Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach
The rapid emergence and spread of new variants of coronavirus type 2, as well as the emergence of zoonotic viruses, highlights the need for methodologies that contribute to the search for new pharmacological treatments. In the present work, we searched for new SARS-CoV-2 papain-like protease inhibit...
- Autores:
-
Osorio, Manuel I.
Yáñez, Osvaldo
Gallardo, Mauricio
Zuñiga-Bustos, Matías
Mulia-Rodríguez, Jorge
López-Rendón, Roberto
García-Beltrán, Olimpo
González-Nilo, Fernando
Pérez-Donoso, José M
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/5568
- Palabra clave:
- Inhibidores
Papaína del SARS-CoV-2
Bioquímica computacional
Binding free energy
Molecular dynamics simulation
Papain-like protease of SARS-CoV-2
Virtual screening
- Rights
- openAccess
- License
- © 2022 by the authors.
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| dc.title.eng.fl_str_mv |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| title |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| spellingShingle |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach Inhibidores Papaína del SARS-CoV-2 Bioquímica computacional Binding free energy Molecular dynamics simulation Papain-like protease of SARS-CoV-2 Virtual screening |
| title_short |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| title_full |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| title_fullStr |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| title_full_unstemmed |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| title_sort |
Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach |
| dc.creator.fl_str_mv |
Osorio, Manuel I. Yáñez, Osvaldo Gallardo, Mauricio Zuñiga-Bustos, Matías Mulia-Rodríguez, Jorge López-Rendón, Roberto García-Beltrán, Olimpo González-Nilo, Fernando Pérez-Donoso, José M |
| dc.contributor.author.none.fl_str_mv |
Osorio, Manuel I. Yáñez, Osvaldo Gallardo, Mauricio Zuñiga-Bustos, Matías Mulia-Rodríguez, Jorge López-Rendón, Roberto García-Beltrán, Olimpo González-Nilo, Fernando Pérez-Donoso, José M |
| dc.subject.armarc.none.fl_str_mv |
Inhibidores Papaína del SARS-CoV-2 Bioquímica computacional |
| topic |
Inhibidores Papaína del SARS-CoV-2 Bioquímica computacional Binding free energy Molecular dynamics simulation Papain-like protease of SARS-CoV-2 Virtual screening |
| dc.subject.proposal.eng.fl_str_mv |
Binding free energy Molecular dynamics simulation Papain-like protease of SARS-CoV-2 Virtual screening |
| description |
The rapid emergence and spread of new variants of coronavirus type 2, as well as the emergence of zoonotic viruses, highlights the need for methodologies that contribute to the search for new pharmacological treatments. In the present work, we searched for new SARS-CoV-2 papain-like protease inhibitors in the PubChem database, which has more than 100 million compounds. Based on the ligand efficacy index obtained by molecular docking, 500 compounds with higher affinity than another experimentally tested inhibitor were selected. Finally, the seven compounds with ADME parameters within the acceptable range for such a drug were selected. Next, molecular dynamics simulation studies at 200 ns, ΔG calculations using molecular mechanics with generalized Born and surface solvation, and quantum mechanical calculations were performed with the selected compounds. Using this in silico protocol, seven papain-like protease inhibitors are proposed: three compounds with similar free energy (D28, D04, and D59) and three compounds with higher binding free energy (D60, D99, and D06) than the experimentally tested inhibitor, plus one compound (D24) that could bind to the ubiquitin-binding region and reduce the effect on the host immune system. The proposed compounds could be used in in vitro assays, and the described protocol could be used for smart drug design. |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-08 |
