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

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

Posted 23 May 2020
bioRxiv DOI: 10.1101/2020.05.22.111526

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-CoV2 infection), and a specificity of 97.2% (N=106 control samples). ### Competing Interest Statement The authors have declared no competing interest.

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