Multicomplex-based pharmacophore modeling ...

Journal of Molecular Graphics and Modelling 75 (2017) 413–423

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Multicomplex-based pharmacophore modeling coupled with molecular dynamics simulations: An efficient strategy for the identification of novel inhibitors of PfDHODH Anu Manhas a , Mohsin Y. Lone a , Prakash C. Jha b,∗ a b

School of Chemical Sciences, Central University of Gujarat, Gandhinagar-382030, Gujarat, India Centre for Applied Chemistry, Central University of Gujarat, Gandhinagar-382030, Gujarat, India

a r t i c l e

i n f o

Article history: Received 6 February 2017 Received in revised form 17 April 2017 Accepted 18 April 2017 Available online 11 May 2017 Keywords: Malaria Dihydroorotate dehydrogenase Pharmacophore Docking Drug-likeness Molecular dynamics

a b s t r a c t Enormous efforts have been made in the past to identify novel scaffolds against the potential therapeutic target, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH). Fourteen different organic molecules have been crystallized to understand the structural basis of the inhibition. However, the pharmacophoric studies carried out so far, have not exploited all the structural information simultaneously to identify the novel inhibitors. Therefore, an attempt was made to construct the pharmacophore hypotheses from the available PfDHODH structural proteome. Among the generated hypotheses, a representative hypothesis was employed as a primary filter to list the molecules with complimentary features accountable for inhibition. Moreover, the auxiliary evaluations of the filtered molecules were accomplished via docking and drug-likeness studies. Subsequently, the stability of the protein-ligand complex was evaluated by using molecular dynamics simulations (MDs). The molecular details of binding interactions of the potential hits were compared with the highly active experimental structure (5FI8) to seek the more potent candidates that can be targeted against PfDHODH. Overall, the combination of screening and stability procedures resulted in the identification of three potent candidates. The drug-likeness of these molecules lie within the acceptable range and consequently increased the opportunities for their development to new anti-malarials. © 2017 Elsevier Inc. All rights reserved.

1. Introduction Malaria is among the world’s deadliest infectious diseases and at present one of the prime causes of mortality [1]. The recent rise of multidrug-resistance has intensified the need of developing new drugs with novel mechanisms of action [2,3]. However, most of the clinical trial antimalarials are targeted against the de novo pyrimidine biosynthetic pathway [4] primarily due to their role in the synthesis of genetic materials [5]. Moreover, the incapability of Plasmodium species in recovering pyrimidines makes the pathway as an attractive choice [6]. One of the key enzymes of this pathway is flavin mononucleotide (FMN)-dependent, dihydroorotate dehydrogenase (DHODH). This enzyme catalyzes the oxidation of l-dihydroorotate (L-DHO) to orotate, the fourth step in the biosynthesis of pyrimidines. Beyond the genetic evidence for its essentiality [7], PfDHODH has been validated and characterized as a novel target for the development of antimalarials [8–10].

Given this importance, numerous crystal structures of PfDHODH have been reported with the different inhibitors [9,11–19]. Various computational, biochemical and structural approaches have been exploited to discover potent and selective inhibitors [10,20–25]. However, the computational strategies opted so far have not incorporated and evaluated all the interactions patterns simultaneously. Therefore, multicomplex-based pharmacophore modeling approach was employed in the present study. This protocol has yielded a top scored hypothesis, which was used as a primary filter against SPECS natural product database. Subsequently, the identified compounds were subjected to docking and molecular dynamics simulations (MDs) to prioritize the candidate molecules that can be targeted against PfDHODH. We anticipate the study will advance the knowledge of targeting PfDHODH using natural compounds and highlight the structurally diverse drug candidates. 2. Materials and methods 2.1. Selection and preparation of protein-ligand complexes

∗ Corresponding author. E-mail address: [email protected] (P.C. Jha). http://dx.doi.org/10.1016/j.jmgm.2017.04.025 1093-3263/© 2017 Elsevier Inc. All rights reserved.

