A Computational Design of siRNA in SARS-CoV-2 Spike Glycoprotein Gene and Its Binding Capability toward mRNA

https://doi.org/10.22146/ijc.68415

Arli Aditya Parikesit(1*), Arif Nur Muhammad Ansori(2), Viol Dhea Kharisma(3)

(1) Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Jakarta 13210, Indonesia
(2) Professor Nidom Foundation, Surabaya 60115, Indonesia
(3) Computational Virology Research Unit, Division of Molecular Biology and Genetics, Generasi Biologi Indonesia Foundation, Gresik 61171, Indonesia
(*) Corresponding Author

Abstract


COVID-19 pandemic has no immediate ending in sight, and any significant increasing cases were observed worldwide. Currently, there are only two main strategies for developing COVID-19 drugs that predominantly use a proteomics-based approach, which are drug repurposing and herbal medicine strategies. However, a third strategy has existed, called small interfering RNA or siRNA, which is based on the transcriptomics approach. In the case of SARS-CoV-2 infection, it is expected to perform by silencing the viral gene, which brings the surface glycoprotein (S) gene responsible for SARS-CoV-2 viral attachment to the ACE2 receptor on the human host cell. This third approach applies a molecular simulation method comprising data retrieval, multiple sequence alignment, phylogenetic tree depiction, 2D/3D structure prediction, and RNA-RNA molecular docking. The expected results are the prediction of 2D and 3D structures of both siRNA and mRNA silenced S genes along with a complex as the result of a docking method formed by those silenced genes. An Insilco chemical interaction study was performed in testing siRNA and mRNA complex’s stability with the confirmation result of a stable complex which is expected to be formed before mRNA reaches the ribosome for the translation process. Thus, siRNA from the S gene could be considered a candidate for the COVID-19 therapeutic agent.

Keywords


COVID-19; SARS-CoV-2; siRNA; S gene; molecular docking

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References

[1] Ansori, A.N.M., Kharisma, V.D., Muttaqin, S.S., Antonius, Y., and Parikesit, A.A., 2020, Genetic variant of SARS-CoV-2 isolates in Indonesia: Spike glycoprotein gene, J. Pure Appl. Microbiol., 14, 971–978.

[2] Turista, D.D.R., Islamy, A., Kharisma, V.D., and Ansori, A.N.M., 2020, Distribution of COVID-19 and phylogenetic tree construction of SARS-CoV-2 in Indonesia, J. Pure Appl. Microbiol., 14, 1035–1042.

[3] Agarwal, A., Rochwerg, B., Lamontagne, F., Siemieniuk, R.A.C., Agoritsas, T., Askie, L., Lytvyn, L., Leo, Y.S., Macdonald, H., Zeng, L., Amin, W., Barragan, F.A.J., Bausch, F.J., Burhan, E., Calfee, C.S., Cecconi, M., Chanda, D., Dat, V.Q., De Sutter, A., Du, B., Freedman, F., Geduld, H., Gee, P., Gotte, M., Harley, N., Hashimi, M., Hunt, B., Jehan, F., Kabra, S.K., Kanda, S., Kim, Y.J., Kissoon, N., Krishna, S., Kuppalli, K., Kwizera, A., Castro-Rial, M.L., Lisboa, T., Lodha, R., Mahaka, I., Manai, H., Mino, G., Nsutebu, E., Preller, J., Pshenichnaya, N., Qadir, N., Relan, P., Sabzwari, S., Sarin, R., Shankar-Hari, M., Sharland, M., Shen, Y., Ranganathan, S.S., Souza, J.P., Stegemann, M., Swanstrom, R., Ugarte, S., Uyeki, T., Venkatapuram, S., Vuyiseka, D., Wijewickrama, A., Tran, L., Zeraatkar, D., Bartoszko, J.J., Ge, L., Brignardello-Petersen, R., Owen, A., Guyatt, G., Diaz, J., Kawano-Dourado, L., Jacobs, M., and Vandvik, P.O., 2020, A living WHO guideline on drugs for covid-19, BMJ, 370, m3379.

