ORIGINAL PAPER

Homology modeling, molecular dynamics, e-pharmacophoremapping and docking study of Chikungunya virus nsP2protease

Kh. Dhanachandra Singh & Palani Kirubakaran & Shanthi Nagarajan &

Sugunadevi Sakkiah & Karthikeyan Muthusamy & Devadasan Velmurgan &

Jeyaraman Jeyakanthan

Received: 3 November 2010 /Accepted: 9 February 2011# Springer-Verlag 2011

Abstract To date, no suitable vaccine or specific antiviraldrug is available to treat Chikungunya viral (CHIKV) fever.Hence, it is essential to identify drug candidates that couldpotentially impede CHIKV infection. Here, we present thedevelopment of a homology model of nsP2 protein basedon the crystal structure of the nsP2 protein of Venezuelanequine encephalitis virus (VEEV). The protein modeledwas optimized using molecular dynamics simulation; thejunction peptides of a nonstructural protein complex werethen docked in order to investigate the possible protein–protein interactions between nsP2 and the proteins cleavedby nsP2. The modeling studies conducted shed light on the

binding modes, and the critical interactions with thepeptides provide insight into the chemical features neededto inhibit the CHIK virus infection. Energy-optimizedpharmacophore mapping was performed using the junctionpeptides. Based on the results, we propose the pharmaco-phore features that must be present in an inhibitor of nsP2protease. The resulting pharmacophore model contained anaromatic ring, a hydrophobic and three hydrogen-bonddonor sites. Using these pharmacophore features, wescreened a large public library of compounds (Asinex,Maybridge, TOSLab, Binding Database) to find a potentialligand that could inhibit the nsP2 protein. The compoundsthat yielded a fitness score of more than 1.0 were furthersubjected to Glide HTVS and Glide XP. Here, we report thebest four compounds based on their docking scores; thesecompounds have IDs of 27943, 21362, ASN 01107557 andASN 01541696. We propose that these compounds couldbind to the active site of nsP2 protease and inhibit thisenzyme. Furthermore, the backbone structural scaffolds ofthese four lead compounds could serve as building blockswhen designing drug-like molecules for the treatment ofChikungunya viral fever.

Keywords Chikungunya virus . Homology modeling .

Desmond . Nonstructural proteins (nsP) . Moleculardynamics simulation (MDS)

Introduction

Chikungunya virus (CHIKV) is a member of the genusAlphavirus of the family Togaviridae. While CHIKV istransmitted to humans by several species of mosquitoes, A.aegypti and A. albopictus are the two main vectors [1]. In

Kh. Dhanachandra Singh, Palani Kirubakaran, and Shanthi Nagarajancontributed equally to this work.

Electronic supplementary material The online version of this article(doi:10.1007/s00894-011-1018-3) contains supplementary material,which is available to authorized users.

K. D. Singh : P. Kirubakaran :K. Muthusamy :J. Jeyakanthan (*)Department of Bioinformatics , Alagappa University,Karaikudi 63003, Indiae-mail: jjkanthan@gmail.com

S. NagarajanBioinformatics Centre, Pondicherry University,Pondicherry 605 014, India

S. SakkiahBioinformatics Centre and Computational Biology Laboratory,Department of Bioinformatics, Bharathiar University,Coimbatore 641 046, India

D. VelmurganCAS, Biophysics and Crystallography, University of Madras,Chennai 600025, Indiae-mail: d_velu@yahoo.com

J Mol ModelDOI 10.1007/s00894-011-1018-3

Africa and Asia, two outbreaks of Chikungunya virus haveoccurred, with time intervals of 7–8 years to 20 yearsbetween consecutive epidemics [2]. In recent years,emerging and re-emerging tropical infectious diseases havebeen shown to have high social and economic impacts [3].The symptoms of CHIKV infection are fever, headache,nausea, vomiting, myalgia, rash, and arthralgia [4]. Wheninfected, patients are observed to have painful puffy feetand ankles, and they experience predominantly chronicpolyarthralgia, typically a rheumatoid arthritis-like illness[5]. Recurrent episodes can occur in some patients,generally in the form of chronic and persistent arthralgia.The CHIKV genome consists of a linear, positive-sense,single-stranded RNA molecule of about 11.8 kb [6].Alphaviruses produce two mRNAs after infection, such asgenomic (49 S) RNA, which is translated into nonstructural(replicase) proteins, and subgenomic (26 S) RNA, whichserves as mRNA for virion structural proteins. Nonstruc-tural proteins (nsP) such as nsPl, nsP2, nsP3 and nsP4 areformed by proteolytic cleavage of a long polyprotein thatincludes individual nonstructural proteins [7].

