ORIGINAL RESEARCH

Predictive 3D-QSAR and HQSAR model generationof isocitrate lyase (ICL) inhibitors by various alignmentmethods combined with docking study

Nirzari Gupta • Vivek K. Vyas • Bhumika Patel •

Manjunath Ghate

Received: 11 February 2013 / Accepted: 22 October 2013

� Springer Science+Business Media New York 2013

Abstract Isocitrate lyase (ICL) is one of the most

important targets in the treatment of Mycobacterium

tuberculosis. In this study a diverse set of 2-benzanilide

derivatives were aligned by two different methods for

CoMFA, CoMSIA, and HQSAR analysis. The best CoM-

FA model was obtained with the internal validation value

(q2) of 0.730 and conventional coefficient (r2) of 0.944.

Various CoMSIA models were generated and cross-vali-

dated. The best cross-validation coefficient (q2) value was

found to be statistically satisfactory (0.688). Both the

models were validated by test set of 10 compounds with

satisfactory prediction value of (r2pred) 0.725 and 0.631 for

CoMFA and CoMSIA, respectively. Cross-validation

coefficient value (q2) of 0.694 and r2 of 0.856 were

obtained for HQSAR study. The docking study reveals that

large hydrophobic pockets occupy R substitutions of these

compounds. An electronically negative surface is observed

near R1 substitution. The results of the 3D-QSAR analysis

corroborate with the molecular docking results, and our

findings will serve as a basis for further development of

better allosteric inhibitors of ICL inhibitors against

M. tuberculosis.

Keywords Isocitrate lyase (ICL) � CoMFA �CoMSIA � Hologram QSAR � Distil � Docking �Mycobacterium tuberculosis � Tripos

Introduction

Mycobacterium tuberculosis is an etiological agent for

tuberculosis (TB). The incidence of TB has steadily risen

in the last years and TB is the world’s second most com-

mon cause of death from infectious diseases after acquired

immunodeficiency syndrome (AIDS) (Ginsburg et al.,

2003). By 2020, it is estimated that one billion people will

be infected, over 125 million people will get sick and over

30 million will die of TB, if control is not further

strengthened. However, the evolution of its new virulent

forms like multidrug resistant (MDR-TB) and extremely

drug resistant (XDR-TB) has become a major threat to

human kind (Rusell et al., 2010; Russell, 2007). TB is

responsible for 2–3 million deaths every year and, proba-

bly, 30 times as many infections (Manabe and Bishai,

2000). A new agent that targets the persistent state of

growth would have a significant impact on the treatment of

TB by shortening the duration of therapy (Nikalie and

Mudassar, 2011). The glyoxylate pathway uses isocitrate

lyase (ICL) and malate synthase to incorporate carbon

during growth of microorganisms on acetate or fatty acids

as the primary carbon source. The glyoxylate cycle is a

reaction sequence in which acetates are converted to suc-

cinates during the energy production and biosynthesis of

cell constituents; this cycle enables bacteria and fungi to

grow on acetate in a hostile environment inside the mac-

rophage where glucose is not available (Ernesto et al.,

2006; Oh et al., 2010). The necessity of ICL to pertain the

infection makes it efficient target for anti-tubercular drug.

The absence of ICL orthologs in mammals should facilitate

the development of glyoxylate cycle inhibitors as novel

drugs for the treatment of TB. Various moieties have been

identified as ICL inhibitors such as 3-nitropropionate

(McFadden and Purohit, 1977), 3-bromopyruvate (Ko and

N. Gupta � V. K. Vyas � B. Patel � M. Ghate (&)

Department of Pharmaceutical Chemistry, Institute of Pharmacy,

Nirma University, Ahmedabad 382 481, Gujarat, India

e-mail: manjunath.ghate@nirmauni.ac.in

N. Gupta

e-mail: nirzari1@gmail.com

123

Med Chem Res

DOI 10.1007/s00044-013-0865-0

MEDICINALCHEMISTRYRESEARCH

McFadden, 1990), 3 phosphoglycerate (Ko et al., 1989),

mycenon (Hautzel et al., 1990), oxalate, and itaconate

(Shin et al., 2005). However, these inhibitors are not

pharmacologically suitable for testing in vivo because of

their toxicity and low activity. TB continues to be a global

health threat, which makes 2-benzanilide an important new

class of therapeutics. In this study, we developed CoMFA

and CoMSIA 3D-QSAR model 2-methoxybenzanilides,

2-hydroxybenzanilides, and their thioxo analogs for iden-

tification of novel benzanilide derivative as ICL inhibitors

(Kozic et al., 2012). A three-dimensional quantitative

structure–activity relationship (3D-QSAR) can be consid-

ered as the ensemble of steric and electrostatic features of

different compounds which are necessary to ensure optimal

supramolecular interactions with a specific biological tar-

get structure and to trigger or to block its biological

response (Caballero 2010). A QSAR model was developed

using CoMFA (comparative molecular field analysis) and

CoMSIA (comparative similarity indices analysis) meth-

ods. CoMFA is a most commonly used 3D-QSAR tech-

nique in drug discovery which was developed by Cramer

et al. (1988). Steric and electrostatic fields are obtained

from CoMFA analysis. In CoMSIA, similarity indices are

calculated at regularly placed grid points for the aligned

molecules. Besides electrostatic and steric fields, CoMSIA

also calculates other descriptors like hydrophobic, hydro-

gen bond donor (HBD) and hydrogen bond acceptor (HBA)