All the PfDHODH complexed with different inhibitors were retrieved from the RCSB Protein Data Bank (PDB) [26]. However,

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the macromolecule–ligand complexes with IC50 activity measures were chosen for the study (ESI Table 1). For the different crystallographic resolutions of the same protein–ligand (4RXO and 5BOO), the one with highest resolution was kept. Subsequently, the selected complexes were prepared by using Protein Preparation Wizard of Accelrys Discovery Studio (DS) version 4.0 (Accelrys, San Diego, USA) [27]. The preparation incorporates the missing atoms, eliminates the crystallographic water, models the missing loops, removes the alternate conformations, insert the hydrogen atoms, assign the acid dissociation constants (Kd ) and protonate the tritrable residues at physiological pH. Additionally, visual examinations were also made for the structural issues. 2.2. Superimposition and pharmacophore development Prior to the common hypothesis pharmacophores (CHP) generation, all the prepared protein–ligand complexes were superimposed via structure comparison tool MatchMaker [28] of USCF (University of California, San Francisco) Chimera 1.10.1 program [29]. The PfDHODH–Genz-669178 complex (4CQ8) was chosen as a reference for the superimposition, on the basis of highest crystallographic resolution. Moreover, the sequence alignment was carried out by using Needleman–Wunsch algorithm [30] and BLOSUM62 [31] scoring matrix with gap opening and extension penalty of 12 and 1 respectively. Rationale behind the superposition was to generate a single coordinate file, integrated with all the essential interactions of the macromolecule–ligand complexes. Consequently, the bioactive conformers of the inhibitors were extracted from the superimposed single coordinate file and subjected for the generation of CHPs. The LUDI built–in program of DS [27] was utilized to transfigure the protein–ligand interactions into the catalyst supported features viz. hydrogen–bond acceptor (HBA), hydrogen–bond donor (HBD), hydrophobic (HY), hydrophobic aliphatic (HY–AL), hydrophobic aromatic (HY–AR), positive ionizable (PI) negative ionizable (NI) and ring aromatic (RA). Additionally, interfeature distance was set to 2 Å and the conformation flexibility was restricted before the generation of CHPs. All the obtained hypotheses were subjected for the different validation procedures in order to list the representative hypothesis.

out by using Ligand Pharmacophore Mapping module of DS [27] to list the capture actives and inactives. The rationale behind the EF and GH studies was to recognize the experimental actives and to evaluate the goodness of hit exhibited by the pharmacophores hypotheses respectively. 2.4. Database preparation and virtual screening Prepare Ligand protocol of DS [27] was used to prepare the SPECS natural product database [42] composed of 813 phytochemicals. This protocol eliminates the structural duplicates, generate the 3D conformers, isomers, tautomers and other user specified functions. A CHARMm force field [41] based BEST conformational search method was employed at default settings for the conformer generation. The prepared database was then screened over the selected hypotheses using Ligand Pharmacophore Mapping module of DS [27]. At default parameters, the flexible mode of pharmacophore fitting was employed to prioritize the natural compounds for the docking studies. 2.5. Molecular docking and its validation FlexX [43,44], an incremental construction (IC) algorithm [43] based module of LeadIt 2.1.8 program [45] was exploited for the docking studies of the screened molecules. The algorithm incrementally assembles the ligand in the binding pocket and thereby introduces conformational flexibility to the ligand. Moreover, the module utilizes the modified Böhm’s scoring function [46] for the calculation of free binding energy (G) of the complex. Prior to docking, the protocol was validated via redocking of the co-crystallized ligand of the selected protein-ligand complex (5FI8). The selection was made on the basis of highest inhibitory activity (0.0063 ␮M) available from the PfDHODH structural proteome. A receptor description file was prepared and the active site was defined around 6.5 Å radius, from the centre of co-crystallized ligand before the commencement of docking. Moreover, top 50 poses were enumerated for each molecule and the best docked conformation of the selected molecules was used for the MDs. 2.6. Molecular dynamics simulations