[4] Kharisma, V.D., and Ansori, A.N.M., 2020, Construction of epitope-based peptide vaccine against SARS-CoV-2: Immunoinformatics study, J. Pure Appl. Microbiol., 14, 999–1005.

[5] Kharisma, V.D., Agatha, A., Ansori, A.N.M., Widyananda, M.H., Rizky, W.C., Dings, T.G.A., Derkho, M., Lykasova, I., Antonius, Y., Rosadi, I., and Zainul, R., 2022, Herbal combination from Moringa oleifera Lam. and Curcuma longa L. as SARS-CoV-2 antiviral via dual inhibitor pathway: A viroinformatics approach, J. Pharm. Pharmacogn. Res., 10 (1), 138–146.

[6] Fernandes, J.D., Hinrichs, A.S., Clawson, H., Gonzalez, J.N., Lee, B.T., Nassar, L.R., Raney, B.J., Rosenbloom, K.R., Nerli, S., Rao, A.A., Schmelter, D., Fyfe, A., Maulding, N., Zweig, A.S., Lowe, T.M., Ares, M., Corbet-Detig, R., Kent, W.J., Haussler, D., and Haeussler, M., 2020, The UCSC SARS-CoV-2 genome browser, Nat. Genet., 52 (10), 991–998.

[7] Parikesit, A.A., 2020, Protein domain annotations of the SARS-CoV-2 proteomics as a blue-print for mapping the features for drug and vaccine designs, J. Mat. Sains, 25, 26–32.

[8] Dai, W., Zhang, B., Jiang, X.M., Su, H., Li, J., Zhao, Y., Xie, X., Jin, Z., Peng, J., Liu, F., Li, C., Li, Y., Bai, F., Wang, H., Cheng, X., Cen, X., Hu, S., Yang, X., Wang, J., Liu, X., Xiao, G., Jiang, H., Rao, Z., Zhang, L.K., Xu, Y., Yang, H., and Liu, H., 2020, Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease, Science, 368 (6497), 1331–1335.

[9] Mansbach, R.A., Chakraborty, S., Nguyen, K., Montefiori, D.C., Korber, B., and Gnanakaran, S., 2021, The SARS-CoV-2 spike variant D614G favors an open conformational state, Sci. Adv., 7 (16), eabf3671.

[10] Fahmi, M., Kharisma, V.D., Ansori, A.N.M., and Ito, M., 2021, Retrieval and investigation of data on SARS-CoV-2 and COVID-19 using bioinformatics approach, Adv. Exp. Med. Biol., 1318, 839–857.

[11] Ansori, A.N.M., Kharisma, V.D., Fadholly, A., Tacharina, M.R., Antonius, Y., and Parikesit, A.A., 2021, Severe acute respiratory syndrome coronavirus-2 emergence and its treatment with alternative medicines: A review, Res. J. Pharm. Technol., 14 (10), 5551–5557.

[12] Beigel, J.H., Tomashek, K.M., Dodd, L.E., Mehta, A.K., Zingman, B.S., Kalil, A.C., Hohmann, E., Chu, H.Y., Luetkemeyer, A., Kline, S., Lopez de Castilla, D., Finberg, R.W., Dierberg, K., Tapson, V., Hsieh, L., Patterson, T.F., Paredes, R., Sweeney, D.A., Short, W.R., Touloumi, G., Lye, D.C., Ohmagari, N., Oh, M., Ruiz-Palacios, G.M., Benfield, T., Fätkenheuer, G., Kortepeter, M.G., Atmar, R.L., Creech, C.B., Lundgren, J., Babiker, A.G., Pett, S., Neaton, J.D., Burgess, T.H., Bonnett, T., Green, M., Makowski, M., Osinusi, A., Nayak, S., Lane, H.C., and ACTT-1 Study Group Members, 2020, Remdesivir for the treatment of Covid-19 - Final report, N. Engl. J. Med., 383 (19), 1813–1826.

[13] O'Dowd, A., 2021, Covid-19: Cases of delta variant rise by 79%, but rate of growth slows, BMJ, 373, n1596.

[14] Torjesen, I., 2021, Covid-19: Delta variant is now UK's most dominant strain and spreading through schools, BMJ, 373, n1445.

