research article virtual screening of acetylcholinesterase...

Research ArticleVirtual Screening of Acetylcholinesterase Inhibitors Using theLipinskirsquos Rule of Five and ZINC Databank

Pablo Andrei Nogara1 Rogeacuterio de Aquino Saraiva2 Diones Caeran Bueno1

Liacutelian Juliana Lissner1 Cristiane Lenz Dalla Corte1 Marcos M Braga1

Denis Broock Rosemberg1 and Joatildeo Batista Teixeira Rocha1

1Departamento de Bioquımica e Biologia Molecular Programa de Pos-Graduacao em Ciencias Biologicas Bioquımica ToxicologicaCentro de Ciencias Naturais e Exatas Universidade Federal de Santa Maria 97105-900 Santa Maria RS Brazil2Grupo de Estudos emBioquımica Farmacologia e ToxicologiaMolecular UnidadeAcademica de SerraTalhadaUniversidade FederalRural de Pernambuco 56900-000 Serra Talhada PE Brazil

Correspondence should be addressed to Joao Batista Teixeira Rocha jbtrochayahoocombr

Received 23 May 2014 Revised 16 July 2014 Accepted 17 July 2014

Academic Editor Miroslav Pohanka

Copyright copy 2015 Pablo Andrei Nogara et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Alzheimerrsquos disease (AD) is a progressive and neurodegenerative pathology that can affect people over 65 years of age It causesseveral complications such as behavioral changes language deficits depression and memory impairments One of the methodsused to treat AD is the increase of acetylcholine (ACh) in the brain by using acetylcholinesterase inhibitors (AChEIs) In this studywe used the ZINC databank and the Lipinskirsquos rule of five to perform a virtual screening and a molecular docking (using AutoDock Vina 111) aiming to select possible compounds that have quaternary ammonium atom able to inhibit acetylcholinesterase(AChE) activity The molecules were obtained by screening and further in vitro assays were performed to analyze the most potentinhibitors through the IC

50value and also to describe the interactionmodels between inhibitors and enzyme bymolecular docking

The results showed that compound D inhibited AChE activity from different vertebrate sources and butyrylcholinesterase (BChE)from Equus ferus (EfBChE) with IC

50ranging from 169 plusmn 046 to 564 plusmn 247 120583M Compound D interacted with the peripheral

anionic subsite in both enzymes blocking substrate entrance to the active site In contrast compound C had higher specificity asinhibitor of EfBChE In conclusion the screening was effective in finding inhibitors of AChE and BuChE from different organisms

1 Introduction

Alzheimerrsquos disease (AD) was first reported by the pathol-ogist Alois Alzheimer in 1907 It is a neurological disordercharacterized by a significant decrease in hippocampal andcortical levels of the neurotransmitter acetylcholine (ACh)[1] with formation of extracellular amyloid plaques andintracellular neurofibrillary tangles that lead to neurotoxicity[2] The AD affects up to 5 of people over 65 yearsrising to 20 of those over 80 years [3] One of the majortherapeutic strategies adopted for AD treatment is based onthe cholinergic hypothesis Clinically AD is associated withcognitive functional and behavioral symptoms which canbe explained by the cholinergic neurotransmission deficitwith the loss of cholinergic neurons [4]

The neurotransmitter ACh plays a key role in learningandmemory processes by activating nicotinic andmuscarinicreceptors of the central nervous system (CNS) [5]The acetyl-cholinesterase (AChE) is an enzyme that hydrolyzes ACh toacetate and choline in the synaptic cleft terminating the AChneurotransmission [6]The inhibitory effect onAChE activityincreases ACh in synaptic cleft with overactivation of thecholinergic transmission [7] Since disruption of cholinergicneurotransmission is involved in different brain functionsthe search for new acetylcholinesterase inhibitors (AChEIs) isrelevant as an early step to selectmolecules that can be used inpreclinical trials as potential pharmacological agents or evento synthesize other kinds of compounds such as insecticides

Virtual screening is established as an effective methodfor filtering compounds in the course of new drug discovery

Hindawi Publishing CorporationBioMed Research InternationalVolume 2015 Article ID 870389 8 pageshttpdxdoiorg1011552015870389

2 BioMed Research International

(a) (b) (c) (d)

Figure 1 Molecular overlapping of the crystal ligands (red) and the best pose of ligands proposed by Auto Dock Vina 111 program (green)for the enzymes 1QON (a) 2VQ6 (b) 3I6M (c) and 2BDS (d) The nonpolar hydrogen atoms were omitted

[8] During this long process it is possible to search forcompounds with specific features that can potentially lead tothe development of effective therapeutic agents For instancethe consideration of the Lipinskirsquos rule of five [9] thatis molecular weight lower than 500Da number of donorhydrogen bonds less than 5 number of acceptor hydrogenbonds less than 10 and the 119909 log119875 lower than 5 is ofimportance for the screening of drugs with pharmacologicalactivity

