An In-Silico Investigation of Antibacterial Phytochemicals

113 Pages·2016·12.77 MB·English

antibiotics
Article
The Search for Herbal Antibiotics: An In-Silico
Investigation of Antibacterial Phytochemicals
MarySnowSetzer1,JavadSharifi-Rad2,3andWilliamN.Setzer1,*
1 DepartmentofChemistry,UniversityofAlabamainHuntsville,Huntsville,AL35899,USA;
[email protected]
2 ZabolMedicinalPlantsResearchCenter,ZabolUniversityofMedicalSciences,Zabol61615-585,Iran;
javad.sharifi[email protected]
3 DepartmentofPharmacognosy,ZabolUniversityofMedicalSciences,Zabol61615-585,Iran
* Correspondence:[email protected];Tel.:[phone]
AcademicEditor:WilliamR.Schwan
Received:26May2016;Accepted:26August2016;Published:12September2016
Abstract: Recently,theemergenceandspreadofpathogenicbacterialresistancetomanyantibiotics
(multidrug-resistant strains) have been increasing throughout the world. This phenomenon is
of great concern and there is a need to find alternative chemotherapeutic agents to combat
these antibiotic-resistant microorganisms. Higher plants may serve as a resource for new
antimicrobials to replace or augment current therapeutic options. In this work, we have carried
out a molecular docking study of a total of 561 antibacterial phytochemicals listed in the
Dictionary of Natural Products, including 77 alkaloids (17 indole alkaloids, 27 isoquinoline
alkaloids,4steroidalalkaloids,and28miscellaneousalkaloids),99terpenoids(5monoterpenoids,
31 sesquiterpenoids, 52 diterpenoids, and 11 triterpenoids), 309 polyphenolics (87 flavonoids,
25chalcones,41isoflavonoids,5neoflavonoids,12pterocarpans,10chromones,7condensedtannins,
11coumarins,30stilbenoids,2lignans,5phenylpropanoids,13xanthones,5hydrolyzabletannins,
and56miscellaneousphenolics),30quinones,and46miscellaneousphytochemicals,withsixbacterial
proteintargets(peptidedeformylase,DNAgyrase/topoisomeraseIV,UDP-galactosemutase,protein
tyrosinephosphatase,cytochromeP450CYP121,andNAD+-dependentDNAligase). Inaddition,
35knowninhibitorsweredockedwiththeirrespectivetargetsforcomparisonpurposes. Prenylated
polyphenolics showed the best docking profiles, while terpenoids had the poorest. The most
susceptibleproteintargetswerepeptidedeformylasesandNAD+-dependentDNAligases.
Keywords: antibiotic resistance; antibacterial phytochemicals; molecular docking; bacterial
proteintargets
1. Introduction
Recently,establishedantibioticshavebecomelesseffectiveagainstnumerousinfectiousorganisms,
and the Centers for Disease Control and Prevention (CDC) has warned of a “post-antibiotic
era”[1]. Thisconcernisheightenedbyourtenuousabilitytodetect,contain,andpreventemerging
infectious diseases. The emergence of pathogenic microbes with increased resistance to existing
antibioticsprovidesamajorincentiveforthediscoveryofnewantimicrobialagents. Theproblems
of drug-resistant pathogens have been reviewed recently [2–5]; there is a pressing need for more
effectiveantibacterialtherapy. Basedonseveralrecentreports,pathogensofimmediateconcernare
methicillin-resistantStaphylococcusaureus(MRSA),acommoncauseofhospital-acquiredinfections,
and which is evolving a resistance to vancomycin [6]; Pseudomonas aeruginosa in which multidrug
resistancehasbecomeproblematic[7];Streptococcuspneumoniae,acommonrespiratorypathogenin
whichmultidrugresistanceisspreading[8];multidrug-resistantstrainsofMycobacteriumtuberculosis[9],
Antibiotics2016,5,30;doi:10.3390/antibiotics5030030 www.mdpi.com/journal/antibiotics
Antibiotics2016,5,30 2of113
which are causing an alarming increase in the incidence of tuberculosis; and virulent strains of
Escherichiacoli,whichcontinuetoemerge[10–12].
Virtual screening using cheminformatics, pharmacophore, or ligand- and structure-based
target prediction methods [13] has emerged as an advantageous alternative to high-throughput
screening for identification of potential lead structures or biological targets for anti-infective drug
discovery. Forexample,BernalandCoy-Barrerahaveusedacombinationofmoleculardockingand
multivariateanalysistoidentifyantifungalandantiviralxanthoneleadcompounds[14]. Rahimiand
co-workershaveusedastructuralsimilaritysearchalongwithmoleculardockingtoidentifypotential
ShigellaflexneriDNAgyraseinhibitors[15]. Moleculardockinghasbeenusedtoidentifybacterial
peptidyl-tRNAhydrolaseasanadditionalalternativetargetforknownantibioticdrugs[16].
