Research Article In Silico Design of Human IMPDH ...downloads.hindawi.com/journals/cmmm/2015/418767.pdf · Research Article In Silico Design of Human IMPDH Inhibitors Using Pharmacophore

Research ArticleIn Silico Design of Human IMPDH Inhibitors UsingPharmacophore Mapping and Molecular Docking Approaches

Rui-Juan Li1 Ya-Li Wang2 Qing-He Wang1 Jian Wang1 and Mao-Sheng Cheng1

1Key Laboratory of Structure-Based Drug Design amp Discovery of Ministry of Education School of Pharmaceutical EngineeringShenyang Pharmaceutical University Shenyang 110016 China2Institute of Medicinal Biotechnology Chinese Academy of Medical Science and Peking Union Medical College Beijing 10050 China

Correspondence should be addressed to Jian Wang jianwangsyphueducn and Mao-Sheng Cheng mschengsyphueducn

Received 20 November 2014 Revised 8 January 2015 Accepted 20 January 2015

Academic Editor John Mitchell

Copyright copy 2015 Rui-Juan Li et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Inosine 51015840-monophosphate dehydrogenase (IMPDH) is one of the crucial enzymes in the de novo biosynthesis of guanosinenucleotides It has served as an attractive target in immunosuppressive anticancer antiviral and antiparasitic therapeutic strategiesIn this study pharmacophoremapping andmolecular docking approaches were employed to discover novel Homo sapiens IMPDH(hIMPDH) inhibitors The Guner-Henry (GH) scoring method was used to evaluate the quality of generated pharmacophorehypotheses One of the generated pharmacophore hypotheses was found to possess a GH score of 067 Ten potential compoundswere selected from the ZINC database using a pharmacophore mapping approach and docked into the IMPDH active site We findtwo hits (ie ZINC02090792 and ZINC00048033) that match well the optimal pharmacophore features used in this investigationand it is found that they form interactions with key residues of IMPDHWe propose that these two hits are lead compounds for thedevelopment of novel hIMPDH inhibitors

1 Introduction

Inosine 51015840-monophosphate dehydrogenase (IMPDH) is arate-limiting enzyme in the de novo synthesis of gua-nine nucleotides It catalyzes the conversion of inosine51015840-monophosphate (IMP) to xanthosine 51015840-monophosphate(XMP) [1 2] and therefore plays an important role in theregulation of cell growth [3]There are two isoforms of Homosapiens IMPDH (hIMPDH) labeled types I and II whichshare 84 amino acid identity hIMPDH type I (hIMPDH1)is the main species in normal leukocytes and hIMPDH typeII (hIMPDH2) predominates over hIMPDH1 in the tumorcells and activated peripheral blood lymphocytes [4ndash8] Genesequence variation in the hIMPDH2 gene may contributeto the large interindividual difference of baseline hIMPDHenzyme activity immunosuppressive efficacy and side effectsin transplant recipients receiving mycophenolic acid [9ndash11]Inhibition of hIMPDH2 has become an important strategyin the treatment of diseases related to immunosuppression

cancer and viral and parasitic infections [12ndash16] Althoughit has long been the belief that chemotherapy would beimproved with selective inhibition of hIMPDH2 this viewhas recently been challenged by the surprising observationthat hIMPDH1 is also an antiangiogenic target [17]

The research of hIMPDH inhibitors is of great signif-icance in providing potentially therapeutic effects againstthis target for disease intervention There are three types ofhIMPDH inhibitors (i) IMP site inhibitors that occupy thebinding position of the natural substrate IMP (ii) NAD+ siteinhibitors that occupy the site of the NAD+NADH cofactorand (iii) allosteric site inhibitors that bind to a site remotefrom the IMP and NAD+ binding pockets Many researchersare interested in developing NAD+ site inhibitors and novelinhibitors of hIMPDH have been reported in the last decade[18] For examplemycophenolatemofetil (MMForCellcept)which is a prodrug of mycophenolic acid (MPA) is anuncompetitive hIMPDH inhibitor that has been approvedfor the prevention of acute rejection in heart kidney or

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2015 Article ID 418767 11 pageshttpdxdoiorg1011552015418767

2 Computational and Mathematical Methods in Medicine

pancreas transplantations when used in combination withcyclosporine A [19 20] However an unfavorable gastroin-testinal toxicity tolerability profile limits the drugrsquos poten-tial for the treatment of other autoimmune disorders Toovercome the limitations of MPA Vertex developed a seriesof phenyl-oxazole urea hIMPDH inhibitors using structure-based drug design and high-throughput screening amongwhich VX-497 has been in phase II development for thehepatitis C virus (HCV) infection [21] In addition tiazofurinhas been found to possess both antiviral and antiproliferativeactivities [22 23] Several compounds such as quinolones[24 25] amides [26] and indoles [27 28] have been reportedto possess potent hIMPDH inhibition activities Howeversafety and selectivity are still deficient and there is a contin-uing effort to develop novel hIMPDH inhibitors

The pharmacophore model can be used to elucidate howdiverse ligands bind to receptor sites and it can predict poten-tial chemical interactions between ligands and a receptor Inaddition this model can be used to discover potent inhibitorsof the target protein from selected database [29ndash31] In thisstudy common feature pharmacophoremodelingwas used touncover novel hIMPDH inhibitors from the ZINC databaseStructure-based docking was then performed to analyze thebindingmodes and affinities of the identified compounds thatshow promise as hIMPDH inhibitors Finally interactionsbetween IMPDH and the potential inhibitors were describedin detail with the aim to design novel drug candidates ofhIMPDH

2 Methods

The common feature pharmacophore model was generatedusing the Common Feature Pharmacophore Generation pro-tocol in the Discovery Studio 30 software program (DS30) Database screening was implemented using the LigandProfiler Protocol in DS 30 [32] Docking studies wereperformed with the glide module in the Schrodinger 2014software program [33]

21 Pharmacophore Generation and Validation

211 Ligand Preparation A set of ligands occupying thehIMPDH NAD+ site with known inhibitory activities werecollected from the literature to establish a common featurepharmacophore All of the structures were constructed in DS30 The most important step in pharmacophore modeling isthe selection of suitable inhibitors that constitute the trainingset For this purpose 22 active hIMPDH inhibitors withdiverse scaffolds [24 27 34ndash42] (Figure 1) were selected asthe training set and generated using parameters from theCHARMm force field All of the structures were minimizedusing the Steepest Descent algorithm followed by the Con-jugate Gradient and Adopted Basis Newton-Raphson algo-rithms with convergence gradient values of 01 kcalsdotmolminus1001 kcalsdotmolminus1 and 0001 kcalsdotmolminus1 respectively A multi-conformer databasewas generated using the poling algorithmwith an energy threshold of 20 kcalsdotmolminus1 and a maximum of255 conformers per molecule

Table 1 Bioactivity of compounds in the training set from literature

Compound 119870

119894[nM] IC50 [nM] Reference

1 12 [34]2 15 [34]3 11 [34]4 6 [35]5 7 [36]6 6 [37]7 30 [38]8 19 [38]9 18 [38]10 30 [38]11 17 [39]12 11 [39]13 17 [39]14 19 [40]15 5 [24]16 lt5 [24]17 lt10 [24]18 11 [24]19 6 [24]20 13 [41]21 7 [42]22 30 [27]