(Zeng and Zhang, 2010). The purpose of the HQSAR study

is to explore individual atomic contribution to molecular

bioactivity with visual display of active centers in a com-

pound. Furthermore, HQSAR result can be also somehow

used as control to assess our CoMFA and CoMSIA results

for HQSAR technique does not require molecular super-

position (Zhu et al., 2005). Therefore, we believed that

these three QSAR models would provide some new useful

information for designing new ICL inhibitors with simpler

structures (Reddy et al., 2012)

Experimental methods and materials

Dataset

In this study, a dataset of 49 compounds obtained from Jan

Kozic et al.’s work consisted of 2-benzanilide derivatives

which showed inhibitory activity toward ICL enzyme

(Kozic et al., 2012); Considering a high deviation in the

biological activity and structural variations among the

compounds of the series it was considered as an ideal series

for performing QSAR analysis. Biological data with neg-

ative logarithm of minimum inhibitory concentration

(MIC) are expressed in mol/liter. The MIC values were

converted to pMIC and subsequently used as a dependent

variable for 3D-QSAR study (Table 1), thus correlating the

data linearly to the free energy change (Murugesan et al.,

2009).

Selection of training and test set

The total set of 49 inhibitors was divided into training set

(39 compounds) for generating 3D-QSAR model and a test

set (10 compounds) for validating the quality of the mod-

els. Selection of the training and test set molecules was

done by considering the fact that test set molecules repre-

sent a range of activity similar to that of the training set.

Thus, the test set was the true representative of the training

set. This was achieved by arbitrarily setting aside 10

compounds as a test set with a regularly distributed bio-

logical data.

Computational details

The CoMFA and CoMSIA studies were performed using

the SYBYL X 1.2 software from Tripos Inc., St. Louis, Mo,

USA. Structures of all the compounds were built using

Sketch option and energy minimized under the Tripos force

field with 0.01 kcal/(mol A). Gasteige–Huckel method was

used to calculate the charges. Energy minimization was

performed by Powell method. 100 iterations after partial

atomic charges were assigned to each atom (Zambre et al.,

2009).

Alignment of dataset

The first step in building a 3D-QSAR model from a set of

ligands is the alignment of the molecules, including posi-

tion, rotation, and conformation. Here the alignment was

performed using two different methods, (a) rigid alignment

using Distill function and (b) pharmacophoric alignment

using DISCOtech. The common substructure for the rigid

alignment can be seen in Fig. 1. Compound 23 (highest

active) was used as the template for alignment of the whole

dataset in rigid alignment (Fig. 2a) (Murugesan et al.,

2009). In the pharmacophoric alignment, the first step in

building a pharmacophore model from a set of ligands was

the alignment of the molecules (Liu et al., 2010). A

pharmacophore contains essential features like hydrogen

bond donor, hydrogen bond acceptor, hydrophobic, aro-

matic ring site, etc. (Fig. 2b). DISCOtech uses clique

detection methods to generate multiple pharmacophore

hypotheses that can be compared and refined. Conformers

were generated using ConfortTM, stochastic methods and

prepare the model using scoring function. Pharmacophore

model scoring function is based on the number of features,

the number of molecules that fit the model, and the inter-

feature distances. Out of the models developed, the highest

Med Chem Res

123

Table 1 Chemical structures and activity of 2-benzanilide derivatives used in 3D-QSAR study

OCH3

NH

O

R

R1

1-18

OCH3

NH

S

R

R1

19-36

OH

NH

O

R

R1

37-49

Compound number R R1 MIC (lmol L-1) pMIC

1 4-Cl 3-NO2 125 8.097

2 4-Cl 4-NO2 250 8.398

3 4-Cl 3-Cl 125 8.097

4 4-Cl 4-Cl 125 8.097

5a 4-Cl 3,4-diCl 125 8.097

6 4-Cl 3-Br 250 8.398

7 4-Cl 4-Br 62.5 7.795

8a 4-Cl 3-CF3 250 8.398

9 4-Cl 4-CF3 125 8.097

10 5-Cl 3-NO2 125 8.097

11 5-Cl 4-NO2 125 8.097

12a 5-Cl 3-Cl 250 8.398

13 5-Cl 4-Cl 62.5 7.795

14 5-Cl 3,4-diCl 125 8.097

15 5-Cl 3-Br 125 8.097

16 5-Cl 4-Br 16 7.204

17 5-Cl 3-CF3 4 6.602

18a 5-Cl 4-CF3 250 8.398

19 4-Cl 3-NO2 8 6.903

20 4-Cl 4-NO2 8 6.903

21 4-Cl 3-Cl 4 6.602

22 4-Cl 4-Cl 4 6.602

23 4-Cl 3,4-diCl 2 6.301

24a 4-Cl 3-Br 32 7.505

25 4-Cl 4-Br 4 6.602

26 4-Cl 3-CF3 4 6.602

27 4-Cl 4-CF3 8 6.903

28a 5-Cl 3-NO2 250 8.398

29 5-Cl 4-NO2 32 7.505

30a 5-Cl 3-Cl 16 7.204

31 5-Cl 4-Cl 16 7.204

32 5-Cl 3,4-diCl 8 6.903

33 5-Cl 3-Br 32 7.505

34a 5-Cl 4-Br 16 7.204

35 5-Cl 3-CF3 16 7.204

36 5-Cl 4-CF3 16 7.204

37a 4-Cl 3-NO2 8 6.903

38 4-Cl 3-CF3 4 6.602

39 4-Cl 4-CF3 4 6.602

40 4-Cl 3-NO2 4 6.602

41 4-Cl 3-CF3 4 6.602

Med Chem Res

123

scored model is considered as the best features are con-

sidered as essential for the ICL inhibition. The Model was

validated using ROC curve method. The ROC curve is a

function of sensitivity versus 1-Specificity, and the area

under the ROC curve (AUC) value is the important way of

measuring the performance of the test.