Untilthebeginningofthetwentiethcentury,virtuallyallmedicineswerederivedfromnatural
sources,mostoftenfromplants,andplantscontinuetoserveassourcesofnewmedicinesandprovide
leadcompoundsfordrugdevelopment. Theseantimicrobialagentsderivedfromhigherplantshave
been reviewed recently [17,18]. In the discovery of new and complementary antibacterial agents,
phytochemicalsthatshowantibacterialactivitycanbeexaminedforpotentialinhibitionofbacterial
targetproteinssuchaspeptidedeformylase(PDF),topoisomeraseIV(TopoIV),DNAgyraseB(GyrB),
proteintyrosinephosphatase(Ptp),UDP-galactopyranosemutase(UGM),cytochromeP450(CYP121),
andNAD+-dependentDNAligase,aswellasphytochemicalinhibitorsofbacterialeffluxpumpsor
quorumsensingproteins,oragentsthatenhancetheimmunesystem.Inthiswork,wehavecarriedout
anin-silicoscreeningofphytochemicalsidentifiedintheDictionaryofNaturalProducts[19]asshowing
antibacterialactivityagainstseveralpotentialbacterialproteintargets.
1.1. PeptideDeformylase
TheprocessofbacterialproteinsynthesisisinitiatedwithN-formylmethionine(f-Met-tRNAi),
whichisgeneratedthroughtheenzymatictransformylationofmethionyl-tRNA(Met-tRNAi)byformyl
methionyltransferase(f-Mett). TheN-formylgroupofthepolypeptidethatemergesfromtheribosome
aftercompletionoftheelongationprocessisremovedbythesequentialactionofpeptidedeformylase
(PDF)[20,21].Methionineaminopeptidase(MAP)thenremovestheN-terminalmethioninedepending
onthenatureofthesecondaminoacidinthepeptidechain[22]. Therefore,deformylationplaysa
pivotalroleinbacterialproteinmaturation,growth,andsurvival;PDFisvitalinavarietyofpathogenic
bacteriabutitisnotrequiredforcytoplasmicproteinsynthesisintheeukaryotes. Hence,PDFhas
beenidentifiedasapotentialantibacterialdrugtarget[23]. BacterialPDFsaremetallohydrolasesthat
use Fe2+ as the catalytic metal ion (which can be replaced with Ni2+ or Zn2+) that is tetrahedrally
coordinatedtotwohistidineresidues,acysteineresidue,andawatermolecule[24].
1.2. DNAGyrase/TopoisomeraseIV
Topoisomerase enzymes control the topological state of DNA within cells and are important
fortheessentialprocessofproteintranslationandcellreplication. Muchattentioninantibacterial
drugdiscoveryhasbeenfocusedontheDNAgyrase(atypeIItopoisomerase)andtopoisomeraseIV.
Thesetypesoftopoisomerasesarepresentinbacteriaandplants,butnotanimals. DNAgyraseand
topoisomeraseIVsharehighstructuralandsequencesimilarity,yetplaydifferentnecessaryrolesin
thereplicationofDNA.Becauseoftheirvitalnatureandmechanismsofaction,topoisomeraseshave
becomekeydrugtargetsforantibacterialdrugdiscovery[25,26].
1.3. UDP-GalactopyranoseMutase
UDP-Galactopyranose mutase (UGM) is the only enzyme known to catalyze the
isomerization of UDP-galactopyranose to UDP-galactofuranose. The enzyme has been identified
in prokaryotes, including Gram-negative bacteria and mycobacteria, as well as eukaryotic
parasites (Leishmania major, Trypanosoma cruzi), nematodes (Caenorhabditis elegans), and fungi
(Aspergillusfumigatus,Cryptococcusneoformans),buthavenotbeenfoundinhighereukaryotes[27].
Antibiotics2016,5,30 3of113
Galactofuranoseresiduesareessentialcomponentsofmycobacterialcellwalls,andthus,UGMhas
beenidentifiedasapotentialtargetforantimycobacterialchemotherapeutics.
1.4. ProteinTyrosinePhosphatase
Proteintyrosinephosphatases(Ptps)havebeensuggestedtobemajorvirulencedeterminants.
