DiscovEpi: automated whole proteome MHC-I-epitope prediction and visualization

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Adaptive immune responses are initiated by antigen presentation, where pathogen peptides bind to MHC class I molecules.
• Accurate prediction of peptides binding to MHC class I (MHC-I) is vital for understanding immune responses and for vaccine and drug design.
• Existing epitope prediction tools often focus on single proteins, limiting large-scale proteome analysis.

Purpose of the Study:

• To develop a computational tool, DiscovEpi, for automated MHC-I epitope prediction across whole proteomes.
• To enable a protein-centric analysis of immunogenicity, including epitope density and binding scores.
• To facilitate comparative analysis of immunogenic potential between different proteomes.

Main Methods:

• Developed DiscovEpi for automated extraction of protein sequences from UniProt and subsequent epitope prediction using NetMHCpan.
• Implemented a protein-centric approach to calculate epitope density and average binding scores for each protein within a proteome.
• Applied DiscovEpi to compare the immunogenic potential of SARS-CoV-2 and influenza A proteomes.

Main Results:

• DiscovEpi successfully interfaces whole proteomes with epitope prediction, providing automated analysis.
• Calculated epitope density and binding scores allowed for ranking proteins by predicted immunogenicity.
• Membrane-associated proteins of SARS-CoV-2 showed higher predicted immunogenic potential compared to influenza A proteins, visualized through epitope maps.

Conclusions:

• Automated whole proteome epitope prediction and visualization aid in identifying immunogenic proteins and regions.
• This approach supports the study of adaptive immune responses and accelerates vaccine design.
• DiscovEpi offers a valuable tool for comparative immunoproteomics and pathogen immune response analysis.