ORI GINAL RESEARCH

QSAR analysis of N-myristoyltransferase inhibitors:antifungal activity of benzofurans

Hemantkumar S. Deokar ÆPurushottamachar Puranik Æ Vithal M. Kulkarni

Received: 8 April 2008 / Accepted: 18 June 2008

� Birkhauser Boston 2008

Abstract Benzofurans are a class of antifungal agents reported to act by selective

inhibition of the N-myristoyltransferase (Nmt) enzyme in fungal cells. A three-

dimensional quantitative structure–activity relationship (3D-QSAR) was performed

on a series of 29 molecules to find correlation between various physicochemical

descriptors and Nmt inhibition. QSAR equations were evaluated using a training set

24 molecules and an external test set of 5 molecules. The statistical quality of the

QSAR models was evaluated using the parameters r2, rcv2 , and the rpred

2 measure. The

results obtained indicated that the enzyme inhibitory activity of benzofuran ana-

logues strongly depends on structural factors as expressed by hydrogen-bond

acceptors and spatial factors as expressed by the principle moment of inertia in the X(PMI_X) and Y directions (PMI_Y).

Keywords 3D-QSAR � GFA � Antifungal � N-myristoyltransferase �Benzofuran analogues

Introduction

Invasive fungal infections have increased dramatically in recent years to become an

important cause of morbidity and mortality in patients. Currently available

antifungal drugs for such infections essentially have three molecular targets:

H. S. Deokar � V. M. Kulkarni (&)

Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth

University, Pune 411038, India

e-mail: vmkulkarni60@gmail.com

P. Puranik

Department of Pharmacology and Experimental Therapeutics,

University of Maryland School of Medicine, 685 West Baltimore Street,

Baltimore, MD 21201-1559, USA

Med Chem Res

DOI 10.1007/s00044-008-9120-5

MEDICINALCHEMISTRYRESEARCH

cytochrome P-450-dependent lanosterol 14a-demethylase (CYP-P45014aDM), ergos-

terol, and 1,3-beta-glucan synthase. The first is a fungistatic target vulnerable to

resistance development; the second, while a fungicidal target, is not sufficiently

different from the host to ensure high selectivity; and the third, a fungistatic

(Aspergillus) or fungicidal (Candida) target, has limited activity spectrum and

potential host toxicity that might preclude dose escalation. Myristoyl CoA:protein

N-myristoyltransferase (Nmt; EC 2.1.3.97) is a cytosolic monomeric enzyme that

catalyzes the transfer of the cellular fatty acid myristate (C14.0) from myristoyl-

CoA to the N-terminal glycine amine of a variety of eukaryotic proteins (Johnson

et al., 1994) Genetic studies have established that Nmt is essential for vegetative

growth and survival of pathogenic fungi such as Sacchromyces cerevisiea. Nmt has

also been proven to be essential for the viability of fungi, including the medically

important Candida albicans (C. albicans) and Cryptococcus neoformans (Duronio

et al., 1989; Weinberg et al., 1995; Lodge et al., 1994). Human Nmt has also been

cloned and characterized. C. albicans Nmt has 451 amino acid residues, with a

sequence identity of 45% to the human Nmt enzyme. Clear difference in the

peptide–substrate specificity between fungal and human Nmts has been exploited,

and Nmt has been identified as a potential chemotherapeutic target for antifungal

agents (Wilcox et al., 1987; Johnson et al., 1994). Hence Nmt is an attractive target

for the design and development of novel antifungal agents with a new mode of

action without disrupting host Nmt. Because of its novel mechanism of action, Nmt

inhibitors are expected to have advantages over azole antifungals in terms of

activity against azole-resistant fungal strain and lack of drug–drug interactions,

which are drawbacks of azole antifungal agents (Benedetti and Bani, 1999).

Although several peptidomimetic inhibitors of C. albicans Nmt (Ca Nmt) have been

reported, their antifungal effect is only marginal (Devadas et al., 1997; Nagarajan

et al., 1997). Hence, there is a need for novel nonpeptidic enzyme inhibitors.

In our laboratory, molecular modeling studies have been performed on various

antifungal targets like such as P45014aDM (Talele et al., 1999) and dihydrofolate

reductase (Gokhale and Kulkarni, 2000b) enzyme for the rational design of new

antifungal agents. We have generated a 3D pharmacophore model using peptidic

Nmt inhibitors and successfully utilized it for the design of nonpeptidic inhibitors

(Karki and Kulkarni, 2001b, 2001c). Recently, Tatsuo et al. reported a novel series

of benzofuran analogs as nonpeptidic Nmt inhibitors (Masubuchi et al., 2001;

