Mol Divers (2013) 17:139–149DOI 10.1007/s11030-013-9425-2

FULL-LENGTH PAPER

Identification of dual Acetyl-CoA carboxylases 1 and 2 inhibitorsby pharmacophore based virtual screening and molecular dockingapproach

Anuseema Bhadauriya · Gaurao V. Dhoke ·Rahul P. Gangwal · Mangesh V. Damre ·Abhay T. Sangamwar

Received: 22 October 2012 / Accepted: 7 January 2013 / Published online: 19 January 2013© Springer Science+Business Media Dordrecht 2013

Abstract Acetyl-CoA carboxylase (ACC) is a crucialmetabolic enzyme that plays a vital role in obesity-inducedtype 2 diabetes and fatty acid metabolism. To identifydual inhibitors of Acetyl-CoA carboxylase1 and Acetyl-CoA carboxylase2, a pharmacophore modelling approachhas been employed. The best HypoGen pharmacophoremodel for ACC2 inhibitors (Hypo1_ACC2) consists of onehydrogen bond acceptor, one hydrophobic aliphatic and onehydrophobic aromatic feature, whereas the best pharma-cophore (Hypo1_ACC1) for ACC1 consists of one additionalhydrogen-bond donor (HBD) features. The best pharma-cophore hypotheses were validated by various methods suchas test set, decoy set and Cat-Scramble methodology. Thevalidated pharmacophore models were used to screen sev-eral small-molecule databases, including Specs, NCI, Chem-Div and Natural product databases to identify the potentialdual ACC inhibitors. The virtual hits were then subjectedto several filters such as estimated IC50 value, quantitativeestimation of drug-likeness and molecular docking analysis.Finally, three novel compounds with diverse scaffolds wereselected as potential starting points for the design of noveldual ACC inhibitors.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11030-013-9425-2) contains supplementarymaterial, which is available to authorized users.

Anuseema Bhadauriya and Gaurao V. Dhoke contributed equally tothis study.

A. Bhadauriya · G. V. Dhoke · R. P. Gangwal · M. V. Damre ·A. T. Sangamwar (B)Department of Pharmacoinformatics, National Institute ofPharmaceutical Education and Research (NIPER), Sect-67,S.A.S. Nagar, Punjab 160 062, Indiae-mail: abhays@niper.ac.in

Keywords Acetyl-CoA carboxylase · HypoGen ·Molecular docking · Pharmacophore mapping ·Virtual screening

Introduction

Obesity has become a serious health problem worldwidein the last few decades due to a high-fat diet and reducedphysical exercise. It accompanies a variety of serious humandiseases, largely type 2 diabetes, cardiovascular diseases,depression and cancer [1]. Obesity is the main cause ofdeath in the case of type 2 diabetes mellitus, which accountsfor 5 % of global deaths [2,3]. According to a WorldHealth Organization global estimate, obesity-induced type2 diabetes-related deaths will increase by more than 50 % inthe coming 10 years [3]. In obesity-induced diabetes, capa-bility of the adipose tissue to store excess nutrients exceedsdue to increased energy intake. This leads to an increasein ectopic fat accumulation in non-adipose tissues, such asmuscle, liver and pancreas, mainly responsible for the devel-opment of insulin resistance and type 2 diabetes mellitus. Ithas been suggested that increased levels of long-chain fattyacyl-CoAs, diacylglycerol and accumulation of intracellu-lar triglyceride, antagonize the metabolic action of insulin,reduce muscle glucose uptake and inhibit hepatic glucoseproduction [4,5]. Therefore, there has been an escalatedresearch interest in the paradigm of lipid disorders alterationby targeting enzymes that are involved in lipid synthesis andoxidation [6,7].

