ORIGINAL RESEARCH

In silico docking studies of bioactive natural plant productsas putative DHFR antagonists

Manoj Kumar • Anuradha Dagar • V. K. Gupta •

Anuj Sharma

Received: 28 December 2012 / Accepted: 28 May 2013

� Springer Science+Business Media New York 2013

Abstract In a bid to come up with effective natural plant

product-based antagonist in antimalarial chemotherapy, we

have built an in-house library of 185 compounds. The binding

site of Plasmodium wild-type DHFR (1J3I) was explored

computationally using AutoDock. The top-screened com-

pounds revealed some novel scaffolds, relative to the general

folate template, with micromolar to nanomolar inhibition

constants. Further structural optimization subjected to the

actual synthesis of these inhibitors can improve their efficacy

as better candidates in the drug design pipeline.

Keywords Malaria � AutoDock � Molecular docking �DHFR � Natural product

Introduction

Reported as one of the leading cause of death, malaria is a

major health problem around the world. World Malaria

report 2011 indicates that about 3.3 billion people, almost

half of the world’s population, are at the risk of malaria

with 216 million cases and 6,55,000 malarial deaths (in

2010) (World Malaria Report 2011, WHO, Fact Sheet). It

is also predicted that global warming and demographic

changes will lead to an increase in the distribution of

clinical malarial cases in the Western world, including

Europe and North America (Bathurst and Hatchel, 2006).

Despite a significant number of antimalarials developed

in the latter half of the twentieth century, there is a crying

need for novel scaffolds, because of the genesis and spread of

drug-resistant strains. Drugs, which have been worst affec-

ted by resistant strains include chloroquine, dihydrofolate

reductase (DHFR) inhibitors, cycloguanil and pyrimeth-

amine (Dondorp et al., 2010). Thanks to the advancements

achieved in the field of proteomics and genomics, the biol-

ogy of the parasite is now well understood, and this has

greatly aided in the rationale-based drug design (Malcom

et al., 2002; Laurence and Michael, 2002).

The dihydrofolate domain of the bifunctional enzyme

dihydrofolate reductase-thymidylate synthase of Plasmo-

dium falciparum (PfDHFR-TS) is an immensely important

target in antimalarial chemotherapy. This enzyme maintains

the intracellular level of tetrahydrofolate cofactor which is

important for cell proliferation and cell growth. Inhibition

of DHFR blocks the NADPH-dependent reduction of

dihydrofolate to tetrahydrofolate and thus prevents DNA

synthesis, resulting in cell death (Schnell et al., 2004).

The receptor had been designed through homology mod-

eling several times, before the crystal structure of the mole-

cule complexed with the third generation folate inhibitor

WR99210 was resolved in 2003. A series of antifolates such

as cycloguanil (a dihydrotriazine), methotrexate (a diamin-

opteridine), pyrimethamine, and trimethoprim (diaminopyr-

imidines) were docked within the binding site of wild-type

DHFR with significant binding constant and free energy.

(Warhust, 1998; Lemke et al., 1999; Rastelli et al., 2000;

Defino et al., 2002; Yuvaniyama et al., 2003).

However, considering resistance against time-tested

molecules, we strongly believe that many more scaffolds

need to be tested and hypothesized against DHFR. To

actualize this aim, we found no better source than mother

nature itself as it produces molecules with unmatched

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00044-013-0654-9) contains supplementarymaterial, which is available to authorized users.

M. Kumar � A. Dagar � V. K. Gupta � A. Sharma (&)

MedChemLab, Department of Chemistry, Indian Institute of

Technology Roorkee, Roorkee 247667, Uttarakhand, India

e-mail: anujs77@gmail.com; anujfcy@iitr.ernet.in

123

Med Chem Res

DOI 10.1007/s00044-013-0654-9

MEDICINALCHEMISTRYRESEARCH

complexity and structural diversity. Indeed, nearly half of

the current pharmaceuticals are either natural products or

directly derived from them (Wong, 2001); the famous

examples being cinchonine and artimisinine. Appreciating

these facts, we decided and made a home-built library of

185 plant-based natural products, as active constituents

from plants having folklore antecedent of being antimala-

rials (Vangapandu et al., 2007; Kaur et al., 2009). Com-

pounds from different classes of natural products like

alkaloids, terpenoids, xanthenes, flavanones, etc. were lis-

ted depending on their structural diversity. We always

hoped that it would help us to hit some new unexpected

templates for lead identification and also might help us to

explore intricacies of pf-DHFR binding pocket.

