Development of quantitative ion physic chemistry properties-activity relationship (QIPAR) and docking simulation for sars-covid-2 protein
DOI:
https://doi.org/10.51316/jca.2021.087Keywords:
SARS-CoV-2, hybrid QIPAR models, docking simulation, Ion-Binding SiteAbstract
Currently, many drugs are being studied and potentially used in the treatment of SARS-CoV-2. Compounds studied are mostly organic substances. This work investigates the ability to inhibit SARS-CoV-2 of various 20 metal ions based on their ability to inhibit several biological systems; the physicochemical properties of metal ions were calculated by quantum chemistry DFT (B3LYP/ LanL2DZ) were used to develop the QIPAR hybrid models. Hybrid models QIPARGA-MLR (k = 4) and QIPARGA-ANN with architecture I(4)-HL(9)-O(1) were developed to predict the biological activity of metal ions. Metal ions were also investigated for their inhibitory potential for the protein SARS-CoV-2 (PDB6LU7) by docking simulation techniques. We predicted the binding sites of metal ions to the active sites of the SARS-CoV-2 protein (PDB6LU7). These studies are consistent with their activities against different biological systems. This research will also contribute to the development of metal oxide nanomaterials.
Downloads
References
D. Zhang, B. Zhang, J. Lv, R. Sa, X. Zhang, Z. Lin, Pharmacological Research 157 (2020) 104882. https://doi.org/10.1016/j.phrs.2020.104882
M. T. Kelleni, Pharmacological Research 157 (2020) 104874. https://doi.org/10.1016/j.phrs.2020.104874
D. L. McKee, A. Sternberg, U. Stange, S. Laufer, C. Naujokat, Pharmacological Research 157 (2020) 104859. https://doi.org/10.1016/j.phrs.2020.104859
R. Yang, H. Liu, C. Bai, Y. Wang, X. Zhang, R. Guo, S. Wu, J. Wang, E. Leung, H. Chang, P. Li, T. Liu, Y. Wang, Pharmacological Research 157 (2020) 104820. https://doi.org/10.1016/j.phrs.2020.104820
Q. Zhao, M. Meng, R. Kumar, Y. Wu, J. Huang, Y. Deng, Z. Weng, L. Yang, International Journal of Infectious Diseases 96 (2020) 131–135. https://doi.org/10.1016/j.ijid.2020.04.086
J. Shang, G. Ye, K. Shi, Y. Wan, C. Luo, H. Aihara, Q. Geng, A. Auerbach, F. Li, Nature 581 (2020) 221–224.
K. Ghosh, A. Amin, S. Gayen, T. Jha, Journal of Molecular Structure 1224 (2021) 129026. https://doi.org/10.1016/j.molstruc.2020.129026
U. Norinder, A. Tuck, K. Norgren, V. M. Kos, Biomedicine & Pharmacotherapy 130 (2020) 110582. https://doi.org/10.1016/j.biopha.2020.110582
G. W. Ejuh, C. Fonkem, Y. T. Assatse, R. A. Y. Kamsi, T. Nya, L. P. Ndukum, J. M. B. Ndjaka, Heliyon 6 (2020) e04647. https://doi.org/10.1016/j.heliyon.2020.e04647
T. P. T. Bui, T. A. M. Tran, T. T. H. Nguyen, T. H. Le, T. H. Tran, T. P. L. Huynh, T. T. Nguyen, T. V. A. Tran, T. Q. Phan, V. T. Pham, V. H. Nguyen, T. Q. Duong, T.T. Nguyen, T. T. Vo, K. H. LaM, T. A. N. Nguyen, ACS Omega 5(14) (2020) 8312–8320. https://doi.org/10.1021/acsomega.0c00772
T. A. M. Tran, T. P. L. Huynh, T. T. H. Nguyen, T.H. Le, T. H. Tran, T. P. T. Bui, T. Q. Duong, T. T. Nguyen, T. V. A. Tran, T. X. D. Nguyen, T. T. Nguyen, V. H. Nguyen, V. T. Pham, T. T. Vo, T. A. N. Nguyen, Biological Chemistry & Chemical Biology 5(21) (2020) 6312-6320.
https://doi.org/10.1002/slct.202000822
Q. B. Thanh, T. P. L. Huynh, T. A. M. Tran, T. Q. Duong, T. P. T. Bui, D. N. Vo, T. Q. Phan, V. T. Pham, Q. D. Duy, T. T. Nguyen, K. H. Lam, T. A. N. Nguyen, RSC Advances, 10 (2020) 30961-30974. https://doi.org/10.1039/D0RA05159D
C. Can, W. Jianlong, Chemosphere 69(10) (2007) 1610–1616. https://doi.org/10.1016/j.chemosphere.2007.05.043
J. T. Mccloskey, M. C. Newman, and S. B. Clark., Environmental Toxicology and Chemistry 15(10) (1996) 1730–1737. https://doi.org/10.51316/jca.2021.087
J. Ying, T. Zhang, M. Tang, Nanomaterials 5 (2015) 1620-1637. https://doi.org/10.3390/nano5041620
K. Roy, Advances in QSAR Modeling Applications in Pharmaceutical, Chemical, Food, Agricultural and Environmental, Springer International Publishing AG, Gewerbestrasse 11, 6330 Cham, Switzerland, 2017.
T. Engel, J. Gasteiger, Applied Chemoinformatics: Achievements and Future Opportunities, Wiley-VCH Verlag GmbH, Weinheim, Germany, 2018.
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, Nature 582 (2020) 289–293.
https://doi.org/10.1038/s41586-020-2223-y
X. Meng, X. Wang, Y. Ma, Y. Wang, Journal of Hazardous Materials 373 (2019) 620–629. https://doi.org/10.1016/j.jhazmat.2019.03.094
M. Petukh, E. Alexov, Asian J. Phys. 23(5) (2014) 735–744.
J. B. Foresman and Æ Frisch, Gaussian, Inc.: Wallingford, CT, 2015.
K. P. Singh, S. Gupta, RSC Advances 4 (2014) 13215. https://doi.org/10.1039/c4ra01274g
QSARIS Reference Guide: Statistical Analysis and Molecular Descriptors, Academic Press, San Diego, USA, 2000.
F. Buontempo, Genetic Algorithms and Machine Learning for Programmers, Andy Hunt, Raleigh, North Carolina, 2019.
D. C. Montgomery, E. A. Peck, and C. G. Vining, Introduction to Linear Regression Analysis Third Edition, Wiley-Interscience, New York, 2001.
M. Dehmer, K. Varmuza, D. Bonchev., Statistical Modelling of Molecular Descriptors in QSAR/QSPR, Wiley-VCH Verlag & Co. KGaA, Weinheim, Germany, 2012.
TIBCO® Data Science – Workbench an academic license for Statistica 13.6.0, 2020.