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相关概念视频

Protein Families02:47

Protein Families

15.8K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
15.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
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...
13.3K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Mechanical Protein Function01:58

Mechanical Protein Function

2.1K
2.1K
Protein and Protein Structure02:15

Protein and Protein Structure

81.4K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
81.4K
Conserved Binding Sites01:49

Conserved Binding Sites

4.4K
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: Sep 13, 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

Published on: November 3, 2011

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机器学习用于蛋白质功能预测.

Yi-Heng Zhu1, Zi Liu2, Yu Ding1

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.

Methods in molecular biology (Clifton, N.J.)
|July 29, 2025
PubMed
概括
此摘要是机器生成的。

准确的蛋白质功能预测对于理解细胞过程和疾病机制至关重要. 本综述对包括深度学习在内的计算方法进行了分类,以加快功能注释,克服实验限制.

关键词:
深度学习是一种深度学习.基因存在学 基因存在学机器学习 机器学习蛋白质功能的预测和预测模板检测 模板检测 模板检测

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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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:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 了解蛋白质功能对于理解细胞过程和疾病机制至关重要.
  • 实验性蛋白质功能注释是精确的,但耗时且昂贵.
  • 需要高效的计算方法来准确预测蛋白质功能.

研究的目的:

  • 审查和分类著名的计算蛋白质功能预测方法.
  • 讨论这些预测方法的应用.
  • 突出基因本体学 (GO) 术语在功能预测中的重要性.

主要方法:

  • 将方法分为基于模板检测,基于统计机器学习,基于深度学习和组合方法的方法.
  • 通过基因本体学 (GO) 术语定义的蛋白质功能的突出的计算预测器的审查.

主要成果:

  • 对蛋白质功能预测的各种计算方法的识别和分类.
  • 讨论各种预测模型的优势和应用.

结论:

  • 计算方法为实验性蛋白质功能注释提供了有效的替代方案.
  • 机器学习和深度学习的进步正在提高预测的准确性.
  • 精确的蛋白质功能预测有助于疾病研究和药物设计.