Identification of Antifungal Targets Based on Computer Modeling

28 Pages·2017·1.88 MB·English

Journal of
Fungi
Review
Identification of Antifungal Targets Based on
Computer Modeling
ElenaBencurova1,†,ShishirK.Gupta1,†,EditaSarukhanyan1,† andThomasDandekar1,2,* ID
1 DepartmentofBioinformatics,AmHubland,Biozentrum,UniversityofWürzburg,
97074Würzburg,Germany;[email protected](E.B.);
[email protected](S.K.G.);[email protected](E.S.)
2 BioComputingUnit,EMBLHeidelberg,69117Heidelberg,Germany
* Correspondence:[email protected];Tel.:[phone]
† Thesethreeauthorscontributedequallytothiswork.
(cid:1)(cid:2)(cid:3)(cid:1)(cid:4)(cid:5)(cid:6)(cid:7)(cid:8)(cid:1)
(cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid:7)
Received:15May2018;Accepted:29June2018;Published:4July2018
Abstract:Aspergillusfumigatusisasaprophytic,cosmopolitanfungusthatattackspatientswithaweak
immunesystem. Arationalsolutionagainstfungalinfectionaimstomanipulatefungalmetabolism
or to block enzymes essential for Aspergillus survival. Here we discuss and compare different
bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways.
Forinstance,phylogeneticanalysisrevealsfungaltargets,whiledomainanalysisallowsustospot
minordifferencesinproteincompositionbetweenthehostandfungi. Moreover,proteinnetworks
between host and fungi can be systematically compared by looking at orthologs and exploiting
information from host–pathogen interaction databases. Further data—such as knowledge of
a three-dimensional structure, gene expression data, or information from calculated metabolic
fluxes—refinethesearchandrapidlyputafocusonthebesttargetsforantimycotics. Weanalyzed
severalofthebesttargetsforapplicationtostructure-baseddrugdesign. Finally,wediscussgeneral
advantagesandlimitationsinidentificationofuniquefungalpathwaysandproteintargetswhen
applyingbioinformaticstools.
Keywords: Aspergillus;metabolicpathways;computationalmodelling;drugdesign
1. Introduction
In the last 40 years, fungal infections have been considered to be of the most common human
diseases[1].Incidenceofinvasivefungaldiseasesisrisingwithhighernumbersoforgantransplantations,
medicalinterventions,chronicgranulomatousdisease,acuteleukemia,andAIDS[2,3]. Fungalsporesare
everywhere:Fungiareanaturalpartoftheenvironmentthatoccupyeverypossiblenicheincludingsoil,
air,water,andevenhospitals. Moreover,fungiareemergingpathogens[4]. Forinstance,Candidaauris,
firstdescribedinJapanin2009iscurrentlyspreadin17countrieson5continents,isdrugresistant,
andisapotentialthreattothepublichealth[5]. Inthisreview,wefocusonAspergillusfumigatus—a
majorfungalpathogenandotherinvasivefungalspecies,suchasCandidaalbicans. A.fumigatusisan
excellentmodelorganismtostudyseverefungalinfections. Theinfectionfirstbeginsatrespiratory
pathwaysandthenspreadsbysepsis. Pronetoanantibioticattackincludethemetabolism,andthe
cellwall. Inthefollowingchapters,wesummarizeandexemplifyhowbioinformaticsmethodscan
identifypotentialdrugtargets,rankthem,andhelptotargetthebestasspecificaspossiblebydrugs.
2. FungalDiseasesandDomainAnalysis
Fungal infections represent a serious health problem worldwide. While superficial infection,
caused by dermatophytes, occurs in approximately 25% of the general population [6], invasive
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fungalinfectionsarelesscommon. These,however,areoftenassociatedwithahighmortalityrate.
Themajorityoffungal-relateddeathsarecausedbythefourgenera:Cryptococcus,Candida,Pneumocystis,
and Aspergillus. Aspergillus causes several types of infection: (i) chronic pulmonary aspergillosis;
(ii)aninvasiveaspergillosis;and(iii)itisastrongaeroallergen,causingallergiesandasthma(reviewed
in [7]). Among the several hundred Aspergillus species, only a few of them are able to infect man,
includingA.fumigatusandA.flavus(reviewedin[8]). Themosteffectivemedicationsagainsthuman
aspergillosisarepolyenes,azoles,andechinocandins,however,theirtoxicity,interactionswithother
drugs,andincreaseddrugresistanceofseveralAspergillusstrainsarethemainreasonstopersistently
seeknoveltreatments/therapies/antidotes.
ThefightagainsteukaryoticpathogensisespeciallychallengingsinceallEukaryoteshavesimilar
typesofcellswithanucleusandbigribosomes,andthesamemetabolicandregulatorypathwaysas
theirmammalianhost. Notonlythetreatmentoffungaldiseaseschallenging,butalsootherinfections
causedbyEukaryotes,e.g.,ApicomplexasuchasPlasmodium,Myriospora,andToxoplasma,wherethe
treatmentwithstandardantibioticsdoesnotwork. Onestrategyistotargetproteinspresentinboth
theeukaryoticpathogenanditshost,butaimingatdifferencesindomainstructuresothatthedrug
affectsmainlytheproteininthepathogen. Zirkeletal. combinedinsilicomodellingandPCRdatato
demonstratethatmethyleneblueselectivelyinhibitsglutathionereductaseofPlasmodiumfalciparum
withoutaffectingthehumanglutathionereductasemuch[9]. PharmacologicallyinterestingCA/C1
peptidases have also shown several functional overlaps in P. falciparum, P. berghei, and their
hosts—MusmusculusandHomosapiens. Domainandregulationdifferencesallowustospecifically
target cathepsinsinplasmodiaincriticalstagesoftheirlifecycle[10].
