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2000
Volume 6, Issue 2
  • ISSN: 2666-7967
  • E-ISSN: 2666-7975

Abstract

Background

The novel coronavirus disease also known as COVID-19 was first detected in December 2019 in Wuhan, China and was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its effect can still be seen in some parts of the world due to the lack of effective antiviral drugs and vaccines for treatment and controlling the pandemic. Chymotrypsin-like protease (3CLpro), also known as the main protease (Mpro) of SARS-CoV-2 plays a vital role during its replication process of the pathogen’s lifecycle and is therefore considered a potential drug target for COVID-19. Hence, targeting the Mpro is an appealing approach for drug development because of its significant role in viral replication and transcription and therefore can act as an attractive drug target to combat COVID-19 as confirmed by researchers through numerous studies so far. Although small molecules dominate the field of drug market so far, peptide inhibitors still represent a class of promising candidates because of their similarity to endogenous ligands, high affinity, and low toxicity. It has been validated that therapeutic peptides can effectively and selectively inhibit the protein-protein interactions in viruses. Hence, it is necessary to design potential peptide inhibitors in order to inhibit the impact of the disease.

Objective

To design peptide inhibitors against the SARS-CoV-2 Main Protease using computational methods.

Methods

This study involves the development of potential target peptides that can act against the Mpro in a competitive mode against histone deacetylase (HDAC2) which had a high-confidence interaction with Mpro. Based on the interaction between Mpro and HDAC2, 13 peptides were designed out of which based on toxicity, binding affinity and binding site prediction, two peptides (peptide2 and peptide4) were screened and subjected to MD simulation.

Results

Our study shows that the two peptides bind to the active site of the Mpro and it attains a higher stability upon binding to the peptides. We also found out that the Mpro has a strong binding affinity with both the peptides (GB = -72.85 kcal/mol for Mpro-peptide2 complex and GB = -46.36 kcal/mol for the Mpro-peptide4 complex).

Conclusion

Even though declaring those peptides as future potent drug candidates would require more analysis and trials, our analysis will surely add value to the future findings and these findings could aid in the development of novel SARS-CoV-2 Mpro peptide inhibitors. These findings could aid in the development of novel SARS-CoV-2 Mpro peptide inhibitors.

