Identification of novel TACE inhibitors using DNN based- virtual screening, molecular dynamics and biological evaluation

References

• Almutairi, K., Nossent, J., Preen, D., Keen, H., & Inderjeeth, C. (2021). The global prevalence of rheumatoid arthritis: A meta-analysis based on a systematic review. Rheumatology International, 41(5), 863–877. https://doi.org/10.1007/s00296-020-04731-0
   PubMed Web of Science ®Google Scholar
• Safiri, S., Kolahi, A. A., Hoy, D., Smith, E., Bettampadi, D., Mansournia, M. A., Almasi-Hashiani, A., Ashrafi-Asgarabad, A., Moradi-Lakeh, M., Qorbani, M., Collins, G., Woolf, A. D., March, L., & Cross, M. (2019). Global, regional and national burden of rheumatoid arthritis 1990–2017: A systematic analysis of the global burden of disease study 2017. Annals of the Rheumatic Diseases, 78(11), 1463–1471. https://doi.org/10.1136/annrheumdis-2019-215920
   PubMed Web of Science ®Google Scholar
• Chancay, M. G., Guendsechadze, S. N., & Blanco, I. (2019). Types of pain and their psychosocial impact in women with rheumatoid arthritis. Women’s Midlife Health, 5(1), 1–9. https://doi.org/10.1186/s40695-019-0047-4
   PubMedGoogle Scholar
• Alpízar-Rodríguez, D., & Finckh, A. (2017). Environmental factors and hormones in the development of rheumatoid arthritis. Seminars in Immunopathology, 39(4), 461–468. https://doi.org/10.1007/s00281-017-0624-2
   PubMed Web of Science ®Google Scholar
• Bilberg, A., Bremell, T., Bjersing, J., & Mannerkorpi, K. (2018). High prevalence of widespread pain in women with early rheumatoid arthritis. Scandinavian Journal of Rheumatology, 47(6), 447–454. https://doi.org/10.1080/03009742.2018.1447683
   PubMed Web of Science ®Google Scholar
• Boyden, S. D., Hossain, I. N., Wohlfahrt, A., & Lee, Y. C. (2016). Non-inflammatory causes of pain in patients with rheumatoid arthritis. Current Rheumatology Reports, 18(6), 1–8. https://doi.org/10.1007/s11926-016-0581-0
   PubMed Web of Science ®Google Scholar
• Burmester, G. R., & Pope, J. E. (2017). Novel treatment strategies in rheumatoid arthritis. Lancet (London, England), 389(10086), 2338–2348. https://doi.org/10.1016/S0140-6736(17)31491-5
   PubMed Web of Science ®Google Scholar
• Lopez-Rodriguez, R., Perez-Pampin, E., Marquez, A., Blanco, F. J., Joven, B., Carreira, P., Ferrer, M. A., Caliz, R., Valor, L., Narvaez, J., Cañete, J. D., Ordoñez, M. D. C., Manrique-Arija, S., Vasilopoulos, Y., Balsa, A., Pascual-Salcedo, D., Moreno-Ramos, M. J., Alegre-Sancho, J. J., Navarro-Sarabia, F., … Gonzalez, A. (2018). Validation study of genetic biomarkers of response to TNF inhibitors in rheumatoid arthritis. PloS One, 13(5), e0196793. https://doi.org/10.1371/journal.pone.0196793
   PubMed Web of Science ®Google Scholar
• Wong, E., Cohen, T., Romi, E., Levin, M., Peleg, Y., Arad, U., Yaron, A., Milla, M. E., & Sagi, I. (2016). Harnessing the natural inhibitory domain to control TNFα converting enzyme (TACE) activity in vivo. Scientific Reports, 6(1), 1–12. https://doi.org/10.1038/srep35598
   PubMedGoogle Scholar
• Murumkar, P. R., Ghuge, R. B., Chauhan, M., Barot, R. R., Sorathiya, S., Choudhary, K. M., Joshi, K. D., & Yadav, M. R. (2020). Recent developments and strategies for the discovery of TACE inhibitors. Expert Opinion on Drug Discovery, 15(7), 779–801. https://doi.org/10.1080/17460441.2020.1744559
   PubMed Web of Science ®Google Scholar
• Ishii, S., Isozaki, T., Furuya, H., Takeuchi, H., Tsubokura, Y., Inagaki, K., & Kasama, T. (2018). ADAM-17 is expressed on rheumatoid arthritis fibroblast-like synoviocytes and regulates proinflammatory mediator expression and monocyte adhesion. Arthritis Research & Therapy, 20(1), 1–8. https://doi.org/10.1186/s13075-018-1657-1
   PubMedGoogle Scholar
• Gooz, M. (2010). ADAM-17: The enzyme that does it all. Critical Reviews in Biochemistry and Molecular Biology, 45(2), 146–169. https://doi.org/10.3109/10409231003628015
   PubMed Web of Science ®Google Scholar
• Charbonneau, M., Harper, K., Grondin, F., Pelmus, M., McDonald, P. P., & Dubois, C. M. (2007). Hypoxia-inducible factor mediates hypoxic and tumor necrosis factor α-induced increases in tumor necrosis factor-α converting enzyme/ADAM17 expression by synovial cells. The Journal of Biological Chemistry, 282(46), 33714–33724. https://doi.org/10.1074/jbc.M704041200