| dc.date.accessioned.none.fl_str_mv |
2025-08-29T21:20:30Z |
| dc.date.available.none.fl_str_mv |
2025-08-29T21:20:30Z |
| dc.type.none.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.content.none.fl_str_mv |
Text |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Osorio, M., Yáñez, O., Gallardo, M., Zuñiga-Bustos, M., Mulia-Rodríguez, J., López-Rendón, R., García-Beltrán, O., González-Nilo, F. y Pérez-Donoso, J. (2022). Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach. Pharmaceuticals, 15(8). DOI: 10.3390/ph15080986 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/ph15080986 |
| dc.identifier.issn.none.fl_str_mv |
14248247 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12313/5568 |
| dc.identifier.url.none.fl_str_mv |
https://www.mdpi.com/1424-8247/15/8/986 |
| identifier_str_mv |
Osorio, M., Yáñez, O., Gallardo, M., Zuñiga-Bustos, M., Mulia-Rodríguez, J., López-Rendón, R., García-Beltrán, O., González-Nilo, F. y Pérez-Donoso, J. (2022). Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach. Pharmaceuticals, 15(8). DOI: 10.3390/ph15080986 10.3390/ph15080986 14248247 |
| url |
https://hdl.handle.net/20.500.12313/5568 https://www.mdpi.com/1424-8247/15/8/986 |
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eng |
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eng |
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8 |
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986 |
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15 |
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Pharmaceuticals |
| dc.relation.references.none.fl_str_mv |
Meister, K.D.; Pandian, V.; Hillel, A.T.; Walsh, B.K.; Brodsky, M.B.; Balakrishnan, K.; Best, S.R.; Chinn, S.B.; Cramer, J.D.; Graboyes, E.M.; et al. Multidisciplinary Safety Recommendations after Tracheostomy during COVID-19 Pandemic: State of the Art Review. Otolaryngol. Head Neck Surg. 2021, 164, 984–1000. Koyama, T.; Weeraratne, D.; Snowdon, J.L.; Parida, L. Emergence of Drift Variants that May Affect COVID-19 Vaccine Development and Antibody Treatment. Pathogens 2020, 9, 324. Callaway, E. Rapid displacement of SARS-CoV-2 variant B.1.1.7 by B.1.617.2 and P.1 in the United States. Nature 2021, 595, 17–18. McCallum, M.; Bassi, J.; De Marco, A.; Chen, A.; Walls, A.C.; Di Iulio, J.; Tortorici, M.A.; Navarro, M.-J.; Silacci-Fregni, C.; Saliba, C.; et al. SARS-CoV-2 immune evasion by the B.1.427/B.1.429 variant of concern. Science 2021, 7994, 6. Yang, Z.; Zhang, S.; Tang, Y.; Zhang, S.; Xu, D.; Yue, S. Clinical Characteristics, Transmissibility, Pathogenicity, Susceptible Populations, and Re-infectivity of Prominent COVID-19 Variants. Aging Dis. 2022, 13, 402–422. Zumla, A.; Chan, J.F.W.; Azhar, E.I.; Hui, D.S.C.; Yuen, K.Y. Coronaviruses-drug discovery and therapeutic options. Nat. Rev. Drug Discov. 2016, 15, 327–347. Shang, J.; Wan, Y.; Luo, C.; Ye, G.; Geng, Q.; Auerbach, A.; Li, F. Cell entry mechanisms of SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2020, 117, 11727–11734. Cavasotto, C.N.; Lamas, M.S.; Maggini, J. Functional and druggability analysis of the SARS-CoV-2 proteome. Eur. J. Pharmacol. 2021, 890, 173705. Ul Qamar, M.T.; Alqahtani, S.M.; Alamri, M.A.; Chen, L.-L. