Investigation of The Potential Inhibitor Effects Of Lycorine On Sars-Cov-2 Main Protease (Mpro) Using Molecular Dynamics Simulations and MMPBSA

Öz

The main protease (Mpro or 3CLpro) plays important roles in viral replication and is one of attractive targets for drug development for SARS-CoV-2. In this study, we investigated the potential inhibitory effect of lycorine molecule as a ligand on SARS-CoV-2 using computational approaches. For this purpose, we conducted molecular docking and molecular dynamics simulations MM-PB(GB)SA analyses. The findings showed that the lycorine ligand was successfully docked with catalytic dyad (Cys145 and His41) of SARS-CoV-2 Mpro with binding affinity changing between -6.71 and -7.03 kcal mol-1. MMPB(GB)SA calculations resulted according to GB (Generalized Born) approach in a Gibbs free energy changing between -24.925-+01152 kcal/mol between lycorine and SARS-CoV-2 which is promising. PB (Poisson Boltzmann) approach gave less favorable energy (-2.610±0.2611 kcal mol-1). Thus, Entropy calculations from the normal mode analysis (ΔS) were performed and it supported GB approach and conducted -23.100±6.4635 kcal mol-1. These results showed lycorine has a druggable potential but the drug effect of lycorine on COVID-19 is limited and experimental studies should be done with pharmacokinetic modifications that increase the drug effect of lycorine.

Anahtar Kelimeler

• Molecular Dynamics Simulation
• MMPBSA
• Gibbs Energy
• SARS-CoV-2

Kaynakça

1. 1. Weiss SR, Navas-Martin S. Coronavirus Pathogenesis and the Emerging Pathogen Severe Acute Respiratory Syndrome Coronavirus. Microbiol Mol Biol Rev. 2005. 69(4):635–64.
2. 2. Ji W, Wang W, Zhao X, Zai J, Li X. Cross-species transmission of the newly identified coronavirus 2019-nCoV. J Med Virol. 2020.
3. 3. Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Biomed. 2020. 91(1):157–60.
4. 4. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020. 579(7798):265–9.
5. 5. Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol. 2019. 17(3):181–92.
6. 6. Zhou P, Yang X Lou, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020. 579(7798):270–3.
7. 7. Ghosh AK, Xi K, Ratia K, Santarsiero BD, Fu W, Harcourt BH, et al. Design and synthesis of peptidomimetic severe acute respiratory syndrome chymotrypsin-like protease inhibitors. J Med Chem. 2005 Nov. 48(22):6767–71.
8. 8. Dömling A, Gao L. Chemistry and Biology of SARS-CoV-2. Chem. 2020. 6(6):1283–95.

