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相关概念视频

Protein Networks02:26

Protein Networks

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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,...
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Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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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...
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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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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Targeted Cancer Therapies02:57

Targeted Cancer Therapies

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
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相关实验视频

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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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药物目标预测基于动态异质图的卷积网络.

Peng Xu, Zhitao Wei, Chuchu Li

    IEEE journal of biomedical and health informatics
    |August 9, 2024
    PubMed
    概括

    本研究引入了动态异质图 (DT-DHG) 用于药物向相互作用 (DTI) 的预测,改善了稀疏网络中的信息利用. DT-DHG增强了图形神经网络的性能,并准确地预测了新的药物向相互作用.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 生物信息学是一种生物信息学.
    • 药物发现 药物发现

    背景情况:

    • 药物向相互作用 (DTI) 的预测对于药物发现和重新定位至关重要.
    • 图形神经网络 (GNN) 对DTI预测有希望,但由于固定的值,在稀疏的网络中可能会丢失信息.
    • 动态异质图 (DT-DHG) 通过优化信息提取来解决这些限制.

    研究的目的:

    • 提出一个新的DTI预测模型,动态异质图 (DT-DHG),可以克服稀疏网络中的信息丢失.
    • 提高在药物向相互作用预测中缺乏信息的利用率.
    • 提高基于GNN的DTI预测方法的稳定性和性能.

    主要方法:

    • 开发了一个动态异质图 (DT-DHG) 模型用于DTI预测.
    • 集成的渐进式学习以动态调整节点的受体场.
    • 使用DT-DHG构建图形而不依赖经验选择的值.

    主要成果:

    • 拟议的DT-DHG模型显著提高了DTI预测的现有GNN性能.
    • 该方法在不同的GNN骨干中显示出稳健性.
    • 在预测新型药物向相互作用方面,DT-DHG的表现优于当前最先进的方法.

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    结论:

    • 动态异质图 (DT-DHG) 有效地解决了DTI预测稀疏网络中的信息丢失问题.
    • 该模型为现有的基于GNN的方法提供了强大而优异的替代方案.
    • 通过准确的新型DTI预测,DT-DHG显示了推动药物发现和重新定位的巨大潜力.