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OJBTM

Online Journal of Bioinformatics ©

Volume 15 (1): 98-105, 2014.

Molecular dynamic simulations docking inhibitors of falcipain - 2

Madhu Sudhana Saddala1, D. kumar Babu1, G. Bhavani1, S. Ayisha2and A. Usha Rani1*

1DBT- Bioinformatics Center, Department of Zoology, Sri Venkateswara University, Tirupati – 517502, A.P., India.

2Department of Microbiology, Sree Vidyanikethan College, Tirupati – 517502, A.P., India.

ABSTRACT

Saddala MS, Babu DK, Bhavani G, Ayisha S, Usha Rani A., Molecular dynamic simulations docking inhibitors of falcipain– 2, Onl J Bioinform., 15 (1): 98-105, 2014.Falcipain-2 promotes intracellular development of the malaria Plasmodium Spp. parasite and degrades the protein hemoglobin. Inhibition of falcipain-2 prevents parasite maturation and therefore the falcipain-2 protein may be a target for antimalarial drugs. Falcipain-2 was energy minimized and subjected to molecular dynamic simulations using NAMD 2.9 software with CHARMM27 force field in water. The receptor structure was minimized by 25,000 steps for 500 ps and simulated 100,000 steps for 2ns. 15560 compounds were screened from PubChem database through structure based virtual screening referencing Mefloquine. The screened compounds were then docked into the active site of falcipain-2 with AutoDock Vina in PyRx Virtual Screening tool. Five compounds CID54578538, CID46233016, CID44361455, CID432301 and CID456309 showed most binding energies of-9.2, -9.1, -8.5, -8.1 and -7.1 kcal/mol respectively. The docking method found these compounds to possess suitable binding energies with falcipain-2 when compared with Mefloquine. Keywords: Falcipain-2, Virtual screening, Docking, PubChem database and PyMol.

INTRODUCTION

Malaria causes illness in hundreds of millions peoples per year (Walsh, 2012) and is due to infection byPlasmodium species P. falciparum, P. vivax, P. ovale, and P. malariae. A fifth one, P. knowlesi, has been recently documented to cause human infections in many countries of Southeast Asia. Most severe diseases and deaths from malaria are caused by P. falciparum. It is

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responsible for about 80% of all malaria cases and is also responsible for about 90% of deaths from malaria in worldwide (Stanley et al., 1991). Erythrocytic malarial parasites degrade hemoglobin as a principal source of amino acids for its growth and survival (Fidel et al., 1995). P. falciparum cysteine protease falcipain-2 hydrolyzes hemoglobin in an acidic food vacuole to provide amino acids for the erythrocytic malaria parasite and is used as a molecular target in this report. The parasite has developed resistance to several antimalarial drugs, most notably chloroquine. Computer Aided Drug Design (CADD) can be used to simulate drug-receptor interactions. Different methods of CADD are profoundly dependent on bioinformatic tools, applications and databases. In this study molecular docking of Mefloquine and its analogs falcipain-2 in order to find out suitable binding areas. ADMET properties and Lipinski’s rule of five (Lipinski, 2000; Selicket al., 2002) were used to identify suitable compounds.

MATERIALS AND METHODS

Preparation of the protein and Simulations The crystal structure of falcipain-2 (PDB: 2GHU) taken in this study was retrieved from RCSB protein data bank (http://www.rcsb.org/pdb). The existing ligands and crystallographic water were removed. It was refined by molecular dynamics in a solvated layer and equilibration methods using NAMD 2.9 (Nanoscale Molecular Dynamics) software (Kale et al., 1999) using CHARMM27 (Schlicketal., 1999) (Chemistry at Harvard Macromolecular Mechanics) force field for protein in water (Schlenkrich et al., 1996). Protein was energy minimized with 25,000 runs for 500ps and simulation with 10,00,000 steps for 2ns. Spherical periodic boundary conditions were included in this study. Finally, the structure having the least RMSD of Cα trace was generated by employing the molecular dynamics simulations which improves the quality of the target protein. The trajectory analysis was analyzed by drawing the graph between Time in Ps on X-axis and RMSD (Å) on Y-axis as shown in Figure1. The quality structure of protein (Figure 2) was used for further analysis.

Figure1: Root mean square deviation (RMSD) during the molecular simulations of

falcipain-2. Time (Ps) was taken in X-axis and RMSD was taken in Y-axis.

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Figure 2: The secondary structure of falcipain-2

Active site Identification Active site of falcipain-2 was identified using CASTp server (Computer Atlas of Surface Topology of protein) (Dundas et al, 2006). A new program, CASTp, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CASTp identification, measurements of surface accessible pockets as well as interior inaccessible cavities by locating, delineating and measuring concave surface regions on three-dimensional structure of proteins. The measurement includes the area and volume of pocket or void by solvent accessible surface model (Richards’ surface) and by molecular surface model (Connolly’s surface), calculated analytically. It can also be used to study surface features and functional regions of proteins.

