THE PREDICTED STRUCTURE FOR THE ANTI-SENSE SIRNA OF THE RNA POLYMERASE ENZYME (RDRP) GENE OF THE SARS-COV-2
Arli Aditya Parikesit, Rizky Nurdiansyah
| Abstract views: 1990
| 
PDF views: 5739
Abstract
The SARS-CoV-2 or COVID-19 pandemic has reached a new height with an unprecedented infection rate and mortality post-world war II history. However, there is no particular designed drug for COVID-19 up to this point. Thus, there exist three strategies for COVID-19 drug design; drug repurposing option, herbal medicine development, and transcriptomics-based drug lead. As the most underutilized option, transcriptomics-based drug lead could be leveraged to deal with SARS-CoV-2 infection. One of the main methods to block the SARS-CoV-2 infection is to inhibit the RNA polymerase enzyme that is responsible to the viral replication. In this regard, the objective of the strategy is to design the anti-sense siRNA drug and lead to inhibit the mRNA of the RNA Polymerase Enzyme (RdRp) gene that encodes the viral RNA Polymerase of the SARS-CoV-2. The Computer-Aided Drug Design (CADD)-based method was leveraged with sequence retrieval of 24 RdRp gene sequences, multiple sequence alignment, phylogenetic tree reconstruction, 2D/3D RNA structure predictions, and RNA-RNA docking. Both the RNAalifold conserved structure from the RdRp genes and the RNAfold structure of the siRNA for blocking the conserved structure are negative or less than 0 kcal/mol. The predicted RNA docking occurred with the best RMSD score of 22.53 Å, which is beyond the accepted threshold of 10-20 Å. Based on the findings, the 2D/3D structures of both the siRNA and mRNA could be elucidated, and the docking between them is feasible. However, this finding should be elucidated in the wet laboratory setting for the final lead validation.
Keywords
Full Text:
PDFReferences
Agustriawan, D., Parikesit, A. A. and Nurdiansyah, R., 2019. Big Data Computation of Drug Design: From the Natural Products to the Transcriptomic-Based Molecular Development. Application of Omics, AI and Blockchain in Bioinformatics Research. WORLD SCIENTIFIC, pp. 59–86.
Akhtari, J., Tafazoli, A., Mehrad-Majd, H. and Mahrooz, A.., 2018. Nanovehicle-based Small Interfering RNA (siRNA) Delivery for Therapeutic Purposes: A New Molecular Approach in Pharmacogenomics. Current Clinical Pharmacology, 13, pp. 173–182.
Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C. and Garry, R. F., 2020. The proximal origin of SARS-CoV-2. Nature Medicine, pp. 1–3.
Ashour, H., M. Elkhatib, W. F., Rahman, M. M. and Elshabrawy, H. A., 2020. Insights into the Recent 2019 Novel Coronavirus (SARS-CoV-2) in Light of Past Human Coronavirus Outbreaks. Pathogens (Basel, Switzerland), 9, pp. 186.
Bajan, S. and Hutvagner, G., 2020. RNA-Based Therapeutics: From Antisense Oligonucleotides to miRNAs. Cells, 9, pp. 137.
Baron, S. A., Devaux, C., Colson, P., Raoult, D. and Rolain, J. M., 2020. Teicoplanin: an alternative drug for the treatment of coronavirus COVID-19? International journal of antimicrobial agents, pp. 105944.
Becker, M. M., Graham, R. L., Donaldson, E. F., Rockx, B., Sims, A. C., Sheahan, T., Pickles, R. J., Corti, D., Johnston, R. E., Baric, R. S. and Denison, M. R., 2008. Synthetic recombinant bat SARS-like coronavirus is infectious in cultured cells and in mice.
Proceedings of the National Academy of Sciences of the United States of America, 105, pp. 19944–19949.
Bernhart, S. H., Hofacker, I. L., Will, S., Gruber, A. R. and Stadler, P. F., 2008. RNAalifold: improved consensus structure prediction for RNA alignments. BMC Bioinformatics, 9, pp. 474.
BNPB, 2020: Home. Covid19.go.id. BNPB (accessed 24 April 2020).
Burnett, J. C. and Rossi, J. J., 2012. RNA-based therapeutics: current progress and future prospects. Chemistry & biology, 19, pp. 60–71.
Cha, Y., Erez, T., Reynolds, I. J., Kumar, D., Ross, J., Koytiger, G., Kusko, R., Zeskind, B., Risso, S., Kagan, E., Papapetropoulos, S., Grossman, I. and Laifenfeld, D., 2018. Drug repurposing from the perspective of pharmaceutical companies. British Journal of Pharmacology, 175(2), pp. 168-180.
