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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
4.1K
Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
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...
12.6K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
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...
12.4K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.7K
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:
12.7K
Protein Networks02:26

Protein Networks

3.9K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
3.9K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.7K
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...
4.7K

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相关实验视频

Updated: May 20, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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GNNSeq:一个基于序列的图形神经网络,用于预测蛋白质-连接物结合亲和力.

Somanath Dandibhotla1, Madhav Samudrala2, Arjun Kaneriya3

  • 1Department of Computer Science, College of Engineering and Computing, George Mason University, Fairfax, VA 22030, USA.

Pharmaceuticals (Basel, Switzerland)
|March 27, 2025
PubMed
概括

一个新的混合模型GNNSeq,只使用序列数据,准确地预测了蛋白质-连接体结合亲和力. 这种高效的方法有助于药物发现,使大规模的虚拟查和识别潜在的候选药物.

关键词:
图表神经网络的神经网络机器学习是机器学习.蛋白联体结合亲和关系 蛋白联体结合亲和关系基于序列的蛋白联体亲和力预测.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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科学领域:

  • 计算化学和化学信息学
  • 机器学习在药物发现中的作用
  • 生物信息学和结构生物学

背景情况:

  • 准确预测蛋白质 - 配体结合亲和力对于有效的药物发现至关重要.
  • 现有的基于序列的模型往往缺乏准确性和稳定性,限制了它们的概括性.
  • 需要的模型不需要预先对接的复合体或结构数据.

研究的目的:

  • 开发GNNSeq,这是一个新的混合机器学习模型,用于预测蛋白质-带结合亲和力.
  • 通过完全利用序列特征来克服现有模型的局限性.
  • 为了提高结合性亲和预测的准确性,稳定性和通用性.

主要方法:

  • GNNSeq集成了一个图形神经网络 (GNN) 与随机森林 (RF) 和XGBoost.
  • 该模型从蛋白质和配体序列中提取分子特征和序列模式.
  • 基于内核的独特的上下文切换设计优化了效率,并动态调整了功能权重.

主要成果:

  • 在PDBbind v.2020精炼集上,GNNSeq实现了0.784的皮尔森相关系数 (PCC),在PDBbind v.2016核心集上达到0.84.
  • 对DUDE-Z数据集的外部验证显示平均AUC为0.74.
  • 结合GNNSeq的混合模型达到0.97的PCC,训练时间高效 (在约1.5小时内完成5000多个复合体).

结论:

  • GNNSeq提供了一个高效和可扩展的解决方案,用于结合亲和力预测.
  • 该模型显示了更好的准确性和通用性,促进了大规模的虚拟选.
  • 通过基于服务器的GUI,GNNSeq是公开可用的,用于具有成本效益的命中识别.