2658-6533Research Results in Biomedicine2658-653310.18413/2658-6533-2022-8-3-0-62809Pharmacology<strong><em>In silico</em> identification of the potential natural inhibitors of SARS-CoV-2 Guanine-N7 methyltransferase</strong><strong><em>In silico</em> identification of the potential natural inhibitors of SARS-CoV-2 Guanine-N7 methyltransferase</strong>RowaiyeAdekunle B.RowaiyeAdekunle B.adekunlerowaiye@gmail.comOnuhOlukemi A.OnuhOlukemi A.kemmieonuh@yahoo.comOladimeji-SalamiJoy A.Oladimeji-SalamiJoy A.soiy143@gmail.comBurDoofanBurDoofandoughfaniyorza@gmail.comNjokuMosesNjokuMosesnjokum2003@yahoo.comIfedilichukwuNma H.IfedilichukwuNma H.ufenma@yahoo.co.ukJohnComfort O.JohnComfort O.commyo2009@yahoo.comBinuyoOlanikeBinuyoOlanikenike-binuyo@yahoo.comUdohFaith P.UdohFaith P.gblackyoung@yahoo.com20228300

Background:&nbsp;The outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 has triggered intense scientific research into the possible therapeutic strategies that can combat the ravaging disease. One of such strategies is the inhibition of an important enzyme that affects an important physiological process of the virus. The enzyme, Guanine-N7 Methyltransferase is responsible for the capping of the SARS-CoV-2 mRNA to conceal it from the host&rsquo;s cellular defense. The aim of the study:&nbsp;This study aims at computationally identifying the potential natural inhibitors of the SARS-CoV-2 Guanine-N7 methyltransferase binding at the active site (Pocket 41). Materials and methods:&nbsp;A library of small molecules was obtained from edible African plants and was molecularly docked against the SARS-CoV-2 Guanine-N7 methyltransferase (QHD43415_13. pdb) using the Pyrx software. Sinefungin, an approved antiviral drug had a binding score of -7.6 kcal/ mol with the target was chosen as a standard. Using the molecular descriptors of the compounds, virtual screening for oral availability was performed using the Pubchem and SWISSADME web tools. The online servers pkCSM and Molinspiration were used for further screening for the pharmacokinetic properties and bioactivity respectively. The molecular dynamic simulation and analyses of the Apo and Holo proteins were performed using the GROMACS software on the Galaxy webserver. Results:&nbsp;With a total RMSD of 77.78, average RMSD of 3.704, total regional (active site) RMSF of 30.61, average regional RMSF of 1.91, gyration of 6.9986, and B factor of 696.14, Crinamidine showed the greatest distortion of the target. Conclusion:&nbsp;All the lead compounds performed better than the standard while Crinamidine is predicted to show the greatest inhibitory activity. Further tests are required to further investigate the inhibitory activities of the lead compounds.

Background:&nbsp;The outbreak of the COVID-19 pandemic caused by the SARS-CoV-2 has triggered intense scientific research into the possible therapeutic strategies that can combat the ravaging disease. One of such strategies is the inhibition of an important enzyme that affects an important physiological process of the virus. The enzyme, Guanine-N7 Methyltransferase is responsible for the capping of the SARS-CoV-2 mRNA to conceal it from the host&rsquo;s cellular defense. The aim of the study:&nbsp;This study aims at computationally identifying the potential natural inhibitors of the SARS-CoV-2 Guanine-N7 methyltransferase binding at the active site (Pocket 41). Materials and methods:&nbsp;A library of small molecules was obtained from edible African plants and was molecularly docked against the SARS-CoV-2 Guanine-N7 methyltransferase (QHD43415_13. pdb) using the Pyrx software. Sinefungin, an approved antiviral drug had a binding score of -7.6 kcal/ mol with the target was chosen as a standard. Using the molecular descriptors of the compounds, virtual screening for oral availability was performed using the Pubchem and SWISSADME web tools. The online servers pkCSM and Molinspiration were used for further screening for the pharmacokinetic properties and bioactivity respectively. The molecular dynamic simulation and analyses of the Apo and Holo proteins were performed using the GROMACS software on the Galaxy webserver. Results:&nbsp;With a total RMSD of 77.78, average RMSD of 3.704, total regional (active site) RMSF of 30.61, average regional RMSF of 1.91, gyration of 6.9986, and B factor of 696.14, Crinamidine showed the greatest distortion of the target. Conclusion:&nbsp;All the lead compounds performed better than the standard while Crinamidine is predicted to show the greatest inhibitory activity. Further tests are required to further investigate the inhibitory activities of the lead compounds.

