molecules Article Global Analysis of Plasmodium falciparum Dihydropteroate Synthase Variants Associated with Sulfadoxine Resistance Reveals Variant Distribution and Mechanisms of Resistance: A Computational-Based Study Rita Afriyie Boateng 1, James L. Myers-Hansen 1, Nigel N. O. Dolling 1 , Benedicta A. Mensah 1, Elia Brodsky 2 , Mohit Mazumder 2 and Anita Ghansah 1,* 1 Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Legon, Accra P.O. Box LG 581, Ghana 2 Pine Biotech, Inc., 1441 Canal St., New Orleans, LA 70112, USA * Correspondence: aghansah@noguchi.ug.edu.gh; Tel.: +23-327-145-535 Abstract: The continual rise in sulfadoxine (SDX) resistance affects the therapeutic efficacy of sulfadoxine-pyrimethamine; therefore, careful monitoring will help guide its prolonged usage. Mutations in Plasmodium falciparum dihydropteroate synthase (Pf dhps) are being surveilled, based on their link with SDX resistance. However, there is a lack of continuous analyses and data on the potential effect of molecular markers on the Pf dhps structure and function. This study explored single-nucleotide polymorphisms (SNPs) in Pf dhps that were isolated in Africa and other countries, highlighting the regional distribution and its link with structure. In total, 6336 genomic sequences from 13 countries were subjected to SNPs, haplotypes, and structure-based analyses. The SNP Citation: Boateng, R.A.; analysis revealed that the key SDX resistance marker, A437G, was nearing fixation in all countries, Myers-Hansen, J.L.; Dolling, N.N.O.; peaking in Malawi. The mutation A613S was rare except in isolates from the Democratic Republic Mensah, B.A.; Brodsky, E.; of Congo and Malawi. Molecular docking revealed a general loss of interactions when comparing Mazumder, M.; Ghansah, A. Global mutant proteins to the wild-type protein. During MD simulations, SDX was released from the active Analysis of Plasmodium falciparum site in mutants A581G and A613S before the end of run-time, whereas an unstable binding of SDX Dihydropteroate Synthase Variants to mutant A613S and haplotype A437A/A581G/A613S was observed. Conformational changes in Associated with Sulfadoxine mutant A581G and the haplotypes A581G/A613S, A437G/A581G, and A437G/A581G/A613S were Resistance Reveals Variant seen. The radius of gyration revealed an unfolding behavior for the A613S, K540E/A581G, and Distribution and Mechanisms of A437G/A581G systems. Overall, tracking such mutations by the continuous analysis of Pf dhps SNPs Resistance: A Computational-Based is encouraged. SNPs on the Pf dhps structure may cause protein–drug function loss, which could Study. Molecules 2023, 28, 145. https://doi.org/10.3390/ affect the applicability of SDX in preventing malaria in pregnant women and children. molecules28010145 Keywords: Pf dhps; sulfadoxine resistance; malaria; molecular dynamics simulations; molecular Academic Editors: Kam-Hung Low docking; haplotype; mutations and Angelo Facchiano Received: 22 November 2022 Revised: 19 December 2022 Accepted: 20 December 2022 1. Introduction Published: 24 December 2022 Sub-Saharan Africa remains a major hub for malaria, with high morbidity and mor- tality, and children under the age of five and pregnant women are the most vulnerable populations [1]. Global efforts toward eliminating malaria are underway but have been hin- Copyright: © 2022 by the authors. dered by increased drug resistance [1,2]. Monitoring the spread of drug-resistant parasites Licensee MDPI, Basel, Switzerland. is paramount to steering control strategies. This article is an open access article One crucial antimalarial therapy against which resistance in the parasite population in distributed under the terms and malaria-endemic regions has rapidly developed is sulfadoxine–pyrimethamine (SP) [3,4]. conditions of the Creative Commons As a result, SP is no longer a recommended treatment for uncomplicated malaria in sub- Attribution (CC BY) license (https:// Saharan African countries and was replaced by the World Health Organization’s (WHO) creativecommons.org/licenses/by/ proposed artemisinin-based combination therapy (ACT) [5]. Currently, however, SP rep- 4.0/). resents the cornerstone therapy for the intermittent preventive treatment of malaria in Molecules 2023, 28, 145. https://doi.org/10.3390/molecules28010145 https://www.mdpi.com/journal/molecules Molecules 2023, 28, 145 2 of 19 sub-Saharan African countries and was replaced by the World Health Organization’s Molecules 2023, 28, 145 (WHO) proposed artemisinin-based combination therapy (ACT) [5]. Currently, how2eovfe1r8, SP represents the cornerstone therapy for the intermittent preventive treatment of malaria in pregnant women and children (IPTp/c) [6] across malaria-endemic regions [7]. pArdegdnitaionntawlloym, tehneranpdy cbhaisleddre onn( aIP cTopm/bci)n[a6t]ioancr ofs sSPm anladr iaam-eondieamquicinre gisio unssed[7 f]o. rA sdedasitoinonal- amllayl,atrhiae racphyembaosperdoponhyalacxoims b(iSnMatCio)n ionf SAPfraincad a[m8].o dAialtqhuoiungehis SuPse defffoicrasceya siosn aglrmadaularlilay cdheecmreoapsirnogp haycrlaoxsiss A(SfrMicCa,) tihneAref raicrea v[8a]r.iaAtlitohnosu ignh SSPP-refsfiisctaacnycies lgervaedlsu walliythdine cArefariscian g[7a,9c,r1o0s]s; Athfurisc, aS,Pth ceorueldar seovoanr ibaet icoonms pinroSmP-isresdi.s tTahnecreefloevree,l sthwerieth iisn aAn furircgaen[7t, 9n,e1e0d]; ttoh uinst,eSnPsicfyo uthlde ssouorvnebilelacnocme opfr oSmP irseesdis.taTnhcer. eTfohrise ,sthhoeurledi sinavnoluvreg menatpnpeinedg otouti nthteen csuifryretnhte gseuorgvreaipllhaniccael odfisStPribreustiisotna nacned. Tsphriseasdh otuo ldasisnevsso lbvoethm tahpep einvgolouutitotnh eofc uSrPr ernetsigsetaongcrea pahnidc atlhde icstornibtiuntuioend aunsed ospf rSePa dast oa amssaelsasribao ctohntthreole ivnotleurtvieonntioofnS. PTrhees iismtapnaccet aonf dkethye mcountatitniounesd ouns ethoef pSrPoatesina mstraulacrtuiarec oanntrdo,l ipnoteternvteinatlliyo,n .itTsh efuinmcptiaocnt owf iklle ypmrouvtiadteio ninssoignhtths e ipnrtoot etihnes tmrueccthuareniasnmds, puontdeenrtliianlliyn,git SsPfu rnecstiisotannwceil,l pparrotviciduelairnlsyi gahcrtsosins tAo ftrhiecam. echanisms underlining SP resistance, particSuPl airsl yaa ccroomssbAinfartiicoan. therapy comprising sulfadoxine (SDX) and pyrimethamine (PYRS)P, iws ahiccohm baicnta tiionn tshyenrearpgyyc otmo priinshinibgits ulPfaladsomxoindieu(mSD Xfa)lcainpadrupmyr imfoeltahtaem sinyent(hPeYsRis)., wPyhriicmheatchtamininsyen ienrhgiybittos iPn.h ifbailtciPpalarsummo ddiuihmydfarlocfiopalartuem refodluactetassyen (tPhfedshisf.r)P, ywrihmereethasa mSDinXe itnarhgibetitss PP.. ffaallcciippaarruumm ddihihyyddrroopftoelraotaetree sdyunctthaassee( P(Pffddhhfpr)s,),w ahne erneazsymSDe Xuptasrtrgeeatms P o. ffa Plcfdiphafrru imn dthihe yfodlraotpe tseyronathteessiysn pthatahswe (aPyf. dOhuprs )u,nadneernstzaynmdeinugp osft rtehaem efofefcPt fodfh Pfrfdihnptsh emfuotlaattieosnysn othne tshies pbainthdwinagy .ofO SuDrXu nisd heirgshtalyn ddienpgeonfdtehnet eofnfe tchteo afvPafidlahbpilsitmy uoft aat icoonms ponletteh eprboitnedinin sgtroufctSuDreX. iPsfdhhigphsl yis dae pbeifnudnecntitononal tehnezayvmaeil atbhialti tycaotaflayzceosm thpele cteonpvreortseiionns torfu 6ct-uhyred.roPxfydmhpesthiysl-a7b,8i-- fduihnyctdioronpatleerninz ypmyreopthhaotscpahtaatley z(DesHthPeP)c toon 7v,e8r dsiiohnydorfo6p-theyrodarotex fyomlloetwhiynlg-7 t,h8-ed aidhdyditrioonp toefr pin- pamyrionpohboesnpzhoaicte a(cDidH (PpPA)BtAo )7 ,d8udriihnygd Pr.o fpatlecirpoaartuemfo flololawtein bgiothsyenatdhdesitiiso. nPfodfhpp-sa mis i3n2o3b eanmzionioc aacciidds( ploAnBgA a)nddu froilndgs Pin. tfoal cai ptrairousmepfhoolastpehbaitoes iysnotmheesraiss.eP (fTdIMhp)s biasr3r2el3 sainmgilneo-daocmidasinlo pnrgoatenidn f(oFlidgsurine t1o).a Ttrhieo sperpohtoeisnp hfeaatetuirseosm ae rwaesell-(sTtIrMuc)tubraerdre leisginhgt-lset-rdaonmdeadin cporroet eoifn p(aFriaglulerle β1-)s.hTeehtes psurorrteoiunnfdeeadtu brye spaerwipehlle-rsatrl uαc-thuerleicdese.i Tghhte- satcrtaivned seidtec oofr tehoef ppraorteailnle ilsβ a- shhiegehtslys fulrerxoibulned tuednnbeyl pfoerrmipehde rbayl αth-he ecloicrees .β-Tshheeeatcst, ivfleanskiteedo bf yth leooppros.t eRinesiesaarchhi gbhyl yCflhietxniubmlestuubn neet laflo.,r imn e2d02b0y, tihnediccoarteedβ -tshhaete tSsD, flXa nbkineddsb ayt ltohoep asc.tiRvees esiatrec, htob yinCtehraitcntu wmitshu bcreutcaial.l, cinat2a0ly2t0i,ci rnedsiicdauteeds [t1h1a]t. S SW D Dh X Xen b fu ibnodusnadt,t hSDe Xac ftuivnectsiioten,st obyin ctoemrapctetwinitgh wcrituhc ipaAl cBaAta, ltyhtiec sruebsisdtruaetse [o1f1 P]. Whennctions by competing with pABA, the substrate of Pf dhps, therebyfdinhhpisb,i tt b ih o ne u gre nd thby , e cinohnivbeirtsiniogn tohfe DcoHnPvPertsoio7n,8 odfi hDyHdProPp ttoer 7o,a8t ed.iThhyidsrloepatdesrotoatteh.e Tkhiilsli nlegaodfs tthoe tPh.ef aklcililpianrgu mof ptahre- aPs. itfealbciypaprruemve pnatirnagsitthee bdyo wprnesvterenatimngs ytnhteh desoiws onfstteretraamhy sdyrnotfhoelasties , othf etenteracehsysdarryofporlaetceu,r tshoer fnoercDesNsaArys ypnrethcuesrisso.rA fodrd DitiNonAa lslyy,ndthruesgisa. cAtidvidtyiticoannalallys,o dbreugac ahciteivveitdy vciaant halesoco bnev aecrshiioenveodf DviHa PthPe tcoopntveerrinsi-osnu lofaf DdeHaPdP-e ntod pmteertianb-soulilcfap droedaudc-etsn[d1 2m].etabolic products [12]. Figure 1. Cartoon representation of the structurall componenttss off Pffddhhppss––PPfhf hpppkk. T. Thhe eamaminion oacaicdi d ranges for botth prrotteeiinnss aarree iinnddiiccaatteeddi ninb baarrss, ,w witihtht htheeP Pf fddhhppsss seeggmmeenntth higighhlilgighhteteddi nind deeeppt eteaal la nd the Pf hppk segment in blue. Mutations occurring in Pf dhps are indicated by spheres in the right panel, which is a magnification of the region indicated on the structure to the left. The image was generated using the PyMOL visualizer. Molecules 2023, 28, 145 3 of 18 Resistance to SP is linked to