Ebiike et al., 2002; Sogabe et al., 2000). Previously we have reported 3D-QSAR on

series of benzofuran analogs as antifungals using comparative molecular field

analysis (CoMFA) (Purushottamachar and Kulkarni, 2003). In order to understand

better the relationship between structure and activity, we report herein a QSAR

using genetic function approximation (GFA), as developed by Rogers (Rogers and

Hopfinger, 1994) for the analysis of fungal N-myristoyltransferase active against C.albicans. GFA is a genetic algorithm which generates a population of equations

rather than a single equation in correlating biological activity with physiochemical

descriptors. GFA considers a combination of Friedman’s multivariate adaptive

regression splines (MARS) algorithm with Holland’s genetic algorithm to evolve a

population that best fits the training data set (Kawakami et al., 1996). This is done as

follows:

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(1) The initial population of equations is generated by a random choice of

descriptors, and the fitness of each equation is scored by a lack-of-fit (LOF)

measure, LOF ¼ LSE

1� cþd�pÞ=m½ �f g2 where LSE is the least square error, c is the

function in the models, d is the smoothing parameter which controls the

number of features contained in all terms of the models, p is the number of

features contained in all basis functions, and m is the number of samples in the

training set.

(2) A pair from the population of equations is chosen at random and crossovers are

performed and progeny equations are generated.

(3) The fitness of each progeny equation is assessed by the LOF measure.

(4) If the fitness of the new progeny equation is better, then it is preserved.

GFA models provide useful information such as the relevance of particular

descriptors to the model and activity prediction; we have been using GFA methods

effectively and have recently applied them to generate QSAR models for antifungal,

antibacterial, antitubercular, anticancer, and antidiabetic agents (Gokhale and

Kulkarni, 2000a; Karki and Kulkarni, 2001a; Kharkar et al., 2002; Wagh et al.,2006; Sachan et al., 2007). A combination of this robust statistical technique

coupled with the use of different descriptors has resulted in the successful prediction

of novel antifungals (Chandavarkar et al., 2003; Chandavarkar et al., 2005).

Results

QSAR models were generated using a training of set 24 molecules (molecules 1–24,

Table 1). Test set molecules (molecules 25–29, Table 1) with regularly distributed

biological activities were used to assess the predictive ability of the generated

QSAR models. Biological activity was used in terms of log (1/IC50) against Ca Nmt.All the molecules used in the study were subjected to conformational search using a

random sampling method, and the lowest-energy conformers were aligned using a

minimum common substructure. Molecules were superimposed onto the lowest-

energy conformer of molecule 23, having the highest biological activity (Fig. 4).

The alignment resulted in the orientation of the molecules in such a way that the

substituent at C2 of the benzofuran ring system oriented towards the X-axis and the

C4-substituted alkylamine group oriented to the Y-axis.During the generation of QSAR models, it was observed that 20,000 crossovers

with a smoothing factor of d = 1.0 resulted in optimum internal and external

predictivity as assessed from the LOF value, rcv2 , and variable usage graphs. Thus

the number of crossovers was set to 20,000.

Since equations generated without restricting the number of the descriptors

(infinite chain length) are usually not relevant, interpretation of QSAR model with

more terms becomes difficult in designing newer molecules. Hence equations with

not more than five terms including a constant were generated.

A total of 32 descriptors (Table 2) were used for the QSAR model generation.

All the statistically significant QSAR models are shown in Table 3. A set of

generated QSAR equations were evaluated for their predictive ability. Observation

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Table 1 Structure and activities of the molecules in the training (1–24) and test (25–29) sets

O O

O

R

Compd R Observed activity Predicted

activity

Residuals

IC50 Log I/IC50

1 O(CH2)2NHC(CH3)3 50 -1.6989 -0.870 -0.8289

2 O(CH2)2OHCH2NHCH(CH3)2 0.98 0.0088 -0.163 0.1718

3 O(CH2)3NHC(CH3)3 1.7 -0.2304 -0.821 0.5906

4 O(CH2)4NHC(CH3)3 4.4 -0.6434 -1.378 0.7346

5

OCH2CH2CH2 N

1.6 -0.2041 -0.713 0.5089

6 O(CH2)5NHC(CH3)3 1.5 -1.1760 -1.664 0.4880

25 O(CH2)2OHCH2NHCH(CH3)2 4.4 -0.6434 -1.369 0.7256

26 O(CH2)2OHCH2NHC(CH3)3 1.2 -0.0792 0.209 -0.2882

NH

X

O

R1O

R

Compd R R1 X Observed activity Predicted

activity

Residuals

IC50 Log I/IC50

7 CH2CH2Ph CH3 N 1.2 -0.0791 -0.142 0.0629

8 CONHPh CH3 N 2.2 -0.3424 0.494 -0.8364

9 CH2SPh CH3 N 0.62 0.2076 0.846 -0.6384

10 COOC2H5 c-C3H5 N 4.4 -0.6434 -0.874 0.2300

11 COOC2H5 CH3 CH 3.3 -0.5185 0.178 -0.6965

12 COOC2H5 CH3 N 1.0 1.0000 0.594 0.4060

21 COOC2H5 Et N 10 -1.0000 0.605 -1.6050

22 COOC2H5 i-propyl N 83 -1.9190 -0.929 -0.9900

29 COOC2H5 H N 79 1.8976 -0.775 -1.1226

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from variable usage indicated that PMI_X, HbondAcc, Density, AlogP, PMI_Y,

