In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19
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 diagno...
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2021-02-01
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doaj-3d810cad4f2b46a1aaf7b89ba641e8bb2021-02-23T09:15:47ZengNature Publishing GroupScientific Reports2045-23222021-02-0111111110.1038/s41598-021-83730-yIn silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19Isabelle Q. Phan0Sandhya Subramanian1David Kim2Michael Murphy3Deleah Pettie4Lauren Carter5Ivan Anishchenko6Lynn K. Barrett7Justin Craig8Logan Tillery9Roger Shek10Whitney E. Harrington11David M. Koelle12Anna Wald13David Veesler14Neil King15Jim Boonyaratanakornkit16Nina Isoherranen17Alexander L. Greninger18Keith R. Jerome19Helen Chu20Bart Staker21Lance Stewart22Peter J. Myler23Wesley C. Van Voorhis24Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Department of Biochemistry, University of WashingtonDepartment of Biochemistry, University of WashingtonDepartment of Biochemistry, University of WashingtonSeattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Center for Global Infectious Disease Research, Seattle Children’s Research InstituteDivision of Allergy and Infectious Diseases, Department of Medicine, Center for Emerging and Re-Emerging Infectious Diseases (CERID), University of WashingtonDivision of Allergy and Infectious Diseases, Department of Medicine, University of WashingtonDepartment of Biochemistry, University of WashingtonDepartment of Biochemistry, University of WashingtonDivision of Allergy and Infectious Diseases, Department of Medicine, University of WashingtonDepartment of Pharmaceutics, University of WashingtonDepartment of Laboratory Medicine and Pathology, University of WashingtonDepartment of Laboratory Medicine and Pathology, University of WashingtonDivision of Allergy and Infectious Diseases, Department of Medicine, Center for Emerging and Re-Emerging Infectious Diseases (CERID), University of WashingtonSeattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)Seattle Structural Genomics Center for Infectious Disease (SSGCID)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).https://doi.org/10.1038/s41598-021-83730-y |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 Scientific Reports |
author_facet |
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 |
author_sort |
Isabelle Q. Phan |
title |
In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 |
title_short |
In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 |
title_full |
In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 |
title_fullStr |
In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 |
title_full_unstemmed |
In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19 |
title_sort |
in silico detection of sars-cov-2 specific b-cell epitopes and validation in elisa for serological diagnosis of covid-19 |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-02-01 |
description |
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). |
url |
https://doi.org/10.1038/s41598-021-83730-y |
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