2.3. Pharmacophore validation The validation of pharmacophore is generally performed to evaluate the quality of the model [32]. However, an essential attribute of a reliable pharmacophore model is to predict the internal and external dataset appropriately [33]. Therefore, all the CHPs obtained, were initially validated by using a test set comprising of experimental actives (59) (ESI Table 2) and presumed inactives (53). The mixing of actives and presumed inactives has been documented as a successful approach for the pharmacophore validation [34–36]. Moreover, the performance of constructed pharmacophore models were envisioned by generating the receiver–operator characteristic (ROC) curves [37]. However, the selection of hypotheses for the enrichment factor (EF) and Güner–Henry (GH) scoring examination [38] were made on the basis of sensitivity, specificity and the area under curve (AUC). To accomplish EF and GH validations, a database of 1106 molecules was made by using 12 experimental actives (ESI Table 3) and 1094 corresponding decoys. The experimental actives were collated from the literature [9,11–19] however, the decoys (molecular descriptor based) were obtained from the ZINC [39] database, by using Decoy Finder [40] at default parameters. Prior to mapping, conformer generation of the database was furnished by a CHARMm force field [41] based FAST conformation generation method, using Prepare Ligand protocol of DS [27] at default parameters. Subsequently, the mapping (flexible) of prepared database was carried

All the simulations were executed in Maestro–Desmond Interoperability tool (academic) [47,48], using OPLS-2005 force field parameters [49]. Prior to simulations, the system was solvated via TIP3P [50] water model in 10 Å3 orthorhombic box and neutralized by incorporating the Cl− ions. Long-range electrostatic interactions were evaluated by smooth particle-mesh Ewald (PME) approximation [51] and the cut off of 9 Å was used for non-bonded interactions using MSHAKE algorithm [52]. Initially, the system was relaxed by exploiting the default six step relaxation protocol, consisting of two steps of minimization (restrained and unrestrained) and four steps of equilibration processes. The equilibration (short MDs runs) was accomplished by using Berendsen thermostat [53] in NVT and NPT ensembles (10 K and 12 ps) with the restraints on the heavy solute atoms. Remaining steps (two) were furnished by using Berendsen thermostat [53] in NPT ensemble (300 K and 24 ps) with and without restrain on heavy solute atoms. Subsequently, a 4 ns production run was carried out on each system with NPT ensemble (300 K and 1 atm) using Nose-Hoover thermostat [54] and Martyna-Tuckerman-Klein barostat [55]. The energies and coordinates were recorded at 1.2 ps and 1 ps respectively. A multiple time step RESPA integrator [56] was utilized for solving the bonded (2.0 fs), short-range non-bonded (2.0 fs) and the long-range non-bonded interactions (6.0 fs). Moreover, the properties like root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), radius-

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Fig. 1. Superimposed protein-ligand complexes of PfDHODH.

