from S. aureus Newman [22]. These 3-D model structures will serve as a useful tool for structure-based drug designing (Figure1). Homology modeling is a multi-step process consisting of template selection, sequence alignment, alignment correction, model building, optimization and model validation. In the series of homology modeling process, templates were chosen and utilized for further validation studies [23]. Homology modeling cannot be performed automatically as it depends on the percentage of sequence identity of template structure with the query sequence [24]. In the present study, the amino-acid sequences of the SEA and SEB-like were blast-searched against the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) using default parameters, revealing 1ESF and 4RGM as the template structure for SEA and SEB-like, respectively, based on their maximum identity and minimum E-value Int. J. Mol. Sci. 2018, 19, 133 3 of 13 Int. J. Mol. Sci. 2018, 19, 133 3 of 13

−124 were(FigureInt. J.94% Mol.2 and).Sci. The 2018 3.03, identity 19 ×, 13310 − 124 and, respectively,E-value between whereas the these 1ESF values and thewere SEA 95% were and 94% 1.69 and × 10 3.03−132 between× 103 of 13 the, −132 4RGMrespectively, and the whereasSEB-like. these values were 95% and 1.69 × 10 between the 4RGM and the SEB-like. were 94% and 3.03 × 10−124, respectively, whereas these values were 95% and 1.69 × 10−132 between the 4RGM and the SEB-like.

FigureFigure 1. 1.1.Workflow WorkflowWorkﬂow of of structure-based structure-based drug drug design.design. The The amino-acid amino-acidamino-acid sequences sequencessequences of of ofstaphylococcal staphylococcal staphylococcal enterotoxinsenterotoxins A A (SEA) (SEA) and and B-likeB-like (SEB-L)(SEB-L) were were obtained obtained from fromfrom Nati NationalNationalonal Center Center Center for for for Biotechnology Biotechnology Biotechnology InformationInformationInformation (NCBI). (NCBI).(NCBI). The TheThe amino- amino-amino- acid sequencesacidacid sequence weres blast-searcheds werewere blast-searchedblast-searched against the against Researchagainst the Collaboratorythe Research Research CollaboratoryforCollaboratory Structural for Bioinformaticsfor Structural Structural Bioi ProteinBioinformaticsnformatics Data Bank Protein (RCSB DataData PDB) Bank Bank to identify(RCSB (RCSB PDB) the PDB) related to toidentify identify protein the structuresthe related related proteinthatprotein were structures structures subsequently that that were were used subsequently subsequently as templates used in homology asas templatestemplates modeling. in in homology homology The quality modeling. modeling. and The reliability The quality quality of and the and reliabilitygeneratedreliability of of modelsthe the generated generated were assessed models models bywerewere NAMD assessedassessed 2.5 by(NAnoscaleby NAMD NAMD 2.5 2.5 Molecular (NAnoscale (NAnoscale Dynamics Molecular Molecular 2.5), Dynamics PROCHECKDynamics 2.5), 2.5), PROCHECKandPROCHECK ERRAT and programs. and ERRAT ERRAT programs. programs.

Figure 2. Amino-acid sequence alignments of staphylococcal enterotoxins A (SEA) and B-like (SEB- Figure 2. Amino-acid sequence alignments of staphylococcal enterotoxins A (SEA) and B-like (SEB-L) FigureL) with 2. Amino-acidtheir template sequence structure alignments sequence. 1ESF of staphylococcal is the template enterotoxinsstructure of SEA, A (SEA) while and 4RGM B-like is that (SEB- with their template structure sequence. 1ESF is the template structure of SEA, while 4RGM is that L) ofwith SEB-like. their template structure sequence. 1ESF is the template structure of SEA, while 4RGM is that of SEB-like. of SEB-like. 2.2. Homology Modeling of the 3-D Structures 2.2. HomologyThe 3-D Modeling models of of SEA the 3-Dand StructuresSEB-like were generated by the MODELLER 9.17 software using the templateThe 3-D structures models of 1ESF SEA and SEB-like4RGM (Figure were gene 3). Coordinatesrated by the MODELLERof structurally 9.17 conserved software regions using the template structures 1ESF and 4RGM (Figure 3). Coordinates of structurally conserved regions

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2.2. Homology Modeling of the 3-D Structures Int. J. Mol. Sci. 2018, 19, 133 4 of 13 Int. J. Mol.The Sci. 3-D 2018 models, 19, 133 of SEA and SEB-like were generated by the MODELLER 9.17 software using4 of 13 the template structures 1ESF and 4RGM (Figure3). Coordinates of structurally conserved regions C-termini and N-termini from the 1ESF and 4RGM templates, and structurally variable regions were C-terminiC-termini and and N-termini N-termini from from the the 1ESF 1ESF and and 4RGM 4RGM templates, templates, and structurally variable regionsregions werewere designated to target sequences according to the satisfaction of the spatial restraints. All side chains of designateddesignated to to target target sequences sequences accord accordinging to to the the satisfaction satisfaction of the spatial restraints. AllAll sideside chainschains ofof the model proteins were set by rotamers. On the basis of the 3-D structures, the valid models of the thethe model model proteins proteins were were set set by by rotamers. rotamers. On On the the ba basissis of of the 3-D structures, thethe validvalid modelsmodels ofof thethe enterotoxins SEA and SEB-like were selected. enterotoxinsenterotoxins SEA SEA and and SEB-like SEB-like were were selected. selected.

