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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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Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
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...
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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
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

3.8K
3.8K
Protein Organization01:24

Protein Organization

6.4K
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....
6.4K

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相关实验视频

Updated: Jun 26, 2025

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

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利用机器学习模型来预测-蛋白相互作用.

Song Yin1, Xuenan Mi2, Diwakar Shukla1,2,3

  • 1Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign Urbana 61801 Illinois USA diwakar@illinois.edu.

RSC chemical biology
|May 10, 2024
PubMed
概括

对生物过程和药物开发至关重要. 机器学习模型现在提供了对蛋白相互作用的高效和准确的预测,克服了传统的计算挑战.

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Identifying Protein-protein Interaction Sites Using Peptide Arrays
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相关实验视频

Last Updated: Jun 26, 2025

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

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Identifying Protein-protein Interaction Sites Using Peptide Arrays
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Identifying Protein-protein Interaction Sites Using Peptide Arrays

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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:

  • 生物化学和分子生物学
  • 计算生物学 计算生物学
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 在生物活动中至关重要,调解高达40%的蛋白质与蛋白质相互作用.
  • 它们的特异性和有效性使得对药物开发具有吸引力.
  • 由于高成本和数据限制,计算预测蛋白复合体具有挑战性.

研究的目的:

  • 为预测蛋白相互作用提供机器学习和深度学习模型的全面审查.
  • 突出机器学习对传统计算方法的优势.

主要方法:

  • 关于机器学习和深度学习模型的最新文献审查,用于预测蛋白相互作用.
  • 分析这些模型的能力和局限性.

主要成果:

  • 机器学习模型为预测蛋白相互作用提供了高效,准确和强大的解决方案.
  • 这些模型解决了传统计算方法的局限性,例如对接和分子动力学模拟.

结论:

  • 机器学习和深度学习在预测蛋白相互作用方面取得了重大进展.
  • 这些模型是理解生物过程和加速药物发现的重要工具.