212 Pharmacophore Generation Pharmacophore modelgeneration was performed using the multiconformer data-base described above with activity values listed in Table 1 Forthemost potent inhibitors (Compounds 1 3 4 5 6 12 15 1617 18 19 20 and 21) the Principal and MaxOmitFeat valueswere set to 2 and 0 respectively For the moderate inhibitors(Compounds 2 7 8 9 10 11 13 14 and 22) the Principaland MaxOmitFeat values were both assigned values of 1The H-bond acceptor (A) H-bond donor (D) hydrophobicaliphatic (H) and hydrophobic aromatic (Z) features wereselected based on the chemical features of the ligands inthe training set using the Feature Mapping protocol in DS30 The value of Maximum Pharmacophore was set to 10The default Minimum Interfeature Distance value of 297 Awas changed to 20 A so that the chemical features locatedclose to each other would be considered when generating thepharmacophoreTheminimum features were set to 4 and themaximum features were set to 7 Default values were used forall other parameters From this approach ten pharmacophorehypotheses were successfully generated These models werevalidated with both the training set and the decoy set

213 Pharmacophore Validation The training set com-pounds were aligned to the ten generated pharmacophorehypotheses using the Ligand Profiler Protocol in DS 30 TheMaximum Omitted Features option was set to minus1 and theScale Fit Values were set to false Default values were used forall other parameters To confirm the quality of these modelsthe ten generated pharmacophore hypotheses were assessedby rank scores and fit values Subsequently some poor

Computational and Mathematical Methods in Medicine 3

O

O

OO

OO

O

O

O

NO

O

O

N

ON

N

N

N

N

N N

NH

NH

NH

NH

ON

NN

H1

OO

O

NNN

O

NN

NH

NH

NH

32

O

N

O

OO

O

NH

NH

NH

O

O

NH

O

NH

NH

NH

N

O

OO

O

O

O

ON

OO

NH

NH

NH

NH

NH

NH

O

O

O

O

ON

OO

NH

O

NH

NH

O

ON

N

N

N

NN

N N

N

N

OO

NH

NH

NH

O

ON

N

N

N

OO

OH

OH

4 5

6 7

10

11 12 13

14 15 16

17 18 19

8 9

F

F

NH

O

O

O

O

O

N

N

NN

N

O

O

O O

O

O

OO

O

O

F

NH

NH

NH

NH

NH

NH N

H

N

N

N

O

O

O

O

NH

N

O

O

O

NH

ON

N

NH

O

O

O

O

O

O

O

F

S

SS

N

O

O

O

NH

ON

O

O

H

222120

Figure 1 Structures of compounds 1ndash22 in the training set for the development of pharmacophore model

models were rejected and the remaining pharmacophoremodels were assessed using another method In generalpharmacophore models are used as 3D queries to searchdatabases to discover novel and potent lead compounds Avalidation of the generated pharmacophore model should beperformed to determine whether the model is able to accu-rately differentiate between active and inactive compounds

The Guner-Henry (GH) scoring method was used to vali-date the pharmacophore hypotheses [43 44] This methodquantifies the merit of the generated model by retrieving theactive compounds from a database containing known activeand inactivemolecules A decoy set containing 729moleculeswas constructed to validate the generated pharmacophore Ofthese 729 molecules 29 molecules were known inhibitors of


hIMPDH whereas the other 700 molecules were obtainedfrom the ZINC database using the Find Similar protocol inDS 30 The GH score method has been successfully appliedto quantify the selectivity of the pharmacophoremodel and todiscover activities from a decoy database The GH score wascalculated using the following formulae

119884 =119867

119886

119879

119886

times 100

119867

119905 =119867

119886

119867

119905

times 100

119864 =

119867

119886119867

119905

119879

119886119879

GH =119867

119886(3119867

119886+ 119867

119905)

4119867

119905119879

119886

(1 minus

119867

119905minus 119867

119886

119879 minus 119879

119886

)

(1)

where119867119886is the number of active hits 119879

119886is the total number

of actives in the decoy set 119867119905is the total number of hits

including actives and decoy molecules 119879 is the total numberof molecules in the decoy set 119884 is the percentage ofknown active compounds obtained from the decoy set 119867

119905

is the percentage of known actives in the hits list 119864 is theenrichment factor and GH is the Guner-Henry score TheGH score ranging from 06 to 1 would indicate an optimalpharmacophore model

22 Database Searching Virtual screening of a chemicaldatabase often leads to the discovery of novel and poten-tial lead compounds for further development The optimalpharmacophore model (hypo-07) was used as the 3D queryfor screening the ZINC database which contains 325881drug-like molecules All screening experiments were per-formed using the Ligand Profiler Protocol in DS 30 Duringthe screening process we performed the virtual screeningusing the BestFlexible conformational procedure with amaximum of 255 conformations generated The MaximumOmitted Features option was set to minus1 and the Scale FitValues were set to false Molecules that possessed all thedesired pharmacophore features were considered to be hitcompounds The so-called ldquoLipinskirsquos rule of fiverdquo was notapplied to selected compounds because we wanted to pre-vent the removal of potential inhibitors Those moleculesthat successfully passed these initial tests were selected forsubsequent molecular docking analysis

23 Molecular Docking Docking study is a necessary stepfor picking out potential hits in virtual screening [45] Toinvestigate the detailed interactions between the virtual hitsand IMPDH the glide module in the Schrodinger softwareprogram was used to perform docking studies

The 3D structure of IMPDH in complex with IMPand MPA (PDB ID 1JR1) has been determined by X-raydiffraction MPA inhibits IMPDH by acting as a replacementfor the nicotinamide portion of the nicotinamide adeninedinucleotide cofactor (NAD+) and a catalytic watermoleculeOur primary aim was to obtain NAD+ site inhibitors thus

the 1JR1 crystal structure was used for docking studies andthe position of MPA was selected as the active site Theprotein was prepared with the Protein Preparation Wizard(PrepWizard) in the Schrodinger software program Thelocation of MPA in the 1JR1 crystal structure was used todefine the size and center of the receptor grid An active site of5 A was set around the MPA ligand To prepare the proteinMPA was deleted IMP was retained hydrogen atoms wereadded water molecules beyond 5 A from the groups weredeleted and the bond orders of the protein were adjusted andminimized up to 030 A rootmean square deviation (RMSD)Then the original conformation of the MPA was dockedinto the NAD+ site of IMPDH using both rigid and flexiblemethods to validate the docking procedureWhen the RMSDvalue ranges between 0 and 2 the program can be used fordocking studies of other ligands The ideal case is when theRMSD value is below 05 Finally the extra precision (XP)mode and other default parameters of the glide module wereused for the docking studies [46] The compounds of thetraining set and the hit compounds were docked into theactive site of IMPDH using the flexible docking strategy topredict potential inhibitors of IMPDH

