The active compound of the sponge Niphates olemda against Plasmodium falciparum phosphoethanolamine methyltransferase (pfpmt) based on in silico study
Abstract
Antimalarial drug resistance to P. falciparum has become a global problem in recent decades. This encourages the need for exploration to find alternative treatments, which come from marine product like Sponge Niphates olemda that contain active substrat. This research was conducted to determine the activity sponge Niphates olemda on P. falciparum phosphoethanolamine methyltransferase (PfPMT) through an in silico study.This research is a pure experimental research using the One Shot Experimental Study research design method. Observations were only made once between the variables studied through three analyzes, namely molecular docking analysis, ADME prediction analysis, and active compound toxicity analysis.The results showed that there were 4 active compounds (Niphateolide A, Kapakahine A, Kapakahine B, and Kapakahine F) that had a better binding affinity than artemisinin so that they had antimalarial potential. However, only Niphateolide A met Lipinski's criteria in ADME analysis, but was more toxic than the other three active compounds and also artemisinin. Based on the results of molecular docking analysis, it was found that the active compound Niphateolide A from the sea sponge Niphates olemda has antimalarial potential on target protein Plasmodium falciparum phosphoethanolamine methyltransferase.
References
Kemenkes. (2021). P2PM. https://p2pm.kemkes.go.id/publikasi/artikel/wilayah-wilayah-endemis-malaria-tinggi-di-indonesia
WHO. (2022). Malaria. https://www.who.int/health-topics/malaria#tab=tab_1
Vika Azkiya Dihni. (2021). Sebanyak 94.610 Kasus Malaria Terjadi di Indonesia pada 2021. https://databoks.katadata.co.id/datapublish/2021/12/20/sebanyak-94610-kasus-malaria-terjadi-di-indonesia-pada-2021
Riskesdas. (2010). RISET KESEHATAN DASAR 2010
Maier, A. G., Matuschewski, K., Zhang, M., & Rug, M. (2019). Plasmodium falciparum. Trends in Parasitology, 35(6), 481–482. https://doi.org/10.1016/j.pt.2018.11.010
Bauserman, M., Conroy, A. L., North, K., Patterson, J., Bose, C., & Meshnick, S. (2019). An overview of malaria in pregnancy. Seminars in Perinatology, 43(5), 282–290. https://doi.org/10.1053/j.semperi.2019.03.018
Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper, C., Costafreda, S. G., Dias, A., Fox, N., Gitlin, L. N., Howard, R., Kales, H. C., Kivimäki, M., Larson, E. B., Ogunniyi, A., … Mukadam, N. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet (London, England), 396(10248), 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6
Singh, J., Mansuri, R., Vijay, S., Sahoo, G. C., Sharma, A., & Kumar, M. (2019). Docking predictions based Plasmodium falciparum phosphoethanolamine methyl transferase inhibitor identification and in-vitro antimalarial activity analysis. BMC Chemistry, 13(1), 43. https://doi.org/10.1186/s13065-019-0551-5
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development q settings. In Advanced Drug Delivery Reviews (Vol. 46). www.elsevier.com/locate/drugdeliv
Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318
Yeung, B. K. S., Nakao, Y., Kinnel, R. B., Carney, J. R., Yoshida, W. Y., Scheuer, P. J., & Kelly-Borges, M. (1996). The Kapakahines, Cyclic Peptides from the Marine Sponge Cribrochalina olemda. The Journal of Organic Chemistry, 61(20), 7168–7173. https://doi.org/10.1021/jo960725e
Kato, H., Nehira, T., Matsuo, K., Kawabata, T., Kobashigawa, Y., Morioka, H., Losung, F., Mangindaan, R. E. P., de Voogd, N. J., Yokosawa, H., & Tsukamoto, S. (2015). Niphateolide A: isolation from the marine sponge Niphates olemda and determination of its absolute configuration by an ECD analysis. Tetrahedron, 71(38), 6956–6960. https://doi.org/https://doi.org/10.1016/j.tet.2015.07.009
Bitencourt-Ferreira, G., & de Azevedo, W. F. (2019). Molegro Virtual Docker for Docking (pp. 149–167). https://doi.org/10.1007/978-1-4939-9752-7_10
Patil, R., Das, S., Stanley, A., Yadav, L., Sudhakar, A., & Varma, A. K. (2010). Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing. PLoS ONE, 5(8), e12029. https://doi.org/10.1371/journal.pone.0012029
Shim, J., Hong, Z. Y., Sohn, I., & Hwang, C. (2021). Prediction of drug-target binding affinity using similarity-based convolutional neural network. Scientific Reports, 11(1). https://doi.org/10.1038/S41598-021-83679-Y
Singh, J., Mansuri, R., Vijay, S., Sahoo, G. C., Sharma, A., & Kumar, M. (2019). Docking predictions based Plasmodium falciparum phosphoethanolamine methyl transferase inhibitor identification and in-vitro antimalarial activity analysis. BMC Chemistry, 13(1), 43. https://doi.org/10.1186/s13065-019-0551-5
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