In silico inhibitors for falcipain-3 in Plasmodium falciparum

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Volume 14 (3): 293-301, 2013

In silico inhibitors for falcipain-3 in Plasmodium falciparum Madhu Sudhana Saddala, P. Gayathri, J. Obaiah, C. Vallamma and A. Usha Rani* Dept. of Zoology, DBT-Bioinformatics Center, Sri Venkateswara University, Tirupati, A.P., India.

ABSTRACT Saddala MS, Gayathri P, Obaiah J, Vallamma C, Usha Rani A., In silico inhibitors for falcipain-3 in Plasmodium falciparum, Onl J Bioinform., 14 (3): 293-301, 2013.Inhibition of falcipain-3 prevents maturation of Plasmodium falciparum, suggesting that the protein could be a target for antimalarial activity. Falcipain-3was energy minimized and subjected to molecular dynamics simulations using NAMD 2.9 software with CHARMM27 force field in water and the receptor structure was minimized with 25,000 steps for 500ps and simulated 10,000 steps for 2ns. 2500 compounds were screened from PubChem database through structure based Virtual screening by referencing Mefloquine. The screened compounds were docked into the active site of the protein using Autodock Vina in PyRx Virtual Screening tool. Results showed that CID3000506, CID40468067, CID65330, CID40692 and CID4046had a highest binding energyof-9.4, -8.9, -8.4, -7.9 and -7.2 kcal/mol, respectively. Lead hit compounds were tested for toxicity and bioavailability with Osiris and Molinspiration online servers. Active site amino acidsHis18, Asp44, Tyr63, Gln110, Tyr115, Asp168, His196, Glu199, Gly453 and Met434could play a role in binding and catalytic activity. KEYWORDS: Falcipain-3, simulations, docking, PubChem database, Autodock Vina INTRODUCTION Malaria is one of the most important infectious diseases in the world wide and affects about 40% of the world’s population (Breman, 2001).It is caused by different species of Plasmodium. Four Plasmodium species have been well known to cause human malaria, namely, 293

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 responsible for about 80% of all malaria cases and is also responsible for about 90% of deaths from malaria (Stanley et al., 1991). In P. falciparum, three papain-like cysteine proteases have been identified, characterized, and isolated thus far. Falcipain-1 was identified in erythrocytic parasites and found to hydrolyze hemoglobin (Salas et al., 1995). However, its low abundance and difficulties in developing expression systems have limited its study. Recently, two closely related cysteine proteases were identified and expressed. Falcipain-2 was shown to be one of the principal trophozoite cysteine proteases and hemoglobinases (Shenai et al., 2000), and more recently, falcipain-3 was identified in the acidic food vacuole of the parasite (Sijwali et al., 2001). Both proteases require a reducing environment and acidic pH for optimal activity. They differ, however, in that falcipain- 3 undergoes efficient transformation into an active enzyme only at acidic pH. It is more active and stable at this pH with greater activity against native hemoglobin. Thus, falcipain-3 is the second P. falciparum hemoglobinase well suited for the hydrolysis of native hemoglobin in the food vacuole. It has been estimated that the concentration of falcipain-2 in trophozoites is 1.8 times that of falcipain-3; however, the latter appears to cleave native hemoglobin about twice as rapidly as the former. Thus, the relative contribution of the two enzymes to the hydrolysis of native hemoglobin is essentially equivalent, making falcipain-2 and falcipain-3 equally important targets for inhibition of hemoglobin degradation (Sijwali et al., 2001). A variety of antimalarial medications are available. In the last 5years, treatment of P. falciparum infections in endemic countries has been transformed by the use of combinations of drugs containing an artemisinin derivative (Govindarajan et al., 2007). Severe malaria is treated with intravenous or intramuscular quinine or, increasingly, the artemisinin derivative artesunate which is superior to quinine for both children and adults (Philip et al., 1998). The parasite has developed resistance to several antimalarial drugs, most notably chloroquine. Computer Aided Drug Design (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions. Different methods CADD are profoundly dependent on bioinformatics tools and applications. Therefore in this study we have performed molecular docking studies of Mefloquine and its analogs with Falcipain-3 in order to find out the best one. The main emphasis of this work was to identify the most appropriate drug molecule against the malaria parasite. There are several parameters such as ADMET properties and Lipinski’s rule of five (Christopher et al., 2000; Selick et al., 2002) that helps in identifying the best lead compounds. In this study, we described inhibitors ofFalcipain-3using molecular docking, molecular dynamics simulation and structure based Virtual screening.

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MATERIALS AND METHODS Protein preparation and MD simulations The X-ray crystallographic structure of the Plasmodium falciparam Falcipain-3 was retrieved from the Protein data bank (http://www.rcsb.org/pdb). The Accession no. of the Falcipain-3PDB ID is 3BPM. 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 (Schlick et al., 1999) (Chemistry at Harvard Macromolecular Mechanics) force field for protein in water (Schlenkrich et al., 1996). Protein was energy minimized with 2500 runs for 500ps and simulation with 1000000 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 Yaxis as shown in Figure1. The quality structure of Falcipain-3 was used for further analysis.

