In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-02-22

AUTHORS

Isabelle Q. Phan, Sandhya Subramanian, David Kim, Michael Murphy, Deleah Pettie, Lauren Carter, Ivan Anishchenko, Lynn K. Barrett, Justin Craig, Logan Tillery, Roger Shek, Whitney E. Harrington, David M. Koelle, Anna Wald, David Veesler, Neil King, Jim Boonyaratanakornkit, Nina Isoherranen, Alexander L. Greninger, Keith R. Jerome, Helen Chu, Bart Staker, Lance Stewart, Peter J. Myler, Wesley C. Van Voorhis

ABSTRACT

Rapid generation of diagnostics is paramount to understand epidemiology and to control the spread of emerging infectious diseases such as COVID-19. Computational methods to predict serodiagnostic epitopes that are specific for the pathogen could help accelerate the development of new diagnostics. A systematic survey of 27 SARS-CoV-2 proteins was conducted to assess whether existing B-cell epitope prediction methods, combined with comprehensive mining of sequence databases and structural data, could predict whether a particular protein would be suitable for serodiagnosis. Nine of the predictions were validated with recombinant SARS-CoV-2 proteins in the ELISA format using plasma and sera from patients with SARS-CoV-2 infection, and a further 11 predictions were compared to the recent literature. Results appeared to be in agreement with 12 of the predictions, in disagreement with 3, while a further 5 were deemed inconclusive. We showed that two of our top five candidates, the N-terminal fragment of the nucleoprotein and the receptor-binding domain of the spike protein, have the highest sensitivity and specificity and signal-to-noise ratio for detecting COVID-19 sera/plasma by ELISA. Mixing the two antigens together for coating ELISA plates led to a sensitivity of 94% (N = 80 samples from persons with RT-PCR confirmed SARS-CoV-2 infection), and a specificity of 97.2% (N = 106 control samples). More... »

PAGES

4290

Journal

TITLE

Scientific Reports

ISSUE

1

VOLUME

11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-021-83730-y

DOI

http://dx.doi.org/10.1038/s41598-021-83730-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1135553150

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33619344


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