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

被引:0
作者
Mahncke, C. [1 ,2 ]
Schmiedeke, F. [3 ]
Simm, S. [4 ,5 ]
Kaderali, L. [4 ]
Broeker, B. M. [3 ]
Seifert, U. [1 ]
Cammann, C. [1 ]
机构
[1] Univ Med Greifswald, Friedrich Loeffler Inst Med Microbiol Virol, D-17475 Greifswald, Germany
[2] Leibniz Inst Virol, Res Unit Emerging Viruses, D-20251 Hamburg, Germany
[3] Univ Med Greifswald, Inst Immunol, D-17475 Greifswald, Germany
[4] Univ Med Greifswald, Inst Bioinformat, D-17475 Greifswald, Germany
[5] Univ Appl Sci Coburg, Inst Bioanalyt, D-96450 Coburg, Germany
来源
BMC BIOINFORMATICS | 2024年 / 25卷 / 01期
关键词
MHC class I; Antigen presentation; Epitope prediction;
D O I
10.1186/s12859-024-05931-2
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
BackgroundAntigen 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.ResultsWe 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.ConclusionAutomated 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.
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页数:10
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