[15] Kusumawati, R.L., Lubis, I., Kumaheri, M.A., Pradipta, A., Faksri, K., Mutiara, M., Shankar, A.H., and Tania, T., 2022, Clinical epidemiology of pediatric COVID-19 Delta variant cases from North Sumatra, Indonesia, Front. Pediatr., 10, 810404.

[16] Burnett, J.C., and Rossi, J.J., 2012, RNA-based therapeutics: Current progress and future prospects, Chem. Biol., 19 (1), 60–71.

[17] Reardon, S., 2021, How the Delta variant achieves its ultrafast spread, Nature, 10.1038/d41586-021-01986-w.

[18] Yu, A.M., Jian, C., Yu, A.H., and Tu, M.J., 2019, RNA therapy: Are we using the right molecules?, Pharm. Ther., 196, 91–104.

[19] Pashkov, E.A., Faizuloev, E.B., Svitich, O.A., Sergeev, O.V., and Zverev, V.V., 2020, The potential of synthetic small interfering RNA-based antiviral drugs for influenza treatment, Vopr. Virusol., 65 (4), 182–190.

[20] Qiu, M., Li, Y., Bloomer, H., and Xu, Q., 2021, Developing biodegradable lipid nanoparticles for intracellular mRNA delivery and genome editing, Acc. Chem. Res., 54 (21), 4001–4011.

[21] Le, T.K., Paris, C., Khan, K.S., Robson, F., Ng, W.L., and Rocchi, P., 2021, Nucleic acid-based technologies targeting coronaviruses, Trends Biochem. Sci., 46 (5), 351–365.

[22] Yu, A.M., Choi, Y.H., and Tu, M.J., 2020, RNA drugs and RNA targets for small molecules: Principles, progress, and challenges, Pharmacol. Rev., 72, 862–898.

[23] Sohrab, S.S., El-Kafrawy, S.A., Mirza, Z., Kamal, M.A., and Azhar, E.I., 2018, Design and delivery of therapeutic siRNAs: Application to MERS-coronavirus, Curr. Pharm. Des., 24 (1), 62–77.

[24] Hua, K., Jin, J., Zhao, J., Song, J., Song, H., Li, D., Maskey, N., Zhao, B., Wu, C., Xu, H., and Fang, L., 2016, miR-135b, upregulated in breast cancer, promotes cell growth and disrupts the cell cycle by regulating LATS2, Int. J. Oncol., 48 (5), 1997–2006.

[25] Cruz, J.A., Blanchet, M.F., Boniecki, M., Bujnicki, J.M., Chen, S.J., Cao, S., Das, R., Ding, F., Dokholyan, N.V., Flores, S.C., Huang, L., Lavender, C.A., Lisi, V., Major, F., Mikolajczak, K., Patel, D.J., Philips, A., Puton, T., Santalucia, J., Sijenyi, F., Hermann, T., Rother, K., Rother, M., Serganov, A., Skorupski, M., Soltysinski, T., Sripakdeevong, P., Tuszynska, I., Weeks, K.M., Waldsich, C., Wildauer, M., Leontis, N.B., and Westhof, E., 2012, RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction, RNA, 18 (4), 610–625.

[26] Hufsky, F., Beerenwinkel, N., Meyer, I.M., Roux, S., Cook, G.M., Kinsella, C.M., Lamkiewicz, K., Marquet, M., Nieuwenhuijse, D.F., Olendraite, I., Paraskevopoulou, S., Young, F., Dijkman, R., Ibrahim, B., Kelly, J., Le Mercier, P., Marz, M., Ramette, A., and Thiel, V., 2020, The international virus bioinformatics meeting 2020, Viruses, 12 (12), 1398.

[27] Marz, M., Beerenwinkel, N., Drosten, C., Fricke, M., Frishman, D., Hofacker, I.L., Hoffmann, D., Middendorf, M., Rattei, T., Stadler, P.F., and Töpfer, A., 2014, Challenges in RNA virus bioinformatics, Bioinformatics, 30 (13), 1793–1799.

[28] Li, B., Cao, Y., Westhof, E., and Miao, Z., 2020, Advances in RNA 3D structure modeling using experimental data, Front. Genet., 11, 574485.

