In silico Screening for Identification of Novel Anti-malarial Inhibitors by Molecular Docking, Pharmacophore Modeling and Virtual Screening

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Medicinal Chemistry, 2015,11, 687-700 687

In silico Screening for Identification of Novel Anti-malarial Inhibitors by Molecular Docking, Pharmacophore Modeling and Virtual Screening

Sidra Batool

¹

, Zeshan Aslam Khan

²

, Warda Kamal

³

, Gohar Mushtaq

⁴

and Mohammad Amjad Kamal*

^5,6

1Department of Biosciences, COMSATS Institute of Information Technology, Park Road, Chak Sha- hzad, Islamabad, 44000, Pakistan; ²Department of Electronic Engineering, International Islamic Uni- versity, H-10 sector, Islamabad, Pakistan; ³Biomediotronics, Enzymoic, 7 Peterlee Pl, Hebersham, NSW 2770, Australia; ⁴Department of Biochemistry, College of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; ⁵King Fahd Medical Research Center, King Abdulaziz University, P. O.

Box 80216, Jeddah 21589, Saudi Arabia; ⁶Enzyomics, 7 Peterlee Pl, Hebersham, NSW 2770, Australia

Abstract: Objective: Drug resistance from affordable drugs has increased the number of deaths from malaria globally. This problem has raised the requirement to design new drugs against multidrug-

resistant Plasmodium falciparum parasite. Methods: In the current project, we have focused on four important proteins of Plasmodium falciparum and used them as receptors against a dataset of four anti-malarial drugs. In silico analysis of these receptors and ligand dataset was carried out using Autodock 4.2. A pharmacophore model was also established using Ligandscout. Results: Analysis of docking experiments showed that all ligands bind efficiently to four proteins of Plas- modium falciparum. We have used ligand-based pharmacophore modeling and developed a pharmacophore model that has three hydrophobic regions, two aromatic rings, one hydrogen acceptor and one hydrogen donor. Using this pharmacophore model, we have screened a library of 50,000 compounds. The compounds that shared features of our pharmacophore model and exhibited interactions with the four proteins of our receptors dataset are short-listed. Conclusion: As there is a need of more anti-malarial drugs, therefore, this research will be helpful in identifying novel anti-malarial drugs that exhibited bindings with four important proteins of Plasmodium falciparum. The hits obtained in this study can be considered as useful leads in anti-malarial drug discovery.

Keywords: Drug design, virtual screening, cheminformatics, docking, pharmacophore modeling, Plasmodium falciparum, anti- malarials, princeton database.

1. INTRODUCTION

Nearly 400 million cases of malaria are reported each year causing about one million deaths worldwide [1]. Plas- modium falciparum is accountable for majority of the deaths caused by malaria particularly amongst young children, mostly happened in the region of Africa. Due to drug resistance from available drugs, effect of most drugs got retarded day by day [2-7].

Several drugs such as quinine, chloroquine and mefloquine (Lariam) are currently in use to treat malaria. The target of these drugs is the inhibition of parasite growth within infected erythrocytes [8]. However, there are clinical reports clearly demonstrating that these drugs may cause certain neurological draw backs, such as paranoia, anxiety and de- pression. There have been several experimental studies fa- voring the hypothesis that these drugs may elicit adverse behavioral effects at the neuromuscular junctions and the synapses. A study carried out by Sieb et al. demonstrates that

*Address correspondence to this author at the King Fahd Medical Research Center, King Abdulaziz University, P. O. Box 80216, Jeddah 21589, Saudi Arabia; Tel: +612-98644812; E-mail: [email protected]

the use of these drugs can significantly affect the miniature end-plate by decreasing its amplitude and decay time [9]. It was also shown that quinine “causes a long-lived open- channel as well as a close-channel block of AChR” at the neuromuscular transmission [9]. Treating malaria has always been a challenge due to the absence of an effective vaccine against malaria as well as resistance of malarial strains to known drugs [10, 11]. Therefore, there is a need to design new antimalarial strategies for the treatment of malaria.