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J. Pharm. Anal. 2020, 10, 313–319. Klemm, T.; Ebert, G.; Calleja, D.J.; Allison, C.C.; Richardson, L.W.; Bernardini, J.P.; Lu, B.G.; Kuchel, N.W.; Grohmann, C.; Shibata, Y.; et al. Mechanism and inhibition of the papain-like protease, PLpro, of SARS-CoV-2. EMBO J. 2020, 39, e106275. Ratia, K.; Pegan, S.; Takayama, J.; Sleeman, K.; Coughlin, M.; Baliji, S.; Chaudhuri, R.; Fu, W.; Prabhakar, B.S.; Johnson, M.E.; et al. A noncovalent class of papain-like protease/deubiquitinase inhibitors blocks SARS virus replication. Proc. Natl. Acad. Sci. USA 2008, 105, 16119–16124. Clasman, J.R.; Everett, R.K.; Srinivasan, K.; Mesecar, A.D. Decoupling Deisgylating and Deubiquitinating Activities of the MERS Virus Papain-Like Protease; Elsevier: Amsterdam, The Netherlands, 2020; Volume 174, ISBN 7654961189. Ratia, K.; Kilianski, A.; Baez-Santos, Y.M.; Baker, S.C.; Mesecar, A. Structural Basis for the Ubiquitin-Linkage Specificity and deISGylating Activity of SARS-CoV Papain-Like Protease. PLoS Pathog. 2014, 10, e1004113. Békés, M.; van der Heden van Noort, G.J.; Ekkebus, R.; Ovaa, H.; Huang, T.T.; Lima, C.D. Recognition of Lys48-Linked Di-ubiquitin and Deubiquitinating Activities of the SARS Coronavirus Papain-like Protease. Mol. Cell 2016, 62, 572–585. Gao, X.; Qin, B.; Chen, P.; Zhu, K.; Hou, P.; Wojdyla, J.A.; Wang, M.; Cui, S. Crystal structure of SARS-CoV-2 papain-like protease. Acta Pharm. Sin. B 2021, 11, 237–245. Fu, Z.; Huang, B.; Tang, J.; Liu, S.; Liu, M.; Ye, Y.; Liu, Z.; Xiong, Y.; Zhu, W.; Cao, D.; et al. The complex structure of GRL0617 and SARS-CoV-2 PLpro reveals a hot spot for antiviral drug discovery. Nat. Commun. 2021, 12, 1–12. Shan, H.; Liu, J.; Shen, J.; Dai, J.; Xu, G.; Lu, K.; Han, C.; Wang, Y.; Xu, X.; Tong, Y.; et al. Development of potent and selective inhibitors targeting the papain-like protease of SARS-CoV-2. Cell Chem. Biol. 2021, 28, 855–865.e9. Bajusz, D.; Rácz, A.; Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform. 2015, 7, 1–13. Yañez, O.; Osorio, M.I.; Areche, C.; Vasquez-Espinal, A.; Bravo, J.; Sandoval-Aldana, A.; Pérez-Donoso, J.M.; González-Nilo, F.; Matos, M.J.; Osorio, E.; et al. Theobroma cacao L. compounds: Theoretical study and molecular modeling as inhibitors of main SARS-CoV-2 protease. Biomed. Pharmacother. 2021, 140, 111764 Alamri, M.A.; Tahir ul Qamar, M.; Mirza, M.U.; Bhadane, R.; Alqahtani, S.M.; Muneer, I.; Froeyen, M.; Salo-Ahen, O.M.H. Pharmacoinformatics and molecular dynamics simulation studies reveal potential covalent and FDA-approved inhibitors of SARS-CoV-2 main protease 3CLpro. J. Biomol. Struct. Dyn. 2021, 39, 4936–4948 Khan, A.; Ali, S.S.; Khan, M.T.; Saleem, S.; Ali, A.; Suleman, M.; Babar, Z.; Shafiq, A.; Khan, M.; Wei, D.Q. Combined drug repurposing and virtual screening strategies with molecular dynamics simulation identified potent inhibitors for SARS-CoV-2 main protease (3CLpro). J. Biomol. Struct. Dyn. 2021, 39, 4659–4670 Wang, E.; Sun, H.; Wang, J.; Wang, Z.; Liu, H.; Zhang, J.Z.H.; Hou, T. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chem. Rev. 2019, 119, 9478–9508. Stewart, J.J.P. Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. J. Mol. Model. 2007, 13, 1173–1213. Řezáč, J.; Hobza, P. Advanced corrections of hydrogen bonding and dispersion for semiempirical quantum mechanical methods. J. Chem. Theory Comput. 2012, 8, 141–151. James, J.P. Stewart MOPAC: A semiempirical molecular orbital program. J. Comput. Aided. Mol. Des. 1990, 4, 1–105. Abad-Zapatero, C. Ligand Efficiency Indices for Drug Discovery. Ligand Effic. Indices Drug Discov. 2013, 469–488. Cavalluzzi, M.M.; Mangiatordi, G.F.; Nicolotti, O.; Lentini, G. Ligand efficiency metrics in drug discovery: The pros and cons from a practical perspective. Expert Opin. Drug Discov. 2017, 12, 1087–1104. Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002, 45, 2615–2623. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. Hopkins, A.L.; Keserü, G.M.; Leeson, P.D.; Rees, D.C.; Reynolds, C.H. The role of ligand efficiency metrics in drug discovery. Nat. Rev. Drug Discov. 