Predicting Mutation-Driven Changes in the SARS-CoV-2 Spike Protein Using Structural Signatures and Neural Networks
Resumo
COVID-19, caused by the SARS-CoV-2 virus, has led to a global pandemic since 2020, resulting in nearly 7 million deaths. The virus’s rapid spread is due to more transmissible variants, many with spike glycoprotein mutations, which are key for cell invasion and a vaccine target. Understanding these mutations is crucial for preventing more dangerous variants. This study developed a computational method to predict the impact of mutations on the spike protein. Using data from 23,472 mutations, molecular modeling, graph-based structural signatures, and a machine-learning approach based on neural networks, the model analyzed 318 proteins, showing the methodology’s effectiveness in assessing the potential of new variants.Referências
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Pires, D. E. V. et al. (2011). Cutoff scanning matrix (csm): structural classification and function prediction by protein inter-residue distance patterns. BMC Genomics, 12(Suppl 4):S12.
Pires, D. E. V. et al. (2013). acsm: noise-free graph-based signatures to large-scale receptor-based ligand prediction. bioinformatics, 29(7):855–861.
Rabaaan, A. A. et al. (2020). Sars-cov-2, sars-cov, and mers-cov: A comparative overview. Le Infezioni in Medicina, 28(2):174–184.
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Wang, M.-Y. et al. (2020). Sars-cov-2: Structure, biology, and structure-based therapeutics development. Frontiers in Cellular and Infection Microbiology, 10:587269.
Webb, B. and Sali, A. (2016). Comparative protein structure modeling using modeller. Current Protocols in Bioinformatics, 54:5.6.1–5.6.37.
Weisblum, Y. et al. (2020). Escape from neutralizing antibodies by sars-cov-2 spike protein variants. eLife, 9:e61312.
Weiss, S. R. and Navas-Martin, S. (2005). Coronavirus pathogenesis and the emerging pathogen severe acute respiratory syndrome coronavirus. Microbiology and Molecular Biology Reviews, 69(4):635–664.
WHO (2023). Who coronavirus (covid-19) dashboard. [link]. Accessed: 2023-09-05.
Yang, H. and Rao, Z. (2021). Structural biology of sars-cov-2 and implications for therapeutic development. Nature Reviews Microbiology, 19(11):685–700.
Zatorski, N. et al. (2022). Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues. Database: The Journal of Biological Databases and Curation, 2022:baac053.
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., and Lipman, D. J. (1990). Basic local alignment search tool. J. Mol. Biol., 215(3):403–410.
Bayat, A. (2002). Bioinformatics. BMJ: British Medical Journal, 324(7344):1018–1022.
Bowie, J. U., Lüthy, and Eisenberg, D. (1991). a method to identify protein sequences that fold into a known three-dimensional structure. science, 253(5016):164–170.
Chen, J., Wang, R., Wang, M., and Wei, G. (2021). prediction and mitigation of mutation threats to covid-19 vaccines and antibody therapies. chemical science, 12(20):6929–6948.
Consortium, T. U. (2023). uniprot: the universal protein knowledgebase in 2023. nucleic acids research, 51(d1):d523–d531.
Cueno, M. E. and Imai, K. (2021). Structural comparison of the sars cov 2 spike protein relative to other human-infecting coronaviruses. Frontiers in Medicine, 7:1–8.
Demšar, J., Zupan, B., Leban, G., Curk, T., Starič, A., Petkovšek, E., Kavšek, B., and Polajnar, M. (2013). orange: data mining toolbox in python. journal of machine learning research, 14(71):2349–2353.
Goujon, M., McWilliam, H., Li, W., Valentin, F., Squizzato, S., Paern, J., and Lopez, R. (2010). A new bioinformatics analysis tools framework at EMBL-EBI. Nucleic Acids Res., 38(Web Server issue):W695–9.
Harvey, W. T. et al. (2021). Sars-cov-2 variants, spike mutations and immune escape. Nature Reviews Microbiology, 19(7):409–424.
Hilario, M. et al. (2004). Classifying protein fingerprints. In boulicaut, j.-f. et al., editors, Knowledge Discovery in Databases: PKDD 2004, volume 3202 of Lecture Notes in Computer Science, pages 209–220. Springer.
Ibrahim, B. et al. (2018). A new era of virus bioinformatics. Virus Research, 251:86–90.
Laskowski, R. A., Macarthur, M. W., Moss, D. S., and Thornton, J. M. (1993). procheck: a program to check the stereochemical quality of protein structures. j. appl. crystallogr., 26(2):283–291.
