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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7698| Predicting Epitope Candidates for SARS-CoV-2 | |
| Akshay Agarwal Kristen Beck Sara Capponi Mark Kunitomi Gowri Nayar Edward Seabolt Gandhar Mahadeshwar Simone Bianco Vandana Mukherjee James Kaufman | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2022.02.09.479786 | |
| https://www.biorxiv.org/content/10.1101/2022.02.09.479786v1 | |
| Epitopes are short amino acid sequences that define the antigen signature to which an antibody binds. In light of the current pandemic, epitope analysis and prediction is paramount to improving serological testing and developing vaccines. In this paper, we leverage known epitope sequences from SARS-CoV, SARS-CoV-2 and other Coronaviridae and use those known epitopes to identify additional antigen regions in 62k SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, discuss where epitopes are located on proteins, and how epitopes can be grouped into classes. We also discuss the mutation density of different regions on proteins using a big data approach. We find that there are many conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike Glycoprotein. | |
| medRxiv and bioRxiv | |
| 10-02-2022 | |
| Preimpreso | |
| https://www.biorxiv.org/ | |
| Inglés | |
| Epidemia COVID-19 | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Predicting Epitope Candidates for SARS COV2.pdf | 13.71 MB | Adobe PDF | Visualizar/Abrir |