2014, 13, 105–121. Anandakrishnan, R.; Aguilar, B.; Onufriev, A.V. H++ 3.0: Automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res. 2012, 40, 537–541. Tian, C.; Kasavajhala, K.; Belfon, K.A.A.; Raguette, L.; Huang, H.; Migues, A.N.; Bickel, J.; Wang, Y.; Pincay, J.; Wu, Q.; et al. Ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 2020, 16, 528–552. Peters, M.B.; Yang, Y.; Wang, B.; Füsti-Molnár, L.; Weaver, M.N.; Merz, K.M. Structural survey of zinc-containing proteins and development of the zinc AMBER force field (ZAFF). J. Chem. Theory Comput. 2010, 6, 2935–2947. Seritan, S.; Bannwarth, C.; Fales, B.S.; Hohenstein, E.G.; Kokkila-Schumacher, S.I.L.; Luehr, N.; Snyder, J.W.; Song, C.; Titov, A.V.; Ufimtsev, I.S.; et al. TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units. J. Chem. Phys. 2020, 152, 224110. Götz, A.W.; Williamson, M.J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R.C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. generalized born. J. Chem. Theory Comput. 2012, 8, 1542–1555. Osorio, M.; Cabrera, M.Á.; Gonzalez-Nilo, F.D.; Pérez-Donoso, J.M. The odd loop regions of XenA and XenB enzymes modulate their interaction with nitro-explosives and provide structural support for their regioselectivity. J. Chem. Inf. Model. 2019, 59, 3860–3870. Fogha, J.; Diharce, J.; Obled, A.; Aci-Sèche, S.; Bonnet, P. Computational Analysis of Crystallization Additives for the Identification of New Allosteric Sites. ACS Omega 2020, 5, 2114–2122. Genheden, S.; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin. Drug Discov. 2015, 10, 449–461. Contreras-García, J.; Johnson, E.R.; Keinan, S.; Chaudret, R.; Piquemal, J.P.; Beratan, D.N.; Yang, W. NCIPLOT: A program for plotting noncovalent interaction regions. J. Chem. Theory Comput. 2011, 7, 625–632. |
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Osorio, Manuel I.55eb1036-fdba-448e-8089-0b4e0e904398-1Yáñez, Osvaldoca398273-de62-4885-9bb8-27948e412147-1Gallardo, Mauricio712077b5-4465-4c15-bf82-42fe3b8bbdae-1Zuñiga-Bustos, Matías1b3e6e9b-33fd-4643-b1fd-36bc7d0734ef-1Mulia-Rodríguez, Jorge980318c7-b760-4c0c-9e7d-8db39c6b0357-1López-Rendón, Roberto34b80954-f992-4714-9a2c-846f5c9156a6-1García-Beltrán, Olimpo6037fb1a-6bfc-4342-9fa2-54cdb7c4e977-1González-Nilo, Fernandobb98aea9-501f-40e8-b23c-5f65718d1fd8-1Pérez-Donoso, José Md26e5265-b1ce-4ab3-9bee-b6774008635b-12025-08-29T21:20:30Z2025-08-29T21:20:30Z2022-08The rapid emergence and spread of new variants of coronavirus type 2, as well as the emergence of zoonotic viruses, highlights the need for methodologies that contribute to the search for new pharmacological treatments. In the present work, we searched for new SARS-CoV-2 papain-like protease inhibitors in the PubChem database, which has more than 100 million compounds. Based on the ligand efficacy index obtained by molecular docking, 500 compounds with higher affinity than another experimentally tested inhibitor were selected. Finally, the seven compounds with ADME parameters within the acceptable range for such a drug were selected. Next, molecular dynamics simulation studies at 200 ns, ΔG calculations using molecular mechanics with generalized Born and surface solvation, and quantum mechanical calculations were performed with the selected compounds. Using this in silico protocol, seven papain-like protease inhibitors are proposed: three compounds with similar free energy (D28, D04, and D59) and three compounds with higher binding free energy (D60, D99, and D06) than the experimentally tested inhibitor, plus one compound (D24) that could bind to the ubiquitin-binding region and reduce the effect on the host immune system. The proposed compounds could be used in in vitro assays, and the described protocol could be used for smart drug