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相关概念视频

Protein Folding01:25

Protein Folding

8.1K
Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
8.1K
Protein and Protein Structure02:15

Protein and Protein Structure

79.7K
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...
79.7K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.5K
VSEPR Theory for Determination of Electron Pair Geometries
34.5K
Conserved Binding Sites01:49

Conserved Binding Sites

1.7K
1.7K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.9K
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...
10.9K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

381
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
381

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相关实验视频

Updated: Jul 20, 2025

Combining Wet and Dry Lab Techniques to Guide the Crystallization of Large Coiled-coil Containing Proteins
11:14

Combining Wet and Dry Lab Techniques to Guide the Crystallization of Large Coiled-coil Containing Proteins

Published on: January 6, 2017

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CoCoNat:一种基于深度学习的新方法,用于线圈-线圈预测.

Giovanni Madeo1, Castrense Savojardo1, Matteo Manfredi1

  • 1Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy.

Bioinformatics (Oxford, England)
|August 4, 2023
PubMed
概括

CoCoNat准确地预测了卷轴-卷轴域 (CCD) 边界,残留物注册表和寡合化状态. 这种新的深度学习方法超越了CCD计算检测和功能注释的当前最先进的工具.

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 预测蛋白质结构的方法

背景情况:

  • 卷曲-卷曲域 (CCD) 是所有生物体中发现的关键蛋白质结构.
  • 精确的CCD计算检测对于蛋白质功能注释至关重要.
  • 现有的方法专注于CCD边界,七度重复模式和寡合化状态预测.

研究的目的:

  • 引入CoCoNat,一种用于预测CCD边界,残留水平注册表和寡合化状态的新型计算方法.
  • 通过先进的深度学习技术,提高CCD预测的准确性和效率.

主要方法:

  • CoCoNat采用了两种最先进的蛋白质语言模型的组合来进行序列编码.
  • 使用三步深度学习程序,然后使用语法限制的隐藏条件随机场来识别和改进CCD.
  • 最后一个神经网络用于预测寡合化状态.

主要成果:

  • 与当前最先进的方法相比,CoCoNat在标准盲测试中实现了优异的性能,用于残留水平和细分水平的CCD预测.
  • 该方法在注册表注释和预测寡合化状态方面显著优于现有的方法.
  • CoCoNat在识别卷轴螺旋边界及其特征图案方面表现出高度准确性.

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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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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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相关实验视频

Last Updated: Jul 20, 2025

Combining Wet and Dry Lab Techniques to Guide the Crystallization of Large Coiled-coil Containing Proteins
11:14

Combining Wet and Dry Lab Techniques to Guide the Crystallization of Large Coiled-coil Containing Proteins

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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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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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结论:

  • CoCoNat代表了对卷轴-卷轴域的计算预测的重大进步.
  • 该方法为蛋白质功能注释和结构分析提供了强大的工具.
  • CoCoNat 的卓越性能为研究蛋白质结构功能关系提供了新的可能性.