Theseenzymesreversetheeffectoftyrosinekinasesbydephosphorylatingthetyrosineresiduesof
hostcellularsubstrateproteinsimportantinhostcellularsignaling,whichattenuateshostimmune
defenses[28,29]. Ptpsareessentialcomponentsfortheinteractionofmycobacteriawithhostcells,
making them attractive protein targets; structural differences between mycobacterial Ptps and
eukaryoticPtpscouldallowforthediscoveryofselectivemycobacterialPtpinhibitors[28,29].
1.5. CytochromeP450CYP121
Severalantifungalazoleandtriazoleagentshavebeenshowntoinhibitmycobacterialcytochrome
P450CYP121[30]. CYP121isessentialforMycobacteriumtuberculosisandthereisacorrelationbetween
antimycobacterial activity and MtCYP121 binding, suggesting that MtCYP121 is the major target
inM.tuberculosis[31]. ThereislowsequencesimilaritybetweenMtCYP121andmammalianP450s,
whichsuggeststhatMtCYP121isapromisingantimycobacterialdrugtarget[32].
1.6. NAD+-DependentDNALigase
DNAligasesareinvolvedinDNAreplication,recombination,andrepairpathwaysbyjoining
adjacent3(cid:48)-hydroxyland5(cid:48)-phosphorylterminiinDNA[33]. BacterialDNAligasesuseNAD+asa
cofactor,whichdifferentiatesthemfromeukaryoticDNAligases,whichuseATP[34]. Thedifferences
betweenbacterialNAD+-dependentDNAligases(LigA)andmammalianATP-dependentDNAligases
suggestthatbacterialLigAshouldbeexcellenttargetsforantibacterialdrugdiscovery[35].
2. ComputationalMethods
Protein-ligand docking studies were carried out based on the structures of verified bacterial
proteindrugtargets.
Bacterialpeptidedeformylase: Bacilluscereus(BcPDF,PDB2OKL[36]);Escherichiacoli(EcPDF,
PDB 1G2A and 1G27 [37], PDB 1LRU [38], PDB 2AI8 [39], PDB 2KMN [40], and PDB 3K6L [41]);
Mycobacteriumtuberculosis(MtPDF,PDB3E3U[42]);Pseudomonasaeruginosa(PaPDF,PDB1LRY[38],
1IX1 [43], and 1S17 [44]); and Staphylococcus aureus (SaPDF, PDB 1Q1Y [43], PDB 3U7K, 3U7M,
and3U7N[45]). InordertotestfortheselectivitytowardbacterialPDFoverhumanPDF,molecular
dockingofthephytochemicalligandswasalsocarriedoutonhumanPDF(HsPDF,PDB4JE7and
4JE8[46]).
Bacterial DNA gyrase B/topoisomerase IV: E. coli (EcGyrB, PDB 1AJ6 [47], EcTopoIVB,
PDB1S16[48]),andM.tuberculosis(MtGyrB,PDB3ZKBand3ZKD[49]).
Bacterialproteintyrosinephosphatase: M.tuberculosis(MtPtpA,PDB1U2Q[28],andMtPtpB,
PDB2OZ5[29]). InordertocompareMycobacteriumPtpdockingoverhumanPtp,moleculardocking
ofthephytochemicalligandswasalsocarriedoutwithhumanPtpB(HsPtpB,PDB2I4Hand2I5X[50]).
MycobacterialUDP-galactopyranosemutase: M.tuberculosis(MtUGM,PDB4RPG,4RPH,4RPJ,
4RPK,and4RPL[51]).
MycobacterialcytochromeP450CYP121: M.tuberculosis(MtCYP121,PDB4IPS[52],4KTF[53],
and5IBE[32]).
Bacterial NAD+-Dependent DNA ligase: E. coli (EcLigA, PDB 2OWO [35] and 4GLX [54]),
M. tuberculosis (MtLigA and PDB 1ZAU [55]), S. aureus (SaLigA, PDB 4CC5 and 4CC6 [56]),
andS.pneumoniae(SpLigAandPDB4GLW[54]).
