Field-based 3D-QSAR modeling and docking studies to explore potent anti-

malarial compounds targeting plasmepsin-2 of Plasmodium falciparum

Tabish Qidwai*

*FoBT, IBST, SRM University, Lucknow 225003, India

E mail:tabish.iet@gmail.com Phone no. +91-09451025175

Abstract

In the current study, a field based 3D-QSAR modeling and molecular docking have been

performed. The developed 3D-QSAR model has been found with R2=0.9457, cross validated

R2 = 0.6431 and R2 for test set compounds (Q2) =0.865. The predictive potential of the model

was evaluated through external validation using test set compounds. The predicted activity

obtained through the model showed good correlation with experimental activity. The values

of Cross-validated R2 > 0.5 and R2 for external test set (Q2), also named R2 predicted > 0.6

indicate that the reported model satisfies the criteria for evaluation of predictive ability of

QSAR model as recommended. Contour maps predicted potentially important structural

requirement to improve anti-malarial activity. Structural features were correlated in terms of

steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor

interactions. It has been found that steric and hydrophobic properties of substituent groups

play crucial roles in bioactivity of the studied compounds while hydrogen bonding

interactions show no obvious impact. Further, molecular docking was performed to identify

the ligand interaction with the active site of known anti-malarial target plasmepsin-2 of P.

falciparum. This study provides valuable information to guide the design of potent and

selective anti-malarial agents targeting plasmepsin-2 of parasite.

Keywords: Field based QSAR; anti-malarial drug; molecular docking; plasmepsin-2.

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1. Introduction

In spite of high throughput rigorous research, Plasmodium falciparum malaria remains a

major cause of deaths worldwide [1]. Prevalence of disease is more in many developing

countries. There are four main Plasmodium species that infect humans, P. falciparum,

P.vivax, P. malariae and P. ovale. Among these, P. falciparum (Pf) causes the most fatal

form of disease and produces maximum mortalities [2]. Globally, around 250 million clinical

cases and 1 million deaths are reported annually [1]. Development of drug resistance in

parasite, low efficacy and safety issues related to majority of the existing anti-malarial drugs

have created trouble in treatment of the disease. Further, vaccine development against malaria

is under clinical trials. Artemisinin based combination therapy (ACT) is used against

uncomplicated P. falciparum malaria as a first line treatment. It is well accepted by patients

and rapidly kills entire blood stages of the parasite as compared to other anti-malarial drugs

[3] nevertheless the parasite is being developed resistance against artemisinin [4,5].

To effectively overwhelm the problem of drug resistances, it appears important to focus on

compounds with new structural classes and new mechanisms of action. The existing

circumstances prompted to explore effective derivatives targeting to parasite specific

pathways. During erythrocytic stage of malaria infection, the host hemoglobin degradation by

the parasite is necessary for its survival. The studies suggested that parasite is not capable of

multiply in the human erythrocytes in vitro in the occurrence of aspartic protease inhibitors

[6, 7]. Enzymes of this degradation pathway are being explored as promising anti-malarial

drug targets [8, 9]. Hence, exploration of drug like compounds targeting enzymes of

aforementioned pathway would be highly attractive.

Currently, quantitative structure-activity relationships (QSAR), pharmacophore modeling and

docking are frequently being used in drug designing. Field based 3D-QSAR approaches such

as comparative molecular field analysis (CoMFA) and comparative molecular similarity

2

indices analysis (CoMSIA) are helpful to correlate the bioactivity of compounds with

structural descriptors. This field based 3D-QSAR model explored the structural requirement

of high potency P. falciparum inhibitors targeting parasite proteases involved in hemoglobin

degradation.