MolRef, RotlBonds, MW, Apol, and LUMO contributed more significantly than all

other descriptors. The frequency distribution of the variables used is shown in

Table 4. The variable terms which appeared in the equation clearly indicate that

Table 1 continued

Compd R R1 X Observed activity Predicted

activity

Residuals

IC50 Log I/IC50

23

O

N

N

CH3 N 0.001 3.0000 1.797 1.2030

24

OO

CH3 N 0.003 1.5228 1.596 -0.0732

NH

N

O

O

O R

Compd R Observed activity Predicted

activity

Residuals

IC50 Log I/IC50

13 2-Cynophenyl 0.017 1.7695 1.328 0.4415

14 Phenyl 0.072 1.1426 0.510 0.6326

15 3-Flurophenyl 0.110 0.9586 1.845 -0.8864

16 2,3-Diflurophenyl 0.0037 2.4317 1.870 0.5670

17 2-Flurophenyl 0.0083 2.0809 1.334 0.7469

18 2,3,4-Triflurophenyl 0.0057 2.2441 2.295 -0.0509

19 2,4-Diflurophenyl 0.0075 2.1249 2.133 -0.0081

20 4-Flurophenyl 0.0052 2.2839 1.155 1.1289

27 4-Chlorophenyl 0.073 1.366 1.343 -0.2069

28 4-Cynophenyl 0.0094 2.0268 1.397 0.6298

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structural and spatial descriptors played an important role in the biological activity

of these molecules. The selection of the best model was based on the values of rcv2 ,

LOF, and rpred2 .

Equation 3 shows better internal predictivity (rcv2 = 0.635), external predictivity

(rpred2 = 0.733), and bootstrap r2 (Bsr2 = 0.731) than all the other equation. The

variable usage graph (Table 4) also indicates the use of the descriptors contained in

Eq. 3. Hence, this equation was selected as the best QSAR model to explain the

Table 2 Descriptors used in the present study

No Descriptor Type Description

1 Vm Spatial Molecular volumea

2 Area Spatial Molecular surface areaa

3 Density Spatial Molecular densitya

4 RadOfGyration Spatial Radius of gyrationa

5 PMI–mag Spatial Principle moment of inertiaa

6 PMI–X Spatial Principle moment of inertia X-component

7 PMI–Y Spatial Principle moment of inertia Y-component

8 PMI–Z Spatial Principle moment of inertia Z-component

9 MW Structural Molecular weighta

10 Rotlbonds Structural Number of rotatable bondsa

11 Hbond acceptor Structural Number of hydrogen bond acceptorsa

12 Hbond donor Structural Number of hydrogen bond donorsa

13 AlogP Thermodynamic Logarithm of partition coefficienta

14 MolRef Thermodynamic Molar refractivitya

15 Dipole–mag Electronic Dipole momenta

16 Dipole–X Electronic Dipole moment–X-component

17 Dipole–Y Electronic Dipole moment–Y-component

18 Dipole–Z Electronic Dipole moment–Z-component

19 Charge Electronic Sum of partial chargesa

20 Apol Electronic Sum of atomic polarizabilitiesa

21 HOMO Electronic Highest occupied molecular orbital energy

22 LUMO Electronic Lowest unoccupied molecular orbital

energy

23 Sr Electronic Superdelocalizability

24 Foct Thermodynamic Desolvation free energy for octanol

25 Fh2o Thermodynamic Desolvation free energy for water

26 Hf Thermodynamic Heat of formation

27 DIFFV MSA Difference volume

28 COSV MSA Common overlap steric volume

29 Fo MSA Common overlap volume ratio

30 NCOSV MSA Non-common overlap steric volume

31 Shape RMS MSA RMS to shape reference

32 SR Vol MSA Volume of shape reference molecule

a Default descriptor

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observed variation in the biological activity of the molecules under study. Terms

involving structural and spatial descriptors contribute to this equation and explain

about 73% variance in the biological activity. The observed and calculated values

for the training and test set molecules are given in Table 1. The plot of calculated

activity values for the training set molecules and the predicted versus the observed

activity values for the test set molecules are shown in Figs. 1 and 2, respectively,

Equations 3, 4, 7, and 8 (Table 3) contain reasonable statistical terms, but Eq. 3 in

Table 3 QSAR equations generated using genetic function approximation for the training set

