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An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro)

Year 2023, , 14 - 36, 15.01.2023
https://doi.org/10.33435/tcandtc.1121985

Abstract

Possible allosteric inhibitors of MPro were investigated using in silico methods. To this end, FDA-approved drugs in the DrugBank database were subjected to virtual screening, and drugs that strongly bind distant from the catalytic site of MPro were identified using molecular docking. Among the identified drugs, Dihydroergotamine (DHE) was chosen for further investigation due to its highest binding score against MPro in the molecular docking experiment. The allosteric inhibition potential of DHE toward MPro was demonstrated by applying some computational tools on the trajectory files which were obtained from the Molecular Dynamics Simulations. Results support that the hydrogen bonding interactions of DHE with GLU278 and THR280, located between Protomer A and Protomer B, affect the structure of the side chain of CYS145 at the catalytic site of MPro. Considering the role of CYS145 in the catalytic cycle, this structural change is likely to be a mechanism for inhibiting MPro.

Thanks

This project was supported by Akdeniz University Scientific Research Projects Coordination Unit. Project ID:5408. The authors acknowledge Harran University High-Performance Computing Center for making computing resources available to us.

References

  • [1] B. Hu, H. Guo, P. Zhou, Z.L. Shi, Characteristics of SARS-CoV-2 and COVID-19, Nature Reviews Microbiology 19 (2021) 141-154.
  • [2] S. Kumar, R. Nyodu, V.K. Maurya, S.K. Saxena, Morphology, genome organization, replication, and pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), Coronavirus Disease 2019 (COVID-19): Epidemiology, Pathogenesis, Diagnosis, and Therapeutics (2020) 23-31.
  • [3] Y.R. Guo, Q.D. Cao, Z.S. Hong, Y.Y. Tan, S.D. Chen, H.J. Jin, K.S. Tan, D.Y. Wang, Y. Yan, The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status, Military Medical Research 7 (2020) 1-10.
  • [4] J.F.W. Chan, K.H. Kok, Z. Zhu, H. Chu, K. K.W. To, S. Yuan, K.Y. Yuen, Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan, Emerging Microbes & Infections 9 (2020) 221-236.
  • [5] J. Zhang, T. Xiao, Y. Cai, B. Chen, Structure of SARS-CoV-2 spike protein, Current Opinion In Virology 50 (2021) 173-182.
  • [6] P. Zhou, X.L. Yang, X.G. Wang, B. Hu, L. Zhang, W. Zhang, H.R. Si, Y. Zhu, B. Li, C.L. Huang, A pneumonia outbreak associated with a new coronavirus of probable bat origin, Nature 579 (2020) 270-273.
  • [7] L. Duan, Q. Zheng, H. Zhang, Y. Niu, Y. Lou, H. Wang, The SARS-CoV-2 spike glycoprotein biosynthesis, structure, function, and antigenicity: implications for the design of spike-based vaccine immunogens, Frontiers In Immunology 11 (2020) 2593.
  • [8] F. Amanat, F. Krammer, SARS-CoV-2 vaccines: status report, Immunity 52 (2020) 583-589.
  • [9] M. Hoffmann, H. Kleine-Weber, S. Pöhlmann, A multibasic cleavage site in the spike protein of SARS-CoV-2 is essential for infection of human lung cells, Molecular Cell 78 (2020) 779-784.
  • [10] C.B. Jackson, M. Farzan, B. Chen, H. Choe, Mechanisms of SARS-CoV-2 entry into cells, Nature Reviews Molecular Cell Biology 23 (2022) 3-20.
  • [11] K. Anand, J. Ziebuhr, P. Wadhwani, J.R. Mesters, R. Hilgenfeld, Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs, Science 300 (2003) 1763-1767.
  • [12] S. Ullrich, C. Nitsche, The SARS-CoV-2 main protease as drug target, Bioorganic & Medicinal Chemistry Letters 30 (2020) 127377.
  • [13] H.J. Lee, C.K. Shieh, A.E. Gorbalenya, E.V. Koonin, N. La Monica, J. Tuler, A. Bagdzhadzhyan, M.M. Lai, The complete sequence (22 kilobases) of murine coronavirus gene 1 encoding the putative proteases and RNA polymerase, Virology 180 (1991) 567-582.
