Machine Learning Approach to Identify Receptor Binding Domain of Spike Glycoprotein as A Potential Vaccine Candidate for COVID-19

Abstract

Recent SARS-CoV-2 outbreaks have spurred continuing efforts to exploit different viral protein targets for therapy, but preventing viral proteins, including in therapeutic and vaccine research, has largely failed. In the lack of clear clinical proof for COVID-19 pathogenesis, a comparison of previous pandemic HCoVs-related immune system reactions could provide insight into COVID-19 pathogenesis. Authors summarize the possible genesis and method of spread of COVID-19, in addition to our present understanding of the viral genome integrity of known outbreak viruses against SARS-CoV-2 in this study. The COVID-19 pandemic continues to be a major concern for health-care systems globally. Accurate and timely identification of SARS Coronavirus 2 (SARS-CoV-2) infection is crucial for limiting dissemination and commencing therapy. The gold standard among test procedures is real-time reverse-transcriptase polymerase-chain reaction (rRT-PCR). Despite the fact that this test has a high specificity and sensitivity, the incidence of erroneously negative findings in patients with symptoms and/or having a positive CT scan remains a difficulty. In this article authors analyze the receptor binding domain of spike glycoprotein to be potential vaccine candidates.