Figure 3. Three-dimensional (3-D) structures of the enterotoxins. The 3-D structure of staphylococcal Figure 3. Three-dimensional (3-D) structures of the enterotoxins. The 3-D structure of staphylococcal enterotoxinFigure 3. Three-dimensional A (SEA) with five (3-D)α-helices structures and 11 of β the-sheets, enterotoxins. and that Theof staphylococcal 3-D structure ofenterotoxin staphylococcal B-like enterotoxin A (SEA) with five α-helices and 11 β-sheets, and that of staphylococcal enterotoxin B-like (SEB-L)enterotoxin with Athree (SEA) α-helices with ﬁve andα-helices 14 β-sheets. and 11 β-sheets, and that of staphylococcal enterotoxin B-like (SEB-L)(SEB-L) with with three three αα-helices-helices and and 14 14 ββ-sheets.-sheets. 2.3. Validation of the 3-D Structures 2.3.2.3. Validation Validation of of the the 3-D 3-D Structures Structures 2.3.1. Molecular Dynamics Studies 2.3.1.2.3.1. Molecular Molecular Dynamics Dynamics Studies Studies The stability of the 3-D SEA and SEB-like structures was examined by the variation in total TheThe stability stability of of the the 3-D 3-D SEA SEA and and SEB-like SEB-like structures structures was examined byby thethe variationvariation inin totaltotal energy versus time. Furthermore, the trajectory graph was also plotted between the root-mean- energyenergy versus versus time. Furthermore,Furthermore, the the trajectory trajectory graph graph was was also also plotted plotted between between the root-mean-square the root-mean- square deviation (RMSD) of carbon backbone trace (C trace) and time in picoseconds (ps) (Figure 4). squaredeviation deviation (RMSD) (RMSD) of carbon of backbonecarbon backbone trace (C trace)trace and(C trace) time inand picoseconds time in picoseconds (ps) (Figure (ps)4). The (Figure steady 4). The steady value stretches out between 0.1 and 0.35 Å, and RMSD value increased initially and Thevalue steady stretches value out stretches between out 0.1 andbetween 0.35 Å, 0.1 and and RMSD 0.35 valueÅ, and increased RMSD initiallyvalue increased and stabilized initially at 1000 and stabilized at 1000 up to 5000 ps, due to lower fluctuation of RMSD values, and can be considered as stabilizedup to 5000 at ps,1000 due up to to lower 5000 ﬂuctuationps, due to lower of RMSD fluctuation values, of and RMSD can be values, considered and can as abe measure considered of the as a measure of the quality and stability of the protein models. a qualitymeasure and of stabilitythe quality of the and protein stability models. of the protein models.

Figure 4. Trajectories of the root-mean-square deviation (RMSD) of carbon backbone trace of FigureFigure 4. 4. TrajectoriesTrajectories of of the the root-mean-square root-mean-square devi deviationation (RMSD) (RMSD) of carbon backbone tracetrace ofof staphylococcal enterotoxins SEA (blue) and SEB-like (SEB-L, red) with respect to simulation time in staphylococcalstaphylococcal enterotoxins enterotoxins SEA SEA (blue) (blue) and and SEB-like SEB-like (SEB-L, (SEB-L, red) red) with with respect respect to tosimulation simulation time time in picoseconds (ps). picosecondsin picoseconds (ps). (ps). 2.3.2. Ramachandran Plot Analysis 2.3.2. Ramachandran Plot Analysis The energetically allowed and favored regions for SEA and SEB-like backbone dihedral angles The energetically allowed and favored regions for SEA and SEB-like backbone dihedral angles Psi (ψ) on the y-axis against Phi (φ) on the x-axis of amino-acid residues were visualized using Psi (ψ) on the y-axis against Phi (φ) on the x-axis of amino-acid residues were visualized using Ramachandran plot (Figure 5). Each dot indicates the angle for an amino-acid, and the areas on the Ramachandran plot (Figure 5). Each dot indicates the angle for an amino-acid, and the areas on the plot with the highest density of dots are called ‘favored regions’ (Figure 5). plot with the highest density of dots are called ‘favored regions’ (Figure 5).