  • [14] J. Ziebuhr, E.J. Snijder, A.E. Gorbalenya, Virus-encoded proteinases and proteolytic processing in the Nidovirales, Journal of General Virology 81 (2000) 853-879.
  • [15] L. Z Zhang, D. Lin, X. Sun, U. Curth, C. Drosten, L. Sauerhering, S. Becker, K. Rox, R. Hilgenfeld, Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors, Science 368 (2020) 409-412.
  • [16] K. Świderek, V. Moliner, Revealing the molecular mechanisms of proteolysis of SARS-CoV-2 M pro by QM/MM computational methods, Chemical Science 11 (2020) 10626-10630.
  • [17] C. Wu, Y. Liu, Y. Yang, P. Zhang, W. Zhong, Y. Wang, Q. Wang, Y. Xu, M. Li, X. Li, Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods, Acta Pharmaceutica Sinica B 10 (2020) 766-788.
  • [18] R. Banerjee, L. Perera, L.V. Tillekeratne, Potential SARS-CoV-2 main protease inhibitors, Drug Discovery Today 26 (2021) 804-816.
  • [19] G. Macip, P. Garcia‐Segura, J. Mestres‐Truyol, B. Saldivar‐Espinoza, M.J. Ojeda‐Montes, A. Gimeno, A. Cereto‐Massagué, S. Garcia‐Vallvé, G. Pujadas, Haste makes waste: a critical review of docking‐based virtual screening in drug repurposing for SARS‐CoV‐2 main protease (M‐pro) inhibition, Medicinal Research Reviews 42 (2022) 744-769.
  • [20] B. Goyal, D. Goyal, Targeting the dimerization of the main protease of coronaviruses: a potential broad-spectrum therapeutic strategy, ACS Combinatorial Science 22 (2020) 297-305.
  • [21] S. Gupta, A.K. Singh, P.P. Kushwaha, K.S. Prajapati, M. Shuaib, S. Senapati, S. Kumar, Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies, Journal of Biomolecular Structure and Dynamics 39 4334-4345.
  • [22] A. Ton, F. Gentile, M. Hsing, F. Ban, A. Cherkasov, Rapid identification of potential inhibitors of SARS‐CoV‐2 main protease by deep docking of 1.3 billion compounds, Mol Inform 39 (2020) 8 e2000028.
  • [23] J. Liang, C. Karagiannis, E. Pitsillou, K.K. Darmawan, K. Ng, A. Hung, T.C. Karagiannis, Site mapping and small molecule blind docking reveal a possible target site on the SARS-CoV-2 main protease dimer interface, Computational Biology and Chemistry 89 (2020) 107372.
  • [24] Z. Lv, K.E. Cano, L. Jia, M. Drag, T.T. Huang, S.K. Olsen, Targeting SARS-CoV-2 proteases for COVID-19 antiviral development, Frontiers in Chemistry (2022) 1221.
  • [25] K. Gunasekaran, B. Ma, R. Nussinov, Is allostery an intrinsic property of all dynamic proteins? Proteins: Structure, Function, and Bioinformatics 57 (2004) 433-443.
  • [26] CHARMM-GUI. MPro Dimer Structure 6M03 2021, December.
  • [27] D.S. Wishart, Y.D. Feunang, A.C. Guo, E.J. Lo, A. Marcu, J.R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, DrugBank 5.0: a major update to the DrugBank database for 2018, Nucleic Acids Research 46 (2018) D1074-D1082.
  • [28] S. Dallakyan, A.J. Olson, Small-molecule library screening by docking with PyRx, In Chemical Biology (2015) 243-250 Springer.
  • [29] G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, A.J. Olson, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, Journal of Computational Chemistry 30 (2009) 2785-2791.
  • [30] Salomon‐Ferrer, R., D. A. Case & R. C. Walker (2013) An overview of the Amber biomolecular simulation package. Wiley Interdisciplinary Reviews: Computational Molecular Science, 3, 198-210.
  • [31] VirginiaTech. 2022, January. H++.
  • [32] J.A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K.E. Hauser, C. Simmerling, ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB, Journal of Chemical Theory and Computation 11 (2015) 3696-3713.
  • [33] J. Wang, W. Wang, P.A. Kollman, D.A. Case, Automatic atom type and bond type perception in molecular mechanical calculations, Journal of Molecular Graphics and Modelling 25 (2006) 247-260.
  • [34] J. Wang, R.M. Wolf, J.W. Caldwell, P.A. Kollman, D.A. Case, Development and testing of a general amber force field, Journal of Computational Chemistry 25 (2004) 1157-1174.