[29] Kang, Y., and Fortmann, C.M., 2013, An alternative approach to protein folding, BioMed Res. Int., 2013, 583045.

[30] Wei, G., Xi, W., Nussinov, R., and Ma, B., 2016, Protein ensembles: How does nature harness thermodynamic fluctuations for life? The diverse functional roles of conformational ensembles in the cell, Chem. Rev., 116 (11), 6516–6551.

[31] Smerlak, M., 2021, Quasi-species evolution maximizes genotypic reproductive value (not fitness or flatness), J. Theor. Biol., 522, 110699.

[32] Li, C.Y., de Veer, S.J., Law, R.H.P., Whisstock, J.C., Craik, D.J., and Swedberg, J.E., 2019, Characterising the subsite specificity of urokinase-type plasminogen activator and tissue-type plasminogen activator using a sequence-defined peptide aldehyde library, ChemBioChem, 20 (1), 46–50.

[33] Seidel, T., Schuetz, D.A., Garon, A., and Langer, T., 2019, The pharmacophore concept and its applications in computer-aided drug design, Prog. Chem. Org. Nat. Prod., 110, 99–141.

[34] Buglak, A.A., Samokhvalov, A.V., Zherdev, A.V., and Dzantiev, B.B., 2020, Methods and applications of in silico aptamer design and modeling, Int. J. Mol. Sci., 21 (22), 8420.

[35] Wang, J., 2020, Fast identification of possible drug treatment of coronavirus disease-19 (COVID-19) through computational drug repurposing study, J. Chem. Inf. Model., 60 (6), 3277–3286.

[36] Parikesit, A.A., and Ramanto, K.N., 2019, The binding prediction model of the iron-responsive element binding protein and iron-responsive elements, Bioinf. Biomed. Res. J., 2 (1), 12–20.

[37] Ivan, J., Nurdiansyah, R., and Parikesit, A.A., 2020, Computational modeling of AGO-mediated molecular inhibition of ARF6 by miR-145, Indones. J. Biotechnol., 25 (2), 102–108.

[38] Valeska, M.D., and Parikesit, A.A., 2020, Structural bioinformatics approach for the molecular models of miR-135b and its silencer as triple negative breast cancer (TNBC) biomarkers, Horiz. Cancer Res., 77, 232–245.

[39] Parikesit, A.A., and Nurdiansyah, R., 2020, The predicted structure for the anti-sense siRNA of the RNA polymerase enzyme (RdRp) gene of the SARS-CoV-2, Berita Biologi, 19 (1), 97–108.

[40] Brister, J.R., Ako-Adjei, D., Bao, Y., and Blinkova, O., 2015, NCBI viral genomes resource, Nucleic Acids Res., 43 (D1), D571–D577.

[41] Giulietti, M., Righetti, A., Cianfruglia, L., Šabanović, B., Armeni, T., Principato, G., and Piva, F., 2018, To accelerate the Zika beat: Candidate design for RNA interference-based therapy, Virus Res., 255, 133–140.

[42] Procter, J.B., Carstairs, G.M., Soares, B., Mourão, K., Ofoegbu, T.C., Barton, D., Lui, L., Menard, A., Sherstnev, N., Roldan-Martinez, D., Duce, S., Martin, D.M.A., and Barton, G.J., 2021, Alignment of biological sequences with Jalview, Methods Mol. Biol., 2231, 203–224.

[43] Velandia-Huerto, C.A., Yazbeck, A.M., Schor, J., and Stadler, P.F., 2022, Evolution and phylogeny of microRNAs — Protocols, pitfalls, and problems, Methods Mol. Biol., 2257, 211–233.

[44] Ender, A., Stadler, P.F., Mörl, M., and Findeiß, S., 2022, RNA design principles for riboswitches that regulate RNase P-mediated tRNA processing, Methods Mol. Biol., 2518, 179–202.

[45] Günzel, C., Kühnl, F., Arnold, K., Findeiß, S., Weinberg, C.E., Stadler, P.F., and Mörl, M., 2021, Beyond plug and pray: Context sensitivity and in silico design of artificial neomycin riboswitches, RNA Biol., 18 (4), 457–467.