The molecular docking is a method that can predictthe most favorable orientation of a molecule (ligand) wheninteracting with a macromolecular target such as an enzymeor a receptor to form a stable complexThe crucial thermody-namic parameter involved in this method is the binding freeenergy (Δ119866binding) which checks the theoretical stability ofthe ligand-protein complex [10] Based on this principle themain objective of the present study was to propose a strategyof virtual screening to unravel potential newAChEIs by usingthe virtual molecules ZINC bank and selecting compoundsthat obey the Lipinskirsquos rule of five which have quaternaryammonium atom (because of the similarity with ACh) Wealso assessed the inhibitory activity of selected compounds invitro in order to check which compounds have the highestinhibitory activity using different AChE sources purifiedAChE from Electrophorus electricus (EeAChE) AChE fromDanio rerio (DrAChE) and human AChE (HsAChE) Fur-thermore in order to verify whether the selected compoundscould also inhibit butyrylcholinesterase (BChE) activity weinvestigated the effects of these compounds on purified BChEfrom Equus ferus (EfBChE)

2 Materials and Methods

21 In Silico Analysis To search for new drugs with bindingaffinity to AChE we used the virtual molecules ZINC bank(httpzincdockingorg) where approximately 55 millionof different molecular structures are deposited [11] Firstwe selected only tridimensional structures of compoundswith quaternary ammonium atom that were in accordancewith the Lipinskirsquos rule of five In addition another rule wasalso included the number of rotatable bonds had to be lessthan 10 [12] The molecules obtained were downloaded and

their geometry optimized using the software Avogadro 094following the MMFF94 method

The molecular docking simulation was used as a secondscreening aiming to search for compounds with higherinhibitory capacity and to propose an interaction modelWe used different crystallographic structures of AChEfrom Protein Data Bank (PDB) (httpwwwpdborg) TheCHIMERA 153 softwarewas used to removemolecules ionsandwater and tominimize the structure of proteins using theGasteiger charges with 500 steps of minimization

After obtaining the ligands and enzymes their structureswere converted to pdbqt format using the Auto Dock Tools154 program inwhich all the rotatable bonds of ligandswereallowed to rotate freely and the receptors were consideredrigid For docking studies we used the Auto Dock Vina 111[13] with 1 A of spacing between the grid points The gridbox was centered on the active site of the enzymes with highresolution allowing the program to search for additionalplaces of probable interactions between the ligands and thereceptor Other configurations were considered default

The type of enzyme species PDB code RMSD valuecoordinates and size of the grid box are shown in Table 1Importantly some enzymes do not present the RMSD valuebecause they do not have inhibitor on their structures Thefigures of structures with RMSD are represented in Figure 1The RMSD value (less than 2 A) is a criterion often used forcorrecting bound structure prediction [14] The redockingswere performed with the same configurations of the previousperformed dockings

For in vitro assays we selected the compounds that pre-sented lower binding energy (Δ119866binding) in all enzymes usedfor the screening The interactions between ligand-proteinwere visualized by Accelrys Discovery Studio Visualizer 25

22 In Vitro Analysis The compounds selected as inhibitorsof AChE activity were obtained commercially from MolPort(httpwwwmolportcombuy-chemicalsindex) They weredissolved in dimethyl sulfoxide (DMSO) at a final concen-tration of 01

The cholinesterase activities were measured based onEllman et alrsquos method [15] The increase of absorbancewas monitored at 412 nm in a reaction mixture containing

BioMed Research International 3

Table 1 Information about the AChE enzymes species PDB code coordinates and size of grid box and RMSD value

Species PDB code Coordinates of grid box Size of grid box RMSD value (A)

Drosophila melanogaster

1DX4119909 30311 119909 44119910 70424 119910 48 mdash119911 10617 119911 52

1QO9119909 28259 119909 44119910 70453 119910 52 mdash119911 12592 119911 44

1QON119909 29403 119909 40

103119910 71508 119910 44119911 11697 119911 40

Torpedo californica

2VQ6119909 5254 119909 48119910 65479 119910 44 194119911 64028 119911 48

3I6M119909 2092 119909 56119910 64337 119910 44 041119911 6475 119911 48

1EA5119909 4957 119909 48119910 64084 119910 44 mdash119911 65094 119911 48

Mus musculus 1J06119909 32473 119909 48119910 20287 119910 40 mdash119911 10477 119911 32