  • [35] A. Jakalian, B.L. Bush, D.B. Jack, C.I. Bayly, Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method, Journal Of Computational Chemistry 21 (2000) 132-146.
  • [36] M.J. Frisch, G. W. Trucks, H. B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, G.A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A.V. Marenich, J. Bloino, B.G. Janesko, R. Gomperts, B. Mennucci, H.P. Hratchian, J.V. Ortiz, A.F. Izmaylov, J.L. Sonnenberg, F.Williams Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V.G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. J. Bearpark, J.J. Heyd, E.N. Brothers, K.N. Kudin, V.N. Staroverov, T.A. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A.P. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, J.M. Millam, M. Klene, C. Adamo, R. Cammi, J.W. Ochterski, R.L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, D. J.Fox, Gaussian 16 Rev. C.01, Wallingford, CT (2016).
  • [37] D. R. Roe, T. E. Cheatham III, PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data, Journal of Chemical Theory and Computation 9 (2013) 3084-3095.
  • [38] O. S. Amamuddy, M. Glenister, T. Tshabalala, Ö. T. Bishop, MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics, Computational and Structural Biotechnology Journal 19 (2021) 5059-5071.
  • [39] C. Chennubhotla, I. Bahar, Signal propagation in proteins and relation to equilibrium fluctuations, PLoS Computational Biology 3 (2007) e172.
  • [40] G. Morra, G. Verkhivker, G. Colombo, Modeling signal propagation mechanisms and ligand-based conformational dynamics of the Hsp90 molecular chaperone full-length dimer, PLoS Computational Biology 5 (2009) e1000323.
  • [41] A.R. Atilgan, P. Akan, C. Baysal, Small-world communication of residues and significance for protein dynamics, Biophysical Journal 86 (2004) 85-91.
  • [42] D.L. Penkler, C. Atilgan, O.Z. Tastan Bishop, Allosteric modulation of human Hsp90α conformational dynamics, Journal of Chemical Information and Modeling 58 (2018) 383-404.
  • [43] E. W. Dijkstra, A note on two problems in connexion with graphs, Numerische Mathematik 1 (1959) 269-271.
  • [44] T. Schreiber, Measuring information transfer, Physical Review Letters 85 (2000) 461.
  • [45] H. Kamberaj, A. van der Vaart, Extracting the causality of correlated motions from molecular dynamics simulations, Biophysical Journal 97 (2009) 1747-1755.
  • [46] D. Nebiu, H. Kamberaj, Symbolic Information Flow Measurement (SIFM): A software for measurement of information flow using symbolic analysis, SoftwareX 11 (2020) 100470.
  • [47] Kamberaj, H. 2020, February, 29. SifmV1.
  • [48] S. Kullback, R.A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics 22 (1951) 79-86.
  • [49] B. Gourévitch, J.J. Eggermont, Evaluating information transfer between auditory cortical neurons, Journal of Neurophysiology 97 (2007) 2533-2543.
  • [50] T.C. McLeish, M.J. Cann, T.L. Rodgers, Dynamic transmission of protein allostery without structural change: spatial pathways or global modes? Biophysical Journal 109 (2015) 1240-1250.
  • [51] D.K. Brown, O.S. Amamuddy, Ö.T. Bishop, Structure-based analysis of single nucleotide variants in the renin-angiotensinogen complex, Global Heart 12 (2017) 121-132.
  • [52] A. Amusengeri, Ö. Tastan Bishop, Discorhabdin N, a South African natural compound, for Hsp72 and Hsc70 allosteric modulation: combined study of molecular modeling and dynamic residue network analysis, Molecules 24 (2019) 188.
  • [53] J. Yoon, A. Blumer, K. Lee, An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality, Bioinformatics 22 (2006) 3106-3108.
  • [54] A. Hacisuleyman, B. Erman, Entropy transfer between residue pairs and allostery in proteins: quantifying allosteric communication in ubiquitin, PLoS Computational Biology 13 (2017) e1005319.
  • [55] R. Nussinov, Introduction to Protein Ensembles and Allostery, ACS Publications, 2016, 6263-6266.
