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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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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...
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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-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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STAG-LLM:通过蛋白语言模型和计算生成的3D结构预测TCR-pHLA结合.

Jared K Slone1, Minying Zhang2, Peixin Jiang2

  • 1Computer Science, Rice University, Houston, 77005, TX, USA.

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概括

预测T细胞受体 (TCR) 和-HLA (pHLA) 的结合对于免疫治疗至关重要. STAG-LLM是一种新的多式模式,使用3D结构和序列来改进约束特异性预测,优于现有方法.

关键词:
几何深度学习的几何深度学习免疫学 免疫学 免疫学蛋白质语言模型的模型蛋白质组学是指蛋白质组学.结构生物信息学 结构生物信息学在TCR,HLA.

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科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 结合T细胞受体 (TCR) 和-HLA (pHLA) 对于适应性免疫至关重要.
  • 准确的结合特异性预测有助于个性化免疫疗法设计.
  • 目前的方法主要使用氨基酸序列,忽略了结构信息.

研究的目的:

  • 为TCR-pHLA结合特异性预测开发一种多式机器学习 (ML) 模型.
  • 将3D结构数据与序列数据集成,以提高预测准确度.
  • 为应对与在ML管道中使用计算生成的3D结构相关的挑战.

主要方法:

  • 开发了STAG-LLM,这是一个多式机器学习模型,结合了蛋白质语言模型和几何深度学习.
  • 利用计算生成的3D蛋白质结构与氨基酸序列一起使用.
  • 纳入策略来管理推断成本,有限的培训数据和结构性噪音.

主要成果:

  • 与现有方法相比,STAG-LLM在预测TCR-pHLA结合特异性方面表现优越.
  • 该模型甚至在较小的训练数据集下实现了高精度.
  • 在体外氨酸扫描实验显示与模型注意力重量相关,验证了预测.

结论:

  • STAG-LLM显示了基于结构的TCR-pHLA结合预测的巨大潜力.
  • 该模型为推进使用模拟3D结构的免疫学和蛋白质学研究提供了基础.
  • 预计STAG-LLM的实用性将随着蛋白质结构和语言模型的进步而增长.