No. Equation LOF r2 arcv2 bBS r2 F-value rpred

2

Model 1 BA = - 2.7589 + 0.0092 PMI_X

– 0.3074 Rotlbonds + 0.4388 Hbond Acc

0.914 0.736 0.630 0.737 18.569 0.682

Model 2 BA = – 1.8205 – 0.5509 Rotlbonds

+ 0.2954 Fh2O + 0.4177Dipole–X

0.914 0.735 0.558 0.738 18.526 0.239

Model 3 BA = - 4.2699 + 0.0070 PMI–X

– 0.0013 PMI–Y + 0.4998 Hbond Acc

0.934 0.730 0.635 0.731 18.013 0.773

Model 4 BA = - 1.4397 + 0.0093PMI_X

– 0.4557 Rotlbonds – 0.1098 Fh2O

0.994 0.727 0.610 0.729 17.763 0.719

Model 5 BA = - 5.2823 + 1.2823 LUMO

– .0019 PMI_Y + 0.8235 Hbond Acc

0.953 0.725 0.599 0.702 17.537 0.742

Model 6 BA = - 21.6399 + 0.0061 PMI_X

+ 0.4200 Sr + 17.9082 Density

0.971 0.719 0.599 0.721 17.084 0.019

Model 7 BA = - 0.6065 + 0.0112 PMI_X

0.0635 MolRef – 0.1123 Foct

0.979 0.717 0.617 0.718 16.879 0.726

Model 8 BA = - 3.3993 + 0.0118 PMI_X

– 0.0002 Apol + 0.3438 HbondAcc

0.986 0.715 0.608 0.716 16.719 0.753

Model 9 BA = - 6.1863 + 0.0088 PMI_X

+ 0.4585 HbondAcc + 0.320 Sr

0.990 0.714 0.593 0.715 16.617 0.482

Model 10 BA = - 0.9557 + 0.0077 PMI_X

– 0.4638 Rotlbonds – 0.1026 Foct

0.992 0.713 0.594 0.715 16.582 0.755

BA in all the equations represents log (1/IC50) in terms of lM, a rcv2 is cross-validated r2, b BS r2 is the

bootstrapping r2, and rpred2 is the predictive r2

Table 4 Frequency distribution

of the variablesDescriptor Frequency

PMI_X 85

HbondAcc 40

Density 22

AlogP 19

PMI_Y 17

MolRef 16

RotlBond 14

MW 11

Apol 10

LUMO 10

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comparison with the other three equations can be characterized by slightly higher rcv2

and rpred2 values. Therefore in the present study Eq. 3 was chosen as a model that

best describes the inhibition of Nmt enzyme of C. albicans by benzofuran analogs.

The statistical significance of the relationship between enzyme inhibition and

physicochemical descriptors was further demonstrated by the randomization

procedure. The r2 values for the 50 trials using permuted data are shown in

Fig. 3. The mean of r2 for 49 randomization tests was used to assess the reliability

of the model; its value was 0.269. It is evident that the r2 value of the original model

is much higher than that of the trials with the permuted data, suggesting robustness

of the model.

Discussion

The C. albicans Nmt inhibition by the series of benzofuran antifungals is thus a

function of PMI_X, PMI_Y, and HbondAcc. PMI_X signifies the principle moment

-2

-1

0

0.5

1

1.5

2

-2 -1 0 1 2

Cal

cula

ted

act

ivit

y

-0.5

-1.5

-2.5 -1.5 -0.5 0.5 1.5 2.5

Observed activity

Fig. 2 Plot of predicted versus observed activity of the molecules of the test set

-2

-1

0

1

2

-2 -1 0 1 2 3 4

Observed activity

2.5

1.5

0.5

-0.5

-1.5

Cal

cula

ted

act

ivit

y

Fig. 1 Plot of calculated versus observed activity of the molecules of the training set

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of inertia of the molecule along X-axis. Positive correlation indicates that the

component of the molecule oriented along the X-axis is significant for better

activity. Similarly, the negative correlation of PMI_Y indicates that the component

of the molecule oriented along the Y-axis should not be bulky. HbondAcc is a

structural parameter and the positive correlation of this term with activity indicates

that, the higher the number of H-bond acceptor groups in the molecule, the more

active it will be. From Table 4 it is evident that the descriptors PMI_X, PMI_Y, and

HbondAcc have been used maximum times. This indicates that these three

descriptors have higher relevance to the prediction of activity.

PMI_X is a spatial descriptor and indicates the orientation and conformational

rigidity of the molecule; its value depends on the total mass distribution within the

Fig. 4 Alignment of the training set molecules

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

r2 o

f G

FA

mo

del

s

Trials

Fig. 3 GFA randomized tests. The first bar shows the r2 value for the model based on the actual data.The other 49 bars show the r2 value for the model based on permuted data

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molecule. Thus the orientation of the substructure of the molecule at C2 position

with its hydrogen bonding acceptor functional group is important for enzyme

inhibition. In the most active molecule (23) the imidazole ring nitrogen is accessible

for hydrogen-bond interaction with active site residues. In the case of molecules 13–20, 22, 27, and 28, the C2 substituent with ether linkage are slightly away from the

direction of the X-axis. This explains the lower activity of these molecules as

compared with 23. Molecule 8, with amide linkage, and molecules 1–6, 25, and 26,

containing ethyl ester, are highly inactive because these groups are oriented away

from the X-axis and also because of the lower hydrogen-bonding capacity of the

amide and ester linkage than the ether functional group.

