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相关概念视频

Clathrin Coated Vesicles01:12

Clathrin Coated Vesicles

9.7K
Clathrin-coated vesicles use endocytosis to transport receptors and lysosomal hydrolases from the Golgi to the lysosome in the late secretory pathway. Clathrin-mediated endocytosis was the first described endocytic process, and Clathrin-coated vesicles remain one of the most well-studied transport vesicles. The molecular machinery that generates clathrin-coated vesicles comprises over 50 proteins that precisely coordinate vesicle formation. Cell surface receptors concentrated in indented sites...
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COP Coated Vesicles00:59

COP Coated Vesicles

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Membrane-enclosed structures called vesicles transport proteins and lipids across the cell. The vesicles derive their cargo from the plasma membrane, Golgi, ER, or endosome. Coated vesicles are spherical, protein-coated carriers with a 50–100 nm diameter that mediate bidirectional transport between the ER and the Golgi. The distribution of proteins between the ER and Golgi complex is dynamic and is maintained by different coated vesicles. Their formation is driven by the assembly of...
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Pinching-off of Coated Vesicles01:32

Pinching-off of Coated Vesicles

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Vesicle budding is orchestrated by distinct cytosolic proteins such as adaptor proteins, coat proteins, and GTPases. To initiate vesicle budding, membrane-bending proteins containing crescent-shaped BAR domains bind to the lipid heads in the bilayer and distort the membrane to form a protein-coated vesicle bud. Adaptors proteins such as AP2 for clathrin-coated vesicles can nucleate on the deformed membrane. Finally, coat proteins such as clathrin or COPI and COPII assemble into a coat forming...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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: Feb 26, 2026

Visualizing Clathrin-mediated Endocytosis of G Protein-coupled Receptors at Single-event Resolution via TIRF Microscopy
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一个可解释的深度学习模型用于使用DCT增强的位置特定得分矩阵预测克拉特林蛋白.

Ali Ghulam1, Tarique Ali2, Rahu Sikander3

  • 1Information Technology Centre, Sindh Agriculture University, Tandojam, Sindh, Pakistan.

Current drug targets
|February 24, 2026
PubMed
概括
此摘要是机器生成的。

这项研究介绍了Pred-CLGRUs,这是一个用于预测Clathrin蛋白 (CP) 功能的计算框架. 该模型实现了93.33%的准确性,证明了它在生物医学研究中对疾病相关蛋白质分析的潜力.

关键词:
囊神经网络是一个神经网络.克拉特林 (Clathrin) 是一个名为克拉特林的词汇.克拉特林蛋白 (CP) 是一种蛋白质.离散的等号变换.有门的经常性单位.长期短期记忆 长期短期记忆职位特定的分数 ma-trix 的分数.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 蛋白质结构和功能预测预测

背景情况:

  • 克拉特林蛋白 (CP) 对于细胞内细胞形成和信号传导等细胞过程至关重要.
  • 脑脊髓功能障碍与神经退行性疾病和癌症等疾病有关.
  • 准确的CP预测对于生物医学研究和疾病理解至关重要.

研究的目的:

  • 开发一种新的计算框架,Pred-CLGRUs,用于预测Clathrin蛋白.
  • 为了增强特征提取和减少噪音,使用位置特定得分矩阵 (PSSM) 和离散等边变换 (DCT).
  • 在前CLGRU框架内评估Gated Recurrent Units (GRUs) 的表现.

主要方法:

  • 使用位置特定得分矩阵 (PSSM) 来获取进化信息,以及离散等边变换 (DCT) 来增强特征.
  • 作为主要的深度学习架构,使用门式循环单位 (GRU).
  • 训练并将GRU与卷积神经网络 (CNN),长期短期记忆 (LSTM) 网络和深度神经网络 (DNN) 相比较.

主要成果:

  • 与GRU一起的Pred-CLGRUs框架实现了93.33%的准确性.
  • 取得了0.86的马修斯相关系数,表明了强大的预测性能.
  • 在Clathrin蛋白预测中表现出高灵敏度 (94.44%) 和特异性 (92.22%).

结论:

  • PSSM-DCT方法有效地增强了用于CP预测的特征提取.
  • 普雷德-CLGRUs模型显示了准确预测克拉特林蛋白的巨大潜力.
  • 未来的工作包括整合异质生物数据和探索像囊神经网络 (CapsNet) 这样的先进模型.