computation Article Pharmacophore-Guided Identification of Natural Products as Potential Inhibitors of Mycobacterium ulcerans Cystathionine γ-synthase MetB Samuel K. Kwofie 1,2,3,* , Nigel N. O. Dolling 1,4 , Emmanuel Donkoh 1,4, Godwin M. Laryea 1, Lydia Mosi 2, Whelton A. Miller III 3,5 , Michael B. Adinortey 6 and Michael D. Wilson 3,4 1 Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Legon, Accra LG 77, Ghana; ndolling5@gmail.com (N.N.O.D.); emmanueldonkoh11@gmail.com (E.D.); gomlaryea@gmail.com (G.M.L.) 2 West African Center for Cell Biology and Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, University of Ghana, Legon, Accra LG 54, Ghana; LMosi@ug.edu.gh 3 Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA; wheltonm@seas.upenn.edu (W.A.M.III); MWilson@noguchi.ug.edu.gh (M.D.W.) 4 Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Legon, Accra LG 581, Ghana 5 Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA 6  Department of Biochemistry, School of Biological Sciences, University of Cape Coast,  Cape Coast CC 033, Ghana; madinortey@ucc.edu.gh * Correspondence: skkwofie@ug.edu.gh Citation: Kwofie, S.K.; Dolling, N.N.O.; Donkoh, E.; Laryea, G.M.; Mosi, L.; Abstract: Buruli ulcer caused by Mycobacterium ulcerans (M. ulcerans) is identified by a pain-free cyst Miller, W.A., III; Adinortey, M.B.; or edema which develops into a massive skin ulcer if left untreated. There are reports of chemore- Wilson, M.D. Pharmacophore-Guided sistance, toxicity, noncompliance, and poor efficacy of current therapeutic options. Previously, we Identification of Natural Products as used cheminformatics approaches to identify potential antimycobacterial compounds targeting major Potential Inhibitors of Mycobacterium receptors in M. ulcerans. In this paper, we sought to identify potential bioactive compounds by ulcerans Cystathionine γ-synthase targeting Cystathionine gamma-synthase (CGS) MetB, a key receptor involved in methionine syn- MetB. Computation 2021, 9, 32. thesis. Inhibition of methionine synthesis restricts the growth of M. ulcerans. Two potent inhibitors https://doi.org/10.3390/ Juglone (IC50 0.7 +/− 0.7 µmol/L) and 9-hydroxy-alpha-lapachone (IC50 0.9 +/− 0.1 µmol/L) were computation9030032 used to generate 3D chemical feature pharmacophore model via LigandScout with a score of 0.9719. The validated model was screened against a pre-filtered library of 2530 African natural products. Academic Editor: Shizuka Uchida Compounds with fit scores above 66.40 were docked against the structure of CGS to generate hits. Three compounds, namely Gentisic 5-O glucoside (an isolate of African tree Alchornea cordifolia), Isos- Received: 21 January 2021 Accepted: 4 March 2021 cutellarein (an isolate of Theobroma plant) and ZINC05854400, were identified as potential bioactive Published: 12 March 2021 molecules with high binding affinities of −7.1, −8.4 and −8.4 kcal/mol against CGS, respectively. Novel structural insight into the binding mechanisms was elucidated using LigPlot+ and molecular Publisher’s Note: MDPI stays neutral dynamics simulations. All three molecules were predicted to possess antibacterial, anti-ulcerative, with regard to jurisdictional claims in and dermatological properties. These compounds have the propensity to disrupt the methionine published maps and institutional affil- synthesis mechanisms with the potential of stagnating the growth of M. ulcerans. As a result of rea- iations. sonably good pharmacological profiling, the three drug-like compounds are potential novel scaffolds that can be optimized into antimycobacterial molecules. Keywords: Buruli ulcer; Cystathionine γ-synthase MetB; Mycobacterium ulcerans; natural products; Copyright: © 2021 by the authors. molecular docking; pharmacophore modeling; antimycobacterial Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons 1. Introduction Attribution (CC BY) license (https:// Mycobacterium ulcerans belongs to the slow-growing environmental mycobacteria creativecommons.org/licenses/by/ family which secretes mycolactone, a toxin that has a strong cytotoxic activity. Buruli ulcer 4.0/). Computation 2021, 9, 32. https://doi.org/10.3390/computation9030032 https://www.mdpi.com/journal/computation Computation 2021, 9, 32 2 of 24 (BU) is an infectious, flesh-eating ailment that affects the skin and subcutaneous tissues [1]. Mycolactone, when present in the human system, induces cell death, necrosis of several cell types, and suppresses the immune response. The disease is characterized by a pain-free cyst, plaque, or edema which develops into a massive skin ulcer if left untreated [2]. BU currently ranks third among mycobacterial diseases that affect humans, with leprosy and tuberculosis placing first and second, respectively. Yet among the three, BU is the least understood [1,2]. BU is prevalent in rural areas of Asia, tropical countries in Africa, Australia, and the Americas with a high focal distribution along water bodies. More than 20,000 cases were reported over the last decade in West Africa, which records the highest prevalence rates; Côte d’Ivoire, Benin, Nigeria, and Ghana are leading the charts [1,3,4]. The transmission mode of M. ulcerans is reportedly less understood but the disease is considered to be related to activities around water bodies [1,2,5]. It is speculated that environmental M. ulcerans enters the body through small cuts in the skin from direct contact with contaminated soil, water, or vegetation, and in some cases, is transmitted through biting water-borne insects [6]. M. ulcerans is an acid-fast bacillus that has a genome size of 5,805,761 base pairs, 4160 protein-coding genes, 771 pseudogenes and consists of two circular replicons. Cys- tathionine gamma-synthase MetB (CGS), a protein encoded by a gene of M. ulcerans, exists as a homotetramer comprising four identical monomers (Chains A, B, C and D) with each having its active site. Present in each active site is the cofactor pyridoxal phosphate (PLP), responsible for activating the protein. More significantly, CGS plays a vital role in the synthesis of methionine, an integral requirement for the growth of M. ulcerans, where it is involved in the early committed step in the methionine biosynthesis pathway. It works as a transferase catalyzing the irreversible reaction between O-succinyl-homoserine and cysteine to produce cystathionine and succinate. It is also involved in selenoamino acid and sulfur metabolism [7]. Therefore, CGS MetB is considered a crucial drug target because of its important role in methionine synthesis. Inhibition of methionine synthesis could restrict the development of M. ulcerans [8]. Current drugs recommended by the World Health Organization (WHO) for the treat- ment of BU are Rifampicin, Streptomycin, and Clarithromycin [9]. These drugs have a long treatment duration and could lead to side effects such as hearing impairment, kidney problems, skin rashes, and vomiting [10,11]. Natural products possess great chemical and structural diversity, biochemical specificity, and other molecular properties that make them promising leads for drug discovery [12]. These unique properties distinguish them from synthetic and combinatorial compound libraries and inspire novel discoveries in chemistry, biology, and medicine [13]. There is a long-term history of usage of natural products and wider public acceptance of drugs of natural product origin [14,15]. Since CGS has been suggested as a drug target [7], the reported work aimed to identify drug-like biomolecules which have the potential to inhibit CGS. Inhibiting the CGS could disrupt the synthesis of methionine critical for the growth of M. ulcerans. This study sought to predict potential novel inhibitors by using ligand-based pharmacophore screening and molecular docking of natural products originating from Africa. In addition, elucidate novel mechanisms of binding and biological activity of the proposed compounds. 2. Materials and Methods 2.1. Target Retrieval and Preparation The 3D X-ray crystallographic structures of CGS of M. ulcerans and homolog CGS of Helicobacter pylori were obtained from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) [16]. For the study, chain A of each 3D crystal structural coordinate file was used. Two experimentally elucidated structures of CGS MetB from M. ulcerans are available. The structure with PDB ID 3QI6 is bound covalently to PLP (cofactor) and the other with PDB ID 3QHX is bound covalently to both PLP and 4-(2-hydroxyethyl)- 1-piperazine ethanesulfonic acid (HEPES) [7]. To select which structure to use for this Computation 2021, 9, x FOR PEER REVIEW 3 of 27 Computation 2021, 9, x FOR PEER REVIEW 3 of 27 ics (RCSB) Protein Data Bank (PDB) [16]. For the study, chain A of each 3D crystal struc- tiucsr a(lR cCoSoBr)d Pinraotte ifnil De awta sB uansekd (.P TDwBo) [e1x6p].e Friomr ethneta slltyu deylu, cihdaaitne dA sotfr uecatcuhr e3sD o cfr yCsGtaSl sMtreutcB- ftruormal Mco.o urldceirnaantse afriele a wvaaisla ubsle.d T. hTew sotr uecxtpuerrei mweitnht aPlDlyB e IlDuc 3iQdaIt6e ids bsotruncdtu croevs aolef nCtlGyS t oM PeLtPB (fcrofmac Mto.r )u lacnerda ntsh ae roe tahveari lwabitlhe . PTDheB sItDru c3tQuHreX w iist hb PoDunBd I Dco 3vQaIle6n istl byo tuon bdo ctohv PaLlePn talyn dto 4 P-(L2P- h(cyodfraocxtoyre)t hanyld) -1th-pe ipotehrearz iwneit eht hPaDnBes IuDlf o3nQicH aXc iids (bHoEuPnEdS c)o [v7a].l eTnot lsye lteoc tb wothhi cPhL sPtr uanctdu r4e- (t2o- Computation 2021, 9, 32 uhsyed frorx tyheitsh sytlu)-d1y-,p tihpe raezsionleu teitohnasn aensdu lRfo-nviacl uaecsid w (eHreE PcoEnSs) i[d7e]r. eTdo. sReelseoctlu wtihoinc hd esstrcuricbteusr eth 3teoo f 24 museea fsourr eth oisf sthtued qyu, athliety r eosfo dluatiao nfosu anndd oRn- vtahleu cersy wstearle c coonntasidneinregd a. Rperosoteluinti. oInt adlesosc irnibcelus dthese measurien ogf t hthee l eqvueall iotfy doeft adial tsae feonu unpdo onn e tlehcet rcoryns dtaeln csoitnyt aminaipn gca alc purloatieoin. aInt dal tshoe i ndciflufrdaec-s tmioensa tspuuadrtyitn,egtrhn te.h Aree lse oavl ueglt eionfne dsraealtn ardiul Rlsee- evonaf luthpeusomnw bee,lr eeac ctproornensm idieeunrmesdi ti.ysR mpelsaaopcl eucdtai loocnunl dasetirsoucnrci tbauenrsdest ht hweeim tdhei fahfsriuagcrhe- of rteiosnoth lupetaqitotunearsln i[t.1 yA6o]s.f Tadh agete aRnfe-ovruanl udreu oldene nothfo ettehcsur ytmhsetba ,ml ace opansrtueamrieni iuonmfg t haisep pqroulataceleintdy. oItnfa tslhstroeu aicnttocumlrueidsc e wms imothde eahls iaugtrh-i ng traeisnotehldue tlfieorvonemsl [ot1hf6ed]. e cTtrahyieslt saRel-elvonagluruapepo hdniecen ldoecatetrsao .t nhTedh em nceslaiotssyuemre a o psft crtahulec tuquluraeat’ilsoi tnyR a-ovnfa dtlhuteh eaistd otimoff ri0ac ctmthioe ndbeplta taetttr-e rn. [t1a7in]A.e Psdya fMrgoeOmnLe rt1ha.9le. r7cu r[ly1es8ot,a1fl9tlh]o wugrmaaspb u,hsaiecp ddr teaomt a.in uTamlhyeiz secp ltohlsaeec r3e Dda ostnrustcrtucrteus’s ra eRnsd-wv taiotl hurehm igso hvtoer ea0sl olt hlauett aibocenhttseed[r1 6]. l[i1g7aT]n.h Pdeys MRas-Ov waLle u1lle. 9a.ds7 e w[n1ao8t,1er9s ]mt whoealesm cuueslaesdsu tirnoe aponrfeatplhyaezreaq tuihoaenl 3i tfDyor so ftfurutrhtchetueart edosom awnicdn smttoro erdaeeml oavtntea aliynllse aidst.t faMrcohimsesd-t he ilnigac nreydssitd aulsl eowsg eirnlal ptahse iwc sadotlaevtrea md. osTtlhreuecucctluleosrsa ielnr f pialreestp rwaurecartteui orien’c sfoRrrp -fvouarraltutheedri sudsotiowng0n stthre aSbmwe tiatsensr-aPl[y1ds7bi]sV.. iMPewyisMesr-O L [i2n0g]1 .r. 9Te.sh7ied[ 1ur8e,s1u 9iln]ti wnthgae ss stuorsulevcdetudtro est awrnuacslty uezrneaeltr hfgieyle 3smD winsetimreu iciznteucdro erisnp aopnrradetpetdoar ruaetsmiionnogv fteohrae ld lSoawwttiasnscs-htPreeddabmlVig iaeanwnadel-rs as y[2s0isw] .u eTslhlineag sr egwsrauotlmetirpnmpg oseltmercubuceltdeusdrein dwp ianrse GpeanRreOartgMioyAn mCfoiSnr i[fm2u1riz]t hewdeir tihdno dpwerfenapsutarlrtea astemiottnian nfgoasrl y fdsooirsw .5M0n0si0tsr sseitanemgps ra.e nsiadl-ues ysisin usthinegs gorlovmedppst reumcbtuerdadlefidl eins GwRerOeMinAcCorSp [o2r1a]t ewdituh sdinefgauthlte sSetwtiinsgs-sP fdobr V50ie0w0 estre[p2s0. ]. The 2.2.r Cesoumltpionugnsdt rSuecletcutrioenw foars Pehnaerrmgyacmopihnoirme iGzenderinatiporne p aration for downstream analysis using 2.2.g CrAoomfteprop uaennmd e Sbxehldaecdutesiotdinvi fneo rlG iPtRehrOaarMtmuAraecC ospSeha[o2rrc1eh] G,w ewniteehr wadteieofrnae u u ltnsaebtltein tgos ifdoern5t0if0y0 isnthepibsi.tors specifi- cally Afofrte Cr Gan exhaustive li2.2. CompSou onfd MSe.l eucltciornanterature searchfosr. PThhairsm leadco ptoh ot,h we ee xwpelorer autinoanb loef re Generation ttoh eid heonmtifoyl oinghs iobfit oCrGs Ss poefc Mifi.