Alphaviruses are enveloped particles, and their genomeconsists of a single-stranded positive-sense RNA moleculeof approximately 12,000 nucleotides. The genome ofCHIKV is considered to have the following gene arrange-ment: 5′ cap-nsP1-nsP2-nsP3-nsP4-(junction region)-C-E3-E2-6 K-E1-poly(A) 3′ [8]. Coding sequences of CHIKVfrom Indian Ocean patients consisted of two open readingframes (ORFs) encoding the nonstructural proteins(consisting of nsP1, 2, 3 and 4) in the form of twopolyprotein precursors in the 5′ two-thirds of the genome,and the other one-third containing one ORF encoding thestructural polyprotein (consisting of C, E3, E2 and E1) [9].

The alphavirus nsP2 protease domain belongs to thepapain superfamily of cysteine proteases. The proteolyticfunction of Semliki Forest virus (an alphavirus) nsP2 hasbeen mapped to its C-terminal domain [10]. The nsP2contains the protease domain responsible for nsP matura-tion [11]; hence, it is clear that the nsP2 is essential foralphavirus replication and exhibits some degree of sequencespecificity among alphaviruses [12], making it an attractivetarget for broad-spectrum antiviral inhibitor development.The nsP2 (799 amino acids) protein from Semliki Forestvirus and Sindbis virus (SINV) specifically cleaves the γ,β-triphosphate bond at the 5' end of RNA. This activity isrestricted to the N-terminal domain, and the C-terminaldomain has no RNA triphosphatase activity [13].

It is necessary to understand the structural aspects ofCHIKV nsP2 and other alphavirus nsP2 proteins in order toraise broad-spectrum viral inhibitors. In this report, weexplain the structural features of the nsP2 protein, which ismodeled based on the crystal structure of Venezuelanequine encephalitis virus (VEEV, PDB ID: 2HWK).

Molecular dynamics simulations (MDs) provide an alterna-tive tool for biological problems that are complementary toexperimental techniques [14]. The modeled nsP2 proteinstructures were refined using MD techniques, and exhaus-tive docking analysis was performed using induced fitdocking [15, 16], which is a robust automated dockingmethod that predicts the conformations of flexible ligandsbound to macromolecular targets. Three peptides, (A)Gly1-Ala2-Gly3-Ile4, (B) Gly1-Cys2-Ala3-Pro4 and (C)Gly1-Gly2-Trp3-Ile4, were docked. The plausible dockingposes obtained provide a great deal of information aboutthe protein-cleaving mechanism of nsP2 protease. Thecritical residues in nsP2 were identified by docking threedifferent peptides in order to identify the residues respon-sible for nonstructural protein cleavage. We propose fivedifferent pharmacophore sites based on the receptor–peptide interactions. The structural and pharmacophoreinformation can be effectively used for broad-spectruminhibitor identification, which, Chikungunya virus aside,should prove useful considering that alphavirus familycandidates are being used as bioweapons.

Materials and methods

All computational analysis was carried out on a Red Hat5.3 Linux platform running on a Lenovo PC with an IntelCore 2 Duo processor and 2 GB of RAM.

Homology model development

Homology modeling is a theoretical method that is used topredict the structure of a sequence with an accuracy that iscomparable to the best results achieved experimentally. Themodeled protein quality is extremely dependent on theidentity between the target and template proteins. Thenonstructural polyprotein (nsP) of Chikungunya virus(strain S27, African prototype) has 2,470 amino acidsand contains four chains, namely nsP1 (1–535), nsP2(536–1333), nsP3 (1334–1863), and nsP4 (1864–2474).The nsP2 protease is an essential protein whose proteolyticactivity is critical for virus replication. On the other hand,experimentally derived protein information is unavailable,so we decided to model the Chikungunya virus nsP2protease using the structure of its closest homolog that hasbeen solved by crystallography.

The nsP2 protein sequence was collected from theSwiss-Prot Protein Database (accession number: Q8JUX6)[8, 17]. A similarity search for nsP2 protease in the ProteinData Bank (http://www.rcsb.org) was performed using theBLAST server [18]. The protein similarity search identifieda very similar protein structure belonging to the nsP2protease of the alphavirus Venezuelan equine encephalitis

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(VEE) [19] (PDB ID: 2HWK), which has 40% sequenceidentity with CHIKV nsP2, so this structure was used as atemplate to generate the model. This was the only templateavailable to model the nsP2 protein. The model wasgenerated using Prime (Schrodinger, LLC, New York,USA) [20], and then the energy was minimized using theOPLS (optimized potentials for liquid simulations) 2005force-field [21].

Model validation

The validation of the structure model obtained from Primewas performed by inspecting the psi/phi Ramachandranplot obtained from PROCHECK analysis [22]. The PROSA[23] test was applied to the final model to check energycriteria against the potential of mean force derived from alarge set of known protein structures. The root mean squaredeviation (RMSD) between the main chain atom of themodel and the template was calculated by superimposingthe structure of the template (2HWK) on the predictedstructure of CHIKV nsP2 protease in order to assess thereliability of the model using PyMol [24].

