Blocking catalytic metal ion binding sites to develop antiviral therapies: exemplified using SARS-CoV-2 and HIV

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Treating COVID19 by inhibition of the RNA dependent-RNA polymerase of the causative virus, SARS-CoV2, is a helpful strategy. In this manuscript we describe a method of inhibiting SARS-CoV2 and other viral polymerases by blocking the binding of catalytic metal ions to the catalytic site in these polymerases. We performed an ~ 900,000 small molecule, in silico , virtual screening for small molecule compounds that would bind the metal ion site on nsp12; the SARS-CoV-2 replicase. We also tested seven of the best scoring “hit” compounds in an in vitro activity assay for HIV reverse transcriptase. We found that even though the in silico screen for compounds had be targeted at nsp12, our compounds, at 10 µM, still had up to 24.4% inhibitory activity on HIV-RT in an enzymatic assay. Docking to a model of HIV-RT found that these seven molecules dock in overlapping pockets an near the catalytic metal ion binding site, occluding it. Presumably these molecules inhibit HIV-RT in the same fashion they were intended to inhibit SARS-CoV-2’s nsp12. Further development of compounds that target catalytic metal ion binding sites can generate antivirals for a variety of viruses or even broad-spectrum antiviral therapeutics. Biological sciences/Biochemistry Biological sciences/Chemical biology Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Microbiology Health sciences/Molecular medicine SARS-CoV-2 COVID19 nonstructural protein 12 HIV reverse transcriptase polymerase inhibitor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction COVID19 is a viral respiratory illness; a pandemic of which began in early December of 2019 [ 1 , 2 ]. The now widely recognized illness is caused by the SARS-CoV2 virus. Vaccines are now available in the United States. Other countries have also developed vaccines. However, these may not be the best option for all individuals due to the potential autoimmune responses and other complications from vaccines [ 5 ]. Additionally, the available COVID19 vaccines may have limited effectiveness against new strains of the SARS-COV2 virus, such as Omicron [ 6 ]. Consequently, a threat from COVID19 and a need for effective therapeutics exists. Additionally, as suggested by the findings in this manuscript, COVID19 therapeutics may be able to be repurposed for the treatment of other viral infections. SARS-CoV2, the virus that causes COVID-19, is a new member of the Orthocoronavirinae subfamily; genus Sarbecovirus. It is a novel coronavirus, and though homologous, it is not MERS-CoV or SARS-CoV [ 4 ]. This novel coronavirus has a similar replication mechanism as other coronaviruses, West Nile virus, Marburg virus, Ebola virus, dengue virus, and hepatitis C virus (HepC) which utilizes a positive-sense, single strand of RNA to encode its genome. The genome of SARS-CoV2 is replicated by non-structural protein 12 (nsp12), an RNA-dependent RNA polymerase (RdRp). This RNA dependency is like reverse transcriptase in HIV and HCV replicase (NS5B) in HepC.[ 7 , 8 ]. The RdRp mechanism is also seen in other positive strand RNA viruses, such as rhinoviruses, that cause common colds, and, not surprisingly, the homologous MERS-CoV and SARS-COV. [ 7 , 8 ]. An existing drug, remdesivir, targets the SARS-CoV-2 virus’s nsp12 [ 9 , 10 ]. Inhibition of viral polymerases has proven effective in treating of viral infections, such as in the cases of HIV and HepC, and the effectiveness of this strategy has led to compassionate use of the drug, remdesivir, in the case of SARS-CoV2 [ 11 – 13 ]. While using existing drugs speeds the treatment to the field more quickly, the threat of second, third, and even more subsequent waves of COVID19, or other pandemic coronaviruses, necessitate treatments specifically targeted to coronaviruses and that have increased potency. This study sought novel, non-nucleoside inhibitors of SARS-CoV2’s nsp12. This strategy will allow for incorporating molecules from a greater chemical space to find more potent coronavirus nsp12-specific lead molecules. Additionally, we discovered that by targeting the viral polymerase's catalytic metal ion binding areas, we found compounds were also partially effective against HIV1 reverse transcriptase displaying up to 24.4% inhibition of HIV-RT at 10 µM compound. These compounds could be helpful against COVID19 and may be developable into a wider spectrum of antivirals. Materials And Methods The virtual screening we carried out was performed before a SARS-CoV2 nsp12 macromolecule experimentally solved structure was available and used a homology model made from SARS-CoV nsp12 6nur.pdb, chain A [ 14 ]. A methodology like that posted online at http://home.fatsilicodatapharm.com was used for the virtual screening. Streamlined and better resourced versions of these methods may be useful in future viral outbreaks. Homology modeling The macromolecule model of SARS-CoV2 nsp12 was created using Bioinformatics Toolkit [ 15 ] as part of a personal interest project while the author was in an academic role. The template used was the cryo-EM structure of SARs-CoV nsp12, 6nur.pdb, chain A [ 14 ]. At the time of beginning of our preliminary screening (circa March 15, 2020), there were no available models of SARS-CoV2 nsp12. A BLAST alignment of the R1ab’s sequence from https://viralzone.expasy.org/8996 [ 16 ] was done using HHPRED. This showed the region of the polypeptide homologous to the SARS-CoV nsp12. A homology model was then produced using MODELLER. An unstructured region of the N-terminus was deleted in the finished model used. The homology model had a backbone rmsd of 0.66 Å when fit to the first SARS-CoV2 cryo-EM structure, 6m71.pdb in DEEPVIEW [ 17 , 18 ]. Macromolecule model preparation The models were prepared for docking in AutoDock MGL Tools 1.5.6 [ 19 ]. The SARs-CoV2 nsp12 and HIV-RT docking model’s hydrogen were added and then merged into nonpolar hydrogen. Gasteiger charges were calculated and AutoDock4 atom types were added. The nsp12 docking model was then saved as a “.pdbqt” file. For the HIV RT model 1rtd.pdb1, the structure’s biological assembly had RNA and small molecules removed, leaving only chains A and B. The HIV RT model was then processed similarly as described. Virtual screening ligand preparation Clean Drug Like sets 50 through 56 were downloaded from the ZINC12 Database (zinc12.docking.org) [ 20 ]. The compressed “.mol2” files were decompressed and then split using Raccoon [ 21 ], available from the Olson laboratory, which also produces AutoDock. A Linux script that employs nested “for each” loops and files in AutoDock MGL Tools 1.5.6, based on scripts in the tutorial, UsingAutoDock4forVirtualScreening_v4.pdf form Scripps Institute, was used to set up ligand files in the proper .pdbqt file for screening. This script also split the ZINC ligand files into 100 folders, numbered 0 to 99, based on the first two numbers in the ZINC Database entry number. Running the virtual screening AutoDock Vina [ 22 ] was used for the virtual screening of ~ 900,000 compound ligand files. Each set was run by a separate instance of a Linux