Journal of Applied Bioinformatics & Computational BiologyISSN: 2329-9533

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Research Article, J Appl Bioinform Comput Biol Vol: 3 Issue: 2

Virtual Screening and Pharmacophore Analysis of Potent Antiviral Inhibitors for Viral Proteins from Ebola virus

Sitansu Kumar Verma*, Soni Yadav and Ankit Singh
Department of Biotechnology, Madhav Institute of Technology and Science,Gwalior, M.P., India
Corresponding author : Sitansu kumar verma
Department of Biotechnology, Madhav Institute of Technology and Science, Gwalior, MP, India- 474005
Tel: +91-9074197804
E-mail:[email protected]
Received: July 11, 2014 Accepted: October 15, 2014 Published: October 20, 2014
Citation: Verma SK, Yadav S, Singh J (2014) Virtual Screening and PharmacophoreAnalysis of Potent Antiviral Inhibitors for Viral Proteins from Ebola virus. J Appl Bioinform Comput Biol 3:2. doi:10.4172/2329-9533.1000111

Abstract

Virtual Screening and Pharmacophore Analysis of Potent Antiviral Inhibitors for Viral Proteins from Ebola virus

Ebola hemorrhagic fever (Ebola HF) is a severe, often-fatal and one of the most virulent disease in primates. In our studies we had aimed to developed novel and potent inhibitor by targeting the VP40 and VP35 protein of the Ebola virus. These proteins are very helpful in virus replication so these are the main drug targets to prevent viral infection Protein sequence of VP40 and VP35 protein from Ebola virus ware retrieved from NCBI with Accession number NP_066245 and NP_066244 respectively and it 3D model ware predicated by using comparative homology modeling program MODELLER 9.10. The templates of VP40 and VP35 for homology modeling were downloaded from protein databank. Models evolution and validation was done by using PROCHECK with Ramachandran plot analysis. In addition we investigated that the antiviral compound bound at the cavity of model. In these studies we investigated interaction behavioral studies of antiviral compound with the modeled proteins after the analysis we select four potent antiviral compound (Famciclovir, Entecvir, Ganciclovir, Oseltamivir) for VP40 and two compound (Navirapine, Valganciclovir) for the VP35.

Keywords: Ebola virus; Homology modeling; Modeller 9.10; Autodock 3.0.5; Pharmacophore

Keywords

Ebola virus; Homology modeling; Modeller 9.10; Autodock 3.0.5; Pharmacophore

Introduction

Ebola virus is a virus named, after a river in the democratic republic of the Congo (formerly Zaire) in Africa, where it was first discovered (CDC,2003a). Total twelve out breaks of Ebola virus have been reported in Sudan, Cango, Uganda, Gabon as of September 14, 2003 (CDC2003 b,WHO, 2003 a). Ebola hemorrhagic fever (Ebola HF) is an important emerging infection in central Africa and has received much attention in present year owing to the documented high case-fatality rates (50%-90%) associated with past outbreaks [1].
Ebola virus one of the member of a family of RNA viruses called the Filoviridae and of the other mononegavirales [2]. It is spared through direct contact with body fluids of a person who is very ill with Ebola virus hemorrhagic fever. Ebola virus mainly cause Hemorrhagic fever which takes place into phase first in incubation periods and second one is late phase fever, Arthritis, fatigue, nausea like symptoms are show by the incubation periods for one week and late symptoms include depression, eye inflammation and hemorrhagic rash over the entire body [3,4]. The main targets of Ebola virus infection are hepatocytes, mononuclear phagocytes infection are hepatocytes mononuclear phagocytes and endothelial cell by synthesizing Ebola virus glycoprotein during infection at the time of replication budding occurs which is main cause of damages and the presence of viral practical effects release of cytokines these cytokines work as signaling molecule for fever and infection [5,6].
Like all filoviruses, Ebola virions are 80 nm in diameter, but their length varies greatly up to 14,000 nm. It consists of an envelope, a nucleocapsid, a polymerase complex, and a matrix protein. The virus capsid is enveloped, the surface proteins are embedded in a lipid bilayer with surface glycoproteins and the Capsid/nucleocapsid is elongated with helical symmetry. The genome is nonsegmented and contains a single molecule of linear negative-sense, single-stranded RNA [7]. The genome is nearly ~19,000 nucleotides long and bears linearly arranged genes that encode seven structural proteins and one non structural protein. Just as with other viruses there are lipids present and located in the envelope [8].These filamentous particles may appear in the shape of a shepherd crook or in the shape of a “U” or a “6” and they may be coiled, toroid or branched [9]. The 3’ terminus is not polyadenylated and the 5’ end is not capped. It codes for 7 structural proteins and one non structural protein. The gene order is 3’-leader -NP-VP35-VP40- GP-VP30-VP24-L-trailer-5’ [10]. Ebola virus consist VP35and VP40 protein which are main component of viral protein. TheVP35 protein is 340 residues long and VP40 consisting 326 residues. These two proteins these two viral proteins are very helpful in viral replication process. As such there is no drug available for the treatment of Ebola hemorrhagic fever. By the targeting these two protein we developed a potent drug against Ebola virus infection.
In this work we studied the interaction behavior of antiviral drug with the biological targets (VP35, VP40). After these studies we finalize two potent inhibitor for VP35 and four inhibitor for VP40 for further studies. Structure based drug designing is an in silico technique which is very helpful in biological target identification, modeling of target protein virtual screening of potent drug compound and molecular docking studies to find out the best lead like compound.