In the present study, a series of 42 compounds derived from tertiary amine and 4-

aminoquinoline were retrieved from the literature and used to explore the correlation of their

structure with inhibitory activity through field based QSAR model using Gaussian field. The

developed model was used to identify the structural features crucial for improving their

activities and facilitate the design of new more effective anti-malarial compounds. Further,

the molecular interactions of compounds with the active site of plasmepsin-2 are examined

using molecular docking. The results provide insight into the contribution of specific

structural moieties of the compounds towards their activity on plasmepsin-2, which allowed

selecting the most potent compounds for use as reference and seed structures to generate

novel ligands. Overall results may be useful for design and discovery of novel and potent

anti-malarial agents against the parasite.

2. Materials and methods

2.1. 3D-QSAR modeling

Field based 3D-QSAR modeling was carried out using Maestro 10.7 of the Schrodinger drug

discovery suite release 2016-4 [10].

2.2. Ligand retrieval

In the current study, a series of 42 compounds were retrieved from the literature [11-14].

Structures of compounds are based on tertiary amines and 4-aminoquinoline. The activities of

compounds were expressed in nano-molar IC50 (fifty percent inhibitory concentration) in the

literature. The nano-molar IC50 values of all compounds were transformed in terms of pIC50

and have been represented (Supplementary Table1). Data of compounds was separated into

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training (35) and test set (7) by means of random selection. The compounds in the collection

are structurally diverse and have diverse activity range. For good reliability of model the

compounds should cover four log order activity range. The studied compounds cover four log

order difference of activity (pIC50 value of 5.069 to 8.444) (Supplementary Table1). All the

compounds of training set are not from the same assay, however compounds of test set are

from the same assay.

2.3. Ligand preparation and optimization

The structures of compounds were prepared by Chem draw software. Further optimization of

compounds has been performed using the LigPrep module of the Maestro [10]. The LigPrep

module generated tautomer with the OPLS_2005 force field. The optimized 3D- structures

were saved in maestro format.

2.4. Ligand alignment

The optimized ligands were subjected to flexible ligand alignment. Flexible ligand alignment

does a quick conformational search. The geometry of each of the structure was minimized to

find the final geometry for 3D-QSAR modeling. For each compound, conformation searching

was carried out by means of simulated annealing and energy minimizations by the

OPLS_2005 force field. For all compounds, the minimum corresponding to putative binding

conformation of lower energy was explored and such ligands were included in 3D-QSAR

model development.

2.5. Shape screening

After ligand alignment shape screening was done for finding the best shape overlapping

conformer for each ligand. Shape screening panel aligns the molecules to a query molecule

on the basis of the shape or atom-type weighted shape. The method involves alignment of the

best conformers from a conformational search.

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2.5. 3D-QSAR model building

Finally shape screened ligand conformations were used to build field based QSAR model.

Ligands were added and training and test set compounds were assigned. 3D-QSAR partial

least-squares (PLS) models have been made with four maximum PLS factors in regression

model. Field-based QSAR (FQSAR) models are not exactly CoMFA and CoMSIA methods.

FQSAR is an implementation with a specific set of parameters. Subsequently different names

have been used: Force Field for CoMFA-like models and Gaussian for CoMSIA like models.

Gaussian includes five Gaussian fields for the model. The Lennard-Jones steric potentials are

used from the OPLS_2005 force field.

2.6. Molecular docking

To find the possible binding mode of the ligands with plasmepsin-2 enzyme, docking was

performed. Protein-ligand docking was performed using Autodoc Vina (PyRx) [15]. The 2D

ligand interaction diagram of docked poses was pictured by Accelrys Discovery Studio Client

3.5. The P. falciparum plasmepsin-2 co-crystallized with inhibitor was retrieved from the

Protein Data Bank (PDB). PDB structure with following information is used as anti-malarial

target for docking:

PDB code: 2IGY

Resolution: 2.6 Å

R-Value Free: 0.264

R-Value Work: 0.211.

Re-docking

To check docking protocol re-docking was carried out. Bound ligand was removed from the

crystal protein. Then ligand was re-docked with the crystal protein. Docked and crystal pose

of ligand was analysed.