3 Results

31 Generation and Validation of the Common Feature Phar-macophore Model of hIMPDH Inhibitors A pharmacophorewhich is known as a powerful tool to identify novel com-pounds with good biological activity can be establishedvia a ligand-based method The common feature pharma-cophore generation protocol in DS 30 was employed withthe training set compounds As suggested by the FeatureMapping protocol pharmacophore models were generatedusing the following features H-bond acceptor (A) H-bonddonor (D) hydrophobic aliphatic group (H) and hydropho-bic aromatic group (Z) Ten pharmacophore hypotheseswereobtained for further investigation based on the training setmolecules All ten pharmacophore hypotheses contain theabove four chemical features Statistical parameters of thegenerated pharmacophore models are listed in Table 2 Rankscores of the ten models range from 21559 kcalsdotmolminus1 to22950 kcalsdotmolminus1 Direct and partial hit values of 1 and 0indicate that the ligands are well mapped onto all of thechemical features of the model (ie there are no missingfeatures) The max fit of all the hypotheses is 4 Clearly thefit values of hypotheses 03 04 07 and 08 (ie hypo-03 -04 -07 -08) are higher than those of hypo-01 -02 -05 -06 -09 and -10 For this reason the former four hypotheseswere selected for further investigation (Table 3) Howeveron the basis of rank scores and fit values alone we couldnot determine which pharmacophore hypothesis is the bestmodel Therefore the Guner-Henry (GH) scoring methodwas used to identify the best pharmacophore model A decoydatabase which was used for pharmacophore identificationand validation was constructed to include 20 independentactive hIMPDH inhibitors 9 inactive hIMPDH inhibitorsand 700 inactive ligands from the ZINC database Notablyall of the 20 active molecules were successfully identifiedby hypotheses 03 04 and 07 and the number of total hits


Table 2 Top ten pharmacophore hypotheses generated by IMPDH inhibitors

Hypo Features Rank Direct hita Partial hitb Max fit01 ZHDA 22950 1111111111111101111111 0000000000000010000000 402 ZHDA 22942 1111111111111111111111 0000000000000000000000 403 ZHDA 22670 1111111111111111111111 0000000000000000000000 404 ZHDA 22526 1111110111111111111111 0000001000000000000000 405 ZHDA 22374 1111110011111111111111 0000001100000000000000 406 ZHDA 21908 1111110011111111111111 0000001100000000000000 407 ZHDA 21800 1111110011111111111111 0000001100000000000000 408 ZHDA 21810 1011111111011111111110 0100000000100000000001 409 ZHDA 21652 1011111011011110111111 0100000100100001000000 410 ZHDA 21559 1111110001110111111111 0000001110001000000000 4aDirect hit indicates whether ldquo1rdquo or not ldquo0rdquo amolecule in the training set mapped every pharmacophore feature in the hypothesis bPartial hit connotes whetherldquo1rdquo or not ldquo0rdquo a particular molecule in the training set mapped all but one pharmacophore feature in the hypothesis

Table 3 The hit values of compounds in the training set mappingto hypotheses 03 04 07 and 08

Comp Hypo-03 Hypo-04 Hypo-07 Hypo-081 3208 3265 3288 31562 3165 3303 3061 38803 3665 3390 3502 37754 3980 3980 4000 38875 3999 3999 3988 37846 3749 3660 3605 35837 3210 3408 3362 35288 2992 2980 2997 30299 2990 2994 2977 364810 3578 3369 3661 307811 2669 2776 2780 286512 2658 27281 2764 283013 2436 2750 2825 314614 2889 3161 2898 225515 3189 3086 3428 399916 3086 3133 3444 326217 3160 3031 3338 347518 3115 3032 3277 365219 3100 3099 3261 374520 3868 3796 3771 299821 3613 3239 3343 330722 2987 3046 3096 2914

was 66 75 and 35 for each hypothesis respectively Otherstatistical parameters such as yield of actives hit rate ofactives enrichment factor and GH score are presented inTable 4 The GH score is one of the standards used to assessthe quality of generated models In this regard hypothesis 07(hypo-07) demonstrates the highest GH score value of 067which indicates a strong capability to choose the active ratherthan inactive molecules from the database

The 3D spatial relationship and geometric parameters ofhypo-07 are shown in Figure 2(a) Figures 2(b)ndash2(d) describethe alignment of hypo-07 with three different inhibitors (ie

compounds 5 (119870119894= 7 nM) 20 (IC

50= 13 nM) and 22 (IC

50=

30 nM) resp) Clearly the four pharmacophore features mapvery well onto compounds 5 and 20 which have previouslybeen shown to be potent inhibitors In addition other potentinhibitors such as compounds 1 3 4 6 15 16 17 18 19 20and 21 but not compound 12 present good hit values (Table3) For the moderately active compound 22 the H-bondacceptor and hydrophobic aliphatic pharmacophore featurescould match with the ligand but the H-bond donor and thehydrophobic aromatic features did not These results revealthat a selected pharmacophore model might be capable ofpredicting the activity of compounds Consequently hypo-07was applied for further studies to identify novel inhibitors

32 Database Screening The best pharmacophore model(ie hypo-07) was used to search the ZINC database whichcontains 325881 compounds for new hIMPDH inhibitorcandidates Among the 325881 compounds 1566 compoundspassed the initial screening and were mapped onto thepharmacophore model which included some compoundsthat are structurally similar to existing hIMPDH inhibitorsand some novel scaffolds Sixty-nine hits were selected basedon the fit value All of these compounds have commonfeatures that can align to the required pharmacophore sites ofhypo-07 The 69 hit compounds were exported as ansdf fileand subjected to further analysis using molecular docking toavoid possible false positive hits from the virtual screeningprocess

33 Molecular Docking Studies of hIMPDH Inhibitors andHit Compounds

331 Docking Validation In this study the validation of thedocking procedure was performed by redocking cocrystal-lized MPA into the active site of IMPDH using both the rigidand flexible methods of the glide module We found thatthe redocked MPA reproduced the binding pose with glidescores of minus665 kcalsdotmolminus1 and minus659 kcalsdotmolminus1 respectivelyThe RMSD of the cocrystallized and experimental poses wasanalyzed and the values of the two methods were 054 A and


Table 4 The evaluation of ligand-based pharmacophore model using the Guner-Henry scoring method

Hypo-03 Hypo-04 Hypo-07 Hypo-08Total number of molecules in the decoy set (119879) 729 729 729 729Total number of actives in the decoy set (119879

119886) 20 20 20 20

Active hits (119867119886) 20 20 20 15

Total hits (119867119905) 66 75 35 54

Yield of actives (119884) 100 100 100 75Hit rate of actives (119867

119905) 3030 2667 5714 2778

Enrichment factor (119864) 1105 972 2083 1013Goodness of hit score (GH) 045 041 067 050GH Guner-Henry

12813

5123

5333

5249

7785

(a) (b)

(c) (d)

