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T Cell Activation and Clonal Selection

T Cell Activation and Clonal Selection

T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...

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Updated: Jul 16, 2025

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

一个强大的深度学习工作流来预测CD8+T细胞表位.

Chloe H Lee^1,2, Jaesung Huh³, Paul R Buckley^1,2

¹MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.

Genome medicine

|September 13, 2023

概括

此摘要是机器生成的。

我们开发了TRAP,这是一种深度学习工具,用于预测CD8+T细胞表位. 即使数据有限,TRAP也可以改善对癌症和病原体的免疫性预测.

关键词:

CD8 + T 细胞表位.计算免疫学计算免疫学深度学习是一种深度学习.皮层预测预测的预测免疫性免疫性免疫性结合MHC的结合方式新史基因标识的识别.自己抗原耐受性耐受性.胸膜选择选择胸膜选择转移学习转移学习疫苗候选人疫苗候选人

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科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

T细胞对于抗癌和病原体的适应性免疫是至关重要的.
识别T细胞抗原是具有挑战性和低通量.
现有的CD8+T细胞表位预测计算方法存在局限性,包括HLA偏差和小数据集的性能差.

研究的目的:

开发一个强大的深度学习工作流程 (TRAP),用于预测CD8+T细胞表位.
提高T细胞表位标识在致病和癌症环境中的准确性和效率.
引入一种新型指标 (RSAT),用于估计病原性的免疫性.

主要方法:

开发了TRAP,这是一个深度学习工作流程,利用转移学习和MHC绑定信息.
TRAP预测来自MHC-I呈现的致病性和自我的CD8+T细胞表位.
引入RSAT指标以估计低置信度预测的免疫性.

主要成果:

在预测质母细胞瘤和SARS-CoV-2的表位方面,TRAP的表现优于现有的算法.
TRAP有效地从有限和不平衡的数据集中提取免疫性特征.
该RSAT指标准确地估计了各种长度和物种的致病性的免疫性.

结论:

TRAP提供了一种新的计算方法,用于准确地预测CD8+T细胞表皮图.
这种工作流可以增强对抗原特异性T细胞反应的理解.
TRAP有助于开发有效的基于T细胞的免疫疗法.