Homo sapiens

3LII119909 91443 119909 52119910 8869 119910 40 mdash119911 minus5859 119911 48

1B41119909 120139 119909 32

mdash119910 108623 119910 52119911 minus132452 119911 36

BChE2BDSlowast

119909 133076 119909 42038Homo sapiens 119910 116113 119910 48

119911 41093 119911 48lowastThis structure was not used in the virtual screening

10mM potassium phosphate buffer pH 74 and 1mMDTNB[551015840-dithiobis-(2-nitrobenzoic) acid] (from Sigma) in thepresence of one of the following enzymes purified AChEfrom Electrophorus electricus (EeAChE)mdash005Uml AChEfrom human erythrocytes (HsAChE) AChE from Daniorerio (DrAChE)mdash05 120583g and purified BChE from Equusferus (EfBChE)mdash005UmL The compounds (or only 01DMSO for the control group) were preincubated with theenzyme during 10 minutes at room temperature and thereaction was started with the addition of acetylthiocholine orbutyrylthiocholine (08mM)

Haemoglobin-free erythrocyte ghosts were preparedaccording to the method previously described [16] Bloodof nonfasted healthy voluntary donors was collected Hep-arinized human blood was centrifuged at 3000 g for 10minThe packed erythrocytes were diluted in 20 volumes (wv) ofhypotonic sodiumpotassium phosphate buffer (67mM pH74) to facilitate the hemolysis followed by centrifugation at30000 g for 30min at 4∘CThe supernatant was removed andthe pellet resuspended in hypotonic phosphate buffer After

two additional washing cycles the pellet was resuspendedin sodiumpotassium phosphate buffer (01M pH 74) andthen centrifuged again at 30000 g for 30min at 4∘C Thesupernatant was gently removed and the pellet was storedAliquots of the erythrocyte ghosts were stored at minus20∘Cuntil usage within one week The sample was diluted 10times for AChE activity measurement Fifty 120583L of the storedghost preparation in a final volume of 200 120583L was usedfor the assay Hemoglobin content from ghost membraneswas measured at 540 nm as the cyano-met-Hb form but nohemoglobin was detected

The DrAChE assay was performed as previously des-cribed by Rosemberg et al (2010) [17] Briefly zebrafishbrains were homogenized on ice in 60 volumes (vw) of Tris-citrate buffer (50mM Tris 2mM EDTA 2mM EGTA andpH 74 with citric acid) using a Potter-Elvehjem-type glasshomogenizer Samples (05 120583g protein) were preincubated for10min at 25∘C and the enzyme activity was further assessedin the absence and the presence of the selected compounds


100

75

50

25

0

1 10 100

ABCD

EFG

EeAC

hE ac

tivity

()

(a)

100

75

50

25

0

1 10 10001

ABCD

EFG

EfBC

hE ac

tivity

()

(b)

Figure 2 Inhibition of purified AChE activity from Electrophorus electricus (a) and BChE activity from Equus ferus (b) by the sevencompounds selected by virtual screening

23 Statistical Analysis The IC50

values were determinedby nonlinear regression (log concentration-inhibition cur-ves) Data were analyzed by one-way analysis of variance(ANOVA) followed by Student-Newman-Keuls test Statisti-cal significance was set at 119875 lt 005 The statistics have beenperformed using GraphPad Prism 5 (version 501 GraphPadSoftware Inc USA)

3 Results and Discussion

The first virtual screening retrieved 382 compounds thatobey the Lipinskirsquos rule of five and have the ammoniumquaternary atom The retrieved compounds were dockedwith the enzymes listed in Table 1 (second screening) Weobtained the mean value of the lower binding free energyfor each molecule resulting in 7 compounds (Table 2) Thesecompounds were further obtained commercially for in vitroassay The in vitro assay was carried out as a third screeningstep in whichwe identified that the compound ldquoDrdquo presentedthe higher anti-AChE activity (for EeAChE DrAChE andHsAChE) being also able to inhibit the EfBChE Theseresults suggest that the respective compound is not specificto acetylcholinesterases because its IC

50was very similar to

all the enzymes tested in this study with values ranging from169 plusmn 046 to 564 plusmn 247 On the other hand the compoundldquoCrdquo presented a higher inhibitory potency against EfBChEwith an IC

50value of 075 plusmn 018 than with AChEs IC

50

values ranging from 9208 plusmn 3973 to 76117 plusmn 1276 Thesedata demonstrate that even if a strategy is adopted to selectspecific AChEIs using in silico analysis it is relevant to assesswhether the potential inhibitors may also alter BChE activityin vitro The IC

50values for both enzymes tested are shown

in Table 3 and the graphics for purified EeAChE and EfBChEare depicted in Figure 2

We further proposed an interaction model for the com-pounds with AChEs enzymes (TcAChE PDB 1EA5 andHsAChE PDB 1B41) andHsBChE (PDB 2BDS) to comparethe interactions of the compounds in each enzyme and toinvestigate the putative mechanisms of inhibition (Figure 3)The TcAChE and compound ldquoCrdquo (Figure 3(a)) have only twocation-120587 interactions (in orange) with Trp84 indicating alower affinity with the enzyme and this fact was confirmedby its IC