Many studies have indicated that different bioinformatics and computational biology tools, such as PseKNC [12,13] or Chou's PseAAC [14,15], can be successfully used in the design of novel drugs [16]. These bioinformatics and computational biology tools can provide useful information such as recombination spots of DNA [17,18], nucleosome positioning in genome [19,20], various PTM (posttranslational modification) sites [21-23], anticancer [24] and antimicrobial peptides [25], interactions between drugs and target proteins in cellular networking [26-29], and sigma-54 promoters [30]. In this study, we use in silico screening or computational approaches to identify novel anti-malarial inhibitors in an effort to provide useful information for stimulating the development of new and effective drugs to treat malaria. We have

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created a dataset of important proteins of Plasmodium falciparum that includes lactate dehydrogenase [31], dUTPase [32], Plasmepsins [33] and falcipain [34]. These four proteins of Plasmodium falciparum are frequently used as drug targets, so we used these proteins as receptors in our study.

A ligand dataset of four anti-malarial drugs is created that includes chloroquine, lumefantrine, mefloquine and quinine.

These four anti-malarial drugs are extensively used as inhibitors against malarial disease, so we have created dataset of these inhibitors as ligand dataset. Docking studies were carried out on receptor and ligand dataset to analyze the bindings. A pharmacophore model was developed using ligand dataset and pharmacophoric features were identified. That pharmacophore model was used for virtual screening of Princeton natural library consisting of 50,000 compounds (www.princetonbio.com). From this library, compounds that shared features of our pharmacophore model were shortlisted and further used for molecular docking studies. Analysis of these compounds was done to find out the binding pattern against receptors. The compounds were selected according to the characteristics of behavioral binding patterns with only those compounds reported that can bind with all receptor proteins. This study further contributes towards the identification of additional potent anti-malarial drugs. A number of in silico studies have been reported that have investigated various computational approaches to treat malaria [35].

2. MATERIALS AND METHODS 2.1. Receptor and Ligand Dataset

In our study, we focused on receptor proteins of Plasmo- dium falciparum, the most lethal type of malaria parasite against humans. Proteins included in the current study were:

(i) Plasmodium falciparum lactate dehydrogenase (funda- mental for the production of ATP via glycolytic pathway within the parasite) (PDB id: 1U4S) [31]; (ii) Plasmodium falciparum deoxyuridine nucleotidohydrolase (PfdUTPase, PDB id: 1VYQ) (an important enzyme for pyrimidine metabolism: therapeutic techniques target pyrimidine metabolism an intervention against malaria) [32]; (iii) aspartic pro- teinases fromPlasmodium falciparum (Plasmepsins) (PDB id: 2IGX) [33] and (iv) Plasmodium falciparum Falcipain-2 (a critical hemoglobinase of Plasmodium falciparum) (PDB id: 3BPF) [34]. The four selected proteins used as drug targets against Plasmodium falciparum are crucially important in different functions of the parasite. Hence, screening new drugs against these receptors could be beneficial in the de- struction of parasite genome. Relevant PDB entries for the receptor dataset were searched from PDB [36] and binding site information for these receptors was gathered. A repre-

sentative PDB structure for each receptor was selected as described earlier. The information regarding the binding pocket of a receptor for its ligand is very important for drug design, particularly for conducting mutagenesis studies [37].

In the literature, the binding pocket of a protein receptor to a ligand is usually defined by those residues that have at least one heavy atom (i.e., an atom other than hydrogen) within a distance of 5Å from another heavy atom of the ligand. Such a criterion was originally used to define the binding pocket of ATP in the Cdk5-Nck5a* complex [38] that later proved quite useful in identifying the functional domains and stimulating the relevant truncation experiments [39]. Similar approach has also been used to define the binding pockets of many other receptor-ligand interactions important for drug design [40-45]. We have used a similar approach for binding site identification of our receptor dataset. Relevant PDB entries for each receptor were carefully analyzed. The binding site residues for each receptor are given in Table 1. Prior to docking studies, ligands were removed from receptors and these protein structures were validated. Ramachandran plot displayed that more than 90% residues of all protein models were present in permissible regions. Parameters such as C tetrahedral distortion, non-bonded interactions, peptide bond planarity, main chain H-bond energy and overall G factor for the structure were also within the allowed values region.

