|Non Structural Protein 2; Protease; Ifn; Chikungunya
|Bioinformatics, combination of biotechnology and computer
science plays on important role in the design of new drug compounds
. Structure based drug design is perhaps the most elegant approach
for discovering compounds exhibiting high specificity and efficacy
and is also important because of its impact on the economics of drug
discovery . Chikungunya is a viral disease that is transmitted to
human beings by mosquito bite of Aedes egypti. It can cause joint
pain, chills, nausea, vomiting, fever, and head ache . The cause of
Chikungunya is mainly due to the replication of CHIKV virus inside
the human body. If Forest day mosquito bites an infected person,
Then the virus gets consign to its body. The virus does not infect
the mosquito itself, but on the adverse, the mosquito bites a healthy
person, the enhanced virus is now transferred into the body of the
healthy person. The Chikungunya virus depends upon the process of
viral tropism for its action. Once the virus enters into a host it searches
for susceptible cells. As soon as it finds one, the virus fuses with the cell
membrane of the cell and it proceeds towards the nucleus of the cell. It
then enters the nucleus and hence captures the biosynthetic machinery
of the cell, and directs the synthesis of viral proteins as well as the
replication of the viral genome. The mode of action of Chikungunya
virus, by which it causes the disease remain to be investigated in detail
and its mechanism of action has not yet been fully characterized accept
the fact that it causes major histopathological changes in the skeletal
muscle tissue, severe inflammation and necrosis of skeletal muscle.
|The non structural proteins play an important role in CHIKV
viral replication, newly synthesized non structural proteins from the
replication that mediates the synthesis of viral RNA’s. This CHIKV
replication causes general host shut off. This will leads to severe
cytopathicity in mammalian cells. Once RNA replication has been
established, CHIKV replication is resistant to inhibition by interferon. The CHIKV actually suppress the antiviral IFN response by preventing
the IFN induced gene expression.Chikungunya virus replication
and propagation mainly depends on the protease activity of the viral
nsP2 protein, which cleaves the viral nsP1234 polyprotein replication
complex into functional unit. There is evidence that this protease
protein plays some role in the replication of virus particle in the human
beings, Therefore nsP2 protease is responsible for cleavages in the
non structural polyprotein that are important for the viral replication
cycle. Therefore this protease constitutes an attractive drug target for
the development of antiviral compounds. Our work is mainly focused
on the interaction of protease protein with the potent novel inhibitor.
These ligands were screened form PubChem database by ADMET
Properties that is aqueous solubility, Blood Brain Barrier penetration,
Hepatotoxicity, Human Intestinal absorption and Plasma Protein
Binding and Drug likeliness properties.
|Materials and Methods
|The protein molecule selected for the docking studies was
chikungunya nsP2 Protease.
|PubChem Database compound contains validated chemical
depiction information. Structures that are stored in PubChem contain calculated properties and description which helps in searching and
filtering of chemical structures. The ligand for nsP2 protease protein
was retrieved from PubChem Compound. The ligand structure,
name and molecular formula are given in Table 2. The ligands were
downloaded as XML file format from PubChem compound. The XML
files were converted into the 3D structure using Open Babel software.
|Dataset and 2d similarity search
|The chemical structure of the Protease inhibitor such as S-adenosyl
methionine was collected from published literature with their biological
activity data. In this study, 2D analogues of the nsP2 protease inhibitors
were retrieved from the PubChem database using PubChem Structure
search tool by means of the 2Dsmiles of the inhibitors. In this similarity
search, compounds with desired physicochemical property were
retrieved using the threshold values for Drug likeliness and exported
in SDF format .
|DS-smarts screening filter was used to screen the screened ligands
from the library of the compounds using the Discovery studio2.5.