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References

  1. HuQ. XiongY. ZhuG.H. The SARS‐CoV‐2 main protease (Mpro): Structure, function, and emerging therapies for COVID‐19.MedComm202233e15110.1002/mco2.151 35845352
     [Google Scholar]
  2. WHO coronavirus disease (COVID-19) dashboard. Banglad Physioth J202010110.46945/bpj.10.1.03.01
     [Google Scholar]
  3. MengistH.M. TebelayD. TengchuanJ. Structural basis of potential inhibitors targeting SARS-CoV-2 main protease.Front Chem.2021962289810.3389/fchem.2021.622898
     [Google Scholar]
  4. DasC. DasD. MattaparthiV.S.K. Computational investigation on the efficiency of small molecule inhibitors identified from indian spices against SARS-CoV-2 Mpro.Biointerface Res. Appl. Chem.202213323510.33263/BRIAC133.235
     [Google Scholar]
  5. KhailanyR.A. SafdarM. OzaslanM. Genomic characterization of a novel SARS-CoV-2.Gene Rep.20201910068210.1016/j.genrep.2020.100682 32300673
     [Google Scholar]
  6. HegyiA. ZiebuhrJ. Conservation of substrate specificities among coronavirus main proteases.J. Gen. Virol.200283359559910.1099/0022‑1317‑83‑3‑595 11842254
     [Google Scholar]
  7. AnandK. ZiebuhrJ. WadhwaniP. MestersJ.R. HilgenfeldR. Coronavirus main proteinase (3CLpro) structure: Basis for design of anti-SARS drugs.Science200330056261763176710.1126/science.1085658 12746549
     [Google Scholar]
  8. HilgenfeldR. From SARS to MERS: crystallographic studies on coronaviral proteases enable antiviral drug design.FEBS J.2014281184085409610.1111/febs.12936 25039866
     [Google Scholar]
  9. LuL. SuS. YangH. JiangS. Antivirals with common targets against highly pathogenic viruses.Cell202118461604162010.1016/j.cell.2021.02.013 33740455
     [Google Scholar]
  10. HuB. GuoH. ZhouP. ShiZ-L. Characteristics of SARS-CoV-2 and COVID-19.Nat. Rev. Microbiol.2022205315510.1038/s41579‑022‑00711‑2 35197601
     [Google Scholar]
  11. MikitshJL ChackoAM Pathways for small molecule delivery to the central nervous system across the blood-brain barrier.Perspect Medicin Chem201466PMC. S1338410.4137/PMC.S13384 24963272
     [Google Scholar]
  12. GoodwinD. SimerskaP. TothI. Peptides as therapeutics with enhanced bioactivity.Curr. Med. Chem.201219264451446110.2174/092986712803251548 22830348
     [Google Scholar]
  13. ZangY. SuM. WangQ. High-throughput screening of SARS-CoV-2 main and papain-like protease inhibitors.Protein Cell2022141pwac01610.1093/procel/pwac016 36726755
     [Google Scholar]
  14. OwenD.R. AllertonC.M.N. AndersonA.S. An oral SARS-CoV-2 Mpro inhibitor clinical candidate for the treatment of COVID-19.Science202137465751586159310.1126/science.abl4784 34726479
     [Google Scholar]
  15. BeigelJ.H. TomashekK.M. DoddL.E. Remdesivir for the treatment of COVID-19: Final report.N. Engl. J. Med.2020383191813182610.1056/NEJMoa2007764 32445440
     [Google Scholar]
  16. HosseinzadehM.H. ShamshirianA. EbrahimzadehM.A. Dexamethasone vs COVID‐19: An experimental study in line with the preliminary findings of a large trial.Int. J. Clin. Pract.2021756e1394310.1111/ijcp.13943 33332726
     [Google Scholar]
  17. NangakuM. KadowakiT. YotsuyanagiH. The japanese medical science federation COVID-19 expert opinion english version.Japan Med. Assoc. J.20214214816210.31662/jmaj.2021‑0002 33997449
     [Google Scholar]
  18. AndreoniM. SticchiL. NozzaS. SarmatiL. GoriA. TavioM. Recommendations of the Italian society for infectious and tropical diseases (SIMIT) for adult vaccinations.Hum. Vaccin. Immunother.202117114265428210.1080/21645515.2021.1971473 34524945
     [Google Scholar]
  19. ZhuK.W. Deuremidevir and Simnotrelvir–Ritonavir for the treatment of COVID-19.ACS Pharmacol. Transl. Sci.2023691306130910.1021/acsptsci.3c00134 37705591
     [Google Scholar]
  20. WilliamsonB.N. FeldmannF. SchwarzB. Clinical benefit of remdesivir in Rhesus macaques infected with SARS-CoV-2.Nature2020585782427327610.1038/s41586‑020‑2423‑5 32516797
     [Google Scholar]
  21. WahlA. GralinskiL.E. JohnsonC.E. SARS-CoV-2 infection is effectively treated and prevented by EIDD-2801.Nature2021591785045145710.1038/s41586‑021‑03312‑w 33561864
     [Google Scholar]
