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相关概念视频

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...
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Protein Complex Assembly02:41

Protein Complex Assembly

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Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...
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Conservation of Protein Domains02:26

Conservation of Protein Domains

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3.1K
Mechanisms of Membrane Domain Formation00:59

Mechanisms of Membrane Domain Formation

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Different physical properties of lipids and proteins allow them to localize and form distinct islands or domains in the membrane. Some membrane domains are formed due to protein-protein interactions, whereas others are formed due to the presence of specific lipids such as sphingolipids and sterols—for example, large proteins, such as bacteriorhodopsin, aggregate and create distinct domains.
Another mechanism for membrane domain formation involves membrane proteins interacting with...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.9K
Membrane Domains01:18

Membrane Domains

5.5K
The membrane domains