比较MPNN和定向进化用于改变RAMR转录因子中的效应体结合位
Alia Clark-ElSayed1, Ethan Creed1, Kaila Nayvelt1
1Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
bioRxiv : the preprint server for biology
|July 17, 2025
概括
计算型蛋白质设计工具在创建全性蛋白质方面遇到了困难. 机器学习对RamR抑制器结合口袋的重新设计未能复制定向进化结果,产生了非功能变体.
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.
- 生物化学 生物化学
背景情况:
- 蛋白质设计的机器学习 (ML) 工具正在快速发展,在稳定性和带结合方面取得了成功.
- 设计具有内在全性机制的蛋白质仍然是一个挑战.
- 体转录因子 (aTF) 通过体通信调节基因表达,是生物传感器发展的潜在目标.
研究的目的:
- 评估LigandMPNN计算工具用于重新设计一个全转录因子的联体结合口袋的有效性.
- 为了将计算重新设计的结果与先前的定向进化努力进行比较,以改变效应特异性.
主要方法:
- 使用LigandMPNN. RamR转录抑制器的连接体结合口袋的计算重新设计.
- 预测野生类型的RamR结构与五种基素化物 (BIA) 复合.
- 对9种重新设计的蛋白质变体进行实验测试,以测试它们在大肠杆菌中的功能性.
主要成果:
- 在计算重新设计结果和以前的定向进化结果之间观察到很少的重叠.
- 九个计算重新设计的RamR变体中,没有一个在大肠杆菌中具有功能.
- 该研究强调了当前ML工具设计全蛋白质的局限性.
结论:
- 目前的计算蛋白质设计方法,包括LigandMPNN,可能不足以设计全性或形状灵活的蛋白质.
- 需要进一步开发蛋白质设计策略,以有效地纳入全性机制.
- 这些发现表明,需要采用替代或补充方法来设计具有复杂调控功能的蛋白质.
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