Acetyl-CoA carboxylases (ACC) are responsible for thebiotin-dependent carboxylation of acetyl-CoA to malonyl-CoA, which is a key intermediate metabolite that controls therate of fatty acid metabolism [8]. ACC is a homo oligomericprotein composed of a carboxyltransferase (CT), a biotin

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carboxyl carrier protein and biotin carboxylase (BC) domains.The conversion of acetyl-CoA to malonyl-CoA by ACC isan ATP-dependent process. The synthesis of malonyl-CoAis a two-step process; the first part of the reaction takes placein the BC-domain and involves a reaction between bicar-bonate ion and enzyme-bound biotin using ATP to form abiotin-carboxylate complex. This biotin-carboxylate com-plex then enters into the CT-domain by means of a narrowchannel between the CT- and BC-domains, here carbon diox-ide is relocated from biotin carboxylate to acetyl-CoA to formmalonyl-CoA. Malonyl-CoA is further consumed by the fattyacid synthase for the de novo fatty acid synthesis and fattyacid chain elongation. It is also being used as an allostericinhibitor of carnitine palmitoyl transferase (CPT1), a keyenzyme that positively regulates mitochondrial β-oxidation.Thus, inhibition of ACC will block the de novo fatty acidsynthesis and enhances β-oxidation of fatty acid which couldbe utilised for the treatment of metabolic disorders, such asobesity and type 2 diabetes [9–11]. In humans, there are twoisoforms of ACC (1 and 2) with distinct subcellular localiza-tion. Acetyl-CoA carboxylase1 (ACC1), a 265-kDa cytosolicprotein, is principally expressed in lipogenic tissues, whileAcetyl-CoA carboxylase2 (ACC2), a 280-kDa protein, ispredominantly expressed in oxidative tissues, such as liver,skeletal muscles and heart [12–14]. It has been found thatACC2 inhibition reduces the intracellular fat by increasing fatutilisation for energy production; on the other hand, the sup-pression of both ACC1 and ACC2 expression using antisenseoligonucleotides stimulates fat oxidation in hepatocytes morepotently than that of inhibiting either ACC1 or ACC2 alone[15]. Therefore, non-specific inhibition of ACC is moreeffective in stimulating fat oxidation than selective ACC2inhibition.

The present study aims to identify the basic structuralrequirements for ACC inhibition, and thereby designingnovel and potent dual inhibitors of ACC1 and ACC2. Aligand-based pharmacophore modelling approach is employedto achieve this goal. A pharmacophore hypothesis for ACC1and ACC2 has been generated by the HypoGen algorithm[16]. The validated pharmacophore hypotheses were usedin virtual screening to identify dual inhibitors of ACC1 andACC2. The screened virtual hits were then subjected to sev-eral filters such as estimated activity, quantitative estima-tion of drug-likeness (QED) and molecular docking studiesby Glide5.5. Finally, we report three novel compounds withdiverse scaffolds as possible candidates for the designing ofpotent dual ACC inhibitors.

Materials and methods

Datasets

In the last couple of years, many compounds were reportedas ACC inhibitors. The datasets of 111 inhibitors for ACC1and 109 inhibitors for ACC2 were selected from the liter-ature, based on the biological assay method and were usedfor the generation of the pharmacophore models [17–24].The inhibitory activities (IC50 value) of ACC1 and ACC2inhibitors spanned across a wide range of 7.4–25,000 andfrom 4 to 26,500 nM, respectively.

During pharmacophore generation, selection of a suit-able training set is an important step to assist in determin-ing the quality of the generated model. HypoGen pharma-cophore hypotheses were generated using a set of 22 com-pounds for both ACC1 and ACC2 inhibitors ( Figs. S1, S2),which were selected based on the principles of structuraldiversity and activity range. The remaining set of com-pounds, 89 and 87, from the original datasets were used asa test set for the validation of the ACC1 and ACC2 gen-erated pharmacophore models, respectively (Figs. S3, S4).For evaluation purposes, the activity values were classifiedon the scale of: highly active (+++, IC50 > 200 nM), mod-erately active (++, 200 nM < IC50 > 2, 000 nM) and leastactive (+, IC50 > 2, 000 nM). Numerous conformations ofall the inhibitors were generated by the BEST method, whichprovides a complete and improved coverage of the conforma-tional space by performing a rigorous energy minimizationand optimising the conformations in both torsional and carte-sian space by the poling algorithm [25]. During this process,the maximum number of conformers was set to 255 with4.0 kcal/mol as the energy cut-off, and all other parameterswere set to default [26]. Pharmacophore models were thengenerated by the HypoGen algorithm implemented in Accel-rys Discovery Studio2.5 (DS2.5) [27].