Fungalparasitestargetvariousanimalspecies(Table1). Mycosesinveterinaryresearcharerarely
reported,asdiagnosisisdifficult. However,theycaninduceaseriousmultipleorganfailure. Dueto
changesinclimatethatpromotetheglobalspreadandproliferation/expansionofmicroorganisms,
pathogenic fungi pose a significant threat as an emerging health hazard [4]. On the other hand,
several animal models have been established for investigation of fungal pathogenesis, host–fungi
interaction,anddevelopmentofantifungaldrugs. Forexample,mice,rabbits,guineapigs,chickens,
andducksrepresentsuchmodelsforthestudyofaspergillosis. Despiteplentyofbenefits,theirsize,
phenotypicdeviation,andgenetichomogeneityofinbredstrainsarelimitingfactorswhen(tryingto
mirror)mimickingtheclinicalsituationinhumans[11].
J.Fungi2018,4,81 3of28
Table1.Commonfungaldiseasesinanimals.Sources:https://www.msdvetmanual.comandhttps://www.britannica.com.
HostSpecies Agent Diseases Description Treatment
Fungusaffectsthegutoflarvae.Outcompeteslarvaefor
Honeybee(Apismellifera) Ascosphaeraapis Chalkbrood nutritionandturnsthelarvaeinto“chalk-like” Apiguard,thymol-basedtreatment
mummies.Veryinfectious.
A.fumigatus,A.flavus, Thefunguscausingthegreenoryellowmummification
Honeybee(Apismellifera) Stonebrood Notreatment
A.niger oflarvae.Veryinfectiousbutnotcommon.
Fungalinfectionattackmostlydisease-weakenedpets,
A.fumigatus,A.flavus, Aspergillosis,Keratomycosis Ketoconazole,Itraconazole,
Allmammals,birds diseaseordrugtherapyispossible.Thenasaland
A.nidulans,A.niger (horses) Fluconazole
pulmonaryformismosttypical.
Systematicmycosisdescribedinhumans,dogs,and AmphotericinBandketoconazole
Dogs,horses(rarelycats) Blastomycesdermatitis Blastomycosis
horses,endemicinNorthAmerica. (dogs),notreatmentforhorses
Thechronicfungaldiseasecausesinflammationand
Histoplasma Notreatment(onlysurgical
Horses,mules,donkey,cattle EpizooticLymphangitis suppurationofthecutaneousandsubcutaneous
farciminosum excisionofallaffectednodes)
lymphaticvesselsandglands.
Mucocutaneousdiseaseinfectingmostlythe
nasopharynx,gastrointestinaltractandexternal AmphotericinB,Fluconazole,
Allmammals,birds C.albicans,C.fumata Candidiasis
genitalia.Oftenoccursinbirds,incatscandidiasisis Chlorhexidine(birds)
rare.Causeofarthritisinhorsesandmastitisincattle.
J. Fungi 2018, 4, x FOR PEER REVIEW 4 of 27
J.FungiA2s0p18e,r4g,i8l1lus fumigatus can be used as model organism for study of ascomycetous patho4goefn28s. The
tendencies in current research are aimed to develop an antimycotic drug that will treat the immuno-
comAprspoemrgiislleuds sftuamteig caatuussecda nbyb etheu siendfecatsiomn owdietlh oArg. afunmisimgatfuosr, Cst.u adlybicoafnsa,s Ccormypytcoectoocucuss,p aanthdo geemnesr.ging
Tmhoeldtesn odfe nthciee soridnerc uMrruencotrraelseesa r[1ch2].a Trehea igmeendomtoesd oevf eilmoppoarntanant tfiumnygcaolt ipcadthruoggetnhsa tarwe ialllrteraeadty tkhneown,
iimncmluudnion-cgo mA.p rfuommiigseadtuss,t aCte. nceaoufosermdabnys, tCh.e ailnbifceacntiso,n PawriatshiteAll.a fpuamraigsaittuicsa,, CH.isatlobpiclaansms,aC crayppstuocloactucums,, and
aPnndeuemmeorcgyisntgism ojlidrsovoefctihi.e orDdeerspMiutec oratlhese [12s]i.gTnhiefigceannot medsiovfeimrspitoyr tanitnf unfguanlgpaatlh oggeennsoamreeasl,r eadsyeveral
ksntuodwine,s/ipnuclbuldicinagtioAn.sf urmeivgeaatules,dC s.tnreeosfso rmspaencs,ifCic. aglbeincaen sc,oPnasrearsvitaeltliaopna raamsitoicna,gH fiustnopglaals msapecacpiesus.l atFuimgu,re 1
aenxdemPnpeulimfioecsy sctiosnjisroevrevceiid.D gesepnietes th(eins igmniafgiceannttad)i vienr sistyevinerfauln gfaulnggein oamneds ,simevperoarltsatnutd ipesa/tphuobgleicnasti.o nHsighly
rceovneaselerdvesdtr egsesnsepse cwifiecreg efnoeuncodn smerovsattliyon aammoonngg tfruanngsamlesmpebcrieasn.e Fpigroutreein1s,e xee.gm.,p ldifiivesercsoen sterarvnesdporter
gfeanmeisli(eins, maacgyelnttraa)nisnfesreavseer alpfurontgeiinans,d iamnpdo rtpahntosppahthootgraennss.feHraisgehsl y(ctoynrosesrinveed; gseenriensew/therreeofonuinned and
mamositnlyogalmycoonsgidtera).n Psmroeteminbsr asnuechp raost ecihnisti,ne .sgy.,ndthivaesres oeft rWan. sdperomrtaetritfiadmis i(lhieisg,halcigyhlttreadn sifne rsaksye bplruoet einin Fs,igure
a1n),d wpehroes pfohuontrda nosnfelyra isne so(ntyer otysipnee ;osfe trhinee s/ptherceieosn ianneda ncadna mseirnvoeg alysc coasniddei)d.aPteros tefoinr sthsuec shpaescicehsi-tsipnecific
synthaseofW.dermatitidis(highlightedinskyblueinFigure1),werefoundonlyinonetypeofthe
treatment [13]. Liu and colleagues identified 240 unique conserved genes among the 10 fungal species
speciesandcanserveascandidatesforthespecies-specifictreatment[13].Liuandcolleaguesidentified
via the comparison of the genome [14]. They found that only 43 of them shared less than 40% identity
240 unique conserved genes among the 10 fungal species via the comparison of the genome [14].
