使用深度学习诱导适合酶的上下文依赖设计产生表达良好,热稳定和活性酶
Lior Zimmerman1, Noga Alon1, Itay Levin1
1Enzymit Ltd., Ness-Ziona 7403626, Israel.
概括
使用CoSaNN和SolvIT的AI驱动的酶工程增强了蛋白质的表达和稳定性. 这种新的方法扩大了工业生物技术应用的酶能力.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 工业酶应用受到表达,稳定性和多样性的限制.
- 新的酶设计是复杂的;扩展自然酶是一种替代方案.
- 当前的方法与复杂的序列结构关系作斗争.
研究的目的:
- 引入CoSaNN,一种用于酶设计的深度学习策略.
- 开发 SolvIT,一个用于预测蛋白质溶解性的图形神经网络.
- 为了设计具有更好的表达和热稳定的酶.
主要方法:
- CoSaNN使用深度学习进行酶结构预测和序列优化.
- CoSaNN 控制酶构造,以探索更广泛的化学空间.
- SolvIT 预测大肠杆菌中的蛋白质溶解度,以优化表达.
主要成果:
- 工程酶显示出优越的表达水平 (54%在大肠杆菌).
- 超过30%的工程酶表现出更高的热稳定性 (Tm).
- 人工智能捕获了高阶相互作用,并保留了全性机制.
结论:
- 人工智能显著提高了酶工程的成功率和效率.
- CoSaNN和SolvIT简化了生物技术的酶设计.
- 这种方法扩大了酶工程中的可访问性和应用.
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