Molecular dynamics simulations

All molecular dynamics (MD) simulations were performedusing the program Desmond [15], which uses a particular“neutral territory” method called the midpoint method [25] toefficiently exploit a high degree of computational parallelism.The OPLS 2005 force-field [21] was used to model theaminoacid interactions in the protein, and the SPC (simplepoint charge) method [26] was used for the water model.Equilibration of the system was carried out using the defaultprotocol provided in Desmond, which consists of a series ofrestrained minimizations and molecular dynamics simula-tions that are designed to slowly relax the system withoutdeviating substantially from the initial protein coordinates.The initial coordinates for the MD calculations were takenfrom the modeled protein. The SPC water molecules werethen added (the orthorhombic dimensions of each water boxwere 10 Å×10 Å×10 Å approximately, which ensured thatthe whole surfaces of the complexes were covered), and thesystem was neutralized by adding Cl− counterions to balancethe net charge of the system. After the construction of thesolvent environment, each complex system was composed ofabout 34,942 atoms. Before equilibration and the longproduction MD simulations, the systems were minimizedand pre-equilibrated using the default relaxation routineimplemented in Desmond. The whole system was subjectedto 300 K for 5 ns of simulation, and the final conformationsof the modeled protein are presented below. The structuralchanges and dynamic behavior of the protein were analyzedby calculating the RMSD and energy.

Active site predictions

The active site of the modeled protein was investigatedusing the SiteMap program [27]. This software generatesinformation on the binding site’s characteristics using novelsearch and analytical facilities: a SiteMap calculationbegins with an initial search step that identifies orcharacterizes—through the use of grid points—one or moreregions on the protein surface that may be suitable forbinding ligands to the receptor. Contour maps are thengenerated, producing hydrophobic and hydrophilic maps[28]. The hydrophilic maps are further divided into donor,acceptor, and metal-binding regions. The evaluation stage,which concludes the calculation, involves assessing eachsite by calculating various properties: the number of sitepoints, a measure of the size of the site; exposure/enclosure,two properties providing different measures of howavailable the site is to the solvent; contact, which measureshow strongly the average site point-interacts with thesurrounding receptor via van der Waals nonbondinginteractions; donor/acceptor character, a property related tothe sizes and intensities of H-donor and H-acceptor regions;and SiteScore, an overall property based on the previousproperties, constructed and calibrated so that the averageSiteScore for a promising binding site is 1.0 [28].

Induced fit docking simulation of peptides

To keep the receptor flexible in the docking protocol, weused a mixed molecular docking/dynamics protocol calledinduced fit docking (IFD) [29], as developed bySchrödinger, LLC (http://www.schrodinger.com/). The IFDprotocol used in this study was carried out in threeconsecutive steps [30]. First, the ligand was docked into arigid receptor model with scaled-down van der Waals(vdW) radii. A vdW scaling of 0.5 was used for both theprotein and ligand nonpolar atoms. A constrained energyminimization was carried out on the protein structure,keeping it close to the original crystal structure whileremoving bad steric contacts. Energy minimization was carriedout using the OPLS 2005 force-field with an implicit solvationmodel until default criteria were met. The active site predictedin SiteMap was used to define the location of the binding siteand the dimension of the energy grids for initial docking. TheGlide XP mode was used for the initial docking, and 20 ligandposes were retained for protein structural refinement. In thesecond step, Prime was used to generate the induced-fitprotein–ligand complexes. Each of the 20 structures from theprevious step was subjected to side-chain and backbonerefinements [30]. All residues with at least one atom locatedwithin 4.0 Å of each corresponding ligand pose wereincluded in the Prime refinement. The refined complexeswere ranked by Prime energy, and the receptor structures

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within 30 kcal mol−1 of the minimum energy structure wereput through to a final round of Glide docking and scoring. Inthe final step, each ligand was redocked into every refinedlow-energy receptor structure produced in the second stepusing Glide XP at default settings.

The binding modes of three different peptides that arebelieved to be cleaved by nsP2 were theoretically identifiedby docking calculations. The prime goal of the nsP2 proteinis to cleave the nonstructural protein complex intoindividual active proteins. No crystal structures or appro-priate templates are available to model the nsP1 and nsP4proteins, so we made use of junction peptide models toexplain the protein–protein interactions. The nsP2 proteasecleaving sites are reported in the Swiss-Prot database; thecleavage site information was obtained by means ofsimilarity analysis carried across alphavirus family members.In the database, two peptides are reported for every junction ofnsP1–nsP2, nsP2–nsP3, and nsP3–nsP4. In the moleculardocking studies, we used the junction peptides as substrates;moreover, we considered that the flanking residues provideaccess to the real system.