with human genes. Analogous to this work, Coronado et al. [15] revealed 187 cell wall-related
Theyfoundthatonly43ofthemsharedlessthan40%identitywithhumangenes. Analogoustothis
proteins in Saccharomyces cerevisiae which were compared with the 17 other fungal genomes,
work,Coronadoetal.[15]revealed187cellwall-relatedproteinsinSaccharomycescerevisiaewhich
including A. fumigatus, C. neoformans, and C. albicans. While the group of adhesins and invasins were
werecomparedwiththe17otherfungalgenomes,includingA.fumigatus,C.neoformans,andC.albicans.
conserved only in the species closely related to the species Saccharomyces, glycosylases, aspartyl
Whilethegroupofadhesinsandinvasinswereconservedonlyinthespeciescloselyrelatedtothe
proteases, and proteins involved in plant pathogenesis showed conserved patterns among all the
speciesSaccharomyces,glycosylases,aspartylproteases,andproteinsinvolvedinplantpathogenesis
tested species [15]. The extensive study of Fedorova et al. focused on the genome comparison of A.
showed conserved patterns among all the tested species [15]. The extensive study of Fedorova et
fumigatus, N. fischeri, and A. clavatus [16]. It showed the presence of several large species-specific and
al. focused on the genome comparison of A. fumigatus, N. fischeri, and A. clavatus [16]. It showed
isolate-specific chromosomal islands that probably contribute to rapid adaptation from saprophytic
thepresenceofseverallargespecies-specificandisolate-specificchromosomalislandsthatprobably
to parasitic lifestyle of Aspergillus.
contributetorapidadaptationfromsaprophytictoparasiticlifestyleofAspergillus.

Figure 1. Comparison of the gene conservation and their phylogenetic proximity among selected
Figure1. Comparisonofthegeneconservationandtheirphylogeneticproximityamongselected
fungal species. The phylogenetic tree was reconstructed using the MUSCLE alignment based on more
fungalspecies.ThephylogenetictreewasreconstructedusingtheMUSCLEalignmentbasedonmore
ththaann2 2000000s isnignlgeleco cpoypgye gneens.esT.h Tehneu nmubmerboefr hoifg hhilgyhcloyn csoernvseedrvgeedn egsefnoeusn fdouinndea icnh esapcehc isepseiscideesp isic dteedpicted
ininm maaggenentat,at,h teheg egneensecso cnosnersverevdeidn iant laeta lsetatswt otwspoe scpieescaierse amrea rmkeadrkinedg riene gnraenedn tahnedu tnhieq uuengiqeunees gaernees are
ccoololorereddi nins ksykyb lbuleu(em (modoifideifdiefdro fmro[m13 []1).3]).
GGeennoommicica nadnpdr opteroomteiocmanica lyasnisailsyscirsu ciisa lfcorruucinadl erfostra nudnindgerfustnagnadlipnagt hofugnengeasli sp.aDtahtoagoebntaeisnise.d Data
forobmtaitnheed ofrmoimcs tahnea olmysiicssa arneaelsysseins tairael efossretnhteiaal nfonro tthatei oannnportoacteisosne sprtohcaetslseeasd thtoatt lheeadre tcoo tghnei trieocnogofnition
tohfe tfhuen fcutinocntaiolndaolm daoinmsaainnsd amndot imfsootiffsp rooft epirnost.eiSnesv. eSreavledraatla dbaastaebsawseesr eweesrtae belissthaebdlisthoesdt utod ysttuhdey the
pprrootteeinind doommaianins.sT. Thheem mosotspt oppoupluarlairs iPsf aPmfam(F i(gFuigreu2re), 2li)s,t liinsgtinmgo rmeothrea nth1a6n,0 1060,0p0ro0t peirnotfeaimn ifliaems[il1i7e]s. [17].
TThheec coonncceepptto offt hteheP fPafmamis issi msipmlep:lee:a cehacehn ternytrisyd ies sdcreisbcerdibbeyd mbyu lmtipulletiapllieg namligennmts,eanntsd, tahneds uthitea bslueitable
aalliiggnnmmeenntti sisr ertertireiveevdedb ybya sae sqeuqeunecnecoer okre ykewyo wrdoqrdu eqruye:rTyh: eTsheee dseaeldig anlmigennmtceonntt caoinnstaainnasr aro nwarsreotwof set of
rreepprreesesenntattaitvievem emmebmerbseorfsa ofaf mai lyf.aSmuiblyse. qSuuenbtsley,qtuheenhtildyd, etnheM ahrikdodvemn oMdealrbkuoivld smthoedfeull lbauliiglndms etnhte, full
walhigicnhminecnlut,d ewshaillchp rointecilnusdeeqsu eanllc epsroofttehien psreoqteuinenfacmesi lyof[ 1t8h].eP pfarmotecoinn tafainmsitlwy o[d18iv]i. siPofnasm:( i)cPonfatmai-nAs two
idsitvhiesimonasn: u(ia)l Plyfacmur-aAte ids tphaer tmcaonvuerailnlyg c1u6r,7a1te2df apmarilti ecsov(Perfainmg 3116.,07)1a2n fdam(iiil)iePsf a(Pmf-aBm, w31h.i0c)h acnodn (siiis)t sPfam-
oBf, auwtohmichat iccaollnysigsetsn eroaft edauctloumstaetriscaplrloyd ugceenderbayteadn aculutostmerast icpdroomduacinedd ebcoym apno siatiuotnomalgaotirci thdmomain
decomposition algorithm (ADDA). A similar approach was implemented for other protein family
J.Fungi2018,4,81 5of28
J. Fungi 2018, 4, x FOR PEER REVIEW 5 of 27
(ADDA).Asimilarapproachwasimplementedforotherproteinfamilyanddomaindatabases—such
asSMART,ProDom,andTIGRFAM—thatalsousemultiplesequencealignmentandprofile-hidden
and domain databases—such as SMART, ProDom, and TIGRFAM—that also use multiple sequence
Markovmodels. TheSMARTdatabaseissynchronizedwithUniProt[19],Ensembl[20]andSTRING
alignment and profile-hidden Markov models. The SMART database is synchronized with UniProt
databases[21]. SMARTcontainsmorethan1300manuallycuratedproteindomains[22],whilethe
[19], Ensembl [20] and STRING databases [21]. SMART contains more than 1300 manually curated
ProDom[23]containsthesetofthedomainfamiliesgeneratedonlyfromUniProt. Theinformation
protein domains [22], while the ProDom [23] contains the set of the domain families generated only
about the microbial proteins is collected in the TIGRFAM (www.jcvi.org/cgi-bin/tigrfams/index.
from UniProt. The information about the microbial proteins is collected in the TIGRFAM
cgi), which contains the protein families hierarchically organized based on their functional role.