- ucalclelrya nfos ru CsiGngS BoLf AMS.T u flrcoerman Ns.C TBhIi [s2 l2e]d. C toG Sth oef Hex.p plyolroarti iwona so ffo tuhned h tom beo lao hgos mofo lCoGg So fo Cf GMS. oufl cMera. nuslc Auerfstaienrgsa wBnLietAhxh SaaT us fesrqtoiuvmee nNlicteCe rBiadIte u[n2rt2ei]ts.y eC aoGrfc Sh4 9,o.wf3 H4e%.w p. eyCrleoGruiS nw oaafb sHl ef.o tpouynilddoer tin oits ibf fyeu ainn hchtoiibomintooalrlolsygs spoiefm cCiifiGlacSra lly toof tMhfoa.r tu ColfcG eMSra.on ufsl Mcwer.iatuhnlc sae sr isanencqse.u Tiethn aiclsseol ie dcdaettnoatlitythyze eoesf xt 4hp9elo. 3crr4ae%taiot. inCoonGf oStf ho Lef- hHcoy.ms ptayotllhoorigio sinsoi nffueCn fGrcotSimoonf OaMl-ls.yuu sclcicmeirnaiylnals-r us- Lto-h tihonamgt BosfLe MAriS.n Tuel fc(reOoraSmnHsN Ss)Ci naBcnIed [i2 tL 2a-]l.csyoCs GctaeStinaolefy H(zUe.snp tiyhPloerr ocitrwKeaBats iIofDon:u oQnfd 1LMt-oc0ybPset5aa;t hMhiooEmnTioBnl_eoH gfrEooLfmPC OXG)-S s[uo2c3fc]M.i nT.yuall-cer- bLl-eh ao1n msshwoosiewthrsina tehs ea(qOt uCSeHGnSce) oaidfn eHdn .tL ipt-yyclyoosrftie 4hi9na.3es4 (f%Uiv.neCi PkGrnoSotwoKfnBH iI.nDph:y ilQboir1tioMirss0f Puc5on;mc MtipoErniTsaiBlnl_ygH sJiEumLgiPlloaXnr)et o[ 2[2t3h4]a.] ,tT αoaf- M. Lbalepu a1lcc sehhroaonnwse,ss i Ynthacnaetgi tCaamGlsbSoi ncoa,f tPaHal.yu zpleyoslwotrhni eihnca,r sea anfitdvio e9n -koHnfyoLdw-crnyo sxitnyah-tαhib-iLoitnaopirnsae ccfhoroomnmpe rO[i2s-i5snu].gc H cJiunogywllo-eLnv-ehr o[,2 mt4oo] ,sa αesr-ine cLearpt(OaicnSh HwoSnh)ee,at nhYdearnL tgh-aceymssebt eininh, eiPb(aUituonlroisPw wrnooitnKu,lB daI nbDde: s9Qu-1HitMaybd0lPreo5 fx;oyMr- αtEh-TeLB at_apHragcEehtLo pPnrXeo )t[e[22i5n3] ].( .CHTGaobwSl eoevf1 eMsrh,. otuowlc aesrst-hat acnesr)Ct,a GitnhS ewo sfhtHreut.hcpetyurl rotehrsie hsoeaf sibnfiohvtiheb ikCtnoGorsSw wnofoi nuHhl.di bp biyteloo srrusi icatonambdl peC rfGiosriSn tgohfeJ u Mtga.rl ogunelcete pr[2arn4o]st,e wαin-e L(rCaep GfaiSrcs hoto fs nMuep,. eYuralicnmegr-am- panosb)e,id nt h,toeP asduterltuoecwrtmunriinense, aosnifm dbio9lta-hHri tCyideGsrS ob xoeytfw -Hαe-.eL pnay ptlhoareciih rao nbndien Cd[2iGn5]Sg. oHpfoo cMwk.ee tuvsle.c reT,rhtaoen sai nswcheeirbrteiat oifnirrssw tw hseeurtpehe etrrhimethn-e se dpocsikendehd itboof bo ti i ntdo erestaecwrhmo iupnlredo stbeimienis luastrirtiuatibcetlsue brfeo trwtoteh edenet ttaehrregmierti nbpeinr obdtienidngi np(Cgo GcmkSeetocsf.h TaMnh.iesu milncshe riaabnnitsdo) ,rcsto hwnesesirstetr eutnhccetuyn r es wdoitchkiend t h C hien ea GcSh opfrHot.epinyl osrtiruanctdurCeG tSo odfeMter.mulicneer abnisnwdienrge fimrsetchsuanpiesrmims paonsde dcotonsdisetteenrmcyi ne witshimini ltahrei t a ai cetivcstib ee tswiteee. ve site.n their binding pockets. The inhibitors were then docked in each protein Tabslter u1.c Ttuhree fitvoe dkentoewrmn iinnheibbiitnodrsi nogf CmGeSc hofa nHieslmicosbaacntedr pcyolnorsii swteitnhc nyawmietsh, iInC th veaalucetsiv, aensdit set.50 ruc- tTuarbesle. 1. The five known inhibitors of CGS of Helicobacter pylori with names, IC50 values, and struc- tureTsa.b le 1. The five known inhibitors of CGS of Helicobacter pylori with names, IC50 values, and structures. Compound IC50 (µM) Structure CompCouonmdpound IC50 (µMI)C50 (µM) SSttrruuccttuurree Juglone 7 ± 0.7 JuglonJeuglone 7 ± 0.7 7 ± 0.7 Computation 2021, 9, x FOR PEER REVIEW 4 of 27 Computation 2021, 9, x FOR PEER REVIEW 4 of 27 α-Lapαa-cLhaopneachone 11 ± 3 11 ± 3 α-Lapachone 11 ± 3 9-Hydroxy-α-Lapachone 9 ± 1 9-Hy9d-rHoxyyd-αro-Lxayp-αac-hLoanpeachone 9 ± 1 9 ± 1 PaulowPanuinlownin 19 ± 2 19 ± 2 Paulownin 19 ± 2 Yangambin 27 ± 6 Yangambin 27 ± 6 2.3. Ligand-Based Pharmacophore Virtual Screening LigandScout version 4.3 [26] was used for ligand-based pharmacophore virtual 2s.c3r.e Leingianngd.- TBhasee d2 DP hsatrrmucatcuorpehso roef Vthiret uinahl iSbcirteoernsi nwge re retrieved from the ZINC database [27] in StLruigcatunrdeS Dcoautat Fvieler s(iSoDnF 4) .f3o r[m26a]t sw anasd luosaede dfo irn tloig Laingdan-bdaSsceodu tp’sh Larimgaancdo-pBhaosered Mvirotdueall- sicnrge ePneirnsgp.e Tcthive e2 Dv4 s.3tr [u2c6t]u. rTehse o df ethfaeu ilnt hseibttiitnogrss wofe OreM reEtGriAev bedes ftr womer eth ues ZedIN inC t hdea tgaebnaesrea [t2io7n] ionf Slitgruacntdu rceo Dnfaotram Failteio (nSsD wF)i tfho r2m00a tcso annfdo rlmoaadtieodn isn btoe iLnigg athned Smcaoxuitm’su Lmig alinmdi-tB saeste pde Mr modoelle-- icnugl eP e[2r8sp].e ctive v4.3 [26]. The default settings of OMEGA best were used in the generation of ligand conformations with 200 conformations being the maximum limit set per mole- c2u.4le. P[2r8e-]F. iltering of the Library for Pharmacophore-Based Screening. A unified library comprising 4067 natural products obtained from AfroDb and 2N.4A. PNrPe-DFBilt weriansg u osf etdh ef Loirb vrairrtyu faolr sPchraeremniancogp. hAorfreo-BDabse adn Sdc rNeeAniNngP.D B were composed of 885 and 3A1 8u2n ciofimedp oliubnrdarsy, recospmepctriivseinlyg. T40h6e7 l ibnraatruyr awl apsr foildteurcetds uosbitnagin Ferde ef rAoDmM AEf-rTooDx bF ialtnedr- NinAgN (FPADFB-D wruasg su4s)e [d2 9f]o tro v eilritmuainl astcer el esnsi ndgru. gA-flrikoeD cbo manpdo uNnAdNs bPaDsBed w oenr eth ceoirm pphoysseidco ocfh e8m85- aicnadl 3p1r8o2fi lceosm. Tphoeu 2n5d3s0, roeustpeuctt icvoemlyp. oTuhned lisb frraormy wthaes ffiilltteerriendg uwseinreg eFmrepel oAyDedM iEn- Tpohxa rFmilatecro-- ipnhgo (rFeA-bFa-sDerdu vgisr4t)u [a2l9 s]c troe enliimngin. ate less drug-like compounds based on their physicochem- ical profiles. The 2530 output compounds from the filtering were employed in pharmaco- p2h.5o. rPeh-baarmseadc ovpihroturea-lB sacsreede Sncirnege.n ing of the Library A total of 2530 pre-filtered compounds were used for pharmacophore-based virtual 2s.c5r.e Pehnainrmg avcioap hLoirgea-BnadsSecdo Suctr eve.n4i.n3 g[ 2o6f ]t hbey L sibcrraereyn ing against the validated pharmacophore modAel .t Tothael coof m25p3o0u pnrdes- fwilteerree dsc creoemnpedou anftdesr wcoenrve eurseiodn f forro pmh “ar.smdaf”c otop h“o.lrdeb-”b.a sed virtual screening via LigandScout v.4.3 [26] by screening against the validated pharmacophore m2.o6d. Vela.l iTdhaeti ocno mofp AoutnoDdso cwk eVrien sac: reened after conversion from “.sdf” to “.ldb”. 2.6.1. Superimposition of Co-Crystallized with Re-Docked Complexes 2.6. Validation of AutoDock Vina: The ligand 4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid (HEPES) was ex- 2t.r6a.c1t.e Sdu fpreormim tphoes ictoio-cnr yosf tCaloli-zCerdy ssttarluliczteudre w oitfh C RGeS-D oofc Mke.d u Clcoermanpsl eaxneds re-docked into the activTeh seit eli. gUasnidn g4 L-(i2g-Ahylidgrno x[3y0e]t,h tyhle) -p1r-pedipicetreadz ibnien deitnhgan peossuel fofn tihc ea HciEdP (EHSE lPigEaSn)d w waass esxu-- tpraecritmedp ofrsoemd wthieth c toh-ecr eyxsptaelrliizmeedn statrlluyc tduertee romf iCnGedS poof sMe o. fu tlcheer acnos- carnyds traell-idzoecdk setdr uicnttuor eth oef atchteiv per ositteei.n U. s ing LigAlign [30], the predicted binding pose of the HEPES ligand was su- perimposed with the experimentally determined pose of the co-crystallized structure of t2h.e6 .p2.r oRtOeiCn .C urve Analysis 2.6.2. ROC Curve Analysis Computation 2021, 9, x FOR PEER REVIEW 4 of 27 9-Hydroxy-α-Lapachone 9 ± 1 Computation 2021, 9, 32 4 of 24 Paulownin 19 ± 2 Table 1. Cont. Compound IC50 (µM) Structure YangaYmabningambin 27 ± 6 27 ± 6 2.3.2 L.3i.gLanigda-nBda-sBeda sPedhaPrhmaarcmopachooprhe oVreirVtuiratlu SaclrSeecnreinengi ng LigLaingdaSncdoSucto uvtevresirosino n4.43. 3[[2266]] wwaass uusseeddf ofrolri glaignadn-bda-sbeadsepdh aprhmaarcmopachooprehovriret uvailrtsucraele n- screinegn.inTgh. eTh2eD 2sDtr sutcrutucrteusreosf otfh tehien ihnihbiibtoitrosrws wereerer ertertireivevededf rforommt htheeZ ZIINNCC ddaattaabbaassee [[2277]] in in SSttrruuccttuurree DDaattaa FFiillee ((SSDDFF)) ffoorrmmaattss aanndd llooaaddeedd iinnttoo LLiiggaannddSSccoouutt’’ss LLiiggaanndd--BBaasseedd MMooddeell-ing ingP Peerrssppeeccttivivee vv44..33 [[2266]].. ThTeh ededfaeufalut sltetsteinttgins gosf OofMOEMGAEG bAestb wesetrew uesreedu inse tdhei ngetnheeragteionne ra- of ltiigoannodf cloignafonrdmcaotniofonrsm waittiho n2s00w ciothnf2o0r0mcaotinofnosr mbeaitniogn tsheb eminagxitmheumm alixmimit usmet plimer imt soelte-per culme [o2l8e]c.u le [28]. 2.4.2 P.4r.eP-Friel-tFerilitnegri nofg tohfe tLhiebLraibryra froyr fPohraPrhmaarcmoapchooprhe-oBrea-sBedas SedcrSeecnreienngi.n g A uAnuifnieifide dliblirbarrayr ycocommpprirsisiningg 4067 natturrall pprorodduuctcstso botbatianiendedfr ofmromAf rAoDfrboDanbd aNndA N- NAPNDPBDwBa ws uase udsfeodr vfoi rt uvairltsucarle esncrineegn. iAnfgr.o ADfbroaDndb NanAdN NPDABNwPDerBe cwoemrep ocsoemdpoofs8e8d5 oanf d88351 82 andc o3m18p2o cuonmdps,oruensdpse,c rtievsepleyc. tTivheelyli. bTrhaery liwbrasryfi lwtearse dfilutesriendg uFsrienegA FDreMe EA-DToMxEF-iTltoexr iFniglte(FrA- F- ingD (FruAgFs-4D) r[2u9g]st4o) e[2li9m] itnoa etleimleisnsadteru legs-lsi kderucogm-lipkoeu cnodmspboausendso nbatsheedir opnh tyhseiciro pc heymsiiccoaclhpermofi-les. icalT phreo2fi5l3e0s. oTuhtep 2u5t3c0o mouptopunt dcosmfrpoomunthdes fifrlotemri nthgew fieltrereinmgp wloeyred eminpplohyaermd ianc opphhaormrea-bcoas-ed phovriret-ubalsescdr eveirntiunagl. screening. 2.5.2 P.5h.aPrhmaarcmopachooprhe-oBrea-sBeda sSedcrSeecnreinengi nofg tohfe tLhiebLraibryra ry A tAotatol toafl 2o5f3205 3p0rep-rfiel-tfierlteedr ecdomcopmoupnodusn dwsewree ureseuds efdorf oprhparhmaramcoapchoporheo-rbea-sbeads evdirvtuiratlu al screscerneienngi nvgiav LiaigLaingdaSncdoSucto vu.t4v.3.4 [.236[]2 6b]y bsycrsecerneienngin aggaaignasitn tshtet hvealviadlaidteadt epdhparhmaramcoapchoporheo re momdeold. eTlh. eT hcoemcopmoupnodusn wdsewree srcerescerneeedn eadftearf tceorncvoenrvseiorsni ofrnofmro “m.sd“.fs”d tfo” “to.ld“b.l”d.b ”. 2.6.2 V.6a. lVidaaltiidoant ioofn Aouf tAouDtoocDko VckinVa:i na 2.6.1. Superimposition of Co-Crystallized with Re-Docked Complexes 2.6.1. Superimposition of Co-Crystallized with Re-Docked Complexes The ligand 4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid (HEPES) was ex- traTchteed lifgraonmd th4-e(2c-oh-ycdryrsotxaylleitzheydl)s-t1r-upciptuerreazoifneC GetShaonf eMsu. lufolcneirca nasciadn d(HreE-PdEoSc)k ewdaisn teox-the tracatcetdiv efrosimte .thUe scino-gcrLyisgtAallliigzned[ 3s0tr],utchtuerpe roefd iCcGteSd obfi nMd.i nuglcperoasnes oafndth ree-HdEocPkEeSd liignaton dthwe as actisvuep seirtiem. pUossinedg wLiigthAtlhigene [x3p0e]r, itmhee nptraeldlyicdteedte rbminidniendgp poosseeo of ft htheec oH-cErPyEstSa llliigzaendds twruacst usure- of pertihmepporsoetdei nw. ith the experimentally determined pose of the co-crystallized structure of the protein. 2.6.2. ROC Curve Analysis 2.6.2. ROTChe Cfiuvrevea cAtinvaelycsoims pounds of CGS from H. pylori together with their decoys were screened against CGS from M. ulcerans. The ROC curve and Area Under Curve (AUC) were generated using easyROC [31]. The ROC curve is quantified by the calculation of the Area under the Curve (AUC) with values between the range of 0 and 1. Another parameter used by LigandScout 4.3 is the Enrichment Factor (EF). EF implies “a ratio of the observed fraction of active compounds in the top few percent of a virtual screen to that expected by random selection” [32]. A good pharmacophore model should be able to significantly distinguish actives from a library composed of inactives and actives. The inactives are decoys of the five known inhibitors. Decoys share similar physical features with the known inhibitors or actives but different chemical structures [33]. Computation 2021, 9, 32 5 of 24 2.7. Virtual Screening of the Library AutoDock Vina interfaced with PyRx v.0.8 [34] was used for all the virtual screen- ing. The pharmacophore hits obtained from screening the pharmacophore model against the filtered library were virtually screened against the energy minimized protein, CGS of M. ulcerans. The pharmacophore hits were imported as “.sdf” format into AutoDock Vina, energy minimized, and converted to “.pdbqt” format. The energy minimization involved the use of the default settings comprising Universal Force Field (UFF) and conju- gate gradients for optimization algorithm with a total number of 200 steps. The AutoDock Vina search space center had X, Y, and Z coordinates, which were set to the spatial coor- dinates of 4.8945 Å, −23.7922 Å, and −37.3622 Å, respectively. Grid box dimensions of 25.00 Å × 25.00 Å × 25.00 Å covered the binding site region. The default exhaustiveness of 8 for AutoDock Vina calculations was used. Ligands that firmly docked in the binding site of the target were selected. Ligands that were not fitted firmly within the binding pocket were eliminated from future analysis. 2.8. Protein-Ligand Interaction LigPlot+ [35] was employed in the characterization of the ligand-protein interactions as 2D schematic diagrams using default settings. 2.9. Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Prediction SwissADME [36] and AdmetSAR [37] were used for Absorption, Distribution, Metab olism, Excretion, and Toxicity (ADMET) predictions. Ligands in SMILES format were used to generate the pharmacological profiles. Both SwissADME [36] and AdmetSAR [37] provide access to parameters and predictive models for the computation of pharmacoki- netics, physicochemical properties, drug-likeness, and toxicity of small molecules. The 2 2 parameters that were used included (but not limited to): polarity (20 Å < TPSA < 130 Å ); solubility (0 < Log S (ESOL) < 6); flexibility (0 < number of rotatable bonds < 9); lipophilicity (−4.0 < LOG P < +5.0); size (150 g/mol < molecular weight < 500 g/mol); hydrogen bond donors ≤ 5; and hydrogen bond acceptors ≤ 1 [36]. Lipinski’s rule of five is based on a set of rules which describe whether a particular compound is “drug-like” or “orally active” [38]. The rule is based on physicochemical parameter ranges for a compound having a molecular weight ≤ 500 Daltons; hydrogen bond donors ≤ 5; a logarithm of n-octanol/ water partition ≤ 5; and hydrogen bond acceptors ≤ 10. A compound may be considered less “orally inactive” and “drug-like” if it violates more than two rules. 