Scientific Reports (Feb 2021)

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

  • 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

DOI
https://doi.org/10.1038/s41598-021-83730-y
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 11

Abstract

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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).