This article was downloaded by: [University of Hong Kong Libraries]On: 15 March 2013, At: 11:20Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Molecular SimulationPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/gmos20

3D QSAR pharmacophore-based virtual screening andmolecular docking studies to identify novel matrixmetalloproteinase 12 inhibitorsUdghosh Singh a , Rahul P. Gangwal a , Rameshwar Prajapati a , Gaurao V. Dhoke a & Abhay T.Sangamwar aa Department of Pharmacoinformatics, National Institute of Pharmaceutical Education andResearch (NIPER), Sect-67, S.A.S. Nagar, Mohali, Punjab, 160062, IndiaVersion of record first published: 25 Oct 2012.

To cite this article: Udghosh Singh , Rahul P. Gangwal , Rameshwar Prajapati , Gaurao V. Dhoke & Abhay T. Sangamwar (2013):3D QSAR pharmacophore-based virtual screening and molecular docking studies to identify novel matrix metalloproteinase 12inhibitors, Molecular Simulation, 39:5, 385-396

To link to this article: http://dx.doi.org/10.1080/08927022.2012.731506

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form toanyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contentswill be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses shouldbe independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims,proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly inconnection with or arising out of the use of this material.

3D QSAR pharmacophore-based virtual screening and molecular docking studies to identifynovel matrix metalloproteinase 12 inhibitors

Udghosh Singh1, Rahul P. Gangwal1, Rameshwar Prajapati, Gaurao V. Dhoke and Abhay T. Sangamwar*

Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sect-67, S.A.S. Nagar,Mohali, Punjab 160062, India

(Received 15 June 2012; final version received 13 September 2012)

Matrix metalloproteinase 12 (MMP-12) is a potential therapeutic target for the treatment of chronic obstructive pulmonarydisorder and other inflammatory disorders. Ligand-based 3D QSAR pharmacophore modelling approach was employed toreveal structural and chemical features necessary for the inhibition of MMP-12. The best HypoGen pharmacophore modelHypo1 for MMP-12 inhibitors contains two hydrogen bond acceptors, one hydrophobic aliphatic and one hydrophobicaromatic feature. Molecular docking studies of all inhibitors showed hydrogen bond interactions with important amino acids(Glu219, Ala182 and Leu181), and these interactions were compared with Hypo1, which shows that the Hypo1 has a goodpredictive ability. The best pharmacophore hypothesis was further cross-validated using test set, decoy set and Cat-Scramblemethodology.Thevalidated pharmacophoremodelHypo1was used for screening the chemical databases of small compounds,including Specs, NCI and ChemDiv, to identify the new compounds that are presumably able to act as MMP-12 inhibitors.The screened virtual hits fromHypo1were subjected to several filters such as toxicity, quantitative estimation of drug-likenessandmolecular docking analysis. Finally, four novel compoundswith diverse scaffolds were selected as possible candidates forthe designing of potent MMP-12 inhibitors.

Keywords: molecular docking; matrix metalloproteinase 12; pharmacophore; virtual screening; 3D QSAR

1. Introduction

Matrix metalloproteinase (MMP) is being extensively

studied for their evident role in carcinogenesis and cellular

invasion by catabolising the extracellular matrix (ECM) [1].

In human, 23 structurally related MMP enzymes are known.

Theseenzymesare zinc-dependent endopeptidases, perform-

ing different catalytic activities and are grouped into

interstitial collagenases (MMP-1, -8, -13, -14) that cleave

fibrillar collagens, gelatinases (MMP-2 and -9) that

efficiently cleave denatured collagen (i.e. gelatin) and

stromelysins (MMP-3, -7, -10, -11) that have a broad

specificity but do not cleave intact fibrillar collagen. Matrix

metalloelastase (MMP-12), which is more distantly geneti-

cally related to other MMPs cleaves other ECM components

as well as elastin [2]. MMP-12 is a 54-kDa protein, secreted

as its pro-form that undergoes self-activation to produce

several active forms of the enzymes. Structurally, it is closely

related to other MMPs. It shares 49% sequence similarity

with MMP-3 and MMP-1. MMP-12 is involved in

inflammatory processes and contributes to tissue remodelling

and tissue degradation. MMP-12 is the primary elastolytic

enzyme of alveolarmacrophages [3]. Studies have suggested

that MMP-12 plays a crucial role in pathophysiology of

chronic obstructive pulmonary disorder (COPD)/emphy-

sema [4]. COPD is characterised by abnormal inflammatory

response of the lung to noxious particles or gas and is mainly

caused by cigarette smoke. COPD is one of the top five

leading causes of mortality, which is expected to increase in

near future due to increased ill habits such as smoking and

also due to environmental factors [5].

Selective blockage of MMP-12 has been shown to be a

valid approach for therapeutic intervention of COPD.

Presently, there are only symptomatic therapies, and

no disease-modifying drugs are available for this indication

[6–8]. In fact, Le Quement et al. [9] reported a selective

MMP-12 inhibitor, AS1117934, which was able to prevent

inflammation induced by exposure to cigarette smoke in

mice. In another study,Markus et al. reported the structure of

the catalytic domain ofMMP-12 complexwith a carboxylate

inhibitor, using the wild-type protein sequence. The

carboxylate inhibitor was shown to coordinate functionally

with catalytic zinc in the S10 pocket [10]. Bhaskaran et al.