Anti-malarial inhibitors were taken as ligand dataset which included chloroquine, lumefantrine, mefloquine and quinine.

The structures of these compounds were taken from Pub- chem database. Fig. (1) shows the 2D structures of four ligands used in this study.

2.2. Molecular Docking Studies

Automated dockings were performed to detect suitable binding orientations and conformations of anti-malarial inhibitors in Plasmodium falciparum proteins using Auto- Dock4.2 tool according to specified instructions [46].

Briefly, Kollman charges and polar hydrogen atoms were allocated to receptor proteins. Non-polar hydrogen atoms were merged and Gasteiger partial charges were assigned to ligands. All rotatable bonds (torsions) were allowed to rotate and random starting positions, random orientations and torsions were used for ligands. Autogrid program was used for generating grid maps. Dimensions of the grid were 120 x 120 x 120 Å³ separated by 0.375 Å, centered at correspond- ing receptor structure. Lamarckian genetic algorithm was used for minimization. Standard protocol for docking rigid and flexible ligand involved 100 runs which consisted of an initial population of 150 randomly placed individuals with 2.5 x 10⁶ energy evaluations. A maximum of 27,000 itera- tions were also used in combination with 0.02 mutation rate, Table 1. Binding pocket residues for the four receptors along with PDB id.

Receptors PDB id Binding Site Residues

Lactate dehydrogenase 1U4S Asn140, Asp168, Arg171, His195, Ala236, Pro246

dUTPase 1VYQ Phe46, Ser92, Ser93, Ser95, Asn103, Gly106, Tyr112, Ala119, Ile117 Plasmepsin 2IGX Trp41, Met75, Phe111, Thr114, Ile123, Tyr192, Phe294

Falcipain-2 3BPF Gln36, Cys42, Tyr78, Gly82, Gly83, Leu84, Leu172, His174, Asn204, Trp206, Asp234

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In silico Methods for Identifying Novel Anti-malarial Inhibitors Medicinal Chemistry, 2015, Vol. 11, No. 7 689

a crossover rate of 0.80 and an elitism value of 1. The information acquired via molecular docking not only can provide useful insights for in-depth understanding of certain subtle action mechanisms (such as the marvelous allosteric mechanism revealed recently by the NMR observations on the M2 proton channel of influenza A virus [47,48]) but can also stimulate new strategies for drug development as demonstrated by a series of previous studies [40,41, 43, 44, 49-55].

Many remarkable biological functions in proteins and DNA and their profound dynamic mechanisms, such as switch between active and inactive states [56, 57], cooperative effects [58], allosteric transition [59] and intercalation of drugs into DNA [60], can be revealed by studying their internal motions as summarized in a comprehensive review [61].

Likewise, to really understand the interaction of a protein receptor with its ligand and to reveal their binding mechanism, we should consider not only the static structures con- cerned but also the dynamical information obtained by simu- lating their internal motions or dynamic process. For a better understanding of the interaction of the receptor with its ligand and their mechanism of binding, the structures can also be studied in their dynamic form rather than just their static form which can be explored in future studies.

2.3. Pharmacophore Generation and Virtual Screening Pharmacophore is a part of molecular structure containing universal chemical features (hydrogen bonds, charge interactions and hydrophobic areas) which determines pre- cise mode of action for a ligand in a macromolecule within a

3D space. In this era, Pharmacophore modeling has become an integrated part of drug development and designing [62].