50value for EeAChE (76117 plusmn 1276 120583M) However

for human AChE (Figure 3(b)) we found a larger number of120587-120587 interactions (in green) of Tyr341 and Trp286 (peripheralanionic subsite) with the molecule ldquoCrdquo indicating a slightincrease in affinity and a decrease in the IC

50value (9208 plusmn

3973 120583M) ForHsBChE (Figure 3(c)) it was possible to detectone hydrogen bond (H-bond) between the compound ldquoCrdquoand Pro285 This H-bond is stronger when compared withthe 120587-120587 or cation-120587 interactions Furthermore the resultsshowed that 120587-120587 and cation-120587 interactions occur stackingbetween the compound ldquoCrdquo and the enzyme in the anionicsubsite (Trp82) All these interactions could explain at leastpartially the high inhibitory potency of the molecule ldquoCrdquo forHsBChE (IC

50= 075plusmn 018 120583M)These results indicated that

the compound ldquoCrdquo is more specific to HsBChEOn the other hand the molecule ldquoDrdquo was able to inhibit

the EeAChE DrAChE HsAChE andHsBChE with a similarpotency (see Table 3) The docking results suggest that forTcAChE occur a larger number of 120587-120587 and cation-120587 inter-actions mainly with the anionic subsite (Trp84) catalytictriad (His440) and peripheral anionic subsite (Tyr334)mdashFigure 3(d) In the presence of the compound ldquoDrdquo a verysimilar conformation was detected for HsBChE (Figure 3(f))but with one H-bond between Asn289 and nitro moiety fromldquoDrdquoThis samenitromoiety has an important role inHsAChEmaking one H-bond with Gly121 (oxyanion hole) Another


Table 2 The seven compounds obtained by molecular docking screening and their features

Compound(ZINC cod) Structure

Molecularweightlowast(gsdotmolminus1)

H-bonddonorlowast

H-bondacceptorlowast 119909 log119875lowast Rotatable

bondslowast

A(1249551)

OOO

HON+ 4826 1 5 11 7

B(1280061) CH3

H3C

H3C

H3CH3C

H3C

O

O

O

O

N+

CH3

CH3

CH3

4786 0 5 242 4

C(1771471)

H3CN+

HO4226 1 2 224 5

D(2417539)

HN

NN

Ominus

N+

N+

O

O

CH3

4665 1 8 173 5

E(4311794)

N+

3004 0 1 103 1


Table 2 Continued



H-bonddonorlowast


bondslowast

F(4372347) H3C

O

N+ CH3

HNN 4246 0 4 173 2

G(4937122)

H3C

O

N+

CH3

HN

N3985 0 4 112 1

lowastData from ZINC databank

Table 3 The IC50 values and Δ119866binding to the compounds AndashG

Compound IC50 (120583M) Δ119866 (kcalsdotmolminus1)EeAChE DrAChE HsAChE EcBChE TcAChE (1EA5) HsAChE (1B41) HsBChE (4BDS)

A 3633 plusmn 314 2623 plusmn 398 1322 plusmn 138 2222 plusmn 106 minus117 minus90 minus106B 5777 plusmn 843 2116 plusmn 144 3755 plusmn 197 402 plusmn 078 minus122 minus98 minus116C 76117 plusmn 1276 23945 plusmn 295 9208 plusmn 3973 075 plusmn 018 minus118 minus99 minus106D 539 plusmn 055 231 plusmn 029 169 plusmn 046 564 plusmn 247 minus130 minus109 minus114E 3416 plusmn 546 2641 plusmn 530 51225 plusmn 30065 1266 plusmn 766 minus108 minus102 minus97F 8416 plusmn 60 1917 plusmn 298 15095 plusmn 6168 3285 plusmn 388 minus116 minus112 minus106G 34439 plusmn 507 4978 plusmn 1528 7003 plusmn 796 951 plusmn 694 minus126 minus113 minus106EeAChE = AChE from Electrophorus electricusDrAChE = AChE fromDanio rerio TcAChE = AChE from Torpedo californicaHsAChE andHsBChE = AChEand BChE from Homo sapiens respectively

H-bond occurs between oxadioazole group and Tyr337(peripheral anionic subsite) (Figure 3(e)) In addition a 120587-120587stacking was observed between Trp286 (peripheral anionicsubsite) and the compound ldquoDrdquoThe different model of whichldquoDrdquo interacts with theHsAChE (Figure 3(e)) could be respon-sible for its more potent inhibitory effect on the enzyme

In all these models proposed above the interactions ofinhibitors with ChEs are expected to prevent the entranceof ACh in the activity site from AChEs and HsBChE conse-quently causing their inhibition It is possible to observe thatboth compounds (C and D) interact rather with peripheralanionic and anionic subsites probably due to the presenceof aromatic residues in both peripheral anionic and anionicsubsites Importantly similar observations were made byother studies involving AChEIs and molecular modeling[18 19] The IC

