In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19.
In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19.
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DOI:
10.1038/s41598-021-83730-y
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发表时间:
2021-02-22
影响因子:
4.6
通讯作者:
Van Voorhis WC
中科院分区:
文献类型:
--
作者:
Phan IQ;Subramanian S;Kim D;Murphy M;Pettie D;Carter L;Anishchenko I;Barrett LK;Craig J;Tillery L;Shek R;Harrington WE;Koelle DM;Wald A;Veesler D;King N;Boonyaratanakornkit J;Isoherranen N;Greninger AL;Jerome KR;Chu H;Staker B;Stewart L;Myler PJ;Van Voorhis WC
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).
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DOI:
10.1093/bioinformatics/bts565
发表时间:
2012-12-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者:
Li W
影响因子:
2.9
作者:
Park, Hahnbeom;Lee, Gyu Rie;Baker, David
通讯作者:
Baker, David
影响因子:
--
作者:
SHANNON, CE
通讯作者:
SHANNON, CE
影响因子:
82.9
作者:
Amanat F;Stadlbauer D;Strohmeier S;Nguyen THO;Chromikova V;McMahon M;Jiang K;Arunkumar GA;Jurczyszak D;Polanco J;Bermudez-Gonzalez M;Kleiner G;Aydillo T;Miorin L;Fierer DS;Lugo LA;Kojic EM;Stoever J;Liu STH;Cunningham-Rundles C;Felgner PL;Moran T;García-Sastre A;Caplivski D;Cheng AC;Kedzierska K;Vapalahti O;Hepojoki JM;Simon V;Krammer F
通讯作者:
Krammer F
影响因子:
10.7
作者:
Katoh K;Standley DM
通讯作者:
Standley DM