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Research ArticleLigand Based Pharmacophore Modeling and Virtual ScreeningStudies to Design Novel HDAC2 Inhibitors

Naresh Kandakatla and Geetha Ramakrishnan

Department of Chemistry Sathyabama University Jeppiaar Nagar Chennai 600119 India

Correspondence should be addressed to Geetha Ramakrishnan icget2011gmailcom

Received 11 July 2014 Revised 8 October 2014 Accepted 22 October 2014 Published 26 November 2014

Academic Editor Gilbert Deleage

Copyright copy 2014 N Kandakatla and G Ramakrishnan This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Histone deacetylases 2 (HDAC2) Class I histone deacetylase (HDAC) family emerged as an important therapeutic target for thetreatment of various cancers A total of 48 inhibitors of two different chemotypeswere used to generate pharmacophoremodel using3DQSAR pharmacophore generation (HypoGen algorithm)module inDiscovery StudioThe best HypoGenmodel consists of fourpharmacophore features namely one hydrogen bond acceptor (HBA) and one hydrogen donor (HBD) one hydrophobic (HYP)and one aromatic centres (RA)This model was validated against 20 test set compounds and this model was utilized as a 3D queryfor virtual screening to validate against NCI andMaybridge database and the hits further screened by Lipinskirsquos rule of 5 and a totalof 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular dockingin the active site of HDAC2 (PDB 3MAX) Finally eight hit compounds NSC108392 NSC127064 NSC110782 and NSC748337from NCI database and MFCD01935795 MFCD00830779 MFCD00661790 and MFCD00124221 fromMaybridge database wereconsidered as novel potential HDAC2 inhibitors

1 Introduction

Histone deacetylases (HDACs) are the enzymes that deacety-lase the epsilon-N-acetyl-lysine group on histone tails ofthe protein and result in tightening of nucleosome structureand gene silencing [1] There are two types of histoneforms which are histone acetylases and histone deacetylases[2] Histone deacetylases (HDACs) are found in animalsplants fungi archaebacteria and eubacteria [3] Histonedeacetylases are generally classified into four different classesnamely HDACs 1ndash3 and 8 belonging to Class I and relatedto homologous to Rpd3 HDAC 4ndash7 9-10 are Class II relatedto Hda1 Sirt 1ndash7 are Class III and are similar to Sir2 andHDAC11 belongs to Class IV Classes I and II are operatedby zinc dependent mechanism and Class III by NAD [4ndash8]Histone deacetylases (HDACs) control the gene expressionand cellular signaling and histone deacetylases 2 (HDAC2) isoverexpressed in solid tumors including colon cancer lungcancer cervical carcinoma breast cancer and kidneycervixcancer and also in Alzheimerrsquos disease [9 10] Several

HDAC inhibitors are in clinical trial namely hydroxamicacid derivatives benzamide derivatives cyclic peptides andshort-chain fatty acids [11] The first histone deacetylase(HDAC) inhibitor SAHA (suberoylanilide hydroxamic acidor vorinostat) approved by FDA for treating cutaneous T-cell lymphoma and other hydroxamic acids are in clinicaltrial The benzamide derivatives which are in clinical tri-als are Entinostat (MS-275 or pyridin-3-yl methyl 4-((2-aminophenyl) carbamoyl) benzyl carbamate) currently inphase II clinical trial for Hodgkin lymphoma phase I trialof advanced leukemia andmyelodysplastic syndrome (MDS)and Mocetinostat (MGCD0103 or N-(2-Aminophenyl)-4-[[(4-pyridin-3-ylpyrimidin-2-yl)amino]methyl] benzamide)in phase II clinical trial for Hodgkin lymphoma phase I trialof advanced leukemia myelodysplastic syndrome (MDS)diffuse large B-cell lymphoma and follicular lymphoma [12ndash15] Ligand based pharmacophore modeling is a major toolin drug discovery and is applied in virtual screening denovo design and lead optimization [16] Different histonedeacetylase (HDAC) inhibitors had been synthesized and

Hindawi Publishing CorporationAdvances in BioinformaticsVolume 2014 Article ID 812148 11 pageshttpdxdoiorg1011552014812148

2 Advances in Bioinformatics

experimental activity was found Different pharmacophoreand virtual screening studies had been reported on histonedeacetylase (HDAC) with known hydroxamic acid deriva-tives and QSAR studies reported on histone deacetylases 2(HDAC2) with N(2-aminophenyl)-benzamides [17ndash19] Inthe present study benzamide derivatives are used to generatethe pharmacophore model and virtual screening studies havebeen done for histone deacetylases 2 (HDAC2) proteins togain knowledge regarding pharmacophore model and virtualscreening This study aims to construct the chemical featurebased on pharmacophore models for histone deacetylases 2(HDAC2)

2 Materials and Methods

21 Data Preparation A training set of 48 histone deacety-lases 2 (HDAC2) inhibitors of two different chemotypeswere selected form previously published data and the IC

50

values were identified using the same biological assay Thechemotype A is N(2-aminophenyl)-benzamide [20ndash31] andchemotype B is N-hydroxy benzamide derivatives (see sup-plementary Figure 1 in the Supplementary Material availableonline at httpdxdoiorg1011552014812148) [32ndash34] 3DQSAR module in Discovery Studio (DS) was used for devel-oping the pharmacophore The 2D structure of compoundswas drawn in ISIS draw and theywere converted into 3D formand conformationalmodels were generated by FASTmethodthe conformers minimized by the CHARMm force field andthe energy threshold value of 20 kcalmol A maximum of255 conformers were developed for each compound andthese conformermodelswere used for hypotheses generationfitting the compound into the hypotheses and estimating theactivity of the compound The training set of 48 moleculeswas chosen with IC

50values with a range from 0014 120583M to

21 120583MThe dataset activity (IC50) was classified based on the

span over four orders of magnitude that is active (IC50le

01 120583M ++++) moderately active (01 le IC50le 1 120583M +++)

less active (1 le IC50le 10 120583M ++) and inactive (IC

50gt

10 120583M +)

22 Pharmacophore Model Generation HypoGen algorithmwas applied to build the pharmacophore model and in thepresent study four features which are hydrogen bond donors(HBD) hydrogen bond acceptors (HBA) ring aromatic(RA) and hydrophobic (HY) were selected to generatethe pharmacophore hypotheses [35] HypoGen generatespharmacophore model based on chemical features of activecompounds in training set The uncertainty value 2 wasselected from default 3 which means the biological activityis two times higher or lower than the true value All otherparameters were kept as default The developed pharma-cophore model was selected based on the highest correlationcoefficient lowest total cost and root mean square deviation(RMSD)

