ABSTRACT
Antibody convergence is the presence of similar antibodies in different individuals—suggesting that the individuals have had exposure to a common antigen, which has stimulated the production of similar, antigen-specific antibodies. We want to be able to identify these shared antibodies, sometimes referred to as “public clones,” as it could lead to development of immunodiagnostic tests against the shared antibodies, and potentially assist in the design of vaccines and therapeutic antibodies. This chapter will focus on the computational methods for identifying these antibodies include clustering together highly similar antibody sequences found in multiple individuals, often using a high percentage sequence identity score as a similarity threshold for clustering. Chapter will cover about the fundamental understanding of the body immune system, antibody function and the importance of cellular component includes mast cells, neutrophils, macrophages, T and B lymphocytes, and plasma cells.
In addition chapter focused on the identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Then the computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. For this it used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (1) they were both mapped, (2) they predicted protective antigens, and (3) they were completely identical to experimentally validated epitopes of SARS-CoV.