Pharmacophore modelling

Considering the chemical nature of both ACC1 and ACC2inhibitors, five features namely hydrogen-bond acceptor(HBA), hydrogen-bond donor (HBD), hydrophobic aliphatic(HYA), hydrophobic aromatic (HYAr) and ring aromatic(RA) were selected to generate pharmacophore hypothe-ses. The minimum and maximum number of features to beincluded during pharmacophore generation was set to 0 and5, respectively. The top ten pharmacophore hypotheses with

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significant statistical parameters were generated by the 3DQSAR Pharmacophore Generation module in the DS2.5 soft-ware package running on an IBM graphic workstation. Theuncertainty value was set to 2, as the activity range in thetraining set compounds barely spans the minimum require-ment of four orders of magnitude as well as to correlate thetraining set compounds with their activity values accurately[28]. The quality of the generated HypoGen pharmacophorehypothesis can be best described in terms of several sta-tistical parameters, such as fixed cost, null cost and totalcost. The best model was selected based on high correla-tion coefficient (r), lowest total cost, highest cost differ-ence and low root mean squared deviation (RMSD) values[29,30].

The best pharmacophore hypotheses were validated todetermine whether they are capable of differentiating betweenactive, inactive and least active compounds and predictingtheir activities accurately. Three different methods (test setprediction, Fischer randomization test and decoy test) wereemployed for the validation of the developed pharmacophorehypotheses. During cost analysis, several theoretical cost val-ues (represented in bit units) were analysed namely fixedcost, null cost and the total cost. In addition to this, threeother cost values that play a vital role in determining thequality of a generated hypothesis are weight cost, configu-ration cost and error cost. In test set validation, the datasetof inhibitors as a representative of the learning series witha wide range of inhibitory activity was used to validatethe best HypoGen pharmacophore hypothesis. A Fischerrandomization (Cat-Scramble) test was employed with thegoal to check whether there is a strong correlation betweenthe chemical structures and the biological activity in the train-ing set. During this validation, 19 random pharmacophorehypotheses were generated to achieve a 95 % confidencelevel.

Decoys are molecules that are supposed to be inactiveagainst a target and used to validate the performance of thevirtual screening workflow. To avoid bias in the enrichmentfactor calculation, decoys should resemble ligands physi-cally, while still being chemically distinct from them [31].For the validation of the best pharmacophore hypotheses,decoy sets were generated by DecoyFinder1.1 [32]. Decoyswere selected if they are similar to the active ligands accord-ing to five physicochemical descriptors (molecular weight,number of rotational bonds, hydrogen bond donor count,hydrogen bond acceptor count and the octanol–water par-tition coefficient). The chemical dissimilarity between thedecoys and the active ligands was calculated according tothe Tanimoto coefficient between MACCS fingerprints. 7ACC1 active and 10 ACC2 active inhibitors were included inthe database to calculate various statistical parameters suchas accuracy, precision, sensitivity, specificity, goodness ofhit score (GH) and enrichment factor (E value). GH and E

value are the two major parameters, which play an importantrole in identifying capability of the generated pharmacophorehypothesis [33].

Pharmacophore-based virtual screening

Pharmacophore-based virtual screening of the commerciallyavailable chemical databases is a fast and precise method,which helps in identification of novel and potential leadssuitable for further development [34]. After applying the Lip-inski’s filters, four commercially available databases (NCI,Specs, ChemDiv and Natural product databases) of diversechemical compounds were mined for the virtual screen-ing process. For database screening, two options are avail-able in DS2.5, namely Fast/Flexible and Best/Flexible. Thevalidated pharmacophore hypotheses as 3D query with theBest/Flexible search option were used for virtual screen-ing of the databases. Initially, the validated pharmacophorehypothesis Hypo1_ACC1 was used to screen the databases.Further, the hits obtained were screened by the best phar-macophore hypothesis Hypo1_ACC2. In both the cases, theMaximum Omitted Features option was set to ‘−1’ to screenthe databases. Hit compounds were screened for their pre-dicted biological activity values. Hits obtained from the phar-macophore screening, which were showing estimated IC50