PMI_Y indicates the conformational and rigidity of the substituent at the C4

position of benzofuran ring, which is important for biological activity. As PMI also

represents the mass distribution of the functional group oriented in the direction of

the Y-axis within the molecule, its negative correlation hints that the substitution at

C4 oxygen should be rigid, not bulky, and optimal, as in molecule 23. Molecule 23

shows that an increase in the space between the C4 oxygen and the amine of the

alkyl group will be detrimental to activity. For example, molecules 4 and 5, which

contain longer alkyl side chains, are inactive. Therefore this indicates the optimum

spacer length to be three. It is observed that molecules containing rigid functional

groups such as 3-pyridylmethyl on the nitrogen of the alkyl amine at the C4 position

of the benzofuran ring are more active than that of nonrigid functional groups such

as isopropyl and tertiary butyl. This may be due to the nonrigid nature of the

isopropyl and tertiary butyl groups, which makes the alkylamino side chain orient

away from the Y-axis, thereby making the molecules lose vital interaction at the

active site of enzyme. The observed variance in the inhibitory activity among 3-

pyridylmethyl substituted molecules must be due to the influence of other

descriptors.

Positive correlation of HbondAcc with the enzyme inhibitory activity indicates

that molecules should have more hydrogen acceptor groups to show potent activity.

Functional groups such as ether, ring nitrogen (tertiary ‘N’), ring oxygen, etc. are

known to act by forming hydrogen bonds with the donor residues in the enzyme

active site. Molecules with the above functional groups are highly active (13–20, 22,

23, 24, 27, and 28), whereas molecules 1–12, 21, 22, 25, 26, and 29 with few

hydrogen bond acceptors groups at the C4 and C2 positions than the most active

molecule are less active. In most of the active molecules ether linkage oxygen has

more hydrogen bond acceptors capacity than thioether, ester oxygen, and amide

carbonyl functional group of inactive molecules (hydrogen bond acceptors capacity:

–N= [ –O– [ COO [ –S–[ CONH [ –C–). Additionally in active molecules, the

pyridyl nitrogen at the C4 side chain also contributes towards activity by its

hydrogen-bond interaction with the enzyme active site (enzyme–ligand interaction,

explained at the end of discussion). Among the inactive molecules, molecules

containing ester oxygen exhibit better activity than those of the thioether-, amide-,

and alkyl linkage-containing molecules 7, 8, and 9, respectively. The importance of

3-pyridylmethylgroups as an additional hydrogen-bond acceptor functional group is

explained by examining molecules 1–6, 12, 25, and 26. In all of these molecules the

ester functional group at the C2 position is common. Whereas the observed potent

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activity of 12 is due to the presence of the 3-pyridylmethyl group. The moderate

activity of the molecule 24, even though it contains ether and 3-pyridylmethyl

groups, may be due to the inaccessibility of the ether functional group to the enzyme

active site.

Thus the overall activity will depend on the combination of the three descriptors.

The results are complimentary to the preliminary structure–activity relationship

(SAR) reported by Tatsuo et al. (Masubuchi et al., 2001; Ebiike et al., 2002; Sogabe

et al., 2000).

The X-ray crystal structure of Ca Nmt (PDB: 1IYL) with the nonpeptidic

inhibitor (23) shows that the pocket of the inhibitor-binding site is composed of

aromatic residues Tyr107, Phe115, Phe117, Tyr119, Phe196, Tyr225, Phe240,

Tyr335, Phe339, and Tyr354. The benzofuran ring is stacked parallel to Tyr225 and

perpendicular to Tyr354, in the proximity Phe117 and Phe339. These residues are

presumed to be important not only for the architecture of the binding site but also

for inhibitor binding. His227 is located in the proximity of the oxygen atom of the

benzofuran ring. The hydrogen-bond interaction of the benzofuran ring with the

imidazole ring of His227 and the geometry of the benzofuran ring may contribute to

the inhibitor binding.

The secondary amine group of the substituent at position C4 of the benzofuran

ring makes a hydrogen bond with the C-terminal carboxylate of Leu451. Thus its

orientation accounts for structure activity. Tyr119 is in the proximity of the pyridine

ring. The change of the substituent from a t-butyl group to a 3-pyridylmethyl group

improves the inhibitory activity due to the additional hydrogen-bond interaction

with the side chain of Asn392 positioned in close proximity to Phe240.

