Author(s):
Rosita Handayani, Tegar Achsendo Yuniarta, Sukardiman, Aiyi Asnawi
Email(s):
rosita.handayani@ff.unair.ac.id
DOI:
10.52711/0974-360X.2025.00079 
Address:
Rosita Handayani1*, Tegar Achsendo Yuniarta2, Sukardiman1, Aiyi Asnawi3
1Pharmaceutical Science Department, Faculty of Pharmacy, Universitas Airlangga, Surabaya, East Java, 60115, Indonesia.
2Faculty of Pharmacy, Universitas Surabaya, Surabaya, East Java, 60293, Indonesia.
3Department of Pharmacochemistry, Faculty of Pharmacy, Universitas Bhakti Kencana, Jl. Soekarno Hatta No.754, Bandung, West Java, 40617, Indonesia.
*Corresponding Author
Published In:
Volume - 18,
Issue - 2,
Year - 2025
ABSTRACT:
Background: Malaria continues to be a serious problem in several countries, marked by an increase in the number of cases and a high morbidity rate. One of the commonly adopted strategies in drug discovery is by performing compound screening using computational tools, known as virtual screening. This technique allows one to screen multitudes of chemical compounds in silico, thus saving cost and time by reducing the amount of tested compound in vitro. Recently, P. falciparum prolyl-tRNA synthetase (PfPRS) is one of the top priority targets to be explored of potent inhibitors. This enzyme plays an important role in attaching L-proline into tRNA, which then will be incorporated into protein sequence. Its inhibition would halt the protein synthesis and kill the parasite. Methods: Hierarchical virtual screening was performed against PfPRS enzyme using 2D followed by 3D similarity method implemented in Infinisee 3.2.0 and SeeSAR 12.1.0, respectively. 1-(pyridin-4-yl) pyrrolidin-2-one based analog, which was previously discovered as potent antimalarial agent, was used as template to screen potential hits from Molport Database of Purchasable Natural Product Compounds. Compounds with high similarity value were evaluated by molecular docking using SeeSAR 12.1.0 approach. The best scoring compounds were subjected into ADMET prediction, molecular dynamics simulation, and in vitro assay against P. falciparum. Results: Two compounds were obtained from virtual screening and molecular docking process, with predicted IC50 value lies on micromolar and nanomolar range. These compounds also satisfy ADMET characteristics in general as well as showing stability during 100 ns molecular dynamics simulation. Bioassay study showed that both compounds yielded < 10 µg/mL inhibitory concentration. Conclusion: This study has discovered two novel compounds using in silico approach, which can be further developed as potential antimalarial agents.
Cite this article:
Rosita Handayani, Tegar Achsendo Yuniarta, Sukardiman, Aiyi Asnawi. Search for Lead Compounds and In Vitro Assay of Potential Inhibitors of Plasmodium falciparum Prolyl-tRNA Synthetase from Natural Compound Database. Research Journal of Pharmacy and Technology.2025;18(2):529-6. doi: 10.52711/0974-360X.2025.00079
Cite(Electronic):
Rosita Handayani, Tegar Achsendo Yuniarta, Sukardiman, Aiyi Asnawi. Search for Lead Compounds and In Vitro Assay of Potential Inhibitors of Plasmodium falciparum Prolyl-tRNA Synthetase from Natural Compound Database. Research Journal of Pharmacy and Technology.2025;18(2):529-6. doi: 10.52711/0974-360X.2025.00079 Available on: https://rjptonline.org/AbstractView.aspx?PID=2025-18-2-11
REFERENCES:
1. Jagannathan P., Kakuru A. Malaria in 2022: Increasing challenges, cautious optimism. Nat Commun. 2022; 13(1): 2678, https://doi.org/10.1038/s41467-022-30133-w.
2. WHO. The 2020 edition of the World Malaria Report. vol. 73. 2020.
3. Agarwal AK., Yadav CP., Kuity P., Mishra J. Development of antimalarial pharmacotherapy and its importance in malaria treatment/public health program. Asian J Pharm Anal. 2022; 12(4): 233–42.
4. Arute JE., Agbamu E., Agare G., Achi CJ., Odili VU., Omomulere A. Physicians’ Antimalarial Utilization Practices in Children below 5 years in a Secondary and Primary Health Facilities in Delta State. Res J Pharm Technol. 2022; 15(6): 2756–60.
5. Ashley EA., Dhorda M., Fairhurst RM., Amaratunga C., Lim P., Suon S., et al. Spread of artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med. 2014; 371(5): 411–23, https://doi.org/10.1056/NEJMoa1314981.
6. Mutiah R., Badiah R., Hayati EK., Widyawaruyanti A. Activity of Antimalarial Compounds from Ethyl Acetate Fraction of Sunflower Leaves (Helianthus annuus L.) against Plasmodium falciparum Parasites 3D7 Strain. Asian J Pharm Technol. 2017; 7(2): 86–90.