Figure 2 (a) 3D spatial relationship and geometric parameters of hypo-07 Pharmacophore features colored as follows H-bond acceptor(green) H-bond donor (magenta) hydrophobic aliphatic group (cyan) and hydrophobic aromatic group (sky blue) (b) Hypo-07 mappingwith active compound 5 (c) Hypo-07 mapping with active compound 20 (d) Hypo-07 mapping with moderate active compound 22

Figure 3 The alignment of redocked (pink) and cocrystallizedligand (cyan) in the active site of IMPDHThe green ligand is IMP

043 A respectively These results showed that docking simu-lations reproduced the crystal complexes very wellThe align-ment of the cocrystallized ligand (green) and redocked ligand(pink) is shown in Figure 3 The glide XP program was suit-able for further studying the binding pose of the novel hits

332 The Docking Results The nonbond interactions wereexamined using the similarly labeled tool in the DiscoveryStudio 40 Visualizer software program various interac-tions between the inhibitors and IMPDH were examinedincluding traditional hydrogen bonding interactions carbon-hydrogen bonding interactions pi-donor hydrogen bondinginteractions and hydrophobic interactions According tothe docking results we can see that the interface pointsof IMPDH include interactions from Pro69 Met70 Asp71His92 His93 Asn94 Cys95 Ala249 Gly251 Thr252 His253Asp256 Arg259 Asp274 Ser275 Gln277 Asn303 Arg322Gly324 Met325 Gly326 Cys327 Gly328 Cys331 Ile332Thr333 Gln334 Asp364 Met414 Gly415 Ala419 Met420and Gln441 On the basis of these results some active sitesand residues were identified from the IMPDH complex

First the docking results of MPA and compound 5(ie VX-497) of the training set are described On onehand research on the known active ligands can help us toidentify the suitable binding modes of the inhibitors in theNAD+ site of IMPDH On the other hand the analysis ofinteractions betweenVX-497 and IMPDHcan rationalize thepharmacophore model from another aspect


(a) (b)

Figure 4 Binding patterns of potent inhibitors with IMPDH The secondary structure of IMPDH is shown as a white rainbow Residuesin the active site are shown in gray lines and labelled as amino acid names IMP is shown in green stick The H-bonding interactions areshown using green dotted lines weak H-bonding interaction is shown using pale-cyan dotted lines and hydrophobic interactions are shownusing pink dotted lines (a) The binding mode of cocrystallized ligand (MPA magenta) in the active site of IMPDH (b) The binding modeof compound 5 (VX-497 magenta) in the active site of IMPDH

MPA exhibits various interactions with the amino acidresidues of the active site as shown in Figure 4(a) Theinteractions between MPA and IMPDH active site residueswere identical with the cases reported by Sintchak andNimmesgern [47]The results suggest that the carbonyl groupof the lactone ring bonds to IMPDH with its oxygen atomto form hydrogen bonding interactions with the backboneNH moiety of Gly326 and the OH group of Thr333 At thesame time the oxygen atom of the hydroxyl group of MPAforms a hydrogen bonding interaction with the OH groupof Thr333 The carbonyl group of COOH forms a hydrogenbonding interaction with the NHmoiety of Gln441The oxy-gen atom (O3) of the methoxy group forms a weak water-hydrogen bonding interaction with H

2O725 and the carbon

atom (C8) of the methoxy group is involved in a carbon-hydrogen bonding interaction with Asp274 IMP which isanother ligand of IMPDH was retained during docking stud-ies because its purine ring forms important 120587-120587 hydrophobicinteractions with MPA

The binding models between VX-497 and IMPDH activesite residues are similar to most of the interactions reportedby Sintchak and Nimmesgern [47] The docking analysisdemonstrates that the oxazole of VX-497 bonds to IMPDHvia its nitrogen atom to form a hydrogen bonding interactionwith the NH moiety of Gly326 The same nitrogen atom alsoforms a water-hydrogen bonding interaction with H

2O794

The benzene ring connected with oxazole makes a 120587-donorhydrogen bonding interaction with H

2O725 The additional

hydrogen bonding interactions are formed between the NHmoiety of the urea group and Asp274 Notably the nitrogenatom of oxazole and theNHmoiety of the urea group serve asaH-bond acceptor and aH-bonddonor respectively which isidentical to the analogous features of the best pharmacophoremodel In addition another benzene ring of VX-497 formsa 120587-120587 hydrophobic interaction with His93 correspondingto the aromatic hydrophobic feature of the pharmacophoremodel At the same time the tetrahydrofuran ring of the inhi-bitor forms a 120587-alkyl hydrophobic interaction with His253The 120587-120587 hydrophobic interactions between the inhibitor andthe purine ring of IMP are also formed (Figure 4(b))

Table 5The glide score of hit compounds compared withMPA andVX-497

Comp Glide XP Gscore (kcalsdotmolminus1) Glide energy(1) ZINC02090792 minus780 minus59779(2) ZINC00048033 minus758 minus59395(3) ZINC00822338 minus757 minus64007(4) ZINC08714541 minus717 minus58716(5) ZINC06662648 minus706 minus60263(6) ZINC00686714 minus698 minus64254(7) ZINC00648305 minus695 minus55144(8) ZINC02081544 minus693 minus62507(9) ZINC00668789 minus690 minus57154(10) ZINC02776094 minus688 minus55596(11) MPA minus659 minus54289(12) VX497 minus756 minus60157

From the above analyses we believe that the role ofGly326 is to serve as a H-bond donor which is necessaryfor potent inhibitors An interaction between Thr333 andthe ligand is also important In addition the critical hydro-gen bonding interaction between the urea NH moiety andAsp274 contributes to the high potency observed for VX-497Some hydrophobic interactions also play important roles inimproving the activities of compounds

The sixty-nine hit compounds that were predicted to bepositive using the pharmacophore screening procedure weresubjected to molecular docking studies The potential com-pounds were selected according to the glide scores and theirinteractions with amino acid residues Notably ten hit com-pounds (see Figure S1 in Supplementary Material availableonline at httpdxdoiorg1011552015418767)were found tohave good glide scores compared to MPA and VX-497 (Table5) Here the binding modes of the two identified compoundswith the highest glide scores from the ZINC database aredescribed They are ZINC02090792 and ZINC00048033which are used for subsequent characterization as potentialinhibitors


(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)

Figure 5 Binding mode and 2D interaction map of hit compounds (a) The binding mode of ZINC02090792 in the active site of IMPDH(b) The 2D interaction map of ZINC02090792 with the active site of IMPDH (c) The binding mode of ZINC00048033 in the active site ofIMPDH (d) The 2D interaction map of ZINC00048033 with the active site of IMPDH

The top virtual hit compound ZINC02090792 showssimilar binding interactions to MPA and VX-497 (Figure5(a)) The oxygen atom of the hydroxyl group serves as aH-bond acceptor to form a hydrogen bonding interactionwith the backbone NH moiety of Gly326 and the hydroxylgroup serves as a H-bond donor to form a hydrogen bondinginteraction withThr333 At the same time both the hydroxylandmethoxy groups of ZINC02090792 formwater-hydrogenbonding interactions with H