(www.jcvi.org/cgi-bin/tigrfams/index.cgi), which contains the protein families hierarchically
Comparedtotheotherdatabases,TIGRFAMshouldonlyhitequivalogs(proteinfamiliesconservedin
organized based on their functional role. Compared to the other databases, TIGRFAM should only
functionsincetheirlastcommonancestor).
hit equivalogs (protein families conserved in function since their last common ancestor).
TheTChleu sCtelursstoefrsO orft hOorlothgooluosgoGurso uGprsou(CpOs (GCsO)Gdast)a dbaatsaeb(ahstet p(h:/tt/pw://wwww.nwc.bnic.nblim.nl.mni.hn.igho.gvo/vC/OCOGG/)/)i sis
basebdaosendt hoen hthyep ohtyhpeostihsethsiast tohratth oorltohgosloshgas rsehtahree sthame seaamrceh aitrecchtiutercet,uthree, tthhree teh-dreiem-denimsieonnsailosntarlu scttruurcetuorfe
thedoof mthaei ndso,mbaioinchs,e bmioiccahleamnidcacle allnudl acreflluunlacrti ofunnsc,taiosnws,e allsa wsethlle aesv tohleu etivoonlaurtyioonrairgyi no,rwighinic, hwahlilcohw aslltohwes
studtyheo fsttuhdeyp ooof rtlhye apnonoortlayt eadnnoortgaatnedis mor.gCanOisGms.s CtaOrtGeds sinta1rt9e9d7 i[n2 41]9a9s7 a[2p4r]o ajesc at ipnrcolujedcti ningcfluuldl-ilnegn gfuthll-
protleeinngstfhr opmrotfievines bfraocmte rfiiavleg beancotemrieasl, goenneomarecsh, aoenael aarncdhaoenael aenudk aornyeo teiuck.aTrhyeotaicn. aTlyhtei canmaelythtioc dmuetsheodd
for CuOseGd fiosr bCaOseGd iso nbatsheed soena trhche soefartcrhia onfg tlreisanogflebsi doifr becidtiiorencatliobneaslt bheistts htiots atoss aigssnigtnh ethoer othrtohloolgosgsa nandd
paraploagrasloagnsd aancdc oarcdcionrgdliyngtolye tsot iemstaitmeaatne dansedp saerpaatreatthe ethpea rpaalroaglos.gCs. OCGOsGus suessefsa fmamilyil-yb-absaesdeda pappproraocahch
whewnhtheen wthheo wlehfoamle iflaymisilcyh iasr achctaerraiczteedribzeadse bdaosendt hoen inthdei vinidduivaildmuaelm mbeermobfetrh oefw thheo lwehfaomlei lfya,meirlgyo, ethrgeo
whotlheep wrohteoilne fparmotielyins hfaamreislyt hsehafurensc tthioen f(us)ndcteisocnr(isb)e ddefsocrritbheedb feosrt -tchhea braecstte-crhizaerdacmteermizebde rmoefmthbeefra omf itlhy.e
Thefparmocileys.s Tohfet hperoacnensso otaft tihoen awnansotmatainonu awllays cmuraantueadllfyo rcueraacthedC fOoGr e[a2c5h] .COG [25].
DespDietespthitee twhoe rwldowrliddweisduec scuescsceosfs tohfe thCeO CGOdGa tdaabtaasbea,sde,u deuteo tao nau nmumbebreorf olfi mlimitaittaiotinosn,st,h teheC COOGGi sis
nolonnog leorncgoenr sciodnesriedderteodb teot bhee tlheea dleeard/efirr/sftircsht ochicoei/cem/mosotsut suesdedin ine vevoloulutitoionn-b-baaseseddp proroteteininc clalassssifiifcicaattioionn..
TheTmhaei nmdarianw dbraacwkboafctkh eoCf OthGe dCaOtaGb adsaetiasbtahsee laisc kthoef plahcykl oogf epnhetyilcorgeesnoelutitci ornesaonludtcioonn tainnudo ucosnutpinduaotue.s
Theuspecdoanted. lTimhei tsaetcioonndis litmheitantuiomnb iesr tohfe anvuamilabbelre ogfe anvoamilaebs,lew gheinchomisesr,e lwathivicehl yiss mrealallti(v6e6lyg esmnoamll e(s6,6
genomes, consisting of 138,458 proteins before the implementation of the EggNOG database [26].
consisting of 138,458 proteins before the implementation of the EggNOG database [26]. This led
This led to the development of the EggNOG database (evolutionary genealogy of genes: non-
to the development of the EggNOG database (evolutionary genealogy of genes: non-supervised
supervised orthologous groups, Figure 2), founded by European Molecular Biology Laboratory
orthologousgroups,Figure2),foundedbyEuropeanMolecularBiologyLaboratory(EMBL)(http:
(EMBL) (http://eggnogdb.embl.de). EggNOG uses an approach for the annotation of the orthologous
//eggnogdb.embl.de). EggNOG uses an approach for the annotation of the orthologous group
group identical to COG. In the first phase, the orthologous groups are constructed using the Smith–
identicaltoCOG.Inthefirstphase,theorthologousgroupsareconstructedusingtheSmith–Waterman
Waterman alignments. Thereafter, groups are identified based on the reciprocal best matches and
alignments. Thereafter, groups are identified based on the reciprocal best matches and triangular
triangular linkage clustering. Compared to COG, EggNOG is completely automatic, the number of
linkageclustering. ComparedtoCOG,EggNOGiscompletelyautomatic,thenumberofthegenesand
the genes and genomes is significantly higher, and a hierarchy of orthologous groups is created [27].
genomesissignificantlyhigher,andahierarchyoforthologousgroupsiscreated[27].Currently,COGis
Currently, COG is implemented in the EggNOG database.
implementedintheEggNOGdatabase.