the accumulation of point mutations at multiple sites in both Pf dhfr and Pf dhps [13]. Thus far, eight resistance mutations (I431V, S436A/F, A437G, K540E, A581G, and A613S/T) in Pf dhps have been reported [14–17]. These mutations, together with the Pf dhfr mutations, confer resistance to the SP drug combination. Although other factors, such as the folate salvage capability of the parasite and host factors, have been established as key factors, the attribution of SDX resistance to Pf dhps has been widely validated via surrogate marker experiments [18,19]. The mutants A581G and A613S, which result in high-resistance phenotypes, have been detected at low frequencies in certain African countries (Ghana, Niger, Tanzania) [20–22]; however, comprehensive data for Central and West Africa remain scarce [14,21]. Intrinsically, a super-resistance genotype carrying A581G or A613S mutations, together with other virulent mutations, may cause high-level resistance to SDX. Nonetheless, these mutations are still emerging, especially in West Africa. There is, therefore, the need to map SP resistance to gain an insight into how widespread these mutations are in Ghana and also across Africa. Analysis of the initial data from Ghanaian parasite isolates has revealed that the prevalence of A581G and A613S mutations in the forest and coastal regions increased (<20%) over a 4-year surveillance period [23]. In this study, we investigated the prevalence and potential impact of drug-resistant Pf dhps mutations in Ghana and other African countries, Southeast Asia, and South Amer- ica, using both genomic and protein structure-based approaches. The prevalence of mu- tations and key haplotypes at the genomic level were estimated. The 3D structure and features of the wild-type (WT) and mutant proteins were generated using homology mod- eling and per-residue energy-based approaches. The impact of mutations on SDX binding was revealed through molecular docking and molecular dynamics simulations. Overall, the detected mutant genotypes and haplotypes, which were observed at high frequencies, may have implications for the continued deployment of SP for IPTp/c, for the preven- tion of malaria in pregnant women and in children. Most importantly, a haplotype, i.e., A437G/A581G/A613S (implicated in conferring resistance) was identified in West Africa and Central Africa. Analysis of the protein structure and function revealed various crucial mechanisms of SDX resistance, which could be used to inform the basic rationale for novel drug discovery approaches. 2. Results 2.1. Prevalence of Pfdhps Mutations A total of 6336 P. falciparum genomes representing malaria-endemic regions, i.e., East- ern Africa (EAF), Central Africa (CAF), Western Africa (WAF), Southeast Asia (SEA), and South America (SAM), were extracted from MalariaGEN Pf3k release 6 [24] and analyzed for the presence of Pf dhps mutations (Table S1 from the Supplementary Materials). The samples from Ghana included archived data collected from 2014 to 2017 [23]. The number of samples that had data available for the key Pf dhps mutations, A437G, K540E/N/Y, and A581G, were 5996, 5923, and 5955, respectively. Similarly, 5964 and 5846 samples had data for Pf dhps, i.e., A613S, and for the haplotype construction, respectively. The overall preva- lence of Pf dhps mutations in each country is shown in Table 1. From the resultant data, seven significant mutations (greater than 1% of the population) were observed (A437G, K540E/N, A581G, A613S/T, and I431V). The key SDX resistance marker, A437G, was nearing fixation in all studied countries, but a significant peak was observed in Malawi (100%) (Table 1). All the SNPs, apart from A437G, were identified to carry both WT and mutant alleles in varying rates of prevalence in the studied sample set. Two different alleles were seen to affect the codons 540 (K540E and K540N) and 613 (A613S and A613T), along with the WT. Interestingly, the rear K540N allele was seen in isolates from Southeast Asia (Thailand; 6.88%, Cambodia; 3.37%, Vietnam; 4.35%) and in one isolate each in Ghana (WAF) and Cameroon (CAF), but not in the EAF region. Moreover, the A613T mutant was only observed in Thailand (0.42%), Cambodia (0.53%), and Kenya (2.38%). With the exception of the Democratic Republic (DR) of Congo Molecules 2023, 28, 145 4 of 18 and Malawi, where the mutant A613S was absent in the analyzed parasite isolates, the mutant showed varying prevalence across the remaining locations. Table 1. Global prevalence of missense mutations detected on Pf dhps. Region Countries Mutations, % (n/N) A437G K540E K540N A581G A613S A613T I431V Ghana 94.94 2.4 0.07 2.18 14.3(1352/1424) (33/1374) (1/1374) (30/1374) (197/1376) 0 (0/1376) 1.4 (2/1374) WAF Gambia 74.64 3.12(309/414) (13/417) 0 (0/417) 0 (0/421) 6.19 (26/420) 0 (0/420) 0 (420) Mali 55.38 0.89 (4/447) 0 (0/447) 0.22 (1/447) 9.17(247/446) (41/447) 0 (0/447) 0 (0/447) Cameroon 95.8(228/238) 0 (0/239) 0.42 (1/239) 22.36 29.11 CAF (53/237) (69/237) 0 (0/237) 19 (46/237) DR Congo 96.72 11.75 3.55(354/366) (43/366) 0 (0/366) (13/366) 0 (0/366) 0 (0/366) 0 (0/366) Kenya 93.55 87.1(116/124) (108/124) 0 (0/124) 0.8 (1/125) 1.59 (2/126) 2.38 (3/126) 0 (0/124) EAF Tanzania 90.77 88.46 28.78(305/336) (299/338) 0 (0/338) (97/337) 0.89 (3/337) 0 (0/337) 0 (0/337) Malawi 100 99.61 4.65(257/257) (257/258) 0 (0/258) (12/258) 0 (0/258) 0 (0/258) 0 (0/258) Thailand 99.79 91.53 6.88 81.59(959/961) (865/945) (65/945) (780/956) 0.1 (1/962) 0.42 (4/962) 0 (0/956) SEA Cambodia 93.03 37.46 37.37 44.36 0.44 0.53(1055/1134) (418/1116) (417/1116) (503/1134) (5/1135) (6/1135) 0 (0/1135) Vietnam 85.6 41.9 4.35 14.57 16.54(214/250) (106/253) (11/253) (37/254) (42/254) 0 (0/254) 0 (0/254) Colombia 17.65 (3/17) 0 (0/17) 0 (0/17) 0 (0/17) 5.88 (1/17) 0 (0/17) 0 (0/17) SAM Peru 55.17(16/29) 13.79 (4/29) 0 (0/29) 31.03 (9/29) 17.24 (5/29) 0 (0/29) 0 (0/29) WAF: West Africa; CAF: Central Africa; EAF: East Africa; SEA: Southeast Asia; SAM: South America. Furthermore, the mutants K540E and A581G, which complete the full (quintuple) and super (sextuple)-SP-resistant haplotypes with Pf dhfr/Pf dhps, i.e., N51IC59RS108N A437G, were higher in the EAF and SEA regions. The prevalence was 87.1–99.6% and 37.5–91.5% for K540E, and 0.8–28.8% and 14.6–81.6% for A581G in the EAF and SEA regions, respectively. Conversely, the prevalence for A613S remained comparable in all countries. There were significant differences in the prevalence of each of the four SNPs across the regions: A437G (χ2 = 1085.6, p < 0.001), K540E/N/Y (χ2 = 5600.5, p < 0.001), A581G (χ2 = 2680, p < 0.001), and A613S/T (χ2 = 624.2, p < 0.001). Similarly, all four point mutations showed significant differences in prevalence in both the East African and Southeast Asian regions: A437G (χ2 = 24.4, p < 0.001) and (χ2 = 102.4, p < 0.001), K540E/N/Y (χ2 = 30.9, p < 0.001) and (χ2 = 867.5, p < 0.001), A581G (χ2 = 90.2, p < 0.001) and (χ2 = 494.1, p < 0.001), A613S/T (χ2 = 17.7, p < 0.001) and (χ2 = 300.4, p < 0.001), respectively. However, in Western and Central African regions, all but K540E/N/Y (χ2 = 5.97, p = 0.202) and A437G (χ2 = 0.35, p = 0.554) exhibited a significant difference in prevalence. In addition, for the South American region, only K540E/N/Y (χ2 = 2.57, p = 0.109) and A613S/T (χ2 = 1.22, p = 0.270) did not exhibit a significant difference. In addition, the I431V mutant allele was only observed in Cameroon (19%) and Ghana (1.4%). Molecules 2023, 28, 145 5 of 18 2.2. Haplotype Frequencies The frequency of key haplotypes is shown in Table 2. In general, 17 different Pf dhps haplotypes were identified. Of these, 9 haplotypes each were found in the three African regions (WAF, CAF, and EAF), while 5 and 14 haplotypes were found in South America and Southeast Asia, respectively. There were significant differences in the frequency of haplotypes across all countries (χ2 = 10736, p < 0.001). In addition, across individual regions, there were significant differences in the frequency of haplotypes in each region: WAF (χ2 = 434.0, p < 0.001), CAF (χ2 = 142.7, p < 0.001), EAF (χ2 = 162.3, p < 0.001), and SEA (χ2 = 1797.6, p < 0.001); however, in the SAM region, there was no significant difference in haplotype frequency (χ2 = 9.20, p = 0.053). Furthermore, 8 of the 17 haplotypes showed relatively high frequencies (Table 2); these haplotypes consisted of two single (AKAS and GKAA), four double (AKGS, GEAA, GKGA, and GKAS), and one triple (GKGS) mutant haplotypes. The single mutation A518G, occurring on haplotype GKAA, exhibited the highest prevalence across all regions (range 0.0–84.7%). Single and double mutants (GKAA and GKAS) were present in all five regions, while another double mutant (GEAA) was present in all regions except SAM. In addition, the double mutant GKGA was found in the CAF, SEA, and SAM regions but not in WAF or EAF. The Central African region alone harbored the double mutant AKGS, while the WAF and EAF regions contained the single mutant AKAS. Interestingly, the triple mutant haplotype, GKGS, was only prevalent in Western and Central Africa. 