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2.3.2. Ramachandran Plot Analysis The energetically allowed and favored regions for SEA and SEB-like backbone dihedral angles Psi (ψ) on the y-axis against Phi (ϕ) on the x-axis of amino-acid residues were visualized using Ramachandran plot (Figure5). Each dot indicates the angle for an amino-acid, and the areas on the plot withInt. theJ. Mol. highest Sci. 2018, 19 density, 133 of dots are called ‘favored regions’ (Figure5). 5 of 13

Figure 5. Ramachandran plot for the staphylococcal enterotoxins A (SEA) and B-like (SEB-L). Figure 5. Ramachandran plot for the staphylococcal enterotoxins A (SEA) and B-like (SEB-L). Different Different colors indicate favored (dark blue and dark brown for non-glycine and glycine residues, colors indicaterespectively) favored and (darkallowed blue (light and blue dark and brown light for brown non-glycine for non-glycine and glycine and glycine residues, residues, respectively) and allowedrespectively) (light blueregions. and Black light and brown red forsquares non-glycine indicate andgeneral glycine residues residues, including respectively) pre-prolines regions. Black and(residues red squares immediately indicate preceding general a residuesproline inincluding sequence, except pre-prolines glycine (residuesand proline) immediately in favored and preceding a prolineallowed in sequence, region, respectively. except glycine Black and red proline) triangles in indicate favored proline and allowedresidues in region, favored respectively. and allowed Black and redregion, triangles respectively. indicate Black proline and residuesred crosses in indicate favored glycine and allowed residues region,in favored respectively. and allowed Blackregion, and red respectively. crosses indicate glycine residues in favored and allowed region, respectively. Introduction of a few errors in the homology modeling is a common phenomenon [25], and more Introductionattention is needed of a few toward errors refinement in the homology and validatio modelingn of the is structure a common obtained. phenomenon All the errors [25], that and more attentionoccurred is needed in homology toward modeling refinement process and were validation assessed and ofthe validated structure after obtained.the refinement All of the protein errors that occurredstructure in homology [25]. The modeling validation processof the SEA were and assessedSEB-like 3-D and models validated obtained after in the this refinement study was also ofprotein carried out using Ramachandran plot, and the corresponding calculations were computed with the structure [25]. The validation of the SEA and SEB-like 3-D models obtained in this study was also RAMPAGE program. The Ramachandran plot results together indicated that 87.1% and 93.2% of the carried out using Ramachandran plot, and the corresponding calculations were computed with the residues of the SEA and SEB-like models, respectively, were in their favored region (Figure 5). The Commented [ST1]: The “and allowed” should be deleted! RAMPAGEvalidation program. results Theusing Ramachandran the Ramachandran plot plot results revealed together the stable indicated conformation that 87.1%of constructed and 93.2% of Response: I AGREE. The values 87.1 and 93.2% is the residuesmodels of with the appropriate SEA and SEB-likestereochemistry, models, indicat respectively,ing that these were models in their can favored be used regionfor further (Figure 5). present ONLY in favored region. The validationanalyses. resultsThe Ramachandran using the Ramachandranplot parameters in plotRAMPAGE revealed program the stable used in conformation our study are probably of constructed modelsthe with most appropriate potent determinants stereochemistry, for the protein indicating structural that evaluation these models and validation can be used [26,27]. for further analyses.Commented [ST2]: Editor Ding agreed with the change! Response: My special thanks to Editor Ding. We highly The Ramachandran2.3.3. Analysis of plot theparameters SEA and SEB-Like in RAMPAGE 3-D Structures program used in our study are probably the most potent determinants for the protein structural evaluation and validation [26,27]. appreciate his kindness. The validation of SEA and SEB-like 3-D structures was further performed using the Structure 2.3.3. AnalysisAnalysis ofand the Verification SEA and Server. SEB-Like The 3-DRMSD Structures values obtained from VERIFY_3D for covalent bonds and covalent angels relative to the standard dictionary value of the SEA model were −0.58 and −2.68 TheÅ, validationand SEB-like of model SEA were and − SEB-like1.78 and −0.39 3-D Å, structures respectively. was The further overall PROCHECK performed G-factor using the of the Structure AnalysisSEA and and Verification SEB-like models Server. was The −3.47 RMSD and − values1.26 Å, respectively, obtained from andVERIFY_3D the verified 3-D for environmental covalent bonds and covalentprofile angels was relative found to to be the suitable standard for both dictionary models. value of the SEA model were −0.58 and −2.68 Å, Several methods are available for comprehensive evaluation of structural alignment in a model, − − and SEB-likewhich include model superposition were 1.78 of and model0.39 into the Å, respectively.native structure The withoverall the structure PROCHECK alignment program, G-factor of the SEA andmeasurement SEB-like models of the average was − distance3.47 and between−1.26 the Å, ba respectively,ckbone atoms andof superimposed the verified proteins 3-D environmental by the profile wasRMSD found and togeneration be suitable of the for Z-score both models. [28,29]. The evaluation of the model also involves the Severaldevelopment methods of a are scoring available function for that comprehensive is able to distinguish evaluation bad and of structural good models. alignment A variety in of a model, which includestatistical superposition criteria have to ofbe modelderived intofrom the various native properties, structure such with as distributions the structure of alignmentpolar residues program, outside or inside the protein [30]. For error corrections, solvation potential that can detect local errors

Int. J. Mol. Sci. 2018, 19, 133 6 of 13 measurement of the average distance between the backbone atoms of superimposed proteins by the RMSD and generation of the Z-score [28,29]. The evaluation of the model also involves the development of a scoring function that is able to distinguish bad and good models. A variety of statistical criteria have to be derived from various properties, such as distributions of polar residues outside or inside the protein [30]. For error corrections, solvation potential that can detect local errors and complete misfolding in protein structures has to be used [31], and packing rules have to be implemented for structure evaluation [32]. A model is considered valid when only a few distortions are present in atomic contacts.