  • [56] J.R. Wagner, C.T. Lee, J.D. Durrant, R.D. Malmstrom, V.A. Feher, R.E. Amaro, Emerging computational methods for the rational discovery of allosteric drugs, Chemical Reviews 116 (2016) 6370-6390.
  • [57] X. Tao, L. Zhang, L. Du, R. Liao, H. Cai, K. Lu, Z. Zhao, Y. Xie, P.H. Wang, J.A. Pan, Y. Zhang, G. Li, J. Dai, Z.W. Mao, W. Xia, Allosteric inhibition of SARS-CoV-2 3CL protease by colloidal bismuth subcitrate, Chem Sci 12 (2021) 14098-14102.
  • [58] L. Strömich, N. Wu, M. Barahona, S.N. Yaliraki Allosteric hotspots in the main protease of SARS-CoV-2, BioRxiv (2020).
  • [59] M. Yuce, E. Cicek, T. Inan, A.B. Dag, O. Kurkcuoglu, F.A. Sungur, Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease, Proteins 89 (2021) 1425-1441.
  • [60] I. Dubanevics, T.C.B. McLeish, Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease, J R Soc Interface 18 (2021) 20200591.
  • [61] M. Carli, G. Sormani, A. Rodriguez, A. Laio, Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations, J Phys Chem Lett 12 (2021) 65-72.
  • [62] T. Sztain, R. Amaro, J.A. McCammon, Elucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease, J Chem Inf Model 61 (2021) 3495-3501.
  • [63] G. Jimenez-Avalos, A.P. Vargas-Ruiz, N.E. Delgado-Pease, G.E. Olivos-Ramirez, P. Sheen, M. Fernandez-Diaz, M. Quiliano, M. Zimic, C.W.G.İ. Peru, Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 M(PRO), Sci Rep 11 (2021) 15452.
  • [64] C.A. Menéndez, F. Byléhn, G.R. Perez-Lemus, W. Alvarado, J.J. de Pablo, Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease, Science Advances 6 (2020) eabd0345.
  • [65] J. Novak, H. Rimac, S. Kandagalla, P. Pathak, V. Naumovich, M. Grishina, V. Potemkin, Proposition of a new allosteric binding site for potential SARS-CoV-2 3CL protease inhibitors by utilizing molecular dynamics simulations and ensemble docking, J Biomol Struct Dyn (2021)1-14.
  • [66] S. Verma, A.K. Pandey, Factual insights of the allosteric inhibition mechanism of SARS-CoV-2 main protease by quercetin: an in silico analysis, 3 Biotech 11 (2021) 67.
  • [67] S. Günther, P.Y. Reinke, Y. Fernández-García, J. Lieske, T.J. Lane, H.M. Ginn, F.H. Koua, C. Ehrt, W. Ewert, D. Oberthuer, X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease, Science 372 (2021) 642-646.
  • [68] F.X. Cantrelle, E. Boll, L. Brier, D. Moschidi, S. Belouzard, V. Landry, F. Leroux, F. Dewitte, I. Landrieu, J. Dubuisson, B. Deprez, J. Charton, X. Hanoulle, NMR Spectroscopy of the Main Protease of SARS-CoV-2 and Fragment-Based Screening Identify Three Protein Hotspots and an Antiviral Fragment, Angew Chem Int Ed Engl 60 (2021) 25428-25435.
  • [69] A. Douangamath, D. Fearon, P. Gehrtz, T. Krojer, P. Lukacik, C.D. Owen, E. Resnick, C. Strain-Damerell, A. Aimon, P. Abranyi-Balogh, J. Brandao-Neto, A. Carbery, G. Davison, A. Dias, T.D. Downes, L. Dunnett, M. Fairhead, J.D. Firth, S.P. Jones, A. Keeley, G.M. Keseru, H.F. Klein, M.P. Martin, M.E.M. Noble, P. O'Brien, A. Powell, R.N. Reddi, R. Skyner, M. Snee, M.J. Waring, C. Wild, N. London, F. von Delft, M.A. Walsh, Crystallographic and electrophilic fragment screening of the SARS-CoV-2 main protease, Nat Commun 11 (2020) 5047.
  • [70] T.J. El-Baba, C.A. Lutomski, A.L. Kantsadi, T.R. Malla, T. John, V. Mikhailov, J.R. Bolla, C.J. Schofield, N. Zitzmann, I. Vakonakis, C.V. Robinson, Allosteric inhibition of the SARS-CoV-2 main protease: insights from mass spectrometry based assays, Angew Chem Int Ed Engl 59 (2020) 23544-23548.
  • [71] Z. Jin, X. Du, Y. Xu, Y. Deng, M. Liu, Y. Zhao, B. Zhang, X. Li, L. Zhang, C. Peng, Y. Duan, J. Yu, L. Wang, K. Yang, F. Liu, R. Jiang, X. Yang, T. You, X. Liu, X. Yang, F. Bai, H. Liu, X. Liu, L.W. Guddat, W. Xu, G. Xiao, C. Qin, Z. Shi, H. Jiang, Z. Rao, H. Yang, Structure of M(pro) from SARS-CoV-2 and discovery of its inhibitors, Nature 582 (2020) 289-293.
Year 2023, , 14 - 36, 15.01.2023
https://doi.org/10.33435/tcandtc.1121985

