通过in silico预测域插入站点来合理设计全蛋白开关
Benedict Wolf1, Pegi Shehu1,2, Luca Brenker1
1Institute of Pharmacy and Molecular Biotechnology, Faculty of Engineering Sciences, Heidelberg University, Heidelberg, Germany.
Nature methods
|August 4, 2025
概括
机器学习工具ProDomino识别了蛋白质域插入的位置,使得能够创建新的可切换蛋白质. 这加速了定制全蛋白的设计,用于诸如诱导基因组工程等应用.
科学领域:
- 蛋白质工程是一种蛋白质工程.
- 合成生物学 合成生物学
- 机器学习在生物学中的应用
背景情况:
- 域插入工程通过结合单独的生物领域来创建具有新功能的蛋白质.
- 确定最佳的插入点,以控制全精子发育是具有挑战性和劳动密集的.
- 现有的方法需要对新蛋白质设计进行广泛的选和优化.
研究的目的:
- 开发一个机器学习管道,ProDomino,用于合理的域重组和插入站点的识别.
- 在生物技术相关系统中实验验验证ProDomino的预测.
- 用ProDomino来演示光遗传和化学遗传蛋白质开关的快速设计.
主要方法:
- 在自然域内插入事件的数据集上训练机器学习模型.
- 使用ProDomino来预测目标蛋白中合适的域插入位.
- 在大肠杆菌和人类细胞中预测位置的实验验证.
- 结合光和化学调节的域作为插入,以创建可切换的蛋白质.
主要成果:
- 在与生物技术相关的蛋白质中,ProDomino成功地确定了功能域插入位.
- 经过实验验证的设计在细菌和人类细胞系统中都显示出强大的性能.
- 开发了新的单元光遗传和化学遗传蛋白开关,包括CRISPR-Cas变体.
- 演示了快速的,定制的全蛋白质的模型引导工程.
结论:
- 普罗多米诺简化了域插入工程,减少了对广泛选的需求.
- 能够有效地创建新的可切换蛋白质,并进行量身定制的全控制.
- 加快合成生物学和基因组工程的先进工具的开发,例如可诱导的CRISPR系统.
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