Figure1: Root mean square deviation (RMSD) during the molecular simulations of Falcipain-3. Time (Ps) was taken in X-axis and RMSD was taken in Y-axis. Simulation parameters The MD simulations system was equilibrated at 250 k for 10 ps with Falcipain-3 atoms fixed, followed by 20 ps MD without restraints. The system was subsequently simulated for 100 ps at300 k with the following parameters. The classical equations of motion were integrated by a leapfrog integrator using a time step at 1 fs. The impulse based ver let-I/r-RESPA method was used perform multiple time stepping: 4 fs long-range electrostatic: 2fs for short range nonbonded forces, and 1 fs for bonded force. The swift function was used to cutoff the LennardJones potential, with the first cut off at 10 Å and the second cutoff at 12 Å. Short range interactions were calculated at intervals of 4 fs. All bonds involving hydrogen atoms were constrained to their equilibrium bond parameters using the SHAKE along them. Langevin 295

dynamics were employed to maintain the pressure at 1 atm, with a Langevin pisten period of 100 fs and oscillation decay time of 50fs.Trajectories were recorded every 200 fs. Subsequently the dynamics behavior and structural changes of the receptor was analyzed by the calculation of energy and the root mean square deviation (RMSD). Active site Identification Active site of 3BPM 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. CAST identification and 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. Falcipain-3 secondary structure and active site are shown in Figures 2 and 3.

Figure2: Secondary structure of Falcipain-3.

Figure3: Binding pocket of Falcipain-3

Ligand Preparation The chemical structure of Mefloquine (CID40692), which display high potency for Falcipain-3 (IC50 = 0.2nm) is display in Figure4. It is a potent, reversible nonpeptidic biaryl inhibitor for Falcipain-3. Therefore, our study used Mefloquine as a query for screening of compounds from PubChem database through structure based virtual screening. Virtual screening 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

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characteristics contained in the Lipinski’s rule of five (Lipinski et al., 1997), or the more relaxed rules revised by Veber et al., 2002. In our work, we have selected 2500 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.

Figure4: Structure of Mefloquine

Ligand-Protein docking studies 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 AutoDock Vina 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 PyMol molecular viewer (http://www.pymol.org/) was employed to analyze the docked structures. RESULTS AND DISCUSSION

297

The target protein (3BPM) was taken from the protein data bank, then the water molecules, hetero atoms and ligands were removed and minimized and subjected to simulation by NAMD tool. Active site of the protein was determined by using Castp server. The refined protein was used for further docking of screened compounds. Two-dimensional structure of Mefloquine was used to query for similar compounds in the PubChem database. Then, approximately 2500 compounds were screened, and the all compounds were saved for further molecular docking. The ligands were taken and docked into target protein active site along with the template drug molecule (Mefloquine) in Autodock Vina in PyRx Virtual Screening tool by which all the ligands were embedded within the active site of the target protein, were observed forming hydrogen bonds with same position as Mefloquine established active site of protein. Top 5 ligands were found, to require lower energy as compared to query drug (Mefloquine), which are used as the therapeutic agents against Falcipain-3 for malaria (Table1). Mefloquine have binding energy 7.2kcal/mol where as CID08830211, CID13650942, CID65739916 and CID65748951 have -9.4, 8.9, -8.4 and -7.9kcal/mol energies respectively. The interactions of ligands and protein graphical view are shown in Figure5. CID3000506

CID40468067

CID65330

CID4046

CID40692

Figure 5: Graphical representation of PubChem compounds and Falcipain-3 protein pocket.

The best binding affinity compounds were obtained through the molecular docking studies. The compound CID3000506 was bound with the binding affinity -9.4kcal/mol by the formation of one hydrogen bond interactions with Asp44 residue within the active site of Falcipain-3 protein. The compound CID40468067 was bound with the binding affinity -8.9kcal/mol by the formation of two hydrogen bond interactions with Gln110 and Tyr115 respectively within the active site of 298

Falcipain-3 protein. The compound CID65330 was bound with the binding affinity -8.4kcal/mol by the formation of two hydrogen bond interactions with Gln110 and Gly453 respectively within the active site of Falcipain-3. The compound CID40692 was bound with the binding affinity -7.9kcal/mol by the formation of one hydrogen bond interactions with Met434within the active site of Falcipain-3 protein. The compound CID4046 (Mefloquine)was bound with the binding affinity -7.2kcal/mol by the hydrophobic, electrostatistic and Van der Waal bonds within the active site of Falcipain-3 protein. The protein and ligand interactions, binding affinity values and hydrogen bond lengths are represented in Table1. Table 1: Binding affinity and bond lengths, of the best four ligands from PubChem database with Falcipain-3, comparison with query drug (Mefloquine) S.No.

