Structure-based discovery of interleukin-33 inhibitors: a pharmacophore modelling, molecular docking, and molecular dynamics simulation approach

References

• E.S. Baekkevold, M. Roussigne, T. Yamanaka, F.-E. Johansen, F.L. Jahnsen, F. Amalric, P. Brandtzaeg, M. Erard, G. Haraldsen, and J.-P. Girard, Molecular characterization of NF-HEV, a nuclear factor preferentially expressed in human high endothelial venules, Am. J. Pathol. 163 (2003), pp. 69–79. doi:https://doi.org/10.1016/S0002-9440(10)63631-0.
   PubMed Web of Science ®Google Scholar
• K. Oboki, S. Nakae, K. Matsumoto, and H. Saito, IL-33 and airway inflammation, Allergy Asthma Immunol. Res. 3 (2011), pp. 81–88. doi:https://doi.org/10.4168/aair.2011.3.2.81.
   PubMed Web of Science ®Google Scholar
• O. Rostan, M.I. Arshad, C. Piquet-Pellorce, F. Robert-Gangneux, J.P. Gangneux, and M. Samson, Crucial and diverse role of the interleukin-33/ST2 axis in infectious diseases, Infect. Immun. 83 (2015), pp. 1738–1748. doi:https://doi.org/10.1128/IAI.02908-14.
   PubMed Web of Science ®Google Scholar
• X. Liu, M. Hammel, Y. He, J.A. Tainer, U.-S. Jeng, L. Zhang, S. Wang, and X. Wang, Structural insights into the interaction of IL-33 with its receptors, Proc. Natl. Acad. Sci. U. S. A 110 (2013), pp. 14918–14923. doi:https://doi.org/10.1073/pnas.1308651110.
   PubMed Web of Science ®Google Scholar
• A.M. Miller, Role of IL-33 in inflammation and disease, J. Inflamm. 8 (2011), pp. 22–34. doi:https://doi.org/10.1186/1476-9255-8-22.
   Google Scholar
• J. Schmitz, A. Owyang, E. Oldham, Y. Song, E. Murphy, T.K. McClanahan, G. Zurawski, M. Moshrefi, J. Qin, X. Li, D.M. Gorman, J.F. Bazan, and R.A. Kastelein, IL-33, an interleukin-1-like cytokine that signals via the IL-1 receptor-related protein ST2 and induces T helper type 2-associated cytokines, Immunity 23 (2005), pp. 479–490. doi:https://doi.org/10.1016/j.immuni.2005.09.015.
   PubMed Web of Science ®Google Scholar
• G. Palmer and C. Gabay, Interleukin-33 biology with potential insights into human diseases, Nat. Rev. Rheumatol. 7 (2011), pp. 321–329. doi:https://doi.org/10.1038/nrrheum.2011.53.
   PubMed Web of Science ®Google Scholar
• G. Palmer, D. Talabot-Ayer, C. Lamacchia, D. Toy, C.A. Seemayer, S. Viatte, A. Finckh, D.E. Smith, and C. Gabay, Inhibition of interleukin-33 signaling attenuates the severity of experimental arthritis, Arthritis Rheum. 60 (2009), pp. 738–749. doi:https://doi.org/10.1002/art.24305.
   PubMed Web of Science ®Google Scholar
• A. Latiano, O. Palmieri, L. Pastorelli, M. Vecchi, T.T. Pizarro, F. Bossa, G. Merla, B. Augello, T. Latiano, G. Corritore, A. Settesoldi, M.R. Valvano, R. D’Incà, L. Stronati, V. Annese, and A. Andriulli, Associations between genetic polymorphisms in IL-33, IL1R1 and risk for inflammatory bowel disease, PLoS One 8 (2013), pp. e62144. doi:https://doi.org/10.1371/journal.pone.0062144.t001.
   PubMed Web of Science ®Google Scholar
• L. Pastorelli, R.R. Garg, S.B. Hoang, L. Spina, B. Mattioli, M. Scarpa, C. Fiocchi, M. Vecchi, and T.T. Pizarro, Epithelial-derived IL-33 and its receptor ST2 are dysregulated in ulcerative colitis and in experimental Th1/Th2 driven enteritis, Proc. Natl. Acad. Sci. U. S. A 107 (2010), pp. 8017–8022. doi:https://doi.org/10.1073/pnas.0912678107.
   PubMed Web of Science ®Google Scholar
• Z. Xiong, R. Thangavel, D. Kempuraj, E. Yang, S. Zaheer, and A. Zaheer, Alzheimer’s disease: Evidence for the expression of interleukin-33 and its receptor ST2 in the brain, J. Alzheimers Dis. 40 (2014), pp. 297–308. doi:https://doi.org/10.3233/JAD-132081.
