Predicting Epitope Candidates for SARS-CoV-2

Agarwal, Akshay; Beck, Kristen L.; Capponi, Sara; Kunitomi, Mark; Nayar, Gowri; Seabolt, Edward; Mahadeshwar, Gandhar; Bianco, Simone; Mukherjee, Vandana; Kaufman, James H.

doi:10.3390/v14081837

Open AccessArticle

Predicting Epitope Candidates for SARS-CoV-2

by

Akshay Agarwal

^1,*

,

Kristen L. Beck

^1,*

,

Sara Capponi

^1,2,

Mark Kunitomi

¹,

Gowri Nayar

¹,

Edward Seabolt

¹

,

Gandhar Mahadeshwar

¹,

Simone Bianco

^1,2,†,

Vandana Mukherjee

¹ and

James H. Kaufman

^1,†

¹

AI and Cognitive Software, IBM Almaden Research Center, San Jose, CA 95120, USA

²

NSF Center for Cellular Construction, San Francisco, CA 94158, USA

^*

Authors to whom correspondence should be addressed.

^†

Current address: Altos Labs, Redwood City, CA 94065, USA.

Viruses 2022, 14(8), 1837; https://doi.org/10.3390/v14081837

Submission received: 14 April 2022 / Revised: 28 July 2022 / Accepted: 29 July 2022 / Published: 21 August 2022

(This article belongs to the Special Issue SARS-CoV-2 Genomics)

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Abstract

Epitopes are short amino acid sequences that define the antigen signature to which an antibody or T cell receptor binds. In light of the current pandemic, epitope analysis and prediction are paramount to improving serological testing and developing vaccines. In this paper, known epitope sequences from SARS-CoV, SARS-CoV-2, and other Coronaviridae were leveraged 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, and discuss where epitopes are located on proteins and how epitopes can be grouped into classes. The mutation density of different protein regions is presented using a big data approach. It was observed that there are 112 B cell and 279 T cell conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike glycoprotein.

Keywords: SARS-CoV-2; epitope; computational biology; mutational analysis; immunology

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MDPI and ACS Style

Agarwal, A.; Beck, K.L.; Capponi, S.; Kunitomi, M.; Nayar, G.; Seabolt, E.; Mahadeshwar, G.; Bianco, S.; Mukherjee, V.; Kaufman, J.H. Predicting Epitope Candidates for SARS-CoV-2. Viruses 2022, 14, 1837. https://doi.org/10.3390/v14081837

AMA Style

Agarwal A, Beck KL, Capponi S, Kunitomi M, Nayar G, Seabolt E, Mahadeshwar G, Bianco S, Mukherjee V, Kaufman JH. Predicting Epitope Candidates for SARS-CoV-2. Viruses. 2022; 14(8):1837. https://doi.org/10.3390/v14081837

Chicago/Turabian Style

Agarwal, Akshay, Kristen L. Beck, Sara Capponi, Mark Kunitomi, Gowri Nayar, Edward Seabolt, Gandhar Mahadeshwar, Simone Bianco, Vandana Mukherjee, and James H. Kaufman. 2022. "Predicting Epitope Candidates for SARS-CoV-2" Viruses 14, no. 8: 1837. https://doi.org/10.3390/v14081837

APA Style

Agarwal, A., Beck, K. L., Capponi, S., Kunitomi, M., Nayar, G., Seabolt, E., Mahadeshwar, G., Bianco, S., Mukherjee, V., & Kaufman, J. H. (2022). Predicting Epitope Candidates for SARS-CoV-2. Viruses, 14(8), 1837. https://doi.org/10.3390/v14081837

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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