[46] Stadler, P.F., 2021, Alignments of biomolecular contact maps, Interface Focus, 11 (4), 20200066.

[47] Yuan, L., Guo, Z.H., Cao, W.J., Luo, Y., and Shi, Y.Z., 2021, An Integrated Tool for RNA 3D Structure Prediction and Analysis, 2021 33rd Chinese Control and Decision Conference (CCDC), 4293–4297.

[48] Krokhotin, A., Houlihan, K., and Dokholyan, N.V., 2015, iFoldRNA v2: Folding RNA with constraints, Bioinformatics, 31 (17), 2891–2893.

[49] Williams, C.J., Headd, J.J., Moriarty, N.W., Prisant, M.G., Videau, L.L., Deis, L.N., Verma, V., Keedy, D.A., Hintze, B.J., Chen, V.B., Jain, S., Lewis, S.M., Arendall, W.B., Snoeyink, J., Adams, P.D., Lovell, S.C., Richardson, J.S., and Richardson, D.C., 2018, MolProbity: More and better reference data for improved all-atom structure validation, Protein Sci., 27 (1), 293–315.

[50] Hanwell, M.D., Curtis, D.E., Lonie, D.C., Vandermeersch, T., Zurek, E., and Hutchison, G.R., 2012, Avogadro: An advanced semantic chemical editor, visualization, and analysis platform, J. Cheminf., 4 (1), 17.

[51] Avery, P., Ludowieg, H., Autschbach, J., and Zurek, E., 2017, Extended Hückel calculations on solids using the Avogadro molecular editor and visualizer, J. Chem. Educ., 95 (2), 331–337.

[52] He, J., Wang, J., Tao, H., Xiao, Y., and Huang, S.Y., 2019, HNADOCK: A nucleic acid docking server for modeling RNA/DNA–RNA/DNA 3D complex structures, Nucleic Acids Res., 47 (W1), W35–W42.

[53] Raden, M., Ali, S.M., Alkhnbashi, O.S., Busch, A., Costa, F., Davis, J.A., Eggenhofer, F., Gelhausen, R., Georg, J., Heyne, S., Hiller, M., Kundu, K., Kleinkauf, R., Lott, S.C., Mohamed, M.M., Mattheis, A., Miladi, M., Richter, A.S., Will, S., Wolff, J., Wright, P.R., and Backofen, R., 2018, Freiburg RNA tools: A central online resource for RNA-focused research and teaching, Nucleic Acids Res., 46 (W1), W25–W29.

[54] Raden, M., Müller, T., Mautner, S., Gelhausen, R., and Backofen, R., 2020, The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment, BMC Bioinf., 21 (1), 15.

[55] Mann, M., Wright, P.R., and Backofen, R., 2017, IntaRNA 2.0: Enhanced and customizable prediction of RNA-RNA interactions, Nucleic Acids Res., 45 (W1), W435–W439.

[56] Salentin, S., Schreiber, S., Haupt, V.J., Adasme, M.F., and Schroeder, M., 2015, PLIP: Fully automated protein-ligand interaction profiler, Nucleic Acids Res., 43 (W1), W443–W447.

[57] Butt, S.S., Badshah, Y., Shabbir, M., and Rafiq, M., 2020, Molecular docking using Chimera and Autodock Vina software for nonbioinformaticians, JMIR Bioinf. Biotechnol., 1, e14232.

[58] WHO, 2021, Novel Coronavirus Disease (Covid-19): Situation Update Report - 50, World Health Organization, New Delhi, India.

[59] Chen, V.B., Wedell, J.R., Wenger, R.K., Ulrich, E.L., and Markley, J.L., 2015, MolProbity for the masses–of data, J. Biomol. NMR, 631 (1), 77–83.

[60] Molprobity, 2021, Molprobity Legend for Structural Validation, http://molprobity.biochem.duke.edu/help/validation_options/summary_table_guide.html.

[61] Laskowski, R.A., and Swindells, M.B., 2011, LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery, J. Chem. Inf. Model., 51 (10), 2778–2786.