23 Pharmacophore Validation The pharmacophore modelis validated by three steps cost analysis Fischerrsquos randomiza-tion test and the test set predictionThe quality of the model

is described in terms of fixed cost total cost and null costThe fixed cost represents the simplest model and it fits thedata perfectly The null cost represents no features with highcost value and it estimates the activity to be average activityof the training set compounds The best model was selectedbased on the difference between the two cost values (null costminus total cost) if the difference between the costs is greaterthan 60 means the model has excellent true correlation Ifthe difference is 40ndash60 the model has prediction correlationof 70ndash90 and if the difference is below 40 it may bedifficult to predict the model Fischerrsquos randomization is thesecond approach to validate the pharmacophore model The95 confidence level was selected to validate the study and19 random spread sheets were constructed This methodgenerates the hypotheses by randomizing the activity ofthe training set compounds The correlation between thestructure and biological activitywas validated by thismethodThefinal sets of validationwere selected using twentyHDAC2inhibitors as given in supplementary Figure 2 The Ligandpharmacophore mapping module in Discovery Studio wasused to map the ligands and estimate the predicted activityof the test set compounds

24 Database Search Virtual screening studies were usedto find novel and potential leads from virtual database forfurther development [36] The virtual screening studies wereused to find novel leads for HDAC2 The Hypo1 model wasused as a 3D query in database screening and the NationalCancer Institute (NCI) database containing 265242moleculesand Maybridge database containing 58723 molecules wereused for screening [37 38] Ligand pharmacophore mappingprotocol was used with flexible search option to screen thedatabase Hit compounds from the database with estimatedactivity less than 01120583M were selected for further screeningusing Lipinskirsquos rule of five compounds have (i) molecularweight less than 500 (ii) hydrogen donors less than 5 (iii)hydrogen acceptors less than 10 and (iv) an octanolwaterpartition coefficient (Log119875) value less than 5

25 Molecular Docking Docking is the binding orientationof small molecules to their protein targets in order to predictthe affinity and activity of the smallmoleculesHence dockingplays an important role in the rational drug designMoleculardocking studies were performed by using LigandFit modulein Discovery Studio [39] There are three stages in LigandFitprotocol (i) docking in which attempt is made to dock aligand into a user defined binding site (ii) in situ ligandminimization and (iii) scoring in which various scoringfunctionswere calculated for each pose of the ligands Proteinpreparationwas themain step in docking and all ligands weredocked into the active site of the receptor Protein preparationinvolves deletion of water molecules and addition of hydro-gen atoms and applying CHARMm force field The activesites were searched using flood filling algorithm The activesite was defined as region of HDAC2 that comes within 12 Afrom the geometric centroid of the ligand Ten poses weregenerated for each ligand during the docking process and thebest poses were selected based on the best orientation of themolecule in the active site and dock score values which was

Advances in Bioinformatics 3

Table 1 Statistical results of the generated pharmacophore models

Hypo Total cost Cost differencea RMS Error cost Correlation Max fit Features1 223598 68459 164 204016 0759 64 HBA HBD HYP RA2 228662 62395 17 209498 0735 69 HBA HBD HYP RA3 238771 53286 182 219538 0689 686 HBA HYP RA RA4 2400 52057 184 22097 0682 716 HBA HYP RA RA5 241524 50533 184 221036 0682 577 HBA HYP HYP RA6 24171 50347 186 222568 0674 698 HBA HYP RA RA7 242261 49796 186 222428 0675 625 HBA HYP RA RA8 242339 49718 185 221941 0677 583 HBA HYP RA RA9 244381 47676 189 22513 0662 684 HBA HYP RA RA10 244543 47514 188 224225 0666 588 HBA HYP HYP RANull cost = 292057 fixed cost = 158138 configuration cost = 1766aCost difference = null cost minus total cost

selected after energy minimization with smart minimizationThe dock score was calculated using the following formula

DockScore (force field)

=minus(ligand

receptor interaction energy+ ligand internal energy)

(1)

Single dock score may fail to obtain active moleculeshence consensus scoring method was applied which consistsof LigScore1 LigScore2 Jain Piecewise Linear Potential(PLP1 and PLP2) and Potential of Mean Force (PMF)The active molecules were selected based on the consensusscoring method and H-bond interaction with the recep-tor The crystal structure of the HDAC2 protein (PDBID 3MAX) was downloaded from the protein data bank(httpwwwrcsborgpdb) The crystal structure of histonedeacetylases 2 (HDAC2) protein has three chains which areA B and C The chain A has higher docking score thanchains B and C so chain A is selected for docking Thehit compounds from the database screening with positiveLipinskirsquos drug likeness were subjected to molecular dockingstudies into the active site of the 3MAX receptor

3 Results and Discussion

31 Pharmacophore Generation Pharmacophore model vir-tual screening and molecular docking studies were per-formed to find novel HDAC2 inhibitors Dataset of 48molecules with structural diversity and four orders of activitymagnitude (0014 to 21 120583M) were selected to develop phar-macophore model using HypoGen algorithm in DiscoveryStudio Four features hydrogen bond donor (HBD) hydrogenbond acceptors (HBA) ring aromatics (RA) and hydropho-bic (HY) were selected Top 10 hypotheses were generatedwith the following features HBA HBD RA and HY Thestatistical parameters such as cost values correlation andRMSD were summarized in Table 1 The best hypothesis wasselected out of 10 hypotheses by the highest cost differenceHypo1 has the highest cost difference between null cost andtotal cost of 6845 correlation coefficient of 075 the lowest

RMS deviation of 164 and configuration cost value of 1766This indicates themodel and the data correlated bymore than90 High correlation coefficient and low RMSD indicate theability to predict the activity of the training set compoundsis high The Hypo1 has a correlation coefficient value (1198772 =075) and the model strongly predicts the activity of trainingset compounds The correlation between the experimentalactivity and predicted activity of training set compoundswas shown in Table 2 For most of the compounds themodel predicts the activity correctly Figure 1(a) shows the 3Dspatial arrangement of all featureswith distance constraints ofHypo1 The features of Hypo1 were mapped onto the activecompound 15 as shown in Figure 1(b) HBD is mapped byamino group HBA is mapped by phosphonate group aro-matic ring is mapped by aromatic group and HY is mappedby hydrophobic group The results indicate that HDAC2inhibition requires the following features HBD HBA RAand HYP Inactive compound 29 was mapped partially ontothe features of Hypo1 as shown in Figure 1(c)The fit value forthemost active and the least active compoundswas generatedto be 539 and 321 respectively

