使用LigandMPNNN的原子上下文条件化蛋白质序列设计
Justas Dauparas1,2, Gyu Rie Lee1,2,3, Robert Pecoraro1,2,4
1Department of Biochemistry, University of Washington, Seattle, WA, USA.
Nature methods
|March 29, 2025
概括
我们开发了LigandMPNN,这是一种用于设计非蛋白质分子的蛋白质序列的深度学习方法. 联MPNN在恢复与小分子,核酸和金属相互作用的蛋白质的原生序列方面优于现有方法.
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.
- 深度学习是一种深度学习.
背景情况:
- 目前的深度学习方法很难在生物分子系统中模拟非蛋白质组件.
- 设计能够结合小分子,核酸和金属的蛋白质对于各种应用至关重要.
研究的目的:
- 介绍LigandMPNN,一种基于深度学习的新型蛋白质序列设计方法.
- 为了使蛋白质设计中非蛋白质组件 (连接体) 的显式建模.
主要方法:
- 开发了LigandMPNN,一个图形神经网络架构.
- 在涉及小分子,核酸和金属的蛋白质序列恢复任务上训练和评估LigandMPNN.
- 与Rosetta和ProteinMPNN相比,LigandMPNN的性能进行了比较.
主要成果:
- 在与小分子相互作用的残留物 (63.3%与50.4%/50.5%对比),核酸 (50.5%与35.2%/34.0%对比) 和金属 (77.5%与36.0%/40.6%对比) 的原生脊柱序列恢复方面,联基MPNN显著优于罗塞塔和蛋白MPNN.
- 联MPNN生成序列和侧链形态,用于详细的结合相互作用分析.
- 使用LigandMPNN设计了100多个经过实验验证的小分子和DNA结合蛋白,实现了高亲和度和结构精度.
结论:
- 联MPNN代表了蛋白质序列设计的重大进步,特别是在涉及非蛋白质组件的系统中.
- 该方法可以设计具有高特异性和亲和力的新型结合蛋白,传感器和酶.
- 通过成功的实验验证和结合亲和力的显著改善,LigandMPNN已经证明了其实用性.
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