Zubair Ahamed, Vandana Kamjula, Bhuvaneswari Kakunuri
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Zubair Ahamed1, Vandana Kamjula2, Bhuvaneswari Kakunuri3*
1Institute of Forest Genetics and Tree Breeding, Coimbatore, Tamil Nadu.
2Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur,
Andhra Pradesh, 522502, India.
3Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur,
Andhra Pradesh, 522502, India.
Volume - 11,
Issue - 2,
Year - 2021
H9N2 avian influenza virus is a low pathogenic endemic strain in the domestic poultry of most of the Asian countries. Attempts have extensively failed in eradicating its diverse strains. To find the drug against the evolutionarily conserved substructures, the target protein sequence is analyzed through sequence and modelled structure for mapping the structurally conserved topology. The available drugs are screened against the deciphered topological map through the predicted ADMET and drug-likelihood scores. This study helps to build a theoretical framework to make the foremost potent drug.
Cite this article:
Zubair Ahamed, Vandana Kamjula, Bhuvaneswari Kakunuri. Targeting influenza at the Topologically conserved substructures. Asian Journal of Pharmacy and Technology. 2021; 11(2):121-9. doi: 10.52711/2231-5713.2021.00020
Zubair Ahamed, Vandana Kamjula, Bhuvaneswari Kakunuri. Targeting influenza at the Topologically conserved substructures. Asian Journal of Pharmacy and Technology. 2021; 11(2):121-9. doi: 10.52711/2231-5713.2021.00020 Available on: https://ajptonline.com/AbstractView.aspx?PID=2021-11-2-5
1. D. T. Mourya et al., “Emerging/re-emerging viral diseases and new viruses on the Indian horizon,” Indian J. Med. Res., vol. 149, no. 4, p. 447, 2019.
2. L.-M. Chen, C. T. Davis, H. Zhou, N. J. Cox, and R. O. Donis, “Genetic compatibility and virulence of reassortants derived from contemporary avian H5N1 and human H3N2 influenza A viruses,” PLoS Pathog, vol. 4, no. 5, p. e1000072, 2008.
3. S. J. Baigent and J. W. McCauley, “Influenza type A in humans, mammals and birds: Determinants of virus virulence, host‐range and interspecies transmission,” Bioessays, vol. 25, no. 7, pp. 657–671, 2003.
4. D. van Riel et al., “Human and avian influenza viruses target different cells in the lower respiratory tract of humans and other mammals,” Am. J. Pathol., vol. 171, no. 4, pp. 1215–1223, 2007.
5. J. K. Taubenberger and J. C. Kash, “Influenza virus evolution, host adaptation, and pandemic formation,” Cell Host Microbe, vol. 7, no. 6, pp. 440–451, 2010.
6. S. Su et al., “Epidemiology, evolution, and pathogenesis of H7N9 influenza viruses in five epidemic waves since 2013 in China,” Trends Microbiol., vol. 25, no. 9, pp. 713–728, 2017.
7. C. L. Black et al., “Influenza vaccination coverage among health care personnel—United States, 2017–18 influenza season,” Morb. Mortal. Wkly. Rep., vol. 67, no. 38, p. 1050, 2018.
8. C. Li et al., “Evolution of H9N2 influenza viruses from domestic poultry in Mainland China,” Virology, vol. 340, no. 1, pp. 70–83, 2005.
9. E. A. Pusch and D. L. Suarez, “The multifaceted zoonotic risk of H9N2 avian influenza,” Vet. Sci., vol. 5, no. 4, p. 82, 2018.
10. D. E. Swayne, “Understanding the complex pathobiology of high pathogenicity avian influenza viruses in birds,” Avian Dis., vol. 51, no. s1, pp. 242–249, 2007.
11. A. S. Lipatov et al., “Domestic pigs have low susceptibility to H5N1 highly pathogenic avian influenza viruses,” PLoS Pathog, vol. 4, no. 7, p. e1000102, 2008.
12. Z. Rui-Hua et al., “Molecular characterization and pathogenicity of swine influenza H9N2 subtype virus A/swine/HeBei/012/2008/(H9N2),” Acta Virol, vol. 55, no. 3, pp. 219–226, 2011.
13. S. J. Gamblin et al., “The structure and receptor binding properties of the 1918 influenza hemagglutinin,” Science (80-.)., vol. 303, no. 5665, pp. 1838–1842, 2004.
