用深度生成模型设计T细胞受体序列的有意义的连续表示
Allen Y Leary1, Darius Scott2, Namita T Gupta2
1Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA. allen.leary@regeneron.com.
Nature communications
|May 20, 2024
概括
我们开发了TCR-VALID,这是一个新的深度学习模型,用于分析T细胞受体 (TCR) 序列. 这个工具可以创建有信息的,低维的表示,以更好地理解免疫反应.
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.
- 机器学习 机器学习
背景情况:
- 结合T细胞受体 (TCR) 抗原对适应性免疫至关重要.
- 由于TCR的高度多样性和蛋白质相互作用的复杂性,阻碍了有效的低维表示用于分析.
- 目前的TCR分析方法在捕捉TCR相互作用的全谱方面存在局限性.
研究的目的:
- 开发一种新的计算框架,用于学习TCR序列的低维表示.
- 为了实现高质量的TCR序列的de novo生成.
- 为TCR集群和分析提供一个可靠的方法.
主要方法:
- 开发了TCR-VALID,一个容量控制的解变量自编码器.
- 在大约1亿个TCR序列的大数据集上训练模型.
- 量化了学习到的表示的属性,包括维度,连续性和解.
主要成果:
- TCR-VALID生成了低维,连续和解的TCR表示.
- 该模型可以实现高质量的TCR序列的de novo生成.
- TCR-VALID表示方便快速准确的TCR集群,性能优于现有方法.
结论:
- TCR-VALID为T细胞受体序列分析和表示学习提供了一种强大的新方法.
- 开发的框架推进了低维的蛋白质表示学习领域.
- 这种方法提高了我们研究TCR抗原相互作用和免疫反应的能力.
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