of-gyration (rGyr), polar surface area (PSA) were envisioned for the stability of protein-ligand complexes. Based on the dynamic behaviour, the simulation run of the promising candidate was extended upto 20 ns (using the above mentioned parameters) in order to scrutinize the stability of the essential interactions. 2.7. Drug-likeness prediction The drug-likeness [57] of the dynamically stable natural compounds was evaluated by employing Lipinski [58] and Veber [59] rule. To accomplish the task, eight descriptors viz. number of hydrogen bond donor (HBD), acceptor (HBA), lipophilicity (AlogP), molecular weight (MW), number of rotatable bonds (NRB), polar surface area (PSA), sum total of HBD and HBA and the number of aromatic rings (AR) were calculated. All the descriptors were computed at default parameters by using Prepare or Filter Ligands protocol of DS [27]. 3. Results and discussion The multiple-complex based pharmacophore models were constructed by exploiting a set of 13 crystallographic structures of PfDHODH in complex with different organic molecules (ESI Table 1). However, prior to pharmacophore generation, all the protein-ligand complexes were superimposed keeping PfDHODH–Genz-669178 complex (4CQ8) as a refrence (Fig. 1). The rationale behind the superposition was to transform the co-ordinates of both receptor and inhibitors to a common frame. Subsequently, the CHPs were constructed by exploiting all the bioactive ligands, extracted from the superimposed protein–ligand complexes. The generated hypotheses identified two common features (HY), among the four developed functional features. In addition, the spatial distribution of the identical features were found to be similar for all the hypotheses. However, all the generated hypothese were subjected for the validation in order to list the representative hypothesis for the virtual screening. 3.1. Pharmacophore validation The validation of pharmacophore is generally performed to evaluate the quality of the model [32]. However, an essential attribute of a reliable pharmacophore model is to predict the internal and external dataset appropriately [33]. Therefore, all the hypotheses obtained from the PfDHODH structural proteome were initially validated by using a test set, comprising of experimental actives (59) (ESI Table 2) and presumed inactives (53). The test set validation was exercised to seek the sensitivity and specificity of the constructed models. It is evident from the ESI Table 4, that the hypothesis 1 and 2 were able to discriminate between the actives and presumed inactives. This scenario can be seen from the corre-

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sponding sensitivity and specificity values (ESI Table 4). Moreover, the rate of accuracy of the models was also predicted by generating ROC plots (Fig. 2). Despite the use of decoys (molecular descriptor based), the efficiency of the hypothesis 1 and 2 was found to be 79.3% and 79.8% respectively. Based on the sensitivity, specificity and AUC, the hypothesis 1 and 2 were chosen for further assessment using GH and EF studies. GH scoring method was used to ensure the quality and performance of the selected hypotheses. A statistical view was offered for the robustness of chosen models by calculating few variables viz. percentage of known actives, true positives and recall of actives. Moreover, false positives and false negatives were also calculated to demonstrate the goodness of hit exhibited by these models. It is evident from Table 1 that the percentage yield of actives (%A) for the hypothesis 1 and 2 is 56.25% and 69.23% respectively. The comparatively low%A can be accredited to the failure of chemical diversity inside the protein pocket, owing to the smaller number of compounds in the database. However, a GH score of 0.7–0.8 indicates the successful goodness of hit [60] exhibited by model in the virtual screening experiments. Despite the non–inclusion of random decoys, the hypothesis 2 fell within the goodness of hit range (Table 1). On contrary, the relative EF–values are used to connote the reliability of virtual screening approach [61,62], owing to the random searching based recognition of actives. The results clearly indicates that the probability of picking of actives is 51.84 and 63.81 times more than that of inactives from the dataset, for the hypothesis 1 and 2 respectively (Table 1). Despite the fact that EF studies were carried out with the limited number of decoys, a significantly good enrichments were observed for both the models. However, a conveniently relevant picture can also be present by scrutinizing the retrieved actives (Ha) and total active hits (A) corresponding to these models. It is apparent that the 75% of actives were correctly identified by these hypotheses whereas; extremely low percentage of 0.64% and 0.37% inactives were retrieved by the hypothesis 1 and 2 respectively. Based on the above validations, hypothesis 2 was chosen for the virtual screening of SPECS natural product database [42]. 3.2. Pharmacophore–based virtual screening The virtual screening of SPECS natural product database [42] was carried out with an aspiration to prioritize the candidate molecules, with essential complimentary features, accountable for the activity. Therefore, hypothesis 2 (ESI Fig. 1) was employed as a 3D search query to reduce the virtual chemical space of SPECS natural compounds [42]. An aggregate of 241 molecules were retrieved by the chosen hypotheses and subjected to the molecular docking studies. 3.3. Molecular docking studies Molecular docking studies were carried out to exploit the ligandreceptor information for searching the similar chemical entities that can bind strongly with the PfDHODH. However, prior to docking, all the co-crystallized ligands of PfDHODH structural proteome (Table 2) were redocked into the corresponding binding pocket. The aspiration was to look for the protein-ligand complex that can be used to furnish the docking of screened molecules as well as to check the robustness of the protocol. It is obvious from the results (Table 2) that all the redocked conformations are in good agreement with the corresponding bound conformations. To be more precise, the RMSD was found to be less than 1.5 Å for all the selected complexes. These results vindicate that all the exploited proteinligand complexes can be used for the docking studies. However, the final choice was made on the basis of experimentally determined half maximal inhibitory concentration (IC50 ). Therefore, all