Priortodocking,allsolventmoleculesandtheco-crystallizedligandswereremovedfromthe
structures.Ifco-factorswerepresent,theywereretainedineachproteinmodel(e.g.,divalentmetalions
inpeptidedeformylases,flavinadeninedinucleotide(FAD)inM.tuberculosisUDP-galactopyranose
Antibiotics 2016, 5, x   4 of 99 
Antibiotics2016,5,30 4of113
Prior to docking, all solvent molecules and the co‐crystallized ligands were removed from the 
musttarsuec,tuarneds. hIfe cmoe‐fainctoMrst CwYerPe1 p2r1e)s.eMnto, tlheceuy lwarerdeo rcektainingedca ilnc uelaacthio pnrsotfeoirn amllocdoeml (pe.ogu., nddivsawleintth meeatcahl of
theiopnrso teiinn s wpeeprteiduen ddeerftoarkmenylausseisn, g Mflaovlieng roadVeinritnuea l Ddioncukcelero(tvideer sio(FnA6D.0),  Mino leMgr.o  AtupbeSr,cuAloasrihs us,
UDP‐galactopyranose mutase, and heme in MtCYP121). Molecular docking calculations for all 
Denmark)[57],withasphere(15Åradius)largeenoughtoaccommodatethecavitycenteredonthe
compounds with each of the proteins were undertaken using Molegro Virtual Docker (version 6.0, 
binding sites of each protein structure in order to allow each ligand to search. If a co-crystallized
Molegro ApS, Aarhus, Denmark) [57], with a sphere (15 Å radius) large enough to accommodate the 
inhibitor or substrate was present in the structure, then that site was chosen as the binding site.
cavity centered on the binding sites of each protein structure in order to allow each ligand to search. 
Ifnoco-crystallizedligandwaspresent,thensuitablysized(>50Å3)cavitieswereusedaspotential
If a co‐crystallized inhibitor or substrate was present in the structure, then that site was chosen as the 
binding sites. Standard protonation states of the proteins based on neutral pH were used in the
binding site. If no co‐crystallized ligand was present, then suitably sized (>50 Å3) cavities were used 
dockingstudies. Eachproteinwasusedasarigidmodelstructure;norelaxationoftheproteinwas
as potential binding sites. Standard protonation states of the proteins based on neutral pH were used 
performed.Assignmentsofthechargesoneachproteinwerebasedonstandardtemplatesaspartofthe
in the docking studies. Each protein was used as a rigid model structure; no relaxation of the protein 
MolegroVirtualDockerprogram;nootherchargeswerenecessarytobeset. Overall,561antibacterial
was performed. Assignments of the charges on each protein were based on standard templates as 
phyptaorct hoef mthiec aMlsohleagvroe bVeiretnuadl oDcokcekde.r Tphroisgrmamol;e ncou loethseert wchaasrgceosm wperries endecoefss7a7rya ltkoa bloe isdest., O99veterarpll,e 5n6o1i ds,
190aflntaivbaocntoeriidasl ,p1h1y9topcohleympihcaelnso hlaicvec obmeepno duoncdkesd,.3 T0hqisu minoolneecus,lea snedt w46asm coismceplrliasnedeo ouf s77p halyktaolcohidesm, 9ic9a ls.
Eactherlpigeannodidsst, ru1c9t0u rfelawvoansobiudsil,t u11s9in gpoSlpyaprhtaenn‘o1l4icf ocromWpinoduonwdss, (v30e rsqiuoinn1o.n1e.0s,, Wanadv ef4u6n cmtiioscnelIlnacn.e,oIruvsi ne,
CAp,hUyStoAch).eFmoircaelsa.c hEalcigha lnigda,nad csotnrufocrtumrea tiwoansa lbsueialtr cuhsianngd Sgpeaortmane‘t1r4y foopr tWimiinzdaotiwosn (wvearssicoanr r1ie.1d.0,o ut
usinWgavtheefuMncMtioFnF Ifnocr.c, eIrfiveilnde,[ 5C8A]., FUleSxAib).l eFloigr aenadchm loigdaenlds,w ae rceonufsoerdmiantiothneald soecakricnhg aanndd gseuobmseeqtruye nt
optoimptiizmaitzioatniosnc hweams ec.aVrraireida boluet oursieinngt atthioe nMsMofFeFa fcohrcoef ftiheeldl i[g5a8n].d Fslewxeibrlee sleigaarcnhde mdoadnedlsr awnekreed ubseadse idn on
thetihrer ed-oracknikngsc aonrde. sFuobrseeqaucehndt oocpktiimngizsaitmioun lsacthioenmteh. eVmaraiaxbilme uormienntuamtiobnesr ooff eiatechra otifo tnhse floigratnhdesd woecrkei ng
searched and ranked based on their re‐rank score. For each docking simulation the maximum 
algorithmwassetto1500,withamaximumpopulationsizeof50,and100runsperligand. TheRMSD
number of iterations for the docking algorithm was set to 1500, with a maximum population size of 
thresholdformultipleposeswassetto1.00Å.Thegeneratedposesfromeachligandweresortedby
50, and 100 runs per ligand. The RMSD threshold for multiple poses was set to 1.00 Å. The generated 
thecalculatedre-rankscore.
poses from each ligand were sorted by the calculated re‐rank score. 
