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T 细胞表位预测及其在免疫治疗中的应用。

T Cell Epitope Prediction and Its Application to Immunotherapy.

机构信息

Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.

出版信息

Front Immunol. 2021 Sep 15;12:712488. doi: 10.3389/fimmu.2021.712488. eCollection 2021.

DOI:10.3389/fimmu.2021.712488
PMID:34603286
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8479193/
Abstract

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.

摘要

T 细胞在控制和驱动免疫反应方面发挥着至关重要的作用，其能够区分来自健康和致病蛋白质的肽段。在这篇综述中，我们专注于目前可用的用于表位预测的计算工具，特别关注旨在识别新抗原的工具，即癌症特异性肽及其在癌症治疗中的免疫治疗中的潜在用途。本综述将介绍这些工具的工作原理、它们使用的数据类型以及它们在各自应用中的优缺点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbe2/8479193/025b165b2839/fimmu-12-712488-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbe2/8479193/c6841cfa1fdb/fimmu-12-712488-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbe2/8479193/025b165b2839/fimmu-12-712488-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbe2/8479193/c6841cfa1fdb/fimmu-12-712488-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbe2/8479193/025b165b2839/fimmu-12-712488-g002.jpg

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J Immunol. 2020 Oct 1;205(7):1962-1977. doi: 10.4049/jimmunol.1900918. Epub 2020 Sep 2.
3
DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning.
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