Materials and methods

AutoDock 4.2 (implemented through graphical user interface

called, AutoDock Tool or ADT) was used for docking simu-

lation. In AutoDock, we employed Lamarckian genetic algo-

rithm (LGA), which is the hybrid of genetic algorithm (GA)

and local search algorithm (LSA), for conformation searching.

This algorithm first built a population of individuals (genes),

each gene being a different random conformation of the

docked compound. The population underwent simulated

evolutionary development with the process of phenotypic

mapping, fitness evaluation, natural selection, crossover, and

elitist selection occurring in each generation. LSA then per-

formed energy minimization on a user-specified proportion of

the population of individuals. Individuals with the lowest

resulting energies are then transformed to next generation, and

the process is repeated (Morris et al., 1998; Garret et al., 1998).

AutoDock

Protein preparation

The crystal structure of wild-type Plasmodium DHFR-TS

complexed with WR99210, NADPH, and dUMP at reso-

lution 2.33 was retrieved from protein data bank (PDB

code: 1J3I). The inhibitor, cofactor NADPH, dUMP, and

all water molecules were removed, leaving only the resi-

dues of the receptor. Preparation of the target protein with

ADT involved the addition of polar hydrogens to the

macromolecule, an essential step to correct the calculation

of partial charge. Finally, Gasteiger charges were calcu-

lated for each atom of the macromolecule.

Ligand library

Before the screening, a chemical collection containing

compounds from natural plant products was built. Selection

of compounds was based on their folklore history from the

literature. Although some of them have been evaluated for

their antimalarial activity by in vivo or in vitro, yet most of

the aspects of mechanism of action and cellular targets of

these natural products are still not fully known (Adebayo

and Krettli, 2011; Kaur et al., 2009; Muthaura et al., 2011;

Newman et al., 2000; Phillipson, 2001; Vangapandu et al.,

2007). Motive of this article is to examine if these diverse

scaffolds can act as DHFR binder. All compounds were

drawn and then converted to 3D by ChemDraw (Cam-

bridgesoft Inc.). Each structure was then energy minimized

by AM1 force field.

Grid parameter setting and docking calculations

The docking area was assigned visually around the pre-

sumed active site. A grid of 76 A 9 76 A 9 76 A with

grid spacing of 0.375 A was positioned around the active

site with all the ligand atom types using AutoGrid. In

addition, an electrostatic map and a desolvation map were

also calculated.

Each docking calculation consisted of 25 million energy

evaluations (ga_num_evals) using Lamarckian genetic

algorithm local search method (GALS). All parameters

were set to defaults except ga_run (100), maximum no. of

generation (ga_num_generation) 27,000, one top individual

to survive to the next generation automatically, mutation

rate (ga_mutation _rate) 0.02, crossover rate (ga_cross-

over_rate) 0.8, local search on an individual in the popu-

lation (ls_search_frequency) 0.06, and the maximum no. of

iterations per local search was set to 300. The docking

results were clustered on the basis of root mean square

deviation (rmsd) and were ranked on the basis of free

energy of binding.

Visualizers

Two programs, Discovery Studio Visualizer (DSV) and

molegro molecular viewer (MMV), were used for 3D

visualization. Both programs have a user-friendly interface

to analyze the protein ligand interactions and also generate

a wide range of molecular representations.

PharmaGist

PharmaGist is a freely available program for pharmaco-

phoric detection. It first demands a set of active compounds

and then generate candidate pharmacophore as an output. It

solves an algorithm by considering multiple flexible align-

ments of the active compounds. This method is quite effi-

cient and fast (Dror et al., 2009).

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Control docking

As a test for confirming the docking algorithm’s ability to

reproduce the co-crystallized pose of inhibitor, WR99210

was docked to the active site of DHFR, from which all ligands

had already been removed. For AutoDock, docking position

of the lowest energy conformation (-8.35 kcal/mol) corre-

sponded well to the co-crystallized inhibitor with rmsd of

1.32 (Fig. 1). The low rmsd value demonstrated the capa-

bility of AutoDock in reproducing the experimental results.