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Table 1 GH scoring and EF parameters for the pharmacophore validation. S. No.

Parameters

Pharmacophore 01

Pharmacophore 02

1 2 3 4 5 6 7 8 9 10

Total molecules in database (D) Total Number of active in database (A) Total Hits (Ht) Active Hits (Ha) % Yield of actives [(Ha/Ht) × 100] % Ratio of actives [(Ha/A) × 100] Enrichment factor (EF) [(Ha × D)/(Ht × A)] False negatives [A-Ha] False positives [Ht-Ha] Goodness of hit score

1106 12 16 9 56.25 75 51.84 3 7 0.60

1106 12 13 9 69.23 75 63.81 3 4 0.70

Table 2 Validation of the docking protocol. Compound Number

PDBs

IC50 (␮M)

Docking Score (kcal/mol)

RMSD (Å)

1 2 3 4 5 6 7 8 9 10 11 12

5FI8 5TBO 5DEL 4ORM 3O8A 3SFK 3I65 3I68 4CQ8 3I6R 4CQ9 4CQA

0.0063 0.0530 0.0160 0.0200 0.0220 0.0380 0.0470 0.0560 0.0800 0.2800 3.5000 13.5000

−19.60 −21.94 −22.98 −26.32 −23.75 −24.02 −21.34 −25.34 −21.02 −16.73 −17.55 −9.08

0.905 0.846 1.018 0.781 0.856 1.053 0.626 0.886 0.899 1.090 1.123 1.157

the filtered molecules (241) were docked into the crystal structure of PfDHODH-DSM422 complex (5FI8) for further analysis. Based on the docking score of the reference ligand (−19.60 kcal/mol, DSM422), the top three hits were selected from the 73 docked molecules (Table 3, ESI Table 5 and ESI Fig. 2). Beyond the energetics, all the selected molecules (spec1, spec2 and spec3) were found to possess reference functionalities (Fig. 3). Moreover, the residues (His185/Arg265) accountable for inhibition were found to interact with the reference functionalities. This indicates that all the three hit molecules (spec1, spec2 and spec3) fits well in the binding pocket of PfDHODH and therefore will exhibit enhanced biological activity. However, to ensure the effective inhibition, binding potential of these compounds within the biological environment was envisioned via MDs. 3.4. Molecular dynamics simulations Dynamic behaviour of the systems under investigation viz. PfDHODH in complex with spec1, spec2 and spec3, along with the reference (5FI8) was carried out for a course of 4 ns. The stability of

all the systems was examined by calculating RMSD and RMSF. It is obvious from Fig. 4 that the protein backbones of all complexes possess RMSD less than 2 Å. This connotes that no large conformational changes have taken place throughout the simulation run. However, the reduction in the RMSF values (Fig. 5, ESI Fig. 3) of the interacting amino acids indicated the binding of the ligands. Based on RMSD and RMSF, it can be concluded that the binding of the ligands have produced no structural variation in the protein. Moreover, to evaluate the stability of ligand in the active site, ligand properties viz. RMSD, RMSF, rGyr, SASA and PSA were computed (Figs. 6 and 7, ESI Figs. 4 and 5). Subsequently, these properties were compared with the reference (DSM422) in order to prioritize the compounds that be targeted against PfDHODH. It is important to note that the fluctuation in ligand positional RMSD of DSM422, spec1, spec2 and spec3 are in the window size of 0.2–0.4 Å, 1.0–1.5 Å, 1.6–2.4 Å and 0.4–0.6 Å respectively (Fig. 6). The conformational changes in 7Bromo-tetrahydro-naphthalene and methoxy moieties of DSM422 and spec3 are respectively accountable for the slight fluctuation of ligand RMSD (Fig. 6). However, the change in ligand RMSD for the spec2 was due to the fluctuating pi–pi interactions of Phe227 with