The descriptors of the selected Eq. 3 are consistent with the interaction of the

molecule with the enzyme active site. The spatial factor PMI_Y (secondary amine

in the molecules) is crucial for enzyme inhibitory activity. Any substituent which

ensures proper orientation of hydrogen-bond acceptor groups in the molecule for

potent enzyme inhibitory activity. Any substituent which has hydrogen-bond

acceptor capacity at positions C4 and C2, which can form hydrogen bond with

Tyr119 and Asn392, respectively, with proper steric bulk and orientation, will

increase the potency of molecule. The benzofuran scaffold can be successfully

replaced with bicyclic or tricyclic rings with proper hydrogen-bond acceptor atoms

to interact with His227 to increase the potency. At the ether linkage containing

molecules at C2 position, the hydrogen bond forming ether oxygen should be near

to the Asn392 and its bulky substituent should be properly oriented towards

hydrophobic aromatic residues Phe115, Phe240 and Phe339 of enzyme active site.

Conclusions

A QSAR model was derived using GFA for a series of benzofuran antifungals. The

best model generated correlates with the antifungal activity with PMI_X, PMI_Y,

and HbondAcc, explaining the significance of the orientation of the molecule along

the X and Y-axes and of hydrogen-bond acceptors. The model has moderate internal

and external predictivity, as shown by the values of rcv2 of 0.635 and of rpred

2 of

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0.773. The statistical significance and robustness of the model has been confirmed

by a randomization study. Thus in the series of benzofuran antifungals, molecules

with substituents with proper orientation towards the X- and Y-axes and hydrogen-

bond accepting capacity may enhance enzyme inhibition activity and, in turn,

antifungal activity. On the basis of the developed QSAR models, novel molecules

can be designed as potential Nmt inhibitors.

Materials and methods

Data set

Twenty-nine molecules selected for the present study were taken from the published

work (Masubuchi et al., 2001; Ebiike et al 2002; Sogabe et al., 2000). These

molecules have been reported as C. albicans Nmt enzyme inhibitors. The negative

logarithm of the 50% inhibitory concentration (IC50) expressed in lM was used as

the biological activity in the 3D-QSAR study, thus correlating the data linearly to

the free energy change. A training set of 24 four molecules (molecules 1–24,

Table 1) was used to generate the QSAR models. The training set molecules were

selected in such a way that they contained information in terms of both their

structural features and biological activity ranges. The most active molecules,

moderately active, and less active molecules were included, to spread out the range

of activities (Golbraikh et al., 2003). To assess the predictive power of the model, a

set of five molecules (molecules 25–29, Table 1) was arbitrarily set aside as the test

set. The test molecules were selected in such way that they truly represent the

training set.

Model building

All molecular modeling studies were carried out using Cerius2 (Version 3.5 and

4.10L) running on Silicon Graphics O2 R5000 and Intel Pentium IV 3.0 GHz

workstations. Structures were constructed and partial charges assigned using the

charge equilibrium method within Cerius2; throughout the study universal force

field 1.02 was used (Rappe et al., 1992). The molecules were subsequently

minimized using the smart minimizer until a root-mean-square deviation of

0.001 kcal/mol A was achieved before use in the study (Rappe and Goddard, 1991).

Conformational sampling

The local minimized geometry was used as the initial structure for conformational

analysis. Conformational ensembles were generated by random sampling using a

rotation increment of 10 kcal/mol for all the torsional angles. In order to restrict the

number of conformers generated to a maximum of 500, conformers with an energy

threshold value of more than 10 kcal/mol above that of the local minimized

structures were rejected, thus selecting only energetically stable conformers.

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Calculation of descriptors

Different physicochemical descriptors were calculated for each molecule in the

study table using the default settings within Cerius2. The descriptors included are

electronic, spatial, structural, thermodynamic, and molecular shape analysis (MSA)

descriptors. All molecules were aligned on the lowest-energy conformer (molecule

23) with energy 210.346 kcal/mol, which was taken as the reference for calculation

of MSA. A complete list of the descriptors used in the study is given in Table 2. For

quantum-mechanical descriptors, the highest occupied orbital energy (HOMO),

lowest unoccupied molecular orbital (LUMO), super delocalizabilty (Sr), etc., were

calculated using the PM3 Hamiltonian.

Generation of QSAR models

QSAR has been a method to derive analysis establishes relationship between

physiochemical properties and biological activity of molecules. In the present study,

QSAR model generation was carried out using the GFA technique using 20,000

crossovers, a smoothness value of 1.00, and other settings at their default values.

GFA was asked to consider not more than four terms in the equation. The set of

equations generated was evaluated on the following basis:

a. LOF

b. Variable term in the equation.

c. Predictivity of the equation (predictive r2 value)

Validation of QSAR models

The overall objective of a QSAR procedure is to derive a model that is optimally

predictive. A model that does a good job of predicting the activities of molecules on

which it is based must be tested to see if any of the data in the test set are data that

affect the model excessively. This is done using the QSAR validation procedure. It

is useful to assess the reliability and significance of QSAR models. Validation can

be done by using cross-validation test, the bootstrap test, and by randomization test.