AfterInt. J. theMol. Sci. ﬁnal 2018 SEA, 19, 133and SEB-like 3-D models were built, their possible active pockets6 of 13 were determined using the Computer Atlas of Surface Topology of Proteins (CASTp) [33,34]. CASTp can be used to analyzeAfter variousthe final conserved SEA and SEB-like regions 3-D of proteins,models we andre built, it provides their possible accessibility active pockets to the bindingwere sites on a proteindetermined that areusing associated the Computer with Atlas the of effectiveness Surface Topology of docking of Proteins [34 (CASTp)]. For instance, [33,34]. CASTp the CASTp can has be used to analyze various conserved regions of proteins, and it provides accessibility to the binding been used to determine the active pocket in the 3-D structure of the Anthrax Toxin Receptor 1 [24]. sites on a protein that are associated with the effectiveness of docking [34]. For instance, the CASTp We conductedhas been aused CASTp to determine analysis the based active onpocket the volumein the 3-D of structure pockets, of the Anthrax atoms liningToxin Receptor the pockets 1 and the area[24]. circumference We conducted of a CASTp mouth analysis openings. based A on total the volume of 41 and of pockets, 37 binding the atoms sites lining were the identiﬁed pockets in the 3-D structureand the ofarea SEA circumference and SEB-like, of mouth respectively. openings. A Among total of 41 these, and 37 the binding best predictedsites were identified binding in site was selectedthe for 3-D further structure docking of SEAanalysis and SEB-like, based respectively on the area. Among and these, volume the ofbest the predicted binding binding site, which site was can help selected for further docking analysis based on the area and volume of the binding site, which can in binding the ligand molecule in all directions. The binding site of SEA with an area of 215.2 Å2 and 2 help in binding3 the ligand molecule in all directions. The binding site of SEA with an area of 215.2 Å a volumeand of a 215.1volume Å ofhad 215.1 14 Å amino-acid3 had 14 amino-acid residues, residues, including including ARG37, ARG37, LYS38, LYS38, GLU41, GLU41, LEU42, LEU42,GLN43, ALA46,GLN43, LEU50, ALA46, PHE223, LEU50, GLY224, PHE223, ALA225, GLY224, GLN226, ALA225, GLY227,GLN226, GLN228GLY227, GLN228 and LEU233, and LEU233, whereas that of SEB-likewhereas with that an of areaSEB-like of 240.6with an Å area2 and of 240.6 a volume Å2 and a of volume 277Å 3ofcontained 277Å3 contained 13 residues, 13 residues, namely namely VAL53, LEU54,VAL53, ASN58, LEU54, HIS59, ASN58, LEU85, HIS59, ASN87, LEU85, ASN87, TYR88, TYR88, ALA114, ALA114, ASN115, ASN115, TYR116, TYR116, TYR117, TYR189 TYR189 and GLN237and (Figure GLN237 S1). (Figure Additionally, S1). Additionally, we used we Swiss-PDB used Swiss-PDB Viewer View to scrutinizeer to scrutinize the structural the structural comparison comparison by superimposition of the template and model, which further strengthens our validation by superimposition of the template and model, which further strengthens our validation (Figure6). (Figure 6).

Figure 6. Superimposition of C-α chain (green) of staphylococcal enterotoxin A (SEA) and template Figure 6. Superimposition of C-α chain (green) of staphylococcal enterotoxin A (SEA) and template 1ESF (red), and of C-α chain (yellow) of staphylococcal enterotoxin B-like (SEB-L) and template 4RGM 1ESF (red),(red). and of C-α chain (yellow) of staphylococcal enterotoxin B-like (SEB-L) and template 4RGM (red). 2.4. Docking Study 2.4. DockingIn Study order to predict the binding modes of pharmacophore models adopted from structural manipulations of the compounds, docking stimulations of the interactions were examined. Using the InMolinspiration order to predict server the [35], binding a library modes of 18 compounds of pharmacophore was screened models for satisfying adopted the fromminimal structural manipulationschemical ofcriteria the compounds, for further analysis. docking Among stimulations these compounds, of the Betulin interactions and 28-Norolean-12-en-3-one were examined. Using the Molinspiration(Table 1) serverwere selected [35], a on library the basis of 18of the compounds criteria to satisfy was screened the Lipinski’s forsatisfying Rule-of-Five the [36] minimal with zero chemical criteriaviolations for further for analysis. docking onto Among enterotoxin these compounds,models. All docking Betulin measurements and 28-Norolean-12-en-3-one were carried out using (Table 1) were selectedFast Rigid on Exhaustive the basis ofDocking the criteria (FRED) to v.2.1, satisfy whic theh is Lipinski’s based on the Rule-of-Five Rigid Body Shape-Fitting [36] with zero (Open violations Eye Scientific Software, Santa Fe, NM, USA), and the files generated were analyzed for their binding for docking onto enterotoxin models. All docking measurements were carried out using Fast Rigid conformations. Open Eye software has been used for docking various drug molecules with the binding site of receptors from different sources [24,37,38]. In our study, analysis was based on the free energy of binding, the lowest docked energy and the calculated RMSD values. All clusters with docking conformations to the lead molecules that showed negative binding energies were considered to have the strongest binding affinity with the enterotoxins. The docking of 28-Norolean-12-en-3-one

