预测抗体和T细胞受体互补性决定区域的结构灵活性
Fabian C Spoendlin1, Monica L Fernández-Quintero2, Sai S R Raghavan2
1Department of Statistics, University of Oxford, Oxford, UK.
Nature machine intelligence
|October 27, 2025
概括
研究人员开发了ITsFlexible,这是一种深度学习工具,用于预测抗体互补性确定区域 (CDR) 中的蛋白质结构灵活性. 该工具准确地将CDR循环分类为刚性或柔性,从而推进了抗原相互作用研究.
科学领域:
- 结构生物学是结构生物学.
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
背景情况:
- 蛋白质的灵活性对于功能至关重要,特别是在影响抗原结合的抗体互补性决定区域 (CDRs) 中.
- 正确预测静态蛋白质结构是可能的 (例如,AlphaFold),但由于训练数据有限,预测灵活性仍然是一个挑战.
研究的目的:
- 开发一种计算方法来预测抗体和T细胞受体CDR3循环的结构灵活性.
- 为了解决灵活性预测培训数据的稀缺性.
主要方法:
- 使用来自蛋白质数据库的120万个循环结构构建了ALL-conformations数据集.
- 开发了ITsFlexible,这是一个使用图形神经网络架构的深度学习工具.
- 训练模型根据抗体结构将CDR循环分类为"刚性"或"灵活".
主要成果:
- 与结晶结构数据集的替代方法相比,ITsFlexible 显示出更高的性能.
- 该模型有效地推广到分子动力学模拟.
- ITsFlexible准确地预测了以前未经描述的CDRH3循环的灵活性,并通过冷电子显微镜验证.
结论:
- ITsFlexible提供了一种可靠的方法来预测CDR循环的灵活性,这对于理解抗体-抗原相互作用至关重要.
- 这项研究强调了深度学习在预测动态蛋白质特性方面的潜力.
- 这种工具可以帮助设计和设计具有特定结合特性的抗体.
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