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Stability of SARS-CoV-2 spike antigens against mutations

Martinez De la Fuente, I.; Malaina, I.; Fedetz, M.; Chruszcz, M.; Grandes, G.; Targoni, O.; Lozano-Perez, A. A.; Shteyer, E.; Ben Ya'akov, A.; Gomez de la Camara, A.; M. Borobia, A.; Carrasco-Pujante, J.; Ignacio Pijoan, J.; Bringas, C.; Perez-Yarza, G.; Ouro, A.; Crawford, M. J.; Shoshan-Barmatz, V.; Zhurov, V.; Lopez, J. I.; Knafo, S.; Tary-Lehmann, M.; Gabaldon, T.; Grbic, M.

2022-10-15 epidemiology
10.1101/2022.10.13.22280980 medRxiv
Show abstract

We have developed a computational method "Multi-Stable Epitope Sequencer" to predict mutation-resistant regions with stability against future viral variability. At the beginning of the pandemic, this approach allowed us to identify a set of eight SARS-CoV-2 spike protein sequences that had the potential to be mutationally stable. We have tested this methodology on the SARS-CoV-2 viral linages that occurred throughout the COVID-19 pandemic. These eight peptide sequences (epitopes) have been preserved in 97% of all SARS-CoV-2 lineages reported in the CoV-GLUE dataset during the pandemic period. Likewise, more than 90% of these peptides remained invariable across the 49 predominant viral variants circulating throughout the pandemic (ECDC-WHO). In addition, the eight selected peptides were preserved in 94.1% of all 28 variants considered of most interest in the CoV-GLUE project. Our analyses confirm the predicted mutational stability of the eight selected short viral peptides over the entire COVID-19 pandemic.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.