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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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Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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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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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.
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相关实验视频

Updated: Jun 6, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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THGB:通过结合树增强和基于直方图的梯度增强来预测配体-受体相互作用.

Liqian Zhou1, Jiao Song1, Zejun Li2

  • 1School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.

Scientific reports
|November 28, 2024
PubMed
概括

THGB是一种用于预测体受体相互作用 (LRIs) 的新计算模型,显著改善了细胞间通信分析. 这种方法在发现新型LRI方面优于现有模型,为湿实验提供了具有成本效益的替代方案.

关键词:
功能选择 功能选择基于直方图的梯度增强可以提高梯度.体-受体相互作用的相互作用树木的增长促进了树木的增长.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 灵受体相互作用 (LRIs) 对于理解生物和医学研究中的细胞间通信至关重要.
  • 实验性发现新的LRI往往是昂贵和耗时的.

研究的目的:

  • 开发一种计算模型,THGB,用于准确和高效地预测新型联体受体相互作用 (LRIs).
  • 为推断细胞间通信提供一个具有成本效益的工具.

主要方法:

  • THGB使用iFeature从连接体-受体 (LR) 对中提取特征信息.
  • 树增强模型用于选择代表性的LR特征.
  • 一个基于直方图的梯度增强模型被用来预测高质量的LRI.

主要成果:

  • 与现有的LRI预测模型 (CellEnBoost,CellGiQ,CellComNet) 和蛋白质与蛋白质相互作用模型 (PIPR) 相比,THGB在六个评估指标中表现出卓越的表现.
  • 使用树增强模型的特征选择在LRI预测方面比PCA,NMF,LLE和TSVD更有效.
  • 一项废除研究证实,具有特征选择的THGB产生的结果比没有更好.

结论:

  • THGB是一种高效的计算工具,用于预测连接体-受体相互作用.
  • 该模型的特征选择策略提高了LRI预测的准确性.
  • THGB为发现新的LRI和推进细胞间通信研究提供了宝贵的资源.