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相关概念视频

Protein Folding01:25

Protein Folding

8.0K
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...
8.0K
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
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
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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
Single-pass Transmembrane Proteins01:25

Single-pass Transmembrane Proteins

5.0K
Integral membrane proteins are tightly associated with the cell membrane and play a crucial role in cell communication, signaling, adhesion, and transport of the molecules. Some integral membrane proteins are present only in the membrane monolayer. For example, the enzyme fatty acid amide hydrolase is present in the cytoplasmic side of the membrane monolayer. In contrast, another type of integral membrane protein, also known as a transmembrane protein, spans across the membrane. Transmembrane...
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Protein Organization01:13

Protein Organization

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Overview
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相关实验视频

Updated: Jun 25, 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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使用结构特征改进alpha-helical跨膜蛋白的alphaFold预测接触.

Aman Sawhney1, Jiefu Li2, Li Liao1

  • 1Department of Computer and Information Sciences, University of Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE 19716, USA.

International journal of molecular sciences
|May 25, 2024
PubMed
概括

来自AlphaFold2的预测蛋白质3D结构可以直接改善残留物接触预测. 利用这些模型的结构特征显著提高了接触预测的准确性,超过了当前最先进的方法.

关键词:
阿尔法螺旋的螺旋阿尔法折叠是什么意思阿尔法折叠联系人地图预测预测机器学习是机器学习.神经网络的神经网络的神经网络蛋白质结构 蛋白质结构蛋白质结构建模模型跨膜蛋白质是一种跨膜蛋白质.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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相关实验视频

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科学领域:

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

背景情况:

  • 残留物接触图对于了解蛋白质结构和功能至关重要.
  • 传统的接触预测仅依赖于顺序特征.
  • 最近在3D结构预测方面的进展,如AlphaFold2,提供了新的机会.

研究的目的:

  • 为了评估AlphaFold2的直接使用,预测3D结构用于残留接触预测.
  • 研究利用3D结构特征来改善接触预测.
  • 开发和测试用于增强残留物接触预测的新方法.

主要方法:

  • 利用基准数据集进行螺旋间残留物接触预测.
  • 使用AlphaFold2预测结构评估接触预测的准确性.
  • 开发了一种从残留对附近的原子结构中提取特征的方法.
  • 训练模型使用实验确定和AlphaFold2预测结构的特征.

主要成果:

  • 直接使用AlphaFold2结构产生了83%的平均精度,超过了顺序方法.
  • 整合3D结构特征显著改善了接触预测.
  • 在持有集中达到超过91.9%的平均精度,在交叉验证中达到89.5%.

结论:

  • 预测的3D蛋白质结构对直接残留接触预测有价值.
  • 利用3D结构特征提供了一种强大的方法来提高接触预测的准确性.
  • 这项工作超过了AlphaFold2在残留物接触预测任务中的表现.