3. ResultsandDiscussion
3. Results and Discussion 
The Molegro Virtual Docking program [57,59] was used to carry out in-silico protein-ligand
The Molegro Virtual Docking program [59,57] was used to carry out in‐silico protein‐ligand 
dockingstudiesusingknownantibacterialphytochemicalswiththestructuresofverifiedbacterial
docking studies using known antibacterial phytochemicals with the structures of verified bacterial 
protein drug targets. A total of 561 antibacterial phytochemicals listed in the Dictionary of
protein drug targets. A total of 561 antibacterial phytochemicals listed in the Dictionary of Natural 
Natural Products [19] were studied, including 77 alkaloids (17 indole alkaloids, 27 isoquinoline
Products [19] were studied, including 77 alkaloids (17 indole alkaloids, 27 isoquinoline alkaloids, 4 
alkaloids,4steroidalalkaloids,and28miscellaneousalkaloids),99terpenoids(5monoterpenoids,
steroidal  alkaloids,  and  28  miscellaneous  alkaloids),  99  terpenoids  (5  monoterpenoids,  31 
31 sesquiterpenoids, 52 diterpenoids, and 11 triterpenoids), 309 polyphenolics (87 flavonoids,
sesquiterpenoids, 52 diterpenoids, and 11 triterpenoids), 309 polyphenolics (87 flavonoids, 25 
25cchhaallccoonneess,, 4411 iissooffllaavvoonnooididss, ,55 nneeooflflaavvoonnoiodisd,s 1,21 2ptpetreorcoacraprapnasn, s1,01 c0hcrhomroomnoesn,e 7s ,c7oncodnendseends etadntnainnns,i ns,
11c1o1u cmouamrianrsi,n3s,0 3s0t isltbilebneoniodisd,s2, 2l ilgignnaannss,,5 5 pphheennyyllpprrooppaannooiiddss, ,1133 xxaannththonoense, s5, 5hyhdyrdorlyozlyabzlaeb tlaentnainnns,i ns,
andan5d6 m56i smceisllcaenlleaonuesoupsh penhoenlioclsi)c,s3),0 3q0u qinuoinnoense,sa,n adnd4 646m misicseclelallnaenoeuoussp phhyytotocchheemmicicaalsls( (sseeee FFiigguurreess 11––23),
wit2h3s),i xwbaitcht ersiiaxl pbraoctteeirniatla rpgreottsei(np eptatridgeetsd e(fpoermptyidlaes ed,eDfoNrmAyglaysrea,s eD/NtoApo igsyormaseer/atsoepoIVis,oUmDePra-sgea laIVct,o se
muUtaDseP,‐gparolatcetionsety romsiunteaspeh, ospprhoatetains e,tcyyrtooscihnreo mpehPo4sp50haCtaYsPe,1 21c,yatoncdhNroAmDe +-dPe4p50e ndCeYnPt1D2N1,A  laingdas e).
AsNaAteDst+‐fdoerpdeoncdkeinntg DaNccAu rlaigcays,et)h. eAcso a-c treysst tfaolrl idzeodckliingga nadccsufrraocmy, ethaec hcop‐crorytestinallsitzreudc tluigraenwdse rfreorme- deaocchk ed
intoprtohteeipnr ostteruincstu.rTeh wederoec krein‐dgoecnkeerdg iienstoa nthder poorot-tmeinesa.n Tshqeu adroecdkidnegv eianteiorgnises( RaMndS Dro)oatr‐meseuamn msqaurairzeedd in
deviations (RMSD) are summarized in Table 1. In order to correct for the known biasing of docking 
Table1. Inordertocorrectfortheknownbiasingofdockingenergies(E )withincreasingmolecular
dock
weiegnhetrg(iMesW  (E)d[o6c0k)– 6w5i]t,hw  ienchraevaesinagls omdoelteecrumlairn ewdeiaghnto r(mMaWli)z e[d60d–o65c]k, inwges choarvee (DalSso  de)tebramseindedo nat he
norm
monleocrumlaarlizweedi gdhotc:kDinSg score= (7D.2Sn×ormE) base/dM onW t1h/e3. molecular weight: DSnorm = 7.2 × Edock/MW⅓. 
norm dock
COMe
HO 2
N Me
MeO N
MeO N OH MeO N CO2Me N H
H H MeO Me
1 2 3
1-Hydroxy-6,7-dimethoxy- 11-Methoxytubotaiwine 12-Methoxy-4-methylvoachalotine
3-methylcarbazole  
Figure1.Cont.