FiguFriegu2r.eC 2o. mCpomarpisaornisobnet bweetweneePnfa Pmfaamn danEdg EggNgoNgodga dtaabtaabseass.es.
Correct and precise protein annotation is important together with the charting of the
evolutionary origin of the proteins. Both help in the identification of novel drug targets. Popular
J.Fungi2018,4,81 6of28
Correctandpreciseproteinannotationisimportanttogetherwiththechartingoftheevolutionary
origin of the proteins. Both help in the identification of novel drug targets. Popular jump-start
approachesusingthosestrategiesarenetwork-basedapproaches. Inthelast10years,thesteadyflow
ofarticlesrelatedtonetwork-basedstrategiesincreasedasaresultofseveralaspects: easyaccessibility
oftheinputdata,timeandcosteffectivity,anduseofthemulti-omicsdatatooptimizetheresearch
process. Thosestrategiesoffercompleteinsightintothepathogenesisandtreatmentofthedisease,
suchaspredictionofnovelproteintargetsanddrugs[28],characterizationofthediseasemodule[29]
and identification of the disease pathway for maximizing the effectivity of the pharmaceutical
component[30,31].
Threemajornetwork-basedstrategiesspeedupdrugdiscovery: analysisofregulatorynetworks,
metabolicnetworks,andprotein–proteininteractionnetworks. Regulatorynetworksarereconstructed
fromthetranscriptomedataandhelptoanalyzetheregulatoryinteractionofthegenesinvolvedinthe
pathogenesis. Severalcomputationalmethodswereestablishedtomodeltheregulatorymechanism
under physiological and pathological conditions, such as a Bayesian network [32] and a Boolean
network[33]. Incontrasttoregulatorynetworks,metabolicnetworkscanbereconstructedbyseveral
methodsthatanalyzethesteady-statefluxdistributionsamongthegenesassociatedwiththenetwork.
Theelementaryfluxmodes,convexbasis-basedpathwayconcepts,fluxbalanceanalysisandchoke
pointanalysesarerequired/crucialinordertoreconstructthemetabolicnetworkrelatedtothedisease.
Themostinteractivemetabolicproteinsfoundinsuchanetworkusuallycorrelatewithahighnumber
of functional associations and have a significant role in pathogenesis [34,35]. The protein–protein
interactionanalysisisfocusedontheinteractomedata. ThereconstructionofthePPInetworkcanbe
performedwithsoftwaretoolssuchasR/Bioconductor(http://www.bioconductor.org)andCytoscape
(http://www.cytoscape.org).TheanalysisofthePPInetworkconsistsofproteinrankingbasedontheir
connectivityandlocation. Drugsthattargetcentralnetworknodesgenerallyhavelethalconsequences
forapathogenortargetedcells,whilethedrugstargetingneighborsofcentralnodessubstantially
manipulatethecorrespondingmetabolicpathway[36].
The identification and comparison of the ortholog groups in host and pathogen is important
to reveal unique differences among the species. Figure 3 illustrates how to scrutinize the protein
domainannotationtospottargetabledifferences. Theglutathionereductasefrombothhumansand
A.fumigatusisanalyzedbyusingtwodifferentdatabases. Thedomaincompositionissimilarinhost
andfungus,however,thereareseveralspecificdifferences. Therearefourdomainsinboth: besides
two cofactor-binding domains and the catalytic domain of glutathione reductase, a dimerization
domainispresent. Maindifferencesbetweentheproteinsconcern—inparticular—thespacingofthe
domainswithrespecttoeachother, localizationoflowcomplexityregions, andminordifferences
inexactdomaincomposition(Figure3,bottom). Thesedifferencescouldallinprinciplebetargeted
andleadtodifferentresponsesagainstsuitabledrugstargetingthese. Interestingly,thedimerization
domain can be targeted with methylene blue, leading to inactivation of the glutathione reductase.
However, the apparent minor differences in domain composition and the resulting overall minor
structuredifferencesimplymoredetrimentaleffectsforthepathogenifmethyleneblueactsonthe
protein. Thestrategyofutilizingadifferentialeffectofmethyleneblueonthepathogenthroughand
bytargetingthesameproteininhostandpathogenandexploitingthesmalldifferencesindetailed
domaincompositionhasbeenusedpreviouslyinmalariaaswellasCandidaresearch[37,38].
Bothofthedatabasesprovidecomplementaryinformation,forinstance,SMARTdepictsbesides
thedomainannotationalsosplicingjunctionsforhumangenes(asred, blue, andblackthinlines)
aswellasthelowcomplexityregions. Insteadoftheannotationfunction,SMARTislinkedtoother
databasessuchasSTRING(predictionoffunctionalinteractingpartners),KEGG(visualizationofa
metabolicpathways),PTMcode(posttranslationalmodificationprediction),andeggNOG(databaseof
orthologousgenes). IncontrasttoSMART,Prodomprovidesmoreinformationregardingtheexact
domaincontentandclassification(graphicalrepresentationofthefamily),allowscomparisonofseveral
J.Fungi2018,4,81 7of28
proteinsbyBlast-PandBlast-X,andislinkedwithSTRAPtoolstoalignandbuildthephylogenetic
trJ.e Feunogfi f2a0m18,i l4y, xb FaOsRe dPEoEnR tRhEeViIrEsWtr u cture. 7 of 27

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rleidneli: nien:trinontr o(pnh(apshea s2e). 2T).hTe haenaanlyasliyss iwsawsa psepreforfromrmede dbyb ySMSMAARRTT ddaatatabbaasese. ;PPaanneel lB(:b D)Domomaiani nmmapapppiningg
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sstatannddo ouutt..