Energy-optimized pharmacophore mapping

Energy-optimized pharmacophores (e-pharmacophores) areobtained by mapping the energetic terms from the Glide XPscoring function onto atom centers. The ligand is dockedwith Glide XP and the pose is refined. The Glide XPscoring terms are computed, and the energies are mappedonto atoms. Next, pharmacophore sites are generated, andthe Glide XP energies from the atoms that comprise eachpharmacophore site are summed. The sites are then rankedbased on these energies, and the most favorable sites areselected for the pharmacophore hypothesis. These pharma-cophores are then used as queries for virtual screening [31].

Pharmacophore sites were automatically generated fromthe protein–ligand docked complex with Phase (Phase,v.3.0, Schrodinger, LLC) using the default set of sixchemical features: hydrogen bond acceptor (A), hydrogenbond donor (D), hydrophobe (H), negative ionizable (N),positive ionizable (P), and aromatic ring (R). Phase treatsmost cationic groups as being exclusively positive ioniz-able. Hydrogen-bond acceptor sites were represented asvectors along the hydrogen bond axis in accordance withthe hybridization of the acceptor atom. Hydrogen-bonddonors were represented as projected points, located at thecorresponding hydrogen-bond acceptor positions in thebinding site. Projected points allow the possibility ofstructurally dissimilar active compounds forming hydrogenbonds to the same location, regardless of their point oforigin and directionality [31].

Each pharmacophore feature site was first assigned anenergetic value equal to the sum of the Glide XP

contributions of the atoms comprising the site. This allowssites to be quantified and ranked on the basis of theseenergetic terms. Glide XP descriptors include terms forhydrophobic enclosure, hydrophobically packed correlatedhydrogen bonds, electrostatic rewards, π–π stacking, π…cation, and other interactions [32]. Sites where less thanhalf of the heavy atoms contribute to the pharmacophorefeature were excluded from the final hypothesis. Thus, ifonly two heavy atoms in a six-membered ring exhibitenergetic interactions, the ring is not considered a pharma-cophore feature [31].

e-Pharmacophore database screening

For the e-pharmacophore approach, explicit matching wasrequired for the most energetically favorable site, providedthat it scored better than −1.0 kcal mol−1. Multiple siteswere included in cases where more than one site had the topscore. Screening molecules were required to match aminimum of 3 sites for a hypothesis with 3 or 4 sites anda minimum of 4 sites for a hypothesis with 5 or more sites.The distance matching tolerance was set to 2.0 Å as abalance between stringent and loose-fitting alignment.Screening of compounds was performed against Asinex(http://www.asinex.com), Maybridge (http://www.maybridge.com), TOSLab (http://www.toslab.com), Binding Database(http://www.bindingdb.org/bind/index.jsp), etc. Database hitswere ranked in order of fitness score, a measure of how wellthe aligned ligand conformer matches the hypothesis basedon RMSD site matching, vector alignments, and volumeterms. The fitness scoring function is an equally weightedcomposite of these three terms and ranges from 0 to 3, asimplemented in the default database screening of Phase. Theligands were selected based on the fitness score. The ligandswith the best fitness scores were docked into the binding sitesof the modeled protein [31].

ADME screening

The QikProp program [33] was used to obtain the ADMEproperties of the compounds. This predicts both physicallysignificant descriptors and pharmaceutically relevantproperties. All of the compounds were neutralized beforebeing used by QikProp. The neutralizing step is essential,as QikProp is unable to neutralize a structure and noproperties will be generated in the normal mode. Theprogram was processed in normal mode, and predicted 44properties for the molecules, consisting of principaldescriptors and physiochemical properties, along with adetailed analysis of log P (octanol/water), QP%, and logHERG. It also evaluated the acceptability of the com-pounds based on Lipinski’s rule of five [34], which isessential for rational drug design.

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Results and discussion

nsP2 model generation

The model was generated based on the nsP2 protease ofVenezuelan equine encephalitis alphavirus (VEEV); thismodel has similar structural features to the templateprotein (Fig. 1). The RMSD between the minimizedprotein model and the template structure was found to be1.1 Å (Fig. 2).The modeled protein was energy minimizedusing the OPLS 2005 force-field. Both domains arecomposed of α-helices and β-strands (Fig. 3). The N-terminus is dominated by an α-helix. The C-terminaldomain contains helices and strands. The central β-sheetsare flanked by α-helices. However, the function of the C-terminal domain of nsP2 is not clear [19]. The three-dimensional structure provides valuable insight intomolecular function and also enables the protein–proteininteraction to be analyzed.