2O794 From the 2D interaction

map (Figure 5(b)) of ZINC02090792 in the active site ofIMPDH it is observed that NH serves as a H-bond donorengaging in an interaction with Asp274 One carbon atom(C3) belonging to the methyl group of ZINC02090792 forms

a 120587-alkyl hydrophobic interaction with His93 and it alsomakes an alkyl-alkyl hydrophobic interactionwithMet414 Inaddition the benzene ring connected with the hydroxyl andmethoxy groups forms120587-120587hydrophobic interactionswith thepurine ring of IMP

ZINC00048033 is another hit compound obtained usingpharmacophore screening and docking validationThe inter-actions between ZINC00048033 and IMPDH are presentedin Figures 5(c) and 5(d) The carbonyl group of the 1H-imidazole-2-(3H)-one ring forms a hydrogen bonding inter-action with the backbone NH moiety of Gly326 and it alsomakes a water-hydrogen bonding interaction with H

2O794

Additionally theNHgroup of 1H-imidazole-2-(3H)-one ring


serves as a H-bond donor to form a hydrogen bonding inter-action with Thr333 The carbonyl group that connects withthe ethoxyl group forms a water-hydrogen interaction withH2O725 One carbon atom (C2) of ZINC00048033 engages

in a carbon-hydrogen bonding interaction with Ser275 Atthe same time a 120587-alkyl hydrophobic interaction betweenthe benzene ring of ZINC00048033 and Met414 is observedSimilar to an active inhibitor of IMPDH the 1H-imidazole-2-(3H)-one ring of ZINC00048033 forms 120587-120587 hydrophobicinteractions with the purine ring of IMP

According to the above results ZINC02090792 andZINC00048033 form interactions with some key residues ofIMPDH (ie Gly326 Thr333 and Asp274) in ways that aresimilar to the cocrystallized ligandMPA and the most potentligand VX-497 Superimpositions of MPA on ZINC02090792and ZINC00048033 bound to the active site are shownin Figure S2 Despite some changes in the interactionsbetween active site residues and inhibitors (MPA VX-497ZINC02090792 and ZINC00048033) the conformations ofthese active site residues are virtually unchanged between thecrystal structures of MPA or other three inhibitors boundto the active site of IMPDH Consequently ZINC02090792and ZINC00048033 serve as lead compounds for developingnovel hIMPDH inhibitors

4 Conclusions

We have established a ligand-based pharmacophore modelfollowed by virtual screening and molecular docking studiesto discover novel hIMPDH inhibitors The common featurepharmacophore models were generated using a training setthat includes 22 active hIMPDH inhibitors Ten hypotheseswere obtained from this analysis which all consisted offour features including one hydrogen-bond acceptor onehydrogen-bond donor one hydrophobic aliphatic group andone hydrophobic aromatic group To confirm the qualityof the pharmacophore models a decoy test set was con-structed and the Guner-Henry (GH) scoring method wasused The hypo-07 model was found to possess the high-est GH score value (067) This result reveals that hypo-07 is an optimal model to discriminate between activeand inactive molecules within the database The hypo-07model was further used to screen the ZINC database toidentify hIMPDH inhibitors and sixty-nine potential can-didates were selected All the compounds can align to therequired pharmacophore features of hypo-07 In additionmolecular docking was performed using the glide module toinvestigate the interactions between the potential candidatesand IMPDH From the docking studies ten compoundswere found to have higher glide scores than known activeinhibitors The top two hit compounds for IMPDH wereZINC02090792 and ZINC00048033 with correspondingglide scores of minus780 kcalsdotmolminus1 and minus758 kcalsdotmolminus1 respec-tively Furthermore the detailed interactions between thehit compounds and IMPDH were analyzed ZINC02090792forms hydrogen bonding interactions with Gly326 Thr333Asp274 and H

2O794 It also preserves a 120587-120587 hydrophobic

interaction with His93 and an alkyl-alkyl hydrophobic inter-action with Met414 ZINC00048033 engages in hydrogen

bonding interactions with Gly326 Thr333 H2O725 and

H2O794 At the same time it forms a carbon-hydrogen

bonding interaction with Ser275 and a 120587-alkyl hydrophobicinteraction with Met414 The two potential hit compoundsboth form 120587-120587 hydrophobic interactions with the purinering of IMP In conclusion the identified hits serve as leadcompounds for developing potential hIMPDH inhibitorsDerivatives of these two hits have been synthesized and anevaluation of their biological activity is now in progress

Conflict of Interests

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

Authorsrsquo Contribution

Rui-Juan Li and Ya-LiWang contributed equally to this work

Acknowledgment

The research was financially supported by the NationalNatural Science Foundation of China (Grant 21372157)

References

[1] M D Sintchak M A Fleming O Futer et al ldquoStructure andmechanism of inosine monophosphate dehydrogenase in com-plexwith the immunosuppressantmycophenolic acidrdquoCell vol85 no 6 pp 921ndash930 1996

[2] L Hedstrom ldquoIMP dehydrogenase structure mechanism andinhibitionrdquo Chemical Reviews vol 109 no 7 pp 2903ndash29282009

[3] E Huberman D Glesne and F Collart ldquoRegulation and role ofinosine-51015840-monophosphate dehydrogenase in cell replicationmalignant transformation and differentiationrdquo Advances inExperimental Medicine and Biology vol 370 pp 741ndash746 1994

[4] M Nagai Y Natsumeda Y Konno R Hoffman S Irinoand G Weber ldquoSelective up-regulation of type II inosine 51015840-monophosphate dehydrogenase messenger RNA expression inhuman leukemiasrdquo Cancer Research vol 51 no 15 pp 3886ndash3890 1991

[5] A Zimmermann J J Gu J Spychala and B S MitchellldquoInosine monophosphate dehydrogenase expression transcrip-tional regulation of the type I and type II genesrdquo Advances inEnzyme Regulation vol 36 pp 75ndash84 1996

[6] A G Zimmermann K L Wright J P-Y Ting and B SMitchell ldquoRegulation of inosine-51015840-monophosphate dehydro-genase type II gene expression in human T cells Role for anovel 51015840 palindromic octamer sequencerdquo Journal of BiologicalChemistry vol 272 no 36 pp 22913ndash22923 1997

[7] A G Zimmermann J J Gu J Laliberte and B S MitchellldquoInosine-51015840-monophosphate dehydrogenase regulation ofexpression and role in cellular proliferation and T lymphocyteactivationrdquo Progress in nucleic acid research and molecularbiology vol 61 pp 181ndash209 1998

[8] J Jain S J Almquist P J Ford et al ldquoRegulation of inosinemonophosphate dehydrogenase type I and type II isoforms inhuman lymphocytesrdquo Biochemical Pharmacology vol 67 no 4pp 767ndash776 2004


[9] J Wang A Zeevi S Webber et al ldquoA novel variant L263F inhuman inosine 51015840-monophosphate dehydrogenase 2 is associ-ated with diminished enzyme activityrdquo Pharmacogenetics andGenomics vol 17 no 4 pp 283ndash290 2007

