Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

References

• Baddipadige, R., Himanshu, V., Gera, N., Bharti, S., & Om, S. (2021). Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors of CYP1B1. Journal of Biomolecular Structure and Dynamics, 40(17), 7975–7990. https://doi.org/10.1080/07391102.2021.1905552
   PubMedGoogle Scholar
• Badhani, B., & Kakkar, R. (2017). In silico studies on potential MCF-7 inhibitors: A combination of pharmacophore and 3D-QSAR modeling, virtual screening, molecular docking, and pharmacokinetic analysis. Journal of Biomolecular Structure and Dynamics, 35(9), 1950–1967. https://doi.org/10.1080/07391102.2016.1202863
   PubMed Web of Science ®Google Scholar
• Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318
   PubMed Web of Science ®Google Scholar
• Bhole, R. P., Bonde, C. G., Bonde, S. G., Chikhale, R. V., & Wavhale, R. R. (2021). Pharmacophore model and atom-based 3D quantitative structure activity relationship (QSAR) of human immunodeficiency virus-1 (HIV-1) capsid assembly inhibitors. Journal of Biomolecular Structure and Dynamics, 39(2), 718–727. https://doi.org/10.1080/07391102.2020.1715258
   PubMed Web of Science ®Google Scholar
• Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 42717. https://doi.org/10.1038/srep42717
   PubMed Web of Science ®Google Scholar
• David, V. D. S., Erik, L., Berk, H., Gerrit, G., Alan, E. M., & Herman, J. C. B. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/10.1002/jcc.20291
   PubMed Web of Science ®Google Scholar
• Dwivedi, M. K., Soni, P. D., Sachan, S., & Tiwari, S. (2015). QSAR studies, and in silico ADME prediction Of P-aminosalicylic acid derivatives as neuraminidase inhibitors. Journal of Drug Delivery and Therapeutics, 5(3), 80–86. https://doi.org/10.22270/jddt.v5i3.1151
   Google Scholar
• Elena, A. G., Emi, T., Rod, S. D., Seiichiro, F., Lance, D. P., Mira, C. P., Weijuan, H., Angie, L., Ha, T. N., Dmitriy, P., Aine, R., Sook, K. B., Magdi, S., Kanta, S., Sun, W., Dayan, W., Richard, J. W., Hui, L. Y., Wenqing, Z., Adam, M., & Larisa, V. G. (2022). Global update on the susceptibilities of human influenza viruses to neuraminidase inhibitors and the cap-dependent endonuclease inhibitor baloxavir. Journal of Antiviral Research, 200(105281), 2018–2020. https://doi.org/10.1016/j.antiviral.2022.105281
   Google Scholar
• Giordano, D., Biancaniello, C., Argenio, M. A., & Facchiano, A. (2022). Drug design by pharmacophore and virtual screening approach. Pharmaceuticals, 15(5), 646. https://doi.org/10.3390/ph15050646
   Web of Science ®Google Scholar
• Golbraikh, A., & Tropsha, A. (2002). Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Molecular Diversity, 5(4), 231–243. https://doi.org/10.1023/a:1020869118689
   PubMed Web of Science ®Google Scholar
• Gubareva, L. V., Sleeman, K., Guo, Z., Yang, H., Hodges, E., Davis, C. T., Baranovich, T., & Stevens, J. (2017). Drug susceptibility evaluation of an influenza A(H7N9)virus by analyzing recombinant neuraminidase proteins. The Journal of Infectious Diseases, 216(suppl_4), S566–S574. https://doi.org/10.1093/infdis/jiw625
   PubMedGoogle Scholar
• Katherine, H., & Kanta, S. (2015). Influenza vaccines: Challenges and solutions. Journal of Cell Host & Microbe, 17(3), 295–300. https://doi.org/10.1016/j.chom.2015.02.012
   PubMed Web of Science ®Google Scholar
• Martin, K., & Andrew, L. (2002). Molecular dynamics simulations of biomolecules. Accounts of Chemical Research, 35(6), 646–652. https://doi.org/10.1021/ar020082r
   Web of Science ®Google Scholar