[11] determined the nuclear magnetic resonance (NMR)

structure of MMP-12 in the absence of an inhibitor, using a

mutant form ofMMP-12, which prevents autolysis. Morales

et al. published the crystal structure of MMP-12 catalytic

domain complex with non-zinc chelating inhibitors. The

study revealed the importance of hydrophobic interactions

between the residues within S10 pocket and the inhibitors,

which gave an insight towards designing of selective

inhibitors for this enzyme [12]. Apart from the structural

details, some promising studies regarding the development

ofMMP-12 inhibitors have alsobeenpublished; these are the

q 2013 Taylor & Francis

*Corresponding author. Email: abhays@niper.ac.in

Molecular Simulation, 2013

Vol. 39, No. 5, 385–396, http://dx.doi.org/10.1080/08927022.2012.731506

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

phosphinic peptides discovered by Dive et al. [13] and the

non-zinc chelating, non-peptidic inhibitors published by

Dublanchet et al. [14]. Elisa et al. [15] reported the design,

synthesis and in vitro evaluation of a new series of

compounds, possessing an arylsulphonyl scaffold, for their

potential as selective inhibitors of MMP-12.

The main aim of this study is to identify the basic

structural requirements for MMP-12 inhibitory activity,

thereby designing novel and potent inhibitors as anti-

inflammatory agents. A ligand-based pharmacophore

modelling approach has been employed to achieve this

goal. The validated pharmacophore hypothesis was used in

virtual screening to identify the potent lead. The screened

virtual hits were subjected to several filters such as estimated

activity, toxicity, quantitative estimation of drug-likeness

(QED) and molecular docking studies using Glide5.5. We

have reported four novel compounds with diverse scaffolds

as possible candidates for the designing of potent MMP-12

inhibitors. Finally, potential lead compounds will be shifted

to the subsequent in vitro and in vivo studies.

2. Materials and methods

2.1 Data-sets

Adatabase of known inhibitors was collected to develop and

validate a pharmacophore model for the identification of

probable MMP-12 inhibitors. The selection of suitable

training set is an important step, which assists in determining

the quality of generated pharmacophore. In the last decade,

numbers of compounds were reported as MMP-12

inhibitors. Out of these, a data-set of 108 inhibitors was

selected from the literature based on the biological assay

method and used for the generation of the pharmacophore

model [16–19]. The inhibitory activities of these inhibitors

were expressed as IC50 value (i.e. the half maximal

inhibitory concentration (IC50) is a measure of the

effectiveness of a compound in inhibiting a biological or

biochemical function), which spanned across a wide range,

0.1–54,400 nM. The workflow followed during pharmaco-

phore modelling is shown in Figure 1.

HypoGen pharmacophore model was created using 16

training set compounds (Figure 2), which were selected

based on the principles of structural diversity and activity

range. The data-set selected covered an activity range of at

least five orders of magnitude, and the most active

compound was also included in the training set. The rest

of the 92 compounds from the data-set were used for the

validation of the generated pharmacophore model. For

evaluationpurpose, the activity valueswere classifiedbased

on an MMP-12 inhibitory activity scale: highly active

(þþþ , IC50 , 50 nM), moderately active (þþ , 50 nM ,

IC50 , 500 nM) and least active (þ , IC50 . 500 nM).

Numerous conformations for all training set compounds

Figure 1. The stepwise description of methodology followed during the pharmacophore modelling of MMP-12 inhibitors.

U. Singh et al.386

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

were generated using Cat-Conf program within DS2.5

software package [20]. The best method was employed for

generating conformations, which provides a complete and

improved coverage of the conformational space by

performing a rigorous energy minimisation and optimising

the conformations in both torsional and Cartesian space

using the poling algorithm [21]. In this method, the

maximum number of conformers was set to 255 with

4.0 kcal/mol as energy cut-off, and all other parameters

were set to default. Pharmacophore models were then

generated using HypoGen algorithm implemented in

Accelrys Discovery Studio2.5 (DS2.5), which generates

hypotheses with features common among active molecules

and missing from the inactive or least active molecules.

2.2 Pharmacophore hypotheses generation

In HypoGen algorithm, the process of pharmacophore

hypothesis generation is accomplished in three steps, namely

a constructive step, a subtractive step and an optimisation

step [22]. In constructive step, hypotheses features common

among the active compounds are identified. The determi-

nation of active compound involves the simple calculation

based on the activity and uncertainty values. Thus,

uncertainty value is a crucial parameter in constructive

step. The hypotheses features common among inactive

compounds are removed from the previous result in the

subtractive step. The consequential hypotheses are then

optimised using simulated annealing to further fine tune the

model parameters, thereby improving model quality.

Figure 2. Chemical structures of training set compounds and their IC50 (nM) values in parentheses.

Molecular Simulation 387

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

The quality of the mapping between a compound and a

hypothesis is indicated by the fit value.

Considering the chemical nature of the inhibitors, a

minimum of zero to a maximum of five features were

selected to generate pharmacophore hypothesis including

hydrogen bond acceptor (HBA), hydrogen bond donor

(HBD), hydrophobic aromatic (HYAr), hydrophobic ali-

phatic (HYA)andnegative ionisable.Basedon these features

of 16 compounds in the training set, 10 pharmacophore

hypotheses with significant statistical parameters were

generated using 3D QSAR pharmacophore generation

module within DS2.5 software package on IBM graphic

workstation. The default uncertainty value of 3 was used

during the generation of the pharmacophore model, which

means that the biological activity of a particular inhibitor is

believed to be located somewhere in the range three times

higher to three times lower of the true value of those

inhibitors. The quality of generated HypoGen pharmaco-

phore models is best described in terms of fixed cost, null

cost, total cost and other statistical parameters. The best

model was selected on the basis of high correlation

coefficient (r), lowest total cost, highest cost difference and

low root mean squared deviation (RMSD) values [23,24].