32 Pharmacophore Validation The pharmacophore modelcan be validated by three methods cost analysis test setprediction and Fischerrsquos randomization test

321 Cost Analysis TheHypoGen algorithm in DS producesthree cost values during the pharmacophore generationwhich are fixed cost total cost and null cost The model isvalidated by the difference between the null cost and totalcost if the model has cost difference above 60 it has thepredictability chance of greater than 90 The Hypo1 havingthe cost difference of 6845 shows significant model (shownin Table 1)

322 Test Set Prediction A good pharmacophore model canpredict not only the activity of the training set compoundsbut also external test set compounds 20 compounds withdifferent activity range were used as a test set to check thepredictability power of the pharmacophore model Ligandpharmacophoremapping protocol with flexible search option


Table 2 The experimental activity and estimated activity of the training set compounds are summarized here

Compound number Exp IC50120583M

Estimated IC50120583M Error Activity

magnitude (exp)Activity

magnitude (est) Fit scores

Compound 1 2 0373 1627 ++ +++ 3981Compound 2 0313 0178 0135 +++ +++ 4301Compound 3 0105 0127 minus0022 +++ +++ 4449Compound 4 0071 0116 minus0045 ++++ +++ 4488Compound 5 034 0133 0207 +++ +++ 4427Compound 6 0115 0117 minus0002 +++ +++ 4484Compound 7 019 0124 0066 +++ +++ 4457Compound 8 078 0252 0528 +++ +++ 4151Compound 9 0049 0202 minus0153 ++++ +++ 4247Compound 10 33 2801 0499 ++ ++ 3106Compound 11 036 0277 0083 +++ +++ 411Compound 12 013 0122 0008 +++ +++ 4465Compound 13 018 0177 0003 +++ +++ 4304Compound 14 014 0088 0052 +++ ++++ 4609Compound 15 0014 0014 0 ++++ ++++ 5392Compound 16 09 2338 minus1438 +++ ++ 3185Compound 17 02 0247 minus0047 +++ +++ 416Compound 18 007 0125 minus0055 ++++ +++ 4456Compound 19 006 0058 0002 ++++ ++++ 4787Compound 20 008 0082 minus0002 ++++ ++++ 4637Compound 21 009 0126 minus0036 ++++ +++ 4451Compound 22 01 0198 minus0098 ++++ +++ 4257Compound 23 05 0817 minus0317 +++ +++ 3641Compound 24 08 1744 minus0944 +++ ++ 3312Compound 25 0039 0251 minus0212 ++++ +++ 4153Compound 26 027 213 minus186 +++ ++ 3225Compound 27 0043 0254 minus0211 ++++ +++ 4149Compound 28 31 2181 0919 ++ ++ 3215Compound 29 21 188 22 + + 3213Compound 30 13 152 1148 + ++ 3372Compound 31 13 1891 minus0591 ++ ++ 3277Compound 32 06 0574 0026 +++ +++ 3795Compound 33 10 024 976 ++ +++ 4172Compound 34 0032 0218 minus0186 ++++ +++ 4215Compound 35 38 2305 1495 ++ ++ 3191Compound 36 0019 0017 0002 ++++ ++++ 5322Compound 37 087 0232 0638 +++ +++ 4188Compound 38 148 1229 0251 ++ ++ 3464Compound 39 347 2075 1395 ++ ++ 3237Compound 40 046 2075 minus1615 +++ ++ 3237Compound 41 026 2115 minus1855 +++ ++ 3228Compound 42 056 0796 minus0236 +++ +++ 3652Compound 43 554 182 372 ++ ++ 3293Compound 44 052 0342 0178 +++ +++ 4019Compound 45 144 2101 minus0661 ++ ++ 3231Compound 46 033 0773 minus0443 +++ +++ 3665Compound 47 181 0696 1114 ++ +++ 3711Compound 48 39 2081 1819 ++ ++ 3235


5835

13089

155267347

10084

3507

(a) (b) (c)

Figure 1 The best pharmacophore model (Hypo1) of HDAC2 inhibitors generated by the HypoGen module (a) the best pharmacophoremodel Hypo1 represented with distance constraints (A) (b) Hypo1 mapping with one of the active compounds 15 and (c) Hypo1 mappingwith one of the least active compounds 29 Pharmacophoric features are colored as follows hydrogen bond acceptor (green) hydrogen bonddonor (magenta) hydrophobic (cyan) and ring aromatic (orange)

was used to map the test set compounds In test set analysisformost of the compounds themodel predicted activity to thetune of less than 10 Out of 20 compounds 17 compoundswere predicted with an error factor less than 5 and 3compounds were predicted with an error factor less than10 The experimental and predicted activities of the test setcompounds were shown in Table 3

323 Fischer Randomization Test Fischer randomizationtest was the third approach to validate the Hypo1 usingDS In this method the experimental activity of the trainingset compounds was randomly scrambled and generates therandom pharmacophore model using the same parametersas used in developing the Hypo1 hypothesis Confidencelevel of 95 was set and it created 19 spread sheets all19 random spread sheets have high cost values than totalcost and correlation value is less than the Hypo1 (supple-mentary Table 1) It clearly shows none of the randomlygenerated pharmacophores has good statistical values thanHypo1 The difference in costs between the HypoGen andFischer randomizations was shown in Figure 2 All the threevalidationsmethods demonstrated thatHypo1 hypothesis hasgood predictability and can be chosen as the best model

33 Database Screening The best pharmacophore modelHypo1 was used as a 3D query to search the NCI (265242)andMaybridge (58723) databases using flexible search optionin DS A total of 6130 compounds from NCI and 1379 fromMaybridge were mapped using the features of Hypo1 Atotal of 1198 and 440 compounds from NCI and Maybridgeshowed HypoGen estimated value of less than 1120583M and wereconsidered for further studies and these compounds werescreened for Lipinskirsquos rule of 5 A total of 625 (382 NCI 243Maybridge) compounds obeyed the rule and were subjectedto molecular docking studiesThe flowchart in Figure 3 was aschematic representation of virtual screening process

34 Molecular Docking The HDAC2 protein has threechains which are A B and C The active compound MS-275(Entinostat) was docked into active sites of all three chainsusing LigandFit module in Discovery Studio and out of

