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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.8K
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:
12.8K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

142
Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
142

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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基于物理和机器学习的组合方法,使用SILCS热点来识别可使用药物的结合部位.

Erik B Nordquist1, Mingtian Zhao1, Anmol Kumar1

  • 1Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States.

Journal of chemical information and modeling
|September 16, 2024
PubMed
概括

我们开发了一种机器学习模型,用SILCS方法识别可用药物的蛋白质结合点. 这种方法通过预测新药类分子的潜在结合口袋来增强药物发现.

科学领域:

  • 计算化学和结构生物学
  • 机器学习在药物发现中的作用
  • 蛋白质 - 配体相互作用

背景情况:

  • 识别可用药物的结合部位,特别是神秘或异质的结合部位,至关重要但具有挑战性.
  • 现有的方法可能会错过在静态蛋白质结构中不明显的部位.
  • 通过连接物竞争和 (SILCS) 方法识别位点使用模拟来捕获蛋白质的灵活性并识别潜在的结合口袋.

研究的目的:

  • 开发一种机器学习 (ML) 模型来排列由SILCS识别的蛋白质结合位点.
  • 预测这些部位容纳类似药物分子的可能性.
  • 推进新型治疗点和候选药物的发现.

主要方法:

  • 利用全原子分子模拟 (SILCS) 来建模蛋白质灵活性和溶液分布.
  • 开发了一种ML模型,根据药物可用性对SILCS识别的"热点"进行排名.
  • 在一组独立的酶和受体上验证了ML模型.

主要成果:

  • ML模型成功地回忆了分别在排名前10和前20的热点中67%和89%的已知连接物结合点.
  • 该模型的决策函数在预测结合位点和它们对新目标的可用性方面被证明是有效的.
  • 识别了被埋藏的绑定口袋,这些口袋被实验结构遗漏了.

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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结论:

  • 用ML增强的SILCS方法显著改善了对orthosteric和allosteric结合位点的识别.
  • 这种方法有助于发现药物样分子,这些分子针对以前无法访问的蛋白质位点.
  • 开发的工具代表了在药物开发中对联体发现和优化的关键进步.