7. Sharma R., Kumawat MK., Sharma GK. In-silico design and molecular docking studies of some novel 4-aminoquinoline-monastrol hybrids for their antimalarial activity. Res J Pharm Technol. 2022; 15(10): 4589–93.
8. Dubey S., Bhardwaj S., Parbhakaran P., Singh E. In silico Prediction of Pyrazoline Derivatives as Antimalarial agents. Asian J Pharm Res. 2022; 12(2): 119–24.
9. Suruse PB., Duragkar NJ., Shivhare UD., Raut SY., Warokar AS. Contribution of Traditional Medicines to the Development of Modern Medicine for Malaria. Res J Pharmacol Pharmacodyn. 2011; 3(1): 1–4.
10. Math AAK., Kaushik M., Krishnamoorthy E. Preliminary screening for cytotoxicity and antiplasmodial activity of fish protein hydrolysates on erythrocytes infected with Plasmodium falciparum 3D7. Res J Pharm Technol. 2023; 16(4): 1721–6.
11. Svensson F., Karlén A., Sköld C. Virtual screening data fusion using both structure- and ligand-based methods. J Chem Inf Model. 2012; 52(1): 225–32, https://doi.org/10.1021/ci2004835.
12. Forte B., Ottilie S., Plater A., Campo B., Dechering KJ., Gamo FJ., et al. Prioritization of Molecular Targets for Antimalarial Drug Discovery. ACS Infect Dis. 2021; 7(10): 2764–76, https://doi.org/10.1021/acsinfecdis.1c00322.
13. Manickam Y., Chaturvedi R., Babbar P., Malhotra N., Jain V., Sharma A. Drug targeting of one or more aminoacyl-tRNA synthetase in the malaria parasite Plasmodium falciparum. Drug Discov Today. 2018; 23(6): 1233–40, https://doi.org/10.1016/j.drudis.2018.01.050.
14. Okaniwa M., Shibata A., Ochida A., Akao Y., White KL., Shackleford DM., et al. Repositioning and Characterization of 1-(Pyridin-4-yl)pyrrolidin-2-one Derivatives as Plasmodium Cytoplasmic Prolyl-tRNA Synthetase Inhibitors. ACS Infect Dis. 2021; 7(6): 1680–9, https://doi.org/10.1021/acsinfecdis.1c00020.
15. Lessel U., Wellenzohn B., Lilienthal M., Claussen H. Searching Fragment Spaces with Feature Trees. J Chem Inf Model. 2009; 49(2): 270–9, https://doi.org/10.1021/ci800272a.
16. Lemmen C., Lengauer T. Time-efficient flexible superposition of medium-sized molecules. J Comput Aided Mol Des. 1997; 11(4): 357–68. https://doi.org/10.1023/A:1007959729800.
17. Jain V., Yogavel M., Oshima Y., Kikuchi H., Touquet B., Hakimi MA., et al. Structure of prolyl-tRNA synthetase-halofuginone complex provides basis for development of drugs against malaria and toxoplasmosis. Structure. 2015; 23(5): 819–29. https://doi.org/10.1016/j.str.2015.02.011.
18. Jo S., Cheng X., Lee J., Kim S., Park S-J., Patel DS., et al. CHARMM-GUI 10 years for biomolecular modeling and simulation. J Comput Chem. 2017; 38(15): 1114–24, https://doi.org/https://doi.org/10.1002/jcc.24660.
19. Jo S., Kim T., Iyer VG., Im W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J Comput Chem. 2008; 29(11): 1859–65, https://doi.org/https://doi.org/10.1002/jcc.20945.
20. Rarey M., Kramer B., Lengauer T., Klebe G. A fast flexible docking method using an incremental construction algorithm. J Mol Biol. 1996; 261(3): 470–89, https://doi.org/10.1006/jmbi.1996.0477.
21. Schneider N., Lange G., Hindle S., Klein R., Rarey M. A consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes: methods behind the HYDE scoring function. J Comput Aided Mol Des. 2013; 27(1): 15–29, https://doi.org/10.1007/s10822-012-9626-2.
22. Wang J., Wolf RM., Caldwell JW., Kollman PA., Case DA. Development and testing of a general amber force field. J Comput Chem. 2004; 25(9): 1157–74, https://doi.org/https://doi.org/10.1002/jcc.20035.
23. Sousa da Silva AW., Vranken WF. ACPYPE - AnteChamber PYthon Parser interfacE. BMC Res Notes. 2012; 5: 367. https://doi.org/10.1186/1756-0500-5-367.
24. Lindorff-Larsen K., Piana S., Palmo K., Maragakis P., Klepeis JL., Dror RO., et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins. 2010; 78(8): 1950–8, https://doi.org/10.1002/prot.22711.
25. Jorgensen WL., Chandrasekhar J., Madura JD., Impey RW., Klein ML. Comparison of simple potential functions for simulating liquid water. \jcp. 1983; 79(2): 926–35, https://doi.org/10.1063/1.445869.
26. Valdés-Tresanco MS., Valdés-Tresanco ME., Valiente PA., Moreno E. gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput. 2021; 17(10): 6281–91. https://doi.org/10.1021/acs.jctc.1c00645.
