In-Silico screening of Pleconaril and its novel substituted derivatives with Neuraminidase of H1N1 Influenza strain
© Basha et al; licensee BioMed Central Ltd. 2012
Received: 6 September 2011
Accepted: 17 February 2012
Published: 17 February 2012
Neuraminidase (NA) is a prominent surface antigen of Influenza viruses, which helps in release of viruses from the host cells after replication. Anti influenza drugs such as Oseltamivir target a highly conserved active site of NA, which comprises of 8 functional residues (R118, D151, R152, R224, E276, R292, R371 and Y406) to restrict viral release from host cells, thus inhibiting its ability to cleave sialic acid residues on the cell membrane. Reports on the emergence of Oseltamivir resistant strains of H1N1 Influenza virus necessitated a search for alternative drug candidates. Pleconaril is a novel antiviral drug being developed by Schering-Plough to treat Picornaviridae infections, and is in its late clinical trials stage. Since, Pleconaril was designed to bind the highly conserved hydrophobic binding site on VP1 protein of Picorna viruses, the ability of Pleconaril and its novel substituted derivatives to bind highly conserved hydrophobic active site of H1N1 Neuraminidase, targeting which oseltamivir has been designed was investigated.
310 novel substituted variants of Pleconaril were designed using Chemsketch software and docked into the highly conserved active site of NA using arguslab software. 198 out of 310 Pleconaril variants analyzed for docking with NA active site were proven effective, based on their free binding energy.
Pleconaril variants with F, Cl, Br, CH3, OH and aromatic ring substitutions were shown to be effective alternatives to Oseltamivir as anti influenza drugs.
KeywordsPleconaril Oseltamivir H1N1 Neuraminidase Docking analysis
Molecular modeling tools help in generating new candidate drug molecules within a short span of time. After generation of new possible drug candidates, the drug and target protein interaction dynamics can be predicted by carrying out a docking analysis. The knowledge so derived is used to predict the strength of association or binding affinity between the two molecules based on scoring functions.
A database of potential drug molecules and target protein structure serve as inputs for docking analysis. The success of the docking analysis is determined by scoring function and search algorithm , which helps in determining the compatibility between the drug and its target protein. This technique is being used extensively to predict the geometries of different bimolecular complexes . Scoring function predicts the strength of the binding affinity between ligand and the protein based on the complex geometry  and search algorithm analyzes the drug molecule for different binding positions with its target molecule, each binding position, which is termed a "pose", is used to generate the snapshot of interactions .
In our present study, a database of 310 novel substituted Pleconaril variants was built by altering the side chains and substituting different aromatic rings into the original Pleconaril molecule. Molecular docking analysis was performed to visualize the interaction of each of these variants with target molecule. An attempt was made to identify Pleconaril variants with best NA binding ability.
1. Preparation of Receptor
The crystal structure of NA of 1918 Spanish flu (A/Brevig Mission/1/18 H1N1) virus (PDB ID: 3BEQ) was obtained from Protein Data Bank (PDB)  with a resolution factor of 1.64 Å and the method incorporated is X-Ray diffraction . Before docking, the crystal structure of the protein was cleaned by removing the water molecules and hydrogen atoms were added to this target protein for correct tautomeric and ionization states of amino acid residues. The modified structure so obtained was saved in .pdb format and used for all docking studies.
2. Preparation of Ligands
Using Arguslab 4.0.1  software, hydrogen bonds were added to each molecule and fidelity of all bonds was checked using "add hydrogens" and "Clean Hybridization" options respectively. Geometry optimization was done using UFF [20–24] Molecular Mechanics (MM) method. Finally Oseltamivir, Pleconaril and all the novel substituted Pleconaril derivatives were saved in .mol format for further docking studies.
3. Determination of Active site
The highly conserved active site of NA, which comprises of 8 functional residues (R118, D151, R152, R224, E276, R292, R371 and Y406) targeting which oseltamivir has been designed was considered as active site for docking analysis.