2.10. Prediction of Activity Spectra for Substances and Structural Similarity Analogues (PASS) Prediction of Activity Spectra for Substances (PASS) [39] was used for the prediction of biological activity based on Bayesian models. Additionally, the hits were screened via the DrugBank [40] to identify similar compounds, derivatives or analogs with antimycobac- terial activity. 2.11. Molecular Dynamics Simulation All molecular dynamics (MD) simulations were executed at 100 ns using GROMACS version 2018 [21] with the SPC water model. The Optimized Potentials for Liquid Simula- tions (OPLS)/All Atom (AA) force field was used for the simulation of the protein alone, whereas the GROMOS96 43a1 force field was employed in the protein–ligand complexes simulations. All MDs were executed on a supercomputing system. The protein topology files were generated for the GROMACS OPLS/AA force field whereas that of the docked complexes were generated utilizing the PRODRG2 using the settings: ‘Yes’ for ‘Chirality, ‘Full’ for ‘Charges’ and ‘No’ for EM. Simulations were carried out in a 1 nm dodecahedron box for the application of periodic boundary conditions, solvated and electro-neutralized with the addition of 13 sodium atoms. Thereafter, energies were minimized in 1100 steps. Equilibration followed by standardization of the temperature of the system to the desired Computation 2021, 9, 32 6 of 24 value for simulation (300 K) and the application of pressure to the system towards the desired density of 1000 kg/m3. All graphs were plotted using Xmgrace [41]. 2.12. Molecular Mechanics Poisson-Boltzmann Surface Area Binding Free Energy Calculations MM-PBSA was employed in the computation of the binding energies of the ligand– protein complexes utilizing g-mmpbsa over 100 ns for the MD simulation using GRO- MACS [42]. Graphs resulting from the MM-PBSA calculations were plotted using the R programming package. The energy terms were calculated over a 100 ns production run, taking 100 snaps over 1 ns interval with default settings. 3. Results and Discussion 3.1. Target Description Cystathionine gamma synthase exists as a homotetramer possessing two individually wrapped homodimers each bearing two actives sites. Within each active site is the cofactor PLP, which binds tightly to the Lys208 moiety and is stabilized by Ser205, Asp183, Asn158, Met87 and Gly86 through hydrogen bond interactions. There are two structures with PDB IDs 3QI6 and PDB ID 3QHX) [16]. The structure with PDB ID 3QHX was preferred over 3QI6 because of a higher resolution value of 1.65 Å compared to 1.91 Å; and R-values of 0.181 and 0.148 as compared to 0.241 and 0.200, respectively. Additionally, the HEPES ligand was found to be only fully resolved in Chain A of the 3QHX structure. On the other hand, 3QI6 did not contain the HEPES ligand, hence was not selected for further downstream analysis. 3.2. Ligand-Based Pharmacophore Virtual Screening The structural alignment between the 3D protein structures of CGS of H. pylori and M. ulcerans yielded an RMSD of 0.66 Å, which was less than the 1.5 Å threshold to be considered as a successful pose (Figure 1) [43]. Consequently, a downstream analysis was undertaken with the inhibitors from CGS of H. pylori. 3.2.1. Pharmacophore Generation For pharmacophore generation, inhibitors with IC50 < 10.0 µM were used to generate the pharmacophore features. Thus, Juglone and 9-Hydroxy-α-lapachone with IC50 values 0.7 +/− 0.7 and 0.9 +/− 0.1 µmol/L, respectively, were used for pharmacophore generation. All the five inhibitors together with their respective decoys were used to validate the pharmacophore model. LigandScout allows the generation of 3D pharmacophores from structural data of ligands rapidly and transparently in a very convenient and automated way [26]. LigandScout implements either the shared- or the merged-feature models for the generation of pharmacophores. The Shared feature model setting selects only features common to training-set molecules. It generates only a few features expected to represent the general binding mechanism of the training set ligands. The merged-feature model setting, on the other hand, selects all features present on each ligand even if those features are not common to most molecules within the training-set. The pharmacophore model was generated with a model score of 0.9719 using a shared feature setting based on the overlap of pharmacophoric features of the two training set compounds of Juglone and 9-hydroxy-lapachone. The features generated via the pharmacophore modeling were 3 hydrogen bond acceptors, 1 hydrogen bond donor, 1 aromatic ring, and 1 hydrophobic interaction (Figure 2). 3.2.2. Validation of Pharmacophore Model The receiver operating characteristic (ROC) curve served to validate the pharma- cophore model. Five inhibitors of CGS of H. pylori [23,24] were used to generate 250 decoys, with 50 decoys for each inhibitor. DUD-E served as a source for the decoys [33]. A library comprising the decoys and the five active compounds labelled “inactives” and “actives”, respectively, was screened against the best pharmacophore model generated. Computation 2021, 9, x FOR PEER REVIEW 7 of 27 Computation 2021, 9, x FOR PEER REVIEW 7 of 27 was generated with a model score of 0.9719 using a shared feature setting based on the overlapw oafs p gheanremraatceodp hwoirthic afe matoudreesl oscfo trhee otwf 0o. 9t7ra1i9n iunsgin sge ta c sohmapreodu nfedast uorfe J usegtltoinneg abnadse 9d- on the hydroxoyv-learplaapch oofn peh. aTrhme afceoaptuhroersi cg efenaetruarteesd o vfi tah teh tew poh tarraminaicnogp sheotr ceo mmopdoeulnindgs wofe Jrue g3l ohnye- and 9- drogenh byodnrdo xayc-cleapptaocrhso, n1e h. yTdhreo fgeeantu broens dg ednoenraotre, d1 vairao mthaet pich rairnmg,a aconpdh 1o rhey mdroodpehloinbgic w ine-re 3 hy- teractiodnr o(Fgiegnu rbeo 2n)d. acceptors, 1 hydrogen bond donor, 1 aromatic ring, and 1 hydrophobic in- teraction (Figure 2). 3.2.2. Validation of Pharmacophore Model 3.2.2. Validation of Pharmacophore Model The receiver operating characteristic (ROC) curve served to validate the pharmaco- phore modeTl.h Fei vree cienihviebri tooprse roaft iCnGg Sc hoaf rHac. tpeyrliosrtii c[ 2(3R,O24C] )w ceurrev ue sseedr vtoed g eton evraaltied 2a5te0 tdheec opyhsa,r maco- with 50p dheocroey ms ofodre el.a Fchiv ien ihnihbiibtoitro. rDs UofD C-EG sSe orvf eHd. pays lao rsio [u23rc,2e4 f]o wr tehree udseecdoy tso [g3e3n].e Ara tleib 2r5a0ry d ecoys, with 50 decoys fo Computation 2021, 9, 32 comprising the decoys an r dea tchhe ifnivheib aitcotirv. eD cUoDm-pEo suenrvdesd la abse all esdou “ricnea fcotirv tehse” daencdo y“as c[t3i3v]e.s A”, library 7 of 24 respectcivoemlyp,r wisainsg s cthreee dneecdo aygsa ainndst tthhee fbiveset apchtiavrem caocmopphoourned ms loadbeell lgeedn “eirnaatecdti.v es” and “actives”, respectively, was screened against the best pharmacophore model generated. Figure 1. FSuigpuerriem1p.oSsuitpioenr iomf 3pqohsxit (ipoanleo yf e3lqlohwx)( opna l4el0OFigure 1. Superimposition of 3qhx (pale yellowy)e l(laoqwu)amonar4inl0eO). T(ahqe uRaMmSaDr ifnoer) t.hTe hsetruRcM- SD for the structural tural alignment was 0.66 Å, which is indicative of the hi gohn s4ilm0Oila (raiqtyu abmetwareinene) .t hTeh tew RoM pSrDot efoinr the struc- structurteusar. alilg anlimgnemntenwt awsa0s. 606.66Å Å, w, whhicichh iiss iindicattiivvee oof fththe ehihgihg hsimsiimlairliatyr ibtyetwbeetewn ethene ttwheo ptwrootepinr otein structures. structures. Computation 2021, 9, x FOR PEER REVIEW 8 of 27 FigFuirgeu 2r.e T2h.e Tphhaermphacaorpmhaocreo pfehaoturreesfe gaetnuerreastegde fnroemra ttheed tfwroo minhtihbeitotwrs.o (Ain) hJuibgiltoonres .(le(fAt)) aJnudg 9lo- ne (left) and hy9d-rhoyxyd-rlaopxayc-hlaopnae c(hriognhte) (srhiogwhtin) gsh sohwareindg pshhaarmreadcopphhaorrmic afecaotpuhreosr cicomfepartiusirnegs Acorommpartiisci RnigngAsr omatic Rings (A(RA),R H),yHdryodgerno gBeondB oDnodnoDrso (nHoBrDs )(,H HByDdr)o, gHeynd Broongde AncBcoepntdorAs (cHceBpAt)o arnsd(H HByAdr)oapnhdobHicy indtreorpach-obic interac- tiotniso (nHs)(. H(B)). T(Bh)e Tachteivaesc tainvde sthaenird otvheerilarpopveedr lpahpaprmedacpohpahromrica fceoaptuhroersi wc hfeicahtu arrees rewphreiscehntaerde irne presented in thet hpehaprhmaarcmopahcoorpe hmoordeeml. oTdhee lr.edT hbaellrse sdhobwal lHsysdrogen bond acceptors (HBA), the yellow ball shows Hydrophobic interactions, the green ball shohwosw HHydyrdorgoegn ebnonbdo ndodnaocrsc e(HptBoDrs) a(nHdB aA b)l,uteh e yellow ball rinsgh roewprsesHenytds raonp Ahroobmicatiinc treinrga.c tions, the green ball shows Hydrogen bond donors (HBD) and a blue ring represents an Aromatic ring. 3.3. Validation of Generated Pharmacophore Model The ROC curve shows the performance of the model to effectively distinguish be- tween a collection of “active” and “inactive” compounds [44]. The AUCs were determined as 1.0, 1.0, 1.0 and 0.70 in the top 1%, 5%, 10% and 100% of the screened library, respec- tively. AUC is assigned values between 0 and 1, with 1 suggesting a theoretically perfect classification showing 100% sensitivity and 100% specificity. Consequently, an AUC closer to 1 is crucial, whilst 0.5 suggests a random classification implying a poor predictive ability of the model [44–47]. An AUC value of 0 implies an incorrect classification, whilst 0.70 or better implies moderate discrimination and hence suitable for classification. Since the overall AUC was 0.7, the model was reasonably good in classification. Additionally, EFs were determined as 51.0, 34.0, 34.0 and 34.0 for 1%, 5%, 10% and 100%, respectively (Figure 3). With three active ligands appearing in the hit results (Figure 3), the selected pharmacophore model had the reasonable capability to distinguish between actives and decoys successfully [48]. Computation 2021, 9, 32 8 of 24 3.3. Validation of Generated Pharmacophore Model The ROC curve shows the performance of the model to effectively distinguish between a collection of “active” and “inactive” compounds [44]. The AUCs were determined as 1.0, 1.0, 1.0 and 0.70 in the top 1%, 5%, 10% and 100% of the screened library, respectively. AUC is assigned values between 0 and 1, with 1 suggesting a theoretically perfect classification showing 100% sensitivity and 100% specificity. Consequently, an AUC closer to 1 is crucial, whilst 0.5 suggests a random classification implying a poor predictive ability of the model [44–47]. An AUC value of 0 implies an incorrect classification, whilst 0.70 or better implies moderate discrimination and hence suitable for classification. Since the overall AUC was 0.7, the model was reasonably good in classification. Additionally, EFs were determined as 51.0, 34.0, 34.0 and 34.0 for 1%, 5%, 10% and 100%, respectively (Figure 3). With three active ligands appearing in the hit results (Figure 3), the selected Computation 2021, 9, x FOR PEER REpVhIEaWrm acophore model had the reasonable capability to distinguish between actives9a onf d27 decoys successfully [48]. FFiigguurree3 3. .S Seelleecctteedd pphhaarrmmaaccoopphhoorree mmooddeell RROOCC ccuurrvvee iinnddiiccaatteedd iinn bblluuee.. DDeetteerrmmiinneedd aatt 11,, 55,, 1100 aanndd 110000%%o of ft hthees esleelcetcetdedd adtaatbaabsaeswe wereerteh tehAe UACUaCn adnEdF EvFa lvuaelsuaess sahs oswhonw. Tnh. eTmhee dmiaendiiasns hiso swhnowbynd boyt ted dotted lines. If the curve were to be close to the median, then it would suggest a poor model. lines. If the curve were to be close to the median, then it would suggest a poor model. 