蛋白MPNN恢复了跨膜β-桶的复杂序列属性
Marissa Dolorfino1,2, Rituparna Samanta1,2,3,4, Anastassia Vorobieva1,2,3
1Structural Biology Brussel, Vrije Universiteit Brussel, Brussels, Belgium.
bioRxiv : the preprint server for biology
|February 14, 2024
概括
深度学习蛋白质设计方法对膜蛋白具有前景,但对复杂的折叠需要准确的骨干输入. 蛋白MPNN优于精细的输入,为潜在的纳米孔应用产生各种序列.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 生物物理学的生物物理.
背景情况:
- 深度学习 (DL) 方法已经推进了蛋白质设计,但仍在与新的膜蛋白 (MP) 和复杂的β-sheet折叠作斗争.
- 目前的DL模型需要对具有挑战性的蛋白质结构进行强有力的评估.
研究的目的:
- 为了对ProteinMPNN深度学习方法进行基准测试,用于新的膜蛋白质设计.
- 为了将ProteinMPNN与罗塞塔弗兰克林2023的能量功能进行比较.
- 评估输入骨干精炼对ProteinMPNN性能的影响.
主要方法:
- 使用跨膜和水溶性β-桶折叠的ProteinMPNN的全面基准.
- 与罗塞塔弗兰克林2023号的能量函数进行比较.
- 用精炼与未精炼输入骨干评估蛋白MPNN性能.
主要成果:
- 精细的输入骨干提高了ProteinMPNN在捕获复杂折叠生物物理的全球序列属性的准确性.
- 与Franklin2023.23相比,ProteinMPNN产生了更多多样化的跨膜β-barrel序列,特别是在孔面位置,与Franklin2023.
- ProteinMPNN设计的序列通过了过器进行实验验证,表明了纳米孔设计的潜力.
结论:
- 在设计β-sheet蛋白质方面,ProteinMPNN的局限性归因于输入骨干精度,而不是固有的软件约束.
- 该研究突出了DL方法在功能性膜蛋白和纳米孔的新设计中的潜力.
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