This Pharmacophoric pattern enables ligand-macromolecule interactions. Searching these chemical patterns in large molecule databases can result in novel scaffolds for the development of lead structures. In this study, Ligand Scout tool [63] was used to develop three dimensional Pharmacophore hypotheses using a set of four anti-malarial compounds:

Chloroquine, Lumefantrine, Mefloquine and Quinine.

Ligand Scout generates structure-based as well as ligand- based Pharmacophore models. The ligand-based strategy derives Pharmacophore models from a set of ligands where the macromolecule structure is absent by considering the conformational flexibility parameter derived from known ligands. It is based on the principle that common structures containing small molecules provide similar biological activity. This approach examines a communal feature pattern that is shared in an active ligand-set [64]. The distances among the Pharmacophoric features were calculated using Discov- ery Studio. The generated Pharmacophore was then used as a 3D query for virtual screening (VS) of natural compounds library taken from Princeton database and to retrieve the compounds from scaffold database that fit the chemical features present in the Pharmacophore model. All screened targets were further filtered according to their ADMET properties and Lipinski's rule of five. The compounds that satisfied Lipinski's rule of five and ADMET properties were selected for molecular docking studies in order to determine suitable orientation of leads and binding affinities with Plasmodium Fig. (1). 2D structures of (a) Quinine, (b) Mefloquine, (c) Chloroquine, (d) Lumefantrine.

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falciparum receptors. Virtual screening approach has been used to narrow down a set of compounds to be tested for activity of proposed drug target. It is initiated by evaluating drug-likeness properties of compounds, and can be followed by ligand-based and structure-based approaches [65]. The latter occurs according to availability of the target structure.

3. RESULTS

3.1. Molecular Docking Analysis

Since this is a computational study, we have used binding information of receptors binding sites for validation of our docking results. The four receptors were subjected to docking studies using Autodock 4.2. Each docked pose was monitored individually for analyzing the binding interactions. Table 2 shows the binding, intermolecular, vdW + Hbond + desolv, electrostatic, final internal energy, torsional energy and system's unbound energies along with inhibition constant values.

Docking analysis revealed that the four ligands showed interactions with binding site residues of all the receptors

included in the study with varying degree. Fig. 2 graphically shows the binding pattern of ligands with binding site of receptors. Table 3 shows residue by residue analysis of binding interactions among receptors and ligands. The strength of docking, aside from docking energy values, was also validated by strong hydrogen bonding. Our ligands dataset exhibited strong hydrogen bonding with receptor residues. The receptor residues involved in hydrogen bonding along with the atoms of both receptors and ligands are shown in Table 4. Hydrogen bond distance among the atoms was measured in Å.

3.2. Evaluation of Pharmacophore Model

The four anti-malarial compounds were subjected to Pharmacophore modeling using Ligand scout. The represen- tative 2D Pharmacophore of each ligand is shown in (Fig. 3).

The features shown in green colors are the hydrogen bond donors (HBD), red colored are the hydrogen bond acceptors (HBA), yellow colored are the hydrophobic group (HD), and aromatic group (AR rings) are depicted in blue color. Collec- tively the Pharmacophoric features for each ligand are shown in Table 5.

Table 2. Energy values for the four ligands with (a) Lactate dehydrogenase, (b) dUTPase, (c) Plasmepsins, (d) Falcipain-2.

Ligands

Binding Energy (kcal/mol)

Ki (M)

Inter- molecular

Energy (kcal/mol)

vdW + Hbond + desolv Energy

(kcal/mol)

Electrostatic Energy (kcal/mol)

Final Total Internal

Torsional Free Energy

(kcal/mol)