33.4.4.. PPhhaarrmaaccoopphhoorree--BBaasseeddS Sccrreeeennininggo offt thheeL Libibrraarryy VVirirttuuaall ssccrreeeenniinngg bbaasseedd oonn ththee ggeenneerraattioionn ooff pphhaarrmmaaccoopphhoorree mmooddeellss isis uusseefufull inin iiddeennttiiffyyiinngg ssttrrucctturralllly novell aanndd ppootetenntitaial llelaedad cocmompopuonudnsd asraisriinsign fgrofmro mchecmheimcailclya ldlyi- dvievresres eddataatbaabsaesse s[4[949].] .TThhe evvaalildidaatetedd pphhaarrmmaaccoopphhoorere mmooddeel lwaass used as a 33D qquueerryy toto ssccrreeeenn tthe library comprriisiing 22553300 ccoommppoouunndds.s C. oCmompopuonudnsd asrea rme amtcahtecdh eadnda nfidltefirletedr oedut obuatsebda soend tohne tphheaprmhaarcmopahcopreh ofirte scfiotrsec goerenegreantedra ftoerd tfhoer pthearpmhacrompahcorpeh moroedmel o[d26e]l. [T2h6]i.s Tphrioscpesrso cdersasstdicralsltyic raeldlyucreds uthces dtahteasdeat toafs emt olfecmuoleles cruelseusltriensgu litni nfgewinerf epwroemr ipsrionmg imsionlge- mcuollesc ufolers mfoorlemcuolaerc udloarckdioncgk. iTnhge. Tcohme cpoomunpdosu nudsesdu sfoedr ffuorthfuert hdeorwdnoswtrneastmre amnalaynsaisly hsaisd hpahdaprmhaarcmopachopreh ofrite sfictosrceosr easboabvoe v6e56 (5T(aTbalbel e2)2.) .TThhe emmaappppiningg ooff these compounds oonnt hthee pphhaarrmmaaccoopphhoorreem mooddeelli sisa alslsoov visisuuaalilzizeedd( F(Figiguurere4 4).). Table 2. Hit molecules arranged in order of decreasing pharmacophore fit score. The compounds were used for further downstream analysis including molecular docking. Name Pharmacophore-Fit Score Pyrogallol 67.16 Chrysophanol 67.04 ZINC00058187 67.02 3-methoxy-4-hydroxyphenol 1-O-beta-D-gluco- 66.97 pyranoside Gossypetin 3,7,8-trimethyl ether 66.88 3′-hydroxyflindulatin 66.88 Vanillin 66.85 4′-methyl gossypetin 66.82 Isoscutellarein 66.81 Corniculatusin 66.75 Sexangularetin 66.75 Computation 2021, 9, x FOR PEER REVIEW 10 of 27 Bucegin 66.74 Computation 9 Isoscutellarein 8-methyl ether 66.74 2021, , 32 9 of 24 Onopordin 66.74 Gentisic acid 66.74 Table 2. Hit molecules arrang1e,8d-idniohrydderroofxdye-c3r,e5a-sdinigmpehtahromxaycxopahnothreofintes core. The compoun6d6s.w74e re used for further downstream analysis including molecular docHkinegr.bacetin 66.74 ZINC14490611 66.72 Name Vanillic acid Pharm6a6co.7p0h ore-Fit Score Pyrogallol 67.16 Chrysophanol Betavulgarin 66.5667 .04 ZINC00058187 67.02 3-methoxy-4-hydroxyphenol 1-O-betaE-Dp-gitlauxcoifpoylriann oside 66.4626 .97 Gossypetin 3,7,8-trPim-heythdyrloexthyebr enzoic acid 66.4606 .88 3′-hydrox2yfl, indulatin 66.88Vanil5li-ndihydroxybenzaldehyde 66.3666 .85 4′2-m,4e′t-hdyilhgyodssryopxeytin-3′-methoxyacetophenone 66.3656 .82 IsoOscumteellgarein 66.81Corniculatau-shinydroxypropioguaiacone 66.3646 .75 5-S(hexyadngroulxayremtinethyl)-2-furancarboxylic acid 66.3646 .75 Bucegin 66.74 Isoscutellarein 8-methyAl eltohe-remodin 66.3606 .74 Onopordin 66.74 Gentisic acid Catechin 66.3606 .74 1,8-dihydroxy-3,5-dimethoZxyINxaCnt0h5o8ne54400 66.1696 .74 Herbacetin 66.74 ZINC124,459-0d6i1h1ydroxybenzyl alcohol 66.1676 .72 Vanillic acid 66.70 Betavulgarin ZINC00013245 66.1626 .56 Epitaxifolin Acetovanillone 66.1616 .42 4P--h(2y-dfroorxmybyenl-z5o-ihc yacid 66.402,5-dihydroxybenzaldedhyrodexymethylpyrrol-1-yl) bu- 66.0676 .362,4′-dihydroxy-3′-methoxyacettoyprhiecn aonceid 66.35 Omega-hydroxypropioguaiacone 66.34 5-(hydroxymethyl)G-2o-fsusryanpceatribno x3y,l8ic-daciimd ethyl ether 65.9676 .34 3,7-dAihloyed-ermoxoydinCatechin -8-methoxy-3-(3′,4′-methylenedi- 66.30 65.8696 .30 ZINC05o8x54y4b0e0nzyl)chroman-4-one 66.19 22,5,-3d-idhyihdroxybenzyl alcohol 66.17ZINyCd0r0o0x1y32-145-(4-hydroxy-3-methoxyphenyl)- 65.8686 .12Acetovanillone 1-propanone 66.11 4-(2-formyl-5-hydroxymethylpyrrol-1-yl) butyric acid 66.07 Gossypetin 3,8-dSihmiektihmyliect haecrid-4-O-gallate 65.865 .97 3,7-dihydroxy-8-methoxy-3-(3′,4′-methyleneZdIiNoxCyb1e3nzyl)chroman-4-one 65.892,3-dihydroxy-1-(4-hydroxy-3-methoxyphenyl)-312-p8r0o5p7a none 65.7675 .88 Shikimic aciGd-e4-nOti-gsiacll atceid 5-O-glucoside 65.4645 .85 ZINC13328057 65.77 Gentisic acid 5-O-glucoside 65.44 FigureF i4g.u Vreis4u. aVliiszuaatliioznat oiofn hoitf hmitomleocluelceusl ems mapappepded oonntoto tthhee pphhaarrmaaccoopphhoorerem modoedl.el. Computation 2021, 9, x FOR PEER REVIEW 11 of 27 ComCopmutpautitoant i2o0n2210, 29,1 x, 9F,O3R2 PEER REVIEW 11 of 27 10 of 24 3.5. Validation of Molecular Docking Protocol 3.5.1. Superimposition of Co-Crystals with Re-Docked Complexes 3.5. Validation of Molecular Docking Protocol Auto3D.5o.cVka Vlidinatai ownaosf Musoeldec fuolra rmDoolcekciunlgar do3.5.1. Superimposition of Co-Crystals wiPthr oRtocckoilng as a result of its ability to ran on all major ope3r.5a.t1in. gSu spyesrtiempso [s5i0ti]o. nHoofwCeov-eCrr, to be u es-Dedo cfkoerd t hCiosm sptulexes ystals with Re-Docked Cdoym, ipt lneexeds ed to be vali- dated. TheA ruet-odDocokckin Vg ionfa HwEaPs EuSse ldig for molecular docking as a resultAutoDock Vina was uasnedd ifnotrom thoel eaccutilvaer dsiotec kwinags uassead o ft oit se vaabliuliatyte t oth ran on all result of its abieli dtyoctok-ran on all ing prmotaojocro lo perating systems [50]. However, to be used for this study, it needed to be vali- datemd.a Tj voirao spuepreartiimngpsoyssintegm ths e[5 p0r]o. jHecotwede vdeorc,ktoinbge puosseeds foovretrh tihses teuxdpye,riitmneenetdaeldlyt iodbene-validated. tified co-crysthae re-dockThe rell-idzoedck sintr iuncgt uofr eH oEfP tEhSe l itgaarngdet ig of HEPES ligand . nTtoh eth bea ascitsi voef swite was used tointo the active shiitcehw waassu tshee e dfa vcatl uthataet the dto evalu HatEeP ock- tEhSe docking has a kinngo pwrottifiepdr ncoo tc oocnolf ovia superimposing the projected docking poses over the expeo-ccroylsvtraimlaliaszuteidpo ensr tairmnudcpt ouosrrieine ognftt athhteieop ntra osrigjneecctte.e Tdith wdeo abcsak seiinxstg roapf cowtsehedis cfohrov wmera stthh teeh ece o r-icmryensttaall lsyt riduen-xfapcetr itmhaetn HtaEllPycE-iSd entified ture ohf aths eac otka-ncrorgywestnt. a Aclolninz RfeoMdrmsStaDrtui ovcnatul aurneed oo off r1tihe.4ne1t3taa tÅirog nwe tsa.isnT cohebe ittab wianaseisds e oxinftrdwaicchtaeitcdihn frgwo tmahsa ttth hAee cuofta-occDrtyotshctaaklt VsHtirnEuaPc -ES has a had thtuer epk oonft oethwnetni taaclr ogtnoef tor. erAmpnra oRtdiMounScDea n tvdhaelou rceire oynfst t1aa.t4lil1oo3ng Årsai nwpchaeisc iot pbwtoaasisen.ee xdTt hirnaed cptiecoadsteifnr gogme tnheathrt aeAtceuodt-o cfDrryooscmtka lVthsitner au cture of superhimadp tothhseiett iaporongt eeotnf. ttAiahnle tRcooM r-ecSprDyrosvtdaaulllucieze etodhf eH1 .c4Er1yP3sEtÅaSl lawongadrsa ropebh-tdiacoi npcekodseedi.n HTdhiEcePa tpEionSsg ceot hgmeantpeAleruaxteteosdD i sfo rcroekmpVr tienh-ea had the sentedsu pinep roiFmtiegpnuotrisaeitl iot5on. roAefp dtrhdoeidt ciuoocn-cearltylhyset,a clflroiyzuesrdt a oHllvoEegPrrElaaSpp hapniicndgp r oei-sndeto.ecTrkahecedti pnHogEs PerEegSsei dncoeumreaspt elecdxoemfrso pimsr irsetihpnrege s-uperim- Tyr11s1e,n Atpesdon s1ii5tni8o , nFMigoeuft3rteh5 e05,c .a onA-dcdr Aydsirttgiao3lnl6ia8zl elwyd,e HrfeoE ruPerEv eSoavaleenrdla, prwep-hidnicoghc k iwneetderrHea pcEtrPienEvgSi orcueossmlidyp uslehesox wecsonmi sapsr erciprsiritne-sge nted in ical (FTigyur1Fr1ei1g 6,u )A.r eTsnh51e.5sA8e,d cMdrieitttii3coa5n0l a,o lavlnye,drf lAoauprgrp3ion6v8ge wrrleeasrpiedp ruienevgse aainlreetde i,rn awdchitcincahtgi vwree soriedf ptuhreeesv fciaocumt stlphyra sitsh iAonwugtnTo yDasro 1cc1rki1t ,-Asn158, Vina cicoaul lM(dF iegtsu3sr5ee0n 6,ti)a. nlTldyhe Arseerp gcl3rici6tai8cteaw la oe srvterrirlkeaivpnepgailnlyeg dc ro,emwsidphuaicrehasb awlree prienodspierc eagvtiivivoeeun os tflh ythes esh ifomawcitl ntahra ste ActtruiintiogcD.a loc(Fki gure 6). VinaT choeusledc ersitsiecnatliaolvlye relapplipcainteg ar setsriidkuinegslya rceominpdaircabtilve eposfet hgievfeanc thteh saitmAiluarto sDetoticnkg.V ina could essentially replicate a strikingly comparable pose given the similar setting. Figure 5. TFhieg uproese5 .gTehneerpaotesde fgoern tehrea tseudpeforirmthpeossiuperimpo sition of the co-crystallized HEPES molecule (sky Figubrelu 5e. )The pose generated for the superimp tioosnit ioofn t hoef tchoe- ccroy-csrtyaslltiazlelidz eHd EHPES molecule (sky blue) and the re-adnodcktehde HreE-dPoEcSk medolHecEuPleE S(gmreoenle)c. uTlheis( gsruepepno).rtTs hAisutsouDpopcokr tVsiAnau EPtoEDS omckoleVciunlae (asskya n effective blue)d aoncdki tnhge prer-odtocked HEPES molecule (green). This supports AutoDock Vin aas aasn a enf efeffcetcivtiev e dockindgo cpkriontgo cporlo. tocol. ocol. Figure 6. LigPlot+ of superimposition between the co-crystallized ligand of 3QHX and the re-docked HEPES ligand. Red circles represent the superimposed molecular interactions between both the co-crystallized and the re-docked ligands. Computation 2021, 9, x FOR PEER REVIEW 12 of 27 Figure 6. LigPlot+ of superimposition between the co-crystallized ligand of 3QHX and the re- docked HEPES ligand. Red circles represent the superimposed molecular interactions between Computation 2021, 9, 32 both the co-crystallized and the re-docked ligands. 11 of 24 3.5.2. ROC Curve Analysis of the Molecular Docking Protocol 3.5.2.RROC cCuurrve Awnitahly csiosmofptuhteeMd oAleUcuCla irsD corcukciinagl Pinro vtoirctoulal screening to evaluate the effi- ciencRyO oCf AcuurtvoeDwoicthk cVoimnap uinte ditsA cUaCpaicsitcyru tcoia dl ifnfevriertnutaial tsec rbeetnwinegento aecvtaivluea ltiegtahnedesf fia-nd inac- tciiveen cmy ofleAcutloeDs oocrk dVeincaoyins i[t3s2c]a.p Tachitey AtoUdCif foerfe nthtiea tRe ObeCtw ceuernvaec tfiovre ltihgea nfdivsea nindhinibaicttoivres of CGS fmroomlec Hul.e psyolorrdi aecgoayins s[t3 2C]G. ST hfreoAmU MC. ouflctehreanRsO aCndc utrhveeirf ocor rtrheespfiovnedininhgib 2it5o0r sdoefcoCyGsS was 0.76 (fFroigmuHre. p7y)l.o rTihaigsa isnusgt gCeGsStsf rtohmat MA.uutloceDraoncska nVdinthae isrhcoowrreesdp oanpdpirnegc2ia5b0ldye gcooyosdw daiss0c.r7i6minative a(Fbiigliutrye i7n) .dTihsitsinsuggugisehstisntgh abteAtwuteoeDno tchkeV 5in ianshhiobwiteodrsa pcoprmecpiarbislyinggo oJudgdliosncrei,m αin-Lataivpeacahbiol-ne, Pau- liotywinnidnisYangam,b tYinagnugisin, ana hmind 9-b g Hin be, tawnede n9t-hHey5dinrohxibitorsydroxy- -Lapachoyn-αe -fL c roa opmma pcrthh isoinnge Jfuroglmon teh, eαi-rL caoprarcehsopnoe,nPdaiunlgo w2n5i0n,eir corresponding 250 decoys. decoys. α Figure 7. ROC plot generated by screening ligands active against H. pylori with their matching decoys Figure 7. ROC plot generated by screening ligands active against H. pylori with their matching dageacionysst tahgeapinrostte itnhest rpurcottuerien, CstGrSucotfuMre. ,u ClcGeraSn os.f TMhe. urelscuerltainnsg. ATUheC rwesaus l0t.i7n6g, wAhUicCh wwaassa 0c.c7e6p,t awbhlei.ch was a3c.6c.epMtaoblelceu. lar Docking of Pharmacophore Hits Molecular docking involves the prediction and identification of ligand orientation 3a.n6d. Mcoonlefocurmlaar tDioonckwinitgh ionf Pa htaarrgmeatecodpahcotriev eHsiittse [51,52]. It involves the identification of lead cMomolpeocuunladrs adgoaciknsint ga tianrgveotlpvreost etihne. Mproeledciucltaior nd oacnkidn gidveianvtiifritcuaatlioscnr eoenf ilniggapnredd iocrtsientation athnedl icgoannfdo–rtmaragteitocno mwpitlhexins tar utacrtugreetebdy aexctpilvoer isnigtet h[5e1c,o5n2f]o. rImt iantvioonlavlessp tahcee iodfetnhetilfiigcaantidosn of lead cwoimthpinotuhnedbsi nadgianignsstit ae otafrtgheett aprrgoettepinro. tMeionl.eTchuelasrc rdeoenckedinpgh vairam vaicrotuphalo rsecrheietsn(iTnagb pler2e)dicts the were used to obtain ligands with low binding energies (Table 3 and Figure 8). The lower ltihgeanbidn–dtianrggeetn ecrogmy,pthleexs tsrtornugcetrurthee bbyin edxinpgloarfifinngi tythoef cthoenfliogramndattioonthael tsapragceet porfo ttehine . ligands wBiintdhiinng tahfefi nbiitnydreinfegrs stioteth oefs ttrheen gtathrgoeftt hperiontteeirnac. tTiohneb sectwreeeennetdw ophorarmmoarecompohleocruel ehsi[t5s3 ](.Table 2) wTaebrlee u3ssehdo wtos othbetafiivne likgnaonwdns iwnhitihb iltoorws obfinCdGinSgo feHne. rpgyileorsi (aTnadblteh e32 a4npdh Farigmuarceo p8h).o Trehe lower thhiets bthinadt idnogc keenderfigrym, ltyhew istthrionntgheer btihned ibnigndpioncgk eatffaifnteitryfi oltfe rtihneg loiguatncdom top otuhne dtsarwgietth protein. Bbindiingg eanfefirngiteys >re−fe7r.s0 tkoc athl/em sotrle. nCghthry osof pthaen ionltreerpaoctritoedn tbheetwloeweens ttwbion doirn gmeonrer gmyolecules [o5f3−].8 T.9akbclea l/3 mshool.wTsh eth2e4 fciovme pkonuonwdsn hiandhibbinitdoirnsg oefn CerGgiSe sowf Hith. ipnytlhoeri raanngde tohfe− 274. 1pthoarmaco- p−h8o.9rek chailt/sm tohla. tS idmoiclakreldy, tfhirem5lkyn wowitnhiinnh tihbeit obrisnhdaidngb inpdoicnkgeet naefrtgeire sfiflatlelrininggw oituhti nctohme pounds wraintghe boifn−di7n.2gt eon−e8rg.8ieksc a>l /−m7.o0l .kcal/mol. Chrysophanol reported the lowest binding energy of −8.9 kcal/mol. The 24 compounds had binding energies within the range of −7.1 to −8.9 kcal/mol. Similarly, the 5 known inhibitors had binding energies falling within the range of −7.2 to −8.8 kcal/mol. Computation 2021, 9, 32 12 of 24 Table 3. Binding energies of top 24 ligands after molecular docking. The ligands with the more negative binding energies are ranked or classified as obtaining the highest binding affinities. No. ZINC ID/Compound Name Binding Energy/kcal/mol Known Inhibitors 1 9-hydroxy-alpha-lapachone −8.8 2 Alpha-Lapachone −8.7 3 Paulownin −8.5 4 Juglone −7.3 5 Yangambin −7.2 Pharmacophore Hits 6 Chrysophanol −8.9 7 Aloe-emodin −8.6 8 Herbacetin −8.5 9 Isoscutellarein −8.4 10 Onopordin −8.4 11 Betavulgarin −8.4 12 ZINC05854400 −8.4 13 ZINC14490611 −8.3 14 Bucegin −8.3 15 Isoscutellarein 8-methyl ether −8.2 16 Sexangularetin −8.2 17 Corniculatusin −8.2 18 4′-methyl gossypetin −8.2 19 1,8-dihydroxy-3,5-dimethoxyxanthone −8.1 20 Epitaxifolin −8.1 21 ZINC13328057 −7.9 22 Catechin −7.7 23 Gossypetin 3,8-dimethyl ether −7.7 24 Gossypetin 3,7,8-trimethyl ether −7.7 25 ZINC00058187 −7.6 26 Shikimic acid−4-O-gallate −7.6 27 3,7-dihydroxy-8-methoxy-3-(3 ′,4′- −7.6 methylenedioxybenzyl)chroman-4-one Computation 2021, 9, x FOR PEER REVIEW 28 3′-hydroxyflindulatin −7.6 14 of 27 29 Gentisic acid 5-O-glucoside −7.1 FigFuigreu r8e. (8A. )( ASu)rSfuacref arceeprreespernetsaetinotna toiof nligoafnldig–atanrdg–ett acrogmetpcleoxm shpolewxinshgo twhei ndgoctkhiengd orecskuinltgs orfe sbuesltts of best 24 hit ligands in the active site region colored cyan. (B) Magnified view showing ligands docked fir2m4lyh iitnl tihgea npdocskient.t he active site region colored cyan. (B) Magnified view showing ligands docked firmly in the pocket. 