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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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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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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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Ligand Binding and Linkage

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通过深度学习推进带对接:虚拟选中的挑战和前景

Xujun Zhang1,2, Chao Shen1,2, Haotian Zhang1,2

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概括

深度学习 (DL) 通过提高速度和准确性来增强基于结构的虚拟选 (SBVS) 的分子对接 (MD). 基于DL的MD (DLLD) 的评估指标,应用场景和物理可信性仍然存在挑战.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 在基于结构的虚拟查 (SBVS) 中,分子对接 (MD) 对于预测蛋白质 - 连接体相互作用至关重要.
  • 传统的MD方法往往简化了速度算法,可能会牺牲准确性.
  • 深度学习 (DL) 在构造预测方面表现有前途,以AlphaFold2为例,这表明它有可能彻底改变MD.

研究的目的:

  • 审查当前DL在SBVS增强MD中的状态.
  • 突出基于DL的MD (DLLD) 的挑战和未来前景.
  • 讨论这个快速发展的领域的贡献和学术见解.

主要方法:

  • 虚拟选 (VS) 和分子对接 (MD) 的概述.
  • 介绍深度学习 (DL) 范式及其与传统方法的偏离.
  • 基于DL的MD (DLLD) 的挑战分析,包括评估指标,应用场景和物理可信性.

主要成果:

  • 与传统的MD相比,DL在绑定姿势预测中提供了更高的准确性和速度.
  • DLLD模型可以实现更高的成功率,但可能会产生物理上不合理的姿势.
  • 观察到从盲目对接到在DL模型中识别结合点的转变.

结论:

  • DLLD具有很大的潜力来推进SBVS.
  • 未来的工作应该专注于提高概括性,平衡速度和准确性,结合蛋白质的灵活性,并确保物理可信性.
  • 对比生成和回归算法对于进一步DLLD开发至关重要.