[10] M-E F Mohamed R F Frye and T Y Langaee ldquoInterpopula-tion variation frequency of human inosine 51015840-monophosphatedehydrogenase type II (IMPDH2) genetic polymorphismsrdquoGenetic Testing vol 12 no 4 pp 513ndash516 2008

[11] T-YWu Y Peng L L Pelleymounter et al ldquoPharmacogeneticsof the mycophenolic acid targets inosine monophosphate dehy-drogenases IMPDH1 and IMPDH2 gene sequence variationand functional genomicsrdquo British Journal of Pharmacology vol161 no 7 pp 1584ndash1598 2010

[12] K Felczak andKW Pankiewicz ldquoRehab ofNAD(P)-dependentenzymes with NAD(P)-based inhibitorsrdquo Current MedicinalChemistry vol 18 no 13 pp 1891ndash1908 2011

[13] L Chen R Petrelli K Felczak et al ldquoNicotinamide adeninedinucleotide based therapeuticsrdquo Current Medicinal Chemistryvol 15 no 7 pp 650ndash670 2008

[14] S B Braun-Sand andM Peetz ldquoInosine monophosphate dehy-drogenase as a target for antiviral anticancer antimicrobial andimmunosuppressive therapeuticsrdquo Future Medicinal Chemistryvol 2 no 1 pp 81ndash92 2010

[15] S K Gorla N N McNair G Yang et al ldquoValidation of IMPdehydrogenase inhibitors in a mouse model of cryptosporidio-sisrdquo Antimicrobial Agents and Chemotherapy vol 58 no 3 pp1603ndash1614 2014

[16] L Hedstrom G Liechti J B Goldberg and D R GollapallildquoThe antibiotic potential of prokaryotic IMP dehydrogenaseinhibitorsrdquo Current Medicinal Chemistry vol 18 no 13 pp1909ndash1918 2011

[17] C R Chong D Z Qian F Pan et al ldquoIdentification of type1 inosine monophosphate dehydrogenase as an antiangiogenicdrug targetrdquo Journal of Medicinal Chemistry vol 49 no 9 pp2677ndash2680 2006

[18] R Petrelli P Vita I Torquati et al ldquoNovel inhibitors of inosinemonophosphate dehydrogenase in patent literature of the lastdecaderdquo Recent Patents on Anti-Cancer Drug Discovery vol 8no 2 pp 103ndash125 2013

[19] M D Vu S Qi D Xu et al ldquoSynergistic effects of mycopheno-latemofetil and sirolimus in prevention of acute heart pancreasand kidney allograft rejection and in reversal of ongoing heartallograft rejection in the ratrdquo Transplantation vol 66 no 12 pp1575ndash1580 1998

[20] A H Miladipour A J Ghods and H Nejadgashti ldquoEffect ofmycophenolate mofetil on the prevention of acute renal allo-graft rejectionrdquo Transplantation Proceedings vol 34 no 6 pp2089ndash2090 2002

[21] ldquoPhase II clinical trial of VX-497 for HCV infection beginsrdquoAIDS Patient Care STDS vol 12 no 12 p 944 1998

[22] A Panattoni F DrsquoAnna and E Triolo ldquoAntiviral activity of tia-zofurin and mycophenolic acid against Grapevine Leafroll-associated Virus 3 in Vitis vinifera explantsrdquo Antiviral Researchvol 73 no 3 pp 206ndash211 2007

[23] K Kiguchi F R Collart C Henning-Chubb and E HubermanldquoCell differentiation and altered IMP dehydrogenase expres-sion induced in human T-lymphoblastoid leukemia cells bymycophenolic acid and tiazofurinrdquo Experimental Cell Researchvol 187 no 1 pp 47ndash53 1990

[24] T GM Dhar S HWatterson P Chen et al ldquoQuinolone-basedIMPDH inhibitors introduction of basic residues on ringD and

SAR of the correspondingmono di and benzofused analoguesrdquoBioorganic and Medicinal Chemistry Letters vol 13 no 3 pp547ndash551 2003

[25] S H Watterson M Carlsen T G M Dhar et al ldquoNovel inhi-bitors of IMPDH a highly potent and selective quinolone-basedseriesrdquo Bioorganic and Medicinal Chemistry Letters vol 13 no3 pp 543ndash546 2003

[26] S H Watterson C Liu T G M Dhar et al ldquoNovel amide-based inhibitors of inosine 51015840-monophosphate dehydrogenaserdquoBioorganic and Medicinal Chemistry Letters vol 12 no 20 pp2879ndash2882 2002

[27] S H Watterson T G M Dhar S K Ballentine et al ldquoNovelindole-based inhibitors of IMPDH introduction of hydrogenbond acceptors at indole C-3rdquo Bioorganic and Medicinal Chem-istry Letters vol 13 no 7 pp 1273ndash1276 2003

[28] R E Beevers G M Buckley N Davies et al ldquoNovel indoleinhibitors of IMPDH from fragments synthesis and ini-tial structure-activity relationshipsrdquo Bioorganic and MedicinalChemistry Letters vol 16 no 9 pp 2539ndash2542 2006

[29] W Tai T Lu H Yuan et al ldquoPharmacophore modeling andvirtual screening studies to identify new c-Met inhibitorsrdquo Jour-nal of Molecular Modeling vol 18 no 7 pp 3087ndash3100 2012

[30] Y-L Wang C-Y Lin K-C Shih J-W Huang and C-Y TangldquoDesign checkpoint kinase 2 inhibitors by pharmacophoremodeling and virtual screening techniquesrdquo Bioorganic andMedicinal Chemistry Letters vol 23 no 23 pp 6286ndash6291 2013

[31] C-Y Lin and Y-LWang ldquoNovel design strategy for checkpointkinase 2 inhibitors using pharmacophore modeling combi-natorial fusion and virtual screeningrdquo BioMed Research Inter-national vol 2014 Article ID 359494 13 pages 2014

[32] Accelrys Inc ldquoDiscovery Studio User manualrdquoDiscovery StudioVersion 30 2011

[33] Schrodinger LLC ldquoSchrodinger user manualsrdquo Glide Version93 2014

[34] T G M Dhar Z Shen J Guo et al ldquoDiscovery of N-[2-[2-[[3-methoxy-4-(5-oxazolyl)phenyl]amino]-5-oxazolyl]phenyl]-N-methyl-4-morpholineacetamide as a novel and potent inhibitorof inosine monophosphate dehydrogenase with excellent invivo activityrdquo Journal of Medicinal Chemistry vol 45 no 11 pp2127ndash2130 2002

[35] D Floryk and T C Thompson ldquoAntiproliferative effects ofAVN944 a novel inosine 5-monophosphate dehydrogenaseinhibitor in prostate cancer cellsrdquo International Journal ofCancer vol 123 no 10 pp 2294ndash2302 2008

[36] J Jain S J Almquist D Shlyakhter and M W Harding ldquoVX-497 a novel selective IMPDH inhibitor and immunosuppres-sive agentrdquo Journal of Pharmaceutical Sciences vol 90 no 5 pp625ndash637 2001