Results and discussion

One of the most difficult yet the most important parts of

any screening study is the process of analyzing the docking

result. This process may be tricky, because inaccuracies of

scoring function can result in errors in ranking. To improve

the success rate, we used the following approaches:

1. Third-generation inhibitor, WR99210, a known active

compound against target protein was docked first; the

results were used as a benchmark.

2. The rank of each compound was determined by the

binding free energy of the lowest energy cluster. In

most of the instances, densely populated cluster

coincided well with the lowest energy cluster, but in

some cases, this was not the case. For example, in the

case of ‘‘4-hydroxycanthin-6-one,’’ two clusters were

resulted. Lowest energy cluster of -8.05 kcal/mol

with a single conformation, while the most populated

cluster possessed remaining 99 % conformation with

average binding energy of -7.80 kcal/mol. In this

case, ranking was considered as binding energy of

-7.80 kcal/mol instead of -8.05 kcal/mol. It is

because ranking by the lowest docked energy can

sometimes favor unreliable single member cluster

prone to disappear in the repeated docking or upon

slight modification of docking parameters. Further,

Fig. 1 Superimposition of crystal (X-ray) WR99210 versus redocked conformation (in purple) with rmsd = 1.32 A (Color figure online)

Table 1 Autodock binding energy, no. of hydrogen bonds, and residues involved in hydrogen-bonding interaction of top scorer

S.No Name of compound Binding energy

(kcal/mol)

Ki (nM) No. of hydrogen

bonds

Residues involved in

hydrogen-bonding interactions

1. Ochralifuanine A -12.07 1.42 3 Ser108 and Tyr170

2. Bischromone–chrobisiamine -11.73 2.51 3 Ala16, Gly44, and Asp194

3. Alianthione -10.28 20.09 3 Ser108, Ala16, and Tyr170

4. Korupensamine -10.25 30.81 2 Ala16 and Ser167

5. Pyrano xanthenone -10.13 37.63 4 Gly44, Ser108, Gly166, and Asp194

6. Ancistrolikokines-A -9.89 56.10 3 Ala16 and Asp 54

7. Calothwaitesixanthene -9.21 176.05 2 Ser167

8. 7-Deacetylkhivorin -9.07 225.88 4 Arg106, Thr107, Thr130, and Val169.

9. 5-Pernylbutein -9.00 252.37 5 Ala16, Val45, Ser111, and Asp194

10. 6-Methylhydroxy angolensate -8.91 294.06 2 Asn42 and Tyr170

11. Aulocarpin -8.52 568.26 1 Ser167

12. WR99210 (Reference) -8.35 758.92 3 Ile14, Asp54, and Ile164

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large clusters are less sensitive to change in docking

parameters, which result in more stable ranking.

Several studies have shown that in docking calcula-

tion, the most populated cluster of the docked ligand

conformations were better predictor of the native state

than the usual approach of selecting the lowest energy

cluster (Cosconati et al., 2010).

3. Furthermore, AutoDock has a typical error of ±2 kcal/

mol in the prediction of the free energy of binding, and

so the estimated free energy of binding should not be

used as a sole criterion for the selection of ligand

ranking. Visual inspection of the docked pose can

greatly help us to increase the success rate. The

following three points were kept in mind:

Fig. 2 Docked poses and binding interactions of bischromone (a and b), ochralifuanine (c), pyrano xanthenone (d), and the docked poses of all

the xanthone derivatives inside the binding cavity (e)

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(A) Was the ligand bound inside a pocket in the receptor?

(B) Were the nonpolar atoms in the ligand docked near

the nonpolar atoms of the receptor? Were the polar

atoms in the ligands docked near the polar atoms in

the receptor?

(C) Hydrogen-bonding and hydrophobic interactions.

We hoped that this protocol would help us to avoid

irrelevant local minima and also provide a strong safeguard

against false positives (Cosconati et al., 2010; Prasad et al.,

2007; Kallibland et al., 2004; Kozakov et al., 2004;

Limogelli et al., 2007).

AutoDock runs resulted in the energy scores between

-4.6 to -12.07 kcal/mol. Out of 185 candidates selected

for screening, 11 compounds displayed higher binding

affinity than the 3rd generation cycloguanil derivative

WR99210. Ochralifuanine and chrobisiamine came out to

be the most promising hits with Ki in nanomolar range. The

structures of these elite molecules were significantly

diverse from classical antifolates. All the top scorers along

with their binding energies, no. of hydrogen bonds, and

interacting residues are listed in Table 1.