values less than 50 nM were selected and subsequently sub-mitted for the evaluation of drug-likeness properties by QEDcalculation. QED value measures the drug-likeness propertyof a compound based on the desirability function and it rangesfrom 0 to 1 [35]. To calculate the QED value physicochemicalproperties were calculated by Accelrys Discovery Studio2.5(DS2.5). The properties calculated were MW, ALOGP, num-ber of HBDs, number of HBAs, molecular PSA, numberof ROTBs and the number of AROMs. Finally, a substruc-ture search was performed against each compound usinga curated reference set of 94 functional moieties that arepotentially mutagenic, reactive or have unfavourable phar-macokinetic properties [36]. The number of matches for eachcompound was captured (ALERTS). The unweighted andweighted QED values were calculated based on the abovementioned molecular properties by following formulae:

QED = exp

(1

n

n∑i=1

ln di

)(1)

QEDw = exp

(∑ni=1 wi ln di∑n

i=1 wi

)(2)

where d is the derived desirability function correspondingto different molecular properties, w is the weight applied toeach function and n is the number of molecular properties[35]. The hit compounds obtained from the QED test weresubjected to molecular docking by Glide5.5 [37].

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Table 1 Statistical parameters of top 10 pharmacophore hypotheses of ACC1 inhibitors generated using HypoGen algorithm

Hypo no. Total cost Cost differencea RMSDb Correlation Features

Hypo1 86.293 71.960 0.800 0.961 1HBA,1HBD,1H YA,1HYAr

Hypo2 93.178 65.075 1.125 0.923 1HBA,1HBD,1HYA,1HYAr

Hypo3 95.725 62.528 1.217 0.909 1HBA,1HBD,1HYA,1HYAr

Hypo4 96.185 62.068 1.242 0.905 1HBA,1HBD,1HYA,1HYAr

Hypo5 100.884 57.37 1.388 0.880 1HBA,1HBD,1HYA,1HYAr

Hypo6 101.148 57.106 1.389 0.880 1HBA,1HBD,1HYA,1HYAr

Hypo7 103.470 54.784 1.439 0.871 2HBA,1HBD,1HYA

Hypo8 104.626 53.628 1.522 0.854 2HBA,1HBD,1HYAr

Hypo9 105.537 52.717 1.501 0.859 2HBA,1HBD,1HYA

Hypo10 106.260 51.994 1.517 0.855 2HBA,1HBD,1HYA

The null cost, the fixed cost and the configuration cost are 158.254, 79.1158 and 14.1238, respectivelyHBA hydrogen bond acceptor, HBD hydrogen bond donor, HYA hydrophobic aliphatic, HYAr hydrophobic aromaticaCost difference between the null and the total costbRMSD, root mean square deviation

Molecular docking

Molecular docking studies assist in the calculation of bindingaffinities, screening and investigation of intermolecular inter-actions between ligands and the target protein. We have car-ried out docking experiments to compare the binding affini-ties of known potent inhibitors with that of the screened hitsand to rank the screened hits on the basis of interactions withamino acid residues of the active site. The Protein Prepara-tion Wizard and LigPrep modules support in the generationof input files and are essential to carry out the moleculardocking. Hydrogen atoms were added to the target, the pro-tonation states for histidine residues were optimized and thehydrogen atoms were minimized up to 0.30 Å RMSD [38].Prepared protein structure of ACC2 was used to generate agrid of 10 Å around the active site by Receptor Grid Genera-tion tool. Extra precision (XP) Glide algorithm was validatedto perform docking studies of the hit molecules by re-dockingthe co-crystallized inhibitor into the grid of target. LigPrepwas used for energy minimizations of the designed ligandswith the OPLS_2005 force field. The default settings for scal-ing the van der Waals radii were selected: a scaling factor of0.8 and a partial charge cut-off of 0.25 with no constraintswere defined for docking these hits.