Discovery studio package contains the smiles of the Reactive functional
group of the database and the compounds screened using the DS_
smarts . Usually many drugs fail in clinical trials because of unrelated
side effects and Bio-Unavailability. To overcome these problems, we
screened drug like molecules that exhibit physicochemical properties
for favorable absorption, distribution, metabolism, excretion
and toxicological parameters. Key physicochemical properties of
compounds such as Molecular weight, ClogP, PSA, Heavy atoms, HBA
(Hydrogen Bond Acceptor), HBD (Hydrogen Bond donor), Rotatable
bonds, and number of Rings were considered in this analysis.
|Discovery Studio provides methods for assessing the disposition
and potential toxicity of a ligand within an organism. The ADMET
protocols contain published models that are used to compute and
analyze Absorption, Distribution, Metabolism, Excretion, and Toxicity
(ADMET) properties . In addition, specific rules were applied to
remove ligands that are not likely drug-like, unsuitable leads, etc. based
on the presence or absence and frequency of certain chemical groups.
|The ADMET functionality test was used to estimate the aqueous
solubility of a set of ligands, estimate the Blood brain barrier penetration
(BBB), estimate Cytochrome P450 (CYP450) 2D6 inhibition, estimate
hepatotoxicity, estimate human intestinal absorption (HIA), estimate
plasma protein binding, assess a broad range of toxicity measures for
a set of ligands and remove ligands that are not likely to be drug-like
|Protein HomologY Recognition Engine developed by Lawrence
Kelley. PHYRE2 was used for protein structure prediction. It was
web-based services for protein structure prediction . PHYRE2 was
used to predict the 3-D structure of a protein using the principles and
techniques of Homology Modelling, Secondary Structure Prediction
and Domain analysis.
|Stuctural Analysis and Verification Server (SAVS) is a server for
analyzing protein structures for validity. SAVS is mainly used for validating the 3D structure of protein whether the protein is stable or
not. Ramachandran plot generated by SAVS plays an important role in
validating the given 3D structure of protein .
|3D ligand site searching
|3DLigandSite is a web server was used to predict the ligandbinding
sites based upon the structure similar to the protein submitted
as query. Ligands bound to structures similar to the query were
superimposed onto the model. This model used to predict the binding
site. 3DLigand Site was used to predict the binding sites using ligands
from homologous structure of query protein nsP2 protease .
|Discovery studio Package was used for drug design and for protein
modeling. It was used for applications such as Catalyst, Modeller, and
CHARMm. In the present work, it was used for 3D Pharmacophore
screening and for checking the ADMET properties of the ligand
|Docking was used to predict both ligand orientation and binding
affinity. In this method the preferred orientation of one molecule
with relation to a second, when bound to each other to form a stable
complex in three dimensional spaces is predicted . Docking was used
for finding the orientation of drugs in particular target. Knowledge
of the preferred orientation in turn was used to predict the strength
of association/binding affinity between two molecules using scoring
|Scoring function was used to predict the binding affinity of one
ligand to the receptor molecule. The function of Scoring was used to
assess the poses prior to real scoring of the docked complex. Another
approach of the scoring function was used to establish a conformational
relationship from the large protein databases and then the stability and
fitness of the pose was evaluated .
|Docking score was used to rank the ligands on the basis of their
relative binding affinities. Evaluating the results was done by analyzing
docked drugs based on their scores . The best docked result was
analyzed by the best score of docking that is greatest value of dock
|GOLD (Genetic Optimization for Ligand Docking) is a program
for calculating the docking models of small molecules in protein
binding site and is provided as part of the gold Suite. The GOLD Suite
provides all the functionality required to set up individual dockings or
virtual screening runs, to post-process docking results and to visualise
and manipulate structures in 3D, pre- and post-docking. Raw PDB files
can be set-up for use within GOLD. No third party software is needed
to prepare the binding site  .
|Results and Discussion
|An integrated approach to model the nsP2 Protease protein
of Chikungunya and to identify the potential nsP2 Chikungunya
protease inhibitors, structure based virtual screening has been used in this study. In order to identify novel, safe and effective inhibitor for
CHIKV Protease, in-silico screening of competitive inhibitors for nsP2
Chikungunya protease by means of screening compound library for
druglikeliness and molecular docking were carried out (Table 1).
