The Internet of Medical Things (IoMT). Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Программы
Год издания: 0
isbn: 9781119769187
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Ellagic acid –2.892 Quercetin –1.249 K-ras oncogene protein Curcumin 2.730 Ellagic acid 0.921 Quercetin –1.154 TP53 Curcumin 1.633 Ellagic acid 0.054 Quercetin –0.809

      In a nutshell, EGFR was successfully docked with curcumin, ellagic acid, and quercetin. Besides that, the same approach of docking simulation was performed for K-ras oncogene protein and TP53. Among the three protein models, EGFR had a strong interaction with ellagic acid due to the lowest energy value while K-ras oncogene protein and TP53 had a strong interaction with quercetin as the binding energy was the lowest. Consequently, result from this study will aid in designing a suitable structure-based drug. However, wet lab must be carried out to verify the results of this study.

      1. Cancer Research UK, Worldwide cancer statistics, 2012, https://www.cancerresearchuk.org/health-professional/cancer-statistics/worldwide-cancer#collapseZero.

      3. American Cancer Society, Causes, risk factors and prevention, 2016, https://www.cancer.org/cancer/non-small-cell-lung-cancer/causes-risks-prevention/ what causes.

      4. Mayo Clinic, Lung cancer, 2018, https://www.mayoclinic.org/diseases-conditions/lung-cancer/diagnosis-treatment/drc-20374627.

      5. El-Telbany, A. and Patrick, C.M., Cancer genes in lung cancer. Genes Cancer, M3, 7–8, 467–480, 2012.

      6. Santos, D.C., Sheperd, F.A., Tsao, M.S., EGFR mutations and lung cancer. Annu. Rev. Pathol.: Mechanisms of Disease, 6, 49–69, 2016.

      7. Bhattacharya, S., Socinski, M.A., T.F., KRAS mutant lung cancer: progress thus far on an elusive therapeutic target. Clin. Transl. Med., 4, 35, 2015.

      8. Halverson, A.R., Silwal-Pandit, L., Meza-Zepeda, L.A. et al., TP53 mutation spectrum in smokers and never smoking lung cancer patients. Front. Genet., 7, 85, 2016.

      9. Basnet, P. and Skalko-Basnet, N., Curcumin: An anti-inflammatory molecule from a curry spice on the path to cancer treatment. Molecules, 6, 6, 4567–4598, 2011.

      10. Healthline, Whyellagic acid is important?, 2020, https://www.healthline.com/health/ellagic-acid.

      11. Kuo, P.-C., Liu, H.-F., Chao, J.-I., Survivin and p53 modulate quercetin-induced cell growth inhibition and apoptosis in human lung carcinoma cells. J. Biol. Chem., 279, 53, 55875–55885, 2004.

      12. Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., Schmidt, T. et al., SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res., 42, W252-8, 2014.

      13. Delano, W.L., The PyMOL molecular graphics system, 2001, http://www.pymol.org.

      14. Gasteiger, E., Hoogland, C., Gattiker, A. et al., Protein identification and analysis tools on the ExPASy server, in: The proteomics protocols handbook, J.M. Walker (Ed.), Humana Press, Totowa, 2015.

      15. Prabi, L.G., Color protein sequence analysis, 1998, https://npsaprabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_color.html.

      16. Costantini, S., Colonna, G., Facchiano, A.M., ESBRI: a web server for evaluating salt bridges in proteins. Bioinformation, 3, 137–138, 2008.

      17. Roy, S., Maheshwari, N., Chauhan, R. et al., Structure prediction and functional characterization of secondary metabolite proteins of Ocimum. Bioinformation, 6, 8, 315–319, 2011.

      18. Geourjon, C. and Deleage, G., SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput. Appl. Biosci., 11, 681–684, 1995.

      20. Wallner, B. and Elofsson, A., Can correct protein models be identified? Protein Sci., 12, 1073–1086, 2003.

      21. Colovos, C. and Yeates, T.O., Verification of protein structures: patterns of non-bonded atomic interactions. Protein Sci., 2, 1511–1519, 1993.

      22. Eisenberg, D., Luthy, R., Bowie, J.U., VERIFY3D: assessment of protein models with three- dimensional profiles. Methods Enzymol., 77, 396–404, 1977.

      23. Jayaram, B., Active site prediction server, 2004, http://www.scftio-iitd.res.in/dock/ActiveSite.jsp.

      24. National Center for Biotechnology Information, Pubchem., 2017, https://pubchem.ncbi.nlm.nih.gov/.

      25. Hui, S.L. and Yang, Z., BSP-SLIM: A blind low-resolution ligand-protein docking approach using theoretically predicted protein structures. Proteins, 80, 93–110, 2012.

      26. Kumar, S., Tsai, C.J., Ma, B. et al., Contribution of salt bridges toward protein thermo-stability. J. Biomol. Struct. Dyn., 1, 79–86, 2000.

      27. Kumar, S. and Nussinov, R., Salt bridge stability in monomeric proteins. J. Mol. Biol., 293, 1241–1255, 2009.

      28. Kumar, S. and Nussinov, R., Relationship between ion pair geometries and electrostatic strengths in proteins. Biophys. J., 83, 1595–1612, 2002.

      29. Parvizpour, S., Shamsir, M.S., Razmara, J. et al., Structural and functional analysis of a novel psychrophilic b-mannanase from Glaciozyma Antarctica PI12. J. Comput. Aided Mol. Des., 28, 6, 685–698, 2014.

      30. Murugesan, D., Ponnusamy, R.D., Gopalan, D.K., Molecular docking study of active phytocompounds from the methanolic leaf extract of vitexnegundo-against cyclooxygenase-2. Bangladesh J. Pharmacol., 9, 2, 146–53, 2014.

      31. Balamurugan, V and Balakrishnan, V., Molecular docking studies of Moringaconcanensisnimmo leaf phytocompounds for brain cancer. Res. Rev.: J. Life Sci., 8, 1, 26–34, 2018.

      32. Santhanakrishnan, D., Sipriya, N., Chandrasekaran, B., Studies on the phytochemistry, spectroscopic characterization and antibacterial efficacy of Salicornia Brachiata. Int. J. Pharm. Pharm. Sci., 6, 6430–6432, 2014.

      33. Kasilingam,