[37] J Jain S J Almquist A D Heiser et al ldquoCharacterization ofpharmacological efficacy of VX-148 a new potent immunosup-pressive inosine 51015840-monophosphate dehydrogenase inhibitorrdquoJournal of Pharmacology and Experimental Therapeutics vol302 no 3 pp 1272ndash1277 2002

[38] H H Gu E J Iwanowicz J Guo et al ldquoNovel diamide-basedinhibitors of IMPDHrdquo Bioorganic and Medicinal ChemistryLetters vol 12 no 9 pp 1323ndash1326 2002

[39] S H Watterson P Chen Y Zhao et al ldquoAcridone-based inhi-bitors of inosine 51015840-monophosphate dehydrogenase discoveryand SAR leading to the identification of N-(2-(6-(4-ethyl-piperazin-1-yl)pyridin-3-yl)propan-2-yl)-2-fluoro-9-oxo-910-dihydroacridine-3-carboxamide (BMS-566419)rdquo Journal ofMedicinal Chemistry vol 50 no 15 pp 3730ndash3742 2007


[40] T Nakanishi Y Kozuki Y Eikyu et al ldquoIn vitro and invivo characterization of AS2643361 a novel and highly potentinosine 51015840-monophosphate dehydrogenase inhibitorrdquo EuropeanJournal of Pharmacology vol 674 no 1 pp 58ndash63 2012

[41] G M Buckley N Davies H J Dyke et al ldquoQuinazolinethionesand quinazolinediones novel inhibitors of inosine monophos-phate dehydrogenase synthesis and initial structure-activityrelationshipsrdquo Bioorganic and Medicinal Chemistry Letters vol15 no 3 pp 751ndash754 2005

[42] T G M Dhar Z Shen C A Fleener et al ldquoThe TosMICapproach to 3-(oxazol-5-yl) indoles application to the synthesisof indole-based IMPDH inhibitorsrdquo Bioorganic and MedicinalChemistry Letters vol 12 no 22 pp 3305ndash3308 2002

[43] R P Gangwal N R Das K Thanki et al ldquoIdentification ofp38120572 MAP kinase inhibitors by pharmacophore based virtualscreeningrdquo Journal ofMolecular Graphics andModelling vol 49pp 18ndash24 2014

[44] S Kalva E R Azhagiya Singam V Rajapandian L M Saleenaand V Subramanian ldquoDiscovery of potent inhibitor for matrixmetalloproteinase-9 by pharmacophore based modeling anddynamics simulation studiesrdquo Journal ofMolecularGraphics andModelling vol 49 pp 25ndash37 2014

[45] G Madhavi Sastry M Adzhigirey T Day R Annabhimojuand W Sherman ldquoProtein and ligand preparation parametersprotocols and influence on virtual screening enrichmentsrdquoJournal of Computer-Aided Molecular Design vol 27 no 3 pp221ndash234 2013

[46] R A Friesner R B Murphy M P Repasky et al ldquoExtra preci-sion glide docking and scoring incorporating amodel of hydro-phobic enclosure for protein-ligand complexesrdquo Journal ofMed-icinal Chemistry vol 49 no 21 pp 6177ndash6196 2006

[47] M D Sintchak and E Nimmesgern ldquoThe structure of inosine51015840-monophosphate dehydrogenase and the design of novel inhi-bitorsrdquo Immunopharmacology vol 47 no 2-3 pp 163ndash1842000

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


MEDIATORSINFLAMMATION

of


Behavioural Neurology

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom


pancreas transplantations when used in combination withcyclosporine A [19 20] However an unfavorable gastroin-testinal toxicity tolerability profile limits the drugrsquos poten-tial for the treatment of other autoimmune disorders Toovercome the limitations of MPA Vertex developed a seriesof phenyl-oxazole urea hIMPDH inhibitors using structure-based drug design and high-throughput screening amongwhich VX-497 has been in phase II development for thehepatitis C virus (HCV) infection [21] In addition tiazofurinhas been found to possess both antiviral and antiproliferativeactivities [22 23] Several compounds such as quinolones[24 25] amides [26] and indoles [27 28] have been reportedto possess potent hIMPDH inhibition activities Howeversafety and selectivity are still deficient and there is a contin-uing effort to develop novel hIMPDH inhibitors

The pharmacophore model can be used to elucidate howdiverse ligands bind to receptor sites and it can predict poten-tial chemical interactions between ligands and a receptor Inaddition this model can be used to discover potent inhibitorsof the target protein from selected database [29ndash31] In thisstudy common feature pharmacophoremodelingwas used touncover novel hIMPDH inhibitors from the ZINC databaseStructure-based docking was then performed to analyze thebindingmodes and affinities of the identified compounds thatshow promise as hIMPDH inhibitors Finally interactionsbetween IMPDH and the potential inhibitors were describedin detail with the aim to design novel drug candidates ofhIMPDH

2 Methods

The common feature pharmacophore model was generatedusing the Common Feature Pharmacophore Generation pro-tocol in the Discovery Studio 30 software program (DS30) Database screening was implemented using the LigandProfiler Protocol in DS 30 [32] Docking studies wereperformed with the glide module in the Schrodinger 2014software program [33]

21 Pharmacophore Generation and Validation

211 Ligand Preparation A set of ligands occupying thehIMPDH NAD+ site with known inhibitory activities werecollected from the literature to establish a common featurepharmacophore All of the structures were constructed in DS30 The most important step in pharmacophore modeling isthe selection of suitable inhibitors that constitute the trainingset For this purpose 22 active hIMPDH inhibitors withdiverse scaffolds [24 27 34ndash42] (Figure 1) were selected asthe training set and generated using parameters from theCHARMm force field All of the structures were minimizedusing the Steepest Descent algorithm followed by the Con-jugate Gradient and Adopted Basis Newton-Raphson algo-rithms with convergence gradient values of 01 kcalsdotmolminus1001 kcalsdotmolminus1 and 0001 kcalsdotmolminus1 respectively A multi-conformer databasewas generated using the poling algorithmwith an energy threshold of 20 kcalsdotmolminus1 and a maximum of255 conformers per molecule

Table 1 Bioactivity of compounds in the training set from literature

Compound 119870

119894[nM] IC50 [nM] Reference

1 12 [34]2 15 [34]3 11 [34]4 6 [35]5 7 [36]6 6 [37]7 30 [38]8 19 [38]9 18 [38]10 30 [38]11 17 [39]12 11 [39]13 17 [39]14 19 [40]15 5 [24]16 lt5 [24]17 lt10 [24]18 11 [24]19 6 [24]20 13 [41]21 7 [42]22 30 [27]

212 Pharmacophore Generation Pharmacophore modelgeneration was performed using the multiconformer data-base described above with activity values listed in Table 1 Forthemost potent inhibitors (Compounds 1 3 4 5 6 12 15 1617 18 19 20 and 21) the Principal and MaxOmitFeat valueswere set to 2 and 0 respectively For the moderate inhibitors(Compounds 2 7 8 9 10 11 13 14 and 22) the Principaland MaxOmitFeat values were both assigned values of 1The H-bond acceptor (A) H-bond donor (D) hydrophobicaliphatic (H) and hydrophobic aromatic (Z) features wereselected based on the chemical features of the ligands inthe training set using the Feature Mapping protocol in DS30 The value of Maximum Pharmacophore was set to 10The default Minimum Interfeature Distance value of 297 Awas changed to 20 A so that the chemical features locatedclose to each other would be considered when generating thepharmacophoreTheminimum features were set to 4 and themaximum features were set to 7 Default values were used forall other parameters From this approach ten pharmacophorehypotheses were successfully generated These models werevalidated with both the training set and the decoy set