2.3 Pharmacophore model validation

The main purpose of validation of the generated pharmaco-

phore model is to determine whether it is capable of

differentiating between active and inactive or least active

compounds and predicting their activities accurately [25]. To

validate quantitative ligand-based pharmacophore model,

three different methods were employed based on cost

analysis, test set prediction and Fischer randomisation test.

During cost analysis, several theoretical cost values

(represented in bit units) were calculated, namely fixed

cost, null cost and total cost. In addition to this, three other

parameters that alsoplayan important role in determining the

qualities of a generated hypothesis are weight cost,

configuration cost and error cost. Weight cost is a value

that depends on the deviation of the featureweight in amodel

from an ideal value of 2. The configuration cost is log 2P,

where P is the number of initial hypotheses created in the

constructive phase, which survived the subtractive phase.

The standard configuration cost value should not be.17.0.

The deviation between the actual and predicted activities of

the training set is signified by the error cost. The fixed cost

represents the entropy of the hypothesis space. The total cost

is calculated for every developed pharmacophore hypothesis

bysummingup these three cost factors.However, error cost is

the major contributor to the total cost. HypoGen also

calculates the cost value for null and ideal hypotheses. The

null cost represents the cost of a hypothesis with no features

that estimate every activity to be the average activity (null

hypothesis). Ideal hypothesis is the most likely hypothesis,

which correlates the data well. The hypothesis does not

reflect a chance correlation if the difference between ideal

and null hypotheses cost value is higher [26]. In test set

validation, the data-set of 92 inhibitors as representative of

the learning series with a wide range of MMP-12 inhibitory

activity was used to validate the best HypoGen pharmaco-

phore model. Fischer randomisation methodology

(Cat-Scramble) was employed with a goal to check whether

there is a strong correlation between the chemical structures

and the biological activity in the training set [27]. During this

validation, 49 random pharmacophore hypotheses were

generated to achieve 98% confidence level.

Finally, for the validation of pharmacophore model

(Hypo1), decoy set was generated using DecoyFinder1.1.

Decoyswere selected if they are similar to the active ligands

according to five physical descriptors (molecular weight,

number of rotational bonds, total HBDs, total HBA and the

octanol–water partition coefficient) without being chemi-

cally similar to any of the active ligands. Twenty active

MMP-12 inhibitors were included in the database to

calculate various statistical parameters such as accuracy,

precision, sensitivity, specificity, goodness of hit score

(GH) and enrichment factor (E-value). GH and E-value are

the two major parameters, which play an important role in

identifying the capability of the generated pharmacophore

hypothesis.

2.4 Virtual screening

Virtual screening of known chemical databases is a fast and

precise method, which helps in identifying novel and

potential leads suitable for further development [28]. It is

advantageous over any de novo design methods because

virtual hits from the pharmacophore screening can be easily

obtained for biological testing [29]. From the available two

(Fast/Flexible and Best/Flexible) database searching

options, we have used Best/Flexible search option for

performing virtual screening. The validated HypoGen

pharmacophore model (Hypo1) was used as 3D query in

database screening. Three commercially available data-

bases (NCI, Specs and ChemDiv) of diverse chemical

compounds have been utilised in virtual screening process.

Maximum omitted features option was set to ‘ 2 1’ to

screen the databases. Hit compounds were screened for

their predicted biological activity values using the Hypo1

pharmacophore model. The compounds that were showing

estimated IC50 values ,50 nM were selected and

subsequently submitted to DEREK for different toxicity

filters. Further screened hits were evaluated for their drug-

likeness properties using QED value. For calculating QED

value, physicochemical properties (MW, ALOGP, number

of HBDs, number of HBAs, molecular PSA, number of

ROTBs and the number of AROMs) were calculated using

the DS2.5. Finally, a substructure search was done for each

compound using a curate reference set of 94 functional

moieties that are potentially mutagenic, reactive or have

U. Singh et al.388

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

unfavourable pharmacokinetic properties. The number of

matches for each compoundwas captured asALERTS [30].

The unweighted and weighted QED values were calculated

based on the above-mentioned molecular properties by

using following formulae:

QED ¼ exp1

n

Xni¼1

ln di

!;

QEDw ¼ exp

Xni¼1

wiln di

Xni¼1

wi

0BBBB@

1CCCCA;

where d is the deriveddesirability function corresponding to

variousmolecular properties,w is theweight applied to each

function and n is the number of molecular properties [31].

The hit compounds passing the QED test were subjected to

molecular docking using Glide5.5.

2.5 Molecular docking studies

To investigate the detailed intermolecular interactions

between the inhibitors andMMP-12, an automated docking

program Glide5.5 [32] was used. 3D structural information

of the target protein was taken from the protein data bank

(PDB ID: 1ROS). The docking protocol was validated by

re-docking of co-crystallised ligand. The protocol followed

for docking studies of virtual hits included processing of the

protein and ligand preparation. During protein preparation,

ligand molecules were deleted, hydrogen atoms were

added, solvent molecules were deleted and bond orders for

crystal protein were adjusted and minimised up to 0.30 A

RMSD. An active site of 10 A was created around the co-

crystallised ligand. Standard precision mode and other

default parameters of Glide software were used for the

docking studies [33]. The final hits were selected based on

the binding mode and molecular interactions shown by the

hit compounds at the active site.

