Computer-Based Immunoinformatic Analysis to Predict Candidate T-Cell Epitopes for SARS-CoV-2 Vaccine Design

Area of Science:

• Immunology
• Virology
• Vaccinology

Background:

• The COVID-19 pandemic, caused by SARS-CoV-2, necessitates effective vaccines.
• Mutations in SARS-CoV-2 reduce the efficacy of antibody-based therapies.
• T-cell immunity offers a potential alternative or complementary strategy for vaccine development.

Purpose of the Study:

• To investigate T-cell immune responses against globally circulating SARS-CoV-2 variants.
• To identify specific viral epitopes that elicit a T-cell response, particularly in the context of mutations.
• To provide a basis for designing next-generation COVID-19 vaccines targeting T-cell mechanisms.

Main Methods:

• Selection of mutated SARS-CoV-2 strains (B.1.1.7, B.1.351, P.1, B.1.617.1, B.1.617.2).
• Collection of overlapping peptides based on S-protein mutation sites.
• Residue scanning and molecular docking to predict peptide-HLA-A*11:01 affinity.
• Experimental validation of epitope binding affinity.

Main Results:

• Predicted and validated the binding affinity of viral epitopes to HLA-A*11:01.
• Identified significant changes in epitope affinity due to viral mutations.
• Confirmed the binding affinity of three epitopes, including 374FSTFKCYGL382, on the spike protein.

Conclusions:

• The epitope 374FSTFKCYGL382 is a promising target for future COVID-19 vaccine design.
• T-cell based vaccine strategies can be improved by considering specific epitopes and viral mutations.
• This study provides a valuable framework for developing broadly protective vaccines against evolving coronaviruses.