TCR-ESM:使用蛋白质语言嵌入来预测TCR--MHC结合
Shashank Yadav1, Dhvani Sandip Vora2,3, Durai Sundar2
1Department of Biomedical Engineering, University of Arizona, Tucson 85721, AZ, USA.
Computational and structural biotechnology journal
|December 26, 2023
概括
预测T细胞受体 (TCR) 与MHC的相互作用对于T细胞治疗至关重要. 我们的TCR-ESM模型,使用来自进化规模建模 (ESM) 的嵌,显著提高了预测准确性.
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:
- 准确的T细胞受体 (TCR) 和结主要基因相容性复合体 (pMHC) 相互作用预测对于T细胞治疗的发展至关重要.
- 目前的方法在准确识别相关目标方面面临挑战,阻碍了治疗进步.
研究的目的:
- 开发和验证一种用于预测TCR-pMHC结合的新型深度学习模型.
- 调查不同TCR链和MHC信息对约束特异性的贡献.
主要方法:
- 使用了由进化规模建模 (ESM) 蛋白质语言模型产生的嵌.
- 开发了TCR-ESM预测模型,包括CDR3α和CDR3β链信息.
- 评估模型性能与独立数据集上的现有预测因素对比.
主要成果:
- 与现有的TCR-pMHC结合预测剂相比,TCR-ESM模型显示出更高的性能.
- 在训练中包括CDR3α和CDR3β链,显著提高了预测准确性.
- 突出了MHC信息在TCR-结合中的关键作用,澄清了以前的不确定性.
结论:
- 深度学习,特别是使用ESM衍生的嵌,为预测TCR-pMHC相互作用提供了一种强大的方法.
- 这些发现强调了TCR链对特异性的重要性,并提供了有关TCR-pMHC识别因素的见解.
- 开发的TCR-ESM模型显示了通用性,并可作为在线工具用于更广泛的研究应用.
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