Docking between receptor and ligands was performed using "Dock a Ligand" option of arguslab 4.0.1 software. A spacing of 0.4 Å between the grid points was used. "ArgusDock" was selected as docking engine. "Regular precision" was selected in docking precision menu, "Dock" was chosen as calculation type, "Flexible" for the ligand and "AScore" was used as the scoring function. A maximum of 150 poses were allowed to be analyzed, binding site box was set to 25 × 25 × 25 angstroms to encompass the entire active site. Each docking run was repeated three times to get best results. Resulted docked molecules were saved in .pdb format and all the docking images were generated using Accelrys® Discovery Studio 3.0 Visualizer software .
Results and discussion
All the 310 variants of Pleconaril molecule were analyzed for binding with the active site of NA. 198 out of these were found to have optimum binding efficiency, based on the binding energy calculations in comparison with Oseltamivir.
Structure, molecular formula, binding energies of Oseltamivir, Pleconaril and best variant of pleconaril
Structure of molecule
Binding energy in K.cal./mol.
Structure, molecular formula, binding energies of 10 best variants of pleconaril
Structure of molecule
Binding energy in K.cal./mol.
Computer aided drug designing and molecular docking analysis are highly effective in creating and analyzing new candidate drug molecules. 198 out of 310 Pleconaril variants analyzed for docking with NA active site were proven effective. Pleconaril variants with F, Br, CH3, Cl, OH and aromatic ring substitutes showed higher levels of NA binding ability. Several interactions such as hydrogen bonds, hydrophobic, hydrophilic interactions, electrostatics and Vanderwaal forces are thought to have played an important role in stabilizing the drug and target complexes based on the theoretical modeling. Thus, based on the above results we propose Pleconaril variants numbered 3, 4 and 11 (Figure 4) with F, Br, CH3, Cl and OH substitutions at R, R1, R2, R3, R4, R5, and R6 positions have a definite potential to be developed as lead compounds for H1N1 Influenza virus. However as it is only a preliminary In-silico investigation in modifying the Pleconaril molecule for anti-influenza activity, further In-vivo validation and conformation of the present findings is required.
The authors wish to thank Dr. N. Ramesh Principal, REVA ISM for extending his help for using the computational facilities at the Dept. of Biotechnology, REVA ISM. T.P. Charanraj, Lecturer, Dept. of Biochemistry, REVA ISM for extending his help in designing the novel substituted Pleconaril derivatives and Prof. H. Junjappa, Professor of Synthetic organic chemistry, REVA ISM for his valuable insights in analyzing and interpretation of the data.
- Noble D, Colatsky TJ: A return to rational drug discovery: computer-based models of cells, organs and systems in drug target identification. Emerging therapeutic targets. 2000, 4: 39-49. 10.1517/14728188.8.131.52.View ArticleGoogle Scholar
- Ekins S, Mestres J, Testa B: In-silico pharmacology for drug discovery: Applications to targets and beyond. Br J Pharmacol. 2007, 152: 21-37. 10.1038/sj.bjp.0707306.PubMedPubMed CentralView ArticleGoogle Scholar
- John Cartar B, Venetia Saunders A: Virology-principles and applications. 2007, India: John Wiley and sons limited publications, 317-322. FirstGoogle Scholar
- Von Itzstein M, Wu WY, Kok GB, Pegg MS, Dyason JC, et al: Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature. 1993, 363: 418-423. 10.1038/363418a0.PubMedView ArticleGoogle Scholar
- Aoki FY, Boivin G, Roberts NA: Influenza virus susceptibility and resistance to Oseltamivir. Anti viral Therapy. 2007, 12: 603-616.Google Scholar