   PubMed Web of Science ®Google Scholar
• Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks : The Official Journal of the International Neural Network Society, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
   PubMed Web of Science ®Google Scholar
• Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug Discovery Today, 23(6), 1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039
   PubMed Web of Science ®Google Scholar
• Gawehn, E., Hiss, J. A., Brown, J. B., & Schneider, G. (2018). Advancing drug discovery via GPU-based deep learning. Expert Opinion on Drug Discovery, 13(7), 579–582. https://doi.org/10.1080/17460441.2018.1465407
   PubMed Web of Science ®Google Scholar
• Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks [Paper presentation]. Proceedings of the fourteenth international conference on artificial intelligence and statistics, June. JMLR workshop and conference proceedings (pp. 315–323).
   Google Scholar
• Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007). Epik: A software program for pKa prediction and protonation state generation for drug-like molecules. Journal of Computer-Aided Molecular Design, 21(12), 681–691. https://doi.org/10.1007/s10822-007-9133-z
   PubMed Web of Science ®Google Scholar
• Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221–234. https://doi.org/10.1007/s10822-013-9644-8
   PubMed Web of Science ®Google Scholar
• Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006). Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. Journal of Medicinal Chemistry, 49(21), 6177–6196. https://doi.org/10.1021/jm051256o
   PubMed Web of Science ®Google Scholar
• Repasky, M. P., Murphy, R. B., Banks, J. L., Greenwood, J. R., Tubert Brohman, I., Bhat, S., & Friesner, R. A. (2012). Docking performance of the glide program as evaluated on the Astex and DUD datasets: A complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. Journal of Computer-Aided Molecular Design, 26(6), 787–799. https://doi.org/10.1007/s10822-012-9575-9
   PubMed Web of Science ®Google Scholar
• Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindah, E. (2015). Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1-2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001
   Google Scholar
• Zoete, V., Cuendet, M. A., Grosdidier, A., & Michielin, O. (2011). SwissParam: A fast force field generation tool for small organic molecules. Journal of Computational Chemistry, 32(11), 2359–2368. https://doi.org/10.1002/jcc.21816
   PubMed Web of Science ®Google Scholar
• Berendsen, H. J., Postma, J. V., Van Gunsteren, W. F., DiNola, A. R. H. J., & Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81(8), 3684–3690. https://doi.org/10.1063/1.448118
   Web of Science ®Google Scholar
• Parrinello, M., & Rahman, A. (1981). Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics, 52(12), 7182–7190. https://doi.org/10.1063/1.328693
   Web of Science ®Google Scholar
• Hess, B., Bekker, H., Berendsen, H. J., & Fraaije, J. G. (1997). LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18(12), 1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
   Web of Science ®Google Scholar
• Valdés-Tresanco, M. S., Valdés-Tresanco, M. E., Valiente, P. A., & Moreno, E. (2021). gmx_MMPBSA: A new tool to perform end-state free energy calculations with GROMACS. Journal of Chemical Theory and Computation, 17(10), 6281–6291. Epub 2021 Sep 29. PMID: 34586825 https://doi.org/10.1021/acs.jctc.1c00645
   PubMed Web of Science ®Google Scholar
• MacNaul, K. L., Hutchinson, N. I., Parsons, J. N., Bayne, E. K., & Tocci, M. J. (1990). Analysis of IL-1 and TNF-alpha gene expression in human rheumatoid synoviocytes and normal monocytes by in situ hybridization. The Journal of Immunology, 145(12), 4154–4166. https://doi.org/10.4049/jimmunol.145.12.4154
   PubMed Web of Science ®Google Scholar
• Moelants, E. A., Mortier, A., Van Damme, J., & Proost, P. (2013). Regulation of TNF‐α with a focus on rheumatoid arthritis. Immunology and Cell Biology, 91(6), 393–401. https://doi.org/10.1038/icb.2013.15
   PubMed Web of Science ®Google Scholar