Abstract

References

  • [1] B. Hu, H. Guo, P. Zhou, Z.L. Shi, Characteristics of SARS-CoV-2 and COVID-19, Nature Reviews Microbiology 19 (2021) 141-154.
  • [2] S. Kumar, R. Nyodu, V.K. Maurya, S.K. Saxena, Morphology, genome organization, replication, and pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), Coronavirus Disease 2019 (COVID-19): Epidemiology, Pathogenesis, Diagnosis, and Therapeutics (2020) 23-31.
  • [3] Y.R. Guo, Q.D. Cao, Z.S. Hong, Y.Y. Tan, S.D. Chen, H.J. Jin, K.S. Tan, D.Y. Wang, Y. Yan, The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status, Military Medical Research 7 (2020) 1-10.
  • [4] J.F.W. Chan, K.H. Kok, Z. Zhu, H. Chu, K. K.W. To, S. Yuan, K.Y. Yuen, Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan, Emerging Microbes & Infections 9 (2020) 221-236.
  • [5] J. Zhang, T. Xiao, Y. Cai, B. Chen, Structure of SARS-CoV-2 spike protein, Current Opinion In Virology 50 (2021) 173-182.
  • [6] P. Zhou, X.L. Yang, X.G. Wang, B. Hu, L. Zhang, W. Zhang, H.R. Si, Y. Zhu, B. Li, C.L. Huang, A pneumonia outbreak associated with a new coronavirus of probable bat origin, Nature 579 (2020) 270-273.
  • [7] L. Duan, Q. Zheng, H. Zhang, Y. Niu, Y. Lou, H. Wang, The SARS-CoV-2 spike glycoprotein biosynthesis, structure, function, and antigenicity: implications for the design of spike-based vaccine immunogens, Frontiers In Immunology 11 (2020) 2593.
  • [8] F. Amanat, F. Krammer, SARS-CoV-2 vaccines: status report, Immunity 52 (2020) 583-589.
  • [9] M. Hoffmann, H. Kleine-Weber, S. Pöhlmann, A multibasic cleavage site in the spike protein of SARS-CoV-2 is essential for infection of human lung cells, Molecular Cell 78 (2020) 779-784.
  • [10] C.B. Jackson, M. Farzan, B. Chen, H. Choe, Mechanisms of SARS-CoV-2 entry into cells, Nature Reviews Molecular Cell Biology 23 (2022) 3-20.
  • [11] K. Anand, J. Ziebuhr, P. Wadhwani, J.R. Mesters, R. Hilgenfeld, Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs, Science 300 (2003) 1763-1767.
  • [12] S. Ullrich, C. Nitsche, The SARS-CoV-2 main protease as drug target, Bioorganic & Medicinal Chemistry Letters 30 (2020) 127377.
  • [13] H.J. Lee, C.K. Shieh, A.E. Gorbalenya, E.V. Koonin, N. La Monica, J. Tuler, A. Bagdzhadzhyan, M.M. Lai, The complete sequence (22 kilobases) of murine coronavirus gene 1 encoding the putative proteases and RNA polymerase, Virology 180 (1991) 567-582.
  • [14] J. Ziebuhr, E.J. Snijder, A.E. Gorbalenya, Virus-encoded proteinases and proteolytic processing in the Nidovirales, Journal of General Virology 81 (2000) 853-879.
  • [15] L. Z Zhang, D. Lin, X. Sun, U. Curth, C. Drosten, L. Sauerhering, S. Becker, K. Rox, R. Hilgenfeld, Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors, Science 368 (2020) 409-412.
  • [16] K. Świderek, V. Moliner, Revealing the molecular mechanisms of proteolysis of SARS-CoV-2 M pro by QM/MM computational methods, Chemical Science 11 (2020) 10626-10630.
  • [17] C. Wu, Y. Liu, Y. Yang, P. Zhang, W. Zhong, Y. Wang, Q. Wang, Y. Xu, M. Li, X. Li, Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods, Acta Pharmaceutica Sinica B 10 (2020) 766-788.
  • [18] R. Banerjee, L. Perera, L.V. Tillekeratne, Potential SARS-CoV-2 main protease inhibitors, Drug Discovery Today 26 (2021) 804-816.
  • [19] G. Macip, P. Garcia‐Segura, J. Mestres‐Truyol, B. Saldivar‐Espinoza, M.J. Ojeda‐Montes, A. Gimeno, A. Cereto‐Massagué, S. Garcia‐Vallvé, G. Pujadas, Haste makes waste: a critical review of docking‐based virtual screening in drug repurposing for SARS‐CoV‐2 main protease (M‐pro) inhibition, Medicinal Research Reviews 42 (2022) 744-769.
  • [20] B. Goyal, D. Goyal, Targeting the dimerization of the main protease of coronaviruses: a potential broad-spectrum therapeutic strategy, ACS Combinatorial Science 22 (2020) 297-305.
  • [21] S. Gupta, A.K. Singh, P.P. Kushwaha, K.S. Prajapati, M. Shuaib, S. Senapati, S. Kumar, Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies, Journal of Biomolecular Structure and Dynamics 39 4334-4345.
  • [22] A. Ton, F. Gentile, M. Hsing, F. Ban, A. Cherkasov, Rapid identification of potential inhibitors of SARS‐CoV‐2 main protease by deep docking of 1.3 billion compounds, Mol Inform 39 (2020) 8 e2000028.
  • [23] J. Liang, C. Karagiannis, E. Pitsillou, K.K. Darmawan, K. Ng, A. Hung, T.C. Karagiannis, Site mapping and small molecule blind docking reveal a possible target site on the SARS-CoV-2 main protease dimer interface, Computational Biology and Chemistry 89 (2020) 107372.
  • [24] Z. Lv, K.E. Cano, L. Jia, M. Drag, T.T. Huang, S.K. Olsen, Targeting SARS-CoV-2 proteases for COVID-19 antiviral development, Frontiers in Chemistry (2022) 1221.
  • [25] K. Gunasekaran, B. Ma, R. Nussinov, Is allostery an intrinsic property of all dynamic proteins? Proteins: Structure, Function, and Bioinformatics 57 (2004) 433-443.