design.application/pdfOsorio, M., Yáñez, O., Gallardo, M., Zuñiga-Bustos, M., Mulia-Rodríguez, J., López-Rendón, R., García-Beltrán, O., González-Nilo, F. y Pérez-Donoso, J. (2022). Search for Novel Potent Inhibitors of the SARS-CoV-2 Papain-like Enzyme: A Computational Biochemistry Approach. Pharmaceuticals, 15(8). DOI: 10.3390/ph1508098610.3390/ph1508098614248247https://hdl.handle.net/20.500.12313/5568https://www.mdpi.com/1424-8247/15/8/986engMDPISuiza898615PharmaceuticalsMeister, K.D.; Pandian, V.; Hillel, A.T.; Walsh, B.K.; Brodsky, M.B.; Balakrishnan, K.; Best, S.R.; Chinn, S.B.; Cramer, J.D.; Graboyes, E.M.; et al. Multidisciplinary Safety Recommendations after Tracheostomy during COVID-19 Pandemic: State of the Art Review. Otolaryngol. Head Neck Surg. 2021, 164, 984–1000.Koyama, T.; Weeraratne, D.; Snowdon, J.L.; Parida, L. Emergence of Drift Variants that May Affect COVID-19 Vaccine Development and Antibody Treatment. Pathogens 2020, 9, 324.Callaway, E. Rapid displacement of SARS-CoV-2 variant B.1.1.7 by B.1.617.2 and P.1 in the United States. Nature 2021, 595, 17–18.McCallum, M.; Bassi, J.; De Marco, A.; Chen, A.; Walls, A.C.; Di Iulio, J.; Tortorici, M.A.; Navarro, M.-J.; Silacci-Fregni, C.; Saliba, C.; et al. SARS-CoV-2 immune evasion by the B.1.427/B.1.429 variant of concern. Science 2021, 7994, 6.Yang, Z.; Zhang, S.; Tang, Y.; Zhang, S.; Xu, D.; Yue, S. Clinical Characteristics, Transmissibility, Pathogenicity, Susceptible Populations, and Re-infectivity of Prominent COVID-19 Variants. Aging Dis. 2022, 13, 402–422.Zumla, A.; Chan, J.F.W.; Azhar, E.I.; Hui, D.S.C.; Yuen, K.Y. Coronaviruses-drug discovery and therapeutic options. Nat. Rev. Drug Discov. 2016, 15, 327–347.Shang, J.; Wan, Y.; Luo, C.; Ye, G.; Geng, Q.; Auerbach, A.; Li, F. Cell entry mechanisms of SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2020, 117, 11727–11734.Cavasotto, C.N.; Lamas, M.S.; Maggini, J. Functional and druggability analysis of the SARS-CoV-2 proteome. Eur. J. Pharmacol. 2021, 890, 173705.Ul Qamar, M.T.; Alqahtani, S.M.; Alamri, M.A.; Chen, L.-L. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J. Pharm. Anal. 2020, 10, 313–319.Klemm, T.; Ebert, G.; Calleja, D.J.; Allison, C.C.; Richardson, L.W.; Bernardini, J.P.; Lu, B.G.; Kuchel, N.W.; Grohmann, C.; Shibata, Y.; et al. Mechanism and inhibition of the papain-like protease, PLpro, of SARS-CoV-2. EMBO J. 2020, 39, e106275.Ratia, K.; Pegan, S.; Takayama, J.; Sleeman, K.; Coughlin, M.; Baliji, S.; Chaudhuri, R.; Fu, W.; Prabhakar, B.S.; Johnson, M.E.; et al. A noncovalent class of papain-like protease/deubiquitinase inhibitors blocks SARS virus replication. Proc. Natl. Acad. Sci. USA 2008, 105, 16119–16124.Clasman, J.R.; Everett, R.K.; Srinivasan, K.; Mesecar, A.D. Decoupling Deisgylating and Deubiquitinating Activities of the MERS Virus Papain-Like Protease; Elsevier: Amsterdam, The Netherlands, 2020; Volume 174, ISBN 7654961189.Ratia, K.; Kilianski, A.; Baez-Santos, Y.M.; Baker, S.C.; Mesecar, A. Structural Basis for the Ubiquitin-Linkage Specificity and deISGylating Activity of SARS-CoV Papain-Like Protease. PLoS Pathog. 2014, 10, e1004113.Békés, M.; van der Heden van Noort, G.J.; Ekkebus, R.; Ovaa, H.; Huang, T.T.; Lima, C.D. Recognition of Lys48-Linked Di-ubiquitin and Deubiquitinating Activities of the SARS Coronavirus Papain-like Protease. Mol. Cell 2016, 62, 572–585.Gao, X.; Qin, B.; Chen, P.; Zhu, K.; Hou, P.; Wojdyla, J.A.; Wang, M.; Cui, S. Crystal structure of SARS-CoV-2 papain-like protease. Acta Pharm. Sin. B 2021, 11, 237–245.Fu, Z.; Huang, B.; Tang, J.; Liu, S.; Liu, M.; Ye, Y.; Liu, Z.; Xiong, Y.; Zhu, W.; Cao, D.; et al. The complex structure of GRL0617 and SARS-CoV-2 PLpro reveals a hot spot for antiviral drug discovery. Nat. 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