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相关概念视频

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...
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Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
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...
12.7K
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-protein Interfaces02:04

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...
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
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

120
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...
120

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A Protocol for Computer-Based Protein Structure and Function Prediction
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SG-ML-PLAP:一种基于结构导向机器学习的评分功能,用于蛋白质 - 配体结合亲缘关系预测.

Sapna Pal1, Ankita Pal1, Debasisa Mohanty1

  • 1Bioinformatics Center, National Institute of Immunology, New Delhi, India.

Protein science : a publication of the Protein Society
|December 11, 2024
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概括
此摘要是机器生成的。

这项研究引入了一种新的基于机器学习的评分函数 (MLSF),用于预测蛋白质-连接体结合亲和力. 开发的MLSF,SG-ML-PLAP,比传统方法更准确,有助于药物发现.

关键词:
欧洲资金交易所 (ECIF) 是一个国际金融机构.停靠的对接方式梯度提升了树木的增长.机器学习的评分功能是机器学习的评分功能.神经网络的神经网络的神经网络蛋白联体结合亲和关系 蛋白联体结合亲和关系随机的森林随机的森林

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相关实验视频

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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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
  • 机器学习 机器学习

背景情况:

  • 预测蛋白质 - 配体结合亲和力对于药物设计至关重要.
  • 机器学习 (ML) 越来越多地被使用,但准确的复合排名仍然是一个挑战.

研究的目的:

  • 开发一种新的基于ML的评分函数 (MLSF),用于准确的结合亲和力预测.
  • 评估开发的MLSF与现有方法的性能.

主要方法:

  • 利用PDBbind数据集中的扩展连接交互指纹 (ECIF) 来构建ML模型.
  • 在评分函数比较评估 (CASF) 数据集和未见复合体上的基准性能.
  • 研究了在训练套件中包括重制蛋白-配体复合物的影响.

主要成果:

  • 开发的MLSF,SG-ML-PLAP,证明了更好的结合亲和力预测准确性.
  • 补充晶体结构与重制复合物增强了MLSF的性能.
  • 在具有ECIF和VINA特征的晶体结构上训练的MLSF显示了晶体和对接复合体的高精度.

结论:

  • 拟议的MLSF与传统的评分功能和其他MLSF相比,提供了更高的性能.
  • SG-ML-PLAP是基于结构的虚拟查和识别新型抑制剂的宝贵工具.
  • 在SG-ML-PLAP网络服务器是免费可访问的,用于更广泛的研究使用.