Validation of the predicted structure

The overall stereochemical quality of the model wasassessed by PROCHECK. The Ramachandran plot showed81.6% of the residues in the most favorable region, 17.4%in the allowed region, 0.7% in the generously allowedregion and 0.3% in the disallowed region; the correspondingvalues for the 2HWK template were 88.6%, 11.0%, 0.0% and0.4%, respectively. These results revealed that the majority ofthe amino acids are in a phi-psi distribution that is consistentwith a right-handed α-helix, and the model is reliable and ofgood quality (see Fig. 1 of the “Electronic supplementarymaterial,” ESM). The G-factors, indicating the quality of thecovalent, dihedral and overall bond angles, were −0.10° fordihedrals, 0.42° for covalent, and −0.11° overall. The overallmain-chain and side-chain parameters, as evaluated byProCheck, are all very favorable. The Ramachandran plotcharacteristic and G-factors confirm the quality of thepredicted model. In order to investigate whether the

Fig. 1 Structure-based sequence alignment of VEEA_nsP2 (PDB ID:2HWK) from Venezuelan equine encephalitis alphavirus nsP2protease with the modeled Chikungunya virus nsP2 protease

(CHIKV_nsP2). The secondary structural elements are shown abovethe alignment. Active site and peptide interaction residues arehighlighted with black triangles

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interaction energy of each residue with the remainder of theprotein is negative, a second test was done to apply energycriteria using a ProSA energy plot. The ProSA analysis ofthe model showed that almost all of the residues hadnegative interaction energies, with very few residuesdisplaying positive interaction energies, as shown in Fig. 2of the ESM.

Molecular dynamics simulations

Molecular dynamics can be used to explain protein structure–function problems, such as folding, conformational flexibilityand structural stability. In the simulations, we monitored thebackbone atoms and the C-α-helix of the modeled protein. TheRMSD values of the modeled structure’s backbone atoms wereplotted as a time-dependent function of the MD simulation.The results support our modeled structure, as they showconstant RMSD deviation throughout the whole simulationprocess. Graphs of potential energy, temperature, pressure andvolume are shown in Fig. 3 of the ESM. The time dependenceof the RMSD (Å) of the backbone atoms of the modeledprotein during a 5 ns simulation is shown in Fig. 4.

The graph clearly indicates that there is a change in theRMSD from 1.0 Å to 3.0 Å in the nsP2 homology modelduring the first 1500 ps, but after that it reaches a plateau.The RMSD values of the backbone atoms in the systemtend to converge after 2000 ps, showing fluctuations ofaround 1 Å. The low RMSD and the simulation timeindicate that, as expected, the 3D structural model of nsP2represents a stable folding conformation.

Induced fit docking (IFD) results for peptides

Molecular docking simulation helps us to understand theplausible binding modes and interactions. In IFD, we are

able to make the receptor flexible, which helps whenrefining the active sites. The active site of the protein lies inthe C-terminal domain (Fig. 5). All three peptides interactwith the C-terminal domain, except for the glycine ofGAGI and the isoleucine of GGYI.

Gly534-Ala535-Gly536-Ile537 (GAGI) interaction

In total, GAGI forms six hydrogen bonds to the active site ofthe nsP2 protein (Fig. 6a). The Gly534 backbone nitrogenforms a trifurcated hydrogen bond with the oxygens ofSer1293, Glu1296 and Glu1157. However, in reality, theNH2 at the starting position can form only two interactions,as it is attached to the preceding residue and hence should bea backbone NH. Ala535 does not interact with the proteinresidues. The Gly536 backbone oxygen forms a hydrogenbond with the nitrogen of Gln1039. The Ile537 side-chainoxygen and nitrogen form hydrogen bonds with the oxygenand nitrogen of His1222 and Lys1239.

Gly1332-Cys1333-Ala1334-Pro1335 (GCAP) interaction

GCAP also forms six hydrogen bonds (Fig. 6b), and all ofthe peptides form hydrogen bonds with the nsP2 protein.The NH2 of Gly1332 forms a trifurcated H-bond with theoxygens of Ser1293, Glu1157 and Glu1297. However, inreality it can form only two bonds, as explained above. Thebackbone oxygen and nitrogen of Cys1333 and Ala1334form hydrogen bonds with the nitrogen and oxygen sidechains of His1222. The side-chain oxygen of Pro1335forms a hydrogen bond with the nitrogen of Lys1045.