|Modeling of CHIKV nsP2 protease protein
|The three-dimensional structure of a protein nsP2 Chikungunya
Protease was predicted by using thePHYRE2. The techniques of
Homology Modelling, Secondary Structure Prediction and Domain
analysis were used in the process. 320 residues of nsP2 Chikungunya
Protease were modelled with 100.0% confidence by the single highest
|First the nsP2 Protease protein sequence was searched against the
Protein Data Bank (PDB). The best template structure was selected
based on the sequence homology. Here 2HWK_Chain A, Crystal
structure of Venezuelan equine encephalitis2 alpha virus nsP2 Protease
domain, shows the highest similarity for the nsP2 Protease Protein of
Chikungunya. The template similarity is 41% and remaining protein
sequence was structure by principle of domain analysis. 3-Dimensional
Structure of CHIKV nsP2 Protease Protein Model is generated using
PHYRE2 (Figure 1).
|Binding site prediction of CHIKV nsP2 protease
|The modelled nsP2 Protease structure was submitted to 3-D Ligand Site Prediction Server. The server found the ligands bound to structures
similar to the query and superimposed them onto the model and to
predict the binding site. The protein structure library currently used by
3DLigandSite is based on the PDB as of 20 January 2010.
|S-adenosyl methionine (SAM) ligand was bound to the structure
similar to the nsP2 Chikungunya Protease (Table 3).
|The binding sites were predicted based on the binding of SAM to
the active. Based on the ligand SAM bound to the structure similar
to the Protein CHIKV_nsP2 Protease. The following residues are
predicted as best binding sites for the lead compound LEU67, GLU68,
ILE85, THR87, PRO88, ARG90, LEU100, LYS103, TYR104, VAL119,
ASN132, ILE134, ALA136, ASN137 AND ARG138 (Figure 2).
|Ligand screening and preparation
|Similarity search and physicochemical property filter: In first
step of Virtual screening using Accelerys Discovery Studio Client
2.5, a 2D similarity search for the available inhibitor was done by the
PubChem structure search with 85% as threshold value. In this search,
totally “11099” analogues were retrieved by using the inhibitor SAM.
|Drug likeliness screening: In the reactive functional group
screening, 197 library compounds wee filtered from 11099 compounds.
The ADMET properties of the compound were analyzed .The
compounds that were obeying the drug likeliness property were
tabulated (Table 4).
|Various steps in the ligand screening and the number of compounds
which passed these tests were given in Table 5. Finally 4 ligands having
best hits were obtained. From 4 ligands 2 ligands shows good Hydrogen
bond interaction and Best Gold Dock Score.
|Gold docking result
|Gold score for the compound LIGAND_3 was 31.6934 with
ADMET_BBB Level -0.712. The binding mode of interaction is shown
in Figure 3. Two hydrogen bonds are established during docking of LIGAND_3 with nsP2, Carbonyl Nitrogen atom in the pyrimidine
ring of LIGAND_3 forms a hydrogen bond to the carbonyl Nitrogen
(N-H…N) of Lys103 and the distance is 2.89Å and Carbonyl Nitrogen
atom in the pyrimidine ring of LIGAND_3 forms a hydrogen bond
to the Carbonyl Oxygen (N-H…O) of same Residue Lys 103 and the
distance is 2.87Å respectively. C10, C15, C18, C20, C23 are involved
in hydrophobic contacts. All other residues such as Leu100, Ala136,
Asn137 and Arg138 were engaged in non-Ligand hydrophobic
interactions. The hydrogen bond interactions are shown using LigPlot
|Gold score for the compound LIGAND_4 was 47.6163 with
ADMET_BBB Level -0.481. The binding mode of the compound to
active site is shown in Figure 4. One hydrogen bond is established
during docking of LIGAND_4 with nsp2. Carbonyl Nitrogen atom
in the pyrimidine ring of LIGAND_4 forms a hydrogen bond to the
carbonyl Oxygen (N-H…O) of Lys103 the bond distance is 3.00Å. C15,
C18, C19, C22, C23, C25, C27, C28, C29, C30, C31, C32 were involved in hydrophobic contacts. All other residues such as Leu100, Tyr104,
Val119, Asn132, Ile134, Ala136 and Asn137 were engaged in non-
Ligand hydrophobic interactions. The hydrogen bond interactions is
shown using LigPlot (Figure 4).
|All the ligand interaction with receptor was available in the output
file. The best confirmation results were found out using Gold Score,
number of Hydrogen bonds formed and Blood Brain Barrier level
(Table 6). Among these tow compound LIGAND_4 [N-butyl-9-[3,
4-dipropoxy-5-(propoxymethyl) oxolan-2-yl] purin-6-amine] was
found to bind with the target more efficiently than other compound
with best gold score(47.6163) and hydrogen bonding interactions with
the peripheral site key residue and Blood Brain Barrier level (-0.481).