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3. Results and Discussion

3.1. Statistical predictions of Field based QSAR

Field based 3D-QSAR (FQSAR) model explores the structural features required for

inhibition potency. Field fractions or Atom type fractions are used to evaluate the molecular

features that are mainly responsible for the activity of the molecule. FQSAR partial-least

square regression model has been constructed by means of the previously generated sets of

alignments with the best shape overlapping conformer for each compound. The FQSAR

method is similar to CoMFA [16] and CoMSIA [17] with a little important difference. Firstly,

steric and electrostatic fields in FQSAR are generated with the OPLS_2005 [18, 19] force

field as opposed to the Tripos force field used in CoMFA. Secondly, FQSAR takes a 30

kcal/mol threshold for both the van-der-Waals and electrostatic interactions, and excludes

grid points that are too close to training set atoms. Lastly, FQSAR uses scaling based on

maximum potential divided by standard deviations of the field over entire training set [20].

Only, electrostatic and steric interaction fields are calculated in CoMFA, while five Gaussian

fields (steric, electrostatic, hydrophobic, H-bond acceptor and H-bond donor) are calculated

in CoMSIA. Overfitting was checked by calculating ratio of Root-mean-square error (RMSE)

in the test set over the standard deviation of the training set and ensuring the value was lesser

than 1.5 by picking a model with a smaller number of partial least square factors [20]. The

compounds (35 compounds of the training set for model construction and 7 compounds of the

test set for model validation) have been shown proper alignments. All the compounds of test

set and training set are not from same literature.

3.1.1. Training set statistics

In the present study, the obtained 3D-QSAR model has shown good statistical parameters,

model expressed 94% variance (R2=0.9457). The value of R2 for the regression (coefficient of

determination) 0.94, means that the model accounts for 94% of the variance in the observed

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activity data. The result of the study showed that experimental and predicted activities

presented a close agreement to the regression line as showed by the fitness graph (Fig. 1).

Cross-validated R2 value is 0.6431 which predicted using leave-one-out approach. The ratio

of the model variance to the observed activity variance is degree of freedom (F). Its value is

134.9 for the developed model, which show a good internal predictivity of the model. Large

values of F indicate a more statistically significant regression. Standard deviation of the

regression and stability of model have been found 0.2106 and 0.759 respectively. High value

of stability indicates good predictability of model (a model that is not sensitive to omissions

from the training set).

Golbraikh and Tropsha 2002 [21] suggested that the predictive ability of a QSAR model can

only be estimated using an external test set of compounds. They suggested criteria for a

QSAR model to have high predictive power. High value of cross-validated R2 and correlation

coefficient R between the predicted and observed activities of compounds from an external

test set should be close to 1. Cross-validation is used to measure predictive ability of model

and explore the overfitting also. It has been stated that, higher the number of descriptors

relative to the number of compounds, the higher is a chance to select those of them that give

high cross-validated R2 values [21]. Over-fitting refers to the phenomenon in which a

predictive model may well describe the relationship between predictors and response, but

may subsequently fail to provide valid predictions for new compounds. Generally, model

overfitting is suspected when the R2 value from the original model is significantly larger

(25%) than the cross-validated R2 value. Difference of R2 and Cross validated R2 value

should not be more than 0.3 [22]. The result of the present study shows difference of R2 and

Cross validated R2 value 0.3 (0.94-0.64=0.30) (Table 1a, 1b). This indicates that reported

model satisfies the criteria of validation as recommended by Golbraikh and Tropsha 2002

[21].