   PubMed Web of Science ®Google Scholar
• C. Pei, M. Barbour, K.J. Fairlie-Clarke, D. Allan, R. Mu, and H.-R. Jiang, Emerging role of interleukin-33 in autoimmune diseases, Immunology 141 (2013), pp. 9–17. doi:https://doi.org/10.1111/imm.12174.
   Web of Science ®Google Scholar
• G.P. Christophi, R.C. Gruber, M. Panos, R.L. Christophi, B. Jubelt, and P.T. Massa, Interleukin-33 upregulation in peripheral leukocytes and CNS of multiple sclerosis patients, Clin. Immunol. 142 (2012), pp. 308–319. doi:https://doi.org/10.1016/j.clim.2011.11.007.
   PubMed Web of Science ®Google Scholar
• M.Y. Mok, F.P. Huang, W.K. Ip, Y. Lo, F.Y. Wong, E.Y. Chan, K.F. Lam, and D. Xu, Serum levels of IL-33 and soluble ST2 and their association with disease activity in systemic lupus erythematosus, Rheumatology 49 (2010), pp. 520–527. doi:https://doi.org/10.1093/rheumatology/kep402.
   PubMed Web of Science ®Google Scholar
• F.Y. Liew, N.I. Pitman, and I.B. McInnes, Disease-associated functions of IL-33: The new kid in the IL-1 family, Nat. Rev. Immunol. 10 (2010), pp. 103–110. doi:https://doi.org/10.1038/nri2692.
   PubMed Web of Science ®Google Scholar
• X. Liu, M. Li, Y. Wu, Y. Zhou, L. Zeng, and T. Huang, Anti-IL-33 antibody treatment inhibits airway inflammation in a murine model of allergic asthma, Biochem. Biophys. Res. Commun. 386 (2009), pp. 181–185. doi:https://doi.org/10.1016/j.bbrc.2009.06.008.
   PubMed Web of Science ®Google Scholar
• Y.H. Kim, T.Y. Yang, C.S. Park, S.H. Ahn, B.K. Son, J.H. Kim, D.H. Lim, and T.Y. Jang, Anti-IL-33 antibody has a therapeutic effect in a murine model of allergic rhinitis, Allergy 67 (2012), pp. 183–190. doi:https://doi.org/10.1111/j.1398-9995.2011.02735.x.
   PubMed Web of Science ®Google Scholar
• A. Holgado, H. Braun, E. Van Nuffel, S. Detry, M.J. Schuijs, K. Deswarte, K. Vergote, M. Haegman, G. Baudelet, J. Haustraete, H. Hammad, B.N. Lambrecht, S.N. Savvides, I.S. Afonina, and R. Beyaert, IL-33trap is a novel IL-33–neutralizing biologic that inhibits allergic airway inflammation, J. Allergy Clin. Immunol. 144 (2019), pp. 204–215. doi:https://doi.org/10.1016/j.jaci.2019.02.028.
   PubMed Web of Science ®Google Scholar
• M. Osbourn, D.C. Soares, F. Vacca, E.S. Cohen, I.C. Scott, W.F. Gregory, D.J. Smyth, M. Toivakka, A.M. Kemter, T. le Bihan, M. Wear, D. Hoving, K.J. Filbey, J.P. Hewitson, H. Henderson, A. Gonzàlez-Cìscar, C. Errington, S. Vermeren, A.L. Astier, W.A. Wallace, J. Schwarze, A.C. Ivens, R.M. Maizels, and H.J. McSorley, HpARI protein secreted by a Helminth parasite suppresses interleukin-33, Immunity 47 (2017), pp. 739–751.e5. doi:https://doi.org/10.1016/j.immuni.2017.09.015.
   PubMed Web of Science ®Google Scholar
• Y.K. Kim, S.H. Cho, A.U. Mushtaq, H. Cho, Y.W. Jung, Y. Byun, and Y.H. Jeon, Discovery of an interleukin 33 inhibitor by molecular docking simulation and NMR analysis, Bull. Korean Chem. Soc. 37 (2016), pp. 117–118. doi:https://doi.org/10.1002/bkcs.10637.
   Web of Science ®Google Scholar
• A. Voet, E.F. Banwell, K.K. Sahu, J.G. Heddle, and K.Y.J. Zhang, Protein interface pharmacophore mapping tools for small molecule protein: Protein interaction inhibitor discovery, Curr. Top. Med. Chem. 13 (2013), pp. 989–1001. doi:https://doi.org/10.2174/1568026611313090003.