Antibiotics2016,5,30 5of113
AAnnttiibbiioottiiccss  22001166,,  55,,  xx     55  ooff  9999  
OO
OOHH NN OO
NN HH
OO
NN
HH
NN NN HH NN
HH MMee HH OOMMee
44 55 66 77
33--PPrreennyylliinnddoollee AAffffiissiinnee ((SS))--AAppppaarriicciinnee AArriissttoollaaccttaamm II   
OO OOHH
HH MMee
NN NN NN NN
HH OOHH
HHOO OO NN HH
NNMMee HH MMeeOO22HHCC HH OOHH
88 99 1100
CCllaauusseennaawwaalllliinnee AA CCrryyppttoohheeppttiinnee DDiipplloocceelliinnee   
NN
HH OOMMee
OO
OO OO NN HH OO
NN HH HH OO OO
HH NN HH NN HH 1122 NNHH
OO IIbbooggaammiinnee NN HHNN
HHOO OO
NN
NNMMee22 NN MMeeOO HH NNMMee22
1111 HH NN HH NN 1155
DDiissccaarriinnee BB HH HH RRuuggoossaanniinnee BB
1133
IIbbooxxyyggaaiinnee   
HHOO22CC OOHH
NN OO
HH HHOO NN
HH
MMeeOO NN NN OO
HHCCOO22MMee HH
1144 1166 1177
IIssoovvooaaccaannggiinnee SSuuaavveeoolliinnddoollee TToouussssaaiinnttiinnee BB   
FiFFgiiugguurerree1  11...I  nIInndddooollleee  aaalllkkkaaallloooiiidddsss  eeexxxaaammmiiinnneeeddd  iinnin  tthhthiissi  swwwooorrkkr..k  .
OO OO OOMMee
MMeeOO
OO OO MMeeOO OO HH
OO NN MMeeOO NN
NN
OO OO MMeeOO
1188 1199 2200
88--AAcceettoonnyyllddiihhyyddrrooaavviicciinnee 88--AAcceettoonnyyllddiihhyyddrroonniittiiddiinnee AAnnttooffiinnee   
Figure2.Cont.
Antibiotics2016,5,30 6of113
Antibiotics 2016, 5, x   6 of 99 
MeO OMe
O MeO
N
O H H
N H
O
OMe N
O
OMe O MeO
21 22 23
Berberine Bisnorthalphenine Cryptopleurine  
OMe MeO OMe MeO
N N N N
Me OMe H Me Me
H O H H O H
O O
OMe OMe
24 25
Berbamine Cepharanthine  
MeO
N
MeO
H O
HO
N
O
H H N
H H MeO
H OMe O OMe
N
OMe
OMe O OMe OMe
26 27 28
Emetine Hydrastine Jatrorrhizine  
OMe OMe
MeO OMe O
O
Me N N Me N OMe O N
H H H H
O
O O
OMe OMe
29 30
Isotrilobine Methothalistyline  
MeO MeO MeO
N N N
MeO Me O Me O
H H H
MeO O O
OH O O
31 32 33
Lauroscholzine N-Demethylthalphenine Thalphenine  
OMe MeO OMe MeO
N N N N
Me OH Me Me MeO Me
H O H H O H
O O
OH OH
34 35
Obamegine Oxyacanthine  
Figure2.Cont.
Antibiotics2016,5,30 7of113
AAnnttiibbiioottiiccss  22001166,,  55,,  xx     77  ooff  9999  
OOMMee MMeeOO OOMMee MMeeOO
MMee NNHH OOOOMHMHee MMeeOO NHNHMMee MMee NNHH OOOOMMMMeeee MMeeOO NHNHMMee
OOMMee MMeeOO OOMMee MMeeOO
OO 3366 OO 3377
PPeennnnssyyllvvaanniinnee TThhaalliiccaarrppiinnee   
OOMMee
OOMMee MMeeOO
MMee NNHH OOOOOOMHMMHMeeee MMMMeeeeOOOO NHNHMMee MMee NNHH OOOOMMMMeeee MMMMeeeeOOOO OO NHNHMMee
OO
OO OOMMee
3388 3399
TThhaalliiaaddaanniinnee TThhaalliiddaassiinnee   
OOMMee MMeeOO
OOMMee OOMMee
NNHH OOOOMMMMeeee OOOO NHNHMMee MMee NNHH OOOOOOMHMMHMeeee MMMMeeeeOOOO NHNHMMee
OO
OOMMee OO
4400 4411
TThhaalliissttyylliinnee TThhaallmmeellaattiinnee   
OOHH OO
OOMMee MMeeOO OOMMee MMeeOO
MMee NNHH OOMMee MMeeOOOO HNHNMMee MMee NNHH OOHH MMeeOO NHNHMMee
OO OO
OOMMee OOMMee
4422 4433
TThhaallmmiirraabbiinnee TThhaallrruuggoossiiddiinnee
OOMMee MMeeOO
MMee NNHH OOHH OO NHNHMMee
OO
OOMMee
4444
TThhaallrruuggoossiinnee   
FiFFgiiuggruuerree2  .22.I.  sIIsosooqqquuuiniinnooollliiinnneee  aaalllkkkaaallloooiiidddsss  eeexxxaaammmiiinnneeeddd  iinnin  tthhthiissi  swwwoorrokkr..k  .