Results and discussion

Pharmacophore modelling

In ligand-based pharmacophore modelling studies, top tenhypotheses were generated using diverse training set com-pounds. Tables 1 and 2 summarize the results obtained during

the pharmacophore hypotheses generation for ACC1 andACC2 inhibitors, respectively. The best pharmacophorehypothesis for each biological target (Hypo1_ACC1 andHypo1_ACC2) was characterized by the best correlationcoefficient, lowest root mean square error and the high-est cost difference. The Hypo1_ACC1 consists of spatialarrangement of the four chemical features namely, presenceof one HBA, one HBD, one HYA, one HYAr feature andthree excluded volumes (Fig. S5); while Hypo1_ACC2 con-sists of one HBA, one HYA, one HYAr feature and threeexcluded volumes (Fig. S6). Table S1 and S2 summarize theHypo1_ACC1 and the Hypo1_ACC2 estimated activity val-ues along with their corresponding error values of the ACC1and ACC2 training set molecules. Among the 22 ACC1 train-ing set compounds, all the active compounds (+++) werepredicted accurately, the two moderately active (++) com-pounds were underestimated as least active (+) and the twoleast active (+) compounds were overestimated as moder-ately active (++); while in the case of ACC2 out of 22 train-ing set compounds, the one moderately active (++) com-pounds were overestimated as active (+++), the two activecompounds (+++) were underestimated as moderately active(++) and the two least active (+) compounds were overes-timated as moderately actives (++). Interestingly, most ofthe highly active compounds from training set were mappedto all the pharmacophoric features of Hypo1_ACC1 andHypo1_ACC2. While in the case of moderately active andthe least active compounds, one or two features were miss-ing or mapping partly with one exception. Figure 1 showsthat the most active compounds from the ACC1 and ACC2training set were mapped accurately over all the features ofHypo1_ACC1 and Hypo1_ACC2, respectively. In the caseof the least active compounds from the ACC1 training set,

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Table 2 Statistical parameters of top 10 pharmacophore hypotheses of ACC2 inhibitors generated using HypoGen algorithm

Hypo no. Total cost Cost differencea RMSDb Correlation Features

Hypo1 85.893 104.18 1.037 0.952 1HBA,1HYA,1HYAr

Hypo2 116.411 73.662 1.992 0.808 3HBA,1HYA

Hypo3 123.770 66.303 2.149 0.772 2HBA,1HYA,1HYAr

Hypo4 124.806 65.267 2.178 0.765 2HBA,1HYA

Hypo5 125.503 64.57 2.174 0.766 1HBA,2HYA,1HYAr

Hypo6 127.584 62.489 2.175 0.766 2HBA,1HYA,1HYAr

Hypo7 129.417 60.656 2.251 0.747 2HBD,1HYA,1HYAr

Hypo8 131.127 58.946 2.273 0.741 2HBA,1HYA,1HYAr

Hypo9 134.397 55.676 2.360 0.717 2HBA,1HYA

Hypo10 140.740 49.333 2.477 0.682 2HBA,1HYA

The null cost, the fixed cost and the configuration cost are 190.073, 72.401 and 7.409, respectivelyHBA hydrogen bond acceptor, HBD hydrogen bond donor, HYA hydrophobic aliphatic, HYAr hydrophobic aromaticaCost difference between the null and the total costbRMSD, root mean square deviation

Fig. 1 a Alignment of mostactive ACC1 inhibitor with bestpharmacophore hypothesis(Hypo1_ACC1). b Alignment ofmost active ACC2 inhibitor withbest pharmacophore hypothesis(Hypo1_ACC2)

Fig. 2 a Alignment of leastactive ACC1 inhibitor with bestpharmacophore hypothesis(Hypo1_ACC1). b Alignment ofleast active ACC2 inhibitor withbest pharmacophore hypothesis(Hypo1_ACC2)

one HBD and one HYAr feature were missing (Fig. 2a),while in case of the ACC2; the least active compound waspartially mapped on all the features (Fig. 2b). FitValue isa measure of the overlap between the features in the phar-macophore and chemical features in the molecule, whichassists in understanding the chemical meaning of the pharma-

cophore hypothesis. The most active compound in the train-ing set of ACC1 and ACC2 inhibitors has shown the fitnessscore of 7.02 and 7.48, respectively. However, the least activecompound has shown the fitness score of 4.04 and 3.80 whenmapped to Hypo1_ACC1 and Hypo1_ACC2 hypothesis,respectively.

