非血红素铁酶的计算稳定使新功能的高效进化成为可能
Brianne R King1, Kiera H Sumida1,2, Jessica L Caruso1
1Department of Chemistry, University of Washington, Seattle, Washington, 98195, USA.
Angewandte Chemie (International ed. in English)
|October 12, 2024
概括
使用ProteinMPNN进行深度学习蛋白质重新设计,提高了酶的稳定性和功能. 稳定酶的演化效率更高,为工业生物催化剂产生更大的活性.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 蛋白质设计和计算生物学
- 合成生物学和工业生物技术
背景情况:
- 酶设计的深度学习工具正在出现,需要在工程工作流程中进行评估.
- 酶Fe (II) /αKG超级家族为潜在的生物催化剂应用提供了多样化的催化功能.
- 定向进化是优化酶特性的一个关键策略.
研究的目的:
- 评估ProteinMPNN深度学习工具对重新设计Fe (II) /αKG酶的有效性.
- 为了提高酶的稳定性,溶解性和表达,同时保持原生和非原生功能.
- 通过使用稳定酶变异来证明增强的定向进化效率.
主要方法:
- 利用ProteinMPNN重新设计Fe(II) /αKG酶,重点关注稳定性和功能.
- 对野生类型和稳定酶的定向进化轨迹进行了系统的比较.
- 确定了结构和序列约束,以在重新设计期间保持催化功能.
主要成果:
- 蛋白MPNN成功地重新设计了Fe (II) /αKG酶,以提高稳定性,溶解性和表达.
- 稳定酶变体表现出更高效的进化,达到80倍的活动增加相比野生类型的6倍增加.
- 基于蛋白MPNN的稳定方法可以将其推广到其他Fe (II) /αKG超级家族成员.
结论:
- 像ProteinMPNN这样的深度学习工具可以在定向进化工作流中例行实施.
- 蛋白MPNN促进了新型生物催化剂的工程,为工业应用提高了性能.
- 这一框架使得稳定,有催化作用的酶可以用于各种生物技术用途.
![Protein Film Infrared Electrochemistry Demonstrated for Study of H2 Oxidation by a [NiFe] Hydrogenase](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F55858.jpg&w=3840&q=50)
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