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相关概念视频

Protein and Protein Structure02:15

Protein and Protein Structure

88.2K
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...
88.2K
Structural Protein Function01:56

Structural Protein Function

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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
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Structural Protein Function01:56

Structural Protein Function

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Protein and Protein Structures02:15

Protein and Protein Structures

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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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相关实验视频

Updated: Feb 8, 2026

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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蛋白质结构预测方法 预测方法

Samantha K Teixeira1, Angélica N Lima2, Pedro Túlio Resende-Lara3,4

  • 1Laboratório de Genética e Cardiologia Molecular, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. samantha.teixeira@hc.fm.usp.br.

Advances in experimental medicine and biology
|February 6, 2026
PubMed
概括
此摘要是机器生成的。

蛋白质结构预测使用计算生物学来从氨基酸序列中确定3D蛋白质结构. 最近的深度学习模型,如AlphaFold2,实现了接近实验的准确性,推进了药物发现和酶工程.

关键词:
终端到终端的模型.免费建模 免费建模蛋白质语言模型的模型基于模板的建模.

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Published on: February 23, 2024

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相关实验视频

Last Updated: Feb 8, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

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RNA Secondary Structure Prediction Using High-throughput SHAPE
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RNA Secondary Structure Prediction Using High-throughput SHAPE

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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:

  • 计算生物学 计算生物学
  • 结构生物学 结构生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 预测蛋白质结构对于理解生物功能至关重要,这源于蛋白质折叠问题.
  • 在过去的四十年中,方法学已经发生了显著的进化,从基于模板的建模到先进的深度学习.
  • 序列决定了三维结构,这是分子生物学中的一个关键关系.

研究的目的:

  • 探索蛋白质结构预测方法的原理,进展和影响.
  • 要突出从传统方法向现代深度学习方法的演变.
  • 讨论这些创新在各种生物研究领域的应用.

主要方法:

  • 基于模板的建模 (TBM) 使用序列同质和线程.
  • 免费建模 (FM) 采用基于物理学的原理进行新结构预测.
  • 先进的混合和端到端深度学习方法 (例如,AlphaFold2,RoseTTAFold) 使用神经网络.

主要成果:

  • 深度学习方法,特别是端到端方法,在预测原子坐标方面已经实现了接近实验的准确性.
  • 蛋白质语言模型直接从序列中学习序列结构功能关系.
  • 创新正在彻底改变结构生物学和相关领域.

结论:

  • 现代蛋白质结构预测方法,特别是深度学习,已经改变了计算生物学.
  • 这些进步使我们能够更深入地理解序列结构功能范式.
  • 应用范围涵盖药物发现,酶工程和疾病研究,强调了该领域的影响.