The following chapters focus on finding host–pathogen differences in a systematic manner, by
Thefollowingchaptersfocusonfindinghost–pathogendifferencesinasystematicmanner,by
looking at host–pathogen interaction networks (Chapter 3). We then give bioinformatics details on
lookingathost–pathogeninteractionnetworks(Chapter3). Wethengivebioinformaticsdetailson
approaches to evaluate the structures of the identified targets and how to design suitable drugs
approaches to evaluate the structures of the identified targets and how to design suitable drugs
(Chapters 4 and 5).
(Chapters4and5).
3. Screens and Counter-Screens for Unique Fungal Protein Targets Using Protein–Protein
3. ScreensandCounter-ScreensforUniqueFungalProteinTargetsUsingProtein–Protein
IInnteterraaccttiioonnN Neettwwoorrkkss aanndd MMeettaabboolliicc NNeettwwoorrkkss
33.1.1..I Inntteerraaccttoommee BBaasseedd AApppprrooaacchheess
BBioiollooggiiccaall nneettwwoorrkkss ccaann hheellpp ttoo iimmpprroovvee oouurr uunnddeerrssttaannddiinngg ooff bbiioolologgicicaal lssyysstetemms saanndd ththe e
ccoommmmuunnicicaatitoionnfl oflwoww itwhiinthtihne mth.ePmro. tePinro–tperiont–epinroitnetienr aicntitoenrancteitowno rnkestw(PoPrIkNs )a(PrePdINir)e ctalrye apdpirleiccatlbyl e
foaprpdlriucagbdleis cfoorv edrryu.gT hdeiysccoavnerbye. uTsheedyt ocaind ebnet ifuysepdro tmo isidinegntdifryu gprtoarmgiestisn,gta dkrinugg itnatrogaectsc,o tuanktinpgro itnetion s
aancdcoiunntetr parcotitoeinnss sapnedc iifintcefroarcttihonesp saptehcoifgiec nfoarn thdet pharothuogghenw ahnidch thhreonucgeht whehihcohs hteanncde tthhee hhoosstt a-snpde tchifie c
nheotwst-osrpkecisifinc ontehtwaromrke dis. nLoat rhgaer-msceadle. Linartegres-psceacliee sinhteorsstp–epcaitehs ohgoesnt–ppraothteoignenn eptwrooterikns n(eHtwP-oPrPksIN (H) Par-e
mPoPrIeNo)v earrea nmiomrpeoovrtearn atnfo iumnpdoartitoanntf oforuthnedautniodne rfsotar ntdhien gunodfethrsetawnadyinsgh oowf tphaet hwoagyesn shionwte rpaactth,iongveandse ,
ainndtersaecizt,e incovnadtreo, laonfdt hseeizheu cmoanntrohlo ostf. thPea thhuomgeanni chopsrto. tPeainthsotgeenndict opriontteeirnasc ttemndai ntoly inwteirthacht umbasinalnyd
bwotittlhe nheucbkss ainndt hbeoPttPleInNec[k3s9 ,i4n0 ]t.hTe hPePcINon [n3e9c,4ti0v]i. tTyhien cthonenheocstti–vpitayt hino gtheen hinotset–rapcattohmogeeins dinitreercatlcytormelea tiesd
toditrhecetlpya rtehloagteedn tfio ttnhees spadtuhroignegn ifnitfnecetsiso dnu[r4i1n]g. iTnhfeecrteiofonr e[4,1h].i gThhedreegforeree, phrigohte idnesgroefet phreopteaitnhso ogfe tnhein
pathogen in HP-PPIN are promising drug targets. The wiring of PPIN can be determined by a variety
of binary and co-complex methods, however, these methods are not suitable for genome-scale
prediction of HP-PPIN. Nevertheless, experimental high throughput assays typically include a high
number of false positive protein–protein interactions [42] so that there is an enduring need for
J.Fungi2018,4,81 8of28
HP-PPINarepromisingdrugtargets. ThewiringofPPINcanbedeterminedbyavarietyofbinaryand
co-complexmethods,however,thesemethodsarenotsuitableforgenome-scalepredictionofHP-PPIN.
Nevertheless,experimentalhighthroughputassaystypicallyincludeahighnumberoffalsepositive
protein–proteininteractions[42]sothatthereisanenduringneedforefficientcomputationalmethods
to scrutinize and counter-screen existing experimental data. Such methods include the interolog
method[43],domaininformation[44],geneontology(GO)annotations[45],andinformationabout
cellularlocalization[46,47].Demonstratingsuchfeaturesinagraphicalrepresentationoftheinteraction
networkprovidesanintuitiveoverviewandusefulinsightstohelpandanalyzecomplicatedrelations
between host and pathogen [48–50]. For instance, the A. fumigatus-human and C. albicans–human
host–pathogenprotein–proteininteractionnetworkwasdeterminedstartingfromhumanandyeast
intraspeciesnetworksandlookingatsequencesimilarproteinsinthesefungiwithsimilarinteractions
withthehost[48]. Suchanetworkrepresentsallpredictedhost–pathogeninteractionswhichwere
foundtobeconservedbysequenceconservation. Todeterminethebestdrugtargetsinpathogens
fromsuchanetwork,itisnecessarytoanalyzethenetworkfurther,inparticularintermsoftopology
oftheproteinsinthenetworkandfunctionofinteractinghost-proteins.
In this pursuit, the availability of the sequenced genome of A. fumigatus [51] has paved the
waytodiscoverdrugtargetsthroughnetworkanalysis[28]. TopologicalanalysisoftheA.fumigatus
interactomerevealsseveralhubsandbottlenecks. Becauseofthehigherconnectivitycomparedto
otherproteinsinthenetwork,hubsareofteninvolvedinmultiplefunctions,whileconnectingproteins,
so-calledbottlenecks,areimportantfortheflowofinformationamongdifferentfunctionalmodules.
Consequently,deletionofahubproteinoraconnectingbottleneckproteincanbelethalforthefungus.