Unbound System's Energy (kcal/mol) (a) Lactate Dehydrogenase Interactions

Chloroquine -5.66 70.81 -8.05 -8.15 0.1 0.35 2.39 0.35

Lumefantrine -6.77 10.99 -10.05 -10.08 0.03 -0.15 3.28 -0.15

Mefloquine -6.35 22.1 -7.84 -6.56 -1.28 0.64 1.49 0.64

Quinine -6.66 13.12 -8.15 -7.66 -0.5 -0.34 1.49 -0.34

(b) dUTPase Interactions

Chloroquine -6.15 30.97 -8.54 -8.01 -0.53 -0.11 2.39 -0.11

Lumefantrine -7.39 3.38 -10.67 -10.46 -0.21 -1.34 3.28 -1.34

Mefloquine -6.21 28.09 -7.7 -6.24 -1.46 -0.56 1.49 -0.56

Quinine -7.28 4.62 -8.77 -8.31 -0.46 -0.09 1.49 -0.09

(c) Plasmepsins Interactions

Chloroquine -7.59 2.71 -9.98 -8.39 -1.59 -0.56 2.39 -0.56

Lumefantrine -7.52 3.05 -10.81 -9.08 -1.73 -1.27 3.28 -1.27

Mefloquine -6.75 11.22 -8.24 -5.32 -3.02 0.09 1.49 0.09

Quinine -7.17 5.52 -8.66 -7.9 -0.76 0.15 1.49 0.15

(d) Falcipain-2 Interactions

Chloroquine -5.94 44.03 -8.33 -7.51 -0.82 0.01 2.39 0.01

Lumefantrine -6.65 13.32 -9.93 -8.9 -1.03 -1.38 3.28 -1.38

Mefloquine -6.62 13.95 -8.12 -6.11 -2.01 -0.22 1.49 -0.22

Quinine -7.05 6.81 -8.54 -7.75 -0.79 -0.67 1.49 -0.67

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Fig. (2). Docking interactions of critical residues of receptors: (a) Lactate dehydrogenase; (b) dUTPase; (c) Plasmepsins; (d) Falcipain-2; each with ligands (i) Chloroquine; (ii) Lumefantrine; (iii) Mefloquine; (iv) Quinine. (Receptor residues and ligands are colored according to atom type: Hydrogen, Grey; Oxygen, Red; Nitrogen, Blue; Sulfur, Orange; Flourine, sky Blue; receptor’s Carbon; Pink; Chloroquine’s Carbon, Green; Lumefantrine’s Carbon, Cyan; Mefloquine’s Carbon, Purple; Quinine’s Carbon, Copper; hydrogen bonds, green dotted lines; residues labeled; distances in Å). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this paper).

Table 3. Interacting residues of all the receptors docked to the four ligands.

Inhibitors

Receptor Binding Residues

Chloroquine Lumefantrine Mefloquine Quinine

Asn140

Asp168

Arg171

His195

Ala236

Lactate Dehydrogenase

Pro246

Phe46

Ser92

Ser93

Ser95

Asn103

Gly106

Tyr112

Ile117

dUTPase

Ala119

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Table 3. contd…

Inhibitors

Receptor Binding Residues

Chloroquine Lumefantrine Mefloquine Quinine

Trp41

Met75

Phe111

Thr114

Ile123

Tyr192

Plasmepsins

Phe294

Gln36

Cys42

Tyr78

Gly82

Gly83

Leu84

Leu172

His174

Asn204

Trp206

Falcipain-2

Asp234

Table 4. Details of hydrogen bonds formed between residues of the receptors and atoms of ligands.

Receptor Inhibitors Binding Residue Hydrogen Bond Distance (Å)

Asn140 NH-N 2.08

Chloroquine

Pro246 O-HN 2.00

Lumefantrine Asn140 NH-O 1.93

Mefloquine Asn140 O-HO 1.75

Lactate Dehydrogenase

Quinine Asn140 NH-N 2.10

Gly106 OH-N 2.38

Chloroquine

Ile117 O-HN 2.20

Lumefantrine Tyr112 O-HO 2.26

Tyr112 OH-F 2.31

O-HN 1.92 NH-O 2.11 Mefloquine

Ile117

O-H 2.18

Asn103 NH-N 2.35

NH-O 2.14 dUTPase

Quinine

Ile117

O-H 2.21

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In silico Methods for Identifying Novel Anti-malarial Inhibitors Medicinal Chemistry, 2015, Vol. 11, No. 7 693 Table 4. contd…