2.3. Establishment of the Complete Protein Structure of Wild-Type and Mutant Pfdhps Prior to modeling, an appropriate template was selected (PDB ID: 6JWX) and assessed using ProSA [25], Verify3D [26], PROCHECK [27], and PDB metrics [28]. From these data, the template showed a resolution of 2.50 nm, indicating a clear density profile. One hundred models were generated, and the top three were evaluated in a similar procedure to that used for the template. From the results, ProSA predicted a Z-score of −10.33 (Figure 2A), indicating that the modeled structure is comparable to experimentally determined X-ray structures in PDB. The structure assessment by VERIFY3D indicated that most residues (85.5%) have averaged 3D–1D scores ≥0.2 (Figure 2B). Additionally, stereochemical checks via the PROCHECK tool revealed that 90.4% of Pf dhps residues fell within the most favorable Mrol e ecguleis o20n23,s 28,( 1F45i gure 2C), implying an acceptable range, i.e., that of a good7- oqf u19 ality structure. Figure 2. Evaluation scores of the modeled structures of WT Pfdhps for evaluation, performed via Figure 2. EvaluatiPornoSAs, cVoerrifey3sD, oPRfOtChHeECmK, aond PeDlBe dmetsritcsr. u(Ac) tPurorSeA sresoulfts,W shoTwinPg fthdath thpe msofdoelerd evaluation, performed via ProSA, Verify3Dstructure is comparable to X-ray structures of a similar size; (B) Verify3D data, indicating that all cla,ssPifiRedO resCiduHesE (aClphKa, ,beatan, ldoopP, pDolBar, mnoneptorlairc estc..) (arAe w)itPhirno thSe Aaccerpetesdu wlitnsdo,ws hanod wing that the modeled structure is compaernavibrolnemetnot; (CX) R-armaaychasntdrraun cptlot by PROCHECK, showing that > 90% of residues have the correct stereochemical structure; andu (Dre) as suopferimapossiemd imilaager ofs tihze em;od(eBled) anVde termifpylat3e D data, indicating that all classified residusetrusct(uarelsp (RhMaS,D b= e0.t19a Å, )l. oop, polar, nonpolar etc.) are within the accepted window and environment; (C) Ra2.m4. Pahcyhsioachnedmircal nanpd SlotrtucbtuyralP PRroOperCtieHs bEetwCeeKn ,Wsihld-oTwypei nangd Mthuatatnt> P9ro0te%ins of residues have the correct stereochemical structuTrabel;e 3a snhdow(s Dda)ta afors tuhep keeyr ipmropperoties of the identified mutations. Overall, several types of changes in amino acid properties arsee odbseirmveda. gThee omfuttahtioensm I43o1dV,e Al4e3d7Ga, anndd template structures (RMSD = 0.19 Å). A581G registered a change from a large hydrophobic residue (A = 89.1 Da, I = 131.2 Da) to a smaller hydrophobic residue (G = 75.1 Da, V = 117.1 Da). Interestingly, the mapping of mutations to structures indicates that A581G occupies the active site of a crucial loop structure that is in close proximity to the widespread resistance markers, A437G and K540E. In addition, I431V lies within the buried β-sheets that form the active site tunnel. K540E and A613S represent a change from a relatively small hydrophobic residue (A = 89.1 Da, K = 146.2 Da) to a polar residue (S = 105.1 Da, E = 147.1 Da). Furthermore, mutant A613S occurs on an alpha-helical structure that is distant from the active site of the protein, whereas K540E is near the active site. Table 3. Identified changes in the amino acid properties. Physiochemical Location on Amino Acid Entropy Mutation Changes Structure Changes Mutation Effect Score Hydrophobic to hy- I431V drophobic Buried Large to small Destabilizing 0.39 Hydrophobic to A437G hydrophobic Surface Large to small Destabilizing 0.66 K540E Basic to polar Surface Small to large Destabilizing 0.37 Molecules 2023, 28, 145 6 of 18 Table 2. Global frequency of key Pf dhps haplotypes. Amino acid changes are highlighted in bold. WAF CAF EAF SEA SAM Ghana Gambia % (n/N) Mali %% (n/N) (n/N) Cameroon % (n/N) DR Congo % (n/N) Kenya % (n/N) Tanzania % (n/N) Malawi % (n/N) Thailand % (n/N) Cambodia % (n/N) Vietnam % (n/N) Colombia % (n/N) Peru % (n/N) A437K540A581 A613 4.25 (58/1366) 23.95 (97/405) 42.60 (190/446) 3.80 (9/237) 3.28 (12/366) 5.69 (7/123) 8.90 (29/326) 0 (0/257) 0.21 (2/935) 7.03 (77/1095) 13.93 (34/244) 82.35 (14/17) 44.83 (13/29) A437K540A581 S613 0.81 (11/1366) 0.99 (4/405) 2.02 (9/446) 0 (0/237) 0 (0/366) 0 (0/123) 0.61 (2/326) 0 (0/257) 0 (0/935) 0.09 (1/1095) 0 (0/244) 0 (0/17) 0 (0/29) A437K540G581 S613 0 (0/1366) 0 (0/405) 0 (0/446) 0.42 (1/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 0 (0/935) 0 (0/1095) 0 (0/244) 0 (0/17) 0 (0/29) A437E540A581 A613 0 (0/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0 (0/366) 0.81 (1/123) 0 (0/326) 0 (0/257) 0 (0/935) 0 (0/1095) 0.82 (2/244) 0 (0/17) 0 (0/29) G437E540A581 A613 2.27 (31/1366) 2.96 (12/405) 0.67 (3/446) 0 (0/237) 8.47 (31/366) 85.37 (105/123) 59.51 (194/326) 94.94 (244/257) 17.43 (163/935) 32.97 (361/1095) 17.21 (42/244) 0 (0/17) 0 (0/29) G437E540A581 S613 0.07 (1/1366) 0.25 (1/405) 0.22 (1/446) 0 (0/237) 0 (0/366) 0 (0/123) 0.31 (1/326) 0 (0/257) 0.11 (1/935) 0.18 (2/1095) 16.39 (40/244) 0 (0/17) 0 (0/29) G437E540A581 T613 0 (0/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 0.43 (4/935) 0.18 (2/1095) 0 (0/244) 0 (0/17) 0 (0/29) G437E540G581 A613 0.07 (1/1366) 0 (0/405) 0 (0/446) 0 (0/237) 3.28 (12/366) 0.81 (1/123) 29.45 (96/326) 4.67 (12/257) 73.69 (689/935) 4.38 (48/1095) 7.38 (18/244) 0 (0/17) 13.79 (4/29) G437K540A581 A613 79.06 (1080/1366) 66.67 (270/405) 47.53 (212/446) 66.67 (158/237) 84.70 (310/366) 3.25 (4/123) 1.23 (4/326) 0.39 (1/257) 0 (0/935) 15.16 (166/1095) 38.11 (93/244) 11.76 (2/17) 6.90 (2/29) G437K540A581 S613 11.35 (155/1366) 5.19 (21/405) 6.73 (30/446) 7.17 (17/237) 0 (0/366) 1.63 (2/123) 0 (0/326) 0 (0/257) 0 (0/935) 0.09 (1/1095) 0 (0/244) 5.88 (1/17) 17.24 (5/29) G437K540A581 T613 0 (0/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0 (0/366) 2.44 (3/123) 0 (0/326) 0 (0/257) 0 (0/935) 0.37 (4/1095) 0 (0/244) 0 (0/17) 0 (0/29) G437K540G581 A613 0 (0/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0.27 (1/366) 0 (0/123) 0 (0/326) 0 (0/257) 1.28 (12/935) 2.01 (22/1095) 1.64 (4/244) 0 (0/17) 17.24 (5/29) G437K540G581 S613 2.05 (28/1366) 0 (0/405) 0.22 (1/446) 21.52 (51/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 0 (0/935) 0 (0/1095) 0 (0/244) 0 (0/17) 0 (0/29) G437N540A581 A613 0 (0/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 0.21 (2/935) 0.09 (1/1095) 0 (0/244) 0 (0/17) 0 (0/29) G437N540G581 A613 0 (0/1366) 0 (0/405) 0 (0/446) 0.42 (1/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 6.63 (62/935) 37.35 (409/1095) 4.51 (11/244) 0 (0/17) 0 (0/29) G437N540G581 S613 0.07 (1/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 0 (0/935) 0 (0/1095) 0 (0/244) 0 (0/17) 0 (0/29) G437Y540A581 A613 0 (0/1366) 0 (0/405) 0 (0/446) 0 (0/237) 0 (0/366) 0 (0/123) 0 (0/326) 0 (0/257) 0 (0/935) 0.09 (1/1095) 0 (0/244) 0 (0/17) 0 (0/29) Molecules 2023, 28, 145 7 of 18 2.4. Physiochemical and Structural Properties between Wild-Type and Mutant Proteins Table 3 shows data for the key properties of the identified mutations. Overall, several types of changes in amino acid properties are observed. The mutations I431V, A437G, and A581G registered a change from a large hydrophobic residue (A = 89.1 Da, I = 131.2 Da) to a smaller hydrophobic residue (G = 75.1 Da, V = 117.1 Da). Interestingly, the mapping of mutations to structures indicates that A581G occupies the active site of a crucial loop structure that is in close proximity to the widespread resistance markers, A437G and K540E. In addition, I431V lies within the buried β-sheets that form the active site tunnel. K540E and A613S represent a change from a relatively small hydrophobic residue (A = 89.1 Da, K = 146.2 Da) to a polar residue (S = 105.1 Da, E = 147.1 Da). Furthermore, mutant A613S occurs on an alpha-helical structure that is distant from the active site of the protein, whereas K540E is near the active site. Table 3. Identified changes in the amino acid properties. Mutation Physiochemical Location on Amino AcidChanges Structure Changes Mutation Effect Entropy Score I431V Hydrophobic tohydrophobic Buried Large to small Destabilizing 0.39 A437G Hydrophobic tohydrophobic Surface Large to small Destabilizing 0.66 K540E Basic to polar Surface Small to large Destabilizing 0.37 A581G Hydrophobic tohydrophobic Buried Large to small Destabilizing 0.31 A613S Hydrophobic topolar Surface Small to large Destabilizing 0.31 A437G/A581G Destabilizing 0.41 A437G/A613S Stabilizing −0.06 A437G/A581G/A613S Destabilizing 0.44 The DynaMut tool was employed to deduce the effect of mutations on the structure. From the results, the mutations and haplotypes of I431V, A437G, K540E, A581G, A613S, A437G/A581G, and A437G/A581G/A613S are predicted to destabilize regions of the active site (entropy score = 0.31 to 0.66). On the other hand, the A437G/A613S haplotype is predicted to stabilize one region in the active site (entropy score = −0.06). 2.5. Evaluation of the Effect of Mutations on Sulfadoxine Binding Molecular docking was performed to fully explore the impact of mutations on SDX binding. This approach represents an important tool for studying protein–ligand inter- actions. Overall, single mutants (I431V, A437G, K540E, A581G, and A613S) and haplo- types composed of double (A437G/A581G, A437G/A613S, and K540E/A581G) and triple (A437G/A581G/A613S) mutations were subjected to docking using AutoDock Vina [29]. The changes to the binding energy scores varied across mutants, with affinities both in- creasing (A437G, −9.4; A613S, −9.4 kcal/mol) and decreasing (I431V, −6.2; K540E, −8.9; A437G/A581G, −8.9; K540E/A581G, −8.9; A437G/A581G/A613S, −7.8 kcal/mol) com- pared with WT (−9.3 kcal/mol). The mutant A581G (−9.3 kcal/mol) exhibited a similar binding affinity to that of the WT. Regarding molecular interactions, a general loss of molec- ular interactions was established in mutant proteins relative to the WT (Figure 3). SDX participated in three H-bonding interactions with Lys582, Ser436, and Ser587 in the WT protein. By comparison, two of these three residues in all mutants were found to participate in H-bonding interactions with SDX, i.e., all of them, with the exception of Ala436. In the presence of all mutants, the H-bonding interaction with Ala436 was replaced with a hydrophobic interaction. With regard to mutant I431V, A437G, and K540E and haplotype Molecules 2023, 28, 145 8 of 18 A581G/A613S and A437G/A581G/A613S, SDX exhibited an unfavorable interaction with Molecules 2023, 28, 145 the residue His584. Additionally, the complete loss of the pi–cation bond with Met5398 owf a19s observed across all the mutants and haplotypes except A581G, A613S and A437G/A581G. FFiigguurree 33.. MMoolleeccuullaarr iinntteerraaccttiioonn ffiinnggeerrpprriinntt ooff SSDDXX aatt tthhee bbiinnddiinngg ssiittee ooff PPffddhhppss WWTT aanndd tthhee mutant pmruottaeinnts p. rIontteeirnasc.t iIonntefiranction fingerprints were generated using the DS visualizer. Hydrogen interactions are indicategde rinp rginretsenw. eUrenfgaevnoerraabteled buosnindgs tahree DshSovwisnu ianl irzeedr.. H(Ay)d SrDogXe ninitnetrearcaticotino nwsitahr e tinhed iWcaTte pdrionteginre. e(nP.aUnenlsfa (vBo–rFa)b) lSeDbXon indtseararectsinhgo wwnithin trheed s.i(nAg)leS mDXutiannttesr aI4c3ti1oVn, wAi4t3h7tGh,e KW54T0pEr, otein. (AP5a8n1eGls, a(Bn–dF A))6S1D3SX, rinestepreaccttivineglyw. (iPthantehles s(Gin–gIl)e) mInutetraancttsioIn43s 1wVi,thA 4th3e7 Gdo, uKb5l4e0 mE,uAta5n8t1sG , and A613S, Are4s3p7eGct/iAve5l8y1. G(P aannedl sA(4G3–7IG))/AIn6t1e3raSc atinodn sthwei ttrhipthlee mdouutabnlet mA4u3ta7nGt/sAA548317GG/A/A61538S1,G reasnpdecAti4v3e7lyG. /TAh6e1 3S panodset/hmeotdriep 4le imn euatacnht dAo4c3k7inGg/ cAlu58st1eGr /isA s6h1o3wS,nr.e spectively. The pose/mode 4 in each docking cluster is shown. 