shell script that employed nest “for each” loop that traversed through the folders set up in the “Virtual screening ligand preparation” section to run AutoDock Vina on each individual, processed ZINC “.pdbqt” ligand file. A 16 by 16 by 18 angstrom search area, centered at approximately CYS 623 in our model (CYS622 in 6m71.pdb), was used as our pocket of interest (POI) for the screening. Resdiues have been renumbered in this manuscript to reflectthe now accepted numbering. The first sets to finish in the virtual screening, Clean Drug Like 50, Clean Drug Like 52 and Clean Drug Like 53 were immediately analyzed in order to purchase lead compounds. Using scripts, log files from each docking were combined, and the best score for each compound was extracted into a new file. These were then sorted. ZINC compound files scoring − 10.1 kcal/mol or better, with more negative scores being better, were included in our lists of “hit” compounds. Analysis and sorting were performed using custom shell scripts and Calc in Libre Office as available on Centos 7. HIV-RT dockings Structure files of the small molecules of interest in this manuscript were docked to a 20x20x20 angstrom search box centered on the alpha carbon of 1rtd.pdb1 chain A MET184 [ 23 ]. MET184 corresponds structurally to the same position on HIV-RT as CYS 623 on SARS-CoV-2 nsp12 in relation to the catalytic metal ions binding site. Molecular Representation Molecules in this manuscript were rendered in AutoDock MGL Tools 1.5.6 [ 19 ] or Biovia Discovery Studio Viewer [ 24 ]. DNA Polymerase Assay We carried out polymerase activity assay of the purified wild-type HIV-1 RT using homopolymeric poly (rA/dT 18 ) or heteropolymeric DNA TP, which consisted of 21-mer DNA PBS primer annealed with 49-mer U5-PBS DNA templates (3’CAG GGA CAA GCC CGC GGT GAC GAT CTC TAA AAG GTG TGA CTG ATT TTC C-5’). Assays were done in a 100-µl volume containing 50 mM Tris-HCl (pH 7.8), 100 µg ml − 1 bovine serum albumin, 2 mM MgCl 2 , 1 mM dithiothreitol, 100 nM TP, 50 µ M dNTPs, and 21 nM enzyme. The homopolymeric rA/dT 18 TP, the reaction mixture contained 20 µM of TTP and 2 µ Ci of 3 H TTP and heteropolymeric TP; all four dNTPs (50 µM each) were included, with one of them being α 32 P -labeled (0.4 µ Ci/nmol of dNTP). Reactions were performed at 37°C for varying times, in the presence and absence of selected small molecules then terminated by the addition of ice-cold 5% trichloroacetic acid (TCA) containing 5 mM inorganic pyrophosphate. We collected insoluble acid materials on Whatman GF/B filters and counted for radioactivity in a liquid scintillation counter. Results Virtual screening and compound of interest selection Our homology modeling resulted in a model that shifted one residue compared to the now accepted numbering for SARS-CoV-2 nsp12. Residue references in this manuscript have been adjusted to reflect the currently accepted numbering. As stated in the “Materials and Methods” section, our model has a RMSD deviation of 0.66 Å when fit to the first SARS-CoV2 cryo-EM structure, 6m71.pdb in DEEPVIEW [ 17 , 18 ]. We targeted the search area depicted in Fig. 1 as our POI. This search area included the surmised metal ion binding sites for SARS-CoV-2. In our screening of about 900,000 compounds from ZINC12’s “Clean Drug Like” we counted small molecule compounds from these sets that scored the same as or better than − 10.1 kcal/mol in AutoDock Vina, with more negative scores being better, as hit molecules. This amounted to the best scoring < 0.05% of the screened small molecule compounds or about 400 to 500 molecules. From this top 0.05% we were able to test a selected, seven of these small molecule compounds (Enamine: Kiev, Ukraine) in an HIV1 RT activity assay. We have analyzed these seven small molecules in this manuscript. They are ZINC8938064 (2-[[2-(3,4-dihydro-2H-1,5-benzodioxepin-7-yl)pyrrolidin-1-yl]methyl]-5-(5-methyl-3-phenyl-1,2-oxazol-4-yl)-1,3,4-oxadiazole), ZINC12545143 (2-(4-oxo-3H-phthalazin-1-yl)-N-(2-phenylquinolin-4-yl)acetamide), ZINC12939070 ([(2S)-1-(2,2-dimethyl-3-oxo-4H-quinoxalin-1-yl)-1-oxopropan-2-yl] 9-oxo-2,3-dihydro-1H-pyrrolo[2,1-b]quinazoline-6-carboxylate), ZINC27385605 ([(3S)-3-(1,3-benzoxazol-2-yl)piperidin-1-yl]-[3-(2,3-dihydroindol-1-ylsulfonyl)phenyl]methanone), ZINC30280395 ((3R)-N-[[4-[(3-fluorophenyl)methoxy]phenyl]methyl]-2-(furan-2-carbonyl)-3,4-dihydro-1H-isoquinoline-3-carboxamide), ZINC12733075 (N-dibenzofuran-2-yl-4-(4-oxo-1H-quinazolin-2-yl)butanamide), and ZINC29112491 ([3-(3,4-dihydro-1H-isoquinolin-2-ylsulfonyl)phenyl]-[2-(4-fluorophenyl)morpholin-4-yl]methanone). Figure 2 shows chemical drawings of the compounds in this manuscript. All are non-nucleotides. Interestingly, while each contains at least one phenol ring, each also represents a novel scaffold in different chemical space. Compounds of interest and SARS-CoV2 nsp12 residue interactions As part of the analysis of the hit molecules our virtual screening Table 1 shows the interacting residues predicted by our dockings in AutoDock Vina and detected through visualization in Discovery Studio Viewer [ 22 , 24 ]. VAL166, ASP452, TYR455, LYS545, MET543, ARG553, ALA554, ARG555, THR556, VAL557, ALA558, PR0620, LYS621, CYS622, ASP623, ARG624, THR680, SER681, ASP760, and LYS798 were each contacted by at least one of the small molecule compounds. TYR455, ASP623, and ARG624 were predicted to be contacted by all of our small molecule compounds in each compound’s best docking pose. These residues are shown in red on the sequence alignment of nsp12 and HIV-RT in S1. Figure 3 shows each compound in the POI as predicted by our dockings and depicted in Discovery Studio viewer. The center of our search area (CYS622) is shown in yellow stick. Our ubiquitous interacting residues TYR455, ASP623, and ARG624 are shown in black, red, and blue stick, respectively. Inhibition of HIV-RT activity Inhibition of HIV1-RT by our small molecule compounds in an enzymatic activity assay of HIV1 reverse transcriptase is illustrated in Fig. 4 . 10 µM of some of our small molecule compounds can inhibit HIV1-RT activity by as much as 24.4%, such as the case of ZINC27385605, that reduced activity to 75.6%. Experiments were carried out using either Mg 2+ or Mn 2+ as the catalytic ion. Data suggests that some compounds may be more effective against different catalytic ions being used for polymerase activity. For example, Mn2 + ZINC29112491 reduced activity by less than 7% versus DMSO control when Mn2 + was the catalytic metal ion but reduced RT activity by nearly 20% when Mg2 + was the catalytic ion present. Small molecule docking to HIV-RT model Upon running the docking of our 7 small molecules of interest to a model of HIV RT, we observed all seven molecules dock in overlapping pockets in close proximity to ASP185 and ASP186 that stabilize metal ion binding in reverse transcriptase (Fig. 5 ) [ 23 ]. This is evidence that our 7 compounds of interest all inhibit SAR CoV2 nsp12 and HIV-RT by the same mechanism. They also have lower, but still respectable AutoDock Vina scores ranging from − 7.8 to -9.1 kcal/mol (Table 2). Discussion Targeting a viral polymerase’s metal ion binding ability could be a practical strategy for therapeutically treating