COVID-19SARS-CoV-2guanine-n7 methyltransferaseinhibitionmolecular dockingmolecular dynamic simulationCOVID-19SARS-CoV-2guanine-n7 methyltransferaseinhibitionmolecular dockingmolecular dynamic simulationСписок литературыWorld Health Organization [Internet]. Infection prevention and control during health care when COVID-19 is suspected. [cited 2021Nov18]. Available from: https://www.who,int/publications-detailWorldometer [Internet]. 2020[cited 2021Nov1]. Available from:https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1?de Wit E, van Doremalen N, Falzarano D, et al. SARS and MERS: recent 345 insights into emerging coronaviruses. Nature Reviews Microbiology. 2016;14:523-34. DOI: https://doi.org/10.1038/nrmicro.2016.81Gorbalenya AE, Baker SC, Baric RS, et al. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nature Microbiology. 2020;5(4):536-44. DOI: https://doi.org/10.1038/s41564-020-0695-zYu C, Deyin G. Molecular mechanisms of coronavirus RNA capping and methylation. Virologica Sinica. 2016;31(1):3-11. DOI: https://doi.org/10.1007/s12250-016-3726-4Li F. Structure, Function, and Evolution of Coronavirus Spike Proteins. Annual Review of Virology. 2016;3:237-61. DOI: https://doi.org/10.1146/annurev-virology-110615-042301Al-Tameemi K, Kabakli R. Novel Coronavirus (2019-nCoV): Disease briefings. Asian Journal of Pharmaceutical and Clinical Research. 2020;13(5):22-7. DOI: http://dx.doi.org/10.22159/ajpcr.2020.v13i5.37436Omrani AS, Saad MM, Baig K, et al. Ribavirin and interferon alfa-2a for severe Middle East respiratory syndrome coronavirus infection: a retrospective cohort study. The Lancet Infectious Diseases. 2014;14(11):1090-5. DOI: https://doi.org/10.1016/S1473-3099(14)70920-XWu C, Liu Y, Yang Y, et al. Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharmaceutica Sinica B. 2020;10(5):766-88. DOI: https://doi.org/10.1016/j.apsb.2020.02.008Shuman S. RNA capping: progress and prospects. RNA. 2015;21(4):735-7. DOI: https://doi.org/10.1261/rna.049973.115Shuman S. What messenger RNA capping tells us about eukaryotic evolution. Nature Reviews Molecular Cell Biology. 2002;3:619-25. DOI: https://doi.org/10.1038/nrm880Topisirovic I, Svitkin YV, Sonenberg N, et al. Cap and cap‐binding proteins in the control of gene expression. Wiley interdisciplinary reviews. RNA. 2011;2(2):277-98. DOI: https://doi.org/10.1002/wrna.52Varshney D, Petit A, Bueren-Calabuig JA, et al. Molecular basis of RNA guanine-7 methyltransferase (RNMT) activation by RAM. Nucleic Acids Research. 2016;44(21):10423-36. DOI: https://doi.org/10.1093/nar/gkw637Chen Y, Cai H, Xiang N, et al. Functional screen reveals SARS coronavirus nonstructural protein nsp14 as a novel cap N7 methyltransferase. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(9):3484-9. DOI: https://doi.org/10.1073/pnas.0808790106Chen Y, Tao J, Sun Y, et al. Structure-function analysis of severe acute respiratory syndrome coronavirus RNA cap guanine-N7-methyltransferase. Journal of Virology. 2013;87(11):6296-305. DOI: https://doi.org/10.1128/JVI.00061-13Sun Y, Wang Z, Tao J, et al. Yeast-based assays for the high-through-put screening of inhibitors of coronavirus RNA cap guanine-N7-methyltranferase. Antiviral Research. 2014;104:156-64. DOI: https://doi.org/10.1016/j.antiviral.2014.02.002Aouadi W, Eydoux C, Coutard B, et al. Toward the identification of viral cap-methyltransferase inhibitors by fluorescence screening assay. Antiviral Research. 2017;144:330-9. DOI: https://doi.org/10.1016/j.antiviral.2017.06.021Decroly E, Canard B. Biochemical principles and inhibitors to interfere with viral capping pathways. Current Opinion in Virology. 