2.6. The Molecular Dynamics of SDX in Wild-Type and Mutant Dhps Binding Sites 2.6. The Molecular Dynamics of SDX in Wild-Type and Mutant Dhps Binding Sites Molecular dynamics simulations were performed for a period of 150 ns. After the remoMvaoll eocuf laarlld ypnearmioidciscs imbouulnatdioarnys wcoernedpiteiorfnosr m(ePdBCfo)r, appoesrti-oddocokfi1n5g0 nasn.aAlyfsteesr thweerree- pmeorvfoarlmofeadll. pTehreio ldigiacnbdou rnodota rmyecaonn dsqituioanrse (dPeBvCia),tpioons t(-RdMocSkDin)g wanaas leyssteismwaetered ptoer efovramlueadt.e Tthhee dligyannadmriocso tomf SeDanX saqt uthaere bdinedviinatgio snite(R oMf tShDe )WwTa sanedst immuattaendtt poreovteailnusa.t eOtvheeradlyl,n SaDmXic bseohfaSvDeXd datiftfheerebnintldyi ntgowsitaerodf tahlel WmTuatanndtsm utant proteins. Overall, SDX behaved differently towardall mutants in comparison to WT (Figinu reco4manpdarFiisgounr etSo1 iWn tTh e (SFuigpuprleems e4n taarnydM aSt1e riianls ).thIne SthuepWplTemperontteairny, aMbaitmeroiadlasl).c Ionn ftohrem WatTio pnraoltdeiinst,r aib buitmioondwala csoonbfsoerrmveadtiowniathl daimstreidbiuatnioRnM wSaDs oofbaseprpvreodx iwmiatthe lay m2.e3d0 inamn R(lMogSvDa loufe a=pp0.r4o).xRimegaaterdlyin 2g.3t0h enmm u(ltoangt vAa5lu8e1 G= 0an.4d). AR6e1g3aSrdpirnogte tinhse, mSDuXtadnet tAac5h8e1dGf raonmd tAh6e1b3iSn dpirnogtesiintes,, tShDerXe bdyetsaccohreindg farolmar gteheR bMinSdDinogf 1s2itea,n tdhe1r5enbmy s(cwoirtihnga alo lgarvgaelu ReMabSoDv eof1 .112), arnesdp 1e5ct invmel y(w(Fitighu ar elo4gB vaanldueF iagbuorveeS 11.i1n),t rheespSuecptpivleemlye (nFtiagruyrMesa 4teBr iaanlsd). SH1o iwn etvheer ,Saupupnliemmoednatlaerqyu Miliabtreiruiamlsw). aHsoewxheivbeirte, da iunnIi4m3o1dVa, lA e4q3u7iGli,bKri5u4m0E w, Kas5 4e0xEh/ibAit5e8d1 iGn, Ia4n3d1VA,4 A374G3/7GA,5 8K15G40(ER,M KS5D40inE/tAhe58r1aGng, eanofd0 A.0433–71.G5/nAm58),1cGom (RpMarSedD winit hthteh eraWngTe. Aofl t0h.o0u3g–h1.5a nsimng),l eccoomnpfoarrmeda twiointahl tdhyen WamTi.c Awlathsoeuxhgihb iate sdinbgyleth ceohnafoprlomtyaptieonAa4l3 d7Gyn/aAm5i8c1 Gw,aSsD eXxhsihbiiftteedd bfryo mtheit shoaprilgoitnyapleb iAnd43in7gGr/eAg5io81nGto, SaDnXea srhbiyftseidte faronmd r ietms oarinigeidnastl abbilnydbinoug nrdegoivoenr ttoh ea rneemarabiny- site and remained stably bound over the remaining simulation time. For mutant A613S, SDX remained unstable throughout the simulation, as seen from the multimodal distribution pattern exhibited (Figure 4B). A peculiar, slight flip of the hydroxyl tail of SDX in haplotype A437G/A581G/A613S resulted in a more widely spread bimodal conformational distribution. Molecules 2023, 28, 145 9 of 18 ing simulation time. For mutant A613S, SDX remained unstable throughout the simulation, as seen from the multimodal distribution pattern exhibited (Figure 4B). A peculiar, slight flip Molecules 2023, 28, 145 of the hydroxyl tail of SDX in haplotype A437G/A581G/A613S resulted in a more w 10 idofe l1y9 spread bimodal conformational distribution. FFiigguurree 44.. KKeerrnneell ddiissttrriibbuuttiioonn pplolott sshhoowwiningg lilgigaanndd RRMMSDSD lolgo gscsocroerse oscoccucrurrinrign gini Wn WT aTnadn md umtauntat nt pprrootteeiinnss.. TThheel looggo offt htheeR RMMSSDDv valauluesesw wasacsa claculclualtaedtedto ttoh ethbea bseasee, we,h werheebrea sbeaesein ed iincadtiecdattehde tchoen stant vcoalnuseta, nwt ivthalaune,a wppitrho axnim aaptpervoxailmueatoef v2a.7lu18e2 o8f2 2. .7T1h8e2w82h. iTtehed wothsirteep droetsse nretptrheesemnet dthiaen m, wedhiearne,a s the whereas the thick black bars in the centers illustrate the interquartile range. (A) WT (pink) and tmhiuctkanbtla scykstbeamrss iwnhthereec SeDntXer ws ialslu rsettraaitneetdh deuinritnergq tuhaer t1i5le0 rnasn sgiem. u(Ala)tiWonT. ((Bpi)n Mk)uatanndt mpruottaenintss ywshteemres wSDheXr ewSaDs Xrewleaasserdet baienfoedred tuhrei nsigmtuhela1ti5o0nn rsusni mtimulea,t icoonm. (pBa)reMdu ttoa nthtep WrotTe i(npsinwkh).e TrehSeD saXmwea Ws rTe leased bsyefsoteremt hise rseipmrueslaetnitoendr ounn etiamche ,pcaonmelp bauret dist oshtohwe nW oTn( pa idnikff)e. rTehnet ssacamlee. WT system is represented on each panel but is shown on a different scale. 22..77.. IImmppaacctt ooff MMuuttaattiioonnss oonn tthhee PPrrootteeiinn BBaacckkbboonnee,, UUssiinngg CC--AAllpphhaa RRMMSSDD TToo eevvaalluuaattee tthhee iimmppaacctt ooff mmuutatatitoionnss oonn ththe estsatbaibliitlyit yofo tfhteh perpotreoitne ibnabckabckobnoe,n teh,et hCe- Cal-pahlpah RaMRMSDS Dwwasa csaclacluclualtaetde.d F. iFgiugruer eS2S 2frforomm ththee SSuupppplelemmeennttaarryy MMaatteerriiaallss aanndd FFiigguurree 55 sshhooww tthhee lliinnee ttrraajjeeccttoorryy pplloott vveerrssuuss tthhee ssiimmuullaattiioonn ttiimmee aanndd tthhee kkeerrnneell ddeennssiittyy ddiissttrriibbuuttiioonn ooff tthhee CC--aallpphhaa RRMMSSDD vvaalluueess,, rreessppeeccttiivveellyy.. FFrroomm tthhee rreessuullttaanntt pplloottss,, rreelliiaabbllee ttrraajjeeccttoorriieess wweerree oobbsesrevrevdedfo rfothr ethane alaynsaelsyassesa llassy satlel mssysetxehmibsi teedxhwibeiltle-edq uwileibllr-aetqeudilbibacraktbeodn ebpaackttbeornnse, epxacteteprtnfso, rexAc4e3p7t Gfo/rA A548317GG/aAn5d8A1G61 a3nGd, Ath6e13pGos, itthioen psoosiftiwonhsic ohf hwahdicah hhiagdh ear hjuigmhper ajut mapp- part oaxpimpraotxeliym4a0tetloy 14000 ntos (1F0i0g unrse (SF2igouf rteh eSS2u opfp tlehme eSnutpapryleMmaetnetraiaryls )M. Raetegrairadlsin).g Rbeagcakrbdoinneg mbaoctkiobno,nteh emWotTioenx,h tihbeit eWd Ta beixmhoibdiateldd ias trbiibmuotidoanl pdaitstterrinbuwtiiothn apnaRttMernSD woiftha papnr oRxMimSaDte loyf 0a.p3p5rnomxim. Iantetlhye 0p.3r5e snemnc.e Ino fthmeu ptaretisoennsc,es oinf gmleu(tuantiiomnso,d sailn)gcloe n(fuonrimmaotdioanl)a lcocnhfaonrgmeastwioenrael ochbsaenrgveesd wfoerreth oebmseurvtaetdio nfosrA th43e7 mG,uKta5t4io0nEs, aAn4d37AG4,3 7KG5/40AE6, 1a3nGd( wAi4t3h7aGn/AR6M13SGD b(wetiwthe eann 0R.M30SaDn db0e.t3w1enenm )0. .3O0n athned o0th.3e1r hnamn)d. ,Oanm uthltei polethceorn fhoarnmda, tiao nmalueltqiupileli bcroiunmforwmaastisoeneanl feoqrutihliebrmiuumta twioanss sIe3e4n1 Vfora nthde Am5u8t1aGtioannsd Ih34a1pVlo atynpde As A58518G1 Gan/dA 6h1a3pSlo, Aty4p3e7sG A/5A815G81/GA,6a1n3Sd, A443377G//A558811G,/ aAn6d1 A3S4.37G/A581G/A613S. Figure 5. Violin plots of the protein backbone RMSD values across WT (in pink) and mutant Pfdhps systems; the interquartile (25th and 75th) is indicated in the black box, with the median in the white dot inside the kernel density plot. Molecules 2023, 28, 145 10 of 19 Figure 4. Kernel distribution plot showing ligand RMSD log scores occurring in WT and mutant proteins. The log of the RMSD values was calculated to the base e, where base e indicated the constant value, with an approximate value of 2.718282. The white dots represent the median, whereas the thick black bars in the centers illustrate the interquartile range. (A) WT (pink) and mutant systems where SDX was retained during the 150 ns simulation. (B) Mutant proteins where SDX was released before the simulation run time, compared to the WT (pink). The same WT system is represented on each panel but is shown on a different scale. 2.7. Impact of Mutations on the Protein Backbone, Using C-Alpha RMSD To evaluate the impact of mutations on the stability of the protein backbone, the C- alpha RMSD was calculated. Figure S2 from the Supplementary Materials and Figure 5 show the line trajectory plot versus the simulation time and the kernel density distribution of the C-alpha RMSD values, respectively. From the resultant plots, reliable trajectories were observed for the analyses as all systems exhibited well-equilibrated backbone patterns, except for A437G/A581G and A613G, the positions of which had a higher jump at approximately 40 to 100 ns (Figure S2 of the Supplementary Materials). Regarding backbone motion, the WT exhibited a bimodal distribution pattern with an RMSD of approximately 0.35 nm. In the presence of mutations, single (unimodal) conformational changes were observed for the mutations A437G, K540E, and A437G/A613G (with an RMSD between 0.30 and 0.31 nm). On the other hand, a multiple conformational Molecules 2023, 28, 145 equilibrium was seen for the mutations I341V and A581G and haplotypes A581G/A1061o3f 1S8, A437G/A581G, and A437G/A581G/A613S. Molecules 2023, 28, 145 Figure 5. Violin plots of the protteiin baacckkbboonnee RMSSD vvaalluueessa accrroosssW WTT( i(ninp pininkk))a annddm muutatannt1tP 1f dofh p1s9 Psyfdsthepmss s;ythsteeminste; rtqhue ainrttielerq(u25atrhtilaen (d257t5ht ha)nids i7n5dthic)a itse dinidnictahteedb lianc kthbeo bxl,awckit hbotxh,e wmitehd itahne imnetdhieawn hinit e tdhoet winhsiidtee dthoet iknesrindeel tdheen kseitrynepll odte.nsity plot. 2.8. Impact of Mutations on Protein Compactness Shown in Figure 6 is the compactness of proteins in the presence and absence of mutations. IItt can be seen from the results that the WT protein exhibits a more compact structure, as iindiiccaatteedd bbyy ththee uunnimimooddaal lddisitsrtirbiubtuiotino nanadn dlolwowere rradraiudsiu osf ogfygraytriaotnio (nRg(R) ogf) o1.f819. 