that virus’s corresponding infection. In this manuscript, we showed evidence that compounds designed to target the catalytic metal ion binding sites in SARS-CoV-2’s replicase, nsp12, in a virtual screening can partially inhibit HIV1 reserve transcriptase. This leads to the hypothesis that by targeting these catalytic metal ion binding sites in viral polymerases, we can find wide spectrum antivirals to increase our readiness for future viral pandemics. The theoretical small molecule docking prediction that our compounds have some interacting residues in common, but not all, shows a strength of our not using different chemical space. Mutation of a residue may exclude some of our compounds from being effective but would presumably not affect all of the compounds’ binding to the same extent, leaving several other options for inhibiting viral polymerases. This study has demonstrated that targeting the catalytic metal ion binding site on the viral replicase via properly executed virtual screening, coupled with in vitro verification testing is an effective strategy for finding a broad spectrum of potentially therapeutic inhibitors. Declarations Acknowledgements Special thanks to Jason Kaelber and the Gaokerena Institute for Molecular Biology for funding that made this study possible, and to Rutgers University, where the corresponding author was employed at the beginning of this study. Thanks to Oleg Trott for assistance troubleshooting the HIV-RT conf file, and Abena Amankwaa, Joana Lopez, and T.W.C.’s other laboratory rotation students at Kean University for useful discussions. Author Contributions V.P. assisted in the experimental laboratory and in the writing of the manuscript. T.W.C secured funding, performed computational and experimental components of the study, and wrote the manuscript. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to ongoing intellectual property filings but are available from the corresponding author on reasonable request. Fair Disclosure Statement (Patent Pending) Small molecule compound(s) discussed in this manuscript are covered under US patent application 17/472,647 invented and owned by Thomas W. Comollo, Ph.D, with a projected publication date of 3/16/2023. References Loeffelholz, M.J. and Y.W. Tang, Laboratory diagnosis of emerging human coronavirus infections - the state of the art . Emerg Microbes Infect, 2020. 9 (1): p. 747–756. Bogoch, II, et al., Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel . J Travel Med, 2020. 27 (2). National Center for Immunization and Respiratory Diseases (NCIRD), D.o.V.D. Coronavirus Disease 2019 (COVID-19) - COVID-19 Forecasts . [internet page] 2020 May 6, 2020 [cited 2020 May 7]; Covid-19 Deaths (Forcast) ]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html#anchor_1587397564229 . Zhu, N., et al., A Novel Coronavirus from Patients with Pneumonia in China, 2019 . N Engl J Med, 2020. 382 (8): p. 727–733. Segal, Y. and Y. Shoenfeld, Vaccine-induced autoimmunity: the role of molecular mimicry and immune crossreaction . Cell Mol Immunol, 2018. 15 (6): p. 586–594. 4th Dose COVID mRNA Vaccines’ Immunogenicity & Efficacy Against Omicron VOC. medRxiv, 2022: p. 2022.02.15.22270948. Snijder, E.J., E. Decroly, and J. Ziebuhr, The Nonstructural Proteins Directing Coronavirus RNA Synthesis and Processing . Adv Virus Res, 2016. 96 : p. 59–126. te Velthuis, A.J., Common and unique features of viral RNA-dependent polymerases . Cell Mol Life Sci, 2014. 71 (22): p. 4403–20. Neogi, U., et al., Feasibility of Known RNA Polymerase Inhibitors as Anti-SARS-CoV-2 Drugs . Pathogens, 2020. 9 (5). Gordon, C.J., et al., Remdesivir is a direct-acting antiviral that inhibits RNA-dependent RNA polymerase from severe acute respiratory syndrome coronavirus 2 with high potency . J Biol Chem, 2020. Eltahla, A.A., et al., Inhibitors of the Hepatitis C Virus Polymerase; Mode of Action and Resistance . Viruses, 2015. 7 (10): p. 5206–24. Grein, J., et al., Compassionate Use of Remdesivir for Patients with Severe Covid-19. N Engl J Med, 2020. Usach, I., V. Melis, and J.E. Peris, Non-nucleoside reverse transcriptase inhibitors: a review on pharmacokinetics, pharmacodynamics, safety and tolerability . J Int AIDS Soc, 2013. 16 : p. 1–14. Kirchdoerfer, R.N. and A.B. Ward, Structure of the SARS-CoV nsp12 polymerase bound to nsp7 and nsp8 co-factors. Nat Commun, 2019. 10 (1): p. 2342. Zimmermann, L., et al., A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core . J Mol Biol, 2018. 430 (15): p. 2237–2243. Hulo, C., et al., ViralZone: a knowledge resource to understand virus diversity . Nucleic Acids Res, 2011. 39 (Database issue): p. D576-82. Guex, N. and M.C. Peitsch, SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling . Electrophoresis, 1997. 18 (15): p. 2714–23. Gao, Y., et al., Structure of the RNA-dependent RNA polymerase from COVID-19 virus . Science, 2020. 368 (6492): p. 779–782. Morris, G.M., et al., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility . J Comput Chem, 2009. 30 (16): p. 2785–91. Irwin, J.J. and B.K. Shoichet, ZINC–a free database of commercially available compounds for virtual screening . J Chem Inf Model, 2005. 45 (1): p. 177–82. Forli, S., et al., Computational protein-ligand docking and virtual drug screening with the AutoDock suite . Nat Protoc, 2016. 11 (5): p. 905–19. Trott, O. and A.J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading . J Comput Chem, 2010. 31 (2): p. 455–61. Huang, H., et al., Structure of a covalently trapped catalytic complex of HIV-1 reverse transcriptase: implications for drug resistance . Science, 1998. 282 (5394): p. 1669–75. BIOVIA, Discovery Studio Viewer . 2019. Tables Table 1. SARS-CoV2 non-structural protein 12 residues predicted to interact with our selected hit compounds Compound | Vina score (kcal/mol) nsp12 residue ZINC08938064 -10.4 ZINC12545132 -10.4 ZINC12939070 -10.2 ZINC27385605 -10.2 ZINC30280395 -10.1 ZINC12733075 -10.1 ZINC29112491 -10.1 VAL166 X X ASP452 X X X TYR455 X X X X X X X LYS545 X MET543 X X X X ARG553 X X X X ALA554 X ARG555 X X X THR556 X X X VAL557 X X ALA558 X X X X X X PRO620 X LYS621 X X X X X CYS622 ASP623 X X X X X X X ARG624 X X X X X X X THR680 X X SER681 X X X X ASP760 X LYS798 X Table 2. Scores of best scoring Vina docking to HIV-RT model conformations depicted in Figure 5. Small Molecule nsp12 Hit Vina Score HIV-RT POI (kcal/mol) ZINC12545143 -8.2 ZINC30280395 -8.5 ZINC29112491 -7.8 ZINC08938064 -9.1 ZINC27385605 -8.5 ZINC12939070 -8.3 ZINC12733075 -7.8 Additional Declarations Competing interest reported. Conflicts of interest for: Viral polymerase inhibitors that block catalytic metal ion binding, may offer clues to developing wide spectrum antiviral therapies By V. Pandey and T.W. Comollo Fair Disclosure Statement (Patent Pending) Small molecule compound(s) discussed in this manuscript are covered under US patent application 17/472,647 invented and owned by Thomas W. Comollo, PhD, with a projected publication date of 3/16/2023. The Authors declare no other conflicts of interest. Supplementary Files S1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2274158","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":153537255,"identity":"3f48435f-b7a1-400b-9343-b45c91612bc4","order_by":0,"name":"Virendra 1 Pandey","email":"","orcid":"","institution":"Rutgers University - New Jersey Medial School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Virendra","middleName":"1","lastName":"Pandey","suffix":""},{"id":153537256,"identity":"7eab3102-c63d-41ab-9ce1-67483abb70c1","order_by":1,"name":"Thomas Comollo","email":"data:image/png;base64,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","orcid":"","institution":"FSDP LLC","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Comollo","suffix":""}],"badges":[],"createdAt":"2022-11-15 03:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2274158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2274158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":29457915,"identity":"1b523910-abcc-4309-8162-17a256c083e7","added_by":"auto","created_at":"2022-11-23 21:10:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSearch area used in our virtual screening\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003e \u003cstrong\u003eA.