3.7. Protein-Ligand Interaction Proteins are known to attain their optimum biological functions by their direct phys- ical interaction with ligands which aids in a better understanding of protein functions and drug development [54]. Hydrogen bonding is one of the most essential intermolecular interactions because it confers much stability to the protein–ligand complex [55–57]. The ligands of interest are those that formed more hydrogen bond interactions with the pro- tein, demonstrating potential specificity which would distinguish a highly specific bind- ing partner from less specific ones [58]. The highest number of hydrogen bonds of six was formed among Shikimic acid-4-O- gallate and the receptor. Epitaxilon and Gentisic acid 5-O glucoside followed with 5 hy- drogen bonds each (Figure 9) and then Betavulgarin with 4 hydrogen bond interactions. The exception was Catechin which formed no hydrogen bonds with the receptor. Juglone and 9-hydroxy-alpha-lapachone were found to have formed 2 and 1 hydrogen bonds with the Asp183 residue, respectively. The shorter the bond length, the stronger the hydrogen bond formed [55]. The short- est hydrogen bond length observed was 2.28 Å, which was formed between 4′-methyl gossypetin and Asp183 residue. A summary of the interaction studies of the top 24 hits together with the five known inhibitors is presented in Table S1. Computation 2021, 9, 32 13 of 24 3.7. Protein-Ligand Interaction Proteins are known to attain their optimum biological functions by their direct physical interaction with ligands which aids in a better understanding of protein functions and drug development [54]. Hydrogen bonding is one of the most essential intermolecular interactions because it confers much stability to the protein–ligand complex [55–57]. The ligands of interest are those that formed more hydrogen bond interactions with the protein, demonstrating potential specificity which would distinguish a highly specific binding partner from less specific ones [58]. The highest number of hydrogen bonds of six was formed among Shikimic acid- 4-O-gallate and the receptor. Epitaxilon and Gentisic acid 5-O glucoside followed with 5 hydrogen bonds each (Figure 9) and then Betavulgarin with 4 hydrogen bond interactions. The exception was Catechin which formed no hydrogen bonds with the receptor. Juglone Computation 2021, 9, x FOR PEER REVIEWa nd 9-hydroxy-alpha-lapachone were found to have formed 2 an1d5 of1 27h ydrogen bonds with the Asp183 residue, respectively. Figure 9. Cont. CComomppuuttaattiioonn 22002211,, 99, ,x3 F2OR PEER REVIEW 16 of 27 14 of 24 Figure 9. Protein-ligand interactions elucidated with LigPlot+. Shikimic acid -4-O-gallate (A), Epitaxi- Figfuorlein 9.( BPr),oatenind-lGigeanntdis iinctearcaicdti5o-nOs -eglulucicdoastiedde w(Cith) rLeipgrPeloset+n. tSehdikbiymibcl uaceids-t4ic-Oks-.gaCllaartbe o(An )a, tEopmi-s are signified taxifolin (B), and Gentisic acid 5-O-glucoside (C) represented by blue sticks. Carbon atoms are sigansifibelda caks bdloactsk adnodts raendd dreodt sdsohtso swhoowx yogxyegneant aotmoms.s.H Hyyddrroopphhoobbiicc ccoonntatactc rterseidsiudeus ewsiwthi tthhet he ligands are ligasnhdosw anrea sshsoewmni a-csi srecmleis-cwirictlhes“ wspitohk “essp”o.kHesy”d. Hroygdernogbeonn bdonindt ienrtaecraticotinonp aptatteternrnssa arree rreepprree-sented as green sendteadsh asl ignreese.n dash lines. 3.8. PhysTichoechsehmoirctael rPtrhofeilibnogn d length, the stronger the hydrogen bond formed [55]. The shortest hyNdroongee onf bthoen d24l elniggatnhdos bvsieorlvateedd wLiapsin2s.2k8i’sÅ r,uwleh sicinhcwe pasref-ofirlmtereidngb eotfw thee nlib4r′-amrye twhaysl gossypetin unadnedrtaAksepn1 t8o3 erleimsidinuaete. Aalls puomtemntairayl “onfotnh-edirnutge-rlaikceti”o ncosmtupdoiuensdosf. tShiemtiolaprl2y4, thhiets ktnoogwetnh er with the inhfiivbietokrns ovwionlatinedh inboitnoer sofi sLpiprienssekni’tse druilnesT. aObthleerS 1p.hysicochemical parameters predicted have been reported (Tables S2 and S3). All 24 ligands showed good physicochemical prop- ert3ie.8s .bPasheyds iocno cthem piacraalmPertoefirlsi nsegt. When compared with four already existing drugs com- prising NStroenpetoomfytchien,2 C4iplirgoaflnodxascvinio, lRaitfeadmpLiicpinin, sakndi’ sCrluarliethsrionmceycpinre, -tfihelt ehritisn gweorfet phree-library was dicutendd teor tsahkoewn bteotteelri mphiynsaicteocahlelmpioctael nptrioaple“rntioesn. -drug-like” compounds. Similarly, the known 3.9i.n Phhiabrimtoacroskvinieotliacst eadndn Toonxeiciotyf LStiupdiniess ki’s rules. Other physicochemical parameters predicted haPvhearbmeeancokreinpeotirctse ddet(eTrambilneess Sth2e afantde oSf3 t)h.e Aadlml 2in4isltiegraedn ddsrusghso inw ae dlivginogo odrgpahnyissmic ochemical aftperr ochpeemrtiiceasl bmaesteadboolinsmth uenptial realimmeinteartisosne ftr.oWm htheen bcoodmy [p5a9r]e. Tdhwe pitahrafmouertearslr meaedasyuerexdis ting drugs wecroe mblpoorids–inbrgaiSnt breaprrtioerm (ByBciBn) ,pCerimpreoafltioonx,a gcainst,roRiinftaemstipniacli anb,saonrpdtiConla, rpietrhmroemabyilcitiyn g, ltyh-e hits were copprroetdeiinct, eadndto cysthoochwrobmetetse Pr4p5h0 y(CsiYcPo)c h(Feimguircea Sl 1p aronpd eTratbieles .S3). Gastrointestinal absorp- tion (GI) is the process where orally administered drugs are absorbed into the blood- str3ea.9m. P[6h0a]r. mCaocmokpionuetnidcss adnednoTtoexdi c“ihtyigSht”u hdaievse a high GI absorption and vice versa. BBB permeatPiohna rism thaec opkoitneenticasl odfe at edrrmugin teos ctrhosesf tahte bolfotohde–bardamin ibnaisrrtieerre tdo dthreu gbrsain atol ibvinindg organism to arefcterptcohres mreilceavlanmt efotarb thoeli samctivuantitoilne olifm siignnaatliionng pfraotmhwtahyes.b Pordedyi[c5ti9n]g. tThhee pperamraemabeitlietrys measured is wveerrye imblpooordta–nbt riani nthbe adrervieerlo(pBmBeBn)t pofe rdmruegast iboenca, ugsaes atr omionlteecsutlien awlilal bnsootr bpet iaobnl,e ptoe rmeability degmloyncsotprartoet ethiner,aapneudticc yatcoticvhitryo mwietshiPn 4t5h0e b(CraYinP p) a(rFeingcuhryemSa1 uannledss Tthabe lbearSr3ie).r iGs paesrt-rointestinal meaabtesdo r[p61ti]o. nHo(wGeIv) eirs, ftohre thpirso scteusdsy,w cohmerpeouonradlsl ythaatd amrei nkinsotewrne dtod crruosgss thaer eBBaBb swoerrbee d into the ignbolroeodd. P-glycoprotein (Pgp) helps to prevent the central nervous system (CNS) from xenobiotisctsr.e Camyto[c6h0r]o.mCeo misopfoorumnsd Cs YdPe1nAot2e, dCY“hPi2gCh9”, ChYavPe2Ca1h9,i gChYGP2IDa6b,s aonrdp tCioYnPa3And4 vice versa. areB BesBsepnetiraml beeactaiounse isoft htheepiro itnetnertiaacltioonf awditrhu cgomtopcoruonsdsst htoe abildo oind –dbruragi nelbimairnraietironto the brain to bind to receptors relevant for the activation of signaling pathways. Predicting the permeability is very important in the development of drugs because a molecule will not be able to demonstrate therapeutic activity within the brain parenchyma unless the barrier is permeated [61]. However, for this study, compounds that are known to cross the BBB were ignored. P-glycoprotein (Pgp) helps to prevent the central nervous system (CNS) from xenobiotics. Cytochrome isoforms CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 are Computation 2021, 9, 32 15 of 24 essential because of their interaction with compounds to aid in drug elimination through metabolism. Toxic and unwanted adverse effects may occur if these isoenzymes are inhibited. Figure S1 shows the pharmacokinetics of the ligands compared to the existing anti-Buruli ulcer drugs. Compounds are considered to have met the pharmacokinetic threshold if they have high GI absorption, ‘No’ for BBB permeation, ‘Yes’ Pgp substrate, and ‘No’ for at least 3 cytochromes. Aloe-emodin, ZINC05854400, ZINC14490611, Epitaxifolin, ZINC13328057, and Cat- echin showed good ADME profiles. Shikimic acid-4-O-gallate and Gentisic acid 5-O- glucoside also showed good ADME profiles except for low GI absorption. The rest of the compounds showed poor ADME profiles which suggest that these compounds could be optimized to meet the aforementioned criteria for a good ADME profile. For the known drugs comprising Streptomycin, Ciprofloxacin, Rifampicin, and Clarithromycin, all but Ciprofloxacin showed low GI. All known drugs were predicted to be Pgp substrates. CYP3A4 was the only cytochrome predicted to be inhibited by Clarithromycin. The other known drugs showed no inhibition against the cytochrome family. Salmonella typhimurium reverse mutation assay (AMES) toxicity [62,63], carcinogenicity and Human Ether-a-go-go- Related Gene Inhibition (hERG I Inhibitor) [64] were employed for toxicity studies. AMES toxicity provides information on whether a particular chemical can cause mutations in the DNA of an organism. This was very essential to this study because administered drugs should not cause mutation in the DNA of patients. Carcinogenicity is the measure of a chemical’s ability to induce cancer or increase the incidence of cancer, whilst the hERG I Inhibitor represents the potential of the compound to inhibit the hERG I receptor resulting in arrhythmia [65]. For this study, we required that these parameters were adequately met for the compounds to be considered as potentially non-toxic. Table S4 shows the toxicity measure of each ligand as well as known inhibitors. For a ligand to be considered as potentially toxic, it is assigned a value of 1, and 0 if non-toxic. Nine ligands were found to be AMES toxic. Juglone was the only inhibitor found to be AMES toxic. Additionally, 3′-hydroxyflindulatin was predicted to be an hERG I inhibitor. Even though it was ob- served not to be carcinogenic and AMES toxic, it was still classified as potentially toxic. All 24 compounds and 5 inhibitors were found not to be carcinogenic. All compounds that were predicted to be toxic could be adequately optimized to generate low toxic analogues. 3.10. Exploring Predicted Leads for Anti-Microbial and Antimycobacterial Activity. Structure-Activity Relationship (SAR) describes the association between a chemical compound and the intrinsic property of a compound to elicit a particular pharmacological effect [66]. PASS predictions are built on SAR analysis of compounds in the database. In essence, the structural features of queried compounds are compared to those of known biological activity to infer the pharmacological profiles [67,68]. Each activity predicted has accompanying Probability of activity (Pa) and Probability of inactivity (Pi) values that determine the probability of a particular substance belonging to a class of active or inactive compounds, respectively. For this study, compounds with Pa > Pi for antibacterial and an- timycobacterial activities were of particular interest since they are considered probable [68]. A total of 23 compounds were predicted as possessing antibacterial activity with Pa > 0.3. Only 3, 7-dihydroxy-8-methoxy-3-(3′,4′-methylenedioxybenzyl) chroman-4-one was not predicted as antibacterial, hence it was eliminated from the prioritization. Gentisic acid 5-O-glucoside was found to have the highest Pa 0.618 and lowest Pi 0.008. The Pa values for the remaining 22 compounds ranged between 0.3 < Pa < 0.6. Additionally, four of the compounds were in the range 0.4 < Pa < 0.5, while the rest of the 17 were within the range 0.3 < Pa < 0.4 (Table 4). Computation 2021, 9, 32 16 of 24 Table 4. Compounds with their Pa and Pi values for predicted antibacterial activity. When Pa > Pi, the compound is worth being pharmacologically profiled. Compounds Pa Pi Gentisic acid 5-O-glucoside 0.618 0.008 ZINC05854400 0.489 0.017 ZINC00058187 0.411 0.027 Sexangularetin 0.405 0.029 Isoscutellarein 8-methyl ether 0.404 0.029 Isoscutellarein 0.403 0.029 Herbacetin 0.399 0.030 Onopordin 0.396 0.031 Bucegin 0.394 0.031 Gossypetin 3,8-dimethyl ether 0.393 0.032 Gossypetin 3,7,8-trimethyl ether 0.384 0.034 3′-hydroxyflindulatin 0.384 0.034 Epitaxifolin 0.381 0.035 ZINC14490611 0.379 0.035 Corniculatusin 0.379 0.035 4′-methyl gossypetin 0.373 0.037 Chrysophanol 0.371 0.038 1,8-dihydroxy-3,5-dimethoxyxanthone 0.368 0.038 Aloe-emodin 0.360 0.040 ZINC13328057 0.358 0.041 Betavulgarin 0.354 0.042 Catechin 0.350 0.043 Shikimic acid-4-O-gallate 0.327 0.065 Medicinal plants that have been suggested to hold inhibitory potential against M. ul- cerans were found to have Chrysophanol, Chrysophanic acid and aloe-emodin as part of the main constituents. More so, glucoside, methyl ether and gallate functional groups were also found to be the main components of the plants with inhibitory potential against M. ulcerans [69]. Therefore, the compounds could hold inhibitory potential against CGS of M. ulcerans. Six compounds comprising Gentisic acid 5-O-glucoside, ZINC05854400, ZINC00058187, Sexangularetin, Isoscutellarein 