Gly1862-Gly1863-Tyr1864-Ile1865 (GGYI) interaction

GGYI forms seven hydrogen bonds with the nsP2 protein(Fig. 6c). Gly1862 forms a trifurcated H-bond with the

Fig. 3 Ribbon diagram of the modeled Chikungunya virus nsp2 protease(CHIKV_nsP2) showing the N- and C-terminal domains. α-Helices, β-strands and loops are colored red, yellow and green, respectively

Fig. 2 Superimposition of template (magenta) and model protein(cyan)

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same residue, but only two H-bonds are possible in reality.The Tyr1864 backbone nitrogen and oxygen form hydrogenbonds with the oxygen of His1222 and nitrogen ofLys1239. The phenolic group of tyrosine forms a hydrogenbond with the oxygen of Gly1176. The backbone nitrogenof Ile1865 forms a hydrogen bond with the nitrogen ofLys1045. The Glide scores, Glide energies and IFD scoresare shown in Table 1.

e-Pharmacophore development and screeningof the database

The e-pharmacophore combines aspects of structure-basedand ligand-based techniques. Incorporating protein–ligandcontacts into ligand-based pharmacophore approaches hasbeen shown to produce enhanced enrichments over usingligand information alone. The method described hereattempts to take a step beyond simple contact scoring by

incorporating structural and energetic information using thescoring function in Glide XP. Seven pharmacophore siteswere predicted, but only five pharmacophore sites arechosen based on the score. The final hypothesis consists ofa hydrophobic group (H), an aromatic ring (R), and threeH-bond donors (D), and their distances are shown in Fig. 7aand b, respectively. The hydrophobic site H23 lies in theCOOH group of Ile537 (GAGI), while the donor D10 liesin the phenol group of Tyr1864 (GGYI). The donor D12lies in the amino group of Gly536 (GAGI), Cys1333(GCAP) and Tyr1864 (GGYI), and the donor D16 lies inthe amino group of Gly534 (GAGI) and Gly1862 (GGYI).These energetically favorable sites encompass the specificinteractions of junction peptides and the nsP2 protein, andthis information should prove helpful in the development ofnew nsP2 inhibitors. With this pharmacophore hypothesis,compound screening was performed against Asinex,Maybridge, Binding Database, TOS Lab, etc.: a total ofmore than 300,000 compounds. To further enrich thescreening, excluded volumes were added to the hypotheses.Receptor-based excluded volumes were included in order tohelp reduce false positives by eliminating inactive com-pounds that cannot simultaneously match the hypothesisand avoid clashing with the receptor. Compounds withfitness scores of more than 1.0 were subjected to Glidehigh-throughput virtual screening (HTVS).

Glide extra precision docking

Molecular docking is a computational technique thatsamples conformations of small compounds at protein-binding sites; scoring functions are used to assess which ofthese conformations best complement the protein-bindingsite. There are two main aspects to assessing the quality ofdocking methods: (i) docking accuracy, which recognizesthe true binding mode of the ligand to the target protein,

Fig. 5 Electrostatic potential surface of the CHIKV_nsP2 and itsactive site pocket. The positively and negatively charged surfaceregions are shown in blue and red, respectively

Fig. 4 RMSD of the backboneatoms of the modeled proteinover a time period of 5 ns

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and (ii) screening enrichment, which measures how muchbetter a docking method is at identifying true bindingligands than random screening.

To speed up the screening of a large set of compounddatabases, we took the refined active site from IFD and made

this active site rigid while screening in HTVS and Glide XPdocking. Twenty compounds were selected from the HTVSfor further Glide XP docking study based on the Glide score.Here, we report the four compounds from this Glide XPdocking study with the best Glide scores (−7.5 to 9.8) and

Table 1 The peptides used in the docking simulations and their corresponding Glide scores and Glide energies

Peptide Junction Glide score Glide energy(kcal/mol)

IFD scorea Interaction(D…H–A)b

H-bondlength (Å)

Gly534-Ala535-Gly536-Ile537 (GAGI) nsP1–nsP2 −7.538 −48.743 −631.584 NH…O Ser1293) 1.830

NH…O(Glu1296) 1.833

NH…O(Glu1157) 1.649

NH(Gln1039)…O 2.008

NH(Lys1239)…O 2.073

NH…O(His1222) 1.828

Gly1332-Cys1333-Ala1334-Pro1335 (GCAP) nsP2–nsP3 −7.399 −46.500 −631.359 NH…O(Ser1293) 1.983

NH…O(Glu1157) 1.643

NH…O(Glu1296) 2.037

NH(His1222)…O 1.914

NH…O(His1222) 1.712

NH(Lys1045)…O 2.107

Gly1862-Gly1863-Tyr1864-Ile1865 (GCYI) nsP3–nsP4 −12.045 −59.956 −635.896 NH…O(Ser1293) 2.454

NH…O(Glu1157) 1.699

NH…O(Glu1296) 2.138

NH…O(His1222) 1.820

OH(Gly1176)…O 1.931

NH(Lys1239)…O 2.228

NH(Lys1045)…N 2.039

a IFD score induced fit docking scoreb D donor, H hydrogen, A acceptor

Fig. 6 Binding modes of thejunction peptides Gly534-Ala535-Gly536-Ile537 (a), Gly1332-Cys1333-Ala1334-Pro1335 (b)and Gly1862-Gly1863-Tyr1864-Ile1865 (c), based on induced fitdocking results