[62] Caboche, S., 2013, LeView: Automatic and interactive generation of 2D diagrams for biomacromolecule/ligand interactions, J. Cheminf., 5 (1), 40.

[63] Maladan, Y., Krismawati, H., Hutapea, H.M.L., Oktavian, A., Fatimah, R., and Widodo, W., 2019, A new Mycobacterium leprae dihydropteroate synthase variant (V39I) from Papua, Indonesia, Heliyon, 5 (3), e01279.

[64] Tüzün, B., and Kaya, C., 2018, Investigation of DNA–RNA molecules for the efficiency and activity of corrosion inhibition by DFT and molecular docking, J. Bio- Tribo-Corros., 4 (4), 69.

[65] Yan, Y., and Huang, S.Y., 2020, Modeling protein–protein or protein–DNA/RNA complexes using the HDOCK webserver, Methods Mol. Biol., 2165, 217–229.

[66] Parikesit, A.A., 2021, “Introductory Chapter: The Emerging Corner of the Omics Studies for Rational Drug Design” in Drug Design - Novel Advances in the Omics Field and Applications, IntechOpen, Rijeka, 4.

[67] Parikesit, A.A., 2018, “Introductory Chapter: The Contribution of Bioinformatics as Blueprint Lead for Drug Design” in Molecular Insight of Drug Design, IntechOpen, Rijeka, 7.

[68] Ivan, J., Agustriawan, D., Parikesit, A.A., and Nurdiansyah, R., 2021, MiRNA-regulated HspB8 as potent biomarkers in low-grade gliomas, Res. J. Biotechnol., 16 (1), 17–25.

[69] Agustriawan, D., Parikesit, A.A., Nurdiansyah, R., Ivan, J., and Ramanto, K.N., 2021, Correlation and transcriptomic analysis revealing potential microRNA-gene interactions associated with breast cancer formation, Res. J. Biotechnol., 16 (2), 16–23.

[70] Medeiros, I.G., Khayat, A.S., Stransky, B., Santos, S., Assumpção, P., and de Souza, J.E.S., 2021, A small interfering RNA (siRNA) database for SARS-CoV-2, Sci. Rep., 11 (1), 8849.

[71] Donia, A., and Bokhari, H., 2021, RNA interference as a promising treatment against SARS-CoV-2, Int. Microbiol., 24 (1), 123–124.

[72] Idris, A., Davis, A., Supramaniam, A., Acharya, D., Kelly, G., Tayyar, Y., West, N., Zhang, P., McMillan, C.L.D., Soemardy, C., Ray, R., O'Meally, D., Scott, T.A., McMillan, N.A.J., and Morris, K.V., 2021, A SARS-CoV-2 targeted siRNA-nanoparticle therapy for COVID-19, Mol. Ther., 29 (7), 2219–2226.

[73] Khaitov, M., Nikonova, A., Shilovskiy, I., Kozhikhova, K., Kofiadi, I., Vishnyakova, L., Nikolskii, A., Gattinger, P., Kovchina, V., Barvinskaia, E., Yumashev, K., Smirnov, V., Maerle, A., Kozlov, I., Shatilov, A., Timofeeva, A., Andreev, S., Koloskova, O., Kuznetsova, N., Vasina, D., Nikiforova, M., Rybalkin, S., Sergeev, I., Trofimov, D., Martynov, A., Berzin, I., Gushchin, V., Kovalchuk, A., Borisevich, S., Valenta, R., Khaitov, R., and Skvortsova, V., 2021, Silencing of SARS-CoV-2 with modified siRNA-peptide dendrimer formulation, Allergy, 76 (9), 2840–2854.

[74] Tenda, E.D., Asaf, M.M., Pradipta, A., Kumaheri, M.A., and Susanto, A.P., 2021, The COVID-19 surge in Indonesia: What we learned and what to expect, Breathe, 17, 210146.

[75] Dyer, O., 2021, Covid-19: Indonesia becomes Asia’s new pandemic epicentre as delta variant spreads, BMJ, 374, n1815.

[76] Kupferschmidt, K., and Wadman, M., 2021, Delta variant triggers new phase in the pandemic, Science, 372 (6549), 1375–1376.



DOI: https://doi.org/10.22146/ijc.68415

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