Top-ranked compounds

Following discussion is based on AutoDock results.

Ochralifuanine A

This compound was first extracted from Strychnos species

seemed to be the most potent hit with AutoDock binding

energy of -12.07 kcal/mol. This compound was bound

tightly to the binding site by hydrogen-bonding interactions

with Ser108 and Tyr170. One side of the binding site was

completely hydrophobic with residues likes Cys15, Ala16,

Leu40, Val45, Leu46, Ile164, and Val195, while lipophobic

residues are concentrated in a small opposite site including

Thr107, Ser108, Ser111, Ser117, Gly165, Gly166, and

Tyr170 (Fig. 2c). Interestingly, ligand’s one pyrrole ring

also showed r–p interaction with Gly166 (established by

Discovery Studio Visualizer).

Bischromone-chrobisiamine

It was placed among the top-ranked compounds with Ki in

nanomolar range (2.51 nM), and its minimum energy

conformation is shown in Fig. 2a, b. The chromenone

moiety occupied a hydrophobic pocket of Ile14, Cys15,

Ala16, Phe58, and Ile164 with only one hydrogen bond

between Ala16 with cyclic carbonyl oxygen, while the

remaining half is buried inside a region rich with amino

acids having low hydropathy index (Asn42, Lys43, Gly44,

Thr107, Ser108, and Ser111). It also showed two hydro-

gen-bonding interactions with Gly44 and Asp194.

Alianthione

It is a rigid pentacyclic compound and was first extracted

from Odyendyea gabonesis. This compound completely

fitted and buried into hydrophobic surface of Cys15, Ala16,

Met55, Phe58, and Leu119. In addition, Ser108, Ala16, and

Tyr170 are involved in hydrogen bonding. Notably, a r–pinteraction with Phe58 was also observed.

Korupensamine and Ancistrolikokines

Both have been isolated from the sample of Ancistrocladus

korupensis and are isoquinoline derivatives. In korupensamine,

Fig. 3 Binding interactions between neighboring residues and a Korupensamine; and b Ancistrolikokines

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the isoquinoline moiety was involved in hydrophobic inter-

actions with Ala16, Phe58, and Ile164 along with a hydro-

gen-bonding interaction with Ala16 and Asp54 (Fig. 3a, b).

The binding inside the hydrophobic pocket was quite similar

to the one observed in the case of bischromone, but the

position of two ligands inside the binding pocket was slightly

different, probably because of the lesser flexibility of these

isoquinoline.

O

O O

O

OHOOHO

NH

N

H

HNNH

HH

H

H

O

O

OO

OH

HO

OH

N

OMe

R

OOH O

OH

O

O

O

OO

O

O

H

HO

OH3C

OO

H3C

OH

Abs

O

O

O

OH

OH

O O

O

O

O O

O R

HO

OHOH

O

OH

N

N

N

NH2

H2N

O OCl

ClCl

HO

O OH

OO

HO

Korupensamine; R= HAncistroealaines-A; R= OH

Ochralifuanine- A

Bischromone- chrobisiamine Alianthione

Pyrano-xanthene

Calothwaitesixanthene

5- Pernylbutein

Aulocarpin

7- Deacetylkhivorin

6- methylhydroxyangolensate

HO

HO

WR99210

Fig. 4 Structures of top scorers

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Xanthone group of compounds

Many xanthones are the active constituents of Pentadesma

butyracea plant extract. All xanthones were located inside

in a mostly hydrophilic pocket lined with Lys43, Gly66,

Thr107, Ser108, and Asp194 with four hydrogen bonds

with Gly44, Ser108, Gly166, and Asp194. Interestingly,

two hydrogen-bonding interactions (with Asp194 and

Gly44) were conserved in all the derivatives (Fig. 2d, e).

Calothwaitesixanthene

The Calophyllum genus (clusiaceace) is known for its well-

established use against several diseases like ulcer and

infection pathologies, and besides has antimicrobial and

molluscicidal activities. One active constituent, xanthone

calothwaitesixanthene, was first isolated from the root bark

of Calophyllum species. This compound was bound deep in

the binding pocket with a slightly different position from

that of bischromone, and one hydroxyl group from ligand

participated in the hydrogen-bonding interaction with

Ser167 NH.