• Mishra, A., Jha, V., & Rajak, H. (2022). Molecular structural investigations of quinoxaline derivatives through 3D-QSAR, molecular docking, ADME prediction and pharmacophore modeling studies for the search of novel antimalarial agent. Journal of the Indian Chemical Society, 99(2), 100343. https://doi.org/10.1016/j.jics.2022.100343
   Web of Science ®Google Scholar
• Mohammad, H., & Parvindokht, B. (2022). Natural inhibitors of SARS-CoV-2 main protease: Structure based pharmacophore modeling, molecular docking and molecular dynamic simulation studies. Journal of Molecular Modeling, 28(9), 279. https://doi.org/10.1007/s00894-022-05286-6
   PubMed Web of Science ®Google Scholar
• Muchtaridi, M., Rina, F. N., Emmy, H. K. I., Aisyah, S. A. R., Amirah, M. G., & Habibah, A. W. (2022). Neuraminidase inhibitor of garcinia atroviridis L. fruits and leaves using partial purification and molecular characterization. Molecules, 27(3), 949. https://doi.org/10.3390/molecules27030949
   PubMed Web of Science ®Google Scholar
• Mukthar, M., Mahamudul, H., Muhammad, M., M., Mohammad, A. S. H., Shariful, I., & Rifat, N. S. (2022). Inhibitory potentiality of secondary metabolites extracted from marine fungus target on avian influenza virus-A subtype H5N8 (neuraminidase) and H5N1 (nucleoprotein): A rational virtual screening. Veterinary and Animal Science, 15, 100231. https://doi.org/10.1016/j.vas.2022.100231
   PubMedGoogle Scholar
• Oranit, D., Dina, S. D., Yuval, I., Ruth, N., & Haim, J. W. (2012). Novel approach for efficient pharmacophore-based virtual screening: method and applications. Journal of Chemical Information and Modeling. 49, 2333–2343. https://doi.org/10.1021/ci900263d
   Google Scholar
• Raies, A. B., & Bajic, V. B. (2018). In silicotoxicology: Comprehensivebenchmarking of multi-labelclassification methods appliedto chemical toxicity data. WIREs Computational Molecular Science, 8(3), 1352. https://doi.org/10.1002/wcms.1240
   Google Scholar
• Ralf, W., Mikhail, M., & Hans, D. K. (2002). Functional balance between haemagglutinin and Neuraminidase in influenza virus infections. Rev. Med. Virol, 12, 159–166. https://doi.org/10.1002/rmv.352
   PubMed Web of Science ®Google Scholar
• Rohini, K., Roy, R., Ramanathan, K., & Shanthi, V. (2019). E-pharmacophore hypothesis strategy to discover potent inhibitor for inluenza treatment. Journal of Theoretical and Computational Chemistry, 18(04), 1950021. https://doi.org/10.1142/S0219633619500214
   Google Scholar
• Roy, K., Mitra, I., Kar, S., Ojha, P. K., Das, R. N., & Kabir, H. (2012). Comparative studies on some metrics for external validation of QSPR models. Journal of Chemical Information and Modeling, 52(2), 396–408. https://doi.org/10.1021/ci200520g
   PubMed Web of Science ®Google Scholar
• Sabina, L., Barbora, L., Daniela, C., Miroslav, O., & Stanislav, M. (2022). Optimization of an inclusion body-based production of the influenza virus neuraminidase in Escherichia coli. Biomolecules, 12, 331. https://doi.org/10.3390/biom12020331
   PubMed Web of Science ®Google Scholar
• Sakkiah, S., Thangapandian, S., & Lee, K. W. (2012). Pharmacophore modeling, molecular docking, and molecular dynamics simulation approaches for identifying new lead compounds for inhibiting aldose reductase 2. Journal of Molecular Modeling, 18(7), 3267–3282. https://doi.org/10.1007/s00894-011-1247-5
   PubMed Web of Science ®Google Scholar
• Shafi, U. K., Nafees, A., Lay, H. C., Rakesh, N., & Thet, T. H. (2022). Natural bioactive compounds as a new source of promising G protein-coupled estrogen receptor (GPER) modulators: Comprehensive in silico approach. Journal of Biomolecular Structure and Dynamics, 40(4), 1617–1628. https://doi.org/10.1080/07391102.2020.1830853
   Web of Science ®Google Scholar