PubChem Compounds

H-bond interactions Structures

Binding Affinity (Kcal/mol)

Protein-----Ligand

1

CID3000506

2

CID40468067

Gln110CO----NH Tyr115CN----OH

-8.9

3

CID65330

Gln110CN----OH Gly453CO----OH

-8.4

CID40692

Met434CO---OH

-7.9

CID4046 (Mefloquine)

Asp44, Gln110, Tyr115, Gly453, Met434

-7.2

4

5

-9.4

Asp44CO---NH

H-bond angle

144.9

110.04 116.68

114.54 126.85

169.90

H-bond distance(Ǻ)

2.88

3.29 2.87

3.30 3.20

3.05

---

The lead hit compounds satisfied the Lipinski’s rule of five with zero violations and also the octanol/water partition coefficient (miLogp), a useful parameter for predicting the drug transport properties like absorption, bioavailability, permeability and penetration. As well as topological molecular polar surface area (TPSA), number of atoms, their molecular weight (MW), number of hydrogen donors and number of hydrogen acceptors. A topological parameter is number of rotatable bonds and it describes the molecular flexibility of these compounds represented in Tables 2 and 3. Our investigation revealed that the selected 299

----

compounds have exhibited significant binding affinity with in the active site of 3BPM protein, when compared to query compound Mefloquine. Based upon this study, it is concluded that these compounds may be used as leads for developing effective antimalarial drugs. Table 2: Molecular physical-chemical properties and Lipinski properties of lead molecules with OSIRIS S.No

PubChem ID

M

I

R

T

Solubility

MW

Drug likeness

Drug score

No. of H acceptors

No. of rotatable bonds

cLop

1

CID3000506

-

-

-

-

45.147

378.1

-3.63

0.04

9

4

4.242

2

CID40468067

-

-

-

-

49.725

379.3

-4.64

0.07

8

4

1.227

3

CID65330

-

-

-

-

45.147

378.6

-5.34

0.04

9

4

4.242

4

CID40692

-

-

-

-

45.147

378.4

-5.21

0.03

9

4

4.242

5

CID4046 (Mefloquine)

-

-

-

-

45.147

378.2

-5.21

0.03

9

4

4.242

Table 3: Parameters for predicting drug transport properties like absorption, bioavailability, permeability and penetration using with Mollinspiration server. PubChem ID

GPCR ligand

Ion channel modulator

Kinase inhibitor

Nuclear receptor ligand

Protease inhibitor

Enzyme inhibitor

TPSA

CID3000506

0.45

0.21

-0.05

0.30

0.36

0.21

45.147

CID40468067 CID65330 CID40692 CID4046 (Mefloquine)

0.45 0.45 0.45 0.45

0.21 0.21 0.21 0.21

-0.05 -0.05 -0.05 -0.05

0.30 0.30 0.30 0.30

0.36 0.36 0.36 0.36

0.21 0.21 0.21 0.21

49.725 45.147 45.147 45.147

CONCLUSIONS In the present work, we have searched for potent anti Falcipain-3 inhibitors through virtual screening on PubChem database based on structure similarity of ligand (Mefloquine). The newly identified cysteine protease enzyme Falcipain-3 is an important target in drug design part therapeutic intervention of malaria disease. Inhibition of Falcipain-3 could result in reducing heamoglobin degradation and further inhibiting the maturation of parasites. Among screened compounds CID3000506, CID40468067, CID65330 and CID40692 have the highest binding affinity and obey the drug properties compared to Mefloquine. Therefore the five compounds were hopeful drug molecule like Mefloquine against malaria disease. ACKNOWLEDGEMENTS Author, Madhu Sudhana Saddala is 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). 300

REFERENCES Breman, J.G., (2001). The ears of the hippopotamus: Manifestations, determinants, and estimates of the malaria burden. Am. J. Trop. Med. Hyg. 64:1–11. Christopher,A Lipinski, (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. Govindarajan, V., Arun Nagaraj and Pundi, N Rangarajan, (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. Laskoswki, R.A., MacArthur, M.W., Moss, D.S, Thornton, J.M., (1993). PROCHECK: A program to check the stereo chemical quality of protein structures. J. Appl. Cryst. 26: 283-291. 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, J Rosenthal, (1998). Proteases of Malaria Parasites: New Targets for Chemotherapy. Emerging Infectious Diseases. 4:49-57. Salas, F., Fichmann, J., Lee, G.K., Scott, M.D., and Rosenthal, P.J., (1995). Functional expression of falcipain, a Plasmodium falciparum cysteine proteinase, supports its role as a malarial hemoglobinase. Infect. Immun. 63:2120–2125. 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. 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.Drug.Discov.Today. 7:109-16. Shenai, B.R., Sijwali, P.S., Singh, A., and Rosenthal, P.J., (2000).Characterization of native and recombinant falcipain-2, a principal trophozoite cysteine protease and essential hemoglobinase of Plasmodium falciparum. J. Biol. Chem. 275: 29000–29010. Sijwali, P.S., Shenai, B.R., Gut, J., Singh, A., and Rosenthal, P.J., (2001).Expression and characterization of the Plasmodium falciparum hemoglobinase falcipain-3.Biochem. J. 360: 481–489. Stanley C., Oaks, (1991). Malaria: Obstacles and Opportunities. National Academy Press, Washington, D.C. Trott O, Olson AJ (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31: 455–461. Wolf LK (2009). PyRx. C&EN 87: 31.

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