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Exhaustive Docking (FRED) v.2.1, which is based on the Rigid Body Shape-Fitting (Open Eye Scientific Software, Santa Fe, NM, USA), and the files generated were analyzed for their binding conformations. Open Eye software has been used for docking various drug molecules with the binding site of receptors from different sources [24,37,38]. In our study, analysis was based on the free energy of binding, the lowest docked energy and the calculated RMSD values. All clusters with docking conformations to the lead molecules that showed negative binding energies were considered to have the strongest binding affinity with the enterotoxins. The docking of 28-Norolean-12-en-3-one to SEA formed a stable complex by the formation of two hydrogen bonds with GLN43 and GLY227 with a binding free energy of −83.97 kcal/mol, whereas that of Betulin to SEA showed GLY227 establishing a hydrogen-bond with Betulin with a binding free energy of −147.39 kcal/mol (Table1 and Figure7). Likewise, the docking Int. J. Mol. Sci. 2018, 19, 133 7 of 13 study of BetulinInt. J. Mol. and Sci. 28-Norolean-12-en-3-one2018, 19, 133 with SEB-like demonstrated that (i) two7 hydrogen of 13 bonds formedfree with energy ASN87 of binding, and the TYR88 lowestresidues docked energy of SEB-like and the calculated stabilized RMSD the values. complex All clusters with Betulin with by a binding freedockingfree energy energy conformations of of binding,−139.97 tothe the kcal/mol,lowest lead moleculesdocked and energy that (ii) showed and HIS59 the negative calculated residue binding RMSD in the energies values. binding wereAll clusters site considered of with SEB-like docking conformations to the lead molecules that showed negative binding energies were considered established ato hydrogen have the strongest bond binding interaction affinity with with 28-Norolean-12-en-3-onethe enterotoxins. The docking of with 28-Norolean-12-en-3-one a binding free energy toto SEAhave formedthe strongest a stable binding complex affinity by the with formation the enterotoxins. of two hydrogen The docking bonds of with 28-Norolean-12-en-3-one GLN43 and GLY227 of −88.20 kcal/mol (Table1 and Figure7). All the docking poses revealed that the tested ligand withto SEA a bindingformed afree stable energy complex of − by83.97 the kcal/mol,formation whereas of two hydrogen that of Betulin bonds withto SEA GLN43 showed and GLY227 molecule interactsestablishingwith a binding with a onehydrogen free or energy two bond aminoof with −83.97 Betulin acids kcal/mol, with in the awhereas binding binding thatfree sites ofenergy Betulin of of SEA −to147.39 SEA and kcal/molshowed SEB-like (TableGLY227 (Figure 1 7). Overall, on theandestablishing basisFigure of7). a thehydrogenLikewise, number bondthe docking of with hydrogen-bond Betulin study with of Betulin a binding interactions and free28-Norolean-12-en-3-one energy the of present −147.39 kcal/mol result with showedSEB-like(Table 1 that and Figure 7). Likewise, the docking study of Betulin and 28-Norolean-12-en-3-one with SEB-like 28-Norolean-12-en-3-onedemonstrated that acts (i) astwo a goodhydrogen inhibitor bonds formed for SEA, with while ASN87 Betulin and TYR88 is for residues SEB-like. of SEB-like Therefore, demonstrated that (i) two hydrogen bonds formed with ASN87 and TYR88 residues of SEB-like we may concludestabilized that the the complex interaction with Betulin between by a thebinding enterotoxin free energy receptor of −139.97 and kcal/mol, the ligand and (ii) is HIS59 a mixture residuestabilized in thethe bindingcomplex site with of SEB-likeBetulin byestablished a binding a hydrogen-bondfree energy of −interaction139.97 kcal/mol, with 28-Norolean-12- and (ii) HIS59 of non-polaren-3-oneresidue and polar in with the types.bindinga binding Similarly,site free of energySEB-like possible of established −88.20 bindingkcal/mol a hydrogen-bond (Table sites 1 of and white interaction Figure spot 7). All with syndrome the 28-Norolean-12- docking virus poses target protein VP26revealeden-3-one (PDB thatwith ID: the 2EDM)a binding tested ligand werefree energy molecule searched of − 88.20interacts using kcal/mol with CASTp one (Table or two [139 and]. amino Figure CASTp acids 7). Allin has the the alsobinding docking been sites poses used of by many drug companiesSEArevealed and thatSEB-like