2.6 Similarity search

PubChem and SciFinder Scholar search tools were used to

confirm the novelty of screened virtual hits. The PubChem

search tool measures the similarity based on the Tanimoto

equation and the dictionary-based binary fingerprint.

The compounds showing partial similarity score $90%

were subjected to screening process using the best

pharmacophore model (Hypo1) as well the molecular

docking studies using the same parameters, which were used

to select the hit molecules from the databases.

3. Results and discussion

3.1 Pharmacophore modelling

The top 10 ligand-based pharmacophore hypotheses were

generated using diverse training set compounds. Table 1

summarises the result obtained during pharmacophore

hypotheses generation. The best pharmacophore hypothesis

Hypo1 was characterised by the best correlation coefficient

0.973, lowest root mean square error 0.787 A2 and the

highest cost difference of 63.17. Hypo1 consists of spatial

arrangement of three chemical features, namely the presence

of two HBAs, one HYA and one HYAr feature (Figure 3).

The activity of all training set compounds was estimated

by using Hypo1 pharmacophore model. Table 2 summarises

the Hypo1-estimated activity values along with their

corresponding error values (i.e. the ratio between calculated

and experimental activity) of training set molecules. Among

16 training set compounds, all active (þþþ), least active

(þ ) compounds were predicted accurately and one

moderately active (þþ) compound was predicted as active

(þ). Interestingly, highly active compoundsweremapped to

all pharmacophore features of Hypo1. While in case of

moderately active and least active compounds, one or two

features were missing or mapping partially with one

exception. All the compounds in the training set were

mapped to HBA and HYAr features, which reveal that these

two features must be mainly responsible for the activity.

Table 1. Statistical parameters of top 10 pharmacophore hypotheses generated using HypoGen algorithm.

Hypo no. Total cost Cost differencea RMSDb Correlation Featuresc

Hypo1 77.96 63.16 0.788 0.973 2HBA, HYA, HYArHypo2 87.27 53.86 1.317 0.920 2HBA, HYA, HYArHypo3 90.02 51.11 1.516 0.889 2HBA, HYA, HYArHypo4 92.86 48.263 1.637 0.869 2HBA, HYA, HYArHypo5 93.78 47.35 1.678 0.862 2HBA, HYA, HYArHypo6 94.04 47.09 1.630 0.872 2HBA, HYA, HYArHypo7 94.32 46.81 1.720 0.853 HBA, HBD, HYAHypo8 95.95 46.17 1.717 0.855 HBA, HBD, HYA, HYArHypo9 95.42 45.70 1.741 0.850 HBA, HBD, HYA, HYArHypo10 95.58 45.55 1.759 0.846 2HBA, HYA, HYAr

Note: The null, the fixed and the configuration costs are 141.13, 70.32 and 15.38, respectively. a Cost difference between the null and the total cost.b RMSD, root mean square deviation. c Abbreviation used for features: HBA, hydrogen bond acceptor; HYA, hydrophobic aliphatic; HYAr, hydrophobicaromatic.

Molecular Simulation 389

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

The most active compound 1 has shown the fitness score of

10.03, whereas the least active compound 16 has shown the

fitness score of 5.23 when mapped to Hypo1. Fit value is a

measure of overlap between the features in the pharmaco-

phore and chemical features in the molecule, which helps in

understanding the chemical meaning of the pharmacophore

hypothesis. In compound 1, the HBA feature corresponds to

oxygen atomof tricyclic dibenzofuran and oneHYAr feature

corresponds to furan ring. For molecules with lesser activity

(compounds 11–16), at least one of the features is missing.

3.2 Pharmacophore model validation

The generated ligand-based pharmacophore hypothesis was

validated by a cost analysis, test set prediction, Fischer

randomisation test and GH score calculation. In the first

validation process, the various cost values calculated during

the pharmacophore generation were evaluated. The

difference of 40–60 bits between the total cost and the

null cost hypotheses confirms the 75–90% chance of

representing a true correlation in the data [34]. The total cost

and null cost values for the generated best pharmacophore

Table 2. Experimental and predicted IC50 activities of the training set molecules based on the Hypo1 pharmacophore model.

Compound Fit value Experimental IC50 (nM) Predicted IC50 (nM) Errora Experimental scaleb Predicted scaleb

1 10.03 0.1 0.15 þ1.52 þþþ þþþ2 9.62 1 0.39 22.55 þþþ þþþ3 7.91 6.3 20.31 þ1.45 þþþ þþþ4 7.86 14 22.69 þ3.6 þþþ þþþ5 7.66 23 35.97 21.22 þþþ þþþ6 7.59 44 42.46 þ1.85 þþþ þþþ7 7.55 58 46.66 21.5 þþ þþþ8 7.14 70 119.21 þ1.19 þþ þþ9 6.97 100 174.32 21.49 þþ þþ10 6.7 260 326.83 þ5.63 þþ þþ11 6.04 1150 1499.98 þ1.3 þ þ12 5.6 2490 4151.09 21.76 þ þ13 5.4 7300 6565.77 þ2.64 þ þ14 5.39 19,900 6641.5 23 þ þ15 5.39 20,100 6676.17 23.01 þ þ16 5.23 54,400 9617.12 25.66 þ þ

a Difference between the predicted and experimental values; ‘þ’ indicates that the predicted IC50 is higher than the experimental IC50; ‘2’ indicates thatthe predicted IC50 is lower than the experimental IC50; a value of 1 indicates that the predicted IC50 is equal to the experimental IC50.

b Activity scale:IC50 , 50 nM ¼ þþþ (highly active); 50 nM # IC50 , 500 nM ¼ þþ (moderately active); IC50 $ 500 nM ¼ þ (low active).