8n9mn.m O.nO tnheth oetohtehre hrahnadn,d a, lal lml muutatnatnst sexehxhibibitietded aa sslilgighhtltyly hhigighheerr Rgg ((1..90–11..9966 nm) compared with WT. Remarkably,, a nottiicceablle mullttiimodal equilibrium was attained for mutant I431V,, whereas a biimodall cconfformatiionall diistriibutiion was seen for the mutant A613S and tthe hapllottypes K540E// A581G and A437G//AA558811GG. . Fiigure 6.. Diisttriibuttiion pplloott ooff tthhee RRgg vvaaluluees soof fWWTT anandd mmutuatnatn Pt fPdfhdphsp ssysytesmtesm. Ts.hTe hinetienrtqeurqarutailret ile rraannggeess ((2255tthh aanndd 7755tthh)) aarree rreepprreesseenntteedd iinn tthhee bbllaacckk bbooxx,, wiitthh tthhee meeddiiaann iinn tthhee whhiittee ddoott iinnssiiddee the ktheer nkeelrdneenl sdietynspitloyt p. lot. 2.9. Effect of Mutations on Per-Residue Fluctuation The impacts of mutations on the per-residue flflexibility dynamics are shown as line pllotts iin Fiigure 7A and iin tthe hiighlly ffllexiiblle regiions mapped tto tthe sttrructture iin Fiigurre 7B.. IIn ggeenneerraall,, ssixixr ergeigoinons se xehxihbiibteitdedh ighhigflhe fxliebxiilbitiylitayc raocsrsoaslsl saylls tseymstse(mroso (trmooeta nmseqauna rsequflaurce- tfuluacttiuonat(ioRnM (SRFM) aSbFo) vaebo3v.5e n3m.5 )n. mTh).e Tseheresge iroengsiocnosn tcaointraeinsi dreuseids u4e0s1 –440016–,440363, –443434–,444649, –446795–, 543725–, 554342,–558414–, 558871,–a5n8d7,6 a1n9d–6 62189. –W62it8h. tWheitehx tcheep teixocneopfti4o6n9 –o4f 7456,9a–ll47re5s, iadlul erseseixdhuiebsit eedxhhibigihteedr flhiegxhibeirl iftlyexinibtihlietym iun tathnets mcoumtapnatsre cdowmiptharWedT .wTihthe mWaTp. pTinhge omfahpigphinlyg floef xhibiglehlrye gfiloenxisbtloe regions to the structure reveals that the majority of flexible regions correspond to the loop structures (residues 401–406, 433–444, 532–535, 540–544, and 619–621) in close proximity to the active site tunnel. However, residues 537–538 and 622–625 are positioned on α- helices that are posterior to the active centers. Molecules 2023, 28, 145 11 of 18 the structure reveals that the majority of flexible regions correspond to the loop structures (residues 401–406, 433–444, 532–535, 540–544, and 619–621) in close proximity to the active Molecules 2023, 28, 145 site tunnel. However, residues 537–538 and 622–625 are positioned on α-helices t 1h2a tofa r1e9 posterior to the active centers. FFiigguurree 77.. LLininee aanndd stsrtuructcuturarla ml mapappipnign gplpotlos,t ss,hsohwoiwngin tghet hfleuflctuucattuioanti oonf roefsirdeusiedsu. (eAs.) L(Ain)eL pinloet plot iinnddiiccaattiinngg tthhee flfleexxiibbllee rreeggiioonnssb beettwweeeennt htheeW WTTa annddm muutatannt ts ysystsetmemssi nint htheep prerseesnecneceo foSf DSDX.XR. eRdedb ars ibnadrisc iantedihciagthel yhiflgehxlyib flelerxeigbiloen rse.g(iBo)nCs.a (rBto) oCnarretoporens erenptarteisoennotaftPiof dn hopf sP, fsdhhopwsi,n sghohwigihnlgy flheigxhiblyle frleegxiiobnles regions (in red) mapped to the reference WT structure. Residues are renumbered, based on the (tienmrpelda)tem naupmpbederitnogt hine PreDfeBr.e nce WT structure. Residues are renumbered, based on the template numbering in PDB. 33.. DDiissccuussssiioonn SSuullffaaddooxxiinnee rreessiissttaannccee iiss aa mmaajjoorr iissssuuee iinn mmaallaarriiaa ttrreeaattmmeenntt aanndd mmaannaaggeemmeenntt [[3300]].. Muuttaattiioonnss ccoonnffeerrrriinngg ddrruugg rreessiissttaannccee ccaann bbee qquuiicckkllyy iiddeennttiiffiieedd vviiaa ccoonnttiinnuuoouuss mmoonniittoorriinngg uuttiilliizziinngg moolleeccuullaarr maarrkkeerrss;; tthhiiss pprroocceessss wiillll hheellpp ttoo pprroovviiddee tthhee uunnddeerrllyyiinngg iinnffoorrmaattiioonn ffoorr ddrruugg ppoolliicciieess [[3311]].. IInn tthhiiss ssttuuddyy,, wee eexxpplloorreedd tthhee muuttaattiioonnss aanndd hhaapplloottyyppeess ooff PPffddhhppss iissoollaatteedd ffrroom Weesstt,, Ceennttrraall,, aanndd EEaasstt Affrriiccaa,, SSoouutthheeaasstt Assiiaa,, aanndd SSoouutthh Ameerriiccaa.. Heerreeiinn,, wee aallssoo ffurrttheerr prreediicctt ttheeiirr pootteenttiiaall eeffffeecctt oon prrootteeiin ssttrrucctturree,, whiicch ccooulld aaiid iin apprroaccheess tto deessiigniing noveell drrugss by rreeveealliing tthee meecchaniissmss iinvollveed iin rreessiissttanccee.. The moossttp prreevvaalelennt tP Pf dfdhhpps sm mutuattaiotinosnfso ufonudnwd ewreeIr4e3 I14V3,1AV4,3 A7G43, 7KG54, 0KE5/4N0E, A/N5,8 1AG5,8a1nGd, Aan6d13 AS/6T1.3AS/Thi.g Ah phriegvha plernecveaolefnthce Poffd thpe sPAfd4h37pGs Am4u3t7atGio mn wutaastioobns ewrvaesd oabcsreorsvseadll accoruonstsr iaelsl, wcohuinletrPie. sfa, lwciphailreu mP. sfalmcipalersumfr osmamtphleesE AfrFomp otphue lEatAioFn psohpouwlaetdiotnh eshporwesedn cteheo fptrheeseKn5ce4 0oEf mthue taKti5o4n0E(T ambluet1a)t.ioTnh e(sTeamblue ta1t)i.o nTshweseer emadudtaitionas llywreerlea teaddtoitiSoDnXallrye sirsetlaanteced, wtoh eSrDeaXs Are4s3is7tGansceele, cwtihvietryedasu rAin4g37IPGT spehleacdtivpirteyv idoursilnygb eIPenTpo bhsaedrv perde[v3i2o,u33sl]y. Vbaereionu osbrseeprovretds i[n3d2i,c3a3t]e. tVhaartioAu4s3 7rGephoartssn ienadrliycartea cthaetd Asa4t3u7rGat ihoansi nnethaerlym arejoarcihtyedo fsAatfurircaatniolno ciant iothnes [m34a,j3o5r]i.tyT hoef lAevfreilcsanof lAoc5a8t1ioGnsa r[e34s,i3g5n]i.f iTchane tlleyvheilgs hoef rAin58t1hGe SaEreA srieggniiofinc,afnotllyo whiegdhebry iEn AthFe( MSEaAla rweigaionnd, Tfoalnlozawneiad) ,bCyA EFA(CF a(mMearloaowni aannddD TRaCnzoanngioa)),, aCnAd FW (ACFam(Gehraonoan) .aSnedv eDraRl sCtuodnigeso)in, danicdat eWthAaFt t(hGehparneav)a. leSnecveroaflK s5t4u0dEieis lionwdiicnatCee nthtraatl tahned pWresvtaelrennAcefr iocfa K[154,306E– 3is8 ].loTwh einP fCdhenptsrAal5 8a1nGd aWnedsAte6rn13 AS/frTicma u[1ta5t,i3o6n–s3w8]e. rTehper ePvfidohupslsy Are5p8o1rGte adnadt aAl6o1w3Sf/rTeq mueuntcaytioinnWs wAeFraen pdreEvAioF,ubsulyt trhepeiorrpteredv alte na cleoiws rferpeoqrutednctyo bine rWapAidFl yanridsi nEgAiFn, Kbeunty tahaenird pUrgeavnadlean[c1e5 ,i3s9 r]e. pFourrttehder mtoo bre, trhape ihdilgyh reisrinpgre ivna Kleenncyeao afnKd5 U40gEanadnad [1A55,3891]G. F, uwrthhiechrmforrme, tthhee hfiuglhl e(rq upirnetvuaplelen)cea nodf Ks5u4p0eEr (asnedx tAup58le1)G-S, Pw-rheiscihs tfaonrmt h tahpel ofutyllp (eqsu,inwtiutphleth) eanPdf dsuhpfrerm (usetxattuiopnle()N-S5P1-Ir,eCsi5st9aRn,t Sh1a0p8lNot)ypanesd, with the Pfdhfr mutation (N51I, C59R, S108N) and Pfdhps mutation (A437G) in EAF and SEA, indicates that resistance originated in these areas and gradually spread to the other endemic areas. This may have been due to the earlier use of SP in these areas than in other endemic areas [40]. The increased prevalence of I431V seen in Cameroon (19%) and Ghana (1.4%) warrants continuous close monitoring. In 2015, a 9.8% prevalence of I431V was reported in Cameroon [15]. Molecules 2023, 28, 145 12 of 18 Pf dhps mutation (A437G) in EAF and SEA, indicates that resistance originated in these areas and gradually spread to the other endemic areas. This may have been due to the earlier use of SP in these areas than in other endemic areas [40]. The increased prevalence of I431V seen in Cameroon (19%) and Ghana (1.4%) warrants continuous close monitoring. In 2015, a 9.8% prevalence of I431V was reported in Cameroon [15]. With regard to the haplotype analysis, the combination of highly established resistance markers, i.e., A437G, K540E, A581G, and A613S, that are present on the same haplotype indicates that these mutations are not being individually selected. We identified a mod- erately high prevalence of the triple mutant haplotype GKGS (A437G/A581G/A613S) in the WAF and CAF regions. This combination (GKGS) may be responsible for conferring moderate to increased SP tolerance in these regions. As a result of missing residues in the WT Pf dhps structure, homology modeling was deployed to remodel the structure. The resolution of the template (2.50 Å) indicates a clear density profile. The model evaluation using ProSA showed that the modeled struc- ture is comparable to experimentally determined X-ray structures in PDB. The structure assessment by VERIFY3D revealed that most residues had averaged 3D–1D scores ≥0.2. Stereochemical checks using PROCHECK achieved an acceptable range, indicating the good quality of the modeled structure. Overall, the assessment of the modeled structure using ProSA, PROCHECK, and Verify3D indicated that the generated structures were of good quality and are therefore reliable for use in further structure-based studies. The observed changes in the physicochemical properties of mutations (changes from small to large amino acids and vice versa) could affect the active site architecture and, thus, the high affinity of SDX binding. Following the results of several studies, it has been postulated that small changes to its core amino acids can alter a protein’s structural conformation, enough to destroy a binding site on the surface, leading to a reduced binding affinity for SDX [41]. The mapping of mutations revealed the close proximity of I431V, A437G, K540E, A581G, and A613S to the active tunnel, and that their ability to cause destabilization or stabilization could result in changes to SDX binding. The destabilization of protein active sites in mutated sequences could result in changes such as fluctuations in temperature or pH, which may lead to the loss of protein structure integrity (denature) and its enzymatic ability. Changes in the Gibbs free energy of mutations I431V, A437G, K540E, A581G, A613S, A437G/A581G, and A437G/A581G/A613S revealed a destabilization effect around the regions of the active site resulting from the loss of hydrogen bonds in neighboring residues. The destabilization effect may suggest conformational changes, which could affect SDX binding [42,43]. SDX assumed an optimal