\u003c/strong\u003e Homology model of SARS-CoV2 nsp12 and search area used in the virtual screening rendered in AutoDock MGL Tools 1.5.6.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/2e52649a345421f4f9e414d7.jpg"},{"id":29457664,"identity":"4acff640-1c20-4dde-a1e0-5956a103dc6d","added_by":"auto","created_at":"2022-11-23 21:02:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmall molecule “hits” featured in this manuscript. \u003c/strong\u003eSelected “hits” from virtual screening, each using a different model of SARS-CoV2’s nsp12, were purchased from Enamine Ltd (Kiev, Ukraine) and were characterized in a viral polymerase activity assay utilizing HIV reverse transcriptase. The small molecules from that pool, featured in this manuscript, have their structures and chemical names listed here. (patent pending)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/0a18ca3120b4d9792cca6007.jpg"},{"id":29458225,"identity":"07d49170-b870-4eda-9ba0-140280446640","added_by":"auto","created_at":"2022-11-23 21:18:48","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmall molecule “hits” featured in this manuscript docked to snp12. \u003c/strong\u003eSelected compounds were purchased from Enamine Ltd (Kyiv, Ukraine) the top docking conformations, returned by AutoDock Vina, of each are shown here in overlapping docking pockets when simulation was performed with our nsp12 model.\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/cac77b34e3f76a2f07d69d13.jpeg"},{"id":29457917,"identity":"9570f793-9bc9-429c-97df-8e84594ef51e","added_by":"auto","created_at":"2022-11-23 21:10:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of HIV1 - Reverse Transcriptase (HIV-RT) by our small molecule compounds. \u003c/strong\u003eModest inhibition was seen with all of our tested compounds compared to DMSO vehicle alone, some even exhibited greater than 20% inhibition of HIV-RT. Top shows results with Mn\u003csup\u003e2+\u003c/sup\u003e as catalytic metal ion. Bottom shows results with Mg\u003csup\u003e2+\u003c/sup\u003e as catalytic metal ion. Please note that the screening was performed for nsp12 from SARS-CoV2 while this assay is performed using HIV reverse transcriptase, a different viral polymerase.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/ace53bd456b7d71d7ea22793.jpg"},{"id":29457916,"identity":"7d5434c1-95c1-4401-b8dc-be7254485585","added_by":"auto","created_at":"2022-11-23 21:10:48","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSmall molecule “hits” featured in this manuscript docked to HIV-RT structure, 1rtd.pdb. \u003c/strong\u003eThe small molecule compounds featured in this manuscript were docked to an HIV-RT macromolecule model made from 1rtd.pdb. In line representation are the top conformations from each molecule docking. In ball and stick are MET184, the center of our search area, and ASP185 and ASP186 that stabilize the binding of the catalytic magnesium ions.\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/c1a43c375b1b2bb6f2244642.jpeg"},{"id":33286306,"identity":"ab17b78c-0d41-4131-a251-c81158bbb03f","added_by":"auto","created_at":"2023-02-22 13:14:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":687193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/207da257-b540-4785-8c61-0a35f0735a4f.pdf"},{"id":29457669,"identity":"83f4dab0-96c8-46df-bd24-e6bae15e12e9","added_by":"auto","created_at":"2022-11-23 21:02:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":140411,"visible":true,"origin":"","legend":"","description":"","filename":"S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2274158/v1/06da020b3460d0b092905f98.pdf"}],"financialInterests":"Competing interest reported. Conflicts of interest for:\nViral polymerase inhibitors that block catalytic metal ion binding, may offer clues to developing wide spectrum antiviral therapies\n\nBy V. Pandey and T.W. Comollo\n\nFair Disclosure Statement (Patent Pending)\nSmall molecule compound(s) discussed in this manuscript are covered under US patent application 17/472,647 invented and owned by Thomas W. Comollo, PhD, with a projected publication date of 3/16/2023.\n\nThe Authors declare no other conflicts of interest.","formattedTitle":"Blocking catalytic metal ion binding sites to develop antiviral therapies: exemplified using SARS-CoV-2 and HIV","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCOVID19 is a viral respiratory illness; a pandemic of which began in early December of 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The now widely recognized illness is caused by the SARS-CoV2 virus. Vaccines are now available in the United States. Other countries have also developed vaccines. However, these may not be the best option for all individuals due to the potential autoimmune responses and other complications from vaccines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, the available COVID19 vaccines may have limited effectiveness against new strains of the SARS-COV2 virus, such as Omicron [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, a threat from COVID19 and a need for effective therapeutics exists. Additionally, as suggested by the findings in this manuscript, COVID19 therapeutics may be able to be repurposed for the treatment of other viral infections.\u003c/p\u003e \u003cp\u003eSARS-CoV2, the virus that causes COVID-19, is a new member of the Orthocoronavirinae subfamily; genus Sarbecovirus. It is a novel coronavirus, and though homologous, it is not MERS-CoV or SARS-CoV [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This novel coronavirus has a similar replication mechanism as other coronaviruses, West Nile virus, Marburg virus, Ebola virus, dengue virus, and hepatitis C virus (HepC) which utilizes a positive-sense, single strand of RNA to encode its genome. The genome of SARS-CoV2 is replicated by non-structural protein 12 (nsp12), an RNA-dependent RNA polymerase (RdRp). This RNA dependency is like reverse transcriptase in HIV and HCV replicase (NS5B) in HepC.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The RdRp mechanism is also seen in other positive strand RNA viruses, such as rhinoviruses, that cause common colds, and, not surprisingly, the homologous MERS-CoV and SARS-COV. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. An existing drug, remdesivir, targets the SARS-CoV-2 virus\u0026rsquo;s nsp12 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInhibition of viral polymerases has proven effective in treating of viral infections, such as in the cases of HIV and HepC, and the effectiveness of this strategy has led to compassionate use of the drug, remdesivir, in the case of SARS-CoV2 [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While using existing drugs speeds the treatment to the field more quickly, the threat of second, third, and even more subsequent waves of COVID19, or other pandemic coronaviruses, necessitate treatments specifically targeted to coronaviruses and that have increased potency. This study sought novel, non-nucleoside inhibitors of SARS-CoV2\u0026rsquo;s nsp12. This strategy will allow for incorporating molecules from a greater chemical space to find more potent coronavirus nsp12-specific lead molecules. Additionally, we discovered that by targeting the viral polymerase's catalytic metal ion binding areas, we found compounds were also partially effective against HIV1 reverse transcriptase displaying up to 24.4% inhibition of HIV-RT at 10 \u0026micro;M compound. These compounds could be helpful against COVID19 and may be developable into a wider spectrum of antivirals.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eThe virtual screening we carried out was performed before a SARS-CoV2 nsp12 macromolecule experimentally solved structure was available and used a homology model made from SARS-CoV nsp12 6nur.pdb, chain A [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A methodology like that posted online at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://home.fatsilicodatapharm.com\u003c/span\u003e\u003cspan address=\"http://home.fatsilicodatapharm.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e was used for the virtual screening. Streamlined and better resourced versions of these methods may be useful in future viral outbreaks.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHomology modeling\u003c/h2\u003e \u003cp\u003eThe macromolecule model of SARS-CoV2 nsp12 was created using Bioinformatics Toolkit [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] as part of a personal interest project while the author was in an academic role. The template used was the cryo-EM structure of SARs-CoV nsp12, 6nur.pdb, chain A [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. At the time of beginning of our preliminary screening (circa March 15, 2020), there were no available models of SARS-CoV2 nsp12. A BLAST alignment of the R1ab\u0026rsquo;s sequence from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://viralzone.expasy.org/8996\u003c/span\u003e\u003cspan address=\"https://viralzone.expasy.org/8996\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was done using HHPRED. This showed the region of the polypeptide homologous to the SARS-CoV nsp12. A homology model was then produced using MODELLER. An unstructured region of the N-terminus was deleted in the finished model used. The homology model had a backbone rmsd of 0.66 \u0026Aring; when fit to the first SARS-CoV2 cryo-EM structure, 6m71.pdb in DEEPVIEW [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMacromolecule model preparation\u003c/h2\u003e \u003cp\u003eThe models were prepared for docking in AutoDock MGL Tools 1.5.6 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The SARs-CoV2 nsp12 and HIV-RT docking model\u0026rsquo;s hydrogen were added and then merged into nonpolar hydrogen. Gasteiger charges were calculated and AutoDock4 atom types were added. The nsp12 docking model was then saved as a \u0026ldquo;.pdbqt\u0026rdquo; file. For the HIV RT model 1rtd.pdb1, the structure\u0026rsquo;s biological assembly had RNA and small molecules removed, leaving only chains A and B. The HIV RT model was then processed similarly as described.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVirtual screening ligand preparation\u003c/h2\u003e \u003cp\u003eClean Drug Like sets 50 through 56 were downloaded from the ZINC12 Database (zinc12.docking.org) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The compressed \u0026ldquo;.mol2\u0026rdquo; files were decompressed and then split using Raccoon [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], available from the Olson laboratory, which also produces AutoDock. A Linux script that employs nested \u0026ldquo;for each\u0026rdquo; loops and files in AutoDock MGL Tools 1.5.6, based on scripts in the tutorial, UsingAutoDock4forVirtualScreening_v4.pdf form Scripps Institute, was used to set up ligand files in the proper .pdbqt file for screening. This script also split the ZINC ligand files into 100 folders, numbered 0 to 99, based on the first two numbers in the ZINC Database entry number.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRunning the virtual screening\u003c/h2\u003e \u003cp\u003eAutoDock Vina [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used for the virtual screening of ~\u0026thinsp;900,000 compound ligand files. Each set was run by a separate instance of a Linux shell script that employed nest \u0026ldquo;for each\u0026rdquo; loop that traversed through the folders set up in the \u0026ldquo;Virtual screening ligand preparation\u0026rdquo; section to run AutoDock Vina on each individual, processed ZINC \u0026ldquo;.pdbqt\u0026rdquo; ligand file. A 16 by 16 by 18 angstrom search area, centered at approximately CYS 623 in our model (CYS622 in 6m71.pdb), was used as our pocket of interest (POI) for the screening. Resdiues have been renumbered in this manuscript to reflectthe now accepted numbering. The first sets to finish in the virtual screening, Clean Drug Like 50, Clean Drug Like 52 and Clean Drug Like 53 were immediately analyzed in order to purchase lead compounds. Using scripts, log files from each docking were combined, and the best score for each compound was extracted into a new file. These were then sorted. ZINC compound files scoring \u0026minus;\u0026thinsp;10.1 kcal/mol or better, with more negative scores being better, were included in our lists of \u0026ldquo;hit\u0026rdquo; compounds. Analysis and sorting were performed using custom shell scripts and Calc in Libre Office as available on Centos 7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHIV-RT dockings\u003c/h2\u003e \u003cp\u003eStructure files of the small molecules of interest in this manuscript were docked to a 20x20x20 angstrom search box centered on the alpha carbon of 1rtd.pdb1 chain A MET184 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. MET184 corresponds structurally to the same position on HIV-RT as CYS 623 on SARS-CoV-2 nsp12 in relation to the catalytic metal ions binding site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Representation\u003c/h2\u003e \u003cp\u003eMolecules in this manuscript were rendered in AutoDock MGL Tools 1.5.6 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] or Biovia Discovery Studio Viewer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDNA Polymerase Assay\u003c/h2\u003e \u003cp\u003eWe carried out polymerase activity assay of the purified wild-type HIV-1 RT using homopolymeric