Chan, J. F. W., Yip, C. C. Y., To, K. K. W., Tang, T. H. C., Wong, S. C. Y., Leung, K. H., Fung, A. Y. F., Ng, A. C. K., Zou, Z. Tsoi, H. W., Choi, G. K. Y., Tam, A. R. Cheng, V. C. C., Chan, K. H., Tsang, O. T. Y. and Yuen, K. Y., 2020. Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp/Hel real-time reverse transcription-polymerase chain reaction assay validated in vitro and with clinical specimens. Journal of Clinical Microbiology, JCM.00310-20.
Chang, Y. chuan., Tung, Y. an, Lee, K. han, Chen, T. fu, Hsiao, Y. chun, Chang, C., Hsieh, T. ting, Su, C. hung, Wang, S. shia, Yu, J. ying, Lin, Y. hsiang, Lin, Y. hung, Tu, Y. chin E. and Tung, C. wei, 2020. Potential therapeutic agents for COVID-19 based on the analysis of protease and RNA polymerase docking. Preprints, 2020020242.
Chen, X., Mangala, L. S., Rodriguez-Aguayo, C., Kong, X., Lopez-Berestein, G. and Sood, A. K., 2018. RNA interference-based therapy and its delivery systems. Cancer and Metastasis Reviews, 37, pp. 107–124.
Cheng, V. C. C., Lau, S. K. P., Woo, P. C. Y., Kwok, Y. Y., 2007. October 1: Severe acute respiratory syndrome coronavirus as an agent of emerging and reemerging infection. Clinical Microbiology Reviews, 20(4), pp. 660-694.
Cui, J., Li, F., Shi, Z. L., 2019, March 1: Origin and evolution of pathogenic coronaviruses. Nature Reviews Microbiology, 17(3), pp. 181-192.
Dong, L., Hu, S. and Gao, J., 2020. Discovering drugs to treat coronavirus disease 2019 (COVID-19). Drug discoveries & therapeutics, 14, pp. 58–60.
Gao, J., Tian, Z. and Yang, X., 2020. Breakthrough: Chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies. Bioscience trends, 2020.01047.
Gordon, C. J., Tchesnokov, E. P., Feng, J. Y., Porter, D. P. and Gotte, M., 2020. The antiviral compound remdesivir potently inhibits RNA-dependent RNA polymerase from Middle East respiratory syndrome coronavirus. The Journal of biological chemistry., jbc.AC120.013056.
Gruber, A. R.; Lorenz, R.; Bernhart, S. H.; Neuböck, R.; Hofacker, I. L., 2008: The Vienna RNA websuite. Nucleic acids research, 36, (Web Server issue):W70-4.
Gumienny, R. and Zavolan, M., 2015. Accurate transcriptome-wide prediction of microRNA targets and small interfering RNA off-targets with MIRZA-G. Nucleic Acids Research, 43, pp. 1380–1391.
Guo, Y. R., Cao, Q. D., Hong, Z. S., Tan, Y. Y., Chen, S. D., Jin, H. J., Tan, K. S., Wang, D. Y. and Yan, Y., 2020. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Military Medical Research, 7, pp. 11.
Hadfield, J., Megill, C., Bell, S. M., Huddleston, J., Potter, B., Callender, C., Sagulenko, P., Bedford, T. and Neher, R. A., 2018. Nextstrain: real-time tracking of pathogen evolution. (Kelso, J., Ed.). Bioinformatics, 34, pp. 4121–4123.
Hall, B. G., 2013. Building phylogenetic trees from molecular data with MEGA. Molecular Biology and Evolution, 30, pp. 1229–1235.
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 Research, 47, pp.W35–W42.
Hofacker, I. L., Fekete, M. and Stadler, P. F., 2002. Secondary Structure Prediction for Aligned RNA Sequences. Journal of Molecular Biology, 319, pp. 1059–1066.
Hofacker, I. L. and Stadler, P. F., 2006. Memory efficient folding algorithms for circular RNA secondary structures. Bioinformatics, 22, pp. 1172–1176.
Kemenkes-RI, 2020. Info Infeksi Emerging Kementerian Kesehatan RI. https://infeksiemerging.kemkes.go.id/. (Accessed 24 April 2020).
Knoff, J.; Yang, B.; Hung, C. F.; Wu, T. C., 2014. Cervical Cancer: Development of Targeted Therapies Beyond Molecular Pathogenesis. Current obstetrics and gynecology reports, 3, pp. 18–32.
Krokhotin, A., Houlihan, K. and Dokholyan, N. V., 2015. iFoldRNA v2: folding RNA with constraints: Fig. 1. Bioinformatics, 31, pp. 2891–2893.