   PubMed Web of Science ®Google Scholar
• The Protein Data Bank. Available at http://www.rcsb.org/pdb (accessed 15 June 2019).
   Google Scholar
• H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, and P.E. Bourne, The protein data bank, Nucleic Acids Res. 28 (2000), pp. 235–242. doi:https://doi.org/10.1093/nar/28.1.235.
   PubMed Web of Science ®Google Scholar
• A. Lingel, T.M. Weiss, M. Niebuhr, B. Pan, B.A. Appleton, C. Wiesmann, J.F. Bazan, W.J. Fairbrother, C. Wiesmann, J.F. Bazan, and W.J. Fairbrother, The structure of interleukin-33 and its interaction with the ST2 and IL-1RAcP receptors – Insight into the arrangement of heterotrimeric interleukin-1 signaling complexes, Structure 17 (2009), pp. 1398–1410. doi:https://doi.org/10.1016/j.str.2009.08.009.
   PubMed Web of Science ®Google Scholar
• S. Günther, D. Deredge, A.L. Bowers, A. Luchini, D.A. Bonsor, R. Beadenkopf, L. Liotta, P.L. Wintrode, and E.J. Sundberg, IL-1 family cytokines use distinct molecular mechanisms to signal through their shared co-receptor, Immunity 47 (2017), pp. 510–523.e4. doi:https://doi.org/10.1016/j.immuni.2017.08.004.
   PubMed Web of Science ®Google Scholar
• J. Yang, R. Yan, A. Roy, D. Xu, J. Poisson, and Y. Zhang, The I-TASSER Suite: Protein structure and function prediction, Nat. Meth. 12 (2015), pp. 7–8. doi:https://doi.org/10.1038/nmeth.3213.
   PubMed Web of Science ®Google Scholar
• Y. Zhang, I-TASSER server for protein 3D structure prediction, BMC Bioinf. 9 (2008), pp. 40. doi:https://doi.org/10.1186/1471-2105-9-40.
   PubMed Web of Science ®Google Scholar
• Molecular Operating Environment (MOE), Version 2014.09, Chemical Computing Group Inc., Montreal, QC, Canada, 2014.
   Google Scholar
• D.S. Wishart, C. Knox, A.C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang, and J. Woolsey, DrugBank: A comprehensive resource for in silico drug discovery and exploration, Nucleic Acids Res. 34 (2006), pp. 668–672. doi:https://doi.org/10.1093/nar/gkj067.
   PubMed Web of Science ®Google Scholar
• Maybridge. Available at http://www.maybridge.com/ (accessed 15 March 2019).
   Google Scholar
• LeadIT, Version 2.0.2, BioSolveIT – GmbH, Germany, 2012.
   Google Scholar
• Sybyl-X Molecular Modeling Software Packages, Version 2.0, TRIPOS Associates, Inc., Louis, USA, 2011.
   Google Scholar
• V.K. Tran-Nguyen, M.T. Le, T.D. Tran, V.D. Truong, and K.M. Thai, Peramivir binding affinity with influenza A neuraminidase and research on its mutations using an induced-fit docking approach, SAR QSAR Environ. Res. 30 (2019), pp. 899–917. doi:https://doi.org/10.1080/1062936X.2019.1679248.
   PubMed Web of Science ®Google Scholar
• Desmond Molecular Dynamics System, D. E. Shaw Research, New York, USA, 2017.
   Google Scholar
• T. Schlick, Molecular dynamics: Basics, in Molecular Modeling and Simulation: An Interdisciplinary Guide: An Interdisciplinary Guide, T. Schlick, ed., Springer New York, New York, 2010, pp. 425–461.
   Google Scholar
• D. Roccatano, A Short Introduction to the molecular dynamics simulation of nanomaterials, in Micro and Nanomanufacturing Volume II, M.J. Jackson and W. Ahmed, eds., Springer International Publishing, Cham, 2018, pp. 123–155.
   Google Scholar
• D. E. Shaw Research, Desmond Users Guide, Release 3.6.1.1, 2014, pp. 15–38.
   Google Scholar
• P.D. Lyne, M.L. Lamb, and J.C. Saeh, Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring, J. Med. Chem. 49 (2006), pp. 4805–4808. doi:https://doi.org/10.1021/jm060522a.
   PubMed Web of Science ®Google Scholar
• P.T.V. Nguyen, H. Yu, and P.A. Keller, Discovery of in silico hits targeting the nsP3 macro domain of chikungunya virus, J. Mol. Model. 20 (2014), pp. 2216. doi:https://doi.org/10.1007/s00894-014-2216-6.
   PubMed Web of Science ®Google Scholar