OOOO OO NN HHOO OO NHNH NN CCHHOO OOOO HH OOHHOOOO OOMMOOeeHH
HH NN
4455 4466 4477
PPiippeerriinnee AAccoonniiccaarraammiiddee LLaassiiooccaarrppiinnee   
Figure3.Cont.
Antibiotics2016,5,30 8of113
AAnnttiibbiioottiiccss  22001166,,  55,,  xx     88  ooff  9999  
OO HHOO OOHH OOMMee
OO NN
OO HH
OO OOMMee NN OO NN NN NN
NN MMee MMee
MMee
OO
4488 4499 5500 5511
44--MMeetthhooxxyy--11--mmeetthhyyll--
LLaassiiooccaarrppiinnee NN--ooxxiiddee CCrryyppttoolleeppiinnee NNeeooccrryyppttoolleeppiinnee
22((11HH))--qquuiinnoolliinnoonnee   
MMee
NN
OOMMee HH
OOMMee
HH
MMeeOO OOHH
MMeeOO NN OO HH HH
NN OO OOMMeeMMee MMee22NN
5522 5533 5544
PPtteelleeiinnee VVeepprriissiinniiuumm CCoonneessssiinnee   
HH
NN HH NN
NNHH22 HH HH
HH HH OO OOMMee HH OO OOHH
HH
HH HH
HH HH HH HH
HH HH
HH22NN HH22NN HH HH22NN HH
5555 5566 5577
IIrreehhddiiaammiinnee AA SSoollaaccaassssiinnee SSoollaannooccaappssiinnee   
HH NN
HH
HH OO
HH
HH HH
HHNN
22 HH
5588
TToommaattiiddiinnee   
FigFuFiirggeuu3rre.e  P33i.. p PPeiiprpieedrriiinddeiinn,eep,, y ppryyrrorrrlooel,leep,,  yppryyrrrorroloilzliizizdiiddiniinneee,,,q  qquuuiininnooollliiinnneee,,,  aaannnddd  sssttteeerrroooiiidddaaalll  aaallklkkaaallooloiiddidss s eexexaxamammiinnieendde  diinni  ntthhtiishs  iwwsoowrrkok.r.  k.
MMeeOO OOMMee MMeeOO
HH22NN OO
OOHH OOHH OO OO HHOO
OOHH OO OO NNHH NNHH NN HHNN NNHH NN
OOMMee HHNN OO MMee
HHOO OO NN OO HHOO
5599 HH NNHH OO
22--((MMeetthhooxxyyaammiinnoo))--44HH--11-- OO 6622
bbeennzzooppyyrraann--33,,44,,55,,77--tteettrrooll 6600 6611 CCeepphhaarraattiinnee AA
AAbbeesssseenniinnee BB MMee22NN AAmmpphhiibbiinnee HH   
Figure4.Cont.
