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相关概念视频

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
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
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
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.4K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
7.4K
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
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

2.7K
Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
2.7K

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

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开始DTA:预测药物标结合亲和力与生物背景特征和开始网络.

Mahmood Kalemati1, Mojtaba Zamani Emani1, Somayyeh Koohi1

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

Heliyon
|February 26, 2025
PubMed
概括

InceptionDTA是一种新的深度学习模型,通过整合生物背景和多尺度特征,准确地预测药物标结合亲和力. 它的性能优于现有的方法,加速药物发现和重新利用.

科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 准确的药物标结合亲和力预测对于有效的药物发现至关重要.
  • 传统的机器学习和现有的深度学习模型在特征提取和可扩展性方面存在局限性.

研究的目的:

  • 介绍InceptionDTA,一个新的深度学习模型,用于预测药物标结合亲和力.
  • 解决现有模型在捕捉生物背景和多尺度特征方面的局限性.

主要方法:

  • 开发了InceptionDTA,利用CharVec进行了增强的蛋白质序列编码与生物背景.
  • 采用灵感来自Inception网络的多尺度卷积架构,用于从蛋白质序列和药物SMILES中提取特征.
  • 在使用热启动,精炼和冷启动设置的基准数据集中评估性能.

主要成果:

  • InceptionDTA显著超过了基于序列,基于变压器和基于图形的深度学习方法.
  • 采用CharVec增强的版本在绝对预测方面取得了很高的准确性.
  • 一个标签编码版本在排名和预测相对约束亲缘关系方面表现强.

结论:

关键词:
这是一个CharVec编码.深度表示学习学习 (deep representation learning) 是一种深度表示学习.药物标结合 afinity 预测 药物标结合 预测启动网络的初始化互动 互动 互动

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  • InceptionDTA提供了一种多功能和有效的方法来预测药物标结合亲和力.
  • 该模型在加速药物重定向和促进新药发现方面显示出前景.
  • 这项工作有助于推进疾病治疗的计算方法.