50values found for the compounds ldquoCrdquo and

ldquoDrdquo are in the range of 120583M that is they are 1ndash3 ordersof magnitude higher than those of tacrine and donepezil

(IC50= 205 plusmn 18 nM and 116 plusmn 16 nM resp [19]) which are

two commercial drugs for treating AD Moreover accordingthe thermodynamic data (Δ119866binding) we observed that notype of correlation occurred with the IC

50values An overall

scheme of the strategy used for this study is depicted inFigure 4

4 Conclusion

In this study we carried out a total of tree hierarchicalscreening steps (two in silico and one in vitro) in orderto search for potential molecules able to act as AChEIsWe found one compound ldquoDrdquo with relevant anti-AChEand anti-BChE activity To our surprise we found onemolecule ldquoCrdquo which inhibited EfBChE more significantlythan it did with AChEs These results suggest that selectingcompounds with pharmacophoric properties (Lipinskirsquos ruleof five) and performing the molecular docking screeningto search potential inhibitors are interesting strategies that


TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)

Figure 3The interactions betweenC andDcompoundswithTcAChE (1EA5 enzyme in green)HsAChE (1B41 enzyme in blue) andHsBChE(4BDS enzyme in yellow) obtained after molecular docking The compound ldquoCrdquo is shown in (a) (b) and (c) and the ldquoDrdquo in (d) (e) and(f) respectively The nonpolar hydrogen atoms were omitted the nitrogen atoms are represented in blue oxygen in red carbon in gray andthe polar hydrogen in white The inhibitors are represented as ball and stick and the amino acids residues as sticks The types of interactionsare represented by dotted lines with their respective distance differentiated by colors 120587-120587 interactions (green) cation-120587 (orange) 120590-120587 (lightblue) and hydrogen bonds (pink)

382

7

2

Lipinskirsquos rule of five

quaternary ammoniumAuto Dock Vina

111

Zinc database

In vitro activity

+

Figure 4 Scheme of the virtual screening used in this study The strategy excludes a large number of compounds for further in vitro assaysas well as the costs and working time

could be used for high throughput screenings aiming todetect new compounds with desirable biological activityWe also reported the importance of aromatics rings in theinhibitors These aromatic moieties in the ligands perform120587-120587 and cation-120587 interactions with several aromatic residueslocated in the gorge of AChE (for instance Trp84 Trp279Phe330 Phe331 and Tyr334) These molecules interact atthe peripheral anionic subsite and anionic subsite of AChEpreventing the hydrolysis of ACh Despite the fact thatsome compounds act as inhibitors of AChE and BChE weemphasize that other approaches such as in vivo studies arenecessary to validate the pharmacological and toxicologicalproperties of these compounds

Conflict of Interests

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

Acknowledgments

This study was supported by FAPERGS (Fundacao deAmparo a Pesquisa do Estado do Rio Grande do Sul) CAPES(Coordenacao de Aperfeicoamento de Pessoal de NıvelSuperior) CNPq (Conselho Nacional de DesenvolvimentoCientıfico e Tecnologico) FINEP (Rede Instituto Brasileiro


de Neurociencia (IBN-Net) no 01060842-00) FAPERGS-PRONEX-CNPQ and INCT-EN (Instituto Nacional deCiencia e Tecnologia em Excitotoxicidade e Neuroprotecao)Cristiane Lenz Dalla Corte and Marcos M Braga wererecipients of a DOCFIX fellowship from FAPERGSCAPES

References

[1] D Harman ldquoA hypothesis on the pathogenesis of Alzheimerrsquosdiseaserdquo Annals of the New York Academy of Sciences vol 786pp 152ndash168 1996

[2] D J Selkoe ldquoCell biology of proteinmisfolding the examples ofAlzheimerrsquos and Parkinsonrsquos diseasesrdquo Nature Cell Biology vol6 no 11 pp 1054ndash1061 2004

[3] L J Launer L Fratiglioni K Andersen et al ldquoRegionaldifferences in the incidence ofDementia in Europe-EURODEMcollaborative analysisrdquo in Alzheimerrsquos Disease and Related Dis-orders Etiology Pathogenesis and Therapeutics K Iqbal D FSwaab B Winblad and H M Wisniewski Eds pp 9ndash15 JohnWiley amp Sons New York NY USA 1999

[4] J L Cummings and C Back ldquoThe cholinergic hypothesisof neuropsychiatric symptoms in Alzheimerrsquos diseaserdquo TheAmerican Journal of Geriatric Psychiatry vol 6 no 2 pp S64ndashS78 1998

[5] P Kasa Z Rakonczay and K Gulya ldquoThe cholinergic system inalzheimerrsquos diseaserdquo Progress in Neurobiology vol 52 no 6 pp511ndash535 1997

