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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
bioRxiv - Bioinformatics Pub Date : 2020-06-02 , DOI: 10.1101/2020.05.22.111526
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 L. Greninger , Keith R. Jerome , Helen Chu , Bart Staker , Lance Stewart , Peter J. Myler , Wesley C. Van Voorhis

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).

中文翻译:

在计算机上检测SARS-CoV-2特异的B细胞抗原决定簇,并在ELISA中验证用于血清学诊断COVID-19

快速生成诊断信息对于理解流行病学和控制新兴传染病(例如COVID-19)的传播至关重要。预测对病原体特异的血清诊断表位的计算方法可能有助于加速新诊断方法的开发。对27种SARS-CoV-2蛋白进行了系统的调查,以评估现有的B细胞表位预测方法,结合对序列数据库和结构数据的全面挖掘,是否可以预测特定蛋白是否适合血清学诊断。其中9个预测已使用SARS-CoV-2感染患者的血浆和血清,以ELISA格式通过重组SARS-CoV-2蛋白进行了验证,并将11个预测与最新文献进行了比较。结果似乎与其中12个预测相符,与3个预测不一致,而另外5个被认为没有结论。我们表明,在我们的前五名候选基因中,有两个,即核蛋白的N末端片段和刺突蛋白的受体结合域,具有最高的灵敏度,特异性和信噪比,可检测COVID-19血清/血浆通过ELISA。将这两种抗原混合在一起以包被ELISA板可产生94%的灵敏度(N = 80个来自RT-PCR确诊SARS-CoV2感染者的样品)和97.2%的特异性(N = 106个对照样品)。通过ELISA检测COVID-19血清/血浆具有最高的灵敏度,特异性和信噪比。将这两种抗原混合在一起以包被ELISA板可产生94%的灵敏度(N = 80个来自RT-PCR确诊SARS-CoV2感染者的样品)和97.2%的特异性(N = 106个对照样品)。通过ELISA检测COVID-19血清/血浆具有最高的灵敏度,特异性和信噪比。将两种抗原混合在一起以包被ELISA板可产生94%的灵敏度(N = 80个来自经RT-PCR确认为SARS-CoV2感染者的样品)和97.2%的特异性(N = 106个对照样品)。
更新日期:2020-06-02
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