poly (rA/dT\u003csub\u003e18\u003c/sub\u003e) or heteropolymeric DNA TP, which consisted of 21-mer DNA PBS primer annealed with 49-mer U5-PBS DNA templates (3\u0026rsquo;CAG GGA CAA GCC CGC GGT GAC GAT CTC TAA AAG GTG TGA CTG ATT TTC C-5\u0026rsquo;). Assays were done in a 100-\u0026micro;l volume containing 50 mM Tris-HCl (pH 7.8), 100 \u0026micro;g ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e bovine serum albumin, 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 1 mM dithiothreitol, 100 nM TP, 50 \u003cem\u003e\u0026micro;\u003c/em\u003eM dNTPs, and 21 nM enzyme. The homopolymeric rA/dT\u003csub\u003e18\u003c/sub\u003e TP, the reaction mixture contained 20 \u0026micro;M of TTP and 2 \u003cem\u003e\u0026micro;\u003c/em\u003eCi of \u003csup\u003e3\u003c/sup\u003eH TTP and heteropolymeric TP; all four dNTPs (50 \u0026micro;M each) were included, with one of them being α\u003csup\u003e32\u003c/sup\u003eP -labeled (0.4 \u003cem\u003e\u0026micro;\u003c/em\u003eCi/nmol of dNTP). Reactions were performed at 37\u0026deg;C for varying times, in the presence and absence of selected small molecules then terminated by the addition of ice-cold 5% trichloroacetic acid (TCA) containing 5 mM inorganic pyrophosphate. We collected insoluble acid materials on Whatman GF/B filters and counted for radioactivity in a liquid scintillation counter.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVirtual screening and compound of interest selection\u003c/h2\u003e \u003cp\u003eOur homology modeling resulted in a model that shifted one residue compared to the now accepted numbering for SARS-CoV-2 nsp12. Residue references in this manuscript have been adjusted to reflect the currently accepted numbering. As stated in the \u0026ldquo;Materials and Methods\u0026rdquo; section, our model has a RMSD deviation of 0.66 \u0026Aring; when fit to the first SARS-CoV2 cryo-EM structure, 6m71.pdb in DEEPVIEW [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We targeted the search area depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e as our POI. This search area included the surmised metal ion binding sites for SARS-CoV-2. In our screening of about 900,000 compounds from ZINC12\u0026rsquo;s \u0026ldquo;Clean Drug Like\u0026rdquo; we counted small molecule compounds from these sets that scored the same as or better than \u0026minus;\u0026thinsp;10.1 kcal/mol in AutoDock Vina, with more negative scores being better, as hit molecules. This amounted to the best scoring\u0026thinsp;\u0026lt;\u0026thinsp;0.05% of the screened small molecule compounds or about 400 to 500 molecules.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom this top 0.05% we were able to test a selected, seven of these small molecule compounds (Enamine: Kiev, Ukraine) in an HIV1 RT activity assay. We have analyzed these seven small molecules in this manuscript. They are ZINC8938064 (2-[[2-(3,4-dihydro-2H-1,5-benzodioxepin-7-yl)pyrrolidin-1-yl]methyl]-5-(5-methyl-3-phenyl-1,2-oxazol-4-yl)-1,3,4-oxadiazole), ZINC12545143 (2-(4-oxo-3H-phthalazin-1-yl)-N-(2-phenylquinolin-4-yl)acetamide), ZINC12939070 ([(2S)-1-(2,2-dimethyl-3-oxo-4H-quinoxalin-1-yl)-1-oxopropan-2-yl] 9-oxo-2,3-dihydro-1H-pyrrolo[2,1-b]quinazoline-6-carboxylate), ZINC27385605 ([(3S)-3-(1,3-benzoxazol-2-yl)piperidin-1-yl]-[3-(2,3-dihydroindol-1-ylsulfonyl)phenyl]methanone), ZINC30280395 ((3R)-N-[[4-[(3-fluorophenyl)methoxy]phenyl]methyl]-2-(furan-2-carbonyl)-3,4-dihydro-1H-isoquinoline-3-carboxamide), ZINC12733075 (N-dibenzofuran-2-yl-4-(4-oxo-1H-quinazolin-2-yl)butanamide), and ZINC29112491 ([3-(3,4-dihydro-1H-isoquinolin-2-ylsulfonyl)phenyl]-[2-(4-fluorophenyl)morpholin-4-yl]methanone). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows chemical drawings of the compounds in this manuscript. All are non-nucleotides. Interestingly, while each contains at least one phenol ring, each also represents a novel scaffold in different chemical space.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCompounds of interest and SARS-CoV2 nsp12 residue interactions\u003c/h2\u003e \u003cp\u003eAs part of the analysis of the hit molecules our virtual screening Table\u0026nbsp;1 shows the interacting residues predicted by our dockings in AutoDock Vina and detected through visualization in Discovery Studio Viewer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. VAL166, ASP452, TYR455, LYS545, MET543, ARG553, ALA554, ARG555, THR556, VAL557, ALA558, PR0620, LYS621, CYS622, ASP623, ARG624, THR680, SER681, ASP760, and LYS798 were each contacted by at least one of the small molecule compounds. TYR455, ASP623, and ARG624 were predicted to be contacted by all of our small molecule compounds in each compound\u0026rsquo;s best docking pose. These residues are shown in red on the sequence alignment of nsp12 and HIV-RT in S1.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows each compound in the POI as predicted by our dockings and depicted in Discovery Studio viewer. The center of our search area (CYS622) is shown in yellow stick. Our ubiquitous interacting residues TYR455, ASP623, and ARG624 are shown in black, red, and blue stick, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInhibition of HIV-RT activity\u003c/h2\u003e \u003cp\u003eInhibition of HIV1-RT by our small molecule compounds in an enzymatic activity assay of HIV1 reverse transcriptase is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. 10 \u0026micro;M of some of our small molecule compounds can inhibit HIV1-RT activity by as much as 24.4%, such as the case of ZINC27385605, that reduced activity to 75.6%. Experiments were carried out using either Mg\u003csup\u003e2+\u003c/sup\u003e or Mn\u003csup\u003e2+\u003c/sup\u003e as the catalytic ion. Data suggests that some compounds may be more effective against different catalytic ions being used for polymerase activity. For example, Mn2\u0026thinsp;+\u0026thinsp;ZINC29112491 reduced activity by less than 7% versus DMSO control when Mn2\u0026thinsp;+\u0026thinsp;was the catalytic metal ion but reduced RT activity by nearly 20% when Mg2\u0026thinsp;+\u0026thinsp;was the catalytic ion present.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eSmall molecule docking to HIV-RT model\u003c/h2\u003e \u003cp\u003eUpon running the docking of our 7 small molecules of interest to a model of HIV RT, we observed all seven molecules dock in overlapping pockets in close proximity to ASP185 and ASP186 that stabilize metal ion binding in reverse transcriptase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This is evidence that our 7 compounds of interest all inhibit SAR CoV2 nsp12 and HIV-RT by the same mechanism. They also have lower, but still respectable AutoDock Vina scores ranging from \u0026minus;\u0026thinsp;7.8 to -9.1 kcal/mol (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTargeting a viral polymerase\u0026rsquo;s metal ion binding ability could be a practical strategy for therapeutically treating that virus\u0026rsquo;s corresponding infection. In this manuscript, we showed evidence that compounds designed to target the catalytic metal ion binding sites in SARS-CoV-2\u0026rsquo;s replicase, nsp12, in a virtual screening can partially inhibit HIV1 reserve transcriptase. This leads to the hypothesis that by targeting these catalytic metal ion binding sites in viral polymerases, we can find wide spectrum antivirals to increase our readiness for future viral pandemics.