  • [26] CHARMM-GUI. MPro Dimer Structure 6M03 2021, December.
  • [27] D.S. Wishart, Y.D. Feunang, A.C. Guo, E.J. Lo, A. Marcu, J.R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, DrugBank 5.0: a major update to the DrugBank database for 2018, Nucleic Acids Research 46 (2018) D1074-D1082.
  • [28] S. Dallakyan, A.J. Olson, Small-molecule library screening by docking with PyRx, In Chemical Biology (2015) 243-250 Springer.
  • [29] G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, A.J. Olson, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, Journal of Computational Chemistry 30 (2009) 2785-2791.
  • [30] Salomon‐Ferrer, R., D. A. Case & R. C. Walker (2013) An overview of the Amber biomolecular simulation package. Wiley Interdisciplinary Reviews: Computational Molecular Science, 3, 198-210.
  • [31] VirginiaTech. 2022, January. H++.
  • [32] J.A. Maier, C. Martinez, K. Kasavajhala, L. Wickstrom, K.E. Hauser, C. Simmerling, ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB, Journal of Chemical Theory and Computation 11 (2015) 3696-3713.
  • [33] J. Wang, W. Wang, P.A. Kollman, D.A. Case, Automatic atom type and bond type perception in molecular mechanical calculations, Journal of Molecular Graphics and Modelling 25 (2006) 247-260.
  • [34] J. Wang, R.M. Wolf, J.W. Caldwell, P.A. Kollman, D.A. Case, Development and testing of a general amber force field, Journal of Computational Chemistry 25 (2004) 1157-1174.
  • [35] A. Jakalian, B.L. Bush, D.B. Jack, C.I. Bayly, Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method, Journal Of Computational Chemistry 21 (2000) 132-146.
  • [36] M.J. Frisch, G. W. Trucks, H. B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, G.A. Petersson, H. Nakatsuji, X. Li, M. Caricato, A.V. Marenich, J. Bloino, B.G. Janesko, R. Gomperts, B. Mennucci, H.P. Hratchian, J.V. Ortiz, A.F. Izmaylov, J.L. Sonnenberg, F.Williams Ding, F. Lipparini, F. Egidi, J. Goings, B. Peng, A. Petrone, T. Henderson, D. Ranasinghe, V.G. Zakrzewski, J. Gao, N. Rega, G. Zheng, W. Liang, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, K. Throssell, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. J. Bearpark, J.J. Heyd, E.N. Brothers, K.N. Kudin, V.N. Staroverov, T.A. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A.P. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, J.M. Millam, M. Klene, C. Adamo, R. Cammi, J.W. Ochterski, R.L. Martin, K. Morokuma, O. Farkas, J. B. Foresman, D. J.Fox, Gaussian 16 Rev. C.01, Wallingford, CT (2016).
  • [37] D. R. Roe, T. E. Cheatham III, PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data, Journal of Chemical Theory and Computation 9 (2013) 3084-3095.
  • [38] O. S. Amamuddy, M. Glenister, T. Tshabalala, Ö. T. Bishop, MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics, Computational and Structural Biotechnology Journal 19 (2021) 5059-5071.
  • [39] C. Chennubhotla, I. Bahar, Signal propagation in proteins and relation to equilibrium fluctuations, PLoS Computational Biology 3 (2007) e172.
  • [40] G. Morra, G. Verkhivker, G. Colombo, Modeling signal propagation mechanisms and ligand-based conformational dynamics of the Hsp90 molecular chaperone full-length dimer, PLoS Computational Biology 5 (2009) e1000323.
  • [41] A.R. Atilgan, P. Akan, C. Baysal, Small-world communication of residues and significance for protein dynamics, Biophysical Journal 86 (2004) 85-91.
  • [42] D.L. Penkler, C. Atilgan, O.Z. Tastan Bishop, Allosteric modulation of human Hsp90α conformational dynamics, Journal of Chemical Information and Modeling 58 (2018) 383-404.
  • [43] E. W. Dijkstra, A note on two problems in connexion with graphs, Numerische Mathematik 1 (1959) 269-271.
  • [44] T. Schreiber, Measuring information transfer, Physical Review Letters 85 (2000) 461.
  • [45] H. Kamberaj, A. van der Vaart, Extracting the causality of correlated motions from molecular dynamics simulations, Biophysical Journal 97 (2009) 1747-1755.
  • [46] D. Nebiu, H. Kamberaj, Symbolic Information Flow Measurement (SIFM): A software for measurement of information flow using symbolic analysis, SoftwareX 11 (2020) 100470.
  • [47] Kamberaj, H. 2020, February, 29. SifmV1.
  • [48] S. Kullback, R.A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics 22 (1951) 79-86.
  • [49] B. Gourévitch, J.J. Eggermont, Evaluating information transfer between auditory cortical neurons, Journal of Neurophysiology 97 (2007) 2533-2543.
  • [50] T.C. McLeish, M.J. Cann, T.L. Rodgers, Dynamic transmission of protein allostery without structural change: spatial pathways or global modes? Biophysical Journal 109 (2015) 1240-1250.
  • [51] D.K. Brown, O.S. Amamuddy, Ö.T. Bishop, Structure-based analysis of single nucleotide variants in the renin-angiotensinogen complex, Global Heart 12 (2017) 121-132.