2017;24:87-96. DOI: https://doi.org/10.1016/j.coviro.2017.04.003Guilloux LV, Peter S, Pierre T. Fpocket: An open-source platform for ligand pocket detection. BMC Bioinformatics. 2009;10:168. DOI: https://doi.org/10.1186/1471-2105-10-168Roy A, Kucukural A, Zhang Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols. 2010;5:725-38. DOI: https://doi.org/10.1038/nprot.2010.5Ramachandran S, Kota P, Ding F, et al. Automated minimization of steric clashes in protein structures. Proteins: Structure, Function and Bioinformatics. 2011;79(1):261-70. DOI: https://doi.org/10.1002/prot.22879Kim S, Chen J, Cheng T, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research. 2019;47(D1):D1102-9. DOI: https://doi.org/10.1093/nar/gky1033Atatreh N, Al Rawashdah S, Al Neyadi SS, et al. Discovery of new butyrylcholinesterase inhibitors via structure-based virtual screening. Journal of Enzyme Inhibition and Medicinal Chemistry. 2019;34(1):1373-9. DOI: https://doi.org/10.1080/14756366.2019.1644329Dallakyan S, Olson AJ. Small-molecule library screening by docking with PyRx. Methods in Molecular Biology. 2015;1263:243-50. DOI: https://doi.org/10.1007/978-1-4939-2269-7_19Gacche RN, Meshram RJ, Dawane AA, et al. Modeling studies of arginase from Helicobacter pylori divulge novel inhibitor-protein interactions. Clinical Proteomics.2016;1(3):54-61. DOI: https://doi.org/10.15761/CPB.1000113Trott O, Olson AJ. Auto Dock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. 2010;31(2):455-61. DOI: https://doi.org/10.1002/jcc.21334Daina A, Olivier M, Vincent Z. Swiss ADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017;7:42717. DOI: https://doi.org/10.1038/srep42717Pires DEV, Blundell TL, Ascher DB. pk CSM: predicting small-molecule pharmacokinetic properties using graph-based signatures. Journal of Medicinal Chemistry. 2015;58(9):4066-72. DOI: https://doi.org/10.1021/acs.jmedchem.5b00104Molinspiration [Internet]. Calculation of Molecular Properties and Bioactivity Score. 2015 [cited 2021Nov18]. Available from: http://www.molinspiration.com/cgi-bin/propertiesMishra SS, Chandra SS, Hemendra PS, et al. In silico ADME, Bioactivity and Toxicity Parameters Calculation of Some Selected Anti-Tubercular Drugs. International Journal of Pharmaceutical and Phytopharmacological Research. 2016;6(6):77-9. DOI: https://doi.org/10.24896/eijppr.2016661DeLano WL. Pymol: An open-source molecular graphics tool. CCP4 Newsletter on protein crystallography. 2020;40:82-92.Salentin S, Schreiber S, Haupt VJ, et al. PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Research. 2015;43(W1):W443-7. DOI: https://doi.org/10.1093/nar/gkv315Lionta E, Spyrou GK, Vassilatis D, et al. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current Topics in Medicinal Chemistry. 2014;14(16):1923-38. DOI: https://doi.org/10.2174/1568026614666140929124445Afghan E, Dannon B, Marius VB, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: update. Nucleic Acids Research. 2016;44(W1):W3-10. DOI: https://doi.org/10.1093/nar/gky379Franco-Ulloa S, Riccardi L, Rimembrana F, et al. Nanomodeler: A webserver for molecular simulations and engineering of nanoparticles. Journal of Chemical Theory and Computation. 2019;15(3):2022-32. DOI: https://doi.org/10.1021/acs.jctc.8b01304Dodda LS, Cabeza VI, Tirado-Rives J, et al. LigParGen web server: an automatic OPLSAA parameter generator for organic ligands. Nucleic Acids Research. 2017;3(W1):W331-6. DOI: https://doi.org/10.1093/nar/gkx312Abraham MJ, Murtola T, Schulz R, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. Software X. 2015;1-2:19-25. DOI: https://doi.org/10.1016/j.softx.2015.06.001Hospital A, Andrio P, Fenollosa C, et al. MDWeb and MDMoby: an integrated web-based platform for molecular dynamics simulations. Bioinformatics. 