AntAibniottibiciost2ic0s1 260,156,, 350, x   9 o9f 9o9f 113
Antibiotics 2016, 5, x   9 of 99 
Antibiotics 201O6, 5, x   O H MeO 9 of 99 
O O H MeO
MeO N O
N N MeO N N O
N N MeO N Me
CCuruc6raHc63OmaH3midNHiedeH HHNH O HMHOHeOOO HHOHOHOO NHOOOOO OOMMeeMMMeeeOOO EEscshcshcs6hcN65ohl5otMzMltiezdeiidniene OOOO
Curc6a3mHiHdHeNHN OO HHHOOHHOOO 6644OOHOH OMOOMeOeOOMe Eschsc6h5oltzidine OO
OO HOON ONNHH DDrordord6er4neinnOinHHH2N2NMeO NNO
HHOO HHO6H6H6H6HHHH O HH6HHN67HNHNN7HHHH DrodreninOOOH2HNHHNNNOOO NNNHHH ONNONHHHMMMeeOOeO OOMMe6OeNHOH9MOHMeMee
HO JeJrevrivnie6ne6 MMatarit6nri7ene O N-BOenMze6oy9lmescaline
Jervine Matrine 6688 N-Benzo6y9lmescaline
MMucurcor6no8innien eH H N-Benzoylmescaline   
OMe OMe Mucronine H N  
OMe OMe N
O OMe O OMe NO
O O O O O
O O O O O O
NN NHOOHOONNHONO ONNHHNH NNNOHOHONOHNNOOONNHNHH HHHNNN HOHHONNON O NNNHHH MMMeeOOeO SShShaa7hh7Oha3id73ihNdi3inidneiene
O O O N
OMOMeNeONMNNHHeNNHNuNmuHmNmum7um7l0uaml0rai7unril0ena reBin Be B NNuNmNuNHmumNH2mmH72um27l1ua7ul1rail1narirenin eSe S S SNSScNNMcucMutMuei7tate2ie7ian72a22n2in2niniene eD D D N,N'N''-THNrHNiis7HNN7o575pHNe5HNnHNtylguanidine
H N,N'N''-Triisopentylguanidine
H N,N'N''-Triisopentylguanidine
MeO
MeO
MeO H
OOO NNMNMeM2ee22 HNHNN
O O
O O O O OO
O AAcAOccOO OO OH OO
O O O OOHH 76
ThOal7igO74lu47c4inone TTuTu7bu7beb6er6eirnrinienee  
ThaTlihgalulicgilnuocinneone   
FFiFiggiFuugiurrgeeru e4r4 e.4.  M.4M M. iMissicsciecselcellelalllanalnaneneoeeouoouusu ssas  laaaklllakkklaaaollliooodiiiddsd sses  xeeexaxxamaammimniinneiendeded i din inn it tn hhthiitssih s wwi swoowrorkkro.k.  r.k .
HOHO HHOHOO OOO OOOHHH OOO OOHOHH
HO
OOHH OOHH
OH OH
7777 7788 7799 8800 8181
77 78 79 80 81
(R)(-RL)i-nLainloaololol ThTyhmymooll TThhyymmooqquuiinnooll --DDoolalabbrirnin -T-hTuhjaupjalicpilnic in 
(R)-Linalool Thymol Thymoquinol -Dolabrin -Thujaplicin 
FiFgiFguiugreurer5 e5. 5.M .M Moononnoootteteerrrpppeeennnoooiiidddsss  eeexxxaaammmiinnineededd  iinni n tthhtihsis iw swoworkrok.r .k .
Figure 5. Monoterpenoids examined in this work. 
HOO OOHH HHOO
O HO OH HO
O H
O OOO OO OOO
O
O O
O O
O O
82 83 84 85
11,11138,-1D8232e-Dheydhyroderorieorliionlin 2,21,01-0B-Bisiasba8bo83olal3addieienn-1-1--oonnee 22--HHyyddrro8ox8x4yy4ccaalalammeenneennee 22-9-2MM--gMeeutteahhioteoh8xnxoy85y-xf85ufyu-rofarunanrenaoon--o -
11,13-Dehydroeriolin 2,10-Bisaboladien-1-one 2-Hydroxycalamenene 9-guaien-8-one  
9-guaien-8-one  
Figure6.Cont.
Antibiotics2016,5,30 10of113
Antibiotics 2016, 5, x   10 of 99 
O O OH
H
O
O
O HO O O
HO HO H H O
O O
86 87 88 89
4,10-Dihydroxy-1,11(13)-
Alantolactone Alliacol A Alliacol B
guaiadien-12,8-olide  
OH
H
H
HO
O O O O H H H
O O H O O O O O
O O OH OH O H O HO H H O
90 91 92 93
Baileyolin Bilobalide A Confertin 4,10-Dihydroxy-1,11(13)-
guaiadien-12,8-olide  
OHC
O
O COH
O 2
OHC HO O
94 95 96 97
Cyperenal Cyperenol Ganodermycin Furanodienone  
H
H OHC H O
O O
O
O O
OHC
O HO H O H COH H O
2
98 99 100 101
Helenalin Hydrogrammic acid Isoalantolactone Ivaxillin  
O O
CHO
O OH CHO HO
H H HO
HO HO H
102 103 104 105
Petrovin A Petrovin B Polygodial Rishitin  
H H H
OH
H H H
106 107 108 109
Xanthorrhizol -Amorphene -Cadinene -Muurolene  
Figure6.Cont.