Friedrich Loeffler-Institute of Medical Microbiology-Virology, University Medicine Greifswald, 17475, Greifswald, Germany.
Research Unit Emerging Viruses, Leibniz Institute of Virology, 20251, Hamburg, Germany.
BMC Bioinformatics. 2024 Sep 27;25(1):310. doi: 10.1186/s12859-024-05931-2.
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.
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.
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.
抗原呈递是启动和塑造适应性免疫反应的关键步骤。为了激活 CD8 T 细胞，病原体衍生肽与主要组织相容性复合体 (MHC) I 类分子结合后在抗原呈递细胞的细胞表面呈现。能够识别这些复合物的 CD8 T 细胞通过其 T 细胞受体被激活，并理想情况下消除受感染的细胞。预测与 MHC I 类 (MHC-I) 结合的潜在肽对于理解特定免疫反应中的病原体识别以及支持药物和疫苗设计至关重要。虽然有可靠的数据库可用于预测表位的算法，但它们主要侧重于预测单个免疫原性蛋白中的表位。
我们开发了 DiscovEpi 工具，在整个蛋白质组学和表位预测之间建立了一个接口。该工具允许在蛋白质组内自动识别所有潜在的 MHC-I 结合肽，并计算每个蛋白质的表位密度和平均结合评分，这是一种以蛋白质为中心的方法。DiscovEpi 提供了一个方便的接口，可在数据库 UniProt 中通过生物体和细胞区室自动进行多次序列提取，然后通过 NetMHCpan 进行后续表位预测。此外，它允许根据预测的免疫原性对蛋白质进行排序，一方面可以比较不同的蛋白质组。通过应用该工具，我们根据所呈现的表位密度和结合评分预测，与流感 A 相比，SARS-CoV-2 的膜相关蛋白具有更高的免疫原性潜力。通过比较流感 A 株和 SARS-CoV-2 的表位图，可以直观地证实这一点。
全自动预测整个蛋白质组学，并随后在序列水平上可视化潜在表位的位置，有助于寻找潜在的免疫原性蛋白质或蛋白质区域，并支持适应性免疫反应和疫苗设计的研究。
