In Silico Study to Predict and Characterize SARS-CoV-2 Surface Glycoprotein

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Avnish Kumar; Bhuvnesh Sharma

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Journal Paper

Paper Information

Journal: VACCINE RESEARCH Year:2020 | Volume:7 | Issue:1 Page(s): 10-16

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Information Journal Paper

Title

In Silico Study to Predict and Characterize SARS-CoV-2 Surface Glycoprotein

Author(s)

Avnish Kumar | Bhuvnesh Sharma | Issue Writer Certificate

Keywords

SARS-CoV-2

COVID-19

Spike protein

Vaccine

Bioinformatics

Antigenic sites

Abstract

Introduction: Coronavirus family member SARS-CoV-2 is a current worldwide threat. It enters into the epithelium membrane of respiratory tract with the help of its antigenic Spike proteins and cause Coronavirus disease 2019 (COVID-19). Methods: Considering SARS-CoV-2 a potent Vaccine or diagnostic candidate, a bioinformatical study was done to determine its structure homology modeling, physiological properties and structure validation with presence of Antigenic sites. Results: The surface glycoprotein of SARSCoV-2 was found to be a stable protein with stereochemically good structure. It also contains 65 Antigenic sites. Conclusion: The present study suggests further wet-lab research to develop a Vaccine or diagnostic kit using this promising surface glycoprotein.

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APA: Copy

Avnish, Kumar, & Bhuvnesh, Sharma. (2020). In Silico Study to Predict and Characterize SARS-CoV-2 Surface Glycoprotein. VACCINE RESEARCH, 7(1), 10-16. SID. https://sid.ir/paper/674868/en

Vancouver: Copy

Avnish Kumar, Bhuvnesh Sharma. In Silico Study to Predict and Characterize SARS-CoV-2 Surface Glycoprotein. VACCINE RESEARCH[Internet]. 2020;7(1):10-16. Available from: https://sid.ir/paper/674868/en

IEEE: Copy

Kumar Avnish, and Sharma Bhuvnesh, “In Silico Study to Predict and Characterize SARS-CoV-2 Surface Glycoprotein,” VACCINE RESEARCH, vol. 7, no. 1, pp. 10–16, 2020, [Online]. Available: https://sid.ir/paper/674868/en

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