Receptor Inhibitors Binding Residue Hydrogen Bond Distance (Å)

Chloroquine Tyr192 OH-N 2.22

Lumefantrine Tyr192 OH-O 2.11

OH-N 1.96 Mefloquine Tyr192

OH-F 2.17 Plasmepsins

Quinine Tyr192 OH-N 2.21

Chloroquine His174 NH-N 2.00

NH-O 2.08 Cys42

SH-O 3.04 Lumefantrine

His174 NH-N 2.47

Cys42 SH-F 2.78

Gly83 NH-F 2.22

Mefloquine

Asp234 O-HO 1.89

Cys42 SH-O 2.90

Falcipain-2

Quinine

His174 NH-O 1.61

Fig. (3). 2D Depiction pharmacophore models: (a) Quinine, (b) Chloroquine, (c) Mefloquine, (d) Lumefantrine.

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Table 5. Pharmacophoric feature of each ligand.

Ligand HBA HBD AR HD

N

N Cl

H H H H

H

H H

H H H

H

H H

H H H H H H

H H H

H H

H

Abs

Chloroquine

+ + ++ +++

N O

Cl Cl

Cl

H H H

H H HH

H HHH

H H

H H HH

H H

H

H HH HH

H H

H H ^Abs

Lumefantrine

+ + ++ +++

N

N O

F F

F

F F F H

H H

H H HH H

H H

Abs

Mefloquine

++ + ++ +++

N N

O O

H H HH

H H

H HH

H H

H

H H H

H H

H HH

Abs

Quinine

+ + ++ +++

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Fig. (4). Alignment of four compounds with features of generated Pharmacophore: Chloroquine in green, Lumefantrine in cyan, Me- floquine in pink, Quinine in blue, hydrophobic regions are shown as yellow spheres, Aromatic ring as blue sphere, hydrogen bond acceptor as red spheres and hydrogen bond donor as green spheres.

(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this paper).

The common featured Pharmacophore is shown in Fig. 4.

The Pharmacophore model generated by the alignment of four ligands consisted of three hydrophobic regions (shown by yellow circles), two aromatic rings (shown by blue circles), one hydrogen acceptor (shown by red circle) and one hydrogen donor (shown by green circle). The distances were measured between the common Pharmacophore features using Discovery Studio. Mean of distances range from low- est to highest and have been measured between the Pharma- cophoric features. The proposed 3D Pharmacophore model and distances among common Pharmacophoric features of the proposed model are shown in (Fig. 5). This common shared pharmacophore model for all ligands was then used for virtual screening of a library containing 50,000 compounds taken from the Princeton Database.

3.3. Virtual Screening of Princeton Database

The pharmacophore model was then used as 3D query to screen the compound libraries. The Princeton library com- prising of 50,000 compounds was screened and 10,550 compounds were identified that shared pharmacophore-like features. The extracted compounds were refined using various filters such as Lipinski’s rule and ADMET properties. Con- sequently, 260 compounds were extracted on the basis of exact pharmacophore features including three hydrophobic regions, two aromatic rings, one hydrogen acceptor and one hydrogen donor. These 260 compounds were subsequently used for docking analysis. Next, receptor and ligand com- plementarities were checked and 160 compounds were filtered on the basis of the following criterion: Compounds having positive binding energies were eliminated leaving 100 compounds for further study. Interactions were mapped for these compounds with the four receptors of Plasmodium falciparum used in this study. We reported those compounds that showed frequent interactions with all the four receptors.

The 2D structures of selected hits are indicated in Fig. 6.

Energy values as a result of docking experiments for these hits are shown in Table 6. Overall, compounds 5 and 7 gave quite low energy values as well as Ki values in docking with all the receptors.