8-methyl ether and Isoscutellarein were predicted as antibacterial with Pa > 0.4 and Pa > Pi. Additionally, the Pa for antimycobacte- rial, anti-ulcerative, antituberculosis, antioxidant and dermatological activities were within the ranges 0.4 < Pa < 0.7, 0.3 < Pa < 0.7, 0.4 < Pa < 0.6, 0.3 < Pa < 0.9 and 0.3 < Pa < 0.6, respectively. The compounds had Pa > Pi for all the predicted biological activities. Gentisic acid 5-O-glucoside was predicted as antimycobacterial, anti-ulcerative, antitu- berculosis, antioxidant and dermatological with Pa 0.623 and Pi 0.009, Pa 0.550 and Pi 0.16, Pa 0.548 and Pi 0.008, Pa 0.637 and Pi 0.004, and Pa 0.457 and Pi 0.041, respectively (Table 5). Additionally, Gentisic acid 5-O-glucoside was predicted to be an isocitrate lyase inhibitor with Pa 0.573 and Pi 0.003. Isocitrate lyase (ICL) and maltase synthase (MS) are two es- sential enzymes in the glyoxylate cycle, a pathway essential to the growth of bacteria [70]. This pathway mediates the persistence of M. tuberculosis because of the intermediates of the tricarboxylic acid (TCA) made available to the organism for glucogenesis as well as other biosynthetic processes. Isocitrate lyase has been suggested as a drug target due to its role in disrupting the pathway when it undergoes conformational changes after binding to a substrate [71]. Therefore, Gentisic acid 5-O-glucoside is an attractive molecule to be explored pharmacologically and potentially specific for the target (CGS). Gentisic acid 5-O-glucoside formed a total of 13 intermolecular bonds comprising five hydrogen and eight hydrophobic bonds. A similarity search through DrugBank revealed Quercetin and Diosmetin as analogs of Isoscutellarein with a similarity score of 0.838 each. Quercetin and Diosmetin have been linked to M. tuberculosis as possessing antimycobacterial and antituberculosis poten- tials [72–74]. Quercetin and Diosmetin belong to the family of flavonoids which are known Computation 2021, 9, 32 17 of 24 to be principal constituents of plants with potency against M. ulcerans [69]. Isoscutellarein had a binding energy value of −8.4 kcal/mol and formed 3 hydrogen bond interactions with critical residues Met87, Asp183 and Glu154, as well as 9 hydrophobic contacts. A structural similarity search via DrugBank with a similarity score of 0.7 involving ZINC05854400 and ZINC00058187 did not reveal any analogs reported to be antimycobac- terial or antibacterial. However, when the threshold was decreased further, Quercetin and Atovaquone emerged with similarity scores of 0.582 and 0.553 for ZINC05854400 and ZINC00058187, respectively. A combination of Atovaquone and Azithromycin has been shown as a viable therapy for Mycobacterium avium complex (MAC) infection [75]. Fur- thermore, ZINC05854400 and ZINC00058187 were among the three compounds (Table 5) predicted as dermatological with Pa > 0.3. ZINC05854400 had a binding energy of −8.4 kcal/mol and formed 2 hydrogen bond interactions with two critical residues Met87 and Asp183, as well as 12 hydrophobic contacts. ZINC00058187 had a binding energy of −7.6 kcal/mol and formed 1 hydrogen bond interaction with Asn158, as well as 7 hy- drophobic contacts. Table 5. Predicted biological activities of compounds with Pa > 0.3 and Pi< Pa. The biological activities comprise antimycobacterial, anti-ulcerative, antituberculosis, antioxidant and dermatological activity. Antimycobacterial Anti-Ulcerative Compounds Pa Pi Compounds Pa Pi Gentisic acid 5-O-glucoside 0.623 0.009 ZINC05854400 0.637 0.008 Isoscutellarein 8-methyl ether 0.568 0.012 Gentisic acid 5-O-glucoside 0.550 0.016 ZINC00058187 0.51 0.018 Isoscutellarein 0.536 0.017 Sexangularetin 0.47 0.024 Isoscutellarein8-methyl ether 0.521 0.02 Isoscutellarein 0.465 0.025 Sexangularetin 0.486 0.026 ZINC05854400 0.446 0.029 ZINC00058187 0.333 0.076 Antituberculosis Antioxidant Compounds Pa Pi Compounds Pa Pi Gentisic acid 5-O-glucoside 0.548 0.008 Isoscutellarein 0.876 0.003 Isoscutellarein 8-methyl ether 0.502 0.02 ZINC05854400 0.837 0.003 ZINC00058187 0.492 0.013 Sexangularetin 0.825 0.003 Sexangularetin 0.443 0.021 Isoscutellarein8-methyl ether 0.777 0.004 Isoscutellarein 0.438 0.022 Gentisic acid5-O-glucoside 0.637 0.004 Dermatological ZINC00058187 0.343 0.017 Compounds Pa Pi ZINC05854400 0.501 0.032 Gentisic acid 5-O-glucoside 0.457 0.041 ZINC00058187 0.356 0.065 3.11. Molecular Dynamics (MD) Simulation of Target Structure and Complexes MD simulation was executed to study the stability and conformational changes of the target protein and ligands in complex with the protein. Five receptor–ligand complexes and the unbound receptor were subjected to MD simulations. The ligands comprise two known inhibitors Juglone and 9-hydroxy alpha-lapachone and potential leads Gentisic acid 5-O glucoside, Isoscutellarein, and ZINC05854400. The stabilities were analyzed over 100 ns simulation time (Figure 10). The RMSD plots were used to assess the stability of Computation 2021, 9, x FOR PEER REVIEW 20 of 27 3.11. Molecular Dynamics (MD) Simulation of Target Structure and Complexes MD simulation was executed to study the stability and conformational changes of the target protein and ligands in complex with the protein. Five receptor–ligand com- plexes and the unbound receptor were subjected to MD simulations. The ligands comprise Computation 2021, 9, 32 two known inhibitors Juglone and 9-hydroxy alpha-lapachone and potential leads G1e8no-f 24 tisic acid 5-O glucoside, Isoscutellarein, and ZINC05854400. The stabilities were analyzed over 100 ns simulation time (Figure 10). The RMSD plots were used to assess the stability of tthhee ccoompplleexxeess aannddt htheet atragregtept rportoetinei.nT. hTehRe MRSMDSDre prreepsreenstesntthse tahvee aravgeeradgies tdaniscteanocfea toofm s atoomf st hoef rtehseid rueesisdautetsh eatp trhoete pinrobtaecink bboancekb[7o6n]e. T[7h6e].b Tahcke bboancekboof nthee otfa rthgee ttparrgoetet inprsohtoeiwn ed shoflwuectdu faltuioctnusaftriomns 0f.r1o5mn m0.1t5o n0m.2 5ton m0.2o5v nemr t hoeveinr itthiael ipneitriiaold poefri5o.dH oof w5.e Hveorw, ietvseurs,t aiti nsueds-an tainaveder ang eaRveMraSgDe oRfM0.2S5Dn omf 0fr.2o5m n5mn sfrtooma b5o nust 5to5 anbs,otuhte 5n5fl nusc,t uthaetend fltuocatbuoautetd3 .t0on ambo, uwth 3e.r0e it nmr,e wmhaeinred its rteamdayinuendt isl t1e0a0dnys upnetriilo 1d0,0a nlbse pite, rieocdo,r dailnbgeita,n reRcMorSdDinogf a0n.2 7RMnmSD. A osfi d0e.2f7r onm .t he Asindiet ifarloflmu ctthuea tiinointisalb efltuwcetuenat0io.2n5s nbmetwaneden0 .03.525n mnmfr oamnd0 0to.353 0nnms, fbrotmh I0s otsoc u30te lnlas,r ebionthan d IsoGsceuntteilsliacre5i-nO agnldu cGoesnidtiesico 5m-Op lgelxuecsomsidime cicokmedpltehxest rmajiemctiockryedo fththe etrtaajregcteotrpyr oft ethine utanrtgilet he pro1t0e0inn sunpteirli tohde, 1a0tt0a innsi npgerainoda,v aetrtagineiRngM aSnD aovfearabgoeu tR0M.2S7Dn mof ianbbooutth 0c.2a7s ensm. T ihne bRoMthS cDasoefs.t he TheZ IRNMCS0D58 5o4f 4t0h0e cZoImNpCle0x58ro54se40to0 acboomupt l0e.x4 4ronsme ftor atbhoeuint i0ti.a4l42 n0mns faonr dthre minaiitniaeld 2s0t enasd aynudn til remfeawinefldu cstueadtiyo nusntaitl tfhewe 6f5luncstuaantidonthse ant trhoes e65to n0s .a4n1dn tmheant r7o5sen sto, w0.h41er nemit arte m75a nins,e wdhsteeraed y it ruenmtialitnheed1 s0t0eandsyp eurniotidl , tahvee r1a0g0i nngs apteRrMiodSD, aovfearabgoiuntg0 .a4t2 RnMmS. DTh oefR aMboSuDt o0f.4th2 en9m-h. yTdhreo xy RMaSlpDh ao-fl athpea c9h-ohnyedrcoxmyp alelpxhrao-sleaptoacahbonuet 0c.o4m5 npmlexf orrosthee toin aitbiaolu2t 00n.4s5a nmd pforor gtrhees sineditisatle a2d0 ily ns aundti lp5ro0gnrseswsehde rseteiatddilryo pupnetidl 5t0o nasb wouhte0re.2 i8t dnrso. pIpt eindc troe asbeoduta 0g.a2i8n ntso. Iatb ionucrte0a.s4e1dn amgawini th to afebwouflt u0c.t4u1a ntimon ws biteht wfeewen fl0u.3ct5unamtiotnos 0b.4e5twnement o0w.3a5r dnsmt htoe 100.405n ns m atorkw.aTrhdes RthMe S1D00o nf st he maJrukg. lTohnee RcoMmSpDl eoxf trhoes eJutoglaobnoeu cto0m.4pnlemx raots5e tnos aabnodurt e0m.4a ninme dat s5t enasd ayntdo raebmoauitn5ed3 nstsewadhye re to iatbionuctr e5a3s ends fworhearbeo uitt i0n.c4r5eansse.dI tftohre anbroeumt a0i.n45e dnsst. eIat dthyeunn rteilm8a6inesdw shteearedyit uronsteil t8o6a nbso ut wh0e.r5e2 itn rsousen ttoil a1b0o0unt s0..5T2h nesr eufnotriel ,10th0e ncso. Tmhpelreexfoesrew, tehree ccoomnpfolermxeast wioenrael lcyonsftoabrmleawtioitnhainllyt he stapbeler iwodithoifnt hthees ipmeuriloadti oonf st.he simulations. FigFuirgeu 1r0e. 1R0M. RSMD SvDervsuerss tuims teim pelopt looft tohfet hbaecbkabcoknbeo noef CofGCSG suSpseurpimerpimospeods ewditwhi tChGCSG cSomcopmlepxleesx.e s. The Them moloelceuculalrard dynyanmamiciscss ismimulualtaiotinosnsw wereereru rnunfo fro1r 01000n sn.s. 3.132..1 M2.M-MPB-SPABS BAinBdiindgi nFgreFe rEeeneErngeyr gCyalCcuallcautiloantiso ns TheT hMeMM-PMB-SPAB SaApparpopacrhoa [c3h2][ w32a]sw uasesdu isne dcailncuclaaltcinugla tthineg bitnhdeinbign fdreineg enfreeregieense orfg tihese of prothteeinp–rloigteainnd– lciogmanpdlecxoems fpollelxoewsinfogl ltohwe icnognttrhibeuctoionntr iobfu vtaionn doefr vWaanadl,e erleWctaraols,taetliecc,t proosltaart ic, solpvoaltaiorns,o alvnadt itohne, Saonldvethnet ASoclcveesnstibAlec cSeusrsfiabclee SAurrefaa c(eSASreAa) (eSnAeSrAgi)ees n[7e7rg].i eGse[n7t7i]s.icG 5e-nOti sgilcu5--O cosgidluec oosbitdaeinoebdt atihnee dletahset lferaeset bfrienedbiningd einngeregnye rogfy −o2f39−.826359 .8k6J/5mkoJ/l m(Toalb(lTea 6b)l ea6n)da nwdasw faosl-fol- lowloewd ebdy bIysoIsscoustceultlealrleairne iwn iwthi t−h7−0.7790.07 9k0J/kmJ/olm. Eovl.eEnv tehnotuhgohu ZghINZCIN05C805548450404 w00aws pasrepdriecdteicdt ed to htoavhea vtheet hleealseta asftfianffiitnyi ttyo tthoet hperoptreoitne iwniwthi tah bainbdinindgin fgrefer eeeneenrgeyrg oyf o3f2302.900.970 k7Jk/mJ/oml, oitl ,witaws as foufnodu ntdo thoavhea voenoen oef otfheth leolwoewset sbtibnidnidnign geneenregrigesie os fo −f 8−.48 .k4ckacl/aml/oml forlofmro mthet hmeomleocluelcaurl ar docking. In terms of energy contribution to the overall binding energy, van der Waal had a larger energy contribution whereas nonpolar solvation energies and electrostatic energies contributed slightly (Table 6). Per-residue decomposition was employed to investigate the energy input of each residue. In general, residues with energies >5.0 kJ/mol or <−5.0 kJ/mol are regarded as plausible key binding moieties [78]. Majority of the residues contributed energies >5.0 kJ/mol or <−5.0 kJ/mol in both Gentisic 5-O glucoside (Figure S2) and ZINC05854400 complexes Computation 2021, 9, x FOR PEER REVIEW 21 of 27 docking. In terms of energy contribution to the overall binding energy, van der Waal had a larger energy contribution whereas nonpolar solvation energies and electrostatic ener- gies contributed slightly (Table 6). Per-residue decomposition was employed to investigate the energy input of each res- idue. In general, residues with energies >5.0 kJ/mol or <−5.0 kJ/mol are regarded as plau- sible key binding moieties [78]. Majority of the residues contributed energies >5.0 kJ/mol or <−5.0 kJ/mol in both Gentisic 5-O glucoside (Figure S2) and ZINC05854400 complexes (Figure 11). Isoscutellarein complex (Figure S3) on the other hand had just one residue, Leu181, with energy contribution >5.0 kJ/mol. Even though, Gentisic 5-O glucoside and Computation 2021, 9, 32 ZINC05854400 complexes were hitherto reported to bind to crucial residues (G19lyo8f 624, Met87, Asn158, Asp183, and Ser205), only Met87 produced the highest energy contribu- tion (Figure 11). Even though, other residues contributed more, they were not located in the binding pocket of the protein. (Figure 11). Isoscutellarein complex (Figure S3) on the other hand had just one residue, TLaebule1 861. ,Enweirtghy etenremrsg oyf cthoen CtrGibSu–ltiigoannd> c5o.0mpklJe/xmeso flr.omE vMeMn -tPhBoSuAg cha,lcGuleantitoinsi. cTh5e-O vagluleusc aorsei de parnedsenZtIeNd Cin0 a5v8e5r4a4g0e0 ± csotamndpalredx edsevwiaetrioenhsi itnh ekrJ/tmo orel.p orted to bind to crucial residues (Gly86, Met87, Asn158, Asp183, and Ser205), only Met87 produced the highest energy contribution (Figure 11). Evenvtahno udgehr ,Woathael r rEelseicdturoestcaotinct ribPuoteldarm Sorlve,at-heSyAwSeAre Enno-t lBoicnadteidngin Enth-e binCdoinmgppooucnkde t of theEnperorgteyi n. Energy tion Energy ergy ergy (KJ/mol) (KJ/mol) (KJ/mol) (KJ/mol) (KJ/mol) Table 6. Energy terms of the CGS–ligand complexes from MM-PBSA calculation. The val−u6e.s32a0r e+/p−r e s e nted in average ± standard deviatioGnesnintiskiJc/ 5m-oOl. glu- 1.793 +/− −633.686 +/− 398.347 +/− −239.865 +/− 2.460 coside 6.832 kJ/mol 68.712 kJ/mol 34.000 kJ/mol 68.428 kJ/mol Compound van der Waal Electrostatic Polar Solvation SASA Energy kJ/molB inding Energy Energy (KJ/mol) Energy−1(K01J/.m94o0l ) Energy (KJ/mol) (KJ/m −9.944 +/− +/− −12.961+/− 54.056 +/− ol) (K−J7/0m.7o9l)0 +/− Gentisic 5-O 1.793 +I/so−scutellare−in6 337.968.164+5/ k− 8.391 J/mol 123.9681.354 k7J+/m/−ol 51.199− k6J.3/m20o+l /− −26309..486159 +k/J/−mol glucoside 6.832 kJ/mol 68.712 kJ/mol 34.000 kJ/mol 2.460 kJ/mol kJ/mol 68.428 kJ/mol Isoscutellarein −101.940 +/− −12.961+/− 54.056 +/− −9.944 +/−−13.820 +/−− 70.790 +/−79.145 kJ/mol 12.615−1k1J/7m.07o5l +/− 5315.169.691k9J /+m/−o ZINC05854400 l 95.188.339 +1/k−J /mol 3.671 60.4 31920k.J9/0m7 o+l/− ZINC05854400 −117.075 +/− 356.3601.95+79/ −kJ/mol 919.459.178 3kJ+//m−ol 77.43−5 1k3J./8m20o+l /− 32504.9.4074+ k/J−/mol 30.579 kJ/mol 91.497 kJ/mol 77.435 kJ/mol 3.671 kJ/mol kJ/mol 54.474 kJ/mol FFiigguurree 1111. .MMMM-P-PBBSASA plpolto otfo Zf IZNICN0C50855845440400 c0omcopmlepxl esxhoswhoinwgi nbginbdiinndgi nfrgeef reeneeergnyer cgoynctroinbutrtiibount ion ppeerr--rreessiidduuee.. 