\u003c/p\u003e \u003cp\u003eThe theoretical small molecule docking prediction that our compounds have some interacting residues in common, but not all, shows a strength of our not using different chemical space. Mutation of a residue may exclude some of our compounds from being effective but would presumably not affect all of the compounds\u0026rsquo; binding to the same extent, leaving several other options for inhibiting viral polymerases.\u003c/p\u003e \u003cp\u003eThis study has demonstrated that targeting the catalytic metal ion binding site on the viral replicase via properly executed virtual screening, coupled with \u003cem\u003ein vitro\u003c/em\u003e verification testing is an effective strategy for finding a broad spectrum of potentially therapeutic inhibitors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpecial thanks to Jason Kaelber and the Gaokerena Institute for Molecular Biology for funding that made this study possible, and to Rutgers University, where the corresponding author was employed at the beginning of this study. Thanks to Oleg Trott for assistance troubleshooting the HIV-RT conf file, and Abena Amankwaa, Joana Lopez, and T.W.C.\u0026rsquo;s other laboratory rotation students at Kean University for useful discussions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthor Contributions\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eV.P. assisted in the experimental laboratory and in the writing of the manuscript. \u0026nbsp;T.W.C secured funding, performed computational and experimental components of the study, and wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData Availability\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to ongoing intellectual property filings but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFair Disclosure Statement (Patent Pending)\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmall molecule compound(s) discussed in this manuscript are covered under US patent application 17/472,647 invented and owned by Thomas W. Comollo, Ph.D, with a projected publication date of 3/16/2023.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLoeffelholz, M.J. and Y.W. Tang, \u003cem\u003eLaboratory diagnosis of emerging human coronavirus infections - the state of the art\u003c/em\u003e. Emerg Microbes Infect, 2020. \u003cb\u003e9\u003c/b\u003e(1): p.\u0026nbsp;747\u0026ndash;756.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBogoch, II, et al., \u003cem\u003ePneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel\u003c/em\u003e. J Travel Med, 2020. \u003cb\u003e27\u003c/b\u003e(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Center for Immunization and Respiratory Diseases (NCIRD), D.o.V.D. \u003cem\u003eCoronavirus Disease 2019 (COVID-19) - COVID-19 Forecasts\u003c/em\u003e. [internet page] 2020 May 6, 2020 [cited 2020 May 7]; Covid-19 Deaths (Forcast) ]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html#anchor_1587397564229\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html#anchor_1587397564229\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, N., et al., \u003cem\u003eA Novel Coronavirus from Patients with Pneumonia in China, 2019\u003c/em\u003e. N Engl J Med, 2020. \u003cb\u003e382\u003c/b\u003e(8): p.\u0026nbsp;727\u0026ndash;733.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegal, Y. and Y. Shoenfeld, \u003cem\u003eVaccine-induced autoimmunity: the role of molecular mimicry and immune crossreaction\u003c/em\u003e. Cell Mol Immunol, 2018. \u003cb\u003e15\u003c/b\u003e(6): p.\u0026nbsp;586\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003e4th Dose COVID mRNA Vaccines\u0026rsquo; Immunogenicity \u0026amp; Efficacy Against Omicron VOC.\u003c/em\u003e medRxiv, 2022: p.\u0026nbsp;2022.02.15.22270948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnijder, E.J., E. Decroly, and J. Ziebuhr, \u003cem\u003eThe Nonstructural Proteins Directing Coronavirus RNA Synthesis and Processing\u003c/em\u003e. Adv Virus Res, 2016. \u003cb\u003e96\u003c/b\u003e: p.\u0026nbsp;59\u0026ndash;126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ete Velthuis, A.J., \u003cem\u003eCommon and unique features of viral RNA-dependent polymerases\u003c/em\u003e. Cell Mol Life Sci, 2014. \u003cb\u003e71\u003c/b\u003e(22): p.\u0026nbsp;4403\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeogi, U., et al., \u003cem\u003eFeasibility of Known RNA Polymerase Inhibitors as Anti-SARS-CoV-2 Drugs\u003c/em\u003e. Pathogens, 2020. \u003cb\u003e9\u003c/b\u003e(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon, C.J., et al., \u003cem\u003eRemdesivir is a direct-acting antiviral that inhibits RNA-dependent RNA polymerase from severe acute respiratory syndrome coronavirus 2 with high potency\u003c/em\u003e. J Biol Chem, 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEltahla, A.A., et al., \u003cem\u003eInhibitors of the Hepatitis C Virus Polymerase; Mode of Action and Resistance\u003c/em\u003e. Viruses, 2015. \u003cb\u003e7\u003c/b\u003e(10): p.\u0026nbsp;5206\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrein, J., et al., \u003cem\u003eCompassionate Use of Remdesivir for Patients with Severe Covid-19.\u003c/em\u003e N Engl J Med, 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUsach, I., V. Melis, and J.E. Peris, \u003cem\u003eNon-nucleoside reverse transcriptase inhibitors: a review on pharmacokinetics, pharmacodynamics, safety and tolerability\u003c/em\u003e. J Int AIDS Soc, 2013. \u003cb\u003e16\u003c/b\u003e: p.\u0026nbsp;1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirchdoerfer, R.N. and A.B. Ward, \u003cem\u003eStructure of the SARS-CoV nsp12 polymerase bound to nsp7 and nsp8 co-factors.\u003c/em\u003e Nat Commun, 2019. \u003cb\u003e10\u003c/b\u003e(1): p.\u0026nbsp;2342.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmermann, L., et al., \u003cem\u003eA Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core\u003c/em\u003e. J Mol Biol, 2018. \u003cb\u003e430\u003c/b\u003e(15): p.\u0026nbsp;2237\u0026ndash;2243.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHulo, C., et al., \u003cem\u003eViralZone: a knowledge resource to understand virus diversity\u003c/em\u003e. Nucleic Acids Res, 2011. \u003cb\u003e39\u003c/b\u003e(Database issue): p.\u0026nbsp;D576-82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuex, N. and M.C. Peitsch, \u003cem\u003eSWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling\u003c/em\u003e. Electrophoresis, 1997. \u003cb\u003e18\u003c/b\u003e(15): p.\u0026nbsp;2714\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao, Y., et al., \u003cem\u003eStructure of the RNA-dependent RNA polymerase from COVID-19 virus\u003c/em\u003e. Science, 2020. \u003cb\u003e368\u003c/b\u003e(6492): p.\u0026nbsp;779\u0026ndash;782.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris, G.M., et al., \u003cem\u003eAutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility\u003c/em\u003e. J Comput Chem, 2009. \u003cb\u003e30\u003c/b\u003e(16): p.