1086420

225

250

275

300

CostsRandom1Random2Random3Random4Random5Random6Random7Random8Random9Random10Random11Random12Random13Random14Random15Random16Random17Random18Random19

Figure 2 Fischer randomization test for 95 confidence level phar-macophore hypotheses versus total cost

NCI database Maybridge253368 58723

Pharmacophore mappingNCI database Maybridge

6130 1379

Estimated activity lt 1NCI database Maybridge

1 198 440

Lipinski rule of fiveNCI database Maybridge

382 243

Molecular docking using ligand fit (DS)

mdashmdash

mdashmdash

mdashmdash

mdashmdash

Figure 3 Schematic representation of virtual screening processimplemented in the identification of HDAC2 inhibitors

three chains chain A has given the best docking scoreand higher H-bond interactions than chains B and C Thedocking score of all three chains with Entinostat was shownin supplementary Table 2 Chain A was selected as an activechain and the final hit compounds from virtual screening


Table 3 The experimental and estimated activity of test compounds





Compound 1 347 0174 3296 ++ +++ 6298Compound 2 10 4253 5747 + ++ 605Compound 3 052 0009 0511 +++ ++++ 5559Compound 4 181 001 18 ++ ++++ 5529Compound 5 01 001 009 ++++ ++++ 5518Compound 6 008 0012 0068 ++++ ++++ 5458Compound 7 02 0015 0185 +++ ++++ 5364Compound 8 09 0017 0883 +++ ++++ 5309Compound 9 144 0019 1421 ++ ++++ 5272Compound 10 046 0019 0441 +++ ++++ 5268Compound 11 1 0027 0973 +++ ++++ 5116Compound 12 10 4033 5967 + ++ 5097Compound 13 009 0033 0057 ++++ ++++ 5034Compound 14 554 036 518 ++ +++ 499Compound 15 026 0046 0214 +++ ++++ 4887Compound 16 033 0048 0282 +++ ++++ 4865Compound 17 148 006 142 ++ ++++ 4771Compound 18 3 0202 2798 ++ +++ 4246Compound 19 05 0239 0261 +++ +++ 4174Compound 20 005 047 minus042 ++++ +++ 3881

studies were docked into active site of 3MAX-AThe dockingscore along with binding orientations and hydrogen bondswas considered for choosing the best pose of the dockedcompounds The docking scores were compared with MS-275 (Entinostat) The docking score of the Entinostat was426 kcalmol and hit compounds from the virtual screeningstudies show better binding than the active compoundEntinostat The Entinostat has the four hydrogen bondinginteractions with Arg39 Cys156 Gly305 and His183 given inFigure 4(a) The hit compounds that scored docking scorehigher than active compound and form interaction withthe crucial amino acids were considered as effective leadsfor designing novel HDAC2 inhibitors 74 compounds fromboth databases showed good interactions in the active site ofthe HDAC2 and scored more than 45 about 20 compoundsthat showed better docking score than active compoundswere chosen as leads and their docking score and H-bondinteractions were listed in supplementary Tables 3 and 4Finally four compounds from NCI namely NSC108392NSC127064 NSC110782 and NSC748337 were identifiedwith good docking score and estimated activity value of026 120583M 047 120583M 037 120583M and 041 120583M respectivelyThe hitNSC108392 (4-(((6-amino-3-methylpyrido[23-b]pyrazin-8-yl)amino)methyl)benzenesulfonamide) has the docking scoreof 1219 kcalmol and forms three hydrogen bond interactionswith Arg39 (3) His145 and Asp181 (2) amino acids shownin Figure 4(b) The binding mode of this compound at theactive site showed that mapping on HBD feature of Hypo1formed interactions with Arg39 and HBA feature of Hypo1

forms the interactions with His145 and Asp181 NSC127064((2S3S4S5R)-2-(1-(benzyloxy)-6-imino-1H-purin-9(6H)-yl)-5-(hydroxymethyl) tetrahydrofuran-34-diol) has thedocking score of 1164 kcalmol and forms seven hydrogenbond interactions with Arg39 Cys156 Gly305 His145(2) Asp181 (2) Trp140 and Gly142 amino acids shown inFigure 4(c) The binding mode and pharmacophore overlayof the compound showed that OH mapped on HBD NH2mapped on HBA and purine moiety mapped on RA forminteraction in the active site NSC110782 (4-(2-((6-amino-3-methylpyrido[23-b]pyrazin-8-yl) amino) ethyl) benzenesulfonamide) has the docking score of 1062 kcalmol andforms four hydrogen bond interactions with His145 Asp181(3) Gly154 and Ala141 shown in Figure 4(d) The bindingmode and pharmacophore overlay of this compound showedthat HBD mapped on NH2 and HBA mapped on pyrazinemoiety have interactions with the amino acid residuesNSC748337 (3-(benzo[d]oxazol-2-yl)-5-(1-(piperidin-4-yl)-1H-pyrazol-4-yl) pyridin-2-amine) has the docking scoreof 1056 kcalmol and forms four hydrogen bond interac-tions with Asp181 (2) His145 Ala141 and His183 shownin Figure 4(e) The nitrogen on piperidine mapped toHBD has interactions with Ala141 amino group onpyridine moiety mapped to HBA of Hypo1 has interac-tions with His183 and Asp181 and nitrogen of pyridinemoiety have interactions with His145 Four compounds fromMaybridge database MFCD01935795 MFCD00830779MFCD00661790 and MFCD00124221 were identifiedas novel HDAC2 inhibitors with estimated activity


His183

Cys156

Gly305Arg39

(a)

His145 Asp181

Arg39

(b)

Trp140Gly142

Arg39

Gly305 His145

Cys156

Asp181

(c)

Ala141

Asp181

His145

Gly154

(d)

Ala141

His183

Asp181

Gly306

His145

(e)

His146

Phe155

Cys156

(f)

Asp181

Arg39

His146His183

Asp269

(g)

His146

His183

Cys156

Ala141

(h)

Gly305

Cys156

His183

(i)