[6] I Silman and J L Sussman ldquoAcetylcholinesterase ldquoclassi-calrdquo and ldquonon-classicalrdquo functions and pharmacologyrdquo CurrentOpinion in Pharmacology vol 5 no 3 pp 293ndash302 2005

[7] A Enz R Amstutz H Boddeke G Gmelin and J MalanowskildquoBrain selective inhibition of acetylcholinesterase a novelapproach to therapy for Alzheimerrsquos diseaserdquo Progress in BrainResearch vol 98 pp 431ndash438 1993

[8] M Jalaie andV Shanmugasundaram ldquoVirtual screening are wethere yetrdquoMini-Reviews in Medicinal Chemistry vol 6 no 10pp 1159ndash1167 2006

[9] C A Lipinski F Lombardo B W Dominy and P J FeeneyldquoExperimental and computational approaches to estimate sol-ubility and permeability in drug discovery and developmentsettingsrdquo Advanced Drug Delivery Reviews vol 23 no 1ndash3 pp3ndash25 1997

[10] D B Kitchen H Decornez J R Furr and J Bajorath ldquoDockingand scoring in virtual screening for drug discovery methodsand applicationsrdquo Nature Reviews Drug Discovery vol 3 no 11pp 935ndash949 2004

[11] J J Irwin and B K Shoichet ldquoZINCmdasha free database of com-mercially available compounds for virtual screeningrdquo Journal ofChemical Information and Modeling vol 45 no 1 pp 177ndash1822005

[12] D F Veber S R Johnson H-Y Cheng B R Smith K WWard and K D Kopple ldquoMolecular properties that influencethe oral bioavailability of drug candidatesrdquo Journal of MedicinalChemistry vol 45 no 12 pp 2615ndash2623 2002

[13] O Trott and A J Olson ldquoSoftware news and update AutoDockVina improving the speed and accuracy of docking with a newscoring function efficient optimization and multithreadingrdquoJournal of Computational Chemistry vol 31 no 2 pp 455ndash4612010

[14] B D Bursulaya M Totrov R Abagyan and C L Brooks IIIldquoComparative study of several algorithms for flexible ligand

dockingrdquo Journal of Computer-Aided Molecular Design vol 17no 11 pp 755ndash763 2003

[15] G L Ellman K D Courtney V Andres Jr and R MFeatherstone ldquoA new and rapid colorimetric determination ofacetylcholinesterase activityrdquo Biochemical Pharmacology vol 7no 2 pp 88ndash95 1961

[16] F Worek P Eyer D Kiderlen H Thiermann and L SziniczldquoEffect of human plasma on the reactivation of sarin-inhibitedhuman erythrocyte acetylcholinesteraserdquo Archives of Toxicol-ogy vol 74 no 1 pp 21ndash26 2000

[17] D B Rosemberg R F da Rocha E P Rico et al ldquoTaurineprevents enhancement of acetylcholinesterase activity inducedby acute ethanol exposure and decreases the level of markers ofoxidative stress in zebrafish brainrdquo Neuroscience vol 171 no 3pp 683ndash692 2010

[18] M J Castro V Richmond C Romero et al ldquoPreparation anti-cholinesterase activity and molecular docking of new lupanederivativesrdquo Bioorganic amp Medicinal Chemistry vol 22 pp3341ndash3350 2014

[19] P Camps X Formosa C Galdeano et al ldquoTacrine-baseddual binding site acetylcholinesterase inhibitors as potentialdisease-modifying anti-Alzheimer drug candidatesrdquo Chemico-Biological Interactions vol 187 no 1ndash3 pp 411ndash415 2010

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(a) (b) (c) (d)

Figure 1 Molecular overlapping of the crystal ligands (red) and the best pose of ligands proposed by Auto Dock Vina 111 program (green)for the enzymes 1QON (a) 2VQ6 (b) 3I6M (c) and 2BDS (d) The nonpolar hydrogen atoms were omitted

[8] During this long process it is possible to search forcompounds with specific features that can potentially lead tothe development of effective therapeutic agents For instancethe consideration of the Lipinskirsquos rule of five [9] thatis molecular weight lower than 500Da number of donorhydrogen bonds less than 5 number of acceptor hydrogenbonds less than 10 and the 119909 log119875 lower than 5 is ofimportance for the screening of drugs with pharmacologicalactivity