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Fig. 3 a Scatter plot ofpredicted pIC50 values againstexperimental pIC50 values forACC1 training (blue triangles)and test set (red diamond’s)inhibitors. b Scatter plot ofpredicted pIC50 values againstexperimental pIC50 values forACC2 training (blue triangles)and test set (red diamond’s)inhibitors. (Color figure online)

Pharmacophore model validation

Validation is one of the important steps in pharmacophoregeneration. Several methods and parameters are available toconfirm the quality of the generated pharmacophore modellike cost analysis, test set prediction, Fischer’s randomizationmethod, goodness of fit and enrichment factor. In the first val-idation process, the various cost values calculated during thepharmacophore generation were evaluated. The difference of40–60 bits between the total cost and the null cost hypothe-ses signifies the 75–90 % chance of representing a true cor-relation in the data. The total cost values for the hypothesesHypo1_ACC1 and Hypo1_ACC2 were 86.293 and 85.893,while the null cost values were 158.254 and 190.073, respec-tively. The cost difference between the null cost and thetotal cost values for the Hypo1_ACC1 was 71.960, whereasfor the Hypo1_ACC2 was 104.18, which characterizes thatboth the pharmacophore hypotheses can significantly cor-relate the data by more than 90 %. Both of the pharma-cophore hypotheses Hypo1_ACC1 and Hypo1_ACC2 haveshown the highest correlation coefficient values of 0.961 and0.952, respectively. In addition, the RMSD values were 0.800and 1.03 Å, respectively. The configuration cost value for theHypo1_ACC1 and Hypo1_ACC2 was found to be 14.123 and7.409, respectively. Among all the generated ten pharma-cophore hypotheses, the Hypo1_ACC1 and Hypo1_ACC2have shown better statistical values including higher correla-tion, greater cost difference, lower RMSD and lower config-uration cost values. On the basis of these validation results,the best pharmacophore hypotheses were carried forwardfor subsequent validation, which includes the activity pre-diction of test set compounds. Most of the compounds inthe ACC1 and ACC2 test sets were predicted correctly withrespect to their experimental biological activity. A coefficientof determination (r2) of 0.790 and 0.757 for the ACC1 andACC2, respectively, shows that there is a good correlation

between the experimental and predicted activities (Fig. 3).In the ACC1 test set validation, it was found that 21 of 25highly active, 18 of 37 moderately active and 26 of 27 the leastactive compounds were predicted correctly. The four highlyactive compounds were underestimated as moderately active;the two moderately active compounds were overestimated asactives and seventeen were underestimated as least actives;the one least active compound was overestimated as moder-ately active. Similarly, in the ACC2 test set validation, 20 of29 highly active, 45 of the 48 moderately active and 10 ofthe 10 least active compounds were predicted correctly. Thenine most active compounds were underestimated as moder-ately active and three moderately active compounds wereoverestimated as actives. Fischer randomization test wasemployed as second validation process to attain 95 % confi-dence level. The 19 random pharmacophore hypotheses weregenerated for both the ACC1 and ACC2 training set, whichwere compared with the original pharmacophore hypothe-ses Hypo1_ACC1 and Hypo1_ACC2, respectively. Figs. S7and S8 show that none of the randomly generated pharma-cophore hypotheses have scored better statistical results thanHypo1_ACC1 and Hypo1_ACC2, respectively. The resultsobtained from Fischer randomization test indicate that thehypotheses Hypo1_ACC1 and Hypo1_ACC2 have not beengenerated by any chance correlation.

