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相关概念视频

Protein Networks02:26

Protein Networks

4.0K
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,...
4.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

12.1K
G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
12.1K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
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...
4.8K

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相关实验视频

Updated: Jul 4, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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基于异质图的预测蛋白质功能注意力技术.

Yingwen Zhao, Zhihao Yang, Lei Wang

    IEEE journal of biomedical and health informatics
    |February 6, 2024
    PubMed
    概括

    这项研究引入了一种用于蛋白质功能预测的新型深度学习方法. 通过将负注释纳入异质图中,它提高了生物研究和药物发现的预测准确性.

    科学领域:

    • 生物信息学是一种生物信息学.
    • 计算生物学 计算生物学
    • 基因组学就是基因组学.

    背景情况:

    • 蛋白质功能预测对于药物发现和了解疾病机制至关重要.
    • 当前的方法往往忽略了负面的实验注释,限制了预测精度.
    • 现有的数据库主要包含积极的蛋白质功能注释.

    研究的目的:

    • 开发一种先进的深度学习方法,用于准确预测蛋白质功能.
    • 为了解决因忽视负注释而导致的精度低估问题.
    • 为了提高未观察到的功能注释的预测.

    主要方法:

    • 构建一个整合蛋白质-蛋白质相互作用,本体结构和负注释的异质图.
    • 使用异质图的注意力技术来学习蛋白质和本体学术语嵌入.
    • 重建积极的蛋白质术语关联,并对未观察到的注释进行评分.

    主要成果:

    • 提出的方法在预测人类,老鼠和阿拉比多普西斯的新蛋白质注释方面表现出卓越的表现.
    • 结合有限的负面注释显著提高了预测性能.
    • 这种方法在蛋白质功能预测方面超过了现有的最先进的方法.

    更多相关视频

    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

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    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

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    相关实验视频

    Last Updated: Jul 4, 2025

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    1.7K
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    68.7K
    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
    06:50

    Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

    Published on: January 26, 2024

    1.8K

    结论:

    • 这种新的深度学习方法有效地利用负注释来改善蛋白质功能预测.
    • 这种方法为生物信息学研究和应用提供了重大进展.
    • 准确的蛋白质功能预测对于生物发现和治疗开发至关重要.