• Sharma, V., Kumar, H., & Wakode, S. (2016). Pharmacophore generation and atom based 3D-QSAR of quinoline derivatives as selective phosphodiesterase 4B inhibitors. RSC Advances, 6 (79), 75805–75819. https://doi.org/10.1039/C6RA11210B
   Web of Science ®Google Scholar
• Sheng, Y. H., & Xiaoqin, Z. (2010). Advances and challenges in protein-ligand docking. International Journal of Molecular Sciences. 11, 3016–3034. https://doi.org/10.3390/ijms11083016
   PubMed Web of Science ®Google Scholar
• So-Jung, S., Mei, C., Louisa, O., Adem, J., Bernadette, P., & Nusrat, H. (2020). Rate of use and effectiveness of oseltamivir in the treatment of influenza illness in high-risk populations: A systematic review and meta-analysis. Journal of Health Science Reports, 4(1), 241. https://doi.org/10.1002/hsr2.214
   Web of Science ®Google Scholar
• Stephani, J. Y. M., Vijayakumar, G., Sunhye, H., & Sun, C. (2015). Role of computer-aided drug design in modern drug discovery. Archives of Pharmacal Research, 38(9), 1686–1701. https://doi.org/10.1007/s12272-015-0640-5
   PubMed Web of Science ®Google Scholar
• Steven, J. G., & John, J. S. (2015). Influenza hemagglutinin and neuraminidase membrane glycoproteins. Journal of Biological Chemistry, 285 (37), 28403–28409. https://doi.org/10.1074/jbc.R110.129809
   Google Scholar
• Tiejun, C., Qingliang, L., Zhigang, Z., Yanli, W., & Stephen, H. B. (2012). Structure-based virtual screening for drug discovery: A problem-centric review. The AAPS Journal, 14(1), 133–141. https://doi.org/10.1208/s12248-012-9322-0
   PubMed Web of Science ®Google Scholar
• Veerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C. P., & Agrawal, R. K. (2011). Validation of QSAR models – Strategies and importance. International Journal of Drug Design and Discovery, 2(3), 511–519.
   Google Scholar
• Vincent, Z., Michel, A. C., Aurelien, G., & Olivier, M. (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
   PubMed Web of Science ®Google Scholar
• Wang, B., Wang, K., Meng, P., Hu, Y., Yang, F., Liu, K., Lei, Z., Chen, B., & Tian, Y. (2018). Design, synthesis, and evaluation of carboxyl-modified oseltamivir derivatives with improved lipophilicity as neuraminidase inhibitors. Bioorganic & Medicinal Chemistry Letters, 28 (21), 3477–3482. https://doi.org/10.1016/j.bmcl.2018.09.014
   PubMed Web of Science ®Google Scholar
• Vora, J., Patel, S., Athar, M., Sinha, S., Chhabria, M. T., Jha, P. C., & Shrivastava, N. (2020). Pharmacophore modeling, molecular docking and molecular dynamics simulation for screening and identifying anti-dengue phytocompounds. Journal of Biomolecular Structure and Dynamics, 38(06), 1726–1740. https://doi.org/10.1080/07391102
   PubMedGoogle Scholar
• Xu, L., Zhao, L., Che, J., Zhang, Q., Cao, R., & Li, X. (2021). Identification of novel influenza polymerase PB2 inhibitors using virtual screening approach and molecular dynamics simulation analysis of active compounds. Bioorganic & Medicinal Chemistry, 52, 116515. https://doi.org/10.1016/j.bmc.2021.116515
   PubMed Web of Science ®Google Scholar
• Yang, J., Hu, J., Zhang, G., Qin, L., Wen, H., & Tang, Y. (2021). Pharmacophore modeling and 3D‑QSAR study for the design of novel α‑synuclein aggregation inhibitors. Journal of Molecular Modeling, 27(9), 260. https://doi.org/10.1007/s00894-021-04881-3
   PubMed Web of Science ®Google Scholar
• Zhang, J., Shan, Y., Pan, X., Wang, C., Xu, W., & He, L. (2011). Molecular docking, 3D-QSAR studies, and in silico ADME prediction of p-aminosalicylic acid derivatives as neuraminidase inhibitors. Chemical Biology & Drug Design, 78(4), 709–717. https://doi.org/10.1111/j.1747-0285.2011.01179.x
   PubMed Web of Science ®Google Scholar