AUC ¼Xn

x¼2

se xð Þ 1� spð Þ xð Þ � 1� spð Þ x� 1ð Þ½ �

where se(x) is the percent of the true positives versus the

total positives at rank position x and (1 - sp)(x) is the

percent of the false positives versus the total negatives at

rank position x (Fawcett, 2006). ROC curve method was

performed using SPSS 15 statistical software. Here, a

decoy set of 147 compounds with 49 active compounds of

the dataset was prepared and 20 conformers of each com-

pound were formed by using a genetic algorithm-based

global optimizer to search the low-energy conformations of

molecules.

CoMFA analysis

CoMFA steric and electrostatic fields were calculated

using QSAR module of SYBYL X1.2. CoMFA uses

Lenard-Jones potential and coulomb potential to calculate

steric and electrostatic fields, respectively (Caballero

et al., 2010). Both steric and electrostatic fields were

calculated for each molecule using sp3-hybridized carbon

atom with 1.52 A van der Waals radiuses and a charge of

?1.0. The energy cut-off values for both steric and

electrostatic fields were set to 30.00 kcal/mol with a dis-

tance-dependent dielectric constant. Column filtering was

set 3.0 kcal/mol to reduce noise and improve efficacy

(Phuong et al., 2004).

CoMSIA analysis

CoMSIA similarity index descriptors were calculated using

the same lattice box as that used in CoMFA calculations

with a grid spacing of 2 A employing a C ?1 probe atom

Table 1 continued

Compound number R R1 MIC (lmol L-1) pMIC

42 4-Cl 4-CF3 4 6.602

43a 5-Cl 3-NO2 8 6.903

44 5-Cl 4-NO2 4 6.602

45 5-Cl 3-Cl 4 6.602

46 5-Cl 4-Cl 4 6.602

47 5-Cl 3,4-diCl 4 6.602

48 5-Cl 4-Br 8 6.903

49 5-Cl 4-CF3 2 6.301

a Test set compounds

Fig. 1 Common substructure of

whole dataset

Fig. 2 a Rigid alignment of

training set of molecules using

Distil. b Pharmacophoric

alignment of training set of

molecules using DISCOtech

Med Chem Res

123

with a radius of 1.0 A. Because of the different shapes of

the Gaussian function, the similarity indices can be cal-

culated at both sides, inside as well as outside of molecular

surface as different shapes of Gaussian function (Jewell

et al., 2001). In general, the CoMSIA contours are more

compacted and centered on the ligand atoms, while

CoMFA contours are scattered at the edge of the ligand

surface. Therefore, CoMSIA result will be helpful in

designing new ligand while CoMFA result should be useful

in exploring the complementary features between ligand

and its receptor active site (McGovern et al., 2010; Perez-

villanueva et al., 2011).

Hologram QSAR (HQSAR)

HQSAR is a technique based on the concept of using

molecular substructures expressed in a binary pattern

(molecular hologram) as descriptors in QSAR models. The

premise of HQSAR is that the two-dimensional (2D) fin-

gerprint encodes the structure of a molecule, which is the

key determinant of all molecular properties (Kulkarni

et al., 2008). 2D chemical database storage and searching

technologies rely on linear notations that define chemical

structures [Wiswesser line-formula notation (WLN); sim-

plified molecular input line entry system (SMILES); SLN–

SYBYL line notation]. The process involves generation of

fragments that are hashed into array bins in the range of 1

to L (length) wherein the array is called molecular holo-

gram and the bin occupancies are the descriptor variables

(Honorio et al., 2005). In this study, fingerprints were

generated for all substructures between four and seven

atoms in size for all molecules. The substructure finger-

prints were then hashed into hologram bins with lengths of

97, 151, 199, 257, 307, and 353. LOO cross-validation was

applied to determine the number of components that yields

a good predictive model. PLS then yields a mathematical

equation that related the molecular hologram bin values to

the inhibition activity of the compounds in the database.

PLS analysis and model validation

CoMFA and CoMSIA models were derived using PLS

regression as implemented in the SYBYL. Calculated

CoMFA and CoMSIA descriptors were used as indepen-

dent variables and pMIC values were used as dependent

variables in the PLS regression analysis. Leave-one-out

(LOO) and cross-validation were initially utilized to eval-

uate the predictive capability q2 and r2cv of the models,

respectively; then 10 cycle bootstrapped run was per-

formed (r2bs) to assess the statistical confidence of the

derived models (Vyas et al., 2013). The optimal number of

components was selected based on the smallest error of

prediction and the highest q2 and r2cv. The PLS analysis

was then repeated with no validation to generate CoMFA

and CoMSIA models. The non-cross-validated models

were assessed by the conventional correlation coefficient

(r2), standard error of prediction (SEE), and F values. The

predictive r2 (r2pred) was based only on the molecules

obtained from the database searching (10 compounds, test

set) and is defined as r2pred = SD-PRESS/SD, where SD is

the sum of the squared deviations between the inhibitory

activity of molecules in the test set and the mean inhibitory

activity of the training set molecules and PRESS is the sum

of the squared deviations between the predicted and actual

activity values for every molecule in the test set (Murum-

kar et al., 2011).