\u0026nbsp;2785\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIrwin, J.J. and B.K. Shoichet, \u003cem\u003eZINC\u0026ndash;a free database of commercially available compounds for virtual screening\u003c/em\u003e. J Chem Inf Model, 2005. \u003cb\u003e45\u003c/b\u003e(1): p.\u0026nbsp;177\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForli, S., et al., \u003cem\u003eComputational protein-ligand docking and virtual drug screening with the AutoDock suite\u003c/em\u003e. Nat Protoc, 2016. \u003cb\u003e11\u003c/b\u003e(5): p.\u0026nbsp;905\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrott, O. and A.J. Olson, \u003cem\u003eAutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading\u003c/em\u003e. J Comput Chem, 2010. \u003cb\u003e31\u003c/b\u003e(2): p.\u0026nbsp;455\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, H., et al., \u003cem\u003eStructure of a covalently trapped catalytic complex of HIV-1 reverse transcriptase: implications for drug resistance\u003c/em\u003e. Science, 1998. \u003cb\u003e282\u003c/b\u003e(5394): p.\u0026nbsp;1669\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBIOVIA, \u003cem\u003eDiscovery Studio Viewer\u003c/em\u003e. 2019.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. SARS-CoV2 non-structural protein 12 residues predicted to interact with our selected hit compounds\u003c/p\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCompound | Vina score (kcal/mol)\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003ensp12 residue\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC08938064 -10.4\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC12545132 -10.4\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC12939070 -10.2\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC27385605 -10.2\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC30280395 -10.1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC12733075 -10.1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eZINC29112491 -10.1\u003c/span\u003e\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVAL166\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eASP452\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTYR455\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLYS545\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMET543\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eARG553\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eALA554\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eARG555\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" 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align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVAL557\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e 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class=\"SimplePara\"\u003ePRO620\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLYS621\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCYS622\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eASP623\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eARG624\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eTHR680\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSER681\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eASP760\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLYS798\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eX\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cp\u003eTable 2. Scores of best scoring Vina docking to HIV-RT model conformations depicted in Figure 5.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSmall Molecule nsp12 Hit\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eVina Score HIV-RT POI (kcal/mol)\u003c/span\u003e\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC12545143\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-8.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC30280395\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-8.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC29112491\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-7.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC08938064\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-9.1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC27385605\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-8.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC12939070\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-8.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZINC12733075\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-7.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2, COVID19, nonstructural protein 12, HIV reverse transcriptase, polymerase, inhibitor","lastPublishedDoi":"10.21203/rs.3.rs-2274158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2274158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite widely disseminated COVID19 vaccinations, infections continue. Treating COVID19 by inhibition of the RNA dependent-RNA polymerase of the causative virus, SARS-CoV2, is a helpful strategy. In this manuscript we describe a method of inhibiting SARS-CoV2 and other viral polymerases by blocking the binding of catalytic metal ions to the catalytic site in these polymerases. We performed an ~\u0026thinsp;900,000 small molecule, \u003cem\u003ein silico\u003c/em\u003e, virtual screening for small molecule compounds that would bind the metal ion site on nsp12; the SARS-CoV-2 replicase. We also tested seven of the best scoring \u0026ldquo;hit\u0026rdquo; compounds in an \u003cem\u003ein vitro\u003c/em\u003e activity assay for HIV reverse transcriptase. We found that even though the \u003cem\u003ein silico\u003c/em\u003e screen for compounds had be targeted at nsp12, our compounds, at 10 \u0026micro;M, still had up to 24.4% inhibitory activity on HIV-RT in an enzymatic assay. Docking to a model of HIV-RT found that these seven molecules dock in overlapping pockets an near the catalytic metal ion binding site, occluding it. Presumably these molecules inhibit HIV-RT in the same fashion they were intended to inhibit SARS-CoV-2\u0026rsquo;s nsp12. Further development of compounds that target catalytic metal ion binding sites can generate antivirals for a variety of viruses or even broad-spectrum antiviral therapeutics.\u003c/p\u003e","manuscriptTitle":"Blocking catalytic metal ion binding sites to develop antiviral therapies: exemplified using SARS-CoV-2 and HIV","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-11-23 21:02:43","doi":"10.21203/rs.3.rs-2274158/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"471c5d08-1c66-4863-9a69-ee1856bfad38","owner":[],"postedDate":"November 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17099417,"name":"Biological sciences/Biochemistry"},{"id":17099418,"name":"Biological sciences/Chemical biology"},{"id":17099419,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":17099420,"name":"Biological sciences/Drug discovery"},{"id":17099421,"name":"Biological sciences/Microbiology"},{"id":17099422,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2023-02-22T13:14:13+00:00","versionOfRecord":[],"versionCreatedAt":"2022-11-23 21:02:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2274158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2274158","identity":"rs-2274158","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}