9. 9. Needle D, Lountos GT, Waugh DS. Structures of the Middle East respiratory syndrome coronavirus 3C-like protease reveal insights into substrate specificity. Acta Crystallogr Sect D Biol Crystallogr. 2015. 71:1102–11.
10. 10. Anand K, Ziebuhr J, Wadhwani P, Mesters JR, Hilgenfeld R. ( 3CL pro ) Structure : Basis for Design of Anti-SARS Drugs. Science (80- ). 2003. 300(June):1763–7.
11. 11. Tahir ul Qamar M, Alqahtani SM, Alamri MA, Chen LL. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants. J Pharm Anal. 2020. (xxxx):1–7.
12. 12. Callaway E. Beyond Omicron: what’s next for COVID’s viral evolution. Nature. 2021. 600(7888):204–7.
13. 13. Shen JW, Ruan Y, Ren W, Ma BJ, Wang XL, Zheng CF. Lycorine: A potential broad-spectrum agent against crop pathogenic fungi. J Microbiol Biotechnol. 2014. 24(3):354–8.
14. 14. Shawky E. In-silico profiling of the biological activities of Amaryllidaceae alkaloids. J Pharm Pharmacol. 2017. 69(11):1592–605.
15. 15. Szlávik L, Gyuris Á, Minárovits J, Forgo P, Molnár J, Hohmann J. Alkaloids from Leucojum vernum and antiretroviral activity of amaryllidaceae alkaloids. Planta Med. 2004. 70(9):871–3.
16. 16. Zhang Y-N, Zhang Q-Y, Li X-D, Xiong J, Xiao S-Q, Wang Z, et al. Gemcitabine, lycorine and oxysophoridine inhibit novel coronavirus (SARS-CoV-2) in cell culture. Emerg Microbes Infect. 2020.
17. 17. He J, Qi WB, Wang L, Tian J, Jiao PR, Liu GQ, et al. Amaryllidaceae alkaloids inhibit nuclear-to-cytoplasmic export of ribonucleoprotein (RNP) complex of highly pathogenic avian influenza virus H5N1. Influenza Other Respi Viruses. 2013. 7(6):922–31.
18. 18. Yang L, Zhang JH, Zhang XL, Lao GJ, Su GM, Wang L, et al. Tandem mass tag-based quantitative proteomic analysis of lycorine treatment in highly pathogenic avian influenza H5N1 virus infection. PeerJ. 2019. 2019(10):1–23.
19. 19. Liu J, Yang Y, Xu Y, Ma C, Qin C, Zhang L. Lycorine reduces mortality of human enterovirus 71-infected mice by inhibiting virus replication. Virol J 2011,. 2011. 8(483):1–9.
20. 20. Li SY, Chen C, Zhang HQ, Guo HY, Wang H, Wang L, et al. Identification of natural compounds with antiviral activities against SARS-associated coronavirus. Antiviral Res. 2005. 67(1):18–23.
21. 21. Jin Y, Sun J, Jeon S, Lee J, Kim S, Rae H, et al. Lycorine, a non-nucleoside RNA dependent RNA polymerase inhibitor, as potential treatment for emerging coronavirus infections. 2020. (January).
22. 22. Jin Z, Du X, Xu Y, Deng Y, Liu M, Zhao Y, et al. Structure of Mpro from COVID-19 virus and discovery of its inhibitors. Nature. 2020.
23. 23. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera -- A visualization system for exploratory research and analysis. J Comput Chem [Internet]. 2004 Oct. 25(13):1605–12. Available from: https://onlinelibrary.wiley.com/doi/10.1002/jcc.20084
24. 24. Morris GM, Ruth H, WILLIAM LINDSTROM, SANNER MF, BELEW RK, GOODSELL DS, et al. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. J Comput Chem. 2009. 30:2785–91.
25. 25. Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JH, et al. General atomic and molecular electronic structure system. J Comput Chem. 1993 Nov. 14(11):1347–63.
26. 26. Pritchard BP, Altarawy D, Didier B, Gibson TD, Windus TL. New Basis Set Exchange: An Open, Up-to-Date Resource for the Molecular Sciences Community. J Chem Inf Model. 2019. 59(11):4814–20.
27. 27. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009 Dec. 30(16):2785–91.
28. 28. BIOVIA DS. Discovery Studio Visualiser. San Diego: Dassault Systèmes D.S. BIOVIA. 2019.
29. 29. Nguyen MN, Tan KP, Madhusudhan MS. CLICK - Topology-independent comparison of biomolecular 3D structures. Nucleic Acids Res. 2011. 39(SUPPL. 2):24–8.
30. 30. Case DA, Cerutti DS, Cheatham TEI, Darden TA, Duke RE, Giese TJ, et al. Amber 2017 reference manual. Univ California, San Fr. 2017. AMBER 2017, University of California, San Francisc.
31. 31. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general amber force field. J Comput Chem. 2004 Jul. 25(9):1157–74.
32. 32. Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J Chem Theory Comput. 2015 Aug. 11(8):3696–713.
33. 33. Hopkins CW, Le Grand S, Walker RC, Roitberg AE. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. J Chem Theory Comput [Internet]. 2015 Apr 14. 11(4):1864–74. Available from: https://pubs.acs.org/doi/10.1021/ct5010406
34. 34. Miller BR, McGee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE. MMPBSA.py : An Efficient Program for End-State Free Energy Calculations. J Chem Theory Comput [Internet]. 2012 Sep 11. 8(9):3314–21. Available from: https://pubs.acs.org/doi/10.1021/ct300418h
35. 35. Roe DR, Cheatham TE. PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. J Chem Theory Comput [Internet]. 2013 Jul 9. 9(7):3084–95. Available from: https://pubs.acs.org/doi/10.1021/ct400341p
36. 36. Sharp KA, Honig B. Calculating total electrostatic energies with the nonlinear Poisson-Boltzmann equation. J Phys Chem [Internet]. 1990 Sep 1. 94(19):7684–92. Available from: https://pubs.acs.org/doi/10.1021/j100382a068
37. 37. Tsui V, Case DA. Theory and applications of the Generalized Born solvation model in macromolecular simulations. Biopolymers [Internet]. 2000. 56(4):275–91. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11754341
38. 38. Hou T, Wang J, Li Y, Wang W. Assessing the Performance of the MM/PBSA and MM/GBSA Methods. 1. The Accuracy of Binding Free Energy Calculations Based on Molecular Dynamics Simulations. J Chem Inf Model [Internet]. 2011 Jan 24. 51(1):69–82. Available from: https://pubs.acs.org/doi/10.1021/ci100275a
39. 39. Murugesan S, Kottekad S, Crasta I, Sreevathsan S, Usharani D, Perumal MK, et al. Targeting COVID-19 (SARS-CoV-2) main protease through active phytocompounds of ayurvedic medicinal plants – Emblica officinalis (Amla), Phyllanthus niruri Linn. (Bhumi Amla) and Tinospora cordifolia (Giloy) – A molecular docking and simulation study. Comput Biol Med [Internet]. 2021 Sep. 136:104683. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0010482521004777
40. 40. Enmozhi SK, Raja K, Sebastine I, Joseph J. Andrographolide As a Potential Inhibitor of SARS-CoV-2 Main Protease: An In Silico Approach. J Biomol Struct Dyn. 2020. 0(0):1–10.
41. 41. Sen Gupta PS, Biswal S, Panda SK, Ray AK, Rana MK. Binding mechanism and structural insights into the identified protein target of COVID-19 and importin-α with in-vitro effective drug ivermectin. J Biomol Struct Dyn [Internet]. 2020 Oct 28. 1–10. Available from: https://www.tandfonline.com/doi/full/10.1080/07391102.2020.1839564
42. 42. Bera K. Binding and inhibitory effect of ravidasvir on 3CL pro of SARS-CoV‐2: a molecular docking, molecular dynamics and MM/PBSA approach. J Biomol Struct Dyn [Internet]. 2021 Mar 8. 1–8. Available from: https://www.tandfonline.com/doi/full/10.1080/07391102.2021.1896388