inthe drugtested (Figure discoveryligand 7). Overall, molecule process on interacts the basis to wi determineofth the one number or two binding ofamino hydrogen-bond acids sites inin the a interactionsbinding number sites ofthe of target SEA and SEB-like (Figure 7). Overall, on the basis of the number of hydrogen-bond interactions the molecules [40present–42]. result showed that 28-Norolean-12-en-3-one acts as a good inhibitor for SEA, while Betulin ispresent for SEB-like. result showedTherefore, that we 28-Norolean-12-en-3-one may conclude that the interaction acts as a goodbetween inhibitor the enterotoxin for SEA, while receptor Betulin and Betulin is a natural product, which is the main source of birch bark [43]. The potent biological theis for ligand SEB-like. is a mixtureTherefore, of wenon-polar may conclude and polar that types. the interaction Similarly, between possible the binding enterotoxin sites of receptor white spot and activity of thissyndromethe ligand compound virusis a mixture target is dueproteinof non-polar to VP26 esterification, (PDBand polar ID: 2EDM) types. and Similarly,were various searched possible methods using binding CASTp of sites [39]. esterification of CASTp white spot has have been proposedalsosyndrome tobeen enhance used virus by target many its activityprotein drug companiesVP26 [44 –(PDB53]. in ID: Thedrug 2EDM) anticancerdiscovery were searched process activity tousing determine of CASTp this binding compound[39]. CASTp sites inhas towarda also been used by many drug companies in drug discovery process to determine binding sites in a human melanomanumber wasof target reported molecules decades [40–42]. ago [45]. Later, several studies have revealed that Betulin number of target molecules [40–42]. is not only melanoma-specific,Betulin is a natural but product, is also which fairly is the active main againstsource of manybirch bark other [43]. cancer The potent cell biological types [46 –48]. activityBetulin of this is acompound natural product, is due towhich esterification is the main, and source various of birch methods bark of[43]. esterification The potent havebiological been The derivativesproposedactivity of Betulinof to this enhance compound have its been activity is due explored [44–53].to esterification The for theiranti, cancerand significant various activity methods of properties this compoundof esterification like toward anti-bacterial have human been [49], antimalarialmelanomaproposed [50], anti-inflammatory to was enhance reported its decades activity [51 ago[44–53].], anthelmintic[45]. Later,The anti severacancer activitiesl studies activity have [of52 revealedthis] and compound anti-HIV that Betulin toward [ is53 not ].human only Similarly, Noroleans aremelanoma-specific,melanoma recognized was reported asbut important decadesis also fairlyago [45]. chemical active Later, aga severainst constituents lmany studies other have of cancerrevealed the cell family that types Betulin Lardizabalaceae,[46–48]. is not onlyThe derivativesmelanoma-specific, of Betulin but have is also been fairly explored active for aga theirinst significantmany other properties cancer celllike typesanti-bacterial [46–48]. [49],The and Noroleananederivatives triterpenoids of Betulin havehave been a wide explored range for of their bioactivities significant [54properties–56]. Other like anti-bacterial studies have [49], shown antimalarial [50], anti-inflammatory [51], anthelmintic activities [52] and anti-HIV [53]. Similarly, that Nortriterpenoids are also cytotoxic against HCT-116, HepG2 and SGC-7901 cancer cell lines [57,58]. Noroleansantimalarial are [50], recognized anti-inflammatory as important [51], chemical anthelmintic constituents activities of [52]the andfamily anti-HIV Lardizabalaceae, [53]. Similarly, and The biologicalNoroleananeNoroleans properties are triterpenoids ofrecognized Betulin, haveas Noroleans important a wide range andchemical of their bioactivities co derivativesnstituents [54–56]. of arethe Other wellfamily studies documented Lardizabalaceae, have shown in literature;thatand Noroleanane triterpenoids have a wide range of bioactivities [54–56]. Other studies have shown that however, noNortriterpenoids reports have are been also available cytotoxic against yet regarding HCT-116, HepG2 the antitoxin and SGC-7901 activity cancer of cell these lines compounds.[57,58].