Fig. 2. ROC plots of the top two pharmacophore hypotheses.

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Table 3 Docking parameters of the top 3 hits. S. No.

Compound

Docking Scorea

Matcha

Lipoa

Ambiga

Clasha

Rota

Matchb

1 2 3

Spec1 Spec2 Spec3

−22.37 −21.42 −20.61

−20.52 −18.20 −16.34

−14.41 −17.98 −11.67

−6.64 −7.16 −3.79

5.41 9.52 4.38

8.40 7.00 1.40

21 22 23

a b

Values in kJ/mol. Number of matched groups in initial and dock poses.

Fig. 3. 2D interaction mapsof (A). 5FI8 (DSM422), (B). spec1, (C). spec2, (D). spec3 with PfDHODH.

dimethyl-phenyl group (Fig. 6). Moreover, the variation in RMSD of spec1 was due to alternate hydrophobic interactions of hydroxylethoxy group with Phe188 and Phe227, eventually followed by the solvent interactions with methoxy-phenyl group after 1 ns (Fig. 6). RMSF for the ligand atoms was calculated for each molecule to check the strength of binding interactions (Fig. 7). The fluctuations were found to be in the window size of 0.51–0.86 Å, 0.73–1.65 Å, 0.74–2.20 Å and 0.62–1.10 Å for DSM422, spec1, spec2 and spec3 respectively. Interestingly, the RMSF plot of spec3 showed a single fluctuation thus depicts that all the atoms (except methoxy) were concealed deep into the binding pocket, therefore confirming the tight binding interactions.

The dynamic behaviour of all the essential protein-ligand interactions was visualized by exploiting the interaction plots. It is obvious from these plots, that the His185 and Arg265 are involved in the hydrogen bonding with the triazolo-pyrimidine group of DSM422 and pyrido-indole moiety of spec3. Moreover, the percentage of these interactions for the course of 4 ns was 92% (His185) and 64% (Arg265) in DSM422 and 93% (His185) and 96% (Arg265) in spec3. Owing to the presence of aromatic rings adjacent to the pyrrole moiety in spec3, favours the delocalization of charge from the pyrrole nitrogen (Fig. 8). This effect encourages the generation of dipole in the molecule which endorse the formation of energetically favourable H-bonding interactions. These attributes are slightly fewer in case of DSM422 due to the presence of single

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Fig. 4. RMSD of protein backbone in complex with (A). DSM422, (B). spec1, (C). spec2, (D). spec3.

aromatic moiety (triazolopymiridine) (Fig. 8). Of all the essential interactions [6], only His185 was found to establish hydrogen bonding interactions with the indole group of spec2 (83%) and hydroxyl-phenyl-ethanone moiety of spec1 (91%) (ESI Fig. 4). Few additional interactions (ESI Fig. 4) have also been observed for the above mentioned systems. One of the essential ligand properties that corroborate human intestinal absorption (HIA), is PSA [63]. Except spec1 (150–165 Å2 ), a significantly good PSA in the range of 50–55 Å2 , 60–70 Å2 and 60 Å2 was observed for spec3, spec2 and DSM422 respectively (ESI Fig. 5). From the above discussion, it can be concluded that spec3 is the most probable candidate that can be targeted against PfDHODH. To further scrutinize the stability of the essential interactions (His185 and Arg265), the simulation run was extended upto 20 ns. Similar kind of behaviour in terms of RMSD, RMSF, and hydrogen bonding interactions was observed (ESI Figs. 6–9). This signifies that spec3 can be considered as a potential candidate (hit) that can be targeted against PfDHODH. 3.5. In-silico assessment of drug-likeness In-silico drug-likeness is often used as an initial tool to exclude the molecules likely to present unacceptable range of pharmacokinetic properties. With this aim, the dynamically inspected molecules (spec1, spec2 and spec3) were evaluated and the results

