非血红素铁酶的计算稳定使新功能的高效演变成为可能
Brianne R King1, Kiera H Sumida2, Jessica L Caruso1
1Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.
bioRxiv : the preprint server for biology
|August 2, 2024
概括
ProteinMPNN是一种深度学习工具,增强生物催化剂工程的酶稳定性. 稳定的酶在针对新功能的定向进化中显示出更大的改进.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 计算生物学和蛋白质设计
- 合成生物学 合成生物学
背景情况:
- 定向进化是设计新型生物催化剂的关键.
- 酶稳定性对于成功的工程至关重要,因为新的催化功能可能会破坏稳定.
- 从稳定的酶支架开始,通常有利于定向进化.
研究的目的:
- 研究深度学习工具ProteinMPNN对重新设计Fe (II) /αKG超级酶家族的实用性.
- 提高酶的稳定性,可溶性和表达性,同时保持原生和非原生催化功能.
- 建立计算序列重新设计作为指导进化的初步步骤.
主要方法:
- 使用的蛋白MPNN用于Fe (II) /αKG酶的计算序列重新设计.
- 在野生类型和稳定酶变体上进行了位点和突变发生.
- 在非原生C-H基化反应中选增加活性.
主要成果:
- 蛋白MPNN成功地重新设计了Fe ((II) /αKG酶 (tP4H和GREE) 以提高稳定性,溶解性和表达.
- 与野生类型酶相比,稳定酶变体在非原生活性中表现出显著更大的增加.
- 简单的结构标准使得稳定,具有催化作用的变体能够产生.
结论:
- 使用ProteinMPNN进行计算序列重新设计是一种有效的策略,用于在定向进化之前稳定酶.
- 这种方法可以加速新型生物催化剂的工程,以增强的特性和功能.
- 蛋白MPNN提供了一种用户友好和可访问的方法,用于改善生物催化剂的酶支架.
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