TheNortriterpenoids biological properties are also cytotoxic of Betulin, against Noroleans HCT-116, and HepG2 their andderivatives SGC-7901 are cancer well celldocumented lines [57,58]. in Hence, the resultsThe biological of their properties antitoxin of activity Betulin, in Noroleans the present and studytheir derivatives revealed aare potential well documented novel biological in property of Betulinliterature; and however, Noroleans. no reports have been available yet regarding the antitoxin activity of these compounds.literature; however, Hence, theno resultsreports ofhave their been antitoxin available activity yet inregarding the present the studyantitoxin revealed activity a potentialof these novelcompounds. biological Hence, property the results of Betulin of their and Noroleans.antitoxin activity in the present study revealed a potential Tablenovel biological 1. Ligand property interactions of Betulin with and enterotoxins Noroleans. along with energy (kcal/mol) values.

Table 1. Ligand interactions with enterotoxins along with energy (kcal/mol) values. Commented [ST3]: Editor Ding agreed with the change of Table 1. Ligand interactionsMolecular with enterotoxins along with energy (kcal/mol)Staphylococcal values. Commented [ST3]: Editor Ding agreed with the change of PubChem Molecular Molecular Staphylococcal Free Energy Compound Name PubChem Molecular Weight Compound Structure Enterotoxin (SE)Free Energy the structure! Compound NameCID Formula MolecularWeight Compound Structure EnterotoxinStaphylococcal (SE) (kcal/mol) the structure! PubChemCID MolecularFormula (g/mol) in InteractionFree(kcal/mol) Energy Compound Name (g/mol)Weight Compound Structure Enterotoxinin Interaction (SE) Response: My special thanks to Editor Ding. We highly CID Formula (kcal/mol) Response: My special thanks to Editor Ding. We highly (g/mol) in Interaction appreciate his kindness. SEASEA −147.39− 147.39 appreciate his kindness. SEA −147.39 Betulin Betulin 72326 72326C30 H50OC302H50O2 442.728442.728 Betulin 72326 C30H50O2 442.728 SEB-likeSEB-like −139.97− 139.97 SEB-like −139.97

SEA −83.97− 28-Norolean-12-en-3- SEASEA −83.97 83.97 101616676 C29H46O 410.686 28-Norolean-12-en-3-one 28-Norolean-12-en-3-one 101616676 101616676C29H 46OC29H46O 410.686410.686 one SEB-like −88.20 SEB-likeSEB-like −88.20− 88.20

Int. J. Mol. Sci. 2018, 19, 133 8 of 13 Int. J. Mol. Sci. 2018, 19, 133 8 of 13

(a) (b)

FigureFigure 7. Docking 7. Docking poses of poses staphylococcal of staphylococcal enterotoxin enterotoxin A (SEA) with A (SEA)Betulin with(a) and Betulin 28-Norolean-12- (a) and en-3-one28-Norolean-12-en-3-one (b) compounds. A (hydrogen-bondb) compounds. interaction A hydrogen-bond is formed interaction between H44 is formed in Betulin between and oxygen H44 (O)in in Betulin GLY227 and residue oxygen of (O) SEA in (a GLY227); while residuetwo hydrogen-bond of SEA (a); while intera twoctions hydrogen-bond are formed by interactions H33 and H

in are28-Norolean-12-en-3-one formed by H33 and H inand 28-Norolean-12-en-3-one O6 in GLN43 and O in and GLY227 O6 in GLN43of SEA, andrespectively O in GLY227 (b); ofDocking SEA, posesrespectively of staphylococcal (b); Docking enterotoxin poses of B-like staphylococcal (SEB-L) with enterotoxin Betulin B-like(c) and (SEB-L) 28-Norolean-12-en-3-one with Betulin (c) and (d) 28-Norolean-12-en-3-one (d) compounds. Hydrogen-bond interaction is formed by O1 in Betulin and compounds. Hydrogen-bond interaction is formed by O1 in Betulin and O in TYR88 and H in ASN87 O in TYR88 and H in ASN87 and TYR88 of SEB-L, respectively (c); while a hydrogen-bond interaction and TYR88 of SEB-L, respectively (c); while a hydrogen-bond interaction is formed between O6 in 28- is formed between O6 in 28-Norolean-12-en-3-one and H in HIS59 residue of SEB-L (d). Residues Norolean-12-en-3-one and H in HIS59 residue of SEB-L (d). Residues located 5 Å from the compounds located 5 Å from the compounds along with bond length are shown. along with bond length are shown.

3. 3.Materials Materials and and Methods Methods 3.1. Source of Data 3.1. SourceThe of amino-acid Data sequences of the SEA (accession number: BAF68155) and SEB-like (accession number:The amino-acid KR819504) sequences were retrieved of the fromSEA the(accessi databaseon number: of the NationalBAF68155) Center and forSEB-like Biotechnology (accession number:Information KR819504) (www.ncbi.nlm.nih.gov were retrieved ).from the database of the National Center for Biotechnology Information (www.ncbi.nlm.nih.gov).

Int. J. Mol. Sci. 2018, 19, 133 9 of 13

3.2. Generation of the 3-D Structure Using Homology Modeling The amino-acid sequences of both enterotoxins SEA and SEB-like were blast-searched against RCSB PDB (www.rcsb.org) to identify the protein structures 1ESF and 4RGM [59]. Furthermore, each of the identiﬁed protein structures was used as a template for homology modelling using MODELLER9.17 (University of California, San Francisco, CA, USA), and the protein structure template was represented as probability density functions (PDFs) for the features restrained. The generated 3-D models were further used to actualize the homology of proteins by ideally fulﬁlling spatial restrictions developed from the alignment [60].

