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A Comparative Modeling and Comprehensive Binding Site Analysis of the South African Beta COVID-19 Variant’s Spike Protein Structure

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Abbreviations

3D:

Three Dimensional

BLAST:

basic local alignment search tool

CASTp:

Computed Atlas of Surface Topography of proteins

COVID-19:

Coronavirus disease of 2019

GMQE:

Global Mean Quality Estimate

PDB:

Protein Data Bank

SARS-Cov-2:

severe acute respiratory syndrome coronavirus 2

SP:

Spike Protein

References

  1. World Health Organization. (2021). Coronavirus disease (COVID-19): Vaccines. https://www. who.int/news-room/q-a-detail/coronavirus-disease-(covid-19)-vaccines?topicsurvey=v8kj13. Accessed on 13 April 2021.

  2. Adebiyi, M., & Olugbara, O. (2021). Binding site identification of COVID-19 main protease 3D structure by homology modelling. Indonesian Journal of Electrical Engineering and Computer Science, 1713–1721.

    Google Scholar 

  3. Mothay, D., & Ramesh, K. V. (2020). Binding site analysis of potential protease inhibitors of COVID-19 using AutoDock. VirusDis, 31(2), 194–199.

    Article  Google Scholar 

  4. Wang, R., Chen, J., Gao, K., Hozumi, Y., Yin, C., & Wei, G.-W. (2021). Analysis of SARS-CoV-2 mutations in the United States suggests presence of four substrains. Communications Biology. https://doi.org/10.1038/s42003-021-01754-6

  5. Liu, T., Tang, G., & Capriotti, E. (2011). Comparative modeling: The state of the art and protein drug target structure prediction. Combinatorial Chemistry and High Throughput Screening, 14, 532–547. https://doi.org/10.2174/138620711795767811

    Article  Google Scholar 

  6. Ringe, D. (1995). What makes a binding site a binding site? Current Opinion in Structural Biology, 5(6), 825–829. https://doi.org/10.1016/0959-440X(95)80017-4

    Article  Google Scholar 

  7. Guo, F., & Wang, L. (2012). Computing the protein binding sites. BMC Bioinformatics, 13–13.

    Google Scholar 

  8. Huang, Y., Yang, C., Xu, X.-F., Xu, W., & Liu, S.-W. (2020). Structural and functional properties of SARS-CoV-2 spike protein: Potential antivirus drug development for COVID-19. Acta Pharmacologica Sinica, 41, 1141–1149.

    Article  Google Scholar 

  9. Hodcroft, E. B., Domman, D., Oguntuyo, K., Snyder, D., Diest, M. V., Densmore, K., & Kevil, C. (2021). Emergence in late 2020 of multiple lineages of SARS-CoV-2 Spike protein variants. MedRxiv. https://doi.org/10.1101/2021.02.12.21251658

  10. Guo, F., & Wang, L. (2012). Computing the protein binding sites. BMC Bioinformatics, 13. https://doi.org/10.1186/1471-2105-13-S10-S2

  11. Xiong, Y., Zhu, X., & Kihara, D. (2013). Binding site identification in target proteins. In M. Lill (Ed.), In silico drug discovery and design (pp. 204–220). Future Science Ltd. https://doi.org/10.4155/EBO.13.165

    Chapter  Google Scholar 

  12. Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., & Schwede, T. (2018). SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Research, 46(W1), W296–W303. https://doi.org/10.1093/nar/gky427

    Article  Google Scholar 

  13. Hasani, H., & Barakat, K. (2017). Homology modeling: An overview of fundamentals and tools. International Review on Modelling and Simulations, 10, 129–147. https://doi.org/10.15866/iremos.v10i2.11412

    Article  Google Scholar 

  14. Adebiyi, M. O., Faola, D., Olatunji, O., Ogundokun, R. O., & Adeniyi, E. A. (2019). Computational prediction of Corynebacterium matruchotti protein’s 3D structure reveals its capacity to bind to DNA domain site in the malaria vector, anopheles. International Journal of Engineering Research and Technology, 1935–1940.

    Google Scholar 

  15. Sajjad, A., Yasir, W., Saba, I., Saadia, B., Wajid, B. S., & Khalid, M. (2021). Structurebased virtual screening identifies multiple stable binding sites at RecA 59 domains of SARS-CoV-2 Helicase Enzyme. Molecules, 26(5), 1446. https://doi.org/10.3390/molecules26051446

    Article  Google Scholar 

  16. Colovos, C., & Yeates, T. O. (1993). Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Science: A Publication of the Protein Society, 2(9), 1511–1519. https://doi.org/10.1002/pro.5560020916

    Article  Google Scholar 

  17. Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., & Fabritiis, G. D. (2017). DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 33(19), 3036–3042. https://doi.org/10.1093/bioinformatics/btx350

    Article  Google Scholar 

  18. Binkowski, T. A., Naghibzadeh, S., & Lianga, J. (2003). CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Research, 31(13), 3352–3355. https://doi.org/10.1093/nar/gkg512

    Article  Google Scholar 

  19. Eisenberg, D., Lüthy, R., & Bowie, J. U. (1997). VERIFY3D: Assessment of protein models with three-dimensional profiles. Methods in Enzymology, 277, 396–404. https://doi.org/10.1016/s0076-6879(97)77022-8

    Article  Google Scholar 

  20. RCSB PDB. (2021). About RCSB PDB: Enabling breakthroughs in scientific and biomedical research and education. Retrieved from RSCB PDB: https://www.rcsb.org/pages/about-us/index

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Acknowledgement

The Authors would like to thank Sol Plaatje University for the support for this chapter via MIT Disciplinary Research Seed Funding- Grant number: MIT3YR2022.

Availability of Data and Materials

The datasets used and analyzed during the current chapter are available in the Protein Data Bank repository, The target protein used can be found at https://www.rcsb.org/structure/7lyo and the template protein can be found at https://www.rcsb.org/structure/7b62 [20].

Competing Interests

The authors declare that they have no competing interests.

Author information

Authors and Affiliations

  1. Sol Plaatje University, Kimberley, South Africa

    Taryn Nicole Michael, Ibidun Christiana Obagbuwa, Albert Whata & Kudakwashe Madzima

Authors
  1. Taryn Nicole Michael
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  2. Ibidun Christiana Obagbuwa
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  3. Albert Whata
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  4. Kudakwashe Madzima
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Editor information

Editors and Affiliations

  1. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Lahby

  2. University of Cagliari Piazza d’Armi, Cagliari, Italy

    Virginia Pilloni

  3. Computer Science and Engineering (AI & ML), Bengal Institute of Technology, Kolkata, India

    Jyoti Sekhar Banerjee

  4. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

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Michael, T.N., Obagbuwa, I.C., Whata, A., Madzima, K. (2023). A Comparative Modeling and Comprehensive Binding Site Analysis of the South African Beta COVID-19 Variant’s Spike Protein Structure. In: Lahby, M., Pilloni, V., Banerjee, J.S., Mahmud, M. (eds) Advanced AI and Internet of Health Things for Combating Pandemics. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-28631-5_18

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  • DOI: https://doi.org/10.1007/978-3-031-28631-5_18

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