Figure 4 Binding orientations of hit compounds (a) Entinostat (b) NSC108392 (c) NSC127064 (d) NSC110782 (e) NSC748337 (f)MFCD01935795 (g)MFCD00830779 (h)MFCD00661790 and (i)MFCD00124221 in the active site of 3MAX-Awith hydrogen bond interac-tions

value of 012 120583M 032 120583M 061 120583M and 068 120583Mrespectively MFCD01935795 (4-(3-ethylthioureido)-N-(5-methylisoxazol-3-yl) benzenesulfonamide) as the dockingscore of 988 kcalmol having three H-bond interactions withCys156 Phe155 and His146 residues is shown in Figure 4(f)The protein-ligand interaction shows HBD of Hypo1 formshydrogen bond interaction with His146 and nitrogen oniso-oxazole mapped with HBA forms hydrogen bond withPhe155 and oxygen of sulphonamide forms bondwithCys156

MFCD00830779 ((E)-N1015840-hydroxy-4-((2-methylthiazol-4-yl)methoxy)benzimidamide) having the docking score of9689 kcalmol forms five hydrogen bonds with Arg39His183 Asp181 (2) Asp269 and His146 amino acids Thebinding mode of the compound shown in Figure 4(g) showsHypo1 of HBA mapped nitrogen of thiazole forms hydrogenbonding with Arg39 HBD mapped on oxygen of N-hydroxyhas hydrogen bond interaction with His183 benzamideof nitrogen has interaction with His146 and nitrogen of


(a) (b) (c)

(d) (e) (f)

(g) (h)

Figure 5The pharmacophore overlay of hit compounds (a) NSC108392 (b)NSC127064 (c) NSC110782 (d)NSC748337 (e)MFCD01935795(f) MFCD00830779 (g) MFCD00661790 and (h) MFCD00124221

formamide has interaction with Asp181 MFCD00661790 (N-(2-(3-(24-difluorophenyl)thioureido)ethyl)-2-(4-hydroxy-phenyl)acetamide) has the docking score of 81 Kcalmolforming four hydrogen bond interactionswithCys156His153His146 and Ala141 amino acids Hypo1 HBD mapped onoxygen shows hydrogen bond interaction with Ala141 HBAmapped on nitrogen forms bonds with His183 nitrogen ofthiourea forms bonds with His146 and acetamide of oxygenhas hydrogen bond with Cys156 the binding interactionsare shown in Figure 4(h) The fourth hit MFCD00124221(N-(4-(3-(23-dichlorophenyl)thioureido)phenyl)-2-hydrox-ybenzamide) has docking score of 6586 kcalmol with threehydrogen bonds with Cys156 Gly305 and His183 The com-plex shown in Figure 4(i) shows benzamide of oxygen withCys156 nitrogen with Gly305 and nitrogen of thiourea withHis183 and shows hydrogen bond interaction

The pharmacophore overlay of the hit compounds wasshown in Figure 5 The identified lead compounds alongwith their estimated IC

50were shown in Figure 6 The

studies show Arg39 Cys156 His145 and His146 were theimportant amino acids in the active site involved in hydrogenbond interaction Based onpharmacophoremodeling virtual

screening and molecular docking studies the compoundslisted in supplementary Table 3 are selected as novel leadsfor effective HDAC2 inhibition All identified hits were withdiverse scaffolds and provide opportunities for designingnovel HDAC2 inhibitors The lead compounds were selectedbased on the docking score and structural diversity The cor-relation between the estimated activity and docking score oftop 10 lead compounds from NCI and Maybridge is 061 and054 respectively which suggests that the selected inhibitorsin the present study could be specific HDAC2 inhibitors

Comparative study on previous developed models withpresent study shows common pharmacophore features werepresent in all studies Pharmacophore model on histone dea-cetylase (HDAC) with known hydroxamic acids and cyclicpeptides shows four pharmacophore features one hydrogenacceptor and one hydrophobic and two aromatic rings [17]and in the study with known hydroxamic acids benzamidesand biphenyl derivatives on HDAC [40] three pharma-cophore features were shown hydrogen acceptors hydrogendonors and hydrophobic aromatic ring Pharmacophoremodel on histone deacetylase (HDAC8) with known hydrox-amic acids has shown four pharmacophore features one


N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)

Figure 6 Identified lead compounds through NCI and Maybridge database search (a) NSC108392 (b) NSC127064 (c) NSC110782 (d)NSC748337 (e) MFCD01935795 (f) MFCD00830779 (g) MFCD00661790 and (h) MFCD00124221

hydrogen acceptor two hydrogen donors and one hydro-phobic group [18] The present developed pharmacophoremodel on HDAC2 with known benzamide derivatives showsfour pharmacophore features one hydrogen acceptor onehydrogen donor and one hydrophobic and one aromaticrings which correlates with the previous studies

The selected eight lead compounds and Entinostat weredocked into the active sites of histone deacetylase (HDAC)(PDB 1ZZ1) and histone deacetylase (HDAC8) (PDB 1T64)

and in both the receptors chain A was selected for dockingThe docking result shows that compounds with HDACand HDAC8 comparably showed lesser docking score andinteractions than HDAC2 The docking scores and H-bondinteractions were shown in supplementary Tables 5 and 6

The combinations of pharmacophore virtual screeningand molecular docking successfully give more potentialinhibitors that can have great impact for future experimentalstudies in diseases associated with HDAC2 inhibition


4 Conclusion

In this study ligand pharmacophore model developed byHypoGen algorithm in DS 10 hypotheses were generatedusing 48 training set compounds with structural diversityThe best pharmacophore Hypo1 was characterized by highcost difference and correlation coefficient comprised ofHBD HBA RA and HY features The Hypo1 was validatedby external test set and Fisherrsquos randomization test suggeststhe model has good predictability The Hypo1 was usedas a 3D query to screen NCI and Maybridge databases625 compounds with estimated activity less than 1120583M andfavourable Lipinskirsquos rule were selected for docking studiesIn molecular docking studies the important interactions withinhibitors and active site residues were determined Based ondocking score and interactions twenty hits were found andfinally eight hits NSC108392 (4-(((6-amino-3-methylpyri-do[23-b]pyrazin-8-yl)amino)methyl)benzenesulfonamide)NSC127064 ((2S3S4S5R)-2-(1-(benzyloxy)-6-imino-1H-purin-9(6H)-yl)-5-(hydroxymethyl)tetrahydrofuran-34-diol) NSC110782 (4-(2-((6-amino-3-methylpyrido[23-b]pyr-azin-8-yl)amino)ethyl)benzenesulfonamide) NSC748337 (3-(benzo[d]oxazol-2-yl)-5-(1-(piperidin-4-yl)-1H-pyrazol-4-yl)pyridin-2-amine) MFCD01935795 (4-(3-ethylthiourei-do)-N-(5-methylisoxazol-3-yl) benzenesulfonamide)MFCD00830779 ((E)-N1015840-hydroxy-4-((2-methylthiazol-4-yl)methoxy)benzimidamide) MFCD00661790 (N-(2-(3-(24-difluorophenyl)thioureido)ethyl)-2-(4-hydroxyphenyl)acet-amide) and MFCD00124221 (N-(4-(3-(23-dichlorophenyl)thioureido)phenyl)-2-hydroxybenzamide) were selectedbased on structural diversity and stability These novel com-pounds can be used for experimental studies for the inhi-bition of HDAC2

