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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

13.4K
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...
13.4K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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...
4.4K
Ligand Binding Sites02:40

Ligand Binding Sites

13.3K
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...
13.3K
Induced-fit Model01:13

Induced-fit Model

82.3K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
82.3K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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相关实验视频

Updated: Sep 15, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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用预训练的图形编码器和语言模型预测激酶抑制剂结合亲和力.

Xudong Guo1, Zixu Ran1, Fuyi Li1,2

  • 1College of Information Engineering, Northwest A&F University, Yangling, 712100, China.

Briefings in bioinformatics
|July 15, 2025
PubMed
概括

预测抑制剂-激酶结合亲和力对于药物发现至关重要. 新的Kinhibit框架使用先进的AI显著提高了预测准确性,为癌症治疗和药物查提供了更好的工具.

关键词:
结合性亲和力是一种结合性亲和力.相反的学习学习学习.图表神经网络的神经网络抑制剂抑制剂的使用激酶激酶的作用是什么

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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相关实验视频

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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科学领域:

  • 计算生物学是一种计算生物学.
  • 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:

  • 准确预测抑制剂-激酶结合亲和力对于药物发现和癌症治疗至关重要.
  • 目前的预测方法在数据表示,特征提取和捕获复杂的激酶抑制剂相互作用方面存在困难.
  • 先进的人工智能 (AI) 和深度学习技术显示出希望,但需要进一步开发复杂的分子相互作用.

研究的目的:

  • 开发一种新的框架,Kinhibit,用于增强抑制剂-激酶结合亲和力预测.
  • 解决现有方法的局限性,包括数据表达和特征提取不足.
  • 提高药物查和生物科学中的计算工具的准确性和有效性.

主要方法:

  • Kinhibit集成了自我监督的图形对比学习,以有效地提取特征.
  • 多视图分子图表表示能够捕捉到各种分子特征.
  • 一个结构信息化的蛋白质语言模型 (ESM-S) 和特征融合优化了抑制剂-激酶相互作用分析.

主要成果:

  • 在预测三种关键的基因激活蛋白激酶 (MAPK) 路径激酶 (RAF,MEK,ERK) 的抑制剂结合时,Kinhibit获得了92.6%的准确性.
  • 该框架在全面的MAPK-All数据集上表现出卓越的性能,准确度为92.9%.
  • 实验结果验证了拟议的特征提取和融合策略的有效性.

结论:

  • 基尼希特在抑制剂-激酶结合亲和力预测方面提供了显著的进步.
  • 该框架为加速药物查提供了一个有希望和有效的计算工具.
  • 这种方法增强了我们理解和向癌症等疾病中酶通路的能力.