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

Abstract

Background: Antigen presentation is a central step in initiating and shaping the adaptive immune response. To activate CD8⁺ T cells, pathogen-derived peptides are presented on the cell surface of antigen-presenting cells bound to major histocompatibility complex (MHC) class I molecules. CD8⁺ T cells that recognize these complexes with their T cell receptor are activated and ideally eliminate infected cells. Prediction of putative peptides binding to MHC class I (MHC-I) is crucial for understanding pathogen recognition in specific immune responses and for supporting drug and vaccine design. There are reliable databases for epitope prediction algorithms available however they primarily focus on the prediction of epitopes in single immunogenic proteins.

Results: We have developed the tool DiscovEpi to establish an interface between whole proteomes and epitope prediction. The tool allows the automated identification of all potential MHC-I-binding peptides within a proteome and calculates the epitope density and average binding score for every protein, a protein-centric approach. DiscovEpi provides a convenient interface between automated multiple sequence extraction by organism and cell compartment from the database UniProt for subsequent epitope prediction via NetMHCpan. Furthermore, it allows ranking of proteins by their predicted immunogenicity on the one hand and comparison of different proteomes on the other. By applying the tool, we predict a higher immunogenic potential of membrane-associated proteins of SARS-CoV-2 compared to those of influenza A based on the presented metrics epitope density and binding score. This could be confirmed visually by comparing the epitope maps of the influenza A strain and SARS-CoV-2.

Conclusion: Automated prediction of whole proteomes and the subsequent visualization of the location of putative epitopes on sequence-level facilitate the search for putative immunogenic proteins or protein regions and support the study of adaptive immune responses and vaccine design.

Keywords: Antigen presentation; Epitope prediction; MHC class I.

Fig. 1

DiscovEpi schematic workflow and output. Workflow of DiscovEpi: A the protein sequence retrieval to generate the dataset; B the prediction parameters encompassing HLA allele, peptide length and NetMHCpan-score threshold; and C the parameters for visualization parameters of the predicted epitopes with the maximum number of top scoring proteins and the maximum protein length to be depicted. The resulting heat map (D) is generated with the parameters shown in A–C where each bar marks the presence of one or multiple overlapping putative epitopes and the intensity indicates the epitope score. The length of the protein is visualized through the light grey background. The proteins are ordered by their epitope density

Fig. 2

Runtime analysis of DiscovEpi. The time elapsed during epitope prediction was compared with the number of proteins in each proteome. Runtime analysis was performed with the Windows executable on Windows 11 with Intel Ultra 9 185H and 32 GB RAM. Sets of reviewed membrane associated proteins of SARS-CoV-2 and 5 strains of Staphylococcus aureus (SAU) were used to cover different proteome sizes. The dotted line describes the linear regression with a slope of 2.61 and p-value of 6.6e−5