The molecular docking is a method that can predictthe most favorable orientation of a molecule (ligand) wheninteracting with a macromolecular target such as an enzymeor a receptor to form a stable complexThe crucial thermody-namic parameter involved in this method is the binding freeenergy (Δ119866binding) which checks the theoretical stability ofthe ligand-protein complex [10] Based on this principle themain objective of the present study was to propose a strategyof virtual screening to unravel potential newAChEIs by usingthe virtual molecules ZINC bank and selecting compoundsthat obey the Lipinskirsquos rule of five which have quaternaryammonium atom (because of the similarity with ACh) Wealso assessed the inhibitory activity of selected compounds invitro in order to check which compounds have the highestinhibitory activity using different AChE sources purifiedAChE from Electrophorus electricus (EeAChE) AChE fromDanio rerio (DrAChE) and human AChE (HsAChE) Fur-thermore in order to verify whether the selected compoundscould also inhibit butyrylcholinesterase (BChE) activity weinvestigated the effects of these compounds on purified BChEfrom Equus ferus (EfBChE)

2 Materials and Methods

21 In Silico Analysis To search for new drugs with bindingaffinity to AChE we used the virtual molecules ZINC bank(httpzincdockingorg) where approximately 55 millionof different molecular structures are deposited [11] Firstwe selected only tridimensional structures of compoundswith quaternary ammonium atom that were in accordancewith the Lipinskirsquos rule of five In addition another rule wasalso included the number of rotatable bonds had to be lessthan 10 [12] The molecules obtained were downloaded and

their geometry optimized using the software Avogadro 094following the MMFF94 method

The molecular docking simulation was used as a secondscreening aiming to search for compounds with higherinhibitory capacity and to propose an interaction modelWe used different crystallographic structures of AChEfrom Protein Data Bank (PDB) (httpwwwpdborg) TheCHIMERA 153 softwarewas used to removemolecules ionsandwater and tominimize the structure of proteins using theGasteiger charges with 500 steps of minimization

After obtaining the ligands and enzymes their structureswere converted to pdbqt format using the Auto Dock Tools154 program inwhich all the rotatable bonds of ligandswereallowed to rotate freely and the receptors were consideredrigid For docking studies we used the Auto Dock Vina 111[13] with 1 A of spacing between the grid points The gridbox was centered on the active site of the enzymes with highresolution allowing the program to search for additionalplaces of probable interactions between the ligands and thereceptor Other configurations were considered default

The type of enzyme species PDB code RMSD valuecoordinates and size of the grid box are shown in Table 1Importantly some enzymes do not present the RMSD valuebecause they do not have inhibitor on their structures Thefigures of structures with RMSD are represented in Figure 1The RMSD value (less than 2 A) is a criterion often used forcorrecting bound structure prediction [14] The redockingswere performed with the same configurations of the previousperformed dockings

For in vitro assays we selected the compounds that pre-sented lower binding energy (Δ119866binding) in all enzymes usedfor the screening The interactions between ligand-proteinwere visualized by Accelrys Discovery Studio Visualizer 25

22 In Vitro Analysis The compounds selected as inhibitorsof AChE activity were obtained commercially from MolPort(httpwwwmolportcombuy-chemicalsindex) They weredissolved in dimethyl sulfoxide (DMSO) at a final concen-tration of 01

The cholinesterase activities were measured based onEllman et alrsquos method [15] The increase of absorbancewas monitored at 412 nm in a reaction mixture containing





1DX4119909 30311 119909 44119910 70424 119910 48 mdash119911 10617 119911 52

1QO9119909 28259 119909 44119910 70453 119910 52 mdash119911 12592 119911 44

1QON119909 29403 119909 40

103119910 71508 119910 44119911 11697 119911 40

Torpedo californica

2VQ6119909 5254 119909 48119910 65479 119910 44 194119911 64028 119911 48

3I6M119909 2092 119909 56119910 64337 119910 44 041119911 6475 119911 48

1EA5119909 4957 119909 48119910 64084 119910 44 mdash119911 65094 119911 48


Homo sapiens

3LII119909 91443 119909 52119910 8869 119910 40 mdash119911 minus5859 119911 48

1B41119909 120139 119909 32

mdash119910 108623 119910 52119911 minus132452 119911 36

BChE2BDSlowast

119909 133076 119909 42038Homo sapiens 119910 116113 119910 48







100

75

50

25

0

1 10 100

ABCD

EFG

EeAC

hE ac

tivity

()

(a)

100

75

50

25

0

1 10 10001

ABCD

EFG

EfBC

hE ac

tivity

()

(b)









50
















H-bonddonorlowast


bondslowast

A(1249551)

OOO

HON+ 4826 1 5 11 7

B(1280061) CH3

H3C

H3C

H3CH3C

H3C

O

O

O

O

N+

CH3

CH3

CH3

4786 0 5 242 4

C(1771471)

H3CN+

HO4226 1 2 224 5

D(2417539)

HN

NN

Ominus

N+

N+

O

O

CH3

4665 1 8 173 5

E(4311794)

N+

3004 0 1 103 1


Table 2 Continued



H-bonddonorlowast


bondslowast

F(4372347) H3C

O

N+ CH3

HNN 4246 0 4 173 2

G(4937122)