Molecular docking study

Selected active molecules were docked using Surflex-Dock

module of SYBYL into the binding site of ICL enzyme.

The crystal structure of ligand bound to ICL (PDB: 1F8M,

resolution: 1.8 A) (Sharma et al., 2000) was used as the

reference for docking studies. Structure-based study was

carried out, and reported to be helpful in validating 3D-

QSAR results (Chen et al., 2010). We performed molecular

docking of all inhibitors (Holt et al., 2008). After docking,

the binding poses of each ligand was analyzed for the

interactions with the active site residues (Aparoy et al.,

2011)

Fig. 3 Receiver operating characteristic (ROC) curve of the phar-

macophore model

Med Chem Res

123

Results and discussion

Alignment of database

In rigid alignment, the highest active compound 23

(MIC = 2 lM) was used as the template for alignment on

the core common substructure (Fig. 1) of whole database

(Table 1). Rigid alignment can be seen in Fig. 2a. In the

pharmacophoric alignment the DISCOtech module was

used (Fig. 2b). In the pharmacophoric alignment, all the

compounds were aligned on two donor sites, two acceptor

sites, one hydrophobic site, and one aromatic site. The

valiation of the model was performed by ROC curve

method. As per Fig. 3, the AUC of ROC curve of model

was found to be 0.78 which demonstrates the reliability of

the model as promising for the alignment.

Result of CoMFA, CoMSIA, and HQSAR analysis

of rigid alignment dataset (align1)

The statistical parameters of standard CoMFA models

constructed with steric and electrostatic fields are given in

Table 2. The q2, r2cv, r2

pred, r2ncv, F, and SEE values were

computed as defined in SYBYL. The PLS analysis showed

a q2 value of 0.730 and r2cv value of 0.715. The non-cross-

validated PLS analysis results in a conventional r2 of

0.944, F = 89.26, and a standard error of estimation (SEE)

of 0.174. In both steric and electrostatic field contributions,

the former accounts for 0.575 while the latter contributes

0.425. The high bootstrapped r2 (0.962) value and low

standard deviation (0.01) suggest a high degree of confi-

dence in the analysis. CoMSIA offered steric and electro-

static, hydrophobic, hydrogen bond donor (HBD) and

acceptor (HBA) field information. These three additional

factors are in combination with steric and electrostatic

fields, and result in best CoMSIA models. Statistically

significant CoMSIA model was obtained by using the

combination of steric, electrostatic, hydrophobic, and HBA

fields (q2 = 0.539, r2cv = 0.627, r2 = 0.911, F = 33,

SEE = 0.230). The corresponding field contributions are

0.103, 0.191, 0.284, 0.244, and 0.177, respectively. CoM-

SIA analysis results are also summarized in Table 2. As per

the HQSAR calculation, the lowest SEE occurred at a

cross-validated q2 of 0.694. The hologram result in the

lowest standard error has a hologram length of 353. The

PLS analysis gave a conventional r2 of 0.856 and a stan-

dard error prediction of 0.3 for all of the studied

compounds.

Pharmacophoric aligned dataset (align2)

The PLS analysis showed a q2 value of 0.167 and

r2cv = 0.287 which are considered as less statistically

significant. The non-cross-validated PLS analysis results in

a conventional r2 of 0.958, F = 101, and a standard error

of estimation (SEE) of 0.153. CoMSIA model was obtained

by using the combination of steric, electrostatic, hydro-

phobic, HBD, and HBA fields (q2 = 0.409, r2 = 0.882,

Table 2 Statistical comparison of align1 and align2 results

Statistical parameters Align1 Align2

CoMFA CoMSIA HQSAR CoMFA CoMSIA HQSAR

q2 0.730 0.539 0.694 0.167 0.409 0.693

r2ncv 0.944 0.911 0.856 0.958 0.882 0.861

r2cv 0.715 0.627 – 0.287 0.355 –

r2bs 0.962 0.974 – 0.983 0.776 –

N 6 9 5 7 2 5

F 89.26 33.0 – 101 62 –

SEE 0.174 0.230 0.300 0.153 0.328 0.400

r2pred 0.735 0.613 0.897 0.641 0.681 0.615

Probability of r2ncv 0.00 0.00 – 0.00 0.00 –

Field contribution

Steric 0.575 0.103 – 0.085 0.414 –

Electrostatic 0.425 0.191 – 0.227 0.586 –

Hydrophobic 0.284 0.168 –

H-bond donor 0.244 0.238 –

H-bond acceptor 0.177 0.282 –

N is the optimal number of components (PLS components), q2 is the leave-one-out (LOO), cross-validation coefficient, r2ncv is the non-cross-

validation coefficient, r2pred is the predictive correlation coefficient, SEE is the standard error of estimation, F is the F-test value, r2

cv is cross-

validation coefficient

Med Chem Res

123

F = 62, SEE = 0.328). The results of the 3D-QSAR using

pharmacophoric alignment and HQSAR calculation is

shown in Table 2. As per the HQSAR calculation, the

lowest standard error occurred at a cross-validated q2 of

0.694 with five optimal components. The hologram result

in the lowest standard error has a hologram length of 353.

