论文标题
个性化工作流程，以识别基于肽的疫苗针对COVID-19的最佳T细胞表位
Personalized workflow to identify optimal T-cell epitopes for peptide-based vaccines against COVID-19
论文作者
论文摘要
针对病毒的传统疫苗旨在靶向其表面蛋白，即抗原，这些抗原可以触发免疫系统以产生特定的抗体来捕获和中和病毒。然而，病毒通常会迅速发展，其抗原容易发生突变，以避免抗体识别（抗原漂移）。 T细胞介导的免疫力可以解决抗体介导的免疫的局限性，该免疫能够识别出在病毒感染细胞上呈现的保守病毒HLA肽。因此，通过将保守区域靶向病毒的基因组，基于T细胞表位的疫苗较少受到突变，并且可能有效地在病毒的不同菌株上工作。在这里，我们提出了一个个性化的工作流程，以根据HLA等位基因和个人的免疫肽组来识别一组最佳的T细胞表位。具体而言，我们的工作流程在免疫肽组上训练机器学习模型，然后预测来自病毒保守区域的HLA肽，这些肽最有可能触发人T细胞的反应。我们应用了工作流程来识别SARS-COV-2病毒的T细胞表位，这导致了全球100多个国家的COVID-19近期大流行。
Traditional vaccines against viruses are designed to target their surface proteins, i.e., antigens, which can trigger the immune system to produce specific antibodies to capture and neutralize the viruses. However, viruses often evolve quickly, and their antigens are prone to mutations to avoid recognition by the antibodies (antigenic drift). This limitation of the antibody-mediated immunity could be addressed by the T-cell mediated immunity, which is able to recognize conserved viral HLA peptides presented on virus-infected cells. Thus, by targeting conserved regions on the genome of a virus, T-cell epitope-based vaccines are less subjected to mutations and may work effectively on different strains of the virus. Here we propose a personalized workflow to identify an optimal set of T-cell epitopes based on the HLA alleles and the immunopeptidome of an individual person. Specifically, our workflow trains a machine learning model on the immunopeptidome and then predicts HLA peptides from conserved regions of a virus that are most likely to trigger responses from the person T cells. We applied the workflow to identify T-cell epitopes for the SARS-COV-2 virus, which has caused the recent COVID-19 pandemic in more than 100 countries across the globe.