Finally, small databases (D) containing 259 compounds(including 7 actives and 252 decoys) for ACC1 and 370 com-pounds (including 10 actives and 360 decoys) for ACC2 weregenerated using DecoyFinder1.1. These databases were usedto validate whether the best hypotheses (Hypo1_ACC1 andHypo1_ACC2) could discriminate the actives from decoysor not. The screening of databases was performed usingthe Hypo1_ACC1 and Hypo1_ACC2 as a 3D structuralquery. The accuracy, precision, sensitivity and specificity ofthe best pharmacophore models were calculated. Moreover,for the analysis of result enrichment factor (E-value) and

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Table 3 The statisticalparameters obtained fromDecoy test

S. no. Parameter Hypo1_ACC1 Hypo1_ACC2

1 Total compounds in database (D) 259 370

2 Total Number of actives in database (A) 7 10

3 Total hits (Ht) 10 14

4 Active hits (TP) 7 10

5 True negative (TN) 249 356

6 Enrichment factor or enhancement (E) 25.90 26.43

7 False negatives (FN=A − TP) 0 0

8 False positives (FP=Ht − TP) 3 4

9 GH score (goodness of hit list) 0.765 0.777

10 Accuracy= (TP + TN)/(TP + TN + FP + FN) 0.988 0.989

11 Precision=TP/(TP + FP) 0.700 0.714

12 Sensitivity=TP/(TP + FN) 1.00 1.00

13 Specificity=TN/(TN + FP) 0.988 0.988

goodness of hit score (GH) were calculated using the follow-ing formulae.

E = (T P × D)

(Ht × A)(3)

G H = (T P/4Ht A)(3A + Ht)

× (1 − ((Ht − T P)/(D − A))), (4)

where D, A, Ht and T P represent the total number ofcompounds of the database, total number of actives, totalnumber of compounds screened by a pharmacophore modeland total number of active compounds screened, respectively.Hypo1_ACC1 and Hypo1_ACC2 have shown an E value of25.90 and 26.43, respectively. The calculated GH score forboth hypotheses was greater than 0.5, which indicates thatthe quality of the developed pharmacophore was significant(Table 3). From the overall validation results, we assure thatthe hypotheses (Hypo1_ACC1 and Hypo1_ACC2) were ableto discriminate between the active and decoys.

Pharmacophore model-based virtual screening

The chronological order followed during the virtual screen-ing process is shown in Fig. 4. Initially, the validated phar-macophore model (Hypo1) of ACC1 inhibitors was used as aquery to search the NCI, Specs, ChemDiv and Natural prod-uct databases, which were pre-filtered by the Lipinski’s ruleof five. 658119 hit compounds mapping on to the pharma-cophore model Hypo1_ACC1 were retrieved, which includedsome compounds structurally similar to that of the existingACC1 inhibitors and some novel scaffolds. Further, thesevirtual hits were subjected to screening using the validatedpharmacophore model Hypo1_ACC2 and a set of 2066 hitcompounds were chosen that showed estimated IC50 values

below 50 nM. The selected screened hits were subsequentlysubmitted for their QED value calculation.

QED values assist in screening the chemical structures bytheir merit relative to the target functions. In contrast to therule-based approaches, QED value presents a better view ofdrug likeness. Primarily, the QED values depend upon thedistribution of the molecular properties, contrasting the rule-based metrics. In some cases, it also recognizes a generallyunfavorable property that may be tolerated when the otherparameters are close to the ideal. The unweighted_QED andweighted_QED values were calculated for screened virtualhits. The QED and QEDw values range from 0.05 to 0.88 and0.05 to 0.91, respectively. The compounds having QED valueabove 0.5 were selected for further analysis by moleculardocking studies to reduce the number of false-positive hitsfrom the virtual screening.