7-Deacetylkhivorin

This limonoid was first obtained from the bark and seed

extract of Khaya grandifoliola. This compound occupied a

surface pit, rich in hydrophilic and polar amino acids Gly44,

Arg106, Thr107, Ser108, Gly166, and Ser167 with four

hydrogen-bonding interactions with Arg106, Thr107, Thr130,

and Val169 (observed by Discovery Studio Visualizer).

Pernylbutein

Many chalcones and flavanones are well known for their

antiplasmodial activities. This compound is extracted from

stem bark of Erythrina abyssinica. In its extended con-

formation, the compound showed a very extensive hydro-

gen bonding with Ala16, Val45, Ser111, and Asp194.

6-Methylhydroxyangolensate

This limonoid was superimposed over chrobisiamine with

hydrogen-bonding interactions with Asn42 and Tyr170 by its

lactone carbonyl oxygen and hydroxyl oxygen, respectively.

Aulocarpin

This labdane diterpenoid is extracted from the seed of

Afromomum aulocarpus and has already been shown to

possess trypanocidal activity. The two fuse cyclic rings

were almost completely buried inside a surface rich with

hydrophobic residues, Ile14, Cys15, Ala16, Ile164, and

were also strongly hydrogen bonded to Ser167 NH with

carboxymethoxy oxygen.

Most of the top scorers have shown one or more com-

mon hydrogen-bonding interaction with Ala16, Gly44,

Ser108, and Asp194 Fig. 4. Similarly, some pocket resi-

dues with high hydropathy index like Ile14, Cys15, Ala16,

Phe58, and Ile164 were also found to be highly conserved,

indicating the significant roles of these residues in binding.

Next, we focused on the structural and spatial character-

istics of the ligands to generate a general pharmacophore.

Ten top-most scorers were submitted to pharmagist server

which solved an algorithm by considering multiple flexible

alignments of the compounds. The results indicated that the

pharmacophore must contain one aromatic center with two

vicinal hydrogen bond acceptors which are mostly meta to

Fig. 5 Common spatial and structural features generated from

pharmagist. Ar Aromatic ring, A1 and A2 are two vicinal acceptors,

while DH denotes a distal hydrophobic group. Distances are in A unit

0 1 2 3 4 5 6 7

Experimental IC50 (microgram/ml)

bind

ing

ener

gy (

kcal

/mol

)

r2= 0.68SD= 0.95y=0.34x-10.14

-7-8

-9-1

0-1

1-1

2

Fig. 6 Correlation between experimental and docking results

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each other as the distance between them is around 4 A and

a distal hydrophobic group which is around 9 A from the

aromatic center (Fig. 5).

Further, a linear regression analysis was performed to

examine whether the docking score can be correlated with

the experimental activities. The predicted binding energies

(AutoDock) were plotted against available experimental

IC50 values from the literature (Vangapandu et al., 2007;

Kaur et al., 2009). A correlation (r2) of 0.68 was found,

which is an acceptable value in such docking practices.

This result suggests that AutoDock have performed well in

predicting the binding energies and also rationalized the

mechanism by which these inhibitors work, Fig. 6.

Conclusion

A structure-based approach was used to search for novel

inhibitors. The binding energies of top-ranked molecules

ranged from -8.52 to -12.07 kcal/mol better than the 3rd

generation cycloguanil derivative WR99210. The och-

ralifuanine and bischromone-chrobisiamine were found to

be the most potent hits. Two factors seemed to be espe-

cially important in binding (1) the residues Ala16, Gly44,

Ser108, and Asp194 which were the most common in

hydrogen-bonding interactions; (2) hydrophobic pocket

residues Ile14, Cys15, Ala16, Phe58, and Ile64. We also

recognized one aromatic ring, two vicinal acceptors, and

one distal hydrophobic group as minimum pharmacophoric

feature. Performance evaluation indicated correlation

coefficient of 0.68 between the predicted binding energy

and experimental activity. Though experimental studies are

indispensable to mark our top scorers as lead and to expel

the false positives, this study, however, will undoubtedly

aid in antimalarial combat strategies and can serve to

providing a new therapeutical in a faster manner.

Acknowledgments Two of the authors M.K. and Anuradha

acknowledge the awards of JRF and M.Tech fellowships from the

Council of Scientific and Industrial Research (India) and the Ministry

of Human Resource and Development (Government of India).