The cross-validation process repeats regression many times on subsets of data.

Usually each molecule is left out in turn, and the r2 value is computed using the

predicted values of the missing molecules (the cross-validated r2). If molecules are

removed N at a time from a total set of M, then N 9 M regressions are performed.

Bootstrapping involves the generation of many new data sets from the original data

set by randomly choosing samples from the original data set (Cramer III et al.,1988). The bootstrap r2 (Bsr2) value is the average squared correlation coefficient. A

Bsr2 value is computed from the subset of variables used one at a time for the

validation procedure. A bootstrap r2 value can be used more than once in computing

the r2 statistic. The randomization test is done as follows: (1) repeatedly permuting

the activity values of the data set, (2) using permuted values to generate QSAR

model, and (3) comparing the resulting mean r2 with the r2 value of the original

QSAR model generated from nonrandomized activity values (Verma et al., 2008). If

Med Chem Res

the original QSAR model is statistically significant, its r2 should be significantly

better than the r2 from permuted data.

Predictivity of the equation

Each QSAR equation was assessed by predicting the activities of the test set

molecules and these predictions were expressed as predictive r2 (rpred2 ) values,

r2predicative ¼

SD� PRESS

SD

where SD is the sum of the squared deviation between biological activities of the

test set molecules and mean activity of the training set, and PRESS is the sum of the

squared deviation between the predicted and actual activities of the test set.

Observed and predicted activities of the molecules along with residuals are

presented in Table 1.

Acknowledgements One of the authors (H.S.D.) is thankful to CSIR, New Delhi for awarding a Senior

Research Fellowship (SRF). The authors would like to thank Dr. Shivajirao S. Kadam Vice-Chancellor,

Bharati Vidyapeeth University, Pune for encouragement.

References

Benedetti MS, Bani M (1999) Metabolism-based drug interactions involving oral azole antifungals in

humans. Drug Metab Rev 31:665–717. doi:10.1081/DMR-100101941

Chandavarkar MA, Kulkarni VM, Shivkumar P, Shetty RS, Bapat UR (2003) 1,2,4-Triazol-1-yl-propan-

2-ol derivatives as anti-fungal agents. World Patent WO 068142.

Chandavarkar MA, Kulkarni VM, Shivkumar P, Shetty RS, Bapat UR (2005) Novel antifungal agents.

U.S. Pat Appl 0020653:A1

Cramer RD III, Bunce JD, Petterson DE, Frank IE (1988) Crossvalidation, bootstrapping, and partial least

squares compared with multiple regression in conventional QSAR studies. Quant Struct Act Relat

7:18–25. doi:10.1002/qsar.19880070105

Devadas B, Freeman SK, Zupec ME, Lu HF, Nagarajan SR, Kishore NS et al (1997) Design and synthesis

of novel imidazole-substituted dipeptide amides as potent and selective inhibitors of Candida

albicans myristoylCoA:protein N-myristoyltransferase and identification of related tripeptide

inhibitors with mechanism-based antifungal activity. J Med Chem 40:2609–2625. doi:10.1021/

jm970094w

Duronio RJ, Towler DA, Heuckeroth RO, Gordon JI (1989) Disruption of the yeast N-myristoyl

transferase gene causes recessive lethality. Science 243:796–800. doi:10.1126/science.2644694

Ebiike H, Masubuchi M, Liu P, Kawasaki K, Morikami K, Sogabe S et al (2002) Design and synthesis of

novel benzofurans as a new class of antifungal agents targeting fungal N-myristoyltransferase. Part

2. Bioorg Med Chem Lett 12:607–610. doi:10.1016/S0960-894X(01)00808-3

Gokhale VM, Kulkarni VM (2000a) Understanding the antifungal activity of terbinafine analogues using

quantitative structure–activity relationship (QSAR) models. Bioorg Med Chem 8:2487–2499. doi:

10.1016/S0968-0896(00)00178-4

Gokhale VM, Kulkarni VM (2000b) Selectivity analysis of 5-(arylthio)-24-diaminoquinazolines as

inhibitors of Candida albicans dihydrofolate reductase by molecular dynamics simulations. J Comput

Aided Mol Des 14:1573–4951. doi:10.1023/A:1008189724803

Golbraikh A, Shen M, Xiao Z, Xiao Y, Lee K, Tropsha A (2003) Rational selection of training and test

sets for the development of validated QSAR models. J Comput Aided Mol Des 17:241–253. doi:

10.1023/A:1025386326946

Johnson DR, Bhatnagar RS, Knoll LJ, Gordon JI (1994) Genetic and biochemical studies of protein

N-myristoylation. Annu Rev Biochem 63:869–914. doi:10.1146/annurev.bi.63.070194.004253

Med Chem Res

Karki RG, Kulkarni VM (2001a) Three-dimensional quantitative structure—activity relationship

(3D-QSAR) of 3-aryloxazolidin-2-one antibacterials. Bioorg Med Chem 9:3153–3160. doi:10.1016/