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3.1.2 Test set statistics

Predictive capacity of a model is judged through external validation by using prediction of

activities of test set compounds. The test set statistics include R2 for external validation also

named as R2 predicted or Q2, Root-mean-square error (RMSE) and Pearson-r. RMSE is Root-

mean-square error in the test set predictions. Pearson r value for the correlation between the

predicted and observed activity for the test set. Model validation is carried out through

external validation with the help of test set compounds. The result of study shows R2 for

external validation (Q2) value = 0.865, which was calculated by leave-one-out method (Table

2b). High value of Q2 indicates that model is good and predictive. The model is good If cross-

validated R2 > 0.5 and R2 for external test set named Q2 (R2 predicted) > 0.6 [21]. High value

of cross-validated R2 and correlation coefficient R between the predicted and observed

activities of compounds from an external test set close to 1 are recommended [21]. The result

of the study has predicted R2 value for external validation more than 0.5 as suggested by Roy

and Roy 2008 [23], showing the robustness of the developed model. The overall statistical

parameters of predicted 3D structure and pIC50 model are good and follow the criteria of

predictive model as suggested by authors [21-23].

3.2. Analysis of contour maps

The main importance of 3D-QSAR modeling is to explore correlation of biological activity

with 3D structural features such as electrostatic distribution, hydrophobic distribution,

hydrogen bond (H-bond) forming ability and orientation of chemical groups [24].

The result of the study shows the field contributions of field based model of Gaussian steric,

electrostatic, hydrophobic, H bond acceptor and H bond donor 0.428, 0.092, 0.238, 0.192,

0.05 respectively (Table 1a and Fig.2). A positive region for a Gaussian field means that it is

favorable for the field property, and a negative means that it is unfavorable. For Gaussian

Steric field type, green colour represents positive region and yellow colour represents the

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negative region. The steric field descriptor elucidated 42.8% of the variance whereas the

electrostatic descriptor accounted for 9.2%. Electrostatic contribution is least among others,

which suggested that the electrostatic interactions were not crucial in explaining the

variations in anti-malarial potency of these molecules. Therefore steric field had greater

influence than other field. In order to predict the effect of contours maps, one more active

compound (2) and one less active compound (44) were included. Fig.3 A and B show the

contour maps of steric field. Green colour denotes the positions where steric or bulky group is

favourable for bioactivity and yellow contour denotes disfavored region. The observation of

yellow regions in the steric contour map suggests that the substitution of less bulky group is

favoured and increases the activity. For electrostatic fields, a positive region is favorable for

positive charges, and a negative region is favorable for negative charges. Fig. 3 C and D

represent the electrostatic contour maps in which blue shows the positive effect of an

electronegative group on the activity (presence of an electronegative group at blue positions

will increase the biological activity of compound) whereas red colour represents the regions

where electronegative groups will reduce the activity of the molecule. Fig. 3 E and F

represent the hydrophobic contour maps in which the yellow region denotes to the area where

hydrophobic group is favourable, while the white region represents the area where a

hydrophobic group is unfavorable. H-bond acceptor contour maps, red contours designate

regions where an H-bond acceptor group is favourable whereas magenta is unfavourable for

activity (Fig. 3 G and H). H-bond donor contour maps, cyan contours show regions where an

H-bond donor group is unfavourable for activity (Fig. 3 I and J).

3.4. Molecular docking

Human hemoglobin digestion is a parasite specific catabolic pathway, necessary for the

parasite. Studies suggested that the parasites are not capable to proliferate in those human red

blood cells having presence of aspartic proteases inhibitors [6, 11]. Plasmepsin-2 has been

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identified as one of the four parasite specific aspartic proteases participated in hemoglobin

degradation in acidic food vacuole [6, 11]. Targeting of P. falciparum plasmepsin-2 is highly

attractive for malaria treatment. The purpose of docking is to evaluate the nature of

interactions between target and ligand. The term ‘target’ refers to those proteins to which the

drug directly binds, and which are responsible for the therapeutic efficacy of the drug.

Compounds were docked into the binding pocket of plasmepsin-2. The scores are expressed

as the binding affinity to characterize the interaction (Table 2). High negative value indicates

good interaction. In the present study, the compounds 2 and 11b docked with plasmepsin-2

target and they showed -6.5 and -7.2 (k cal/mole) binding affinities respectively. Compound

11b with binding affinity -7.2 has good interaction with target as compared to compound 2.

