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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Protein Organization01:24

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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用DFMDock进行蛋白质对接的统一采样和排名.

Lee-Shin Chu1, Sudeep Sarma1, Jeffrey J Gray1

  • 1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

bioRxiv : the preprint server for biology
|October 10, 2024
PubMed
概括

一个新的扩散模型DFMDock统一了蛋白质对接采样和排名. 它的性能优于以前的方法,在姿势预测和排名方面取得了更高的成功率,而不需要单独的信心模型.

科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 蛋白质对接对于理解分子相互作用和药物设计至关重要.
  • 当前的扩散模型通常需要单独的组件来进行姿势采样和信心评分.
  • 现有的方法在准确排名预测的蛋白质-连接体姿势方面面临挑战.

研究的目的:

  • 引入DFMDock,这是蛋白质对接的统一扩散模型.
  • 将姿势采样和排名整合到一个单一,高效的框架中.
  • 为了提高蛋白质对接预测的成功率和准确性.

主要方法:

  • 开发了DFMDock,这是一个具有双输出头的扩散模型,用于力和能量预测.
  • 在训练力预测中采用了否定力匹配目标.
  • 将能量梯度与预测力对齐,以实现基于能量的排名.
  • 利用预测的力量进行采样和预测的能量来排名停靠姿势.

主要成果:

  • DFMDock实现了44%的采样成功率,明显超过了DiffDock-PP的8%的表现.
  • 在对接基准5.5.5上,DFMDock的成功率为16%,而DiffDock-PP的成功率为0%.

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  • 该模型的能量预测形成了一个结合道,类似于基于物理学的方法,表明精确的能量景观捕获.

    • 结论:

    • DFMDock成功地使用单一的扩散模型统一了蛋白质对接中的采样和排名.
    • 拟议的力量匹配和能量对齐方法提高了预测的准确性和效率.
    • DFMDock代表了结构生物信息学和药物发现的扩散模型应用的重大进步.