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相关概念视频

Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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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...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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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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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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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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相关实验视频

Updated: Jun 28, 2025

Diagonal Method to Measure Synergy Among Any Number of Drugs
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Diagonal Method to Measure Synergy Among Any Number of Drugs

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多模式药物标绑定亲和力预测使用图局部子结构.

Xun Peng, Chunping Ouyang, Yongbin Liu

    IEEE journal of biomedical and health informatics
    |April 10, 2024
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了MLSDTA,一种用于药物标结合亲和力 (DTA) 预测的多式联络深度学习模型. 它集成了图形和序列数据,通过捕获关键的分子亚结构,超过现有方法.

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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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    相关实验视频

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    Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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    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 (DTA) 预测对于有效的药物开发至关重要.
    • 目前用于DTA预测的深度学习方法通常仅依赖于序列或图形信息,可能导致信息丢失 (例如,缺少原子数据) 或包含不相关的特征.
    • 现有的模型可能缺乏分子特征的结构化表示.

    研究的目的:

    • 提出MLSDTA,一种新的多式联络深度学习模型,用于增强药物标结合亲和力预测.
    • 从药物和目标中全面整合图形和序列信息,以提高DTA预测的准确性.
    • 通过结合本地亚结构信息和增强分子特征表示来解决现有方法的局限性.

    主要方法:

    • 开发了MLSDTA,一个多式联网DTA预测模型,集成图形和序列数据,使用交叉注意力机制进行特征融合.
    • 实现了自适应结构意识的聚合,以生成捕获本地亚结构信息的图形表示.
    • 利用DropNode策略来提高分子表示的独特性.

    主要成果:

    • 与最先进的模型相比,MLSDTA在用于DTA预测的两个基准数据集上表现优越.
    • 多模式方法有效地整合了多样化的分子信息,导致更准确的结合亲和力预测.
    • 纳入本地基结构和增强分子区分有助于模型的效率提高.

    结论:

    • 通过利用多式联络数据融合,MLSDTA提供了一种强大而有效的方法来预测药物标结合亲缘关系.
    • 该模型能够捕获本地亚结构信息并增强分子区别的能力,代表了DTA预测的重大进步.
    • 这些发现证实了MLSDTA的可行性和优越性,为其在加速药物发现管道中的应用铺平了道路.