Ligand Preparation The analogs of drug Mefloquinewere selected for further docking study because drug Mefloquine, which display high potency for falcipain-2 (IC50 = 0.2nm) is display in Figure3. It is a potent, reversible nonpeptidicbiaryl inhibitor for falcipain-2. Therefore Mefloquine may become an initial potent candidate of drug against falcipain-2. Our study used Mefloquine as a query compound for screening of compounds from PubChem database through structure based virtual screening. It has been emerged as a complementary approach to high throughput screening and has become an important in silico technique in the pharmaceutical industry (Lengauer et al., 2004). The structure based virtual screening begins with the identification of potential ligand binding sites on the target proteins. Usually, molecules that meet the criteria for biological activity fulfill characteristics contained in the Lipinski’s rule of five (Lipinski et al., 1997).

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Figure 3: Query compound of Mefloquine

In our work, we have selected 15560 docked ligands based on structure similarity with query Mefloquine compound from PubChem database. The AutoDock Vina in PyRx Virtual Screening Tool URL http://pyrx.scripps.edu (Wolf, 2009; Trott and Olson, 2010) was used for the screening of selected ligands from PubChem database and energy minimization.

Docking simulations Docking is a computational method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Docking has been widely used to suggest the binding modes of protein inhibiters. Most docking algorithms are able to generate a large number of possible structures, thus they also require a means to score each structure to identify those that of greatest interest. Docking was performed using AutoDockVina in PyRx Virtual Screening tool (Wolf, 2009; Trott and Olson, 2010).

PubChem screened compounds were docked into active site of refined model. Lamarkian genetic algorithm was used as number of individual population (150), max number of energy evaluation (25000000), max number of generation (27000), Gene mutation rate (0.02), crossover rate (0.8), Cauchy beta (1.0) and GA window size (10.0).The grid was set whole protein due to the multi binding pocket at X=3.42, Y=-56.23, Z=98.32 and dimension Å) at X=89.92, Y=98.56, Z=98.32 and exhaustiveness 8. The pose for a given ligands identified on the basis of highest binding energy. Only ligand flexibility was taken into account and the proteins were considered to be rigid bodies. The resulting complexes were clustered according to their root mean square deviation (rmsd) values and binding energies, which were calculated using the Autodock scoring function. Further characterization via MD simulations was conducted using complexes that were selected according to their binding energy values and the interactions made with the surrounding residues.The resulting conformations were visualized in the PyMol Viewer tool(http://www.pymol.org/).

RESULTS AND DISCUSSION

Falcipain-2 is an attractive target for malaria disease. Although several inhibitors of falcipain-2 have been clinically validated for the treatment of malaria during the past years, the search for new active compounds against falcipain-2 is still considerably challenging. Our work aimed at using molecular docking and virtual screening to filter millions of compounds to identify new anti falcipain-2 inhibitors.

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Three – dimensional structure of the target protein was taken from the PDB (Protein Data Bank) entry 2GHU. Processing of the target protein included energy minimized. The refinement of structure of protein was used for the dock. AutoDockVina was used for the docking studies. For the docking of ligands into target protein binding pockets (Sousa et al., 2006) and to estimate the binding affinities of docked ligands.The docked conformation corresponding to the lowest binding energy was selected as the most probable binding conformation. The total screened 15560 compounds were docked into the active site of falcipain-2. The best fivePubChem compounds (Figure4) showed best binding energies and significant energies with target protein of falcipain2 the values are represented in Table 1.

54578538

46233016

44361455

432301

456309

Mefloquine (Query)

Figure 4: Best binding affinity lead compounds

Table 1: Protein and ligands binding energy values

S.No. Protein – Ligand Binding Affinities (K.cal/mol)

1 2GHU _54578538 -9.2

2 2GHU _46233016 -9.1

3 2GHU _44361455 -8.5

4 2GHU _432301 -8.1

5 2GHU _456309 -7.1

6 2GHU _40692 (Mefloquine) -5.2

Which all the ligands were embedded within the active site of target protein were observed forming hydrogen bonds with same position as Mefloquine established active site of target protein. Active site residues such as Gln156 and Asp155 play a key role in the hemoglobin degradation for parasite growth and survival. The best docked compounds such as CID54578538, CID46233016, CID44361455, CID432301 and CID456309 were found to be shown highest binding energies of, -9.2, -9.1, -8.5, -8.1 and -7.1 kcal/mol respectively (Table 2).

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Table 2:Binding affinity and bond lengths, of the best five lead compounds from PubChem database with falcipain2, comparison with query drug (Mefloquine)

S.No Compounds

Hydrogen bonding interactions Residues

Binding Affinities

(K.cal/mol)

Hydrogen bond

distance (Ǻ) Ligand Protein

1 54578538 OH---- CO----

----OC ----NC

GLN156 ASP155

-9.2 2.2 3.2

2 46233016 OH---- OH----

----OC ----NC

GLN156 ASP155

-9.1 2.2 3.1

3 44361455 CO---- CO----

----NC ----NC

ASP155 GLN156

-8.5 3.3 3.3

4 432301 CO---- ----OC GLN156 -8.1 3.2

5 456309 CO---- CH----

----NC ----OC

ASP155 GLN156

-7.1 3.2 2.2

6 Mefloquine

(Query) CO---- ----NC ASP155 -5.2 3.3

Hydrogen bonds play a role in stabilizing the protein-ligand complex (Gao et al., 2005). The PubChem database compounds also exhibit several hydrogen bonding moieties. The compound CID54578538 was bound with the binding affinity -9.2 by the formation of two hydrogen bonds to Gln156 and Asp155 active site residues of protein. The compound CID46233016 was bound with the binding affinity -9.1 by the formation of two hydrogen bonds with Gln156 and Asp155 active site residues of protein.The lead compounds and their interactions with active site of residues are represented in Figure 5.

