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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

11.9K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
11.9K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.1K

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相关实验视频

Updated: May 2, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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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
PubMed
概括
此摘要是机器生成的。

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.

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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.