  22. XieY. YinW. ZhangY. Design and development of an oral remdesivir derivative VV116 against SARS-CoV-2.Cell Res.202131111212121410.1038/s41422‑021‑00570‑1 34584244
     [Google Scholar]
  23. ZhangJ.L. LiY.H. WangL.L. Azvudine is a thymus-homing anti-SARS-CoV-2 drug effective in treating COVID-19 patients.Signal Transduct. Target. Ther.20216141410.1038/s41392‑021‑00835‑6 34873151
     [Google Scholar]
  24. UnohY. UeharaS. NakaharaK. Discovery of S-217622, a noncovalent oral SARS-CoV-2 3CL protease inhibitor clinical candidate for treating COVID-19.J. Med. Chem.20226596499651210.1021/acs.jmedchem.2c00117 35352927
     [Google Scholar]
  25. YanV.C. MullerF.L. Why remdesivir failed: Preclinical assumptions overestimate the clinical efficacy of remdesivir for COVID-19 and Ebola.Antimicrob. Agents Chemother.20216510e01117e0112110.1128/AAC.01117‑21 34252308
     [Google Scholar]
  26. HeilmannE. CostacurtaF. MoghadasiS.A. SARS-CoV-2 3CLpro mutations selected in a VSV-based system confer resistance to nirmatrelvir, ensitrelvir, and GC376.Sci. Transl. Med.202315678eabq736010.1126/scitranslmed.abq7360 36194133
     [Google Scholar]
  27. NoskeG.D. de Souza SilvaE. de GodoyM.O. Structural basis of nirmatrelvir and ensitrelvir activity against naturally occurring polymorphisms of the SARS-CoV-2 main protease.J. Biol. Chem.2023299310300410.1016/j.jbc.2023.103004 36775130
     [Google Scholar]
  28. ZhouY. GammeltoftK.A. RybergL.A. Nirmatrelvir-resistant SARS-CoV-2 variants with high fitness in an infectious cell culture system.Sci. Adv.2022851eadd719710.1126/sciadv.add7197 36542720
     [Google Scholar]
  29. JochmansD. LiuC. DonckersK. The substitutions L50F, E166A and L167F in SARS-CoV-2 3CLpro are selected by a protease inhibitor in vitro and confer resistance to nirmatrelvir.BioRxiv202210.1101/2022.06.07.495116
     [Google Scholar]
  30. HuangC. ShuaiH. QiaoJ. A new generation Mpro inhibitor with potent activity against SARS-CoV-2 Omicron variants.Signal Transduct. Target. Ther.20238112810.1038/s41392‑023‑01392‑w 36928316
     [Google Scholar]
  31. IketaniS. MohriH. CulbertsonB. Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir.Nature2023613794455856410.1038/s41586‑022‑05514‑2 36351451
     [Google Scholar]
  32. MoghadasiS.A. HeilmannE. MoraesS.N. Transmissible SARS-CoV-2 variants with resistance to clinical protease inhibitors.bioRxiv202250309910.1101/2022.08.07.503099
     [Google Scholar]
  33. SasiV.M. UllrichS. TonJ. Predicting antiviral resistance mutations in SARS-CoV-2 main protease with computational and experimental screening.Biochemistry202261222495250510.1021/acs.biochem.2c00489 36326185
     [Google Scholar]
  34. RambautA. HolmesE.C. O’TooleÁ. A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology.Nat. Microbiol.20205111403140710.1038/s41564‑020‑0770‑5 32669681
     [Google Scholar]
  35. GordonD.E. JangG.M. BouhaddouM. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.Nature2020583781645946810.1038/s41586‑020‑2286‑9 32353859
     [Google Scholar]
  36. VajdaS. YuehC. BeglovD. New additions to the ClusPro server motivated by CAPRI.Proteins201785343544410.1002/prot.25219 27936493
     [Google Scholar]
  37. KozakovD. HallD.R. XiaB. The ClusPro web server for protein–protein docking.Nat. Protoc.201712225527810.1038/nprot.2016.169 28079879
     [Google Scholar]
  38. YanY. TaoH. HeJ. HuangS.Y. The HDOCK server for integrated protein–protein docking.Nat. Protoc.20201551829185210.1038/s41596‑020‑0312‑x 32269383
     [Google Scholar]
  39. YanY. ZhangD. ZhouP. LiB. HuangS.Y. HDOCK: A web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy.Nucleic Acids Res.201745W1W365-7310.1093/nar/gkx407 28521030
     [Google Scholar]
  40. EisenbergD. LüthyR. BowieJ.U. VERIFY3D: Assessment of protein models with three-dimensional profiles.Methods Enzymol199727739640410.1016/S0076‑6879(97)77022‑8 9379925
     [Google Scholar]
  41. ColovosC. YeatesT.O. Verification of protein structures: patterns of nonbonded atomic interactions.Protein Sci.1993291511151910.1002/pro.5560020916
     [Google Scholar]