Figure 3. (Colour online) The chemical features of best pharmacophore hypothesis (Hypo1) with their inter-feature distance constraintsin angstrom (A) HBA indicated as green-vectored spheres, hydrophobic features indicated as cyan spheres, and exclusion volumesindicated as grey sphere.

U. Singh et al.390

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

hypothesis (Hypo1) were 77.96 and 141.13, respectively.

The cost difference between the null cost and total cost

values for the Hypo1 was 63.17, which represents that

Hypo1 can significantly correlate the data bymore than 90%.

Hypo1 showed the highest correlation coefficient value of

0.973, thereby indicating the high predictive ability of

Hypo1. In addition, RMSD value for Hypo1 was 0.787 A2,

which further supports the predictive ability of the top

pharmacophore model. Among the 10 generated pharmaco-

phore hypotheses, Hypo1 has shown better statistical values

including higher correlation (0.973), greater cost difference

(63.17) lower RMSD (0.787) and configuration cost (15.38)

values. Based on these validation results, Hypo1 was

considered as the best pharmacophore hypothesis to be

carried out further for subsequent analyses. The activity

prediction of test compounds was employed as second

validation process. Hypo1 was used to estimate the activity

of the test set compounds. Most of the compounds in the

test set were predicted correctly for their biological activity.

A correlation coefficient of 0.848 shows a good correlation

between the experimental and predicted activities (Figure 4).

In detail, 54 of 66 highly active, 4 of 10 moderately active

and 9 of 16 least active compounds were predicted correctly.

A total of 12 highly active compounds were underestimated

asmoderately active; sixmoderately active compoundswere

overestimated as active and seven least active compounds

Figure 4. (Colour online) Scatter plot of predicted pIC50 against experimental pIC50 for training (blue diamonds) and test set (redtriangles) compounds.

Figure 5. (Colour online) (a) Pharmacophore model aligned with the most active compound 4 (IC50 ¼ 0.1 nM). (b) Pharmacophoremodel aligned with the least active compound 50 (IC50 ¼ 11,800 nM).

Molecular Simulation 391

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

were overestimated as moderately active. The most active

compound 4 in the test set mapped over Hypo1 (Figure 5(a))

shows that all the features were being mapped accurately

with Hypo1, whereas in the least active compound 50 of the

test set, oneHBA and oneHYA featureweremissing (Figure

5(b)). Fischer randomisation test was employed as third

validation process to attain 98% confidence level. The

pharmacophore hypotheses generated during the 49 random

generation runs were compared with the original pharma-

cophore hypothesis, Hypo1. Figure 6 shows that none of the

randomly generated pharmacophore hypotheses have scored

better statistical results than Hypo1. The results obtained

from Fischer randomisation test indicate that Hypo1 has not

been generated by any chance correlation.

Finally, a small database (D) containing 1100

compounds was generated using DecoyFinder1.1, which

includes 20 active and 1080 decoys for MMP-12. This

database was used to validate whether the hypothesis

(Hypo1) could discriminate the active from decoys or not.

Database screeningwas done usingHypo1 as a 3D structural

query. The accuracy, precision, sensitivity and specificity of

the best pharmacophore model (Hypo1) for the decoy set

were found to be 0.99, 0.78, 0.90 and 0.99, respectively.

Moreover, for the analysis of result enrichment factor

(E-value) and GH were calculated using the following

formulae:

E ¼ðTP £ DÞ

ðHt £ AÞ;

GH¼ ðTP=4HtAÞð3AþHtÞ£ ð12 ððHt2TPÞ=ðD2AÞÞÞ;

where D, A, Ht and TP represent the total number of

compounds of the database, the total number of actives, the

total number of compounds screened by a pharmacophore

model and the total number of active compounds screened,

respectively. Hypo1 has shown an E-value of 43.04 and the

calculated GH score for Hypo1 (0.807) was .0.5, which

indicates that the quality of developed pharmacophore was

significant (Tables 3 and 4). From the overall validation

results, we can assure that hypothesis (Hypo1) was able to

discriminate between the active and decoys. Hence, we have

used Hypo1 hypothesis to select or discriminate the suitable

MMP-12 inhibitors.

3.3 Pharmacophore model-based virtual screening

The sequential virtual screening was done as shown in

Figure 7. The validated pharmacophoremodel (Hypo1)was

Figure 6. (Colour online) The difference in the cost value of hypotheses between the initial spreadsheet and 49 random spreadsheetsafter Cat-Scramble run (Fischer randomisation test).

U. Singh et al.392

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

used as a query to search the NCI, Specs and ChemDiv

databases, which comprised 87,374, 276,807 and 843,113

compounds, respectively. Mapping of 821,192 hit com-

pounds to the pharmacophore model was done, which

included some compounds structurally similar to the

existingMMP-12 inhibitors and some novel scaffolds were

also emerged. A set of 4755 hit compounds were selected

which had shown estimated IC50 values below 50 nM.

Screened hits were also evaluated for different toxicities

such as carcinogenicity, chromosome damage, genotoxi-

city, hERG channel inhibition, hepatotoxicity, mutageni-

city and thyroid toxicity using DEREK. Hit compounds

selected after screening through Derek filters were

subsequently submitted to the QED calculation.