14. C. S. Copeland, R. W. Doms, E. M. Bolzau, R. G. Webster, and A. Helenius, “Assembly of influenza hemagglutinin trimers and its role in intracellular transport.,” J. Cell Biol., vol. 103, no. 4, pp. 1179–1191, 1986.
15. Y. Shtyrya et al., “Adjustment of receptor-binding and neuraminidase substrate specificties in avian–human reassortant influenza viruses,” Glycoconj. J., vol. 26, no. 1, pp. 99–109, 2009.
16. A. Mehle, “Unusual influenza A viruses in bats,” Viruses, vol. 6, no. 9, pp. 3438–3449, 2014.
17. R. König et al., “Human host factors required for influenza virus replication,” Nature, vol. 463, no. 7282, pp. 813–817, 2010.
18. G. Cattoli et al., “Evidence for differing evolutionary dynamics of A/H5N1 viruses among countries applying or not applying avian influenza vaccination in poultry,” Vaccine, vol. 29, no. 50, pp. 9368–9375, 2011.
19. J.-H. Lu, X.-F. Liu, W.-X. Shao, Y.-L. Liu, D.-P. Wei, and H.-Q. Liu, “Phylogenetic analysis of eight genes of H9N2 subtype influenza virus: a mainland China strain possessing early isolates’ genes that have been circulating,” Virus Genes, vol. 31, no. 2, pp. 163–169, 2005.
20. J. Pu et al., “Evolution of the H9N2 influenza genotype that facilitated the genesis of the novel H7N9 virus,” Proc. Natl. Acad. Sci., vol. 112, no. 2, pp. 548–553, 2015.
21. M. T. Koday et al., “A computationally designed hemagglutinin stem-binding protein provides in vivo protection from influenza independent of a host immune response,” PLoS Pathog., vol. 12, no. 2, p. e1005409, 2016.
22. W. He et al., “Alveolar macrophages are critical for broadly-reactive antibody-mediated protection against influenza A virus in mice,” Nat. Commun., vol. 8, no. 1, pp. 1–14, 2017.
23. P. Zimmermann and N. Curtis, “The influence of probiotics on vaccine responses–A systematic review,” Vaccine, vol. 36, no. 2, pp. 207–213, 2018.
24. A. P. Gultyaev, M. I. Spronken, M. Funk, R. A. M. Fouchier, and M. Richard, “Insertions of codons encoding basic amino acids in H7 hemagglutinins of influenza A viruses occur by recombination with RNA at hotspots near snoRNA binding sites,” RNA, p. rna-077495, 2020.
25. B. Morrissey, M. Streamer, and K. M. Downard, “Antigenic characterisation of H3N2 subtypes of the influenza virus by mass spectrometry,” J. Virol. Methods, vol. 145, no. 2, pp. 106–114, 2007.
26. A. Runthala and S. Chowdhury, “Protein Structure Prediction: Are We There Yet?,” in Knowledge-based systems in biomedicine and computational life science, Springer, 2013, pp. 79–115.
27. V. Kamjula, A. Kanneganti, R. Metla, K. Nidamanuri, S. Idupulapati, and A. Runthala, “Decoding the vital segments in human ATP-dependent RNA helicase,” Bioinformation, vol. 16, no. 2, p. 160, 2020.
28. W. Eber and J. Zimmermann, “Evaluating and Retrieving Parameters for Optimizing Organizational Structures in Real Estate and Construction Management,” Period. Polytech. Archit., vol. 49, no. 2, pp. 155–164, 2018.
29. A. Runthala and S. Chowdhury, “Refined template selection and combination algorithm significantly improves template-based modeling accuracy,” J. Bioinform. Comput. Biol., vol. 17, no. 02, p. 1950006, 2019.
30. S. K. Shikhin Garg and A. Runthala, “Improved protein model ranking through topological assessment,” Computational Biology and Bioinformatics: Gene Regulation. CRC Press, pp. 406–424, 2016.
31. S. Garg, S. Kakkar, and A. Runthala, “Improved Protein Model Ranking through Topological Assessment,” in Computational Biology and Bioinformatics, CRC Press, 2016, pp. 410–428.