Thetop20%ofnetworkproteinsbasedon(hub)degreerankand(bottleneckconnectivityrankcanbe
consideredtobefunctionallyimportant,andre-rankingofprotein“iP”canbecalculatedbasedon
HBrank(iP)thatgivesbothranksequalweight
HBrank(iP) =Rank (iP)+Rank (iP)
de be
Here“iP”indicatestheithproteininthereconstructedPPI,Rank istherankingofprotein“iP”
de
basedondegreescoreandRank istherankingofproteins“iP”basedonthe“betweenness”score
be
(i.e.,thenumberofinterconnectedproteins).
The biggest challenge for computational prediction of the interactome is the lack of
available verified interactions and the relevant annotation information in most of the pathogens.
Therefore,severalapproacheshavebeendevelopedtoincreasetheinteractionconfidenceincluding
domain–domaininteractions(DDIs)[52],subcellularlocalization[46],andGOannotation[45]filters.
Domain–domaininteractionshavebeenusedbyseveralauthorsasanapproachtobetterselectrelevant
PPIs. However,DDIscanalsobeusedtofilterinterologinteractions[52]. Thisensuresthemostreliable
interolog interactions that are supported by at least one experimentally verified PPI. For domain
mappingPfam[53]andInterPro[54]arethebestsources,whileDOMINE[55]andDIMA3.0[56]are
goodresourcestofindinformationofinteractingdomainsaccordingtocrystallizedstructures.
Todeterminearobustorthologyrelationship,InParanoid[57,58]orOrthoMCL[59]canbeused.
ExperimentallyverifiedPPIscanbeobtainedfromhighlycurateddatabasessuchastheDatabaseof
InteractingProteins[60]. SeveralotherdatabasesalsocontainexperimentalverifiedPPIs: String[21],
BioGrid[61]andcommoninterfaceresourcessuchasPSICQUIC[62]. Thelattercanalsobeusedto
deriveatemplateforthemappingofputativePPIs.
Similar subcellular localization of two interacting proteins ensures their chances to meet.
Moreover, multiple localized proteins increase the possibility of protein interaction with different
localizedpartners. WoLFPSORT[63],KnowPred[64],andYLoc[65]arerobusttoolstodetermine
thesubcellularlocalizationofeukaryoticpathogenicproteins. Likewise,similarGO-annotationsof
twoproteinsalsoindicatetheirco-involvementinthesamebiologicalfunctionofmolecularprocesses.
Therefore,todeterminethis,GOdatabase(TheGeneOntology,2017),David[66],andaplatformsuch
J.Fungi2018,4,81 9of28
asBlast2GO[67]areagoodchoice. ThenetworkcanbeconstructedwithCytoscape[68]withdifferent
plugins(http://apps.cytoscape.org)forfurtherdetailednetworkanalysis.
SeveraltopologicallyimportantproteinsthatalsoparticipateinA.fumigatusmetabolismwere
identifiedaspotentialantimycotictargets,suchas1,3-β-glucanosyltransferase(Gel2,AFUA_6G11390)
andphosphatetransporter(Pho88,AFUA_5G01960)[28].
3.2. MetabolicModelingApproach
AnotherreliablemethodtosystematicallyidentifydrugtargetsofA.fumigatusreliesonthefungal
metabolism[28]. Thisincludesdirectmetabolicnetworkmodelingusingelementarymodeanalysis
(EMA)andfluxestimatesusinggeneexpressiondata. OnecancombinetranscriptomedatawithEMA,
lookingathighlevelexpression, centralpositioninthemetabolism, andtheresulting(calculated)
strongandcentralmetabolicfluxasdetailedlaterinthemanuscript. Thisapproachallowsustotarget
metabolicenzymesofthefungusaftertranscriptomeanalysisandsearchforthecondition-specific
highlyexpressedenzymesduringimportantphasesoftheinfection.
Metabolic network modeling is used to predict and analyze the flux distribution of different
physiologicalconditionsinbiologicalsystems[69]. Metabolicprocessesareakeycomponentofthe
factorsthatdeterminethedynamicsandvirulenceofthepathogens. Inthehost–pathogeninteraction,
manyinteractionsrelyonmetabolicprocesses. Thisincludesthecompetitionforcommonresources,
exchange of nutrients, and chemical communications. Metabolic networks can be reconstructed
forthepathogenon boththesmall-scaleandgenomescale. Small-scalereconstructionfocuseson
a specific metabolic pathway network. For instance, the central carbohydrate metabolism may be
easilycalculated,pointingoutcentralenzymesinvolvedinmanypathwaysorcarryingalotofflux.