Materials and Methods

Data retrieval
The amino acid sequence of VP35 and VP40 of Ebola virus were retrieved from NCBI (www.ncbi.nlm.nih.gov/) with accession no. respectively. For the homology modeling structure templates of VP35 and VP40 were downloaded from protein data bank (www.resb. org/pdb) with PDBID 3ks-4 A and 3es6-A respectively. Dynamic programming based aligh2d module in Modeller 9.10 is used for the sequence alignment of targets with corresponding temples [11].
Molecular modeling of VP35 and VP40 protein
Homology model of VP35 and VP40 protein were modeled by using Modeller 9.10 program. MODELLER is a program for comparative modeling and to calculate sequence and structure alignment. After alignment of VP35 and VP40 with template 3ks4 A and 3es6 A respectively we generate five models for the target. Modeller objective function, statistical function and dope score which use the standard modeller energy function for assessment of model help in validation of the model of VP35.
Model evaluation and validation
The predicted 3D model was finalized by Ramachandran plot providing the position of residue in particular segment based on psi and phi angles between Na-Ca and Ca-C. The models are validated by using the PROCHECK programs to check the steriochemical property of modeled structure [12]. The PROCHECK also calculated the parameters like bond length, bond angle main chain and side chain properties, residue by residue property. It also calculates the RMS distance from planarity and destroyed geometry plots. PROCHECK assess whether the geometry of the residue in given protein structure is normal or unusual, as compared with steriochemical parameters derived from well define high resolution structures [13].
Energy minimization of models
The model stricter of 40 and VP35 was further optimizing by energy minimization by Gromos96, which implemented in Swiss PDB viewer software. This tool is form the molecular dynamic of the entire bonded and non banded atom with the model structure [14].
Active site analysis
The active sites were revealed by Q site finder. It is an energy based method for the prediction of protein ligand binding site it uses the interaction energy between the protein and simple van der waals probe to locate energetically favorable binding site. Energetically favorable probe site were cluster according to their spatial proximity and cluster were ranked according to the sum of interaction energy [15].
Docking studies and Pharmacophore modeling
Molecular docking is a key tool in structural molecular biology and computer added drug designing these docking studies are performed to product the predominant binding models of legend with a protein of know 3D structured. Molecular docking between anti viral drug and VP35 and VP40 protein model structures were done using Autodock 3.2 [16,17]. This program used a simulated annealing approach for explore the conformational space between the ligand and target protein. The energy evaluation process is done by using grid-based molecular affinity potential, the docking was performed on the basis of Lamarckian Genetic Algorithm. The energy grids were built within a box of dimension 56, 58, 58 Å for VP35 and 72, 68, 78 Å for VP40 with a spacing of 0.375 Å.

