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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.
Phan, Isabelle Q; Subramanian, Sandhya; Kim, David; Murphy, Michael; Pettie, Deleah; Carter, Lauren; Anishchenko, Ivan; Barrett, Lynn K; Craig, Justin; Tillery, Logan; Shek, Roger; Harrington, Whitney E; Koelle, David M; Wald, Anna; Veesler, David; King, Neil; Boonyaratanakornkit, Jim; Isoherranen, Nina; Greninger, Alexander L; Jerome, Keith R; Chu, Helen; Staker, Bart; Stewart, Lance; Myler, Peter J; Van Voorhis, Wesley C.
  • Phan IQ; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Subramanian S; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, USA.
  • Kim D; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Murphy M; Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA, USA.
  • Pettie D; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Carter L; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Anishchenko I; Institute for Protein Design (IPD), University of Washington, Seattle, WA, USA.
  • Barrett LK; Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
  • Craig J; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Tillery L; Institute for Protein Design (IPD), University of Washington, Seattle, WA, USA.
  • Shek R; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Harrington WE; Institute for Protein Design (IPD), University of Washington, Seattle, WA, USA.
  • Koelle DM; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Wald A; Institute for Protein Design (IPD), University of Washington, Seattle, WA, USA.
  • Veesler D; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • King N; Department of Biochemistry, University of Washington, Seattle, WA, USA.
  • Boonyaratanakornkit J; Institute for Protein Design (IPD), University of Washington, Seattle, WA, USA.
  • Isoherranen N; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Greninger AL; Division of Allergy and Infectious Diseases, Department of Medicine, Center for Emerging and Re-Emerging Infectious Diseases (CERID), University of Washington, Seattle, WA, USA.
  • Jerome KR; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Chu H; Division of Allergy and Infectious Diseases, Department of Medicine, Center for Emerging and Re-Emerging Infectious Diseases (CERID), University of Washington, Seattle, WA, USA.
  • Staker B; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Stewart L; Division of Allergy and Infectious Diseases, Department of Medicine, Center for Emerging and Re-Emerging Infectious Diseases (CERID), University of Washington, Seattle, WA, USA.
  • Myler PJ; Seattle Structural Genomics Center for Infectious Disease (SSGCID), Seattle, WA, USA.
  • Van Voorhis WC; Division of Allergy and Infectious Diseases, Department of Medicine, Center for Emerging and Re-Emerging Infectious Diseases (CERID), University of Washington, Seattle, WA, USA.
Sci Rep ; 11(1): 4290, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1096333
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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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).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Enzyme-Linked Immunosorbent Assay / Epitopes, B-Lymphocyte / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Systematic review/Meta Analysis Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-83730-y

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Enzyme-Linked Immunosorbent Assay / Epitopes, B-Lymphocyte / SARS-CoV-2 / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Systematic review/Meta Analysis Limits: Humans Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-83730-y