Fig. (4) shows the binding of these screened compounds from Princeton Database in binding cavity of Lactate dehydrogenase (1U4S), dUTPase (1VYQ), Plasmepsin (2IGX), and Falcipain-2 (3BPF). Figure 7 clearly shows that the eight ligands exhibit binding with all of the four receptors at their binding sites.

4. DISCUSSION

In this study, we have selected proteins as receptor dataset from the most virulent human malaria parasite,

“Plasmodium falciparum” that includes lactate dehydrogenase, dUTPase, Plasmepsins, and Falcipain-2, followed by collection of binding site information for these receptors.

Analysis of docking between ligand dataset consisting of four ligands and these receptors has revealed that the tested

Fig. (5). 3D Pharmacophore model of four compounds showing geometrical relationship among Pharmacophore features. Aromatic ring groups (AR) are demonstrated by two pairs of brown meshed spheres, cyan sphere representing a hydrophobic group (HD), hydrogen bond donor (HBD) by a pair of magenta spheres, and hydrogen bond acceptor (HBA) by a pair of green spheres. The smaller sphere is for assisting in localization of the HBA atom on a ligand, while the larger one indicates the location of HBD atom on the receptor. (b) Distances (Å) between the centers of selected features are labeled. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this paper).

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Fig. (6). 2D Representation of Princeton naturally extracted compounds, Compounds 1-3 belongs to carboxylate family and compounds 4-8 belongs to chromen-4-one family.

Table 6. Energy values of the screened ligands with (a) Lactate dehydrogenase, (b) dUTPase, (c) Plasmepsins, (d) Falcipain-2.

Ligands

Ki (M)

vdW + Hbond +

desolv Energy (kcal/mol)

(kcal/mol)

Unbound System's Energy (kcal/mol) (a) Lactate Dehydrogenase Interactions

1 -6.36 21.8 -8.75 -8.75 0.01 -0.75 2.39 -0.75

2 -7.12 6.04 -8.91 -8.92 0.01 -0.49 1.79 -0.49

3 -7.36 4.05 -9.15 -9.05 -0.09 -0.37 1.79 -0.37

4 -7.75 2.1 -9.54 -9.4 -0.14 -0.96 1.79 -0.96

5 -8.29 834.8nM -9.78 -9.78 -0.01 -0.44 1.49 -0.44

6 -7.12 6.09 -8.9 -8.8 -0.11 -0.78 1.79 -0.78

7 -8.74 391.0nM -10.23 -10.3 0.07 -1.12 1.49 -1.12

8 -7.37 3.95 -8.56 -8.39 -0.17 -0.55 1.19 -0.55

(b) dUTPase Interactions

1 -6.32 23.22 -8.71 -8.51 -0.2 0.51 2.39 0.51

2 -7.12 6.04 -8.91 -8.84 -0.07 -0.69 1.79 -0.69

3 -7.02 7.11 -8.81 -8.75 -0.07 -0.7 1.79 -0.7

4 -7.16 5.68 -8.95 -8.96 0.01 -0.7 1.79 -0.7

5 -8.61 491.66nM -10.1 -10.07 -0.02 -0.65 1.49 -0.65

6 -7.38 3.9 -9.17 -9.07 -0.1 -0.58 1.79 -0.58

7 -10.01 45.84nM -11.5 -11.5 0.0 -1.18 1.49 -1.18

8 -7.45 3.49 -8.64 -8.68 0.04 -0.53 1.19 -0.53

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In silico Methods for Identifying Novel Anti-malarial Inhibitors Medicinal Chemistry, 2015, Vol. 11, No. 7 697 Table 6. contd….