orientation at the binding site via molecular docking, inter- acting with crucial substrate-binding amino acids. Molecular docking employs force-fields and knowledge-based statistical and empirical scoring functions to provide a perspective strength of ligand–protein interactions (i.e., binding energies) [29]. The observed increased or decreased binding energies/modes of SDX in WT and mutant systems are likely to be the result of mutation-induced changes to the active site. Although there were dif- ferences in binding energies between WT and mutant systems, a significant difference in binding affinity between WT and the mutant I431V (3.1 kcal/mol) might reveal the decreased binding affinity of SDX to Pf dhps, and, thus, could contribute to SP resistance. The decreased protein–ligand binding energy scores and molecular interactions, such as H-bonding in the presence of mutations I431V, K540E, A437G/A581G, K540E/A581G, and A437G/A581G/A613S, may reflect reduced molecular recognition and binding com- pared with WT. The greater decrease in binding affinity exhibited by mutant I431V protein, compared with WT Pf dhps, suggests that the change affects SDX binding. During MD simulations with the WT system, SDX attained a relatively bimodal con- formation, indicating a more stable orientation unique for catalysis. The release of SDX during the early stages of the simulations in mutant A581G and A613S suggests a novel mechanism of action that is adapted to promote resistance. Interestingly, simulation of the mechanism of action has been previously reported for other mutations in Mycobacterium Molecules 2023, 28, 145 13 of 18 tuberculosis, implicated in resistance [44]. A more rigid conformation exhibited by mutants and haplotypes A437G, K540E, A437G/A581G, and K540E/A581G could limit SDX flexi- bility, which is crucial for catalysis. A multimodal shift by mutant A437G/A613S indicates that there are several positions of equilibrium. The protein backbone RMSD is the simulation-based measurement of the effect of mutations on protein stability. The multiple conformations seen for the mutations I431V, A437G, A613S, and A581G, and the haplotypes A437G/A581G, A581G/K540E, and A437G/A581G/A613S suggest a higher level of protein backbone instability. Rg repre- sents the compactness of proteins both with and without mutations. The more compact structure exhibited by the WT protein is vital for catalysis. On the other hand, the slightly higher Rg exhibited by all mutants is indicative of increased protein unfolding. The no- ticeable bimodal conformational distribution for mutations A613S, A437G/A581G, and K450E/A581G and the multimodal distribution for mutant I431V might indicate a high degree of denaturation [44–46]. Overall, the differences between the WT and mutant systems are the basis for the mechanism of resistance, promoting protein fitness in the populations. Although SP plays an effective role in preventing malaria in vulnerable populations [32], it is necessary to pay closer attention to the profiles of these mutations to ensure the sustainability of SP for IPTp/c. 4. Materials and Methods 4.1. Plasmodium Falciparum Sequence Acquisition and Analysis 4.1.1. Study Data Retrieval and Preprocessing Genomic sequences from 29 countries, spanning West, Central, and East Africa, South- east Asia, and South America, were selected from the MalariaGEN Plasmodium falciparum (Pf3k) Community Project (release version 6 of the database) [24] in variant call format (VCF). Gene variants on chromosome 8 were extracted. Additionally, individual-level data collected from Begoro and the Cape Coast in the forest and coastal ecological zones of Ghana over 4 years (2014–2017) were included [23]. These data consisted of 150 and 181 genomic sequences from Begoro and the Cape Coast, respectively. Pf dhps genetic variants on chromosome 8 (position: 547,896–551,057) were extracted for all populations using BCFtools version 1.9 and in-house Python scripts. Prior to variant extraction, biallelic single-nucleotide polymorphisms (SNPs) obtained for all populations were quality con- trolled using the following rules: only SNPs that passed all VCF filters were maintained; isolates with >10% of missing SNPs were removed; SNPs with >5% of missing SNP data were removed, using PLINK v1.9 [47]. The SNPs that remained were imputed and phased using Beagle v5.1. The extracted Pf dhps sequences were then translated into amino acids, using the in-house Python script. 4.1.2. Sequence Data Analyses and Statistics The sequence data for Pf dhps were first analyzed for SNPs (A437G, A581G, A613S/T, K540E/N/Y) and defined as either wild-type (WT)—“isolate with no mutation detected” or mutant—“isolate with mutation detected”. Subsequently, haplotypes for the Pf dhps were constructed and grouped as follows: WT—“isolate with no mutation detected” or either single, double, triple, or quadruple mutants for isolate with 1, 2, 3, or 4 mutant alleles, respectively. The prevalence of mutations was estimated as the proportion of isolates with a mutant allele among the total number of successfully analyzed isolates. The prevalence of mutations and haplotypes in Pf dhps, i.e., I431V, A437G, A581G, A613S/T, and K540E/N/Y, were then estimated for each country. The differences in the prevalence of drug-resistant alleles and haplotypes among countries were assessed using Chi-square (χ2) and/or Fisher’s exact tests. The STATA software package, version 12 (StataCorp LP, College Station, TX, USA), was used to perform the statistical analyses. Statistical significance was inferred for p-values < 0.05. Molecules 2023, 28, 145 14 of 18 4.2. Structure-Based Analysis 4.2.1. Wild-Type and Mutant Structure Retrieval and Assessment The WT three-dimensional (3D) structure of Pf dhps-HPPK (PDB ID: 6JWQ) was obtained from the Protein Data Bank (PDB) [28]. As a result of the presence of missing residues in the available structure, homology modeling was employed to remodel the full- length protein structure using the MODELLER (version 9.18) tool [48]. Initially, the protein interactive modeling (PRI-MO) protein structure prediction server was used to select a suitable template, based on the highest sequence identity and query coverage to the target sequence [49]. All crystalized water molecules and unwanted ligands were removed from the structure. Additionally, the PROCHECK [27], Verify3D [26], and ProSA [25] tools were utilized to further authenticate the template. Considering the listed criteria, the sequence of Pf dhps was extracted from PlasmoDB. The template–target alignment from MAFFT [50] was employed to generate “pir” files for modeling. A total of 100 models were generated using a “very slow refinement” molecular dynamics level. The optimal model was chosen by rating all generated models using the normalized discrete optimized potential energy (z-DOPE) scoring profile [51] and validating the three models with the highest scores, as per the template. A consensus result of the different validation tools was evaluated to select the best model. Additionally, the protein structure of mutations was manually inserted at their appropriate residue positions, using the Discovery Studio (DS) visualization tools [52]. DS was employed to minimize the possible structural variations in mutant structures. 4.2.2. Mutation Mapping and Molecular Docking The identified mutations were mapped to the generated structure, and other physio- chemical property changes in the amino acid residues and the effect of mutations on the protein were evaluated using PyMOL [53] and the DynaMut tool [54], respectively. The DynaMut tool evaluates the impact of mutations on protein stability and dynamics. A positive score indicates a destabilization effect, while a negative score represents a stabi- lization effect. To predict the binding effect of SDX across mutant and wild-type Pf dhps proteins, molecular docking was performed using AutoDock Vina. Prior to docking, the 3D structure of SDX was retrieved from DrugBank (ID: DB01299) [55] in SMILES format. The chemical compound was constructed and minimized to the lowest energy geometry, using RDKit [56]. Initial docking validation was conducted to evaluate the consistency of the AutoDock Vina docking poses. The protein and ligand pdbqt input files were generated using AutoDockTools 1.5.6 (ADT) [57], where all nonpolar hydrogens were fused and par- tial charges were assigned using the Gasteiger–Huckel method. Initially, each protein was subjected to blind docking simulations with 320 exhaustiveness, using a cuboid box with a diameter of 120 × 120 × 120 and a grid spacing of 0.375. The intermolecular interactions between SDX and each protein were determined using LigPlot+ [58] and DS 2D plots. 4.2.3. All-Atom Molecular Dynamics Simulations of Pf dhps WT and Mutant Proteins A total of 150 ns all-atom molecular dynamics (MD) simulations were conducted using GROMACS (version 2019) [59] for Pf dhps WT and mutant proteins with SDX compounds, bound to the active site. The AMBER03 force field [60] and the ACPYPE tool [61] were used to create input files for the structure and the ligand topology, respectively, which were GROMACS-compatible. A total of nine systems (WT) and eight mutant systems (I431V, A437G, K540E, A581G, A613S, A437G/A613S, A437G/A581G, K540E/A581G, and A437G/A581G/A613S) were solvated in a cubic box, with a minimal gap of 1 Å between the box edge and the protein, using the TIP3P water model [62]. Then, 0.15 M NaCl was used to neutralize all systems. The relaxed systems converged to a maximum force of 1000 kJ/mol/nm after the solved systems had been initially minimized for 5000 steps, us- ing the steepest descent algorithm. Following minimization, systems were equilibrated at a constant number, volume, and temperature (NVT) using the modified Berendsen thermo- stat algorithm at a 300 K temperature and constant volume [63], and then at NPT (constant number of particles, pressure, and temperature) using the Parrinello–Rahman barostat al- Molecules 2023, 28, 145 15 of 18 gorithm at 1 bar pressure and at a constant volume and temperature [64]. Systems coupling groups and time restrictions were defined in each ensemble at fs. The LINCS holonomic constraints algorithm [65] was used to restrict all bonds; however, the particle-mesh Ewald (PME) algorithm [66] was configured to take long-range electrostatic interactions into ac- count. The Center for High-Performance Computing (CHPC), Cape Town, South Africa, was used to implement the overall MD protocol. After every 10 ps, structural coordinates were written, and periodic boundary conditions (PBC) were eliminated. Post-MD analyses, such as ligand and C-alpha RMSD, RMSF, and Rg analyses, were performed. 