  42. LaskowskiR.A. HutchinsonE.G. MichieA.D. WallaceA.C. JonesM.L. ThorntonJ.M. PDBsum: A web-based database of summaries and analyses of all PDB structures.Trends Biochem. Sci.1997221248849010.1016/S0968‑0004(97)01140‑7 9433130
     [Google Scholar]
  43. Badaczewska-DawidA.E. NithinC. WroblewskiK. KurcinskiM. KmiecikS. MAPIYA contact map server for identification and visualization of molecular interactions in proteins and biological complexes.Nucleic Acids Res.202250W1W474-8210.1093/nar/gkac307 35524560
     [Google Scholar]
  44. KurcinskiM. JamrozM. BlaszczykM. KolinskiA. KmiecikS. CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site.Nucleic Acids Res.201543W1W419-2410.1093/nar/gkv456 25943545
     [Google Scholar]
  45. BlaszczykM. KurcinskiM. KouzaM. Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking.Methods201693728310.1016/j.ymeth.2015.07.004 26165956
     [Google Scholar]
  46. ZhouP. JinB. LiH. HuangS.Y. HPEPDOCK: A web server for blind peptide–protein docking based on a hierarchical algorithm.Nucleic Acids Res.201846W1W443-5010.1093/nar/gky357 29746661
     [Google Scholar]
  47. WeiL. YeX. SakuraiT. MuZ. WeiL. ToxIBTL: Prediction of peptide toxicity based on information bottleneck and transfer learning.Bioinformatics20223861514152410.1093/bioinformatics/btac006 34999757
     [Google Scholar]
  48. XueL.C. RodriguesJ.P. KastritisP.L. BonvinA.M. VangoneA. PRODIGY: A web server for predicting the binding affinity of protein–protein complexes.Bioinformatics201632233676367810.1093/bioinformatics/btw514 27503228
     [Google Scholar]
  49. EberleR.J. SevenichM. GeringI. Discovery of All d-peptide inhibitors of SARS-CoV-2 3C-like protease.ACS Chem. Biol.202318231533010.1021/acschembio.2c00735 36647580
     [Google Scholar]
  50. XiongG. WuZ. YiJ. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties.Nucleic Acids Res.202149W1W5W1410.1093/nar/gkab255 33893803
     [Google Scholar]
  51. TianC. KasavajhalaK. BelfonK.A.A. ff19SB: Amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution.J. Chem. Theory Comput.202016152855210.1021/acs.jctc.9b00591 31714766
     [Google Scholar]
  52. Eduardo Sanabria-ChanagaE. Betancourt-CondeI. Hernández-CamposA. Téllez-ValenciaA. CastilloR. In silico hit optimization toward AKT inhibition: Fragment-based approach, molecular docking and molecular dynamics study.J. Biomol. Struct. Dyn.201937164301431110.1080/07391102.2018.1546618 30477412
     [Google Scholar]
  53. Salomon-FerrerR. GötzA.W. PooleD. Le GrandS. WalkerR.C. Routine microsecond molecular dynamics simulations with Amber on gpus. 2. Explicit solvent particle mesh ewald.J. Chem. Theory Comput.2013993878388810.1021/ct400314y 26592383
     [Google Scholar]
  54. CaseD.A. CheathamT.E.III DardenT. The Amber biomolecular simulation programs.J. Comput. Chem.200526161668168810.1002/jcc.20290 16200636
     [Google Scholar]
  55. MichaelF. ChenL.Y. ChanR. LiangH. TIP3P and TIP4P in molecular dynamics simulations of erythrocyte aquaporins.In TACCSTER Proceedings202011473510.26153/tsw/11475
     [Google Scholar]
  56. JorgensenW.L. ChandrasekharJ. MaduraJ.D. ImpeyR.W. KleinM.L. Comparison of simple potential functions for simulating liquid water.J. Chem. Phys.198379292693510.1063/1.445869
     [Google Scholar]
  57. DardenT. YorkD. PedersenL. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems.J. Chem. Phys.19939812100891009210.1063/1.464397
     [Google Scholar]
  58. RyckaertJ.P. CiccottiG. BerendsenH.J.C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes.J. Comput. Phys.197723332734110.1016/0021‑9991(77)90098‑5
     [Google Scholar]
  59. BerendsenH.J.C. PostmaJ.P.M. van GunsterenW.F. DiNolaA. HaakJ.R. Molecular dynamics with coupling to an external bath.J. Chem. Phys.19848183684369010.1063/1.448118
     [Google Scholar]
  60. RoeD.R. CheathamT.E.III PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data.J. Chem. Theory Comput.2013973084309510.1021/ct400341p 26583988
     [Google Scholar]
  61. ChenF. LiuH. SunH. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein–protein binding free energies and re-rank binding poses generated by protein–protein docking.Phys. Chem. Chem. Phys.20161832221292213910.1039/C6CP03670H 27444142
     [Google Scholar]
