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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
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...
14.9K
Ligand Binding Sites02:40

Ligand Binding Sites

8.7K
8.7K
Entropy and Solvation02:05

Entropy and Solvation

8.3K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
8.3K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

5.5K
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...
5.5K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.0K
4.0K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

14.9K
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:
14.9K

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相关实验视频

Updated: Jan 18, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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分子:分子动力学和优化深度学习,用于输入法规化的分类和不确定性意识的连接体评估.

Ivan Cucchi1, Elena Frasnetti2, Francesco Frigerio3

  • 1Dipartimento di Matematica "F. Casorati", Università di Pavia, Via Ferrata 5, Pavia 27100, Italy.

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概括

这项研究引入了一种双模态深度神经网络,通过整合蛋白质动态和结构数据来预测药物作用模式. 该模型能够快速选化合物,提高药物发现效率.

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科学领域:

  • 计算化学是一种计算化学.
  • 药理学 药理学是指药理学的学科.
  • 生物物理学的生物物理.

背景情况:

  • 机器学习 (ML) 和深度学习 (DL) 正在推动药物发现.
  • 整合基于结构的数据可以提高ML模型的预测.
  • 以前的研究表明,ML模型可以使用分子动力学 (MD) 数据来分类酶配体.

研究的目的:

  • 开发一种新的双模态深度神经网络分类器,用于预测复合作用模式.
  • 单独且高效地处理动态和结构数据.
  • 为了利用一个多样化的激酶数据集,有280个经过实验解决的结构.

主要方法:

  • 策划了多样化的激酶数据集 (280个结构).
  • 开发了一个双模态深度神经网络分类器.
  • 训练并评估动态和结构数据模型.

主要成果:

  • 在预测化合物作用模式方面取得了强大的分类性能.
  • 证明了有效的不确定性处理.
  • 强调了将蛋白质动态数据纳入数据的重要性.
  • 通过计入动态数据保持高性能.

结论:

  • 开发的双模态深度神经网络有效预测复合作用模式.
  • 蛋白质动态数据对于准确的预测至关重要.
  • 该方法允许快速的化合物选和优先级,而不需要广泛的模拟.