5. Conclusions Here, the prevalence of circulating SDX-resistant Pf dhps mutations was determined, and their effects on protein functionality were revealed via computational approaches, including SNP analysis, energy-based analysis, molecular docking, and MD simulation. The prevalence of mutants and their corresponding haplotype prevalence were assessed across Africa and other regions. The analysis indicated that the mutation A613S had variable prevalence across most of the studied regions, except for isolates from DR Congo and Malawi. Molecular interactions from docking revealed a loss of interactions in mutants relative to the WT protein. Interestingly, SDX formed unfavorable bonds with the residue His586 in the mutant K540E and haplotype A437G/A613S. The ligand RMSD in the MD simulations indicated that SDX dissociates from the active site of mutants A581G and A613S before the end of the simulation run time. SDX remained unstable in mutants I431V, A613S, and A437G/A581G/A613S. The global protein RMSD indicated several examples of conformational dynamics in mutants with I431V, A581G, A613S, A437G/A581S, A581G/K540E, and A437G/A581G/A613S. Additionally, Rg revealed that mutations I431V, A613S, A437G/A581G, and K540E/A581G are associated with protein unfolding behavior. Overall, continuous analysis of Pfdhps SNPs is encouraged to track mutations. The molecular effects of SNPs on the structure of Pfdhps could cause the loss of protein function. These findings may have implications for drug deployment for IPTp/c and the mechanisms of drug resistance and should be monitored. Further interaction studies with mutated proteins expressed in the presence of SDX should be conducted to provide insight into the mechanism of protein destabilization and the degree of loss of protein function, due to destabilization. Supplementary Materials: The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/molecules28010145/s1. Figure S1: Kernel distribution plot showing the ligand RMSD scores occurring in WT and mutant proteins. The white dots represent the median, whereas the thick black bars in the centers illustrate the interquartile range. (A) WT (pink) and mutant systems where SDX was retained during the 150 ns simulation. (B) Mutant proteins where SDX was released before the simulation run time, compared to the WT (pink). The same WT system is represented on each panel but on a different scale; Figure S2: Line plot of protein RMSD showing protein stability indices; Table S1: Breakdown summary of analysis set samples organized by geography. Regions are categorized into five regions. These comprised East Africa (EAF), West Africa (WAF), Central Africa (CAF), Southeast Asia (SEA) and Southern America (SAM). Author Contributions: Conceptualization, R.A.B. and A.G.; data curation, R.A.B., J.L.M.-H. and N.N.O.D.; formal analysis, R.A.B., J.L.M.-H. and N.N.O.D.; funding acquisition, A.G.; methodology, R.A.B., J.L.M.-H., N.N.O.D., B.A.M., E.B. and M.M.; project administration, A.G.; supervision, E.B., M.M. and A.G.; visualization, R.A.B.; writing—original draft, R.A.B.; writing—review and editing, R.A.B., J.L.M.-H., N.N.O.D. and A.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by DELTAS Africa Initiative under the Wellcome Trust (DELGEME grant number 107740/Z/15/Z). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)’s Alliance for Accelerating Excellence in Science in Africa (AESA) and is supported by the New Partnership for Africa’s Development Planning and Coordinating Agency (NEPAD Agency), with funding from the Wellcome Trust (DELGEME grant 107740/Z/15/Z) and the UK government. The views expressed in this publication are those of the author(s) and not necessarily those of the AAS, NEPAD Agency, Wellcome Trust, or the UK government. Molecules 2023, 28, 145 16 of 18 Data Availability Statement: Data will be made available upon request. Acknowledgments: We acknowledged CHPC for making their computational resources available for our use in this study. Conflicts of Interest: The authors declare no conflict of interest. Sample Availability: Sample of the compound are available from the authors. References 1. World Health Organization. World Malaria Report 2021. World Health Organization. Available online: https://apps.who.int/iris/ handle/10665/350147 (accessed on 15 October 2022). 2. World Health Organization. World Malaria Report 2019. World Health Organization. Available online: https://www.who.int/ publications/i/item/9789241565721 (accessed on 15 October 2022). 3. Sibley, C.H.; Hyde, J.E.; Sims, P.F.G.; Plowe, C.V.; Kublin, J.G.; Mberu, E.K.; Cowman, A.F.; Winstanley, P.A.; Watkins, W.M.; Nzila, A.M. Pyrimethamine-sulfadoxine resistance in Plasmodium falciparum: What next? Trends Parasitol. 2001, 17, 582–588. [CrossRef] [PubMed] 4. Curtis, J.; Duraisingh, M.T.; Warhurst, D.C. In vivo selection for a specific genotype of dihydropteroate synthase of Plasmodium falciparum by pyrimethanine-sulfadoxine but not chlorproguanil-dapsone treatment. J. Infect. Dis. 1998, 177, 1429–1433. [CrossRef] [PubMed] 5. Nosten, F.; White, N.J. Artemisinin-based combination treatment of falciparum malaria. Am. J. Trop. Med. Hyg. 2007, 77, 181–192. [CrossRef] [PubMed] 6. Ofori, M.; Ansah, E.; Agyepong, I.; Ofori-Adjei, D.; Hviid, L.; Akanmori, B. Pregnancy-associated malaria in a rural community of ghana. Ghana Med. J. 2009, 43, 13–18. 7. Henry, M.; Florey, L.; Youll, S.; Gutman, J.R. An analysis of country adoption and implementation of the 2012 WHO recommen- dations for intermittent preventive treatment for pregnant women in sub-Saharan Africa. Malar. J. 2018, 17, 364. [CrossRef] [PubMed] 8. World Health Organization. World Malaria Report 2018. World Health Organization. Available online: https://apps.who.int/iris/ handle/10665/275867 (accessed on 15 March 2022). 9. Kayentao, K.; Garner, P.; Van Eijk, A.M.; Naidoo, I.; Roper, C.; Mulokozi, A.; MacArthur, J.R.; Luntamo, M.; Ashorn, P.; Doumbo, O.K.; et al. Intermittent preventive therapy for malaria during pregnancy using 2 vs 3 or more doses of sulfadoxine-pyrimethamine and risk of low birth weight in Africa: Systematic review and meta-analysis. JAMA—J. Am. Med. Assoc. 2013, 309, 594–604. [CrossRef] [PubMed] 10. White, N.J. Intermittent Presumptive Treatment for Malaria. PLoS Med. 2005, 2, e3. [CrossRef] 11. Chitnumsub, P.; Jaruwat, A.; Talawanich, Y.; Noytanom, K.; Liwnaree, B.; Poen, S.; Yuthavong, Y. The structure of Plasmodium falciparum hydroxymethyldihydropterin pyrophosphokinase-dihydropteroate synthase reveals the basis of sulfa resistance. FEBS J. 2020, 287, 3273–3297. [CrossRef] 12. Roland, S.; Ferone, R.; Harvey, R.J.; Styles, V.L.; Morrison, R.W. The characteristics and significance of sulfonamides as substrates for Escherichia coli dihydropteroate synthase. J. Biol. Chem. 1979, 254, 10337–10345. [CrossRef] 13. Cowman, A.F.; Morry, M.J.; Biggs, B.A.; Cross, G.A.M.; Foote, S.J. Amino acid changes linked to pyrimethamine resistance in the dihydrofolate reductase-thymidylate synthase gene of Plasmodium falciparum. Proc. Natl. Acad. Sci. USA 1988, 85, 9109–9113. [CrossRef] 14. Koukouikila-Koussounda, F.; Bakoua, D.; Fesser, A.; Nkombo, M.; Vouvoungui, C.; Ntoumi, F. High prevalence of sulphadoxine- pyrimethamine resistance-associated mutations in Plasmodium falciparum field isolates from pregnant women in Brazzaville, Republic of Congo. Infect. Genet. Evol. 2015, 33, 32–36. [CrossRef] [PubMed] 15. Chauvin, P.; Menard, S.; Iriart, X.; Nsango, S.E.; Tchioffo, M.T.; Abate, L.; Awono-Ambéné, P.H.; Morlais, I.; Berry, A. Prevalence of Plasmodium falciparum parasites resistant to sulfadoxine/pyrimethamine in pregnant women in Yaoundé Cameroon: Emergence of highly resistant pfdhfr/pfdhps alleles. J. Antimicrob. Chemother. 2015, 70, 2566–2571. [CrossRef] [PubMed] 16. Xu, C.; Sun, H.; Wei, Q.; Li, J.; Xiao, T.; Kong, X.; Wang, Y.; Zhao, G.; Wang, L.; Liu, G.; et al. Mutation Profile of pfdhfr and pfdhps in Plasmodium falciparum among Returned Chinese Migrant Workers from Africa. Antimicrob. Agents Chemother. 