  • [52] A. Amusengeri, Ö. Tastan Bishop, Discorhabdin N, a South African natural compound, for Hsp72 and Hsc70 allosteric modulation: combined study of molecular modeling and dynamic residue network analysis, Molecules 24 (2019) 188.
  • [53] J. Yoon, A. Blumer, K. Lee, An algorithm for modularity analysis of directed and weighted biological networks based on edge-betweenness centrality, Bioinformatics 22 (2006) 3106-3108.
  • [54] A. Hacisuleyman, B. Erman, Entropy transfer between residue pairs and allostery in proteins: quantifying allosteric communication in ubiquitin, PLoS Computational Biology 13 (2017) e1005319.
  • [55] R. Nussinov, Introduction to Protein Ensembles and Allostery, ACS Publications, 2016, 6263-6266.
  • [56] J.R. Wagner, C.T. Lee, J.D. Durrant, R.D. Malmstrom, V.A. Feher, R.E. Amaro, Emerging computational methods for the rational discovery of allosteric drugs, Chemical Reviews 116 (2016) 6370-6390.
  • [57] X. Tao, L. Zhang, L. Du, R. Liao, H. Cai, K. Lu, Z. Zhao, Y. Xie, P.H. Wang, J.A. Pan, Y. Zhang, G. Li, J. Dai, Z.W. Mao, W. Xia, Allosteric inhibition of SARS-CoV-2 3CL protease by colloidal bismuth subcitrate, Chem Sci 12 (2021) 14098-14102.
  • [58] L. Strömich, N. Wu, M. Barahona, S.N. Yaliraki Allosteric hotspots in the main protease of SARS-CoV-2, BioRxiv (2020).
  • [59] M. Yuce, E. Cicek, T. Inan, A.B. Dag, O. Kurkcuoglu, F.A. Sungur, Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease, Proteins 89 (2021) 1425-1441.
  • [60] I. Dubanevics, T.C.B. McLeish, Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease, J R Soc Interface 18 (2021) 20200591.
  • [61] M. Carli, G. Sormani, A. Rodriguez, A. Laio, Candidate Binding Sites for Allosteric Inhibition of the SARS-CoV-2 Main Protease from the Analysis of Large-Scale Molecular Dynamics Simulations, J Phys Chem Lett 12 (2021) 65-72.
  • [62] T. Sztain, R. Amaro, J.A. McCammon, Elucidation of Cryptic and Allosteric Pockets within the SARS-CoV-2 Main Protease, J Chem Inf Model 61 (2021) 3495-3501.
  • [63] G. Jimenez-Avalos, A.P. Vargas-Ruiz, N.E. Delgado-Pease, G.E. Olivos-Ramirez, P. Sheen, M. Fernandez-Diaz, M. Quiliano, M. Zimic, C.W.G.İ. Peru, Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 M(PRO), Sci Rep 11 (2021) 15452.
  • [64] C.A. Menéndez, F. Byléhn, G.R. Perez-Lemus, W. Alvarado, J.J. de Pablo, Molecular characterization of ebselen binding activity to SARS-CoV-2 main protease, Science Advances 6 (2020) eabd0345.
  • [65] J. Novak, H. Rimac, S. Kandagalla, P. Pathak, V. Naumovich, M. Grishina, V. Potemkin, Proposition of a new allosteric binding site for potential SARS-CoV-2 3CL protease inhibitors by utilizing molecular dynamics simulations and ensemble docking, J Biomol Struct Dyn (2021)1-14.
  • [66] S. Verma, A.K. Pandey, Factual insights of the allosteric inhibition mechanism of SARS-CoV-2 main protease by quercetin: an in silico analysis, 3 Biotech 11 (2021) 67.
  • [67] S. Günther, P.Y. Reinke, Y. Fernández-García, J. Lieske, T.J. Lane, H.M. Ginn, F.H. Koua, C. Ehrt, W. Ewert, D. Oberthuer, X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease, Science 372 (2021) 642-646.
  • [68] F.X. Cantrelle, E. Boll, L. Brier, D. Moschidi, S. Belouzard, V. Landry, F. Leroux, F. Dewitte, I. Landrieu, J. Dubuisson, B. Deprez, J. Charton, X. Hanoulle, NMR Spectroscopy of the Main Protease of SARS-CoV-2 and Fragment-Based Screening Identify Three Protein Hotspots and an Antiviral Fragment, Angew Chem Int Ed Engl 60 (2021) 25428-25435.
  • [69] A. Douangamath, D. Fearon, P. Gehrtz, T. Krojer, P. Lukacik, C.D. Owen, E. Resnick, C. Strain-Damerell, A. Aimon, P. Abranyi-Balogh, J. Brandao-Neto, A. Carbery, G. Davison, A. Dias, T.D. Downes, L. Dunnett, M. Fairhead, J.D. Firth, S.P. Jones, A. Keeley, G.M. Keseru, H.F. Klein, M.P. Martin, M.E.M. Noble, P. O'Brien, A. Powell, R.N. Reddi, R. Skyner, M. Snee, M.J. Waring, C. Wild, N. London, F. von Delft, M.A. Walsh, Crystallographic and electrophilic fragment screening of the SARS-CoV-2 main protease, Nat Commun 11 (2020) 5047.
  • [70] T.J. El-Baba, C.A. Lutomski, A.L. Kantsadi, T.R. Malla, T. John, V. Mikhailov, J.R. Bolla, C.J. Schofield, N. Zitzmann, I. Vakonakis, C.V. Robinson, Allosteric inhibition of the SARS-CoV-2 main protease: insights from mass spectrometry based assays, Angew Chem Int Ed Engl 59 (2020) 23544-23548.
  • [71] Z. Jin, X. Du, Y. Xu, Y. Deng, M. Liu, Y. Zhao, B. Zhang, X. Li, L. Zhang, C. Peng, Y. Duan, J. Yu, L. Wang, K. Yang, F. Liu, R. Jiang, X. Yang, T. You, X. Liu, X. Yang, F. Bai, H. Liu, X. Liu, L.W. Guddat, W. Xu, G. Xiao, C. Qin, Z. Shi, H. Jiang, Z. Rao, H. Yang, Structure of M(pro) from SARS-CoV-2 and discovery of its inhibitors, Nature 582 (2020) 289-293.
There are 71 citations in total.