32. Y. Zhang and J. Skolnick, “Scoring function for automated assessment of protein structure template quality,” Proteins Struct. Funct. Bioinforma., vol. 57, no. 4, pp. 702–710, 2004.
33. H. Ashkenazy et al., “ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules,” Nucleic Acids Res., vol. 44, no. W1, pp. W344–W350, 2016.
34. P. Artimo et al., “ExPASy: SIB bioinformatics resource portal,” Nucleic Acids Res., vol. 40, no. W1, pp. W597–W603, Jul. 2012, doi: 10.1093/nar/gks400.
35. G. M. Morris et al., “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility,” J. Comput. Chem., vol. 30, no. 16, pp. 2785–2791, 2009.
36. L. Jendele, R. Krivak, P. Skoda, M. Novotny, and D. Hoksza, “PrankWeb: a web server for ligand binding site prediction and visualization,” Nucleic Acids Res., vol. 47, no. W1, pp. W345–W349, 2019.
37. V. B. Siramshetty, J. Nickel, C. Omieczynski, B.-O. Gohlke, M. N. Drwal, and R. Preissner, “Withdrawn—a resource for withdrawn and discontinued drugs,” Nucleic Acids Res., vol. 44, no. D1, pp. D1080–D1086, 2016.
38. A. Daina, O. Michielin, and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,” Sci. Rep., vol. 7, p. 42717, 2017.
39. P. Ertl, B. Rohde, and P. Selzer, “Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties,” J. Med. Chem., vol. 43, no. 20, pp. 3714–3717, 2000.
40. C. A. Lipinski, “Lead-and drug-like compounds: the rule-of-five revolution,” Drug Discov. Today Technol., vol. 1, no. 4, pp. 337–341, 2004.
41. A. Bairoch et al., “The universal protein resource (UniProt),” Nucleic Acids Res., vol. 33, no. suppl_1, pp. D154–D159, 2005.
42. A. Runthala and S. Chowdhury, “Iterative optimal TM_Score and Z_Score guided sampling significantly improves model topology,” in Proceedings of the International MultiConference of Engineers and Computer Scientists, 2014, vol. 1.
43. S. Neetu, K. Sunil, A. Ashish, K. Jayantee, and M. Usha Kant, “Microstructural abnormalities of the trigeminal nerve by diffusion-tensor imaging in trigeminal neuralgia without neurovascular compression,” Neuroradiol. J., vol. 29, no. 1, pp. 13–18, 2016.
44. A. Runthala, “Probabilistic divergence of a TBM methodology from the ideal protocol,” bioRxiv, 2020.
45. M. Biasini et al., “Swiss-Model: modelling protein tertiary and quaternary structure using evolutionary information,” Nucleic Acids Res., vol. 42, no. W1, pp. W252–W258, 2014.
46. X. Robert and P. Gouet, “Deciphering key features in protein structures with the new ENDscript server,” Nucleic Acids Res., vol. 42, no. W1, pp. W320–W324, 2014.
47. B. Webb and A. Sali, “Comparative protein structure modeling using Modeller,” Curr. Protoc. Bioinforma., vol. 54, no. 1, pp. 5–6, 2016.
48. C. Colovos and T. O. Yeates, “Verification of protein structures: patterns of nonbonded atomic interactions,” Protein Sci., vol. 2, no. 9, pp. 1511–1519, 1993.
49. L. Zimmermann et al., “A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core,” J. Mol. Biol., vol. 430, no. 15, pp. 2237–2243, 2018.
50. G. E. Crooks, G. Hon, J.-M. Chandonia, and S. E. Brenner, “WebLogo: a sequence logo generator,” Genome Res., vol. 14, no. 6, pp. 1188–1190, 2004.
51. K. Fukao et al., “Combination treatment with the cap-dependent endonuclease inhibitor baloxavir marboxil and a neuraminidase inhibitor in a mouse model of influenza A virus infection,” J. Antimicrob. Chemother., vol. 74, no. 3, pp. 654–662, 2019.
52. R. A. Laskowski and M. B. Swindells, “LigPlot+: multiple ligand–protein interaction diagrams for drug discovery.” ACS Publications, 2011.
53. C. Bäuerle et al., “Coherent control of single electrons: a review of current progress,” Reports Prog. Phys., vol. 81, no. 5, p. 56503, 2018.