QED values aid in screening chemical structures by

their merit relative to the target functions. In comparison to

the rule-based approaches, QED provides a better view of

drug-likeness. TheQEDvalues are based on the distribution

of molecular properties, unlike the rule-based metrics. It

also identifies the cases in which a generally unfavourable

property may be tolerated when the other parameters are

close to the ideal. The unweighted_QED and weight-

ed_QED values were calculated for screened virtual hits.

The QED and QEDw values range from 0.052 to 0.914 and

0.055 to 0.908, respectively. The compounds showing QED

value above 0.5 were selected for further analysis by

molecular docking studies to avoid the false-positive hits

from virtual screening.

3.4 Molecular docking studies

The hit compounds from virtual screening along with 20

training set compounds were docked into the active site of

MMP-12 using Glide5.5. To validate the docking protocol,

co-crystallised ligand (DEO) was re-docked into the active

site ofMMP-12withRMSDof 0.384.DEO is stabilised by a

greater number of hydrogen bonds and numerous

hydrophobic interactions with amino acids lining the

large hydrophobic pocket. The carboxylic acid group of

DEO chelates with zinc by forming two hydrogen bonds.

The ethoxy group has shown the hydrophobic contacts with

the side chains of Phe248 and Leu214. Also, the phenyl

fragment of the phthalamide group makes hydrophobic

contacts with the side chain of His222. Further, two phenyl

rings show p–p interaction with His218 and Tyr240.

The carboxylic group forms the hydrogen bond with the

side chain atomofGlu219. It has also shownhydrogen bond

interaction between the carbonyl oxygen and backbone N

atom of Ala182 and Leu181.

The most active compound 1 in the training set has

scored a best docking score value of 212.34. Ten hit

compounds having glide score .212.00 were selected

and checked for the binding mode analysis. Similar

scaffolds from virtual hits were also rejected based on

their docking score and active site interactions. Finally,

four hit compounds ZINC00990677, ZINC03900852,

ZINC02113246 and ZINC00627455 (Figure 8) with high

docking score were selected as the best hits (Table 3).

Molecular overlay of these hits with the co-crystallised

ligand revealed their similar binding orientations at the

active site. Figure 9 shows the docking pose of top virtual hit

(ZINC00990677) into the active site of MMP-12. Two

hydrogen bond interactions of all top virtual hits with

backbone NH of Leu181 and side chain atom of Glu219

were conserved. The binding interaction patterns observed

during docking studieswere complementarywith that of the

pharmacophoric hydrophobic features and HBA feature,

which confirms that the developed pharmacophore

hypothesis has good predictability.

3.5 Similarity analysis

The PubChem [35] and SciFinder Scholar [36] search

tools were employed as a similarity search tool to confirm

the novelty of the hit compounds. The partially similar

compounds were tested with the pharmacophore hypoth-

esis Hypo1. Most of the partially similar compounds were

Table 3. Predicted IC50 (nM), docking score, fit value and QED values of top four virtual hits.

Compounds Predicted IC50 (nM) Docking score Fit value Unweighted_QED Weighted_QED

ZINC00990677 36.62 213.61 2.99 0.74 0.72ZINC03900852 33.97 212.48 2.96 0.70 0.72ZINC02113246 25.70 212.08 2.82 0.69 0.72ZINC00627455 49.91 211.98 2.75 0.76 0.75

Table 4. The statistical parameters obtained from decoy test.

Sr. no. Parameter Hypo1

1 Total compounds in database (D) 11002 Total number of actives in database (A) 203 Total hits (Ht) 234 Active hits (TP) 185 True negative (TN) 10756 Enrichment factor or enhancement (E) 43.047 False negatives (FN ¼ A 2TP) 28 False positives (FP ¼ Ht 2 TP) 59 GH score (goodness of hit list) 0.80710 Accuracy ¼ (TP þ TN)/(TP þ TN þ FP þ FN) 0.9911 Precision ¼ TP/(TP þ FP) 0.7812 Sensitivity ¼ TP/(TP þ FN) 0.9013 Specificity ¼ TN/(TN þ FP) 0.99

Molecular Simulation 393

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

not able to fit in the geometric constraints of Hypo1, and

hence predicted low MMP-12 inhibitory activity. Also, the

docking scores for the hit molecules were far better than

partially similar compounds. Molecular docking studies

also proved that these compounds are not suitable to form

hydrogen bond interactions with the critical residues such

as Glu219, Ala182 and Leu181. The novelty of the four hit

compounds was further confirmed by the SciFinder

Scholar search as these compounds have not been

experimentally reported for MMP-12 inhibitory activity

Figure 7. Flow chart showing sequential virtual screening techniques followed to identify novel MMP-12 inhibitors.

Figure 8. Structures of the final hits obtained through sequential virtual screening using Hypo1.

U. Singh et al.394

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

earlier. Hence, we suggest that these four compounds are

novel scaffolds for MMP-12 inhibitors.

4. Conclusions

In this study, we have developed a ligand-based

pharmacophore model for a different class of MMP-12

inhibitors. The best pharmacophore hypothesis Hypo1 was

validated using different methods to evaluate its prediction

power over the diverse test set compounds. The highly

predictive hypothesis was further used in virtual searching

for new MMP-12 inhibitors. Three diverse chemical

databases were used in virtual searching. The hits from

the virtual screening were filtered based on the estimated

activity values, several toxicity filters and QED values. The

resulted drug-like compounds were docked into the active

site of MMP-12 using Glide5.5. Finally, the molecular

docking analysis was carried out to select positive virtual

hits. Novelty of these hit compounds was confirmed with

PubChem and SciFinder Scholar search tools. Combining

all these results, four new compounds were presented as

possible lead candidates to be used as novel and potent

MMP-12 inhibitors. Further in vitro testing of virtual hits

would be ensued to confirm the success rate of this study and

to optimise the hits thereafter.