- Magden J, Kääriäinen L, Ahola T: Inhibitors of virus replication:recent developments and prospects. Appl Microbiol Biotechnol. 2005, 66: 612-621. 10.1007/s00253-004-1783-3.PubMedView ArticleGoogle Scholar
- Effects of Pleconaril Nasal Spray on Common Cold Symptoms and Asthma Exacerbations Following Rhinovirus Exposure (Study P04295AM2). 2007, ClinicalTrials.gov. U.S. National Institutes of Health, Retrieved 2011-12-26
- Florea N, Maglio D, Nicolau D: Pleconaril, a novel antipicornaviral agent. Pharmacotherapy. 2003, 23 (3): 339-48. 10.1592/phco.23.3.339.32099.PubMedView ArticleGoogle Scholar
- Kitchen DB, Decornez H, Furr JR, Bajorath J: Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Review Drug Discovery. 2004, 3: 935-949. 10.1038/nrd1549.View ArticleGoogle Scholar
- Irawin Kuntz D, Elaine Meng C, Shoichet Brain K: Structure-Based Molecular Design. Accounts of Chemical research. 1994, 27 (5): 117-123. 10.1021/ar00041a001.View ArticleGoogle Scholar
- Jain AN: Scoring functions for protein-ligand docking. Curr Protein Pept Sci. 2006, 7 (5): 407-20. 10.2174/138920306778559395.PubMedView ArticleGoogle Scholar
- Shoichet BK, Kuntz ID, Bodian DL: Molecular docking using shape descriptors. J Comput Chem. 2004, 13 (3): 380-397.View ArticleGoogle Scholar
- Bernstein FC, Koetzle TF, Williams GJ, Meyer EE, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M: The Protein Data Bank: A Computer-based Archival File For Macromolecular Structures. J Mol Biol. 1977, 112: 535-10.1016/S0022-2836(77)80200-3.PubMedView ArticleGoogle Scholar
- Xiaojin Xu, Xueyong Zhu, Raymond Dwek A, James Stevens, Ian Wilson A: Structural Characterization of the 1918 Influenza Virus H1N1 Neuraminidase. J Virol. 2008, 82 (21): 10493-10501. 10.1128/JVI.00959-08.View ArticleGoogle Scholar
- National Center for Biotechnology Information: PubChem Compound Database; CID = 65028, (accessed Dec. 26, 2011), [http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=65028&loc=ec_rcs]
- National Center for Biotechnology Information: PubChem Compound Database; CID = 1684, (accessed Dec. 26, 2011), [http://pubchem.ncbi.nlm.nih.gov/summary/summary.cgi?cid=1684&loc=ec_rcs]
- Bolton E, Wang Y, Thiessen PA, Bryant SH: PubChem: Integrated Platform of Small Molecules and Biological Activities. Chapter 12 IN Annual Reports in Computational Chemistry. 2008, American Chemical Society, Washington, DC, 4:Google Scholar
- 2007, ACD/ChemSketch Freeware, version 11.01, Advanced Chemistry Development, Inc., Toronto, ON, Canada, www.acdlabs.com
- Mark A: Thompson, ArgusLab 4.0.1, Planaria Software LLC, Seattle, WA, [http://www.arguslab.com]
- Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM: UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc. 1992, 114: 10024-10035. 10.1021/ja00051a040.View ArticleGoogle Scholar
- Rappe AK, Colwell KS, Casewit CJ: Application of a Universal force field to metal complexes. Inorg Chem. 1993, 32: 3438-3450. 10.1021/ic00068a012.View ArticleGoogle Scholar
- Rappe AK, Goddard WA: Charge Equilibration for molecular dyanamics simulations. J Phys Chem. 1991, 95: 3358-3363. 10.1021/j100161a070.View ArticleGoogle Scholar
- Casewit CJ, Colwell KS, Rappe' AK: Application of a universal force field to organic molecules. J Am Chem Soc. 1992, 114: 10035-10046. 10.1021/ja00051a041.View ArticleGoogle Scholar
- Casewit CJ, Colwell KS, Rappe' AK: Application of a universal force field to main group compounds. J Am Chem Soc. 1992, 114: 10046-10053. 10.1021/ja00051a042.View ArticleGoogle Scholar
- Accelrys Software Inc., 2011, Discovery studio Visualizer 3.0,, [http://accelrys.com/products/discovery-studio/visualization-download.php]