Details

Primary Language English
Subjects Chemical Engineering
Journal Section Research Article
Authors

Mehmet Murat Yaşar 0000-0001-6211-0350

Ekrem Yaşar 0000-0003-0575-7267

Nuri Yorulmaz 0000-0003-4959-2302

Emin Tenekeci 0000-0001-5944-4704

İsmail Hakkı Sarpün 0000-0002-9788-699X

Erol Eroğlu 0000-0002-5401-987X

Publication Date January 15, 2023
Submission Date May 26, 2022
Published in Issue Year 2023

Cite

APA Yaşar, M. M., Yaşar, E., Yorulmaz, N., Tenekeci, E., et al. (2023). An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro). Turkish Computational and Theoretical Chemistry, 7(1), 14-36. https://doi.org/10.33435/tcandtc.1121985
AMA Yaşar MM, Yaşar E, Yorulmaz N, Tenekeci E, Sarpün İH, Eroğlu E. An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro). Turkish Comp Theo Chem (TC&TC). January 2023;7(1):14-36. doi:10.33435/tcandtc.1121985
Chicago Yaşar, Mehmet Murat, Ekrem Yaşar, Nuri Yorulmaz, Emin Tenekeci, İsmail Hakkı Sarpün, and Erol Eroğlu. “An in Silico Investigation of Allosteric Inhibition Potential of Dihydroergotamine Against Sars-CoV-2 Main Protease (MPro)”. Turkish Computational and Theoretical Chemistry 7, no. 1 (January 2023): 14-36. https://doi.org/10.33435/tcandtc.1121985.
EndNote Yaşar MM, Yaşar E, Yorulmaz N, Tenekeci E, Sarpün İH, Eroğlu E (January 1, 2023) An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro). Turkish Computational and Theoretical Chemistry 7 1 14–36.
IEEE M. M. Yaşar, E. Yaşar, N. Yorulmaz, E. Tenekeci, İ. H. Sarpün, and E. Eroğlu, “An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro)”, Turkish Comp Theo Chem (TC&TC), vol. 7, no. 1, pp. 14–36, 2023, doi: 10.33435/tcandtc.1121985.
ISNAD Yaşar, Mehmet Murat et al. “An in Silico Investigation of Allosteric Inhibition Potential of Dihydroergotamine Against Sars-CoV-2 Main Protease (MPro)”. Turkish Computational and Theoretical Chemistry 7/1 (January 2023), 14-36. https://doi.org/10.33435/tcandtc.1121985.
JAMA Yaşar MM, Yaşar E, Yorulmaz N, Tenekeci E, Sarpün İH, Eroğlu E. An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro). Turkish Comp Theo Chem (TC&TC). 2023;7:14–36.
MLA Yaşar, Mehmet Murat et al. “An in Silico Investigation of Allosteric Inhibition Potential of Dihydroergotamine Against Sars-CoV-2 Main Protease (MPro)”. Turkish Computational and Theoretical Chemistry, vol. 7, no. 1, 2023, pp. 14-36, doi:10.33435/tcandtc.1121985.
Vancouver Yaşar MM, Yaşar E, Yorulmaz N, Tenekeci E, Sarpün İH, Eroğlu E. An in silico investigation of allosteric inhibition potential of Dihydroergotamine against Sars-CoV-2 Main Protease (MPro). Turkish Comp Theo Chem (TC&TC). 2023;7(1):14-36.

Journal Full Title: Turkish Computational and Theoretical Chemistry


Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)