Ligands

Ki (M)

vdW + Hbond +

desolv Energy (kcal/mol)

(kcal/mol)

Unbound System's Energy (kcal/mol) (c) Plasmepsins Interactions

1 -6.0 40.33 -8.38 -8.33 -0.05 -0.84 2.39 -0.84

2 -7.44 3.49 -9.23 -9.19 -0.04 -0.22 1.79 -0.22

3 -8.34 766.7nM -10.13 -10.09 -0.04 -0.48 1.79 -0.48

4 -7.95 1.49 -9.74 -9.81 0.07 -0.17 1.79 -0.17

5 -9.1 215.24nM -10.59 -10.62 0.04 -0.72 1.49 -0.72

6 -7.83 1.83 -9.62 -9.63 0.01 -0.65 1.79 -0.65

7 -10.79 12.42nM -12.28 -12.23 -0.04 -1.39 1.49 -1.39

8 -8.37 736.84nM -9.56 -9.55 -0.01 -0.44 1.19 -0.44

(d) Falcipain-2 Interactions

1 -5.37 114.95 -7.76 -7.71 -0.05 -0.72 2.39 -0.72

2 -6.42 19.64 -8.21 -8.08 -0.13 -0.68 1.79 -0.68

3 -6.25 26.18 -8.04 -7.9 -0.14 -0.87 1.79 -0.87

4 -6.02 38.38 -7.81 -7.97 0.15 -0.9 1.79 -0.9

5 -7.36 4.03 -8.85 -8.83 -0.02 -0.68 1.49 -0.68

6 -7.16 5.6 -8.95 -8.83 -0.13 -1.0 1.79 -1.0

7 -8.04 1.29 -9.53 -9.45 -0.07 -1.22 1.49 -1.22

8 -7.25 4.84 -8.44 -8.35 -0.09 -0.58 1.19 -0.58

Fig. (7). Binding mode of screened compounds from Princeton Database with (a) lactate dehydrogenase, (b) dUTPase, (c) Plas- mepsins, (d) Falcipain-2. Ligands are shown in sticks enveloped in mesh while receptors are shown in sticks and labeled.

compounds interacted well with binding sites of the receptors along with favorable energy values. Using the ligand dataset, a pharmacophore model was created and pharmacophoric features were identified which comprised of three hydrophobic regions, two aromatic rings, one hydrogen acceptor and one hydrogen donor. This pharmacophore model was used for virtual screening of a library taken from Prince- ton Database. The approaches of pharmacophore modeling and virtual screening [65-69] have also been used for finding new drugs. The retrieved compounds were filtered by apply- ing filters like Lipinski’s rule of five and ADMET properties. Hits showing exact pharmacophore-like features were further short-listed and subjected to docking analyses against the four proteins of receptor dataset. Finally, we identified eight hits that exhibited binding with selected proteins of Plasmodium falciparum. In this study, we have shortlisted compounds by features of our pharmacophore model that also showed suitable binding orientations with the binding sites of receptor dataset. We propose that this knowledge will be useful in identifying the chemical features of anti- malarial inhibitors and also help in designing novel anti- malarial drugs with improved activity. As pointed out by Chou et al. [70] and demonstrated in a series of recent publi-

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cations [18, 29, 30, 71, 72], user-friendly and publicly acces- sible web-servers represent the future direction for developing practically more useful models and prediction methods or demonstrating novel findings. We will make an attempt in our future work to provide a web-server for the approach and findings presented in this paper. The current study extends our previous bioinformatics knowledge for designing new drug candidates [73, 74].

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest in the current study.

ACKNOWLEDGEMENTS

Authors are thankful to Comsats Institute of Information Technology, Islamabad for providing computational platform for carrying out experiments.

ABBREVIATIONS

PDB = Protein Data Bank

dUTPase = Deoxyuridine 5'-Triphosphate Nucleo- tidohydrolase

2D = 2 Dimensional

3D = 3dimensional

ADMET = Absorption, Distribution, Metabolism and Excretion

VS = Virtual screening

HBA = Hydrogen bond acceptor HBD = Hydrogen bond donor

AR = Aromatic group

HD = Hydrophobic group

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Received: September 26, 2014 Revised: February 26, 2015 Accepted: March 01, 2015