213 Pharmacophore Validation The training set com-pounds were aligned to the ten generated pharmacophorehypotheses using the Ligand Profiler Protocol in DS 30 TheMaximum Omitted Features option was set to minus1 and theScale Fit Values were set to false Default values were used forall other parameters To confirm the quality of these modelsthe ten generated pharmacophore hypotheses were assessedby rank scores and fit values Subsequently some poor


O

O

OO

OO

O

O

O

NO

O

O

N

ON

N

N

N

N

N N

NH

NH

NH

NH

ON

NN

H1

OO

O

NNN

O

NN

NH

NH

NH

32

O

N

O

OO

O

NH

NH

NH

O

O

NH

O

NH

NH

NH

N

O

OO

O

O

O

ON

OO

NH

NH

NH

NH

NH

NH

O

O

O

O

ON

OO

NH

O

NH

NH

O

ON

N

N

N

NN

N N

N

N

OO

NH

NH

NH

O

ON

N

N

N

OO

OH

OH

4 5

6 7

10

11 12 13

14 15 16

17 18 19

8 9

F

F

NH

O

O

O

O

O

N

N

NN

N

O

O

O O

O

O

OO

O

O

F

NH

NH

NH

NH

NH

NH N

H

N

N

N

O

O

O

O

NH

N

O

O

O

NH

ON

N

NH

O

O

O

O

O

O

O

F

S

SS

N

O

O

O

NH

ON

O

O

H

222120






119884 =119867

119886

119879

119886

times 100

119867

119905 =119867

119886

119867

119905

times 100

119864 =

119867

119886119867

119905

119879

119886119879

GH =119867

119886(3119867

119886+ 119867

119905)

4119867

119905119879

119886

(1 minus

119867

119905minus 119867

119886

119879 minus 119879

119886

)

(1)





119905






3 Results









compounds 5 (119870119894= 7 nM) 20 (IC

50= 13 nM) and 22 (IC

50=








119886) 20 20 20 20

Active hits (119867119886) 20 20 20 15

Total hits (119867119905) 66 75 35 54


119905) 3030 2667 5714 2778


12813

5123

5333

5249

7785

(a) (b)

(c) (d)







(a) (b)






2O794









(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















O

O

OO

OO

O

O

O

NO

O

O

N

ON

N

N

N

N

N N

NH

NH

NH

NH

ON

NN

H1

OO

O

NNN

O

NN

NH

NH

NH

32

O

N

O

OO

O

NH

NH

NH

O

O

NH

O

NH

NH

NH

N

O

OO

O

O

O

ON

OO

NH

NH

NH

NH

NH

NH

O

O

O

O

ON

OO

NH

O

NH

NH

O

ON

N

N

N

NN

N N

N

N

OO

NH

NH

NH

O

ON

N

N

N

OO

OH

OH

4 5

6 7

10

11 12 13

14 15 16

17 18 19

8 9

F

F

NH

O

O

O

O

O

N

N

NN

N

O

O

O O

O

O

OO

O

O

F

NH

NH

NH

NH

NH

NH N

H

N

N

N

O

O

O

O

NH

N

O

O

O

NH

ON

N

NH

O

O

O

O

O

O

O

F

S

SS

N

O

O

O

NH

ON

O

O

H

222120






119884 =119867

119886

119879

119886

times 100

119867

119905 =119867

119886

119867

119905

times 100

119864 =

119867

119886119867

119905

119879

119886119879

GH =119867

119886(3119867

119886+ 119867

119905)

4119867

119905119879

119886

(1 minus

119867

119905minus 119867

119886

119879 minus 119879

119886

)

(1)





119905






3 Results









compounds 5 (119870119894= 7 nM) 20 (IC

50= 13 nM) and 22 (IC

50=








119886) 20 20 20 20

Active hits (119867119886) 20 20 20 15

Total hits (119867119905) 66 75 35 54


119905) 3030 2667 5714 2778


12813

5123

5333

5249

7785

(a) (b)

(c) (d)







(a) (b)






2O794









(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of


















119884 =119867

119886

119879

119886

times 100

119867

119905 =119867

119886

119867

119905

times 100

119864 =

119867

119886119867

119905

119879

119886119879

GH =119867

119886(3119867

119886+ 119867

119905)

4119867

119905119879

119886

(1 minus

119867

119905minus 119867

119886

119879 minus 119879

119886

)

(1)





119905






3 Results









compounds 5 (119870119894= 7 nM) 20 (IC

50= 13 nM) and 22 (IC

50=








119886) 20 20 20 20

Active hits (119867119886) 20 20 20 15

Total hits (119867119905) 66 75 35 54


119905) 3030 2667 5714 2778


12813

5123

5333

5249

7785

(a) (b)

(c) (d)







(a) (b)






2O794









(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of























compounds 5 (119870119894= 7 nM) 20 (IC

50= 13 nM) and 22 (IC

50=








119886) 20 20 20 20

Active hits (119867119886) 20 20 20 15

Total hits (119867119905) 66 75 35 54


119905) 3030 2667 5714 2778


12813

5123

5333

5249

7785

(a) (b)

(c) (d)







(a) (b)






2O794









(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of



















119886) 20 20 20 20

Active hits (119867119886) 20 20 20 15

Total hits (119867119905) 66 75 35 54


119905) 3030 2667 5714 2778


12813

5123

5333

5249

7785

(a) (b)

(c) (d)







(a) (b)






2O794









(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















(a) (b)






2O794









(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















(a)

ARG259

ARG322

ASH364

SER275

SER276

71

70

ASP

ASP

274ASP

PRO69

337LEU

MET414

MET420

HIE93

HIE92

ALA419

ASN94

GLY415

GLN

GLN

GLN

441

GLY

GLY

324

GLY326

MET325

327CYS

95CYS

331CYS

333THR

THR

303ASN

MET

ALA249

251

253252

256HIS

283

277

OOO

OH

NH

N

N

H2O

(b)

(c)

ARG322

ASH364

GLY302SER

275

SER276

274

273

ASPPRO69

LEU

337LEU

MET

MET

414

MET420

HIE93

ALA419

ASN94

GLY

GLY

415

GLN441

324

GLY326

325

327CYS

331 333

303

CYS THR

ASN

VAL272

O

OO

O

NH

NH

NH

N

H2O

H2O

(d)







2O794






4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of




















4 Conclusions











Acknowledgment


References
























































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of































































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of






























of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





















of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















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Research Article In Silico Design of Human IMPDH ...downloads.hindawi.com/journals/cmmm/2015/418767.pdf · Research Article In Silico Design of Human IMPDH Inhibitors Using Pharmacophore