条件为产生热友蛋白序列的神经网络可以增加热稳定性
Evan Komp1, Christian Phillips2, Lauren M Lee3
1Chemical Engineering, University of Washington, Seattle, WA, USA. komp.evan@gmail.com.
Scientific reports
|April 23, 2025
概括
研究人员开发了通过杆翻译 (NOMELT) 实现的化温度神经优化,以设计热稳定蛋白质. 这种新的方法使用神经机器翻译来预测和排名蛋白质的热稳定性,在设计的蛋白质变体中显示出显著的改进.
科学领域:
- 蛋白质工程是一种蛋白质工程.
- 计算生物学是一种计算生物学.
- 生物技术是生物技术.
背景情况:
- 设计具有增强热稳定的蛋白质对于各种生物技术应用至关重要.
- 现有的方法往往需要大量的数据或同质信息,限制了它们的范围.
- 了解生物体温度适应和蛋白质热友性之间的关系是关键.
研究的目的:
- 推出NOMELT,一种基于神经机器翻译的新方法,用于设计和排序高温稳定的蛋白质.
- 为了证明NOMELT在预测和增强蛋白质化温度方面的能力.
- 探索利用生物生长温度来设计蛋白质的潜力.
主要方法:
- 训练一个神经机器翻译模型 (NOMELT) 在400多万个来自适应不同温度的生物的蛋白质同类对上.
- 应用NOMELT来设计和实验验证Drosophila melanogasterEngrailed Homeodomain的一个变体.
- 评估NOMELT的零射击预测能力,以对蛋白质家族的实验融化和半激活温度进行排名.
主要成果:
- 一种被设计的Drosophila melanogaster Engrailed Homeodomain的变体显示化温度增加了15.5K.
- 诺梅尔特证明了对排名蛋白质热稳定性指标的零射击预测能力.
- 该模型成功地学习了热友性,将其与一般的自然变异区分开来,而不需要大量的数据集.
结论:
- 诺梅尔特为热稳定蛋白质的合理设计提供了一种有前途的方法.
- 利用生物体生长温度是依赖环境的蛋白质工程的一个有效策略.
- 该方法有可能加速用于高温应用的蛋白质的开发.
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