Acknowledgement

The authors acknowledge financial support from Department ofScience and Technology (DST), New Delhi.

Note

1. Authors with equal contribution.

References

[1] S.E. Gill and W.C. Parks, Metalloproteinases and their inhibitors:Regulators of wound healing, Int. J. Biochem. Cell Biol. 40 (2008),pp. 1334–1347.

[2] H. Nagase, R. Visse, and G. Murphy, Structure and function ofmatrix metalloproteinases and TIMPs, Cardiovasc. Res. 69 (2006),pp. 562–573.

[3] S.D. Shapiro, D.K. Kobayashi, and T.J. Ley, Cloning andcharacterization of a unique elastolytic metalloproteinase producedby human alveolar macrophages, J. Biol. Chem. 268 (1993),pp. 23824–23829.

[4] R.D. Hautamaki, D.K. Kobayashi, R.M. Senior, and S.D. Shapiro,Requirement for macrophage elastase for cigarette smoke-inducedemphysema in mice, Science 277 (1997), pp. 2002–2004.

[5] P.R.A. Pauwels and K.F. Rabe, Burden and clinical features ofchronic obstructive pulmonary disease (COPD), Lancet 364 (2004),pp. 613–620.

[6] D.P. Tashkin, B. Celli, S. Senn, D. Burkhart, S. Kesten, S. Menjoge,andM. Decramer, A 4-year trial of tiotropium in chronic obstructivepulmonary disease, N. Engl. J. Med. 359 (2008), pp. 1543–1554.

[7] M.G. Belvisi, D.J. Hele, and M.A. Birrell, New anti-inflammatorytherapies and targets for asthma and chronic obstructive pulmonarydisease, Expert Opin. Ther. Targets 8 (2004), pp. 265–285.

[8] C. Bergeron and L.P. Boulet, Structural changes in airwaydiseases*, Chest 129 (2006), pp. 1068–1087.

[9] C. Le Quement, I. Guenon, J.Y. Gillon, S. Valenca, V. Cayron-Elizondo, V. Lagente, and E. Boichot, The selective MMP-12inhibitor, AS111793 reduces airway inflammation in mice exposedto cigarette smoke, Br. J. Pharmacol. 154 (2008), pp. 1206–1215.

[10] M.A. Markus, B. Dwyer, S. Wolfrom, J. Li, W. Li, K. Malakian,J. Wilhelm, and D.H.H. Tsao, Solution structure of wild-type humanmatrix metalloproteinase 12 (MMP-12) in complex with a tight-binding inhibitor, J. Biomol. NMR 41 (2008), pp. 55–60.

[11] R. Bhaskaran, M.O. Palmier, N.A. Bagegni, X. Liang, and S.R. VanDoren, Solution structure of inhibitor-free human metalloelastase(MMP-12) indicates an internal conformational adjustment, J. Mol.Biol. 374 (2007), pp. 1333–1344.

[12] R. Morales, S. Perrier, J.M. Florent, J. Beltra, S. Dufour,I. De Mendez, P. Manceau, A. Tertre, F. Moreau, and D. Compere,Crystal structures of novel non-peptidic, non-zinc chelatinginhibitors bound to MMP-12, J. Mol. Biol. 341 (2004),pp. 1063–1076.

[13] V. Dive, D. Georgiadis, M. Matziari, A. Makaritis, F. Beau, P.Cuniasse, and A. Yiotakis, Phosphinic peptides as zinc metallopro-teinase inhibitors, Cell. Mol. Life Sci. 61 (2004), pp. 2010–2019.

[14] A.C. Dublanchet, P. Ducrot, C. Andrianjara, M. O’Gara, R. Morales,D. Compere, A. Denis, S. Blais, P. Cluzeau, K. Courte, J. Hamon,F. Moreau, M.-L. Prunet, and A. Tertre, Structure-based design andsynthesis of novel non-zinc chelating MMP-12 inhibitors,Bioorg. Med. Chem. Lett. 15 (2005), pp. 3787–3790.

[15] E. Nuti, L. Panelli, F. Casalini, S.I. Avramova, E. Orlandini,S. Santamaria, S. Nencetti, T. Tuccinardi, A. Martinelli, andG. Cercignani, Design, synthesis, biological evaluation, and NMRstudies of a new series of arylsulfones as selective and potent matrixmetalloproteinase-12 inhibitors, J. Med. Chem. 52 (2009),pp. 6347–6361.

[16] W. Li, J. Li, Y. Wu, F. Rancati, S. Vallese, L. Raveglia, J. Wu,R. Hotchandani, N. Fuller, and K. Cunningham, Identification of anorally efficacious matrix metalloprotease 12 inhibitor for potentialtreatment of asthma, J. Med. Chem. 52 (2009), pp. 5408–5419.

[17] I.P. Holmes, S. Gaines, S.P. Watson, O. Lorthioir, A. Walker,S.J. Baddeley, S. Herbert, D. Egan, M.A. Convery, andO.M.P. Singh, The identification of b-hydroxy carboxylic acids asselective MMP-12 inhibitors, Bioorg. Med. Chem. Lett. 19 (2009),pp. 5760–5763.