Hereby, some previous knowledge or bioinformatics data or biochemical data can help to reveal
promisingtargetsincentralfungalpathways. Incontrast,genome-scalereconstructionrequiresthe
completegenomesequenceandrefinedannotationofallthereactionsandbiomassdata[70]. Thisis
verytime-consumingandlabor-intensive,however,thesumofcalculationsthendirectlypointsout
thebesttargetsinpathogens. Thus,LeeandcoworkersreconstructedamodelofStaphylococcusaureus
andidentifiedvariousantimicrobialdrugtargets[71]. Theauthorsconstructedthemetabolicnetwork
usingthegenomeannotation,functional-pathwayanalysis,andcomparativegenomicapproaches
and finally performed flux balance analysis (FBA)-based in silico single and double gene deletion
experimentstoidentifynoveldrugtargets. Similarly,toanalyzefungaldrugtargetscomprehensively,
genome-scale metabolic network of A. fumigatus can be reconstructed [28]. Reconstruction means
startingfrombiochemicaldatabasestogetafulllistofallinvolvedenzymes,andcriticallyreexamining
thislisttakinggenomeannotation,domain,andsequenceanalysisintoaccount. Otherwise,onecannot
besureonthefullrepertoireofenzymeinthisorganism. Next,EMAtakesthiswell-establishedlistof
enzymesandtheirreactions(theso-calledstoichiometricmatrix)tocalculatetheelementaryfluxmodes
(EFMs). ThesetofEFMsisuniqueandcanrepresentanypossiblephysiologicalsituationinacellular
system. Asidefromrecoveringcanonicalpathways,theEFMpredictionapproachalsopredictsalarge
numberofunobservedpotentialpathwaysthatemergefromacombinationofactivereactionsinthe
network[72]. Forthisreason,theEFMapproachisidealtoexploretherichnessinherentinametabolic
network and consequently to elucidate novel pathways [73,74]. To reconstruct a model for EMA,
thesetofdirectionalmetabolicreactionsisaprerequisite. Definingthedirectionalityofthereactions
andfindingalltheoptimalEFMsinabigreactionsetisparticularlychallenging. Henceoneneedsa
setofrulesthatcanbefollowedtosolvetheproblemswithenzymedirectionalityandtohaveatool
thatcanbreakupbigreactionsetsintosmallerconnectednetworksthatareeasiertocalculateusing
theEMA[75]. ThesameapproachwasusedintheworkonA.fumigatus[28]. Inbrief,usingtheKEGG
database[76,77]andliterature,thecompleteprimarymetabolismfromA.fumigatuswasmodelled
inametabolicnetwork, includingmajorcarbohydratemetabolism(glycolysis, pentosephosphate
pathway,TCAcycle),lipid,fattyacidmetabolism,nucleotidebiosynthesis,aminoacidbiosynthesis
and degradation, and fermentation pathways. The steady state of the system was computed
J.Fungi2018,4,81 10of28
usingtheYANAsoftwarepackage. Tocalculatecondition-specificstrengthsofdifferentmetabolic
fluxes,transcriptomedata-setswereusedasconstraintstofitthemetabolicmodeltoestimateflux
distributions[78,79]foroptimalgrowthconditionsandchangesunderbiofilmconditions. Thetraining
procedurealsoinvolvedalgorithms(Gradientdescent: BFGS—Boyden–Fletcher–Goldfarb–Shannon
optimizationmethod)thathavebeenconstantlydevelopedfurther[80,81].
Othermetabolism-basedapproachesincludetheinhibitionofanenzymethatconsumesaunique
substrate. Thisresultsintheaccumulationoftheuniquesubstratewhichispotentiallytoxictothecell.
Moreover,theinhibitionofanenzymethatproducesauniqueproductmayresultinthestarvationof
theuniqueproduct,potentiallycripplingessentialcellfunctions[82]. Suchmetabolicallyimportant
enzymesareknownaschokepointenzymesinmetabolicnetworksandcouldbeusedaspotential
drugtargets. Forinstance,drugtargetsinPlasmodiumfalciparumwereidentifiedusingchokepoint
analysis [82]. These approaches become complicated, however, when identifying drug targets for
eukaryoticpathogens.
Metabolicenzymes,aswellastheirregulators,areincreasinglyconsideredviabledrugtargets[83].
OftenthepathogenicgenesofA.fumigatuscouldbeinducedbyconditionssuchaschangesduring
invasioninitiation[84],irondeficiency[85],andunderhypoxiaadaptation[86]. Torevealkeyenzymes
involved in metabolic adaptation as prospective antimycotic targets, one needs to look at several
related transcriptome datasets. Therefore, three GEO datasets were reanalyzed and the enzymes
relatedtometabolismwereidentified[28]. Amongtheupregulatedgenesinreanalyzedtranscriptome
datasets,severalgeneswerefoundtobeimportantforA.fumigatusgrowthandhencewerevaluable
antimycotictargets[28]. Toaccessthegenomeorproteome,NCBI(https://www.ncbi.nlm.nih.gov/)
or organism-specific portals can be used. For instance, AspGD [87] contains the omics data of
severalAspergilli. ForthereconstructionofthefungalmetabolicnetworkKEGG[76,77],Brenda[88],
Reactome [89] re-evaluated by information from scientific literature represent the best resources.
KEGGcontainshighlycuratedinformationthatmakesitthebestone-stopoption.
Metabolic models can also be derived on orthology based frameworks such as Raven and
Merlin. Sequence similarity searches can be improved by using sensitive Blast parameters or
reverse blast [90]. The recommended parameters for the best detection of orthologs as reciprocal
besthitsarethecombinationofsoftfilteringwithaSmith–Watermanfinalalignment(the-F“mS”
-s T options in NCBI’s BLASTP). Such considerations are important in order to avoid annotation
errors which would mislabel the potential drug target [91]. Once the reaction network is created
for FBA, one can select a number of metabolic modeling tools [92] with specific advantages and
limitations. For EMA, we recommend the tool YanaSquare [78] and updated versions (https:
//www.biozentrum.uni-wuerzburg.de/bioinfo/computing/). Itprovidesaneasyinterfaceforstarters
andexperts. OnecanalsouseMetatool[93]fortheEMAbutitsfunctionalityislimitedincomparison
withYanaSquare. NCBIGEO[94]andArrayExpress[95]arethemajorrepositoriesofgeneexpression
datawhichthencanbeanalyzedwithR[96]Thebuilt-intollGEO2Rcanalsobeusedtoanalyzethe
expressiondatadepositedattheNCBIGEOdatabase.
Importantmetabolicproteinswereidentifiedaspotentialantifungaltargetsbyfluxcomputations,
for instance, the first enzyme of the riboflavin biosynthetic pathway GTP cyclohydrolase II (Rib1,
AFUA_1G13300)wasidentifiedaspotentialtargetwhichpossessesacapabilityaspacemakerinthe
correspondingpathway[28].
3.3. Homology-BasedApproach
Essentialgenesofpathogenscanserveasdrugtargetsiftheydonothavesignificanthomology
withhostorganisms. DatabaseofEssentialGenes(DEG)[97]containsgenesreportedintheliterature
to be essential for the organism tested under at least one condition for both the prokaryotes and
eukaryotes. Ifthepathogenunderinvestigationisnotlistedinthedatabase,aBlast-basedsearchwith
stringente-valuethreshold<1×10−10andabitscore≥500,ororthologypredictioncanbeusedto
identifythepotentialessentialgenesaswepreviouslydidforthepathogenS.marcescens[98].