27. Xiong G., Wu Z., Yi J., Fu L., Yang Z., Hsieh C., et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021; 49(W1): W5–14, https://doi.org/10.1093/nar/gkab255.
28. Lipinski CA., Lombardo F., Dominy BW., Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997; 23(1): 3–25. https://doi.org/https://doi.org/10.1016/S0169-409X(96)00423-1.
29. Veber DF., Johnson SR., Cheng H-Y., Smith BR., Ward KW., Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002; 45(12): 2615–23. https://doi.org/10.1021/jm020017n.
30. Trager W., Jensen JB. Human malaria parasites in continuous culture. Science. 1976; 193(4254): 673–5. https://doi.org/10.1126/science.781840.
31. Ekasari W., Widya Pratiwi D., Amanda Z., Suciati., Widyawaruyanti A., Arwati H. Various Parts of Helianthus annuus Plants as New Sources of Antimalarial Drugs. Evid Based Complement Alternat Med. 2019; 2019: 7390385, https://doi.org/10.1155/2019/7390385.
32. Hu G., Kuang G., Xiao W., Li W., Liu G., Tang Y. Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening. J Chem Inf Model. 2012; 52(5): 1103–13. https://doi.org/10.1021/ci300030u.
33. Eckert H., Bajorath J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov Today. 2007; 12(5–6): 225–33. https://doi.org/10.1016/j.drudis.2007.01.011.
34. Chupakhin E., Babich O., Prosekov A., Asyakina L., Krasavin M. Spirocyclic motifs in natural products. Molecules. 2019; 24(22). https://doi.org/10.3390/molecules24224165.
35. Stefanachi A., Leonetti F., Pisani L., Catto M., Carotti A. Coumarin: A Natural, Privileged and Versatile Scaffold for Bioactive Compounds. Molecules. 2018; 23(2). https://doi.org/10.3390/molecules23020250.
36. Bhat M., Belagali SL., Mamatha S V., Sagar BK., Sekhar EV. Chapter 7 - Importance of quinazoline and quinazolinone derivatives in medicinal chemistry. In: Atta-ur-Rahman BT-S in NPC, editor. vol. 71. Elsevier; 2021. p. 185–219.
37. Ferreira LG., Dos Santos RN., Oliva G., Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015; 20(7): 13384–421, https://doi.org/10.3390/molecules200713384.
38. Kuhn B., Guba W., Hert J ér. ôm., Banner D., Bissantz C., Ceccarelli S., et al. A Real-World Perspective on Molecular Design. J Med Chem. 2016; 59(9): 4087–102, https://doi.org/10.1021/acs.jmedchem.5b01875.
39. Leeson PD., Springthorpe B. The influence of drug-like concepts on decision-making in medicinal chemistry. Nat Rev Drug Discov. 2007:881–90, https://doi.org/10.1038/nrd2445.
40. Yuniarta TA., Wawo JE., Kesuma D. Molecular Docking Study of 1‑ (Pyridin-4-YL)Pyrrolidine-2-One Derivate Against PROLYL-tRNA Synthetase in Plasmodium Falciparum. Pharmacoscript. 2022;5(2):157–71.
41. Bulusu G., Desiraju GR. Strong and Weak Hydrogen Bonds in Protein-Ligand Recognition. J Indian Inst Sci. 2020;100(1):31–41, https://doi.org/10.1007/s41745-019-00141-9.
42. Liu K., Watanabe E., Kokubo H. Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations. J Comput Aided Mol Des. 2017; 31(2): 201–11, https://doi.org/10.1007/s10822-016-0005-2.
43. Tuccinardi T. What is the current value of MM/PBSA and MM/GBSA methods in drug discovery? Expert Opin Drug Discov. 2021;16(11):1233–7, https://doi.org/10.1080/17460441.2021.1942836.
44. Shearer TW., Smith KS., Diaz D., Asher C., Ramirez J. The Role of In Vitro ADME Assays in Antimalarial Drug Discovery and Development. Comb Chem High Throughput Screen. 2005;8(1).
45. Kar S., Roy K., Leszczynski J. In Silico Tools and Software to Predict ADMET of New Drug Candidates BT - In Silico Methods for Predicting Drug Toxicity. In: Benfenati E, editor. New York, NY: Springer US; 2022. p. 85–115.
46. Guengerich FP., Bartleson CJ. Principles and methods of toxicology. 1994.
47. Crispim M., Verdaguer IB., Silva SF., Katzin AM. Suitability of methods for Plasmodium falciparum cultivation in atmospheric air. Mem Inst Oswaldo Cruz. 2022;117(13):1–8, https://doi.org/10.1590/0074-02760210331.
48. O’Neill D H; Boardman, P; Phillipson, J D; Warhurst, D C MJB. Plants as Sources of Antimalarial Drugs Part. 1. In vitro Test Method for the Evaluation of Crude Extracts from Plants. Planta Med. 1985;51(05):394–8, https://doi.org/10.1055/s-2007-969529.