神经网络模型用于基于序列的TCR和HLA协会预测
Si Liu1, Philip Bradley1,2,3, Wei Sun1,3,4,5
1Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, USA.
bioRxiv : the preprint server for biology
|June 9, 2023
概括
T细胞受体 (TCR) 和人类白细胞抗原 (HLA) 的关联对于理解免疫活性至关重要. 一种新的深度学习方法DePTH预测了这些关联,并揭示了与免疫治疗后癌症患者生存相关的HLA相似性.
科学领域:
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- T细胞利用T细胞受体 (TCRs) 来识别由人类白细胞抗原 (HLA) 蛋白呈现的外来抗原.
- TCRs编码一个人的免疫史,特定的TCR与特定的HLA等位基因相关,需要研究TCR-HLA关联.
- 了解TCR-HLA相互作用对于表征免疫反应和开发向疗法至关重要.
结论:
- DePTH为剖析TCR-HLA关联及其功能影响提供了一个强大的工具.
- 正如DePTH所预测的那样,HLA等位基因之间的功能相似性与免疫检查点阻塞疗法后的患者存活率有关.
- 这项工作将计算预测与癌症免疫学中的临床相关性联系起来.
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