Plasmepsin-2 is co-crystalized with ligand 2IGY (A2T). Amino acid residues in plasmepsin-

2 are interacted with A2T via vander Waals, electrostatic and sigma and pi interaction (Fig.

4). The present study showed that the compound 39 is more active than compound 40

however docking result has suggested less interaction of compound 39 (-5.6) for the target as

compared to compound 40 (-6.0). Adeniyi and Ajibade 2013 [25] compared the

appropriateness of Autodock, Gold and Glide for the Docking. The Gold docking results are

showed in terms of the values of fitness which means the higher the fitness the better the

docked interaction of the complexes, while the Glide and Autodock, which are predicted

docking energy score which means the lower the score the better the interaction. They

showed that predictions from Autodock and Gold correlate better with experimental data

[25].

The 2D protein-ligand interaction diagram for compound 39 has been predicted amino acid

residues namely ASP214, GLY36, GLY 216, ILE 32, ILE123, ILE 212, MET15, PHE111,

PHE120, SER 37, THR114, THR217, TYR192 are interacted with vander Waals force of

attraction (Fig.4). SER 218 and Tyr 77 are interacted through electrostatic attraction.

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Similarly, compound 41 is more active than compound 42 but compound 41 has less

interaction for the target. In compound 41 amino acid residues, ASN210, ASN76, ILE133,

ILE212, LEU131, MET75, MET75, PHE294, PRO295, SER 79, SER132, SER37, TYR12,

THR298, VAL 78, VAL 296 are interacted through vander Waals force of attraction. ASN 76

and GLU 74 and are interacted through electrostatic attraction. Compound 10r has shown -

7.6 k cal/mole binding affinity (the most negative value) and hence shown highest interaction

with target. On the contrary, compound 34 is more active than compound 35 and also has

high interaction with target as compared to compound 35. From the docking results it seems

that there is no linear pattern of binding affinity of compounds with target and activity. It may

appear that studied compounds either may target to other proteins or may involve multiple

targets. Table 2 depicts the predicted interaction of potential anti-malarial compounds with

target plasmepsin-2.

3.5 Re-docking

Re-docking was done to validate the docking method. The ligand bound crystal protein

(2IGY) was split using Pymol. Then re-docking was performed and superimposition was

checked. The value of root mean square deviation (RMSD) has been found 0.59 Angstrom.

Validation of docking protocol means we need to take crystallographic complex protein with

ligand in it and perform the docking of the same complex. Then we need to check for the

RMSD values. If docking protocol is able to produce similar docking pose of a ligand with

respect to the biological configuration of the same ligand in the crystal structure of complex

protein then it means docking is validated (Fig.5). Lower the value of RMSD means higher

the accuracy of docking. RMSD values less than 1.5 Angstrom are always wise to consider.

However, it should be kept in mind, that RMSD is not always the best descriptor to choose

docking protocol. Docking poses have to be examined for presence of important interactions

as well. The result of the present study showed that compounds have been found to exhibit

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good interaction with plasmepsin-2 of P. falciparum. This information would be beneficial

for docking based exploration of anti-malarial agents.

4. Conclusions

While a large number of studies are going on to handle the chemotherapy in P. falciparum

infection, the malaria remains a major cause of mortality especially in tropical regions so far.

Novel and effective drugs are highly desirable to control and minimize the problems in

malaria chemotherapy. In conclusion, this study reported statistically authentic 3D-QSAR

model resulting from tertiary amine and 4-aminoquinoline analogues using field-based

approach. The model has good predictive power evidenced by the high values of R2, Q2 and

R2 for external validation. Contour map analysis suggested that steric and hydrophobic

properties of substituents groups have more contribution in activity modulation. The

interactions recognized through docking of ligands with anti-malarial target plasmepsin-2

contributed useful cues. The 3D contour maps predicted through field-based 3D-QSAR

modeling and binding mode between inhibitor and plasmepsin-2 could provide valuable

understanding into design of novel inhibitors. Finally, the results of the study would be

helpful for optimization of above-mentioned class of compounds for better activity and may

be crucial for further lead optimization.