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] A Mai S Massa R Ragno et al ldquo3-(4-Aroyl-1-methyl-1H-2-pyrrolyl)-N-hydroxy-2-alkylamides as a new class of synthetichistone deacetylase inhibitors 1 Design synthesis biologicalevaluation and binding mode studies performed through threedifferent docking proceduresrdquo Journal of Medicinal Chemistryvol 46 no 4 pp 512ndash524 2003

[2] C Monneret ldquoHistone deacetylase inhibitorsrdquo European Jour-nal of Medicinal Chemistry vol 40 no 1 pp 1ndash13 2005

[3] D D Leipe and D Landsman ldquoHistone deacetylases acetoinutilization proteins and acetylpolyamine amidohydrolases aremembers of an ancient protein superfamilyrdquo Nucleic AcidsResearch vol 25 no 18 pp 3693ndash3697 1997

[4] X-J Yang and E Seto ldquoThe Rpd3Hda1 family of lysinedeacetylases from bacteria and yeast to mice and menrdquo NatureReviews Molecular Cell Biology vol 9 no 3 pp 206ndash218 2008

[5] A V Bieliauskas andMKH Pflum ldquoIsoform-selective histonedeacetylase inhibitorsrdquo Chemical Society Reviews vol 37 no 7pp 1402ndash1413 2008

[6] J M Denu ldquoThe Sir2 family of protein deacetylasesrdquo CurrentOpinion in Chemical Biology vol 9 no 5 pp 431ndash440 2005

[7] X-J Yang and S Gregoire ldquoClass II histone deacetylases fromsequence to function regulation and clinical implicationrdquoMolecular and Cellular Biology vol 25 no 8 pp 2873ndash28842005

[8] I V Gregoretti Y-M Lee and H V Goodson ldquoMolecular evo-lution of the histone deacetylase family functional implicationsof phylogenetic analysisrdquo Journal of Molecular Biology vol 338no 1 pp 17ndash31 2004

[9] O H Kramer ldquoHDAC2 a critical factor in health and diseaserdquoTrends in Pharmacological Sciences vol 30 no 12 pp 647ndash6552009

[10] M Kilgore C A Miller D M Fass et al ldquoInhibitors of class1 histone deacetylases reverse contextual memory deficits in amouse model of alzheimerrsquos diseaserdquo Neuropsychopharmacol-ogy vol 35 no 4 pp 870ndash880 2010

[11] J MWagner B HackansonM Lubbert andM Jung ldquoHistonedeacetylase (HDAC) inhibitors in recent clinical trials forcancer therapyrdquo Clinical Epigenetics vol 1 no 3-4 pp 117ndash1362010

[12] D R Walkinshaw and X-J Yang ldquoHistone deacetylaseinhibitors as novel anticancer therapeuticsrdquo Current Oncologyvol 15 no 5 pp 237ndash243 2008

[13] A Jona N Khaskhely D Buglio et al ldquoThe histone deacetylaseinhibitor entinostat (sndx-275) induces apoptosis in hodgkinlymphoma cells and synergizes with bcl-2 family inhibitorsrdquoExperimental Hematology vol 39 no 10 pp 1007e1ndash1017e12011

[14] A Younes Y Oki R G Bociek et al ldquoMocetinostat for relapsedclassical Hodgkinrsquos lymphoma an open-label single-arm phase2 trialrdquoThe Lancet Oncology vol 12 no 13 pp 1222ndash1228 2011

[15] H M Prince M J Bishton and S J Harrison ldquoClinical studiesof histone deacetylase inhibitorsrdquo Clinical Cancer Research vol15 no 12 pp 3958ndash3969 2009

[16] S-Y Yang ldquoPharmacophore modeling and applications in drugdiscovery challenges and recent advancesrdquo Drug DiscoveryToday vol 15 no 11-12 pp 444ndash450 2010

[17] S Vadivelan B N Sinha G Rambabu K Boppana and S A RP Jagarlapudi ldquoPharmacophoremodeling and virtual screeningstudies to design some potential histone deacetylase inhibitorsas new leadsrdquo Journal of Molecular Graphics and Modelling vol26 no 6 pp 935ndash946 2008

[18] T Sundarapandian J Shalini S Sugunadevi and L K WooldquoDocking-enabled pharmacophore model for histone deacety-lase 8 inhibitors and its application in anti-cancer drug discov-eryrdquo Journal of Molecular Graphics and Modelling vol 29 no 3pp 382ndash395 2010

[19] N Kandakatla G Ramakrishnan S Vadivelan and SJagarlapudi ldquoQSAR studies of N-(2-Aminophenyl)-Benzamidederivatives as histone deacetylase2 inhibitorsrdquo InternationalJournal of PharmTech Research vol 4 no 3 pp 1110ndash1121 2012

[20] R R Frey C K Wada R B Garland et al ldquoTrifluoromethylketones as inhibitors of histone deacetylaserdquo Bioorganic ampMedicinal Chemistry Letters vol 12 no 23 pp 3443ndash3447 2002

[21] P Siliphaivanh P Harrington D J Witter et al ldquoDesign ofnovel histone deacetylase inhibitorsrdquo Bioorganic amp MedicinalChemistry Letters vol 17 no 16 pp 4619ndash4624 2007

[22] D J Witter P Harrington K J Wilson et al ldquoOptimization ofbiaryl selective HDAC1amp2 inhibitors (SHI-12)rdquo Bioorganic andMedicinal Chemistry Letters vol 18 no 2 pp 726ndash731 2008


[23] J L Methot P K Chakravarty M Chenard et al ldquoExplorationof the internal cavity of histone deacetylase (HDAC) withselective HDAC1HDAC2 inhibitors (SHI-12)rdquo Bioorganic ampMedicinal Chemistry Letters vol 18 no 3 pp 973ndash978 2008