The PLS analysis gave a conventional r2 of 0.861 and a

standard error prediction of 0.400 for all of the studied

compounds.

Statistical results of Align2 showed a low value of q2

and r2cv as compared to Align1. Due to the statistical sig-

nificance, further analysis was carried out using Align1

model. Optimization of CoMSIA study was performed

using steric, electrostatic, hydrophobic, hydrogen bond

donor, and hydrogen bond acceptor fields. 3D-QSAR

models were generated using the above fields in different

combinations, and the results of study are summarized in

Table 3. CoMSIA models showed higher correlation and

high predictive properties. In most of the models, hydro-

phobic field was a common factor indicating the impor-

tance of lipophilicity for the present series of molecules.

We found that the CoMSIA descriptors such as electro-

static, hydrophobic, and HBD fields played a significant

role in the prediction of biological activity. A satisfactory

value of q2 of 0.688 was obtained with this model. The

predictive abilities of 3D-QSAR models were further val-

idated using a test set of 10 compounds, not included in the

model generation study. The predicted r2 (r2pred) values of

CoMFA and CoMSIA models are 0.725 and 0.631,

respectively, for Align1 (Table 3). Plot of experimental

and predicted pMIC of training and test set using CoMFA,

CoMSIA, and HQSAR is depicted in Fig. 4 (Table 4). The

residual activity differences can be seen as hologram in

Fig. 5. The histogram of residual activity suggests the

absence of any outlier compound in the training set whose

residual activity is above one.

CoMFA contour maps

The steric contour map for the CoMFA model with the

most active compound 23 (MIC = 2 lM) is shown in

Fig. 6a. In this figure, the green-colored contours represent

regions of high steric tolerance, while the yellow contours

represent regions of low steric bulk tolerance. A large

green contour present near the C-3 position of terminal

phenyl ring indicated that substitution with the groups

which results in increasing the steric tolerance would favor

the activity. This can be seen in case of the most active

compound 23 (MIC = 2 lM) whose –F group is oriented

in this region. It can also be seen by comparing the

structure and activity of 38 (MIC = 4 lM) and 37

(MIC = 8 lM); 38 contains –CF3 group at C-3 position,

which is more appropriate for sterically favored green-

colored region as compared to –NO2 group at C-3 position

of terminal phenyl ring in compound 37. Compounds 26,

32, 40, 41, and 43 showed good activity due to bulky group

at C-3. A second small green contour and a bulky/steric

unfavorable yellow contour were observed away from

molecular area, so there is no significance of steric bulk

property in this area. A second big green contour observed

in the vicinity of the first phenyl indicated the presence of

steric bulk to the activity of the compounds. All the

Table 3 Optimization of CoMSIA analysis for align1

Features q2 r2ncv N F SEE S E H D A

SEHDA 0.539 0.911 9 33 0.230 0.103 0.191 0.284 0.244 0.177

SHDA 0.555 0.928 9 41 0.208 0.130 – 0.366 0.318 0.186

EHDA 0.513 0.911 9 32 0.230 – 0.212 0.316 0.270 0.201

SEHA 0.537 0.912 9 33 0.228 0.128 0.276 0.344 – 0.252

SEDA 0.494 0.884 9 24 0.263 0.156 0.262 – 0.362 0.220

SHE 0.631 0.914 9 34 0.227 0.226 0.341 0.433 – –

SED 0.641 0.880 9 23 0.268 0.193 0.355 – 0.452 –

SEA 0.450 0.870 9 21 0.278 0.204 0.418 – – -0.378

SHA 0.478 0.922 9 38 0.216 0.176 – 0.469 – 0.355

SDA 0.545 0.892 9 26 0.254 0.240 – – 0.472 0.289

EHD 0.688 0.920 9 37 0.218 – 0.254 0.419 0.328 –

EHA 0.524 0.912 9 33 0.228 – 0.307 0.394 – 0.299

EDA 0.497 0.881 9 23 0.266 0.313 – 0.412 0.275

ADH 0.571 0.917 9 35 0.222 – – 0.332 0.414 0.254

Bold values provide the combination of descriptors which is found to be most statistically significant among all CoMSIA combinations

N is the optimal number of components (PLS components); q2 is the leave-one-out (LOO) cross-validation coefficient; r2ncv is the non-cross-

validation coefficient; SEE is the standard error of estimation; F is the Fischer’s F value; S is steric; E is electrostatic; H is hydrophobic; D is

H-bond donor; A is H-bond acceptor

Med Chem Res

123

compounds of this series follow the same scaffold in their

structures.

The electrostatic contour map for the CoMFA model is

shown in Fig. 6b. In this figure, the blue contours represent

the regions of high electrostatic tolerance, while the red

contours represent regions of low electrostatic bulk toler-

ance. A big blue contour covers the thioamide linkage

between the phenyl rings. Hydrogen atom of –NH group of

thioamide and amide group of all the molecules were

shown to be perfectly fit for the blue contour. The reason

being, they have their electropositive hydrogen atom, fill-

ing the blue contour. However, there is an overlap of a

small red contour map at this region which is very complex

to interpret. A second big blue contour observed near the

C-2 of the first phenyl ring suggests that electropositive

substituents in these regions would increase the activity.