References

Adebayo JO, Krettli AU (2011) Potential antimalarials from Nigerian

plants: A review. J Ethnopharmacol 133:289–302

Bathurst I, Hatchel C (2006) Medicines for malaria venture. Trends

parasitol 22:301–307

Cosconati S, Forli S, Alex LP, Harris R, Goodsell DS, Oslon AJ

(2010) Virtual screening with AutoDock: theory and practices.

Expert Opin Drug Discov 5:597–607

Defino RT, Santos-filho OA, Figueroa JD (2002) Molecular modeling

of wild type and antifolate resistance mutant Plasmodium

falciparum DHFR. Biophys Chem 98:287–300

Dondorp AM, Yeung S, White L, Nguonc et al (2010) Artimisinine

resistance: current status and scenarios for containment. Nat Rev

Microbiol 8:272–280

Dror O, Duhovny S, Inbar Y et al (2009) Novel approach for efficient

pharmacophore-based virtual screening: method and application.

J Chem Inf Model 49:2333–2343

Garret MM, David SG, Robert SH, Ruth H, William E et al (1998)

Automated docking using Lamarckian genetic algorithm and an

empirical binding free energy function. J Comput Chem

19:1639–1662

Kallibland P, Mancera RL, Todorov NP (2004) Assessment of

multiple binding modes in ligand-protein docking. J Med Chem

47:3334–3337

Kaur K, Jain M, Kaur T, Jain R (2009) Antimalarials from nature.

Bioorg Med Chem 7:3229–3256

Kozakov D, Clodfelter KH, Vajda S, Camacho CJ (2004) Optimal

clustering for detecting near native conformation in protein

docking. J Med Chem 89:867–875

Laurence F, Michael PW (2002) A proteomic view of Plasmodium

falciparum life cycle. Nature 419:520–526

Lemke T, Christensen IT, Jorgensen FS (1999) Toward an under-

standing of drug resistance in malaria, three dimensional

structure of Pf–DHFR by homology building. Bioorg Med

Chem 7:1003–1011

Limogelli V, Marinelli L, Consconati SJ, Braun HA, Schmidt B,

Novellino E (2007) Ensemble docking approach on BACE-1,

pharmacophore perception and guideline for drug design.

ChemMedChem 2:667–678

Malcom J, Gardner et al (2002) Genome sequence of the human

malaria parasite Plasmodium falciparum. Nature 419:498–511

Morris GM, Goodsell DS et al (1998) Automated docking using a

Lamarckian genetic algorithm and empirical binding free energy

function. J Comput Chem 19:1639–1662

Muthaura et al (2011) Investigation of some medicinal plants

traditionally used for treatment of malaria. Exp Parasitol

127:609–626

Newman JD, Cragg GM, Snader KM (2000) The influence of natural

products upon drug discovery. Nat Prod Rep 17:215–234

Phillipson JD (2001) Phytochemistry and medicinal plants. Phyto-

chemistry 56:237–243

Prasad JC, Goldstone JV, Camacho CJ, Vajda S, Stegeman JJ (2007)

Ensemble modeling of substrate binding to cytochrome P-450:

analysis the catalytic difference between CY1A orthologs.

Biochemistry 46:2040–2654

Rastelli G, Sirawaraporn W, Sompornpisut P, Vilaivan T et al (2000)

Interaction of pyrimethamine, cycloguanil, WR99210 and their

analogues with Plasmodium falciparum dihydrofolate reductase:

structural basis of antifolate resistance. Bioorgan Med Chem

8:1117–1128

Schnell JR, Dyson HJ, Wright PE, (June (2004) Structure, dynamics

and catalytic function of dihydrofolate reductase. Annu Rev

Biophys Biomol Struct 33:119–140

Vangapandu S, Jain M, Kaur K et al (2007) Recent advance in

antimalarial drug development. Med Res Rev 27:65–107

Warhust DC (1998) Antimalarial drug discovery: development of

inhibitors of dihydrofolate reductase active in drug resistance.

Drug Discov Today 3:538–546

Wong K (2001) Mother Nature’s medicine cabinet, scientists scour

the earth in search of miracle drug. In: Rennie J (ed) Scientific

American. April/2001/2

World Malaria Report 2011, WHO-Fact Sheet

Yuvaniyama J, Chitnumsub P, Kamchonwongpaisan S et al (2003)

Insight into antifolate resistance from malarial DHFR-TS

structure. Nat Struct Biol 10:357–365

Med Chem Res

123