Molecular docking

In order to reduce the number of false positive screenedvirtual hits, docking analysis was performed at the activesite of human ACC2 using Glide5.5. The docking proto-cols were validated by re-docking the co-crystallized lig-and CP-640186 into the active site of ACC2 (PDB ID:3FF6) with a root mean squared deviation of 0.731 Å. Itbinds at the dimer interface and interacts with the residues(Val2128, Arg2158, Gly2159, Phe2160, Ser2161, Leu2229,Glu2230, Glu2232, Gly2233 and Gly2236 from the chain Aand Thr1960, Ala1964, Leu1965 and Val1968 from the chainB) from both chains of the ACC2 protein. The carbonyl groupadjacent to the anthracene of CP-640186 forms the hydro-gen bond with the main-chain amide N atom of Glu2230.Another weak hydrogen bond was observed between the

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Fig. 4 Sequential virtual screening followed to identify dual ACC inhibitors

Table 4 Docking score, fit value, predicted ACC1 inhibitory activity, ACC2 inhibitory activity and QED values of top virtual hits

Compound Docking score Hypo1_ACC1fitvalue

Hypo1_ACC2fitvalue

ACC1 estimatedIC50 (nM)

ACC2 estimatedIC50 (nM)

Unweighted_QED value

Weighted_QEDvalue

ZINC33086616 −7.226 6.67 7.25 25.77 05.48 0.549 0.555

ZINC19924472 −7.181 6.99 7.13 12.30 7.189 0.514 0.525

ZINC33086598 −7.060 6.51 6.65 37.53 21.72 0.517 0.529

carbonyl group adjacent to the morpholine and the amide Natom of Gly2162. All screened virtual hits (712) were dockedinto the active site of ACC2, and fifteen hit compounds havinga docking score > −7.00 kcal/mol were selected and checkedfor their binding mode. Similar scaffold hit compounds were

also rejected based on their docking score and active siteinteractions. Finally, three hit compounds ZINC33086616,ZINC19924472 and ZINC33086598 (Fig. S9) with the high-est docking score were selected as the best hits (Table 4).Molecular overlay of these hits with the most active com-

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Fig. 5 The docking pose of co-crystallized ligand (CP-640186) and top virtual hit (ZINC33086616) into the active site of ACC2 protein (PDBID: 3FF6)

Fig. 6 a Alignment ofZINC19924472 with bestpharmacophore hypotheses(Hypo1_ACC1 andHypo1_ACC2). b Alignment ofZINC33086616 with bestpharmacophore hypotheses(Hypo1_ACC1 andHypo1_ACC2) c Alignment ofZINC33086598 with bestpharmacophore hypotheses(Hypo1_ACC1 andHypo1_ACC2)

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pound of the training set revealed their similar binding ori-entations at the active site. The docking poses of CP-640186and the top virtual hit ZINC33086616 in the active site isshown in Fig. 5. One hydrogen-bond interactions betweenthe NH of Glu2230 and the carbonyl group of all inhibitorsand screened virtual hits were conserved. Figure 6 showsthe mapping of the top three virtual hits on the best phar-macophore hypotheses (Hypo1_ACC1 and Hypo1_ACC2).Further, these three hit compounds were confirmed by thePubChem [39] and SciFinder scholar [40] search tools didn’tfind these compounds reported as ACC inhibitors. Hence, wesuggest that these three compounds could be novel scaffoldsas ACC inhibitors.

Conclusions

In this study, we have developed ligand-based pharmacophoremodels for different classes of ACC1 and ACC2 inhibitors.The best pharmacophore hypotheses Hypo1_ACC1 andHypo1_ACC2 were validated by the different methods toevaluate their prediction power over the diverse test setcompounds. The highly predictive hypotheses were furtherused in virtual searching for the identification of dual ACCinhibitors. Four diverse chemical databases were used in vir-tual searching. The hits from the virtual screening were fil-tered based on the estimated activity values and the QEDvalues. The resultant drug-like compounds were docked intothe active site of ACC2 using Glide5.5. Finally, the mole-cular docking analysis was performed to select the positivevirtual hits. As far as we are aware, ACC1 and ACC2 activ-ity of these compounds has not been reported in the litera-ture as explored by PubChem and SciFinder Scholar searchtools. Combining all these results, three new compoundswere identified as potential lead candidates to be used as dualACC inhibitors. The proposed pharmacophore models canbe further used for rational design of dual ACC inhibitors.Further in vitro testing of virtual hits would be ensued toconfirm the success rate of this study and to optimize the hitsthereafter.

Acknowledgments The authors acknowledge financial support fromDepartment of Science and Technology (DST), New Delhi.

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