obtained are presented in Fig. 9. It is obvious from the results that spec1, spec2 and spec3 behaved well within the limits provided by Lipinski [58] and Veber [59]. From the plots itemize in Fig. 9, MW of all the compounds was found to be less than 500, which is considered desirable for the non-optimized hit compounds. Among the aforementioned descriptors, the oral absorption (AlogP) for the entire compounds was found to be within the lipophilicity domain. However, it is recommended that the lipophilicity should lie within the range of 1–3 (2.6), to attain the desired physico-chemical properties during drug development process [64]. In this respect, spec1 (2.89) and spec3 (2.44) were found close to the desirable range. The number of ARs (≤3) has restricted the development of drug candidates in the past [65] therefore, visual inspection was made for all the selected candidates. It was observed that the spec1, spec2 and spec3 possess two, three and three ARs respectively. Overall, the computed descriptors indicated that these natural compounds can be listed as the potential candidates in terms of drug-likeness. Moreover, it is well documented in the literature, that the compounds obeying these rules are supposed to increase the chance of development to new anti-malarial drugs [66]. In addition, the oxygenated chalcones and alkaloids to which these compounds belongs, have been classified as an antiplasmodial agents [67,68]. Beyond the above descriptions, the detailed study regarding the importance of drug-likeness in prioritizing the natural compounds

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Fig. 5. RMSF of protein residues in complex with (A). DSM422, (B). spec1, (C). spec2, (D). spec3.

Fig. 6. Pictorial representation of the groups responsible for the fluctuation (Left) and RMSD plot of ligand in the active site (Right) (A). DSM422, (B). spec1, (C). spec2, (D). spec3.

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Fig. 7. RMSF of the ligands (A). DSM422, (B).spec1, (C).spec2, (D).spec3.

Fig. 8. Representation of the interactions of DSM422 and Spec3 with the PfDHODH.

for anti-malarial drug development has been extensively explored [68]. 4. Conclusions The aspiration behind the present study was to identify druglike molecules that can effectively inhibit the PfDHODH. In this pursuit, multicomplex-based pharmacophore modeling coupled with docking and MDs were exploited. Based on the rigorous validation and expertise, we have identified a set of potential candidates (spec1, spec2 and spec3) from the SPECS natural product database that can be targeted against PfDHODH. Despite the higher docking energies of the potential candidates, only spec3 has shown

the optimal dynamic behaviour in comparison to the reference (5FI8). Moreover, the drug-likeness analysis of spec3 suggested its potential for further drug development. The importance of spec3 also dwells in its scaffold, which may serve as a template for the design of novel series of potent inhibitors. Therefore, employing this protocol for screening the other compound repositories would be very useful for the scaffold hoping of potential candidates. We recommend experimental evaluation to further validate these computational findings. Competing interest The authors have declared no competing interest.

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Fig. 9. Graphical representation of drug-likeness features.

Acknowledgements Anu Manhas and Mohsin Yousuf Lone acknowledge the University Grants Commission (UGC), Govt. of India for financial assistance. Mohd. Athar is highly acknowledged for his assistance throughout the work. PCJ acknowledge Central University of Gujarat-Gandhinagar (CUG) for providing basic infrastructure and

facilities. PCJ also acknowledges SERB, DST for project grant through grant number EMR/2016/003025.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jmgm.2017.04. 025.

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