A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

Area of Science:

• Immunology
• Molecular Biology
• Biotechnology

Background:

• T cells are crucial for adaptive immunity, recognizing peptide antigens presented by Major Histocompatibility Complex (MHC) proteins.
• Current methods for assessing peptide-MHC binding lack high-throughput capabilities, limiting large-scale epitope discovery.
• Understanding peptide-MHC interactions is vital for vaccine development and immunotherapy.

Purpose of the Study:

• To develop and validate a high-throughput yeast display platform for assessing peptide-MHC binding.
• To apply this platform to SARS-CoV-2 and dengue virus peptides for human class II MHCs.
• To compare experimental findings with existing computational predictions and identify discrepancies.

Main Methods:

• Yeast display technology was employed to create a platform for screening tens of thousands of user-defined peptides.
• A comprehensive peptide library covering the SARS-CoV-2 proteome and four dengue virus serotypes was assessed.
• Binding affinities to human class II MHCs (HLA-DRB1*01:01, HLA-DRB1*04:02, HLA-DRB1*04:04) were measured.

Main Results:

• The platform successfully screened large peptide libraries for binding to specific human class II MHC molecules.
• Experimental data showed general agreement with known MHC-binding motifs but also revealed validated computational false positives and negatives.
• The study identified potential viral epitopes and provided insights into viral conservation and MHC binding relationships.

Conclusions:

• The yeast display platform offers a high-throughput, experimentally validated method to assess peptide-MHC binding.
• This approach complements and enhances existing computational prediction tools and experimental datasets.
• The platform facilitates epitope identification and can aid in understanding immune responses to viral pathogens.