Fig. 4 a Plot of experimental and predicted activity using CoMFA

model. b Plot of experimental and predicted activity using CoMSIA

model. c Plot of experimental and predicted activity using HQSAR model

Table 4 Actual and predicted activity of training and test set com-

pounds used in 3D-QSAR (align1)

Compound pMIC Predicted activity

CoMFA CoMSIA HQSAR

1 8.097 8.099 7.997 8.099

2 8.397 8.351 8.415 8.266

3 8.097 7.834 7.803 7.949

4 8.097 8.056 7.945 7.948

5 8.097 7.44 7.461 7.954

6 8.398 8.32 8.179 8.169

7 7.795 7.897 7.905 7.888

8 8.397 7.13 7.183 7.537

9 8.097 8.067 8.096 7.97

10 8.097 8.018 8.036 7.983

11 8.097 8.051 8.124 8.15

12 8.397 7.947 7.945 7.833

13 7.795 7.775 7.724 7.832

14 8.097 8.147 8.059 7.838

15 8.097 7.98 8.012 8.053

16 7.204 7.665 7.661 7.772

17 6.602 6.747 6.972 7.421

18 8.398 7.505 8.08 7.854

19 6.903 7.068 7.035 6.952

20 6.903 6.741 6.785 7.138

21 6.602 6.466 6.807 6.804

22 6.602 6.775 6.639 6.819

23 6.301 6.518 6.629 6.808

24 7.505 6.554 6.858 7.021

25 6.602 6.516 6.535 6.76

26 6.602 6.935 6.691 6.446

27 6.903 6.823 6.753 6.841

28 8.398 6.818 7.496 7.164

29 7.505 7.368 7.594 7.35

30 7.204 6.751 7.286 7.016

31 7.204 7.024 7.193 7.032

32 6.903 6.593 7.02 7.021

33 7.505 7.687 7.659 7.234

34 7.204 6.943 7.178 6.973

35 7.204 7.223 6.698 6.659

36 7.204 7.153 7.175 7.054

37 6.903 6.608 5.758 6.638

38 6.602 6.725 6.821 6.621

39 6.602 6.694 6.515 6.619

40 6.602 6.653 6.634 6.626

41 6.602 6.45 6.494 6.56

42 6.602 6.708 6.672 6.642

43 6.903 6.61 5.758 6.638

44 6.602 6.503 6.629 6.805

45 6.602 6.485 6.596 6.488

46 6.602 6.716 6.209 6.486

Med Chem Res

123

CoMSIA contour maps

The electrostatic contour map of the CoMSIA analysis can

be seen in Fig. 7a. CoMSIA electrostatic contour map was

similarly placed as CoMFA electrostatic contour map.

However, the presence of red contour near the methoxy

group indicates that electronegative group around this

region would increase the inhibitory activity. The presence

of electronegative oxygen of hydroxyl group in compounds

37–49 (MIC B 8 lM) accounts for good activity. The

hydrophobic contour map of CoMSIA analysis based on

the atomic hydrophobicity distribution displays more

clearly the hydrophobic interactions (Fig. 7b). The region

of yellow contour around the –F group of terminal phenyl

ring indicated that the addition of hydrophobic substituents

at these position may increase the activity. Compound 23

(MIC = 2 lM) has better activity due to the presence of

yellow contour at –F-substituted phenyl ring, because –F

group is much more lipophilic than hydrogen; so incor-

porating a –F group in a molecule will make it more

lipophilic. Compound 10 (MIC = 125 lM) and compound

28 have a –NO2 group substitution at this position which is

less hydrophobic in nature as compared with the alkyl

groups like –Pr and –Bu, and thus less active, compara-

tively. The hydrophobic favored regions around the ter-

minal phenyl ring are similar to the steric favored regions.

A small yellow contour present near the C-4 of the terminal

phenyl ring indicates the addition of hydrophobic sub-

stituent for good activity. The bulky unfavorable gray

region is observed away from molecular area.

Molecular docking study

Docking studies revealed that all inhibitors are docked into

the allosteric site of the ICL crystal structure (PDB ID:

1F8M) and are in similar orientation. The protein structure

contains the crystal structure of pyruvic acid as bound

ligand. The inhibitor conformations obtained from docking

and pyruvic acid has low root mean square deviation

(RMSD \ 1.5 A) indicating a highly conserved binding

mode and the reliability of Surflex-Dock for docking

studies. The inhibitor molecules bind to an allosteric site

adjacent to the Mg?2–pyruvic acid-binding region and

other surrounding protein residues through hydrogen

bonding and hydrophobic interactions. The structural

superposition of the six active molecules (pyruvic acid, 17,

23, 38, 42, and 49) in their docked conformations is shown

in Fig. 8. Pyruvic acid is a co-crystallized allosteric

inhibitor in the 1F8M crystal structure. Highly conserved

Mg?2 ion is observed in other ICL crystal structures (PDB

IDs: 1F8I and 1F61). Figure 9 indicates the docking of the

most active molecule 23 from Table 1 and its imposition

with pyruvic acid. The molecule 23 also forms several

interactions with TRP93, LEU348, THR347, and LYS190.