2012;28(9):1278-9. DOI: https://doi.org/10.1093/bioinformatics/bts139Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews.1997;23(1-3):3-25. DOI: https://doi.org/10.1016/S0169-409X(96)00423-1Okoli PT, Nzute VC, Durojaye OA, et al. An in-silico pharmacokinetics study on Cis-heptadeca-1, 9-Diene-4, 6-Diyne-3, 8-Diol: A nutraceutical compound with anticancer properties. Asian Food Science Journal. 2019;7:1-7. DOI: https://doi.org/10.9734/afsj/2019/v7i329972Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry. 1999;1(1):55-68. DOI: https://doi.org/10.1021/cc9800071Veber DF, Johnson SR, Cheng HY, et al. Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry. 2002;45(12):2615-23. DOI: https://doi.org/10.1021/jm020017nAl Wasidi AS, Hassan AS, Naglah AM. In vitro cytotoxicity and druglikeness of pyrazolines and pyridines bearing benzofuran moiety. Journal of Applied Pharmaceutical Science. 2020;10(4):142-8. DOI: https://doi.org/10.7324/JAPS.2020.104018Clark DE. What Has Polar Surface Area Ever Done for Drug Discovery? Future Medicinal Chemistry. 2011;3(4):469-84. DOI: https://doi.org/10.4155/fmc.11.1Clemons PA, Bodycombe NE, Carrinski HA, et al. Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles. Proceedings of the National Academy of Sciences of the United States of America. 2010;107(44):18787-92. DOI: https://doi.org/10.1073/pnas.1012741107Feldmann C, Miljković F, Yonchev D, et al. Identifying Promiscuous Compounds with Activity against Different Target Classes. Molecules. 2019;24(22):4185. DOI: https://doi.org/10.3390/molecules24224185Imran M, Choudhary MA, Kumar N, et al. Synthesis, spectroscopic characterization and Petra Osiris Molinspiration (POM) analyses of dicarboxylic acid amides. International Journal of Pharmaceutical Sciences and Research. 2016;7(5):1915-27. DOI: https://doi.org/10.13040/IJPSR.0975-8232.7(5).1915-27Khan T, Dixit S, Ahmad R, et al. Molecular docking, PASS analysis, bioactivity score prediction, synthesis, characterization and biological activity evaluation of a functionalized 2-butanone thiosemicarbazone ligand and its complexes. Journal of Chemical Biology. 2017;10:91-104. DOI: https://doi.org/10.1007/s12154-017-0167-yLin J, Sahakian DC, De Morais SM, et al. The role of absorption, distribution, metabolism, excretion and toxicity in drug discovery. Current Topics in Medicinal Chemistry. 2003;3(10):1125-54. DOI: https://doi.org/10.2174/1568026033452096Bachmakov I, Werner U, Endress B, et al. Characterization of &beta;‐adrenoceptor antagonists as substrates and inhibitors of the drug transporter P‐glycoprotein 1. Fundamental and Clinical Pharmacology. 2006;20(3):273-82. DOI: https://doi.org/10.1111/j.1472-8206.2006.00408.xAmin ML. P-glycoprotein Inhibition for Optimal Drug Delivery. Drug Target Insights. 2013;7:27-34. DOI: https://doi.org/10.4137/DTI.S12519Varma MV, Ashokraj Y, Dey CS, et al. P-glycoprotein inhibitors and their screening: a perspective from bioavailability enhancement. Pharmacological Research. 2003;48(4):347-59. DOI: https://doi.org/10.1016/S1043-6618(03)00158-0Ondieki G, Nyagblordzro M, Kikete S, et al. Cytochrome P450 and P-Glycoprotein-Mediated Interactions Involving African Herbs Indicated for Common Noncommunicable Diseases. Evidence-based Complementary and Alternative Medicine. 2017;2582463. DOI: https://doi.org/10.1155/2017/2582463Zhou SF. Drugs behave as substrates, inhibitors and inducers of human cytochrome P450 3A4. Current Drug Metabolism. 2008;9(4):310-22. DOI: https://doi.org/10.2174/138920008784220664Sliwoski G, Kothiwale S, Meiler J, et al. Computational methods in drug discovery. Pharmacological Reviews. 