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相关概念视频

Protein Organization01:24

Protein Organization

6.3K
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....
6.3K
Protein and Protein Structure02:15

Protein and Protein Structure

79.2K
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...
79.2K
Protein and Protein Structures02:15

Protein and Protein Structures

10.4K
10.4K
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
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
10.8K
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...
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相关实验视频

Updated: Jun 17, 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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在蛋白质结构预测中使用人工智能驱动的深度学习技术.

Lingtao Chen1, Qiaomu Li1, Kazi Fahim Ahmad Nasif1

  • 1College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA.

International journal of molecular sciences
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

这篇评论探讨了计算式蛋白质结构预测,强调了从传统方法到尖端人工智能 (AI) 模型 (如AlphaFold) 的进步. 它评估了AI.

关键词:
阿尔法折叠是什么意思阿尔法折叠人工智能的人工智能是人工智能.生物信息学是一种生物信息学.计算方法 计算方法深度学习是一种深度学习.医疗保健 医疗保健 医疗保健 医疗保健机器学习是机器学习.蛋白质建模模型中的蛋白质.蛋白质结构 蛋白质结构变压器变压器变压器变压器

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 生命科学中的人工智能

背景情况:

  • 蛋白质结构预测对于理解蛋白质的功能和行为至关重要.
  • 传统的方法包括同质模型,ab initio模型和线程.
  • 这一领域随着人工智能 (AI) 的整合而迅速发展.

研究的目的:

  • 为蛋白质结构预测提供计算模型的全面审查.
  • 涵盖从已建立的蛋白质建模技术到最先进的AI框架的演变.
  • 讨论这些模型的性能和应用.

主要方法:

  • 审查已建立的蛋白质建模技术 (同质,初始,线程).
  • 基于深度学习的人工智能模型的分析 (AlphaFold,RoseTTAFold,ProteinBERT).
  • 使用CASP和CAMEO排名和指标 (TM-score,GDT_TS,lDDT) 的模型性能比较.

主要成果:

  • 人工智能模型,特别是深度学习方法,显著提高了蛋白质结构预测的准确性.
  • 已建立的框架已经整合了人工智能技术,提高了他们的能力.
  • 性能比较显示,通过CASP和CAMEO评估,预测准确度取得了实质性的进展.

结论:

  • 人工智能已经彻底改变了蛋白质结构预测,实现了前所未有的准确性.
  • 在预测动态蛋白质行为,结构变化和相互作用方面仍然存在挑战.
  • 未来的研究方向包括探索这些复杂的动态,并扩大药物设计和开发中的应用.