54578538

46233016

44361455

432301

456309

Mefloquine (Query)

Figure 5:Graphical view of protein and lead compound interactions

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The compound CID44361455 was bound with the binding affinity -8.5 by the formation of two hydrogen bonds with Gln156 and Asp155 active site residues of protein. The compound CID432301 was bound with the binding affinity -8.1 by the formation of one hydrogen bondwith Gln156 active site residue of protein. The compound CID44361455 was bound with the binding affinity -7.1 by the formation of two hydrogen bonds with Gln156 and Asp155 active site residues of protein.Our investigation revealed that the selected compounds have exhibited significant binding affinity within the active site of falcipain-2, when compare to query drug Mefloquine.

ACKNOWLEDGEMENTS

Author, Madhu Sudhana Saddala is especially grateful to the University Grants Commission, New Delhi for the financial assistance with the award of BSR-Meritorious fellowship. This work was carried out in DBT-Bioinformatics Infrastructure Facility (BIF), Department of Zoology, Sri Venkateswara University, Tirupati (BT/BI/25/001/2006).

REFERENCES

Christopher Lipinski, A., (2000). Drug-like properties and the causes of poor solubility and poor permeability.Journal of Pharmacological and Toxicological Methods. 44:235-249.

Dundas, J., Ouyan, Z., Tseng, J., Binkowski, A., Turpaz, Y., Liang, J., (2006). CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic acids Res. 34: W116-W118.

Fidel Salas, Jenny Fichmann, Garson, K., Lee Mark, D., Scott and Rosenthal, P. J., (1995).Functional expression of falcipain, a Plasmodium falciparum cysteine proteinase, supports its role as a malarial hemoglobinase.Infection and Immunity. 63:2120-2125.

Gao, F., Bren, N., Burghardt, T. P., Hansen, S., Henchman, R. H., Taylor, P., McCammon, J. A., Sine S. M., (2005). Agonist-mediated conformational changes in acetylcholine-binding protein revealed by simulation and intrinsic tryptophan fluorescence. J. Biol. Chem. 280: 8443–8451.

GovindarajanPadmanaban, ArunNagaraj, V., and PundiRangarajan, N., (2007).Drugs and drug targets against malaria.Current science.92:1545-1555.

Kale, L., Skeel, R., Bhandarkar, M., Brunner, R., Gursoy, A., Krawetz, N., Phillips, J., Shinozaki, A., Varadarajan, K., Schulten, K., (1999). NAMD2: greater scalability for parallel molecular dynamics. J. Comput. Phy. 15: 283–312.

Lengauer, T., Lemmen, C., Rarey, M., Zimmermann, M., (2004).Novel technologies for virtual screening.Drug Discovery Today. 9: 27-34.

Lipinski, C.A., Lombardo, F., Dominy, B.W., Feeny, P.J., (1997).Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings.Adv. Drug Delivery Rev. 23: 3–25.

Philip Rosenthal, J., (1998). Proteases of Malaria Parasites: New Targets for Chemotherapy.Emerging Infectious Diseases.4:49-57.

Schlenkrich, M., Brickmann, J., MacKerell, A.D., Jr Karplus, M., (1996). Empirical potential energy function for phospholipids: criteria for parameter optimization and applications. In: Merz KM, Roux B (Eds) Biological membranes: a molecular perspective from computation and experiment. Birkhauser, Boston, MA, pp 31–81.

105

Schlick, T., Skeel, R., Brunger, A., Kale, L., Board, J.A., Jr Hermans, J., Schulten, K., (1999). Algorithmic challenges in computational molecular biophysics. J. Comput. Phys. 151: 9–48.

Selick, H. E., Beresford, A. P., Tarbit, M. H., (2002). The emerging importance of predictive ADME simulation in drug discovery.DrugDiscovToday.7:109-16.

Sousa, S. F.,Fernandes, P. A., Ramos, M. J., (2006). Protein-ligand docking: current status and future challenges. Proteins. 65: 15–26.

Stanley, C. Oaks., (1991). Malaria: Obstacles and Opportunities.National Academy Press.Washington, D.C. pg234-239.

Trott, O., Olson, A.J., (2010). AutoDockVina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J ComputChem, 31: 455–461.

Walsh, J. A., (2012). Disease problems in the Third World. Ann. N. Y. Acad.Sci. 569:1-16. Wolf, L.K., (2009). PyRx. C&EN 87: 31.