2019, 63, e01927-18. [CrossRef] 17. Jiang, T.; Chen, J.; Fu, H.; Wu, K.; Yao, Y.; Eyi, J.U.M.; Matesa, R.A.; Obono, M.M.O.; Du, W.; Tan, H.; et al. High prevalence of Pfdhfr-Pfdhps quadruple mutations associated with sulfadoxine-pyrimethamine resistance in Plasmodium falciparum isolates from Bioko Island, Equatorial Guinea. Malar. J. 2019, 18, 101. [CrossRef] [PubMed] 18. A-Elbasit, I.E.; Alifrangis, M.; Khalil, I.F.; Bygbjerg, I.C.; Masuadi, E.M.; Elbashir, M.I.; Giha, H.A. The implication of dihydrofolate reductase and dihydropteroate synthetase gene mutations in modification of Plasmodium falciparum characteristics. Malar. J. 2007, 6, 108. [CrossRef] [PubMed] 19. Ngondi, J.M.; Ishengoma, D.S.; Doctor, S.M.; Thwai, K.L.; Keeler, C.; Mkude, S.; Munishi, O.M.; Willilo, R.A.; Lalji, S.; Kaspar, N.; et al. Surveillance for sulfadoxine-pyrimethamine resistant malaria parasites in the Lake and Southern Zones, Tanzania, using pooling and next-generation sequencing. Malar. J. 2017, 16, 236. [CrossRef] Molecules 2023, 28, 145 17 of 18 20. Gesase, S.; Gosling, R.D.; Hashim, R.; Ord, R.; Naldoo, I.; Madebe, R.; Mosha, J.F.; Joho, A.; Mandia, V.; Mrema, H.; et al. High resistance of Plasmodium falciparum to sulphadoxine/pyrimethamine in Northern Tanzania and the emergence of dhps resistance mutation at codon 581. PLoS ONE 2009, 4, e4569. [CrossRef] 21. Grais, R.F.; Laminou, I.M.; Woi-Messe, L.; Makarimi, R.; Bouriema, S.H.; Langendorf, C.; Amambua-Ngwa, A.; D’Alessandro, U.; Guérin, P.J.; Fandeur, T.; et al. Molecular markers of resistance to amodiaquine plus sulfadoxine-pyrimethamine in an area with seasonal malaria chemoprevention in south central Niger. Malar. J. 2018, 17, 98. [CrossRef] 22. Myers-Hansen, J.L.; Abuaku, B.; Oyebola, M.K.; Mensah, B.A.; Ahorlu, C.; Wilson, M.D.; Awandare, G.; Koram, K.A.; Ngwa, A.A.; Ghansah, A. Assessment of antimalarial drug resistant markers in asymptomatic Plasmodium falciparum infections after 4 years of indoor residual spraying in Northern Ghana. PLoS ONE 2020, 15, e0233478. [CrossRef] 23. Mensah, B.A.; Aydemir, O.; Myers-Hansen, J.L.; Opoku, M.; Hathaway, N.J.; Marsh, P.W.; Anto, F.; Bailey, J.; Abuaku, B.; Ghansah, A. Antimalarial drug resistance profiling of Plasmodium falciparum infections in Ghana using molecular inversion probes and next-generation sequencing. Antimicrob. Agents Chemother. 2020, 64, e01423-19. [CrossRef] 24. Ahouidi, A.; Ali, M.; Almagro-Garcia, J.; Amambua-Ngwa, A.; Amaratunga, C.; Amato, R.; Amenga-Etego, L.; Andagalu, B.; Anderson, T.J.C.; Andrianaranjaka, V.; et al. An open dataset of Plasmodium falciparum genome variation in 7000 worldwide samples. Wellcome Open Res. 2021, 6, 42. [PubMed] 25. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007, 35, W407–W410. [CrossRef] [PubMed] 26. Eisenberg, D.; Lüthy, R.; Bowie, J.U. VERIFY3D: Assessment of protein models with three-dimensional profiles. Methods Enzymol. 1997, 277, 396–404. [PubMed] 27. Laskowski, R.A.; MacArthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Crystallogr. 1993, 26, 283–291. [CrossRef] 28. Deshpande, N.; Addess, K.J.; Bluhm, W.F.; Merino-Ott, J.C.; Townsend-Merino, W.; Zhang, Q.; Knezevich, C.; Xie, L.; Chen, L.; Feng, Z.; et al. The RCSB Protein Databa Bank: A redesigned query system and relational database based on the mmCIF schema. Nucleic Acids Res. 2005, 33, D233–D237. [CrossRef] [PubMed] 29. Trott, O.; Olson, A.J. AutoDock Vina. J. Comput. Chem. 2010, 31, 445–461. 30. Xu, C.; Wei, Q.; Yin, K.; Sun, H.; Li, J.; Xiao, T.; Kong, X.; Wang, Y.; Zhao, G.; Zhu, S.; et al. Surveillance of Antimalarial Resistance Pfcrt, Pfmdr1, and Pfkelch13 Polymorphisms in African Plasmodium falciparum imported to Shandong Province, China. Sci. Rep. 2018, 8, 12951. [CrossRef] 31. Vestergaard, L.S.; Ringwald, P. Responding to the challenge of antimalarial drug resistance by routine monitoring to update national malaria treatment policies. Am. J. Trop. Med. Hyg. 2007, 77, 153–159. [CrossRef] 32. Rupérez, M.; González, R.; Mombo-Ngoma, G.; Kabanywanyi, A.M.; Sevene, E.; Ouédraogo, S.; Kakolwa, M.A.; Vala, A.; Accrombessi, M.; Briand, V.; et al. Mortality, Morbidity, and Developmental Outcomes in Infants Born to Women Who Received Either Mefloquine or Sulfadoxine-Pyrimethamine as Intermittent Preventive Treatment of Malaria in Pregnancy: A Cohort Study. PLoS Med. 2016, 13, e1001964. [CrossRef] 33. Braun, V.; Rempis, E.; Schnack, A.; Decker, S.; Rubaihayo, J.; Tumwesigye, N.M.; Theuring, S.; Harms, G.; Busingye, P.; Mockenhaupt, F.P. Lack of effect of intermittent preventive treatment for malaria in pregnancy and intense drug resistance in western Uganda. Malar. J. 2015, 14, 372. [CrossRef] 34. Kaingona-Daniel, E.P.S.; Gomes, L.R.; Gama, B.E.; Almeida-De-Oliveira, N.K.; Fortes, F.; Ménard, D.; Daniel-Ribeiro, C.T.; Ferreira-Da-Cruz, M.D.F. Low-grade sulfadoxine-pyrimethamine resistance in Plasmodium falciparum parasites from Lubango, Angola. Malar. J. 2016, 15, 309. [CrossRef] [PubMed] 35. Jiang, T.; Cheng, W.; Yao, Y.; Tan, H.; Wu, K.; Li, J. Molecular surveillance of anti-malarial resistance Pfdhfr and Pfdhps polymorphisms in African and Southeast Asia Plasmodium falciparum imported parasites to Wuhan, China. Malar. J. 2020, 19, 209. [CrossRef] [PubMed] 36. Ruh, E.; Bateko, J.P.; Imir, T.; Taylan-Ozkan, A. Molecular identification of sulfadoxine-pyrimethamine resistance in malaria infected women who received intermittent preventive treatment in the Democratic Republic of Congo. Malar. J. 2018, 17, 17. [CrossRef] [PubMed] 37. Berzosa, P.; Esteban-Cantos, A.; García, L.; González, V.; Navarro, M.; Fernández, T.; Romay-Barja, M.; Herrador, Z.; Rubio, J.M.; Ncogo, P.; et al. Profile of molecular mutations in pfdhfr, pfdhps, pfmdr1, and pfcrt genes of Plasmodium falciparum related to resistance to different anti-malarial drugs in the Bata District (Equatorial Guinea). Malar. J. 2017, 16, 28. [CrossRef] [PubMed] 38. Esu, E.; Tacoli, C.; Gai, P.; Berens-Riha, N.; Pritsch, M.; Loescher, T.; Meremikwu, M. Prevalence of the Pfdhfr and Pfdhps mutations among asymptomatic pregnant women in Southeast Nigeria. Parasitol. Res. 2018, 117, 801–807. [CrossRef] [PubMed] 39. Spalding, M.D.; Eyase, F.L.; Akala, H.M.; Bedno, S.A.; Prigge, S.T.; Coldren, R.L.; Moss, W.J.; Waters, N.C. Increased prevalence of the pfdhfr/phdhps quintuple mutant and rapid emergence of pfdhps resistance mutations at codons 581 and 613 in Kisumu, Kenya. Malar. J. 2010, 9, 338. [CrossRef] 40. Amimo, F.; Lambert, B.; Magit, A.; Sacarlal, J.; Hashizume, M.; Shibuya, K. Plasmodium falciparum resistance to sulfadoxine- pyrimethamine in Africa: A systematic analysis of national trends. BMJ Glob. Health 2020, 5, e003217. [CrossRef] 41. Alberts, B.; Johnson, A.; Lewis, J.; Raff, M.; Roberts, K.; Walter, P. Molecular Biology of the Cell, 4th ed.; Protein Function; Garland Science: New York, NY, USA, 2002. Molecules 2023, 28, 145 18 of 18 42. Gruber, S.; Löf, A.; Hausch, A.; Jöhr, R.; Obser, T.; König, G.; Schneppenheim, R.; Brehm, M.A.; Benoit, M.; Lipfert, J. A Conformational Transition of Von Willebrand Factor’s D’D3 Domain Primes It For Multimerization. Blood Adv. 2021, 6, 5198–5209. [CrossRef] 43. Fang, M.; Zhang, Q.; Wang, X.; Su, K.; Guan, P.; Hu, X. Inhibition Mechanisms of (−)-Epigallocatechin-3-gallate and Genistein on Amyloid-beta 42 Peptide of Alzheimer’s Disease via Molecular Simulations. ACS Omega 2022, 7, 19665–19675. [CrossRef] 44. Amamuddy, O.S.; Musyoka, T.M.; Boateng, R.A.; Zabo, S.; Bishop, Ö.T. Determining the unbinding events and conserved motions associated with the pyrazinamide release due to resistance mutations of Mycobacterium tuberculosis pyrazinamidase. Comput. Struct. Biotechnol. J. 2020, 18, 1103–1120. [CrossRef] 45. Boateng, R.A.; Bishop, Ö.T.; Musyoka, T.M. Characterisation of plasmodial transketolases and identification of potential inhibitors: An in silico study. Malar. J. 2020, 19, 442. [CrossRef] [PubMed] 46. Amamuddy, O.S.; Baoteng, R.A.; Barozi, V.; Nyamai, D.W.; Bishop, Ö.T. Novel dynamic residue network analysis approaches to study homodimeric allosteric modulation in SARS-CoV-2 Mpro and in its evolutionary mutations. Comput. Struct. Biotechnol. J. 2021, 19, 6431–6455. [CrossRef] [PubMed] 47. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; De Bakker, P.I.W.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [CrossRef] [PubMed] 48. Sali, A. MODELLER: A Program for Protein Structure Modeling Release 9.12, r9480. Rockefeller Univ. 2013. 49. Hatherley, R.; Brown, D.K.; Glenister, M.; Bishop, Ö.T. PRIMO: An Interactive Homology Modeling Pipeline. PLoS ONE 2016, 11, e0166698. [CrossRef] 50. Katoh, K.; Standley, D.M. MAFFT: Iterative Refinement and Additional Methods. Methods Mol. Biol. 2014, 1079, 131–146. 51. Shen, M.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 2006, 15, 2507–2524. [CrossRef] 52. Accelrys Software Inc. Discovery Studio Modeling Environment, release 3.5.; Accelrys Softw. Inc.: San Diego, CA, USA, 2012. 53. Schrödinger, L.; DeLano, W. PyMOL. 2020. Available online: http://www.pymol.org/pymol (accessed on 12 April 2019). 54. Rodrigues, C.H.M.; Pires, D.E.V.; Ascher, D.B. DynaMut: Predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic Acids Res. 2018, 46, W350–W355. [CrossRef] 55. Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018, 46, D1074–D1082. [CrossRef] 56. Landrum, G. RDKit: Open-Source Cheminformatics Software. 2021. Available online: http://www.rdkit.org/ (accessed on 10 May 2019). 57. El-Hachem, N.; Haibe-Kains, B.; Khalil, A.; Kobeissy, F.H.; Nemer, G. AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study. Methods Mol. Biol. 2017, 1598, 391–403. 58. Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple Ligand–Protein Interaction Diagrams for Drug Discovery. J. Chem. Inf. Model. 2011, 51, 2778–2786. [CrossRef] 59. Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25. [CrossRef] 60. Pearlman, D.A.; Case, D.A.; Caldwell, J.W.; Ross, W.S.; Cheatham, T.E.; DeBolt, S.; Ferguson, D.; Seibel, G.; Kollman, P. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 1995, 91, 1–41. [CrossRef] 61. Sousa da Silva, A.W.; Vranken, W.F. ACPYPE—AnteChamber PYthon Parser interfacE. BMC Res. Notes 2012, 5, 367. [CrossRef] 62. Mark, P.; Nilsson, L. Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K. J. Phys. Chem. A 2001, 105, 9954–9960. [CrossRef] 63. Lemak, A.S.; Balabaev, N.K. On The Berendsen Thermostat. Mol. Simul. 1994, 13, 177–187. [CrossRef] 64. Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981, 52, 7182–7190. [CrossRef] 65. Hess, B.; Bekker, H.; Berendsen, H.J.C.; Fraaije, J.G.E.M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 1997, 18, 1463–1472. [CrossRef] 66. Petersen, H.G. Accuracy and efficiency of the particle mesh Ewald method. J. Chem. Phys. 1995, 103, 3668. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.