3.3. Molecular Dynamics The generated 3-D structures were further improved with a blend of sub-atomic elements and equilibration strategies by NAMD programming for lipids and proteins, along with Transferable Intermolecular Potential with 3 Points model for water [61]. The security extending, edge bowing, torsional and other compelling field parameters for SEA and SEB-like were specifically connected to guarantee redress hybridization using Chemistry at Harvard Macromolecular Mechanics 27 (CHARMM27) driven from the field protein parameters [62,63]. The vitality of the structures was upgraded for side chains, and dissolvable with 100,000-stage minimization to remove any bad contacts with 12 Å cutoff (switching function starts at 10 Å) for van der Waals interactions. A mix time-venture of 2 femtoseconds (fs) interim was used, allowing a different time-venturing calculation in which interaction of all covalent bonds involving hydrogens were figured at each time step [64]. Short-run non-fortified communications were registered for consistent time step and long-extend electrostatic powers were processed for every four-time step. The combined rundown of the non-fortified communications was recalculated for every ten-time venture with a couple list separation of 13.5 Å. The short scope of non-reinforced communications was within 12 Å, and smoothing procedure was used for van der Waals associations at the 10 Å cutoff. An equilibrated framework was recreated for two ps with a 500 kcal/mol/Å limitation on the protein spine under constant pressure of one atmospheric pressure (atm) steady weight and constant temperature of 310 Kelvin (K) (typical weight and temperature). The Langevin damping coefficient was set to 20 ps unless generally stated [65]. The structure with the most minimal vitality and steady low RMSD was utilized for further reviews. The final refined 3-D models of SEA and SEB-like were dissected by using Ramachandran plot calculations computed with the RAMPAGE (http://mordred.bioc.cam.ac.uk/~rapper/rampage.php) program [26]. Subsequently, the ERRAT diagram (Structure Evaluation server, http://services.mbi.ucla. edu/ERRAT/) and PROCHECK program (http://services.mbi.ucla.edu/PROCHECK) were utilized to perform environmental profiling for deciding stereo substance nature of the protein structures [66].

3.4. Binding Site Identification Binding sites of SEA and SEB-like were identified using the CASTp server, (http://sts.bioe.uic. edu/castp/). CASTp identifies all feasible pockets in the targeted protein structure and measures both the solvent-accessible surface area (Lee-Richards molecular surface) and solvent-excluded molecular surface (MS, Connolly surface) area, and pockets and pocket mouth openings, as well as cavities.

3.5. Substrate Docking The chemical structures of Betulin and 28-Norolean-12-en-3-one compounds were built with ChemSketch software suite v.15.01 (ACD/Structure Elucidator, Advanced Chemistry Development, Inc., Toronto, ON, Canada, http://www.acdlabs.com) and optimized using Molinspiration server (http://www.molinspiration.com)[35,39]. Ligands were docked with the binding sites of SEA and SEB-like by extremely FRED v.2.1 (Open Eye Scientific Software, Santa Fe, NM, USA). FRED enabled us to implement multi conformer docking in two steps. First, a conformational screening of the ligand was conducted, and then all relevant low-energy conformations were rigidly placed in the binding Int. J. Mol. Sci. 2018, 19, 133 10 of 13 sites. These two steps allowed the formation of the rigid structure by the translational degree of freedom. The FRED process was used for the series of shape-based filters, and the default scoring function was assigned based on the Gaussian shape fitting [67].

4. Conclusions In our study, the stable 3-D SEA and SEB-like models were constructed and further used for docking with the inhibitors Betulin and 28-Norolean-12-en-3-one. Docking results revealed several important amino-acid residues in the binding sites of SEA and SEB-like which play essential role in maintenance of their conformation and are directly implicated in substrate binding. The interactions between the inhibitors and the binding sites of SEA and SEB-like projected in this study are useful for the in-depth understanding of the binding mechanisms of inhibitors and active domain of enterotoxins. The bioinformatic tools described in this study are simple but highly effective and robust. The results revealed that the predicted conformations are in close agreement with the experimental structures available in literature. The method used in the current study provides a platform for in silico validation and control of S. aureus infection, resulting in effective control of food poisoning-related effects.

Supplementary Materials: Supplementary materials can be found at www.mdpi.com/1422-0067/19/1/133/s1. Acknowledgments: All the authors are grateful to the Department of Microbiology and Biotechnology, Karnatak University Dharwad, for providing the facilities. Author Contributions: Sudisha Jogaiah designed and coordinated the study. Mahantesh Kurjogi, Praveen Satapute and Jayasimha Rayalu Daddam performed the experiments. Sudisha Jogaiah, Mostafa Abdelrahman and Venkatesh Ramu analyzed the data with the input from Lam-Son Phan Tran, Mahantesh Kurjogi, Sudisha Jogaiah and Lam-Son Phan Tran wrote the manuscript. All the authors revised and approved the manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Abbreviations

3-D Three-Dimensional atm Atmospheric Pressure CASTp Computer Atlas of Surface Topology of Proteins CHARMM Chemistry at Harvard Macromolecular Mechanics FRED Fast Rigid Exhaustive Docking K Kelvin MS Molecular Surface NAMD NAnoscale Molecular Dynamics NCBI National Center for Biotechnology Information PDB Protein Data Bank PDFs Probability Functions ps and fs Pico- and Femtoseconds RCSB Research Collaboratory for Structural Bioinformatics RMSD Root-Mean-Square Deviation SEA Staphylococcal Enterotoxin A SEB Staphylococcal Enterotoxin B SEs Staphylococcal Enterotoxins

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