用RF扩散进行蛋白质结构和功能的新设计
Joseph L Watson1,2, David Juergens1,2,3, Nathaniel R Bennett1,2,3
1Department of Biochemistry, University of Washington, Seattle, WA, USA.
Nature
|July 11, 2023
概括
一个新的深度学习框架,RoseTTAFold扩散 (RF扩散),可以设计多种功能蛋白. 这种方法在制造蛋白质结合剂,对称组件和各种应用的支架方面表现出色.
科学领域:
- 计算生物学
- 蛋白质工程
- 药物发现中的人工智能
背景情况:
- 深度学习已经推进了蛋白质的设计,但对于新型结合剂设计和对称架构等各种挑战的总体框架缺乏.
- 由于复杂的几何结构和序列结构关系,在图像和语言方面取得成功的扩散模型已经显示出有限的蛋白质建模成功.
研究的目的:
- 为蛋白质设计开发一个通用的深度学习框架,解决各种挑战.
- 在各种设计任务中创建具有高性能蛋白质脊柱的生成模型.
主要方法:
- 微调RoseTTAFold结构预测网络的蛋白质结构检测任务.
- 开发RoseTTAFold扩散 (RF扩散),一种蛋白质骨干的生成模型.
主要成果:
- 在无条件和受拓限制的蛋白质单体设计,粘合剂设计,对称的寡合体设计和支架中,RF扩散表现出色.
- 数百种设计的蛋白质的实验性表征,包括对称组件和结合剂,验证了该方法的功率和普遍性.
- 低温电子显微镜证实了设计的粘合剂的高精度,几乎与设计模型相同.
结论:
- 射频扩散提供了一个强大而通用的深度学习框架,用于从分子规格设计多种功能性蛋白质.
- 该方法显著提升了人工智能在蛋白质工程和新蛋白质设计方面的能力.
- 射频扩散为创造新疗法,酶和生物材料开辟了新的途径.
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