Hence, LIGAND_4 has been emerged as a promising Anti-viral drug
candidate with potential symptomatic and disease-modifying effects.
|CHIKV nsP2 protease was responsible for diseases, therefore,
identify the drug which inhibits the expression of nsP2 protein
prevent the inhibition of interferon during the infection of virus .
The nsP2 protease was responsible for cleavage in the nonstructural
polyprotein that were crucial for the viral replication cycle. Therefore
this nsP2 protease constitutes an attractive target for the development
of antiviral compounds . The inhibitor blocks the protease when
new viral particle break off from on infected cells. Now days there is no
proper inhibitor for inhibit the Chikungunya protease activity.
|The study of enzymatic activity of the purified protease nsP2 has
ability to cleave peptidyl-7-amino-4-methylcoumarine; this kind of
substrate was used to study the activity of a wide range of proteases .
Still there was no PDB_ID for the structure of CHIKV nsP2 Protease.
Therefore first we retrieve the sequence of nsP2 CHIKV(37997)
Protease from UNIPROT_KB and then the protein was Modelled by the query sequence of Venezuelan equine encephalitis virus(VEEV)
nsP2 structure . The C-terminal region of alphavirus nsP2 protease
could play a similar role to the CHIKV nsp2 protease. The PDB_ID for
VEEV virus nsP2 Protease was 2HWK, it shows 40% template similarity
for CHIKV nsP2 protease. The Chikungunya protease activity was
slightly inhibited by NaCl . But inhibition of CHIKV protease
activity by NaCl is not that much effective. Chloroquine , Quinine
, Carbomide, Saquinavir, Indanavir, Ritonavir were the Antiviral
drug for CHIKV replication. But these replication compounds were
cause some side effects like Dizziness, Nausea, High Blood Sugar, Signs
of Allergy, etc. Therefore in our study main was mainly focused to
identified the novel inhibitor free from side effect and free from allergy.
|Docking work was performed with novel potent inhibitor or lead
compound retrieved from PUBCHEM database through screening
technique, which fulfill the Drug likeliness and ADMETox property.
The Lead compounds were Screened form PUBCHEM Database. Based
on PUBCHEM structure similarity search 85% of similar structure of
SAM was retrieved. Total number of compound Screened in structure
similarity screening was 11099 and the following screened compounds
were screened with ADMETox Properties. After this screening 197
Lead compounds were obtained. Then 197 Lead Compound were
screened based on Drug Likeliness Property. 4 Best Lead Compound
were obtained at last. These 4 ligands were free form side effects
because the obtained ligands were screened by ADMETox properties
and Physicohemical properties for drug likeliness. Docking work was
performed with 4 Lead Compound with the target protein CHIKV
nsP2 Protease Protein using GOLD SUITE. Among the 4 compounds,
LIGAND_4 has best score (47.6163) compared with other compounds.
Hydrogen bonding exists with the residue LYS103 Key residue.
|This study describes about the nsP2 Protease that have demonstrated
to be a promising drug target for the treatment of Chikungunya. The
interaction between Chikungunya nsP2 protease protein against the
ligands was studied by using various computational methods. Based on
the hydrogen bonds, docking score and Blood Brain Barrier level value
the docking result was analyzed. The results were compared within
themselves to find out the best ligands which can inhibit the property of
the viral protein. Based on this observations LIGAND_4 [N-butyl-9-[3,
4-dipropoxy-5-(propoxymethyl) oxolan-2-yl] purin-6-amine] is found
to bind with the target more efficiently than LIGAND_3 compound
with best gold score and hydrogen bonding interactions with the
peripheral site key residue. Hence, LIGAND_4 has been emerged as
a promising Anti-viral drug candidate with potential symptomatic
and disease-modifying effects. Further, QSAR studies can be done to
identify conformational changes.
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