Lüthy, R., Bowie, J. U., and Eisenberg, D. (1992). assessment of protein models with three-dimensional profiles. nature, 356(6364):83–85.
Mariano, D. C. B., Santos, L. H., Machado, K. D. S., Werhli, A. V., de Lima, L. H. F., and de Melo-Minardi, R. C. (2019). A computational method to propose mutations in enzymes based on structural signature variation (SSV). Int. J. Mol. Sci., 20(2):333.
Mirdita, M. et al. (2022). colabfold: making protein folding accessible to all. nature methods, 19(6):679–682.
Moreira, E. U. M., Mariano, D. C. B., and de Melo-Minardi, R. C. (2024). computational analysis of mutations in sars-cov-2 variants spike protein and protein interactions. In features, transmission, detection, and case studies in covid-19, pages 123–139. elsevier.
Nieto-Torres, J. L. et al. (2015). Severe acute respiratory syndrome coronavirus e protein transports calcium ions and activates the nlrp3 inflammasome. Virology, 485:330–339.
Paiva, V. d. A. et al. (2022). Protein structural bioinformatics: An overview. Computers in Biology and Medicine, 147:105695.
Pires, D. E. V. et al. (2011). Cutoff scanning matrix (csm): structural classification and function prediction by protein inter-residue distance patterns. BMC Genomics, 12(Suppl 4):S12.
Pires, D. E. V. et al. (2013). acsm: noise-free graph-based signatures to large-scale receptor-based ligand prediction. bioinformatics, 29(7):855–861.
Rabaaan, A. A. et al. (2020). Sars-cov-2, sars-cov, and mers-cov: A comparative overview. Le Infezioni in Medicina, 28(2):174–184.
Ramachandran, G. N., Ramakrishnan, C., and Sasisekharan, V. (1963). stereochemistry of polypeptide chain configurations. j. mol. biol., 7(1):95–99.
Ribeiro, R. et al. (2023). Molecular modeling study of natural products as potential bioactive compounds against sars-cov-2. Journal of Molecular Modeling, 29(6):183.
Shen, M.-y. and Sali, A. (2006). Statistical potential for assessment and prediction of protein structures. Protein Science, 15(11):2507–2524.
Shukla, N. et al. (2023). Covid variants, villain and victory: A bioinformatics perspective. Microorganisms, 11(8):2039.
Sievers, F., Wilm, A., Dineen, D., Gibson, T. J., Karplus, K., Li, W., Lopez, R., McWilliam, H., Remmert, M., Söding, J., Thompson, J. D., and Higgins, D. G. (2011). Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega. Mol. Syst. Biol., 7(1):539.
Wang, M.-Y. et al. (2020). Sars-cov-2: Structure, biology, and structure-based therapeutics development. Frontiers in Cellular and Infection Microbiology, 10:587269.
Webb, B. and Sali, A. (2016). Comparative protein structure modeling using modeller. Current Protocols in Bioinformatics, 54:5.6.1–5.6.37.
Weisblum, Y. et al. (2020). Escape from neutralizing antibodies by sars-cov-2 spike protein variants. eLife, 9:e61312.
Weiss, S. R. and Navas-Martin, S. (2005). Coronavirus pathogenesis and the emerging pathogen severe acute respiratory syndrome coronavirus. Microbiology and Molecular Biology Reviews, 69(4):635–664.
WHO (2023). Who coronavirus (covid-19) dashboard. [link]. Accessed: 2023-09-05.
Yang, H. and Rao, Z. (2021). Structural biology of sars-cov-2 and implications for therapeutic development. Nature Reviews Microbiology, 19(11):685–700.
Zatorski, N. et al. (2022). Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues. Database: The Journal of Biological Databases and Curation, 2022:baac053.
Publicado
02/12/2024
Como Citar
MOREIRA, Eduardo U. M.; MORAIS, Leandro; ARAUJO, Sheila C.; LEMOS, Rafael P.; BASTOS, Ana Luísa A.; LIMA, Alessandra; MARIANO, Diego; MELO-MINARDI, Raquel C. de.
Predicting Mutation-Driven Changes in the SARS-CoV-2 Spike Protein Using Structural Signatures and Neural Networks. In: SIMPÓSIO BRASILEIRO DE BIOINFORMÁTICA (BSB), 17. , 2024, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 167-178.
ISSN 2316-1248.
DOI: https://doi.org/10.5753/bsb.2024.245606.