H3C

O

N+

CH3

HN

N3985 0 4 112 1











50values An overall


4 Conclusion



TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)


382

7

2



111

Zinc database

In vitro activity

+





Acknowledgments




References




























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





1DX4119909 30311 119909 44119910 70424 119910 48 mdash119911 10617 119911 52

1QO9119909 28259 119909 44119910 70453 119910 52 mdash119911 12592 119911 44

1QON119909 29403 119909 40

103119910 71508 119910 44119911 11697 119911 40

Torpedo californica

2VQ6119909 5254 119909 48119910 65479 119910 44 194119911 64028 119911 48

3I6M119909 2092 119909 56119910 64337 119910 44 041119911 6475 119911 48

1EA5119909 4957 119909 48119910 64084 119910 44 mdash119911 65094 119911 48


Homo sapiens

3LII119909 91443 119909 52119910 8869 119910 40 mdash119911 minus5859 119911 48

1B41119909 120139 119909 32

mdash119910 108623 119910 52119911 minus132452 119911 36

BChE2BDSlowast

119909 133076 119909 42038Homo sapiens 119910 116113 119910 48







100

75

50

25

0

1 10 100

ABCD

EFG

EeAC

hE ac

tivity

()

(a)

100

75

50

25

0

1 10 10001

ABCD

EFG

EfBC

hE ac

tivity

()

(b)









50
















H-bonddonorlowast


bondslowast

A(1249551)

OOO

HON+ 4826 1 5 11 7

B(1280061) CH3

H3C

H3C

H3CH3C

H3C

O

O

O

O

N+

CH3

CH3

CH3

4786 0 5 242 4

C(1771471)

H3CN+

HO4226 1 2 224 5

D(2417539)

HN

NN

Ominus

N+

N+

O

O

CH3

4665 1 8 173 5

E(4311794)

N+

3004 0 1 103 1


Table 2 Continued



H-bonddonorlowast


bondslowast

F(4372347) H3C

O

N+ CH3

HNN 4246 0 4 173 2

G(4937122)

H3C

O

N+

CH3

HN

N3985 0 4 112 1











50values An overall


4 Conclusion



TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)


382

7

2



111

Zinc database

In vitro activity

+





Acknowledgments




References




























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


100

75

50

25

0

1 10 100

ABCD

EFG

EeAC

hE ac

tivity

()

(a)

100

75

50

25

0

1 10 10001

ABCD

EFG

EfBC

hE ac

tivity

()

(b)









50
















H-bonddonorlowast


bondslowast

A(1249551)

OOO

HON+ 4826 1 5 11 7

B(1280061) CH3

H3C

H3C

H3CH3C

H3C

O

O

O

O

N+

CH3

CH3

CH3

4786 0 5 242 4

C(1771471)

H3CN+

HO4226 1 2 224 5

D(2417539)

HN

NN

Ominus

N+

N+

O

O

CH3

4665 1 8 173 5

E(4311794)

N+

3004 0 1 103 1


Table 2 Continued



H-bonddonorlowast


bondslowast

F(4372347) H3C

O

N+ CH3

HNN 4246 0 4 173 2

G(4937122)

H3C

O

N+

CH3

HN

N3985 0 4 112 1











50values An overall


4 Conclusion



TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)


382

7

2



111

Zinc database

In vitro activity

+





Acknowledgments




References




























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





H-bonddonorlowast


bondslowast

A(1249551)

OOO

HON+ 4826 1 5 11 7

B(1280061) CH3

H3C

H3C

H3CH3C

H3C

O

O

O

O

N+

CH3

CH3

CH3

4786 0 5 242 4

C(1771471)

H3CN+

HO4226 1 2 224 5

D(2417539)

HN

NN

Ominus

N+

N+

O

O

CH3

4665 1 8 173 5

E(4311794)

N+

3004 0 1 103 1


Table 2 Continued



H-bonddonorlowast


bondslowast

F(4372347) H3C

O

N+ CH3

HNN 4246 0 4 173 2

G(4937122)

H3C

O

N+

CH3

HN

N3985 0 4 112 1











50values An overall


4 Conclusion



TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)


382

7

2



111

Zinc database

In vitro activity

+





Acknowledgments




References




























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table 2 Continued



H-bonddonorlowast


bondslowast

F(4372347) H3C

O

N+ CH3

HNN 4246 0 4 173 2

G(4937122)

H3C

O

N+

CH3

HN

N3985 0 4 112 1











50values An overall


4 Conclusion



TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)


382

7

2



111

Zinc database

In vitro activity

+





Acknowledgments




References




























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


TcAChE

(a)

HsAChE

(b)

HsBChE

(c)

TcAChE

(d)

HsAChE

(e)

HsBChE

(f)


382

7

2



111

Zinc database

In vitro activity

+





Acknowledgments




References




























Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



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 virtual screening of acetylcholinesterase...

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