2013;66(1):334-95. DOI: https://doi.org/10.1124/pr.112.007336Bell EW, Zhang Y. DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism. Journal of Cheminformatics. 2019;11:40. DOI: https://doi.org/10.1186/s13321-019-0362-7Sarkar A, Kellogg GE. Hydrophobicity-shake flasks, protein folding and drug discovery. Current Topics in Medicinal Chemistry. 2010;10(1):67-83. DOI: https://doi.org/10.2174/156802610790232233Jeffrey GA. An Introduction to Hydrogen Bonding. Oxford University Press; 1997.Gao J, Bosco DA, Powers ET, et al. Localized thermodynamic coupling between hydrogen bonding and microenvironment polarity substantially stabilizes proteins. Nature Structural and Molecular Biology. 2009;16:684-90.DOI: https://doi.org/10.1038/nsmb.1610Salentin S, Haupt VJ, Daminelli S, et al. Polypharmacology rescored: Protein&ndash;ligand interaction profiles for remote binding site similarity assessment. Progress in Biophysics and Molecular Biology. 2014;116(2-3):174-86. DOI: https://doi.org/10.1016/j.pbiomolbio.2014.05.006Chen D, Oezguen N, Urvil P, et al. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Science advances. 2016;2(3):e1501240. DOI: https://doi.org/10.1126/sciadv.1501240Desiraju G, Steiner T. The Weak Hydrogen Bond. In Structural Chemistry and Biology; Oxford University Press: Oxford; 2001.Ching LL, Koh C, Reker D, et al. Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. Scientific Reports. 2019;9:7703. DOI: https://doi.org/10.1038/s41598-019-43125-6de Freitas RF, Schapira M. A systematic analysis of atomic protein-ligand interactions in the PDB. MedChemComm. 2017;8:1970-81. DOI: https://doi.org/10.1039/C7MD00381AKumar S, Nussinov R. Close-range electrostatic interactions in proteins. ChemBioChem. 2002;3(7):604-17. DOI: https://doi.org/10.1002/1439-7633(20020703)3:7&lt;604::AID-CBIC604&gt;3.0.CO;2-XKurczab R, Śliwa P, Rataj K, et al. Salt Bridge in Ligand&ndash;Protein Complexes&mdash;Systematic Theoretical and Statistical Investigations. Journal of Chemical Information and Modeling. 2018;58(11):2224-38. DOI: https://doi.org/10.1021/acs.jcim.8b00266Maiorov VN, Crippen GM. Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. Journal of Molecular Biology. 1994;235(2):625‐34. DOI: https://doi.org/10.1006/jmbi.1994.1017Kirchmair J, Markt P, Distinto S, et al. Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection&mdash;what can we learn from earlier mistakes? Journal of Computer-Aided Molecular Design. 2008;22:213-28. DOI: https://doi.org/10.1007/s10822-007-9163-6Fuglebakk E, Echave J, Reuter N. Measuring and comparing structural fluctuation patterns in large protein datasets. Bioinformatics. 2012;28(19):2431-40. DOI: https://doi.org/10.1093/bioinformatics/bts445Sneha P, Doss CGP. Molecular dynamics: new frontier in personalized medicine. Advances in Protein Chemistry and Structural Biology. 2016;102:181-224. DOI: https://doi.org/10.1016/bs.apcsb.2015.09.004Sun Z, Liu Q, Qu G, et al. Utility of B-Factors in Protein Science: Interpreting Rigidity, Flexibility, and Internal Motion and Engineering Thermostability. Chemical Reviews. 2019;119(3):1626-65. DOI: https://doi.org/10.1021/acs.chemrev.8b00290Carson M, Bugg C, DeLucas L, et al. Comparison of homology model to the experimental structure of a novel serine protease. Acta Crystallographica Section D: Structural Biology. 1994;D50:889-99. DOI: https://doi.org/10.1107/S0907444994004907David CC, Jacobs DJ. Principal component analysis: a method for determining the essential dynamics of proteins. Methods in Molecular Biology. 