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Glide energies (−29 to −49 kcal mol−1), which suggest strongenzyme–ligand interactions. The chemical names of the fourlead compounds and their corresponding database identity(ID) numbers are: [5-(5-fluoro-2,4-dioxopyrimidin-1-yl)-3,4-dihydroxyoxolan-2-yl]methyl phosphate (27943: BindingDatabase); 4-[hydroxy-(4-methylphenyl)methylidene]-5-(3-hydroxyphenyl)-1-(2-hydroxypropyl)pyrrolidine-2,3-dione(21362: TOSLab); N-(2-methyl-4-nitrophenyl)-2-[[5-(6-oxo-cyclohexa-2,4-dien-1-ylidene)-1,2-dihydro-1,2,4-triazol-3-yl]sulfanyl]acetamide (ASN 01107557: Asinex); and N-(5-

ethyl-1,3,4-thiadiazol-2-yl)-2-[[5-(6-oxocyclohexa-2, 4-dien-1-ylidene)-1,2-dihydro-1,2,4-triazol-3-yl]sulfanyl]acetamide(ASN 01541696: Asinex). The chemical structures of theselead molecules are illustrated in Fig. 8, and the bindingmodes of these four lead molecules and their interactingresidues are shown in Fig. 9a–d and Table 2. The residuesLys1045, Gly1176, His1222 and Lys1239 are involved inligand interactions and are also important residues for thepeptide interaction. The nsP2 protease sequences available inUniProt were retrieved (UniProt IDs: Q8JUX6, D7R997,

Fig. 7 Common pharmacophore hypothesis (DDDHR) based on the alignment of junction peptides (a) and the distance between the pharmacophoresites (b). Geometry of the pharmacophore. Orange torus aromatic ring feature, green sphere hydrophobic feature, light blue sphere donor feature

Fig. 8 Structures of the fourlead molecules along with theircompound IDs and databasenames

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D7R977, D7R966, D7R942, D7R973, A6MH22, D7R926,D7R999, D7R979, A0FJ31, D7R944, D7R9A1, Q1H8W7,Q1EL94, C7G0W2, A6MH12, B4YIR3, A9LMA5,A5Y7Y4, D2KBP9, C7AE71, Q1W368, D2KBQ5,D2CY36 and C7ADY7) and multiple sequence alignmentwas performed in ClustalW. We found that there were nochanges in the residues at the active site we reported.Multiple sequence alignment of the reported sequences fornsP2 protease is shown in Fig. 4 of the ESM. Hence, thereported active site does not change very often .

Predicted ADME properties

We analyzed 44 physically significant descriptors [33] andpharmaceutically relevant properties of the four leadcompounds, including molecular weight, H-bond donors,H-bond acceptors, log P (octanol/water), log P MDCK, logKp (skin permeability), humoral absorption, and theirpositions according to Lipinski’s rule of five (Table 3 andTable 4). Lipinski’s rule of five is a rule of thumb toevaluate drug likeness; in other words, to determine if a

Fig. 9 Binding modes of thefour potential ligands to theactive site of nsP2 protease. Thecompound database IDs of thelead molecules are as follows:27943: Binding Database (a),21362: TOSLab (b), ASN01107557: Asinex (c), ASN01541696: Asinex (d)

Table 2 Pharmacophore results for the best hit compounds, and Glide XP docking results

Lead moleculesa Interaction (D…H–A)b H-bond length (Å) Align score Fitness score Glide score Glide energy (kcal/mol)

27943 OH… O(His1222) 1.982 1.015 1.029 −9.601 −42.576OH… O(His1222) 1.932

NH(Lys1239)…O 1.766

21362 OH …O(Gly1176) 1.974 0.927 1.046 −9.279 −48.690NH(His1222)…O 2.161

OH …O(His1222) 1.621

NH(Lys1239)…O 1.706

NH(Lys1045)…O 1.893

OH…O(Leu1203) 2.156

ASN 01107557 NH(Lys1045)…O 1.833 0.981 1.170 −8.57 −32.436NH…O(Leu1203) 1.812

NH…O(Lys1239) 2.956

ASN 01541696 NH…O(Leu1202) 1.848 1.240 1.009 −7.640 −29.999NH(Lys1045)....O 1.790

NH(Lys1045)....O 2.707

a Ligand IDs are: 43077: Binding Database; 21362: TOSLab; ASN 01107557 and ASN 01541696: Asinex databaseb D donor, H hydrogen, A acceptor

