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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Ligand Binding Sites02:40

Ligand Binding Sites

14.9K
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...
14.9K
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K
Conserved Binding Sites01:49

Conserved Binding Sites

1.9K
1.9K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

64.0K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
64.0K
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K

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相关实验视频

Updated: Jan 18, 2026

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

Published on: January 26, 2024

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协作学习宏观结合趋势和微观残留相互作用,以预测-蛋白相互作用.

Li Zeng, Yang Liu, Zu-Guo Yu

    IEEE journal of biomedical and health informatics
    |September 8, 2025
    PubMed
    概括

    MMPepPro是一个新的计算框架,通过整合宏观层面的结合亲和力和微观层面的残留相互作用来增强-蛋白相互作用预测. 这种双层方法提高了治疗性类药物开发的准确性.

    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:

    • -蛋白相互作用对于治疗药物开发至关重要.
    • 识别这些相互作用的传统实验方法耗时且资源密集.
    • 需要计算方法来准确地预测-蛋白相互作用,无论是在分子和残留水平.

    研究的目的:

    • 开发一种新的计算框架,MMPepPro,用于准确预测蛋白相互作用.
    • 为了全面建模,将宏观层面的结合亲和力与微观层面的残留物相互作用特征集成.
    • 克服现有的单级预测方法的局限性.

    主要方法:

    • 开发了MMPepPro,一个双层生物特征协作互动学习框架.
    • 集成的分子级和氨基酸级特征用于全面的建模.
    • 在19187个蛋白复合物的数据集上训练模型.

    主要成果:

    • 与最先进的方法相比,MMPepPro在所有评估指标上都表现出卓越的表现.
    • 该模型在预测蛋白相互作用方面取得了很高的准确性.
    • 在四个额外的数据集中验证了通用化性能.

    更多相关视频

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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    Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance
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    Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance

    Published on: August 26, 2025

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    相关实验视频

    Last Updated: Jan 18, 2026

    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

    2.5K
    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
    10:58

    Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

    Published on: July 25, 2013

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    Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance
    10:07

    Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance

    Published on: August 26, 2025

    544

    结论:

    • 在计算性蛋白相互作用预测方面,MMPepPro提供了显著的进步.
    • 双层方法提高了预测的准确性和普遍性.
    • 这种方法可以加速开发基于的治疗方法.