The docking studies demonstrated that the QSAR contour

maps collaborate with molecule 23 docking interaction

with protein allosteric site. The allosteric site has one

hydrophobic groove which contains the ILE36, THR347,

LEU348, and SER317. The R substitution of the com-

pounds lies within this region. The chlorine atom of the

first benzene ring occupies this pocket. One small electri-

cally negative pocket can be seen near ASP108. The

Table 4 continued

Compound pMIC Predicted activity

CoMFA CoMSIA HQSAR

47 6.602 6.407 6.412 6.493

48 6.903 6.83 6.894 6.427

49 6.301 6.466 6.517 6.508

Fig. 5 a Histogram of CoMFA residual values for the training set.

b Histogram of CoMSIA residual values for the training set.

c Histogram of HQSAR residual values for the training set

Med Chem Res

123

carbonyl group of the amide linkage fulfill this space. One

hydrogen bond was found between TRP93 and compound

23 at the distance of 2.20 A. The –OCH3 of the compound

23 stabilizes the SER91, LEU90, and THR93 pocket.

Conclusions

Isocitrate lyase is proven to be a promising target for the

treatment of M. tuberculosis. 3D-QSAR techniques

Fig. 6 a The CoMFA steric contour map represented by green (favored) and yellow (disfavored) polyhedra. b The CoMFA electrostatic contour

map represented by blue (favored) and red (disfavored) polyhedra. Compound 23 is shown inside the field

Fig. 7 a The CoMSIA electrostatic contour map represented by blue

(favored) and red (disfavored) polyhedra. b The CoMSIA hydropho-

bic contour map represented by green (favored) and gray (disfavored)

polyhedra. c The CoMSIA HBD contour map represented by cyan

(favored) and violet (disfavored) polyhedra. Compound 23 is shown

inside the field (Color figure online)

Med Chem Res

123

(CoMFA and CoMSIA) and HQSAR were applied for the

first time on the series of 2-benzanilide derivatives as ICL

inhibitors. All the models (CoMFA, CoMSIA, and

HQSAR) were found to be satisfactory according to the

statistical parameters. Contour map analysis of CoMFA

and CoMSIA will assist in prediction of ICL activity with

appropriate accuracy. That was further correlated with

receptor active site. Combined 3D-QSAR and molecular

docking analysis corroborate each other, and these results

will help to better interpret the structure–activity relation-

ship of these ICL inhibitors and provide valuable insights

into rational drug design. The present QSAR approach

along with docking studies provides useful information to

design the novel derivatives with higher selectivity and

efficacy.

Acknowledgments The authors would like to thank Nirma Uni-

versity, Ahmedabad, India for providing computer facility to com-

plete this work.

Conflict of interest The authors declare that they have no conflict

of interest.

References

Aparoy P, Suresh GK, Reddy K, Reddanna P (2011) CoMFA and

CoMSIA Studies on 5-hydroxyindole-3-carboxylate derivatives

as 5-lipoxygenase inhibitors: generation of homology model and

docking studies. Bioorg Med Chem Lett 21:456–462

Caballero J (2010) 3D-QSAR (CoMFA and CoMSIA) and pharma-

cophore (GALAHAD) studies on the differential inhibition of

aldose reductase by flavonoid compounds. J Mol Graph Model

29:363–371

Caballero J, Fernandez M, Coll D (2010) Quantitative structure–

activity relationship of organosulphur compounds as soybean

15-lipoxygenase inhibitors using CoMFA and CoMSIA. Chem

Biol Drug Des 76:511–517

Chen Y, Li Z, Chen HF (2010) Computational study of CCR5

antagonist with support vector machines and three dimensional

quantitative structure activity relationship methods. Chem Biol

Drug Des 75:295–309

Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular

field analysis (CoMFA). J Am Chem Soc 110:5959–5967

Ernesto J, Elıas M, McKinney JD (2006) M. tuberculosis isocitrate

lyases 1 and 2 are jointly required for in vivo growth and

virulence. Nat Med 11:638–644

Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit

Lett 27:861–874

Ginsburg AS, Grosset JH, Bisha JH (2003) Fluoroquinolones,

tuberculosis, and resistance. Lancet Infect Dis 3:432–442

Hautzel R, Anke H, Sheldrick WS (1990) Mycenon, a new metabolite

from a Mycena species TA 87202 (basidiomycetes) as an

inhibitor of isocitrate lyase. J Antibiot 43:1240–1244

Holt PA, Chaires JB, Trent JO (2008) Molecular docking of

intercalators and groove-binders to nucleic acids using autodock

and surflex. J Chem Inf Model 48:1602–1615

Honorio KM, Garratt RC, Andricopulo AD (2005) Hologram

quantitative structure-activity relationships for a series of

farnesoid X receptor activators. Bioorg Med Chem Lett

15:3119–3125

Jewell NE, Turner DB, Willett P, Sexton GJ (2001) Automatic

generation of alignments for 3D QSAR analyses. J Mol Graph

Model 20:111–121

Ko YH, McFadden BA (1990) Alkylation of isocitrate lyase from

Escherichia coli by 3-bromopyruvate. Arch Biochem Biophys

278:373–380

Ko YH, Vanni P, McFadden BA (1989) The interaction of

3-phosphoglycerate and other substrate analogs with the gly-

oxylate- and succinate-binding sites of isocitrate lyase. Arch

Biochem Biophys 274:155–160

Kozic J, Novotna E, Volkova M, Stolarıkova J, Trejtnar F, Vinsova J

(2012) Synthesis and in vitro antimycobacterial activity of

2-methoxybenzanilides and their thioxo analogues. Eur J Med

Chem 56:387–395

Kulkarni SS, Patel MR, Talele TT (2008) CoMFA and HQSAR

Studies on 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as

phosphodiesterase10A inhibitors. Bioorg Med Chem 16:3675–

3686

Liu G, Ju X, Cheng J, Liu Z (2010) 3D-QSAR studies of insecticidal

anthranilic diamides as ryanodine receptor activators using

CoMFA, CoMSIA and DISCOtech. Chemosphere 78:300–306

Fig. 8 Superposition of five active molecules in their docked

conformation in allosteric binding site of ICL. Yellow dotted line

indicates hydrogen bond interactions (Color figure online)