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chemical compound with a certain pharmacological orbiological activity has properties that would likely make itan orally active drug in humans. The rule describesmolecular properties that are important in the drug’spharmacokinetics in the human body, including its ADME.However, the rule does not predict whether a compound ispharmacologically active. The four selected compoundswere in the acceptable range of Lipinski’s rule of five. Forthe four lead compounds, the partition coefficient (QP logP(o/w)) and the water solubility (QP log S), which arecrucial when estimating the absorption and distribution ofdrugs within the body, ranged between −1.690 to 1.727and −1.582 to −4.691, respectively, while the cellpermeability (QP PCaco), a key factor governing drugmetabolism and its access to biological membranes, rangedfrom 0.345 to 95. Overall, the percentage human oralabsorptions for the compounds ranged from 25% to 100%.

All of these pharmacokinetic parameters are within theacceptable range defined for human use, thereby indicatingtheir potential for use as drug-like molecules.

Conclusions

Our main objective of this work was to identify the residuesinvolved in the cleavage mechanism through theoreticalcalculations. The identification of inhibitors for Chikungunyavirus has been hampered but a lack of structural insight intoany proteins. Therefore, we have chosen to model the nsP2protein, which plays a vital role in activating the nonstructuralprotein complex by cleaving the proteins into subunits ofnsP1, nsP2, nsP3 and nsP4. The model was further validatedby molecular dynamics simulation and various validation

Table 3 Principal descriptors calculated by Qikprop simulation

Lead moleculesa Molecular weightb (g/mol) Molecular volumec (Å) PSAd HB donorse HB acceptorsf Rotatable bondsg

27943 342.174 851.553 192.165 5.000 13.600 3

21362 367.401 1145.733 118.162 2.000 7.200 4

ASN 01107557 385.397 1154.389 139.73 3.000 6.750 4

ASN 01541696 362.424 1099.505 130.068 3.000 7.750 5

a Ligand IDs: 43077: Binding Database; 21362: TOSLab; ASN 01107557 and ASN 01541696: Asinex databaseb Molecular weight of the moleculec Total solvent-accessible volume in cubic angstroms using a probe with a radius of 1.4 Åd Van der Waals surface areas of polar nitrogen and oxygen atomse Estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution. Values are averages taken over anumber of configurations, so they can be non-integerf Estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution. Values are averages taken over anumber of configurations, so they can be non-integerg Number of rotatable bonds

Table 4 Physiochemical descriptors calculated by Qikprop simulation

Lead moleculesa QP log P(o/w)b QP log Sc QP PCacod QP log HERGe QP PMDCKf % Human oral absorptiong

27943 −1.690 −1.582 0.345 0.090 0.269 1

21362 2.238 −4.109 112.378 −5.323 46.585 3

ASN 01107557 2.228 −5.106 54.703 −6.284 31.997 3

ASN 01541696 1.727 −4.691 95.051 −5.903 90.432 3

a Ligand IDs: 43077: Binding Database; 21362: TOSLab; ASN 01107557 and ASN 01541696: Asinex databaseb QP log P for octanol/water (−2.0, – 6.5)c Predicted aqueous solubility, log S. S in mol dm–3 is the concentration of the solute in a saturated solution that is in equilibrium with the crystalline solid(−6.5, – 0.5)d Apparent Caco-2 permeability (nm/s) (<25 poor, >500 great)e log HERG, HERG K+channel blockage (concern below −5)f Apparent MDCK permeability (nm/s) (<25 poor, >500 great)g % Human oral absorption in GI (±20%) (<25% is poor)

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tools. Again, the model was subjected to flexible peptidedocking and further e-pharmacophore mapping was carriedout. Ligands that had a fitness score of more than 1.0 weresubjected to a rigid docking study. As per our dockinganalysis, the residues Gln1039, Lys1045, Glu1157, Gly1176,His1222, Lys1239, Ser1293, Glu1296 and Met1297 showcrucial interactions with the nonstructural protein complex tobe cleaved, and were considered an individual functional unit.Chikungunya virus replication and propagation depends onthe nsP2 protein; so a chemical compound that inhibits thisprotein by targeting the key residues specified above will bepotentially applicable therapeutically. Based on the dockingresults, we can report four chemical compounds that may bepotential inhibitors of nsP2 protease. Furthermore, thebackbone structural scaffolds of these four lead compoundscould serve as building blocks in the design of drug-likemolecules for the treatment of Chikungunya viral fever.Besides targeting the Chikungunya virus, the inhibitors mayact against other members of the Alphavirus genus due to thehigh sequence similarity among alphavirus proteins, whichthus provides a clear potential path towards the identificationof broad-spectrum drugs.

Acknowledgments The authors like to thank the Department ofBioinformatics, Alagappa University, Karaikudi, India for its supportand providing the facilities for this work.

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