[24] K J Wilson D J Witter J B Grimm et al ldquoPhenylglycineand phenylalanine derivatives as potent and selective HDAC1inhibitors (SHI-1)rdquo Bioorganic andMedicinal Chemistry Lettersvol 18 no 6 pp 1859ndash1863 2008

[25] P Tessier D V Smil A Wahhab et al ldquoDiphenylmethylenehydroxamic acids as selective class IIa histone deacetylaseinhibitorsrdquo Bioorganic amp Medicinal Chemistry Letters vol 19no 19 pp 5684ndash5688 2009

[26] S Raeppel N Zhou F Gaudette et al ldquoSAR and biologicalevaluation of analogues of a small molecule histone deacetylaseinhibitor N-(2-aminophenyl)-4-((4-(pyridin-3-yl)pyrimidin-2-ylamino)methyl)benzamide (MGCD0103)rdquo Bioorganic ampMedicinal Chemistry Letters vol 19 no 3 pp 644ndash649 2009

[27] RW Heidebrecht Jr M Chenard J Close et al ldquoExploring thepharmacokinetic properties of phosphorus-containing selectiveHDAC 1 and 2 inhibitors (SHI-12)rdquo Bioorganic and MedicinalChemistry Letters vol 19 no 7 pp 2053ndash2058 2009

[28] K V Butler and A P Kozikowski ldquoChemical origins ofisoform selectivity in histone deacetylase inhibitorsrdquo CurrentPharmaceutical Design vol 14 no 6 pp 505ndash528 2008

[29] P Jones and C Steinkuhler ldquoFrom natural products to smallmolecule ketone histone deacetylase inhibitors development ofnew class specific agentsrdquo Current Pharmaceutical Design vol14 no 6 pp 545ndash561 2008

[30] L Zuomei and K Murakami ldquoCombination therapyrdquo US20090124631 A1

[31] J C Bressi A J Jennings R Skene et al ldquoExploration ofthe HDAC2 foot pocket synthesis and SAR of substituted N-(2-aminophenyl)benzamidesrdquo Bioorganic andMedicinal Chem-istry Letters vol 20 no 10 pp 3142ndash3145 2010

[32] J B Joseph and B Sriram ldquoUses of selective inhibitors of hdac8for treatment of t-cell proliferative disordersrdquo US Patent no20080112889 A1 2008

[33] D V Smil S Manku Y A Chantigny et al ldquoNovel HDAC6isoform selective chiral small molecule histone deacetylaseinhibitorsrdquo Bioorganic amp Medicinal Chemistry Letters vol 19no 3 pp 688ndash692 2009

[34] G Giannini M Marzi M D Marzo et al ldquoExploring bis-(indolyl)methane moiety as an alternative and innovative CAPgroup in the design of histone deacetylase (HDAC) inhibitorsrdquoBioorganic and Medicinal Chemistry Letters vol 19 no 10 pp2840ndash2843 2009

[35] H Li J Sutter and R Hoffmann ldquoHypoGen an automatedsystem for generating predictive 3D pharmacophore modelsrdquoin Pharmacophore Perception Development and use in DrugDesign O F Guner Ed pp 173ndash189 International UniversityLine La Jolla Calif USA 2000

[36] T Langer and G Wolber ldquoPharmacophore definition and 3Dsearchesrdquo Drug Discovery Today Technologies vol 1 no 3 pp203ndash207 2004

[37] National Cancer Institute (NCI) database httpdtpncinihgov

[38] Maybride Database httpwwwmaybridgecom[39] C M Venkatachalam X Jiang T Oldfield and M Wald-

man ldquoLigandFit a novel method for the shape-directed rapiddocking of ligands to protein active sitesrdquo Journal of MolecularGraphics and Modelling vol 21 no 4 pp 289ndash307 2003

[40] N Noureen S Kalsoom and H Rashid ldquoLigand based phar-macophore modelling of anticancer histone deacetylase inhib-itorsrdquo African Journal of Biotechnology vol 9 no 25 pp 3923ndash3931 2010

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


experimental activity was found Different pharmacophoreand virtual screening studies had been reported on histonedeacetylase (HDAC) with known hydroxamic acid deriva-tives and QSAR studies reported on histone deacetylases 2(HDAC2) with N(2-aminophenyl)-benzamides [17ndash19] Inthe present study benzamide derivatives are used to generatethe pharmacophore model and virtual screening studies havebeen done for histone deacetylases 2 (HDAC2) proteins togain knowledge regarding pharmacophore model and virtualscreening This study aims to construct the chemical featurebased on pharmacophore models for histone deacetylases 2(HDAC2)

2 Materials and Methods

21 Data Preparation A training set of 48 histone deacety-lases 2 (HDAC2) inhibitors of two different chemotypeswere selected form previously published data and the IC

50

values were identified using the same biological assay Thechemotype A is N(2-aminophenyl)-benzamide [20ndash31] andchemotype B is N-hydroxy benzamide derivatives (see sup-plementary Figure 1 in the Supplementary Material availableonline at httpdxdoiorg1011552014812148) [32ndash34] 3DQSAR module in Discovery Studio (DS) was used for devel-oping the pharmacophore The 2D structure of compoundswas drawn in ISIS draw and theywere converted into 3D formand conformationalmodels were generated by FASTmethodthe conformers minimized by the CHARMm force field andthe energy threshold value of 20 kcalmol A maximum of255 conformers were developed for each compound andthese conformermodelswere used for hypotheses generationfitting the compound into the hypotheses and estimating theactivity of the compound The training set of 48 moleculeswas chosen with IC

50values with a range from 0014 120583M to

21 120583MThe dataset activity (IC50) was classified based on the

span over four orders of magnitude that is active (IC50le

01 120583M ++++) moderately active (01 le IC50le 1 120583M +++)

less active (1 le IC50le 10 120583M ++) and inactive (IC

50gt

10 120583M +)

22 Pharmacophore Model Generation HypoGen algorithmwas applied to build the pharmacophore model and in thepresent study four features which are hydrogen bond donors(HBD) hydrogen bond acceptors (HBA) ring aromatic(RA) and hydrophobic (HY) were selected to generatethe pharmacophore hypotheses [35] HypoGen generatespharmacophore model based on chemical features of activecompounds in training set The uncertainty value 2 wasselected from default 3 which means the biological activityis two times higher or lower than the true value All otherparameters were kept as default The developed pharma-cophore model was selected based on the highest correlationcoefficient lowest total cost and root mean square deviation(RMSD)