Fig. 9 3D view of the docked conformation of molecule 23 (orange)

superimposed on co-crystallized pyruvic acid (red) in the allosteric binding

site of ICL. The hydrogen bonds are shown in yellow broken line, and the

conserved Mg?2 ion is represented as violet ball (Color figure online)

Med Chem Res

123

Manabe YC, Bishai WR (2000) Latent Mycobacterium tuberculosis-

persistence, patience, and winning by waiting. Nat Med

6:1327–1329

McFadden BA, Purohit S (1977) Itaconate, an isocitrate lyase-

directed inhibitor in Pseudomonas indigofera. J Bacteriol

131:136–144

McGovern DL, Mosier PD, Roth BL, Westkaemper RB (2010)

CoMFA analyses of C-2 position salvinorin A analogs at the

kappa-opioid receptor provides insights into epimer selectivity.

J Mol Graph Model 28:612–625

Murugesan V, Prabhakar YS, Katti SB (2009) CoMFA and CoMSIA

studies on thiazolidin-4-one as anti-HIV1 agents. J Mol Graph

Model 27:735–743

Murumkar PR, Le L, Truong TN, Yadav MR (2011) Determination of

structural requirements of influenza neuraminidase type A

inhibitors and binding interaction analysis with the active site

of A/H1N1 by 3D-QSAR CoMFA and CoMSIA modeling. Med

Chem Commun 2:710–719

Nikalie AG, Mudassar P (2011) Multidrug-resistant Mycobacterium

tuberculosis: a brief review. Asian J Biol Sci 4:101–115

Oh KB, Jeon HB, Han YR, Lee YJ, Park J, Lee SH, Yang D, Kwon

M, Shin J, Lee HS (2010) Bromophenols as Candida albicans

isocitrate lyase inhibitors. Bioorg Med Chem Lett 20:6644–6648

Perez-villanueva J, Medina-franco JL, Caulfield TR, Hernandez-

campos A, Hernandez-luis F, Yepez-mulia L (2011) Comparative

molecular field analysis (CoMFA) and comparative molecular

similarity indices analysis (CoMSIA) of some benzimidazole

derivatives with trichomonicidal activity. Eur J Med Chem

46:3499–3508

Phuong T, Minh TK, Van NT, Phuong HT (2004) Synthesis and

antifungal activities of phenylenedithioureas. Bioorg Med Chem

Lett 14:653–656

Reddy BM, Tanneeru K, Meetei PA, Guruprasad L (2012) 3D-QSAR and

molecular docking studies on substituted isothiazole analogs as

inhibitors against MEK-1 kinase. Chem Biol Drug Des 79:84–91

Rusell DG, Barry CE, Flynn JL (2010) Tuberculosis: what we don’t

know can, and does, hurt us. Science 328:852–856

Russell DG (2007) Who puts the tubercle in tuberculosis? Nat Rev

Microbiol 5:39–47

Sharma V, Sharma S, Hoener K, Bentrup Z, Mckinney JD, Russell

DG, Jacobs WR, Sacchettini JC (2000) Stucture of isocitrate

lyase, a persistence of Mycobacterium tuberculosis. Nat Struct

Biol 7:663–668

Shin DS, Kim S, Yang HC, Oh KB (2005) Cloning and expression of

isocitrate lyase, a key enzyme of the glyoxylate cycle, of

Candida albicans for development of antifungal drugs. J Micro-

biol Biotechnol 15:652–655

Vyas VK, Bhatt HG, Patel PK, Jalu J, Chintha C, Gupta N, Ghate M

(2013) CoMFA and CoMSIA studies on C-aryl glucoside

SGLT2 inhibitors as potential anti-diabetic agents. SAR QSAR

Environ Res http://dx.doi.org/10.1080/1062936X.2012.751553

Zambre VP, Murumkar PR, Giridhar R, Yadav MR (2009) Structural

investigations of acridine derivatives by CoMFA and CoMSIA

reveal novel insight into their structures toward DNA G-quad-

ruplex mediated telomerase inhibition and offer a highly

predictive 3D-model for substituted acridines. J Chem Inf Model

49:1298–1311

Zeng H, Zhang H (2010) Combined 3D-QSAR modeling and

molecular docking study on 1,4-dihydroindeno[1,2-c]pyrazoles

as VEGFR-2 kinase inhibitors. J Mol Graph Model 29:54–71

Zhu W, Chen G, Hu L, Luo X, Gui C, Luo C, Puah CM, Chen K,

Jiang H (2005) QSAR Analyses on ginkgolides and their

analogues using CoMFA, CoMSIA, and HQSAR. Bioorg Med

Chem 13:313–322

Med Chem Res

123