  62. GenhedenS. RydeU. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities.Expert Opin. Drug Discov.201510544946110.1517/17460441.2015.1032936 25835573
     [Google Scholar]
  63. chen J, Yin B, Pang L, Wang W, Zhang JZH, Zhu T. Binding modes and conformational changes of FK506-binding protein 51 induced by inhibitor bindings: insight into molecular mechanisms based on multiple simulation technologies.J. Biomol. Struct. Dyn.20203872141215510.1080/07391102.2019.1624616 31198099
     [Google Scholar]
  64. DuQ. QianY. YaoX. XueW. Elucidating the tight-binding mechanism of two oral anticoagulants to factor Xa by using induced-fit docking and molecular dynamics simulation.J. Biomol. Struct. Dyn.202038262563310.1080/07391102.2019.1583605 30806177
     [Google Scholar]
  65. GaoJ. WangY. ChenQ. YaoR. Integrating molecular dynamics simulation and molecular mechanics/generalized Born surface area calculation into pharmacophore modeling: A case study on the proviral integration site for Moloney murine leukemia virus (Pim)-1 kinase inhibitors.J. Biomol. Struct. Dyn.202038258158810.1080/07391102.2019.1571946 30678548
     [Google Scholar]
  66. JoshiT. JoshiT. SharmaP. ChandraS. PandeV. Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae pv. Oryzae by targeting peptide deformylase.J. Biomol. Struct. Dyn.202139382384010.1080/07391102.2020.1719200 31965918
     [Google Scholar]
  67. SkM.F. RoyR. KarP. Exploring the potency of currently used drugs against HIV-1 protease of subtype D variant by using multiscale simulations.J. Biomol. Struct. Dyn.2021393988100310.1080/07391102.2020.1724196 32000612
     [Google Scholar]
  68. SunH. DuanL. ChenF. Assessing the performance of MM/PBSA and MM/GBSA methods. 7. Entropy effects on the performance of end-point binding free energy calculation approaches.Phys. Chem. Chem. Phys.20182021144501446010.1039/C7CP07623A 29785435
     [Google Scholar]
  69. TianS. JiC. ZhangJ.Z.H. Molecular basis of SMAC-XIAP binding and the effect of electrostatic polarization.J. Biomol. Struct. Dyn.202139274375210.1080/07391102.2020.1713892 31914860
     [Google Scholar]
  70. WanY. GuanS. QianM. Structural basis of fullerene derivatives as novel potent inhibitors of protein acetylcholinesterase without catalytic active site interaction: Insight into the inhibitory mechanism through molecular modeling studies.J. Biomol. Struct. Dyn.202038241042510.1080/07391102.2019.1576543 30706763
     [Google Scholar]
  71. WangE. SunH. WangJ. End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design.Chem. Rev.2019119169478950810.1021/acs.chemrev.9b00055 31244000
     [Google Scholar]
  72. ZhangW. YangF. OuD. Prediction, docking study and molecular simulation of 3D DNA aptamers to their targets of endocrine disrupting chemicals.J. Biomol. Struct. Dyn.201937164274428210.1080/07391102.2018.1547222 30477404
     [Google Scholar]
  73. DasC. DasD. MattaparthiV.S.K. Effect of mutations in the SARS-CoV-2 Spike RBD region of delta and delta-plus variants on its interaction with ACE2 receptor protein.Lett Appl NanoBioSci202212411810.33263/LIANBS124.118
     [Google Scholar]
  74. DasC. MattaparthiV.S.K. Impact of mutations in the SARS-CoV-2 spike RBD Region of BA.1 and BA.2 variants on its interaction with ACE2 receptor protein.Biointerface Res. Appl. Chem.202213435810.33263/BRIAC134.358
     [Google Scholar]
  75. DasC. HazarikaP.J. DebA. JoshiP. DasD. MattaparthiV.S.K. Effect of double mutation (L452R and E484Q) in RBD of spike protein on its interaction with ACE2 receptor protein.Biointerface Res. Appl. Chem.20221319710.33263/BRIAC131.097
     [Google Scholar]
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Computational Design of Peptide Inhibitors Targeting the SARS-CoV-2 Main Protease
Coronaviruses 6, e130624230993 (2025); https://doi.org/10.2174/0126667975319992240612053235
/content/journals/covid/10.2174/0126667975319992240612053235
/content/journals/covid/10.2174/0126667975319992240612053235
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  • Article Type:     Research Article
Keyword(s): 3CLpro; COVID-19; HDAC2; MD simulation; peptide inhibitor; SARS-CoV-2

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