Result and Discussion

Molecular modeling of VP40 protein
The sequence alignment of the query VP40 sequence (NP_066245) of Ebola virus and templet 3es6-A was shown in Figure 1. MODELLER 9.10 was used to generate 3D model of query Ebolavirus protein at resolution 3.23 based on template protein 3es6-A with identity of 93.2%. Reliability of new model for VP40 was identified by Ramachandranplot. PROCHECK analysis of the model structure from MODELLER 9.10 through Ramachandranplot showed that 89.9% of the total residues in most favored region and 8.3% in additional in generously allowed region was 0.9%. Only LYS 231, ASP 259, GLN 239, GLN 266 residues were located in the disallowed region which is having 0.9% of the total protein. The model of VP40 was submitted in protein model database with PMID-ID: PM0078671 (25). GROMOS96 program is used for energy minimization of the VP40 model structure of Ebola virus.
Molecular modeling of VP35 protein
The sequence alignment of the query VP35 sequence (NP_066244) of Ebola ‘Virus and template 3ks4-A was shown in Figure 2. MODELLER 9.10 was used to generate 3D model of query Ebola virus protein at resolution 2.40 A based on template protein 3ks4-A with identity of 88.3%. Reliability of new model for VP40 was identified by Ramachandran plot. PROCHECK analysis of the model structure from MODELLER 9.10 through Ramachandranplot showed that 99.0% of the total residues in most favored region and 0.0% in additional in generously allowed region was 1.0%. Only SER 97 residues were located in the disallowed region which is having 0.0% of the total protein. The model of VP35 was submitted in protein model database with PMID-ID: PM0078672 [25]. GROMOS96 program is used for energy minimization of the VP35 model structure.
Active site analysis
Q-site finder detected the fallowing putative functional site residue for the targeted VP40 protein HIS102, CYS268, SER156, HIS272, THR62, ASP101, ASP150, ASP59, ASP151, ASN157 and ASP269 for VP40 protein was shown in Figure 3A. In this VP40 protein the binding site cavity volume was 492 cubic angstroms and it also showed that the coordinates the binding sit box around predicted site had minimum coordinates 2, 31, 4 and maximum coordinates 24, 50, and 27.
Functional site of VP35 consisted of following amino acid residues- ALA1, LYS2, ARG5, VAL23, GLN24, VAL25, CYS27, LYS28, LYS31 was shown in Figure 3B. The Q-site finder shows that binding site cavity volume of model VP35 protein was 124 cubic angstroms and it also showed that the coordinates the binding sit box around predicted site had minimum coordinates -9, 36, 79 and maximum coordinates -22, 20, 65.

Results

Docking studies was carried out using Autodock software to predict the interaction behavior of ligand with protein and residue involve in this interaction. The entire fifty antiviral compounds were docked with the 3D modelled structure of VP40 and VP35 of Ebolavirus mention in Table 1. After the docking analysis of VP40 with the antiviral compound, four compounds Famciclovir (-15.37), Entecvir (-15.28), Ganciclovir (-15.23), Oseltamivir (-15.01) are screened out for the best potent inhibiter shown in Figure 4a. After the docking result of the 50 antiviral Compound with VP35 we select two antiviral compounds Navirapine (-10.34), valganciclovir (-10.17) as potent inhibitor shown in Figure 4b.
Table 1: Virtual screening results of potent antiviral compounds and viral proteins from Ebola virus.
pharmacophore modeling
After the molecular docking analysis we choose six antiviral compounds (four for VP40 and two for VP 35) on the basis of highest scoring function for pharmacophore modeling. This pharmacophore mapping will furnish a new insight to design novel molecule that can enhance or inhibit the function of the target and useful to prevent infection. Ligand Scout was used to develop different type of chemical features of these six compounds. The pharmacophore model generated by ligand scout for the training set shown three main features Hydrogen bond acceptor, Hydrogen bond donor, and aromatic ring. The representation of pharmacophore of each compound is shown in Figure 5. The feature identified in red colour is Hydrogen bond acceptor, green colour is Hydrogen bond donor and aromatic ring are shown in blue colour.

Conclusion

This study demonstrates that VP40 and VP30 proteins of Ebola virus possess a flexible ligand binding site pocket which may bind ligands of different structural properties. These proteins exhibit an impressive affinity for antiviral compounds. Hence, these antiviral compounds Famciclovir, Entecvir, Ganciclovir and Oseltamivir (for VP40) and Navirapine and Valganciclovir (for the VP35) can be brought under study which may produce better results. Three shared feature pharmacophore (hydrogen bond Donor, hydrogen bond Acceptor & aromatic ring) are generated using Ligand Scout to discover the essential features of this antiviral compound, which are invaluable to examine the potential lead of VP40 and VP35. Our analysis will certainly guide the biologists to design putative drugs againstVP40 and VP35 protein in wet lab to prevent Ebola virus infection.

Acknowledgments

The authors are grateful to Director, Madhav Institute of Technology and Science, Gwalior, M.P., India for providing necessary facilities and encouragement. The authors are also thankful to all faculty members of the Institute of Department of Biotechnology, Madhav Institute of Technology and Science, Gwalior, M.P., India for their generous support during the course of experimental work and manuscript preparation.

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