3.13. Leads Summary Gentisic acid 5-O glucoside, Isoscutellarein and ZINC05854400 have been identified as potential leads against CGS of M. ulcerans (Table 7). Even though, ZINC05854400 had the highest binding free energy of 320.907 kJ/mol with CGS of M. ulcerans from the MM-PBSA calculations, its antimycobacterial potential can be explored experimentally. However, it could be exploited as a scaffold for the development of antimycobacterial drugs. The antimycobacterial potential of the compounds Sexangularetin, Isoscutellarein 8-methyl ether, and ZINC00058187 could also be further investigated. Computation 2021, 9, x FOR PEER REVIEW 22 of 27 Computation 2021, 9, x FOR PEER REVIEW 22 of 27 3.13. Leads Summary Computation 2021, 9, x FOR PEER REVIEW Gentisic acid 5-O glucoside, Isoscutellarein and ZINC05854400 have been iden2t2i foife 2d7 3a.1s 3p. oLteeandtsi aSlu lmeamdasr ya gainst CGS of M. ulcerans (Table 7). Even though, ZINC05854400 had the highest binding free energy of 320.907 kJ/mol with CGS of M. ulcerans from the MM- P3B.1S3AG. L ecenatldicssui Scla uatmcioimdn as5r,- yiOt s galnutciomsyidceo,b Iascotsecruiatle lplaorteeinnt iaanl dca ZnI bNeC e0x5p8l5o4r4e0d0 e hxapveer ibmeeennt aidlleyn. tHifioewd - aesv peor,t eitn ctioaul llde abdes eaxgpalionistte dC GasS a o sf M. ulcerans (Table 7). EveGentisic acid 5-O glucosidec, aIsffooslcdu tfeolrl atrheei nd eavnedl oZpImNCe nn tthough, ZINC05854400 had the highest binding free en 05 o85f 4a4n0t0im hyacvoeb baecetenr iiadle dnrtiufigesd. Tash ep oatnetnimtiaylc loebaadcst eargiaali npsot t eerngtyia lo fo f3 2th0.e9 0c7o mkJp/mouonl dws iSthe xCaGngS of M. ulcerans from the MM- PBSA calculations, its antimCyGcSo boafc Mter. iuallc peroatnesn (tTiaal bclaen 7 b).e E evxe unl atrheotuing, hI,s oZsIcNuCte0l5la8r5e4in40 80- mhaed- tthhyel hether, and ZINC00058187 could also be further investig paloterde.d experimentally. How- ever, iitg choeusltd b bined eixnpgl ofrieteed e naes rag ysc oaff f3o2ld0. 9fo07r tkhJe/m doelv weloitphm CeGnSt ooff aMn.t iumlcyercaonbsa cfrtoermia lt hder uMgMs. - TPhBe SaAn tcimalcyucloabtiaocntesr, iiatls paontteimntyiaclo obfa cttheer icaol mpoptoeunntidasl cSaenx abneg euxlpalroertiend, eIsxopsecruimtelelnatraelilny .8 How-Tevabelre, i7t. Mcooulledcu blees ewxipthlo tihteeidr naasm ae ssc aanfdfo 2lDd fsotrru tchtuer edse. vTehleo mpmolecules have been suggested a -sm e- thpoytle entthiaelr a, natnimd yZcoINbaCct0e0r0ia5l 8le1a8d7s c. ould also be further investiegnatte odf. antimycobacterial drugs. Computation 2021, 9, 32 The antimycobacterial potential of the compounds Sexangularetin, Isoscutellare2in0 o8f-2m4 e- Ttahbylel e7t. hMero,l eacnudle sZ wINitCh 0th0Ie0Ui5rP 8nA1a8mC7 e Ncso aaunmldde 2 aOlstoh ebre further investigated. Compound Name D structures. The molecul2eDs h Savtreu bceteunr esu ggested as potential antimycobacterial leads.N ames Table 7. Molecules with their names and 2D structures. The molecules have been suggested as Table 7. Molecules with their npaomtenestiaanl adn2tDimsytcruobctaucrteeIrsiU.aTlP hlAeeaCmds oN. leacmulee sOhtahveerb een suggested as potential antimycobacte- rial leads. Compound Name 2D Structure 2 NamI-UhyPdArCox Ny-5 e-s am[3e, 4O,5th-terir- Compound Name CGoemntpisoicu nacdIiU dNP 5aA-mOCe N ame OthyeNdr Nraomaxmyese-6s- 22DD S St trruuccttuurree glucoside (hydroxymethyl)oxan- 2-2h-yydl]rooxxyyb-e5n-[z3o,4ic,5 a-tcriid- Gentisic acid 5-O hydroxy-6- glucoside (h2y-hdyrodxryomxye-t5h-[y3l,)4o,x5a-tnr-i- Gentisic 2acidGentisic acid 5-O glucoside -hyd 5ro-Oxy -5-2[3-y,4l,]5o-hxtryyihbdyerdnorxzooyxi-yc6- -6a - cid (ghlyudcrosxiydme ethyl)o(xhaynd-2r-oyxl]yoxmyebtehnyzlo)iocxaacnid- 2-yl]oxybenzoic acid 5,7,8-trihydroxy-2- Isoscutellarein (4-hydroxy- phenyl)chromen-4-one 5,7,8-trihydroxy-2- Isoscutellarein Isoscutellarein5 ,7,8-trihyd(4ro-hxyy-d2r-oxy-(4-hydroxyphenyl)chromen-4-on phe5n,7y,l8)-cthrirhoymderno-x4y e --o2n-e Isoscutellarein (4-hydroxy- phenyl)chromen-4-one ZINC05854400 ZINC05854400 GartaninGartanin ZINC05854400 Gartanin ZINC05854400 Gartanin 4. 4C. oCnocnluclsuiosinosns We have used molecular docking and ligand-based pharmacophore modeling to iden- tify thrWeeeA hfarivcea nunseadtu mraol lpercoudlaurc tdsoacskipnogt eanntdia llingoavnedl-lbeaasdedc opmhparomunacdospahgoarien sm t tohdeelCinGgS to o4fi.dM Ce.onuntilcfyleur atshniorse.nesT Ahefrcicoamn pnoautunrdasl cpormodpuricstisn agsG peontteinsitciaal cnidov5e-lO legaldu ccoosmidpeo, uIsnodssc uatgeallianrset i nthe anCdGZSI NofC M05.8 u54lc4e0ra0nhsa. vTehteh ceopmopteonutniadlst ocoinmhpibriitsitnhge Gacetnivtiitsiiecs aocfidC G5-O glucoside, Isoscutel- l4a.r CeiWonn eac nlhudas ZvioeInN usC se0d58 m54o4l0e0c uhlaavre d tohceking and ligand-based pharm S, and possibly delay potential to inhibit the activitieasc oopf hCoGreS , manodd eplionsgsi bto thideegnrtoifwy ththorefet hAefm ly ricyacno bnaacttuerraiul mprovdiautchtse adsi spruoptetniotinalo fnmoveetlh lieoandin ceosmynptohuensdiss. aAgdadinitsito nth-e alldyeCG, la Sth y oeW ythewe hegrareovwep rtuehsd eoidcf t tehmdeo tmloeycpucoolsabsrae csdtserium via the disruption of methionine synthesis. Addi- tionallfy M. ulcerans. The compoun odacnsk ticinbogam captnerrdiis ailnli,ggaa nGntdeimn-btyiascsioecbd aa ccpitdhea r5ira-mOl,a acgnoltupi-chuoolsrcied rema, toIivsdoees,lcaiuntgde lt-o dleairdrmeeinant oiaflnyo , gtthhiceraeyle awd ZIN CAc etfirvreiic p0585tai4e rn4s e .d0n0T iacte hhtuaer dta vehl t r ope erpoomdsusoelcestssc uaalnse tspibwoatecertneetrisiahallo ,n waonnvteitmlo lyhecaovdbe acfocatmveorpiraoalub, nlaednpsti h-auaglracmeinraasctto ivt-hee, logaCniGcdaS ld poerfro mMfial.e tous,llocbegirniacdnasiln. agTchteienv the p eictroigemise.p sT,ohaunen dth otreene timal tosb icnodminpogrliec inhibit the activities of C msinuecgleh sGa nweinsetmriess ics.h Saoicnwidcne 5tt-hoOe h sgatlv Ge Sfa, vaonrda bploes spihbalyr delay the growth o udcoysiisdpe,r iImsoasrciulytel -- commlaarpecuiontla oatginoidcna aZll ,IpNerxoCpf0iel fe tsh, e mycobacterium via the disruption of methion5ri8m54be4nin0t0adl ihncaogvr ereon tbehoregr apietoisot,e nannotidfa tlbh tieon rdienisnhugilbt simt iteshceehx aapnceitdsivmieitnsi.et .sST i inncee s tyhnet hsetusidsy. Aisd pdri-i- tionally, they were predicted to possess antibacterial, antimycobacteorhifa eCl,sG caaSnf,tf ioa-nluddlsc eporofastthsiviebel,y maodnldelc aduyel ertshmecaa gtnorolbowegatihcda aolp fa ttechtdeiv amistiyaecsso.k bTealhecetoe trnhiurfoemer tvmhieao ldtehecesu idgleinss rowufpentreieox tns-h gooefwn menre attthoioi ohnnaCivnGee S fsayivnnohtrhiabebistloiesr .sp Afhodardr-i- trmetaiaotcinoaglollmgyi,yc tachlo ebpyar cowtfielrreieas ,lp birnienfdedcicitntioegdn e stn,oee srppgoiesecssie,a slaslny adBn ubtiirbnuadlciitnuegrlci aemlr,.e acnhtainmisymcosb. aScitnecreia tlh, ea nsttiu-udlyc eisra ptirvi-e, and dermatological activities. The three molecules were shown to have favorable phar- Sumppalceomloengtiacrayl Mpraotfeirlieasl,s :bTinhdeifnogll oewnienrggiaerse, aavnadil abbilnedoinnlgin emaetchhtatpnsi:s/m/sw. wSiwn.cmed tphie.c osmtu/d2y0 7is9 -p3ri- 197/9/3/32/s1, Figure S1: A heat map showing the pharmacokinetic properties of potential leads, known drugs and inhibitors predicted as GI absorption, BBB permeant, and Pgp substrate, as well as CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4 inhibitors. Red denote “Yes” whilst blue denotes “No” to cytochrome inhibition, respectively. Additionally, low Gastrointestinal (GI) absorption is denoted by green, whilst high is denoted by violet. Figure S2: Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) plot of binding free energy contribution per residue of Gentesic 5-O glucoside complex. Fluctuations by hitherto predicted critical residues are shown in red. Figure S3: Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) plot of binding free energy contribution per residue of Isoscutellarein complex. Fluctuations by hitherto predicted critical residues are shown in red. Table S1: The interaction studies of the top 24 pharmacophore hit compounds following molecular docking arranged in descending order of the number of hydrogen Computation 2021, 9, 32 21 of 24 bonds formed. Known inhibitors are in red. Table S2: Physicochemical Properties of 24 ligands and 4 known anti-Buruli ulcer drugs. Some of the drugs violated Lipinski’s rule. Known inhibitors, as well as known drugs, are in red. Table S3: Physicochemical properties of the top 24 ligands and 4 known anti-Buruli ulcer drugs showing other physicochemical parameters. Known inhibitors and known drugs are in red. Table S4: Toxicity results of 24 ligands with their respective structures predicted as AMES toxicity, carcinogens, and hERG I Inhibitor. Author Contributions: S.K.K., L.M., N.N.O.D. and M.D.W. conceptualized the research project. Computational and data analysis was predominantly undertaken by S.K.K., N.N.O.D., E.D., G.M.L. with inputs from W.A.M.III, M.B.A. and M.D.W., S.K.K., N.N.O.D., E.D. and M.D.W., altogether wrote the first draft. Draft revisions and agreement on the final draft were done by all authors before submission. All authors have read and agreed to the published version of the manuscript. Funding: There was no funding obtained for this project. Data Availability Statement: All data and their identifications (IDs) used in this work are available in the manuscript and supplementary file. Acknowledgments: We are grateful to the entire staff of the Biomedical Engineering Department and the Parasitology Department, University of Ghana for their invaluable support. We are also thankful to WACCBIP, University of Ghana for granting us access to the supercomputing system, Zuputo. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations CGS Cystathionine Gamma Synthase PLP Pyridoxal Phosphate BU Buruli Ulcer MetB Methionine B NANPDB North African Natural Product Database AfroDB Library of natural products from African origin SDF Structure Data File UFF Universal Force Field PDB Protein Data Bank MD Molecular Dynamics HPC High-Performance Computing GROMACS GROningen MAchine for Chemical Simulations TPSA Topological Polar Surface Area RMSD Root Mean Square Deviation EF Enrichment Factor ESOL Effective Solubility DUD-E Database of Useful (Docking) Decoys—Enhanced AMES Salmonella typhimurium reverse mutation assay Log P Logarithm of the octan-1-ol/water partition coefficient MW Molecular Weight ID Identification P-gp Permeability glycoprotein CYP Cytochromes P450 References 1. Portaels, F.; Silva, M.T.; Meyers, W.M. Buruli ulcer. Clin. Dermatol. 2009, 27, 291–305. [CrossRef] 2. Evans, M.R.W.; Thangaraj, H.S.; Wansbrough-Jones, M.H. Buruli ulcer. Curr. Opin. Infect. Dis. 2000, 13, 109–112. [CrossRef] [PubMed] 3. World Health Organization. Buruli ulcer disease: Mycobacterium ulcerans infection: An overview of reported cases globally. Wkly. Epidemiol. Rec. 2004, 79, 194–199. 4. Ampah, K.A.; Asare, P.; De-Graft Binnah, D.; Maccaulley, S.; Opare, W.; Röltgen, K.; Pluschke, G.; Yeboah-Manu, D. Burden and historical trend of Buruli ulcer prevalence in selected communities along the Offin River of Ghana. PLoS Negl. Trop. Dis. 2016, 10, e0004603. [CrossRef] 5. Zhang, T.; Bishai, W.R.; Grosset, J.H.; Nuermberger, E.L. Rapid assessment of antibacterial activity against Mycobacterium ulcerans by using recombinant luminescent strains. Antimicrob. Agents Chemother. 2010, 54, 2806–2813. [CrossRef] [PubMed] Computation 2021, 9, 32 22 of 24 6. Merritt, R.W.; Walker, E.D.; Small, P.L.; Wallace, J.R.; Johnson, P.D.; Benbow, M.E.; Boakye, D.A. Ecology and Transmission of Buruli Ulcer Disease: A systematic review. PLoS Negl. Trop. Dis. 2010, 4, e911. [CrossRef] [PubMed] 7. Clifton, M.C.; Abendroth, J.; Edwards, T.E.; Leibly, D.J.; Gillespie, A.K.; Ferrell, M.; Dieterich, S.H.; Exley, I.; Staker, B.L.; Myler, P.J.; et al. Structure of the cystathionine γ-synthase MetB from Mycobacterium ulcerans. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 2011, 67, 1154–1158. [CrossRef] 8. Berney, M.; Berney-Meyer, L.; Wong, K.W.; Chen, B.; Chen, M.; Kim, J.; Wang, J.; Harris, D.; Parkhill, J.; Chan, J.; et al. Essential roles of methionine and S-adenosylmethionine in the autarkic lifestyle of Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA 2015, 112, 10008–10013. [CrossRef] 9. Walsh, D.S.; Portaels, F.; Meyers, W.M. Buruli Ulcer: Advances in understanding Mycobacterium ulcerans infection. Dermatol. Clin. 2011, 29, 1–8. [CrossRef] 10. Yotsu, R.R.; Richardson, M.; Ishii, N. Drugs for treating Buruli ulcer (Mycobacterium ulcerans disease). Cochrane Database Syst. Rev. 2018, 2018, CD012118. [CrossRef] 11. Zhang, Y.; MacArthur, C.; Mubila, L.; Baker, S. Control of neglected tropical diseases needs a long-term commitment. BMC Med. 2010, 8, 67. [CrossRef] 12. Kwofie, S.K.; Adobor, C.; Quansah, E.; Bentil, J.; Ampadu, M.; Miller, W.A., 3rd; Wilson, M.D. Molecular docking and dynamics simulations studies of OmpATb identifies four potential novel natural product-derived anti-Mycobacterium tuberculosis compounds. Comput. Biol. Med. 2020, 122, 103811. [CrossRef] 13. Siddiqui, A.A.; Iram, F.; Siddiqui, S.; Sahu, K. Role of natural products in drug discovery process. Int. J. Drug Dev. Res. 2014, 6, 172–204. Available online: https://www.ijddr.in/drug-development/role-of-natural-products-in-drug-discovery-process. php?aid=5524 (accessed on 15 February 2019). 14. Ekor, M. The growing use of herbal medicines: Issues relating to adverse reactions and challenges in monitoring safety. Front. Pharm. 2014, 4, 177. [CrossRef] 15. Dias, D.A.; Urban, S.; Roessner, U. A historical overview of natural products in drug discovery. Metabolites 2012, 2, 303–336. [CrossRef] 16. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235–242. [CrossRef] [PubMed] 17. Kleywegt, G.J.; Alwyn Jones, T. Model building and refinement practice. Methods Enzymol. 1997, 277, 208–230. [CrossRef] 18. Lill, M.A.; Danielson, M.L. Computer-aided drug design platform using PyMOL. J. Comput. Mol. Des. 2011, 25, 13–19. [CrossRef] [PubMed] 19. Yuan, S.; Chan, H.C.S.; Hu, Z. Using PyMOL as a platform for computational drug design. Wires Comput. Mol. Sci. 2017, 7, e1298. [CrossRef] 20. Bordoli, L.; Schwede, T. Automated protein structure modeling with SWISS-MODEL Workspace and the protein model portal. Methods Mol. Biol. 2012, 857, 107–136. [CrossRef] [PubMed] 21. Spoel, D.V.D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005, 26, 1701–1718. [CrossRef] 22. NCBI Resource Cordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2013, 41, D8–D20. [CrossRef] 23. Boutet, E.; Lieberherr, D.; Tognolli, M.; Schneider, M.; Bairoch, A. UniProtKB/Swiss-Prot. In Plant Bioinformatics: Methods and Protocols; Edwards, D., Totowa, E., Eds.; Humana Press: Totowa, NJ, USA, 2007; pp. 89–112. [CrossRef] 24. Kong, Y.H.; Zhang, L.; Yang, Z.Y.; Han, C.; Hu, L.H.; Jiang, H.L.; Shen, X. Natural product juglone targets three key enzymes from Helicobacter pylori: Inhibition assay with crystal structure characterization. Acta Pharmacol. Sin. 2008, 29, 870–876. [CrossRef] [PubMed] 25. Kong, Y.; Wu, D.; Bai, H.; Han, C.; Chen, J.; Chen, L.; Hu, L.; Jiang, H.; Shen, X. Enzymatic characterization and inhibitor discovery of a new Cystathionine γ-Synthase from Helicobacter pylori. J. Biochem. 2008, 143, 59–68. [CrossRef] 26. Wolber, G.; Langer, T. LigandScout: 3-D Pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 2005, 45, 160–169. [CrossRef] [PubMed] 27. Irwin, J.J.; Shoichet, B.K. ZINC—A free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177–182. [CrossRef] [PubMed] 28. Gidaro, M.C.; Alcaro, S.; Secci, D.; Rivanera, D.; Mollica, A.; Agamennone, M.; Giampietro, L.; Carradori, S. Identification of new anti-Candida compounds by ligand-based pharmacophore virtual screening. J. Enzym. Inhib. Med. Chem. 2016, 31, 1703–1706. [CrossRef] [PubMed] 29. Lagorce, D.; Bouslama, L.; Becot, J.; Miteva, M.A.; Villoutreix, B.O. FAF-Drugs4: Free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics 2017, 33, 3658–3660. [CrossRef] [PubMed] 30. Heifets, A.; Lilien, R.H. LigAlign: Flexible ligand-based active site alignment and analysis. J. Mol. Graph. Model. 2010, 29, 93–101. [CrossRef] [PubMed] 31. Goksuluk, D.; Korkmaz, S.; Zararsiz, G.; Karaagaoglu, E.A. easyROC: An interactive web-tool for ROC curve analysis using R language environment. R J. 2016, 8, 213. [CrossRef] 32. Lätti, S.; Niinivehmas, S.; Pentikäinen, O.T. Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization. J. Cheminf. 2016, 8, 45. [CrossRef] Computation 2021, 9, 32 23 of 24 33. Mysinger, M.M.; Carchia, M.; Irwin, J.J.; Shoichet, B.K. Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking. J. Med. Chem. 2012, 55, 6582–6594. [CrossRef] 34. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem. 2010, 31, 455–461. [CrossRef] [PubMed] 35. Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple ligand–protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011, 51, 2778–2786. [CrossRef] [PubMed] 36. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 1–13. [CrossRef] 37. Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Lee, P.W.; Tang, Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012, 52, 3099–3105. [CrossRef] [PubMed] 38. Benet, L.Z.; Hosey, C.M.; Ursu, O.; Oprea, T.I. BDDCS, the Rule of 5 and drugability. Adv. Drug Deliv. Rev. 2016, 101, 89–98. [CrossRef] 39. Lagunin, A.; Stepanchikova, A.; Filimonov, D.; Poroikov, V. PASS: Prediction of activity spectra for biologically active substances. Bioinformatics 2000, 16, 747–748. [CrossRef] 40. Wishart, D.S.; Knox, C.; Guo, A.C.; Cheng, D.; Shrivastava, S.; Tzur, D.; Gautam, B.; Hassanali, M. DrugBank: A knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008, 36, D901–D906. [CrossRef] 41. Turner, P.J. XMGRACE, Version 5.1. 19. In Center for Coastal and Land-Margin Research; Oregon Graduate Institute of Science and Technology: Beaverton, OR, USA, 2005. 42. Kumari, R.; Kumar, R.; Lynn, A. g_mmpbsa—A GROMACS Tool for High-Throughput MM-PBSA Calculations. J. Chem. Inf. Model. 2014, 54, 1951–1962. [CrossRef] 43. Hevener, K.E.; Zhao, W.; Ball, D.M.; Babaoglu, K.; Qi, J.; White, S.W.; Lee, R.E. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J. Chem. Inf. Model. 2009, 49, 444–460. [CrossRef] 44. Hajian-Tilaki, K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp. J. Intern. Med. 2013, 4, 627–635. Available online: https://pubmed.ncbi.nlm.nih.gov/24009950/ (accessed on 2 March 2019). 45. Greiner, M.; Pfeiffer, D.; Smith, R.D. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 2000, 45, 23–41. [CrossRef] 46. Jiménez-Valverde, A. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Glob. Ecol. Biogeogr. 2011, 21, 498–507. [CrossRef] 47. Lasko, T.A.; Bhagwat, J.G.; Zou, K.H.; Ohno-Machado, L. The use of receiver operating characteristic curves in biomedical informatics. J. Biomed. Inform. 2005, 38, 404–415. [CrossRef] [PubMed] 48. Lewis, R.A.; Sirockin, F. 2/3D Pharmacophore definitions and their application. Ref. Modul. Chem. Mol. Sci. Chem. Eng. 2016. [CrossRef] 49. Çifci, G.; Aviyente, V.; Akten, E.D. Molecular docking study based on pharmacophore modeling for novel phosphodiesteraseiv inhibitors. Mol. Inform. 2012, 31, 459–471. [CrossRef] 50. Dallakyan, S.; Olson, A. Small-molecule library screening by docking with PyRx. Methods Mol. Biol. 2015, 1263, 243–250. [CrossRef] 51. Lionta, E.; Spyrou, G.; Vassilatis, D.K.; Cournia, Z. Structure-based virtual screening for drug discovery: Principles, applications and recent advances. Curr. Top. Med. Chem. 2014, 14, 1923–1938. [CrossRef] 52. Gimeno, A.; Ojeda-Montes, M.J.; Tomás-Hernández, S.; Cereto-Massagué, A.; Beltrán-Debón, R.; Mulero, M.; Pujadas, G.; Garcia-Vallvé, S. The Light and Dark Sides of Virtual Screening: What is there to know? Int. J. Mol. Sci. 2019, 20, 1375. [CrossRef] [PubMed] 53. Kastritis, P.L.; Bonvin, A.M.J. On the binding affinity of macromolecular interactions: Daring to ask why proteins interact. J. R. Soc. Interface 2013, 10. [CrossRef] 54. Du, X.; Li, Y.; Xia, Y.L.; Ai, S.M.; Liang, J.; Sang, P.; Ji, X.L.; Liu, S.Q. Insights into protein–ligand interactions: Mechanisms, models, and methods. Int. J. Mol. Sci. 2016, 17, 144. [CrossRef] 55. Herschlag, D.; Pinney, M.M. Hydrogen bonds: Simple after all? Biochemistry 2018, 57, 3338–3352. [CrossRef] [PubMed] 56. Bulusu, G.; Desiraju, G. Strong and weak hydrogen bonds in protein–ligand recognition. J. Indian Inst. Sci. 2019, 100, 31–41. [CrossRef] 57. Hubbard, R.; Haider, M. Hydrogen bonds in proteins: Role and strength. Encycl. Life Sci. 2010, 1. [CrossRef] 58. Schreiber, G.; Keating, A.E. Protein binding specificity versus promiscuity. Curr. Opin. Struct. Biol. 2011, 21, 50–61. [CrossRef] [PubMed] 59. Wang, G.Y.; Zheng, H.H.; Zhang, K.Y.; Yang, F.; Kong, T.; Zhou, B.; Jiang, S.X. The roles of cytochrome P450 and P-glycoprotein in the pharmacokinetics of florfenicol in chickens. Iran. J. Vet. Res. 2018, 19, 9–14. Available online: https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC5960766/ (accessed on 19 June 2019). 60. Devadasu, V.R.; Deb, P.K.; Maheshwari, R.; Sharma, P.; Tekade, R.K. Physicochemical, pharmaceutical, and biological considera- tions in GIT absorption of drugs. In Dosage Form Design Considerations; Tekade, R.K., Ed.; Academic Press: Cambridge, MA, USA, 2018; pp. 149–178. 61. Suenderhauf, C.; Hammann, F.; Huwyler, J. Computational prediction of blood-brain barrier permeability using decision tree induction. Molecules 2012, 17, 10429–10445. [CrossRef] Computation 2021, 9, 32 24 of 24 62. Samiei, M.; Asgary, S.; Farajzadeh, M.; Bargahi, N.; Abdolrahimi, M.; Kananizadeh, U.; Dastmalchi, S. Investigating the mutagenic effects of three commonly used pulpotomy agents using the ames test. Adv. Pharm. Bull. 2015, 5, 121–125. [CrossRef] 63. Wang, W.Q.; Duan, H.X.; Pei, Z.T.; Xu, R.R.; Qin, Z.T.; Zhu, G.C.; Sun, L.W. Evaluation by the Ames assay of the mutagenicity of UV filters using benzophenone and benzophenone-1. Int. J. Environ. Res. Public Health 2018, 15, 1907. [CrossRef] 64. Yasuda, C.; Yasuda, S.; Yamashita, H.; Okada, J.; Hisada, T.; Sugiura, S. The human ether-a-go-go-related gene (hERG) current inhibition selectively prolongs action potential of midmyocardial cells to augment transmural dispersion. J. Physiol. Pharm. 2015, 66, 599–607. Available online: https://pubmed.ncbi.nlm.nih.gov/26348084/ (accessed on 21 June 2019). 65. Danker, T.; Möller, C. Early identification of hERG liability in drug discovery programs by automated patch clamp. Front. Pharm. 2014, 5, 203. [CrossRef] [PubMed] 66. Guha, R. On exploring structure activity relationships. Methods Mol. Biol. 2013, 993, 81–94. [CrossRef] 67. Benchabane, Y.; Di Giorgio, C.; Boyer, G.; Sabatier, A.S.; Allegro, D.; Peyrot, V.; De Méo, M. Photo-inducible cytotoxic and clastogenic activities of 3,6-di-substituted acridines obtained by acylation of proflavine. Eur. J. Med. Chem. 2009, 44, 2459–2467. [CrossRef] [PubMed] 68. Stepanchikova, A.V.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. Prediction of Biological Activity Spectra for Substances: Evaluation on the Diverse Sets of Drug-Like Structures. 2003. Available online: https://www.ingentaconnect.com/content/ben/ cmc/2003/00000010/00000003/art00003 (accessed on 8 May 2019). 69. Tsouh Fokou, P.V.; Nyarko, A.K.; Appiah-Opong, R.; Tchokouaha Yamthe, L.R.; Ofosuhene, M.; Boyom, F.F. Update on medicinal plants with potency on Mycobacterium ulcerans. Biomed. Res. Int. 2015, 2015, 1–16. [CrossRef] 70. Kwofie, S.K.; Dankwa, B.; Odame, E.A.; Agamah, F.E.; Doe, L.; Teye, J.; Agyapong, O.; Miller, W.A., 3rd; Mosi, L.; Wilson, M.D. In Silico screening of isocitrate lyase for novel anti-buruli ulcer natural products originating from Africa. Molecules 2018, 23, 1550. [CrossRef] 71. Dunn, M.F.; Ramírez-Trujillo, J.A.; Hernández-Lucas, I. Major roles of isocitrate lyase and malate synthase in bacterial and fungal pathogenesis. Microbiology 2009, 155, 3166–3175. [CrossRef] [PubMed] 72. Lirio, S.B.; Macabeo, A.P.; Paragas, E.M.; Knorn, M.; Kohls, P.; Franzblau, S.G.; Wang, Y.; Aguinaldo, M.A. Antitubercular constituents from Premna odorata Blanco. J. Ethnopharmacol. 2014, 154, 471–474. [CrossRef] 73. Sasikumar, K.; Ghosh, A.R.; Dusthackeer, A. Antimycobacterial potentials of quercetin and rutin against Mycobacterium tuberculosis H37Rv 3. Biotech 2018, 8, 427. [CrossRef] 74. Butova, T.; Zaitseva, S.; Butov, D.; Stepanenko, G. Morphological changes in experimental tuberculosis resulting from treatment with quercetin and polyvinylpyrrolidone. Int. J. Mycobacteriol. 2016, 5, S103–S104. [CrossRef] [PubMed] 75. Hughes, W.T.; Dankner, W.M.; Yogev, R.; Huang, S.; Paul, M.E.; Flores, M.A.; Kline, M.W.; Wei, L.J. Pediatric AIDS Clinical Trials Group 254 Team. Comparison of atovaquone and azithromycin with trimethoprim-sulfamethoxazole for the prevention of serious bacterial infections in children with hiv infection. Clin. Infect. Dis. 2005, 40, 136–145. [CrossRef] [PubMed] 76. Molecular Docking, Estimating Free Energies of Binding, and AutoDock’s Semi-Empirical Force Field. Dr. Sebastian Raschka. 2014. Available online: https://sebastianraschka.com/Articles/2014_autodock_energycomps.html (accessed on 10 February 2020). 77. Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 2015, 10, 449–461. [CrossRef] [PubMed] 78. Kwofie, S.K.; Broni, E.; Teye, J.; Quansah, E.; Issah, I.; Wilson, M.D.; Miller, W.A., 3rd; Tiburu, E.K.; Bonney, J. Pharmacoinformatics- based identification of potential bioactive compounds against Ebola virus protein VP24. Comput. Biol. Med. 2019, 113, 103414. [CrossRef] [PubMed]