23 Pharmacophore Validation The pharmacophore modelis validated by three steps cost analysis Fischerrsquos randomiza-tion test and the test set predictionThe quality of the model

is described in terms of fixed cost total cost and null costThe fixed cost represents the simplest model and it fits thedata perfectly The null cost represents no features with highcost value and it estimates the activity to be average activityof the training set compounds The best model was selectedbased on the difference between the two cost values (null costminus total cost) if the difference between the costs is greaterthan 60 means the model has excellent true correlation Ifthe difference is 40ndash60 the model has prediction correlationof 70ndash90 and if the difference is below 40 it may bedifficult to predict the model Fischerrsquos randomization is thesecond approach to validate the pharmacophore model The95 confidence level was selected to validate the study and19 random spread sheets were constructed This methodgenerates the hypotheses by randomizing the activity ofthe training set compounds The correlation between thestructure and biological activitywas validated by thismethodThefinal sets of validationwere selected using twentyHDAC2inhibitors as given in supplementary Figure 2 The Ligandpharmacophore mapping module in Discovery Studio wasused to map the ligands and estimate the predicted activityof the test set compounds

24 Database Search Virtual screening studies were usedto find novel and potential leads from virtual database forfurther development [36] The virtual screening studies wereused to find novel leads for HDAC2 The Hypo1 model wasused as a 3D query in database screening and the NationalCancer Institute (NCI) database containing 265242moleculesand Maybridge database containing 58723 molecules wereused for screening [37 38] Ligand pharmacophore mappingprotocol was used with flexible search option to screen thedatabase Hit compounds from the database with estimatedactivity less than 01120583M were selected for further screeningusing Lipinskirsquos rule of five compounds have (i) molecularweight less than 500 (ii) hydrogen donors less than 5 (iii)hydrogen acceptors less than 10 and (iv) an octanolwaterpartition coefficient (Log119875) value less than 5

25 Molecular Docking Docking is the binding orientationof small molecules to their protein targets in order to predictthe affinity and activity of the smallmoleculesHence dockingplays an important role in the rational drug designMoleculardocking studies were performed by using LigandFit modulein Discovery Studio [39] There are three stages in LigandFitprotocol (i) docking in which attempt is made to dock aligand into a user defined binding site (ii) in situ ligandminimization and (iii) scoring in which various scoringfunctionswere calculated for each pose of the ligands Proteinpreparationwas themain step in docking and all ligands weredocked into the active site of the receptor Protein preparationinvolves deletion of water molecules and addition of hydro-gen atoms and applying CHARMm force field The activesites were searched using flood filling algorithm The activesite was defined as region of HDAC2 that comes within 12 Afrom the geometric centroid of the ligand Ten poses weregenerated for each ligand during the docking process and thebest poses were selected based on the best orientation of themolecule in the active site and dock score values which was






=minus(ligand


(1)
















5835

13089

155267347

10084

3507

(a) (b) (c)






1086420

225

250

275

300





6130 1379


1 198 440


382 243


mdashmdash

mdashmdash

mdashmdash

mdashmdash













His183

Cys156

Gly305Arg39

(a)

His145 Asp181

Arg39

(b)

Trp140Gly142

Arg39

Gly305 His145

Cys156

Asp181

(c)

Ala141

Asp181

His145

Gly154

(d)

Ala141

His183

Asp181

Gly306

His145

(e)

His146

Phe155

Cys156

(f)

Asp181

Arg39

His146His183

Asp269

(g)

His146

His183

Cys156

Ala141

(h)

Gly305

Cys156

His183

(i)





(a) (b) (c)

(d) (e) (f)

(g) (h)









N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)







4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






=minus(ligand


(1)
















5835

13089

155267347

10084

3507

(a) (b) (c)






1086420

225

250

275

300





6130 1379


1 198 440


382 243


mdashmdash

mdashmdash

mdashmdash

mdashmdash













His183

Cys156

Gly305Arg39

(a)

His145 Asp181

Arg39

(b)

Trp140Gly142

Arg39

Gly305 His145

Cys156

Asp181

(c)

Ala141

Asp181

His145

Gly154

(d)

Ala141

His183

Asp181

Gly306

His145

(e)

His146

Phe155

Cys156

(f)

Asp181

Arg39

His146His183

Asp269

(g)

His146

His183

Cys156

Ala141

(h)

Gly305

Cys156

His183

(i)





(a) (b) (c)

(d) (e) (f)

(g) (h)









N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)







4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









5835

13089

155267347

10084

3507

(a) (b) (c)






1086420

225

250

275

300





6130 1379


1 198 440


382 243


mdashmdash

mdashmdash

mdashmdash

mdashmdash













His183

Cys156

Gly305Arg39

(a)

His145 Asp181

Arg39

(b)

Trp140Gly142

Arg39

Gly305 His145

Cys156

Asp181

(c)

Ala141

Asp181

His145

Gly154

(d)

Ala141

His183

Asp181

Gly306

His145

(e)

His146

Phe155

Cys156

(f)

Asp181

Arg39

His146His183

Asp269

(g)

His146

His183

Cys156

Ala141

(h)

Gly305

Cys156

His183

(i)





(a) (b) (c)

(d) (e) (f)

(g) (h)









N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)







4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology











His183

Cys156

Gly305Arg39

(a)

His145 Asp181

Arg39

(b)

Trp140Gly142

Arg39

Gly305 His145

Cys156

Asp181

(c)

Ala141

Asp181

His145

Gly154

(d)

Ala141

His183

Asp181

Gly306

His145

(e)

His146

Phe155

Cys156

(f)

Asp181

Arg39

His146His183

Asp269

(g)

His146

His183

Cys156

Ala141

(h)

Gly305

Cys156

His183

(i)





(a) (b) (c)

(d) (e) (f)

(g) (h)









N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)







4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


(a) (b) (c)

(d) (e) (f)

(g) (h)









N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)







4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


N

N

N

N

N

S

O

O

N

(a)

NH

N NO

NN

O

HO

HOHO

H

H

HH

(b)

N

N

NHN

S

O

O

H2N NH2

(c)

N

N

O

N

NN

N

(d)

HN

HNS

S NH

NO

O

O

(e)

N

HO

OS

NNH2

(f)

HO

HN

O

NH

S

NH

F F

(g)

NH

OOH

HN NH

S

Cl

Cl

(h)







4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


4 Conclusion




References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

research article ligand based pharmacophore modeling and ...than means, the model has excellent true...

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