Structure-Based Modeling of SARS-CoV-2 Peptide/HLA-A02 Antigens

Area of Science:

• Immunology
• Computational Biology
• Structural Biology

Background:

• SARS-CoV-2 infection elicits CD4 and CD8 T cell responses in individuals with varying disease severity.
• Understanding T cell epitopes is crucial for developing COVID-19 diagnostics and therapeutics.
• Characterizing SARS-CoV-2 T cell epitopes can inform vaccine design and immunotherapies.

Purpose of the Study:

• To develop and apply a computational method for modeling SARS-CoV-2 CD8 T cell epitopes.
• To create accurate 3D models of SARS-CoV-2 epitopes binding to HLA-A*02:01.
• To identify potential cross-reactive epitopes shared with common cold coronaviruses.

Main Methods:

• Utilized RosettaMHC, a comparative modeling approach leveraging Protein Data Bank structures.
• Modeled 8-10 residue epitopic peptides predicted to bind HLA-A*02:01.
• Compared electrostatic surfaces of SARS-CoV-2 epitope models with homologous coronavirus complexes.

Main Results:

• Generated accurate 3D models for putative SARS-CoV-2 CD8 epitopes.
• Made these models publicly available via an online database (https://rosettamhc.chemistry.ucsc.edu).
• Identified potential cross-reactive epitopes recognized by shared T cell receptors (TCRs).

Conclusions:

• RosettaMHC provides a valuable tool for characterizing SARS-CoV-2 T cell epitopes.
• The generated models can aid in understanding the structural basis of T cell recognition and immunogenicity.
• This work facilitates the development of diagnostic and therapeutic interventions for COVID-19.