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相关概念视频

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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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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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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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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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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相关实验视频

Updated: Jun 21, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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通过考虑特定领域的特征来预测药物向相互作用的多功能框架.

Shuo Liu1,2, Jialiang Yu2, Ningxi Ni2

  • 1School of Pharmacy, Lanzhou University, Gansu 730000, China.

Journal of chemical information and modeling
|July 8, 2024
PubMed
概括

这项研究引入了E-DIS,这是一种用于预测药物向相互作用 (DTI) 的整体模型. 通过结合一般和特定特征,E-DIS增强了对新数据的概括性,提高了药物发现效率.

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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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相关实验视频

Last Updated: Jun 21, 2025

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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:

  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 预测药物向相互作用 (DTI) 对于药物发现至关重要,但由于昂贵的实验而受到阻碍.
  • 深度学习模型对DTI预测有前途,但由于训练数据有限,通常无法将其推广到新型药物标对.

研究的目的:

  • 开发一种新的策略,E-DIS (集成领域特定特征的深度学习模型),以提高DTI预测模型的概括能力.
  • 通过捕获多样化的域-通用和域-特定特征来增强蛋白质和连接体的表征.

主要方法:

  • 一组模型 (E-DIS) 被训练以捕捉域通用和域特定的特征.
  • 在不同领域训练了多个"专家"模型,以学习和调整特定领域的信息,而无需访问未见的领域数据.
  • 该方法旨在调整模型以适应销售之外的数据.

主要成果:

  • 在四个基准数据集中,E-DIS在域内和跨域设置中显著改善了模型性能.
  • 与现有的DTI预测技术相比,该方法显示出优越的域泛化能力.
  • 通过有效捕捉各种特征,E-DIS提供了蛋白质和配体的全面表示.

结论:

  • 通过有效地结合域名通用和域名特定特征,E-DIS战略代表了DTI预测的重大进步.
  • 这种方法提高了DTI预测模型的概括能力,为更有效的药物发现铺平了道路.
  • E-DIS提供了一个可靠的解决方案来预测药物向相互作用,特别是对于新鲜或未见的药物向对.