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相关概念视频

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...
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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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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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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Updated: Jul 2, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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机器学习用于序列和基于结构的蛋白质-连接物相互作用预测.

Yunjiang Zhang1, Shuyuan Li1, Kong Meng1

  • 1Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China.

Journal of chemical information and modeling
|February 22, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型准确地预测药物向相互作用,加速药物发现. 本综述考察了用于蛋白质 - 连接体相互作用预测的计算方法,数据集和模型,突出了应用和未来方向.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.药物发现 药物发现功能工程的特点工程.机器学习 机器学习蛋白质 - 连接物结合亲和力.蛋白质 - 配体相互作用序列和结构的顺序和结构.

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相关实验视频

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

  • 计算化学和生物信息学
  • 药物的发现和开发.
  • 在药理学中的机器学习.

背景情况:

  • 药物开发是昂贵和耗时的.
  • 准确预测药物向相互作用对于有效的药物发现至关重要.
  • 机器学习 (ML) 方法在预测蛋白质-连接体相互作用方面表现有前途.

研究的目的:

  • 审查用于蛋白质 - 连接体相互作用预测的计算方法.
  • 根据序列和结构数据对ML模型进行分类和总结.
  • 讨论该领域的应用,评估,可解释性,挑战和未来方向.

主要方法:

  • 在蛋白质 - 配体相互作用研究中使用的数据集的概述.
  • 检查各种蛋白质和连接体表示方法.
  • 使用基于序列和基于结构的标准对ML模型 (经典和深度学习) 的分类.

主要成果:

  • 用于蛋白质 - 配体相互作用预测的各种ML模型的总结.
  • 讨论评估指标和模型可解释性技术.
  • 探索这些模型在药物研究中的应用.

结论:

  • 计算方法,特别是ML,提供了一个强大的方法来预测药物向相互作用.
  • 需要进一步的研究来应对当前的挑战,并推进该领域的未来方向.
  • 本综述为药物发现和计算生物学研究人员提供了全面的概述.