S0968-0896(01)00186-9

Karki RG, Kulkarni VM (2001b) A feature based Pharmacophore for Candida albicans MyristoylCoA:

protein N-myristoyltransferase inhibitors. Eur J Med Chem 36:147–163. doi:10.1016/S0223-

5234(00)01202-2

Karki RG, Kulkarni VM (2001c) Computer-aided design and synthesis of Candida albicansN-myristoyltransferase inhibitors as antifungal agents. Indian Drugs 38:406–413

Kawakami Y, Inoue A, Kawai T, Wakita M, Sugimoto H, Hopfinger AJ (1996) The rationale for E2020

as a potent acetylcholinesterase inhibitor. Bioorg Med Chem Lett 4:1429–1446. doi:10.1016/0968-

0896(96)00137-X

Kharkar PS, Desai B, Gaveria H, Varu B, Loriya R, Naliapara Y et al (2002) Three-dimensional

quantitative structure-activity relationship of 14-dihydropyridines as antitubercular agents. J Med

Chem 45:4858–4867. doi:10.1021/jm020217z

Lodge JK, Machelski E, Toffaletti DL, Perfect JR, Gordon JI (1994) Targeted gene replacement demonstrates

that myristoyl-CoA: protein N-myristoyltransferase is essential for viability of Cryptococcus

neoformans. Proc Natl Acad Sci USA 91:12008–12012. doi:10.1073/pnas.91.25.12008

Masubuchi M, Ken-ichi K, Ebiike H, Ikeda Y, Tsujii S, Sogabe S et al (2001) Design and synthesis of

novel benzofurans as a new class of antifungal agents targeting fungal N-myristoyltransferase Part 1.

Bioorg Med Chem Lett 11:1833–1837. doi:10.1016/S0960-894X(01)00319-5

Nagarajan SR, Devadas B, Zupec ME, Freeman SK, Brown DL, Lu HF et al (1997) Conformationally

constrained [p-(omega-aminoalkyl)phenacetyl]-L-seryl-L-lysyl dipeptide amides as potent pepti-

domimetic inhibitors of Candida albicans and human myristoyl-CoA:protein N-myristoyl

transferase. J Med Chem 40:1422–1438. doi:10.1021/jm9608671

Purushottamachar P, Kulkarni VM (2003) 3D-QSAR of N-myristoyltransferase inhibiting antifungal

agents by CoMFA and CoMSIA methods. Bioorg Med Chem 5:3487–3497. doi:10.1016/S0968-

0896(03)00305-5

Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM (1992) UFF a full periodic table force field

for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114:10024–10039.

doi:10.1021/ja00051a040

Rappe AK, Goddard WA (1991) Charge equilibration for molecular dynamics simulations. J Phys Chem

95:3358–3363. doi:10.1021/j100161a070

Rogers D, Hopfinger AJ (1994) Application of genetic function approximation to quantitative structure-

activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci

34:854–866. doi:10.1021/ci00020a020

Sachan N, Kadam SS, Kulkarni VM (2007) Human protein tyrosine phosphatase 1B inhibitors: QSAR by

genetic function approximation. J Enzyme Inhib Med Chem 22:267–276. doi:10.1080/1475636060

1051274

Sogabe S, Masubuchi M, Sakata K, Fukami TA, Morikami K, Shiratori Y et al (2000) Crystal structures

of Candida albicans N-myristoyltransferase with two distinct inhibitors. Chem Biol 9:1119–1128.

doi:10.1016/S1074-5521(02)00240-5

Talele TT, Kulkarni SS, Kulkarni VM (1999) Development of pharmacophore alignment models as input

for comparative molecular field analysis of a diverse set of azole antifungal agents. J Chem Inf

Comput Sci 39:958–966. doi:10.1021/ci990020o

Verma J, Khedkar VM, Prabhu AS, Khedkar SA, Malde AK, Coutinho EC (2008) A comprehensive

analysis of the thermodynamic events involved in ligand—receptor binding using CoRIA and its

variants. J Comput Aided Mol Des 22:91–104. doi:10.1007/s10822-008-9172-0

Wagh NK, Deokar HS, Juvale DC, Kadam SS, Kulkarni VM (2006) 3D-QSAR of histone deacetylase

inhibitors as anticancer agents by genetic function approximation. Indian J Biochem Biophys

43:360–371

Weinberg RA, McWherter CA, Freeman SK, Wood DC, Gordon JI, Lee SC (1995) Genetic studies reveal

that myristoylCoA: protein N-myristoyltransferase is an essential enzyme in Candida albicans. Mol

Microbiol 16:241–250. doi:10.1111/j.1365-2958.1995.tb02296.x

Wilcox C, Hu JS, Olson EN (1987) Acylation of proteins with myristic acid occurs cotranslationally.

Science 238:275–278. doi:10.1126/science.3685978

Med Chem Res