2014;1084:193-226. DOI: https://doi.org/10.1007/978-1-62703-658-0_11Sawle L, Ghosh K. Convergence of Molecular Dynamics Simulation of Protein Native States: Feasibility vs Self-Consistency Dilemma. Journal of Chemical Theory and Computation. 2016;12(2):861-9. DOI: https://doi.org/10.1021/acs.jctc.5b00999Kasahara K, Fukuda I, Nakamura H. A Novel Approach of Dynamic Cross Correlation Analysis on Molecular Dynamics Simulations and Its Application to Ets1 Dimer-DNA Complex. PLoS ONE. 2014;9(11):e112419. DOI: https://doi.org/10.1371/journal.pone.0112419Mubashir H, Saba S, Sung SY, et al. Molecular Docking and Dynamic Simulation of AZD3293 and Solanezumab Effects Against BACE1 to Treat Alzheimer&#39;s Disease. Frontiers in Computational Neuroscience. 2018;12:1-34. DOI: https://doi.org/10.3389/fncom.2018.00034Nolan LL. Molecular target of the antileishmanial action of sinefungin. Antimicrobial Agents and Chemotherapy. 1987;31(10):1542-8. DOI: https://doi.org/10.1128/AAC.31.10.1542Kumar YM, Park S, Chae S, et al. Sinefungin, a natural nucleoside analogue of S-adenosylmethionine, inhibits Streptococcus pneumoniae biofilm growth. BioMed Research International. 2014;2014:156987. DOI: https://doi.org/10.1155/2014/156987Trager W, Tershakovec M, Chiang PK, et al. Plasmodium falciparum: antimalarial activity in culture of sinefungin and other methylation inhibitors. Experimental Parasitology. 1980;50(1):83-9. DOI: https://doi.org/10.1016/0014-4894(80)90010-7Dube DK, Mpimbaza G, Allison AC, et al. Antitrypanosomal activity of sinefungin. American Journal of Tropical Medicine and Hygiene. 1983;32(1):31-3. DOI: https://doi.org/10.4269/ajtmh.1983.32.31Ihsan MF, Yanuar A. Molecular Dynamics Simulations of several selected compounds from the herbal database of Indonesia results of molecular docking against DNA Methyltranferase enzyme. International Journal of Applied Pharmaceutics. 2018;10(1):285-90. DOI: https://doi.org/10.22159/ijap.2018.v10s1.63Pugh CS, Borchardt RT, Stone HO. Sinefungin, a potent inhibitor of virion mRNA(guanine-7-)-methyltransferase, mRNA(nucleoside-2&#39;-)-methyltransferase, and viral multiplication. Journal of Biological Chemistry. 1978;253(12):4075-7. DOI: https://doi.org/10.1016/S0021-9258(17)34682-3Kuroda Y, Yamagata H, Nemoto M, et al. Antiviral effect of sinefungin on in vitro growth of feline herpesvirus type 1. Journal of Antibiotics. 2019;72:981-5. DOI: https://doi.org/10.1038/s41429-019-0234-4Jenny M, Wondrak A, Zvetkova E, et al. Crinum Latifolium Leave Extracts Suppress Immune Activation Cascades in Peripheral Blood Mononuclear Cells and Proliferation of Prostate Tumor Cells. Scientia Pharmaceutica. 2011;79(2):323-35. DOI: https://doi.org/10.3797/scipharm.1011-13Ikewuchi CC, Ikewuchi JC, Ifeanacho MO. Bioactive phytochemicals in an aqueous extract of the leaves of Talinum triangulare. Food Science and Nutrition. 2017;5(3):696-701. DOI: https://doi.org/10.1002/fsn3.449Ripim NSM, Fazil N, Ibrahim SNK, et al. Antiviral Properties of Orthosiphon stamineus Aqueous Extract in Herpes Simplex Virus Type 1 Infected Cells. Sains Malaysiana. 2018;47(8):1725-30. DOI: http://dx.doi.org/10.17576/jsm-2018-4708-11Hossain MA, Ismail Z. Quantification and enrichment of sinensetin in the leaves of Orthosiphon stamineus. Arabian Journal of Chemistry. 2016;9(2):S1338-S1341. DOI: https://doi.org/10.1016/j.arabjc.2012.02.016Scalbert A, Andres-Lacueva C, Arita M, et al. Databases on Food Phytochemicals and Their Health-Promoting Effects. Journal of Agricultural and Food Chemistry. 2011;59(9):4331-48. DOI: https://doi.org/10.1021/jf200591dEe GCL, Razis AFA. Phytochemical Constituents and Biological Activities of Melicopelunu-ankenda. Molecules. 2018;23(10):2708.DOI: https://doi.org/10.3390/molecules23102708