[18] W. Li, J. Li, Y. Wu, J. Wu, R. Hotchandani, K. Cunningham,I. McFadyen, J. Bard, P. Morgan, and F. Schlerman, A selectivematrix metalloprotease 12 inhibitor for potential treatment of

Figure 9. (Colour online) The interactions of top hit compound(ZINC00990677) from the virtual screening with the active siteof MMP-12 (PDB ID: 1ROS).

Molecular Simulation 395

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3

chronic obstructive pulmonary disease (COPD): Discovery of (S)-2-(8-(methoxycarbonylamino)dibenzo[b,d]furan-3-sulfonamido)-3-methyl-butanoic acid (MMP408), J. Med. Chem. 52 (2009), pp. 1799–1802.

[19] Y. Wu, J. Li, J. Wu, P. Morgan, X. Xu, F. Rancati, S. Vallese,L. Raveglia, R. Hotchandani, and N. Fuller,Discovery of potent andselective matrix metalloprotease 12 inhibitors for the potentialtreatment of chronic obstructive pulmonary disease (COPD),Bioorg. Med. Chem. Lett. 22 (2011), pp. 138–143.

[20] Discovery Studio Version 2.5 (DS 2.5) User Manual, Accelrys, Inc.,San Diego, CA, 2009.

[21] A. Smellie, S.L. Teig, and P. Towbin, Poling: Promotingconformational variation, J. Comput. Chem. 16 (1995),pp. 171–187.

[22] Y. Kurogi and O.F. Guner, Pharmacophore modeling and three-dimensional database searching for drug design using catalyst,Curr. Med. Chem. 8 (2001), pp. 1035–1055.

[23] J. Wang, M. Yan, D. Zhao, Y. Sha, F. Li, and M. Cheng,Pharmacophore identification of PAK4 inhibitors, Mol. Simul. 36(2010), pp. 53–57.

[24] R. Singh, A. Balupuri, and M.E. Sobhia, Development of3D-pharmacophore model followed by successive virtual screening,molecular docking and ADME studies for the design of potent CCR2antagonists for inflammation-driven diseases, Mol. Simul. (2012)doi: 10.1080/08927022.08922012.08701743.

[25] N. Bharatham, K. Bharatham, and K.W. Lee, Pharmacophoreidentification and virtual screening for methionyl-tRNA synthetaseinhibitors, J. Mol. Graph. Model. 25 (2007), pp. 813–823.

[26] G.V. Dhoke, R.P. Gangwal, and A.T. Sangamwar, A combinedligand and structure based approach to design potent PPAR-alphaagonists, J. Mol. Struct. 1028 (2012), pp. 22–30.

[27] S. Gupta, A. Fallarero, P. Jarvinen, D. Karlsson, M.S. Johnson,P.M. Vuorela, and C.G. Mohan, Discovery of dual binding siteacetylcholinesterase inhibitors identified by pharmacophore model-

ing and sequential virtual screening techniques, Bioorg. Med.Chem. Lett. 21 (2011), pp. 1105–1112.

[28] S. Gupta and C. Gopi Mohan, 3D-pharmacophore model basedvirtual screening to identify dual-binding site and selectiveacetylcholinesterase inhibitors, Med. Chem. Res. 20 (2011), p. 1422.

[29] E.P. Semighini, C.A. Taft, and C.H.T.P. Silva, Homologymodelling, virtual screening and molecular dynamics of theMARK3 KA1 domain for cancer drug design, Mol. Simul. 37 (2011),pp. 1186–1206.

[30] R. Brenk, A. Schipani, D. James, A. Krasowski, I.H. Gilbert,J. Frearson, and P.G. Wyatt, Lessons learnt from assemblingscreening libraries for drug discovery for neglected diseases,ChemMedChem 3 (2008), pp. 435–444.

[31] G.R. Bickerton, G.V. Paolini, J. Besnard, S. Muresan, andA.L. Hopkins, Quantifying the chemical beauty of drugs,Nat. Chem. 4 (2012), pp. 90–98.

[32] Glide Version 5.5, Schrodinger, LLC, New York, 2009.[33] P.S. Ambure, R.P. Gangwal, and A.T. Sangamwar, 3D-QSAR and

molecular docking analysis of biphenyl amide derivatives as p38amitogen-activated protein kinase inhibitors, Mol. Divers. 16 (2012),pp. 377–388.

[34] B.W. Dymock, X. Barril, P.A. Brough, J.E. Cansfield, A. Massey,E. McDonald, R.E. Hubbard, A. Surgenor, S.D. Roughley, andP. Webb, Novel, potent small-molecule inhibitors of the molecularchaperone Hsp90 discovered through structure-based design,J. Med. Chem. 48 (2005), pp. 4212–4215.

[35] Y. Wang, E. Bolton, S. Dracheva, K. Karapetyan, B.A. Shoemaker,T.O. Suzek, J. Wang, J. Xiao, J. Zhang, and S.H. Bryant,An overview of the PubChem BioAssay resource, Nucleic AcidsRes. 38 (2010), pp. D255–D266.

[36] A.B. Wagner, SciFinder Scholar 2006: An empirical analysis ofresearch topic query processing, J. Chem. Inf. Model. 46 (2006),pp. 767–774.

U. Singh et al.396

Dow

nloa

ded

by [

Uni

vers

ity o

f H

ong

Kon

g L

ibra

ries

] at

11:

20 1

5 M

arch

201

3