Conflict of interest

No conflict of interest is declared.

Acknowledgement

The author thanks to Professor B.N. Mishra for providing innovative suggestions.

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Table 1a. Statistical parameters of developed field based 3D-QSAR (Gaussian field) model for training set compounds.

Statistical parameters ValueR2 for the regression (the coefficient of determination) 0.9457R2 Cross validated (R2 for cross validation) 0.6431Standard deviation of the regression (SD) 0.2106The ratio of the model variance to the observed activity variance (F)

134.9

The significance level of F when treated as a ratio of Chi-squared distributions (P)

3.87E-19

Stability of the model predictions to changes in the training set composition

0.759

Field type ContributionSteric 0.428Electrostatic 0.092Hydrophobic 0.238H bond acceptor 0.192H bond donor 0.05

Table 1b. Statistical parameters of developed field based 3D-QSAR (Gaussian field) model for test set compounds.

Statistical parameters ValueR2 for external validation of test set (Q2) 0.865Root-mean-square error in the test set predictions (RMSE) 0.18Pearson r value for the correlation between the predicted and observed activity for the test set

0.9333

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Table 2. Demonstration of binding affinities resulting from docked poses of anti-malarial agents on plasmepsin-2 of P. falciparum.

Ligand ID Binding affinity (k cal/mole)

Ligand ID Binding affinity (k cal/mole)

2 -6.5 31 -5.8 11b -7.2 30 -6.0 11f -6.9 36 -6.6 18 -6.2 13 -6.2 20 -5.8 16 -6.5 21 -6.3 26 -5.7 22 -6.5 33 -5.9 23 -6.3 39 -5.6 4 -5.8 34 -6.0 14 -6.1 41 -5.8 29 -6.3 25 -6.5 10 -6.1 38 -5.8 47 -5.7 45 -6.6 9 -6.2 37 -6.5 27 -6.2 43 -6.4 32 -5.8 35 -6.0 24 -5.7 42 -5.6 28 -6.2 40 -6.0 8 -5.8 44 -6.9 12 -6.1 6ci -7.2 46 -6.7 10r -7.6

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(a) (b)

Figure1. The predicted vs experimental pIC50 values for 42 compounds of model. (a)

Predictions for pIC50 values of ligands in training set (b) and predictions for pIC50 values of

ligands in test set.

Steric Electrostatic Hydrophobic H bond acceptor

H bond donor

0

5

10

15

20

25

30

35

40

45

Perc

ent fi

eld

cont

ributi

on

Figure 2. Gaussian field contribution in activity modulation as predicted by 3D-QSAR

model.

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A. Steric contour map (solid and mesh) of Comp. 44

B. Steric contour map (solid and mesh) of Comp. 2

C. Electrostatic contour map (solid and mesh) of Comp. 44

D. Electrostatic contour map (solid and mesh) of Comp. 2

E. Hydrophobic contour map of Comp. 44 F. Hydrophobic contour map of Comp.2

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G. H-bond acceptor contour map of Comp. 44 H. H-bond acceptor contour map of Comp.2

I. H-bond donor contour map of Comp. 44 J. H-bond donor contour map of Comp. 2

Figure 3. Contour maps around the template compound 44 as well as compound 2.

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Compound 4

Compound 8

20

Compound 34

Compound 35

21

Compound 39

Compound 40

22

A2T (crystal ligand)

Figure 4. Predicted binding models between plasmepsin-2 and ligands. The 2D ligand-

interaction diagram illustrates the major interactions between the ligand and the active sites

amino acid residues of plasmepsin-2. The residues forming the potential interactions are

given as colour.

Figure 5: Superimposition of the co crystallized conformation of small molecule and

reproduced the conformation in the given binding site by using re-docking.

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