Kumar, M. and Carmichael, G. G., 1998. Antisense RNA: Function and Fate of Duplex RNA in Cells of Higher Eukaryotes. Microbiology and Molecular Biology Reviews, 62, pp. 1415–1434.
Lai, A., Bergna, A., Acciarri, C., Galli, M. and Zehender, G., 2020. Early phylogenetic estimate of the effective reproduction number of SARS‐CoV‐2. Journal of Medical Virology, jmv.25723.
Langedijk, J., Mantel-Teeuwisse, A. K., Slijkerman, D. S. and Schutjens, M. H. D. B., 2015. August 1: Drug repositioning and repurposing: terminology and definitions in literature. Drug Discovery Today.
Leelananda, S. P. and Lindert, S., 2016. Computational methods in drug discovery. Beilstein Journal of Organic Chemistry, 12, pp. 2694-2718.
Li, C., Penet, M. F., Wildes, F., Takagi, T., Chen, Z., Winnard, P. T., Artemov, D. and Bhujwalla, Z. M., 2010. Nanoplex delivery of siRNA and prodrug enzyme for multimodality image-guided molecular pathway targeted cancer therapy. ACS Nano, 4, pp. 6707–6716.
Li, F. S.; Weng, J. K., 2017, July 31: Demystifying traditional herbal medicine with modern approaches. Nature Plants, 3, pp. 17109.
Li, G. and De Clercq, E., 2020. Therapeutic options for the 2019 novel coronavirus (2019-nCoV). Nature Reviews Drug Discovery, 19, pp. 149–150.
Li, T., Zhang, Y., Fu, L., Yu, C., Li, X., Li, Y., Zhang, X., Rong, Z., Wang, Y., Ning, H., Liang, R., Chen, W., Babiuk, L. A. and Chang, Z., 2005. siRNA targeting the Leader sequence of SARS-CoV inhibits virus replication. Gene Therapy, 12, pp. 751–761.
Liang, T., 2020: Handbook of COVID-19 Prevention and Treatment. Zhenjiang.
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F. and Hofacker, I. L., 2011. ViennaRNA Package 2.0. Algorithms for Molecular Biology, 6, pp. 26.
Lu, H., 2020. Drug treatment options for the 2019-new coronavirus (2019-nCoV). Bioscience trends, 14(1), pp. 69-71.
Luk, H. K. H., Li, X., Fung, J., Lau, S. K. P. and Woo, P. C. Y., 2019. July 1: Molecular epidemiology, evolution and phylogeny of SARS coronavirus. Infection, Genetics and Evolution, 71, pp. 21-30.
Lung, J., Lin, Y., Yang, Y., Chou, Y., Shu, L., Cheng, Y., Liu, H. Te. and Wu, C., 2020. The potential chemical structure of anti-SARS-CoV-2 RNA-dependent RNA polymerase. Journal of medical virology., jmv.25761.
Mansoor, M. and Melendez, A. J., 2008. Advances in Antisense Oligonucleotide Development for Target Identification, Validation, and as Novel Therapeutics. Gene Regulation and Systems Biology, 2, GRSB.S418.
Mattick, J. S. and Rinn, J. L., 2015. Discovery and annotation of long noncoding RNAs. Nature structural & molecular biology., 22, pp. 5–7.
Meng, H., Mai, W. X., Zhang, H., Xue, M., Xia, T., Lin, S., Wang, X., Zhao, Y., Ji, Z., Zink, J. I. and Nel, A. E., 2013. Codelivery of an optimal drug/siRNA combination using mesoporous silica nanoparticles to overcome drug resistance in breast cancer in vitro and in vivo. ACS Nano, 7, pp. 994–1005.
Mercer, T. R., Dinger, M. E. and Mattick, J. S., 2009. Long non-coding RNAs: insights into functions. Nature Reviews Genetics, 10, pp. 155–159.
NUS, 2020: COVID-19 Science Report: Therapeutics. Singapore.
Okonechnikov, K., Golosova, O. and Fursov, M., 2012. Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics, 28, pp. 1166–1167.
Parikesit, A. A. and Anurogo, D., 2016. Prediksi Struktur 2-Dimensi Non-Coding Rna Dari Biomarker Kanker Payudara Triple-Negative Dengan Vienna Rna Package. Chimica et Natura Acta, 4, pp. 27.
Parikesit, A. A., Utomo, D. H. and Karimah, N., 2018a. Determination of secondary and tertiary structures of cervical cancer lncRNA diagnostic and siRNA therapeutic biomarkers. Indonesian Journal of Biotechnology, 23, pp. 1.
Parikesit, A. A. and Anurogo, D., 2018b. 3D Prediction of Breast Cancer Biomarker from The Expression Pathway of Lincrna-Ror/Mir-145/Arf6. Jurnal Sains dan Teknologi, 2, pp. 10–19.
Parikesit, A. A. and Nurdiansyah, R., 2018c. Generating Two-Dimensional Repertoire of siRNA Linc-ROR and siRNA mRNA ARF6 from the lincRNA-RoR/miR-145/ARF6 expression Pathway that involved in the progression of Triple Negative Breast Cancer. IOP Conference Series: Materials Science and Engineering, 299, pp. 012059.
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 (Data Sets).
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C. and Ferrin, T. E., 2004. UCSF Chimera--a visualization system for exploratory research and analysis. Journal of computational chemistry, 25, pp. 1605–1612.
Phan, T., 2020. Genetic diversity and evolution of SARS-CoV-2. Infection, Genetics and Evolution, 81, pp. 104260.
Riccio, F., Talapatra, S. K., Oxenford, S., Angell, R., Mazzon, M. and Kozielski, F., 2019. Development and validation of RdRp Screen, a crystallization screen for viral RNA-dependent RNA polymerases. Biology Open, 8.
Rother, M., Milanowska, K., Puton, T., Jeleniewicz, J., Rother, K. and Bujnicki, J. M., 2011. ModeRNA server: an online tool for modeling RNA 3D structures. Bioinformatics, 27, pp. 2441–2442.
Sætrom, P. and Snøve, O., 2004. A comparison of siRNA efficacy predictors. Biochemical and Biophysical Research Communications, 321, pp. 247–253.
Shannon, A., Tuyet Le, N. T., Selisko, B., Eydoux, C., Alvarez, K., Guillemot, J. C., Decroly, E., Peersen, O., Ferron, F. and Canard, B., 2020. Remdesivir and SARS-CoV-2: structural requirements at both nsp12 RdRp and nsp14 Exonuclease active-sites. Antiviral research, 178, pp. 104793.
Shen, K. L.; Yang, Y. H., 2020, February 5: Diagnosis and treatment of 2019 novel coronavirus infection in children: a pressing issue. World Journal of Pediatrics. https://doi.org/10.1007/s12519-020-00344-6
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. Current Pharmaceutical Design, 24, pp. 62–77.
Song, Z., Xu, Y., Bao, L., Zhang, L., Yu, P., Qu, Y., Zhu, H., Zhao, W., Han, Y. and Qin, C., 2019. January 1: From SARS to MERS, thrusting coronaviruses into the spotlight. Viruses, 11(1), pp. 59.
Tafer, H., Ameres, S. L., Obernosterer, G., Gebeshuber, C. A,. Schroeder, R., Martinez, J. and Hofacker, I. L., 2008. The impact of target site accessibility on the design of effective siRNAs. Nature biotechnology, 26, pp. 578–583.
Tang, Y., Zhu, W., Chen, K. and Jiang, H., 2006. New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discovery Today: Technologies, 3, pp. 307–313.
Thomford, N. E., Dzobo, K., Chimusa, E., Andrae-Marobela, K., Chirikure, S., Wonkam, A. and Dandara, C., 2018. Personalized Herbal Medicine? A Roadmap for Convergence of Herbal and Precision Medicine Biomarker Innovations. OMICS A Journal of Integrative Biology, 22, pp. 375–391.
Titze-de-Almeida, R., David, C. and Titze-de-Almeida, S. S., 2017. July 1: The Race of 10 Synthetic RNAi-Based Drugs to the Pharmaceutical Market. Pharmaceutical Research, 34(7), pp. 1339-1363.
Valeska, M. D., Adisurja, G. P., Bernard, S., Wijaya, R., Aldino, M. and Parikesit, A. A., 2019. The Role of Bioinformatics in Personalized Medicine: Your Future Medical Treatment. Cermin Dunia Kedokteran, 46, pp. 785–788.
Wahlestedt, C., 2006, June 1: Natural antisense and noncoding RNA transcripts as potential drug targets. Drug Discovery Today, 11(11-12), pp. 503-508.
Wang, J., 2020: Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study, 10.1021/acs.jcim.0c00179.
Wang, M., Cao, R., Zhang, L., Yang, X., Liu, J., Xu, M., Shi, Z., Hu, Z., Zhong, W. and Xiao, G., 2020. March 1: Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Research, 30(3), pp. 269-271.
WHO, 2020a: Situation Reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/. Accessed 6th of March 2020
WHO, 2020b: Coronavirus disease 2019. World Health Organization., 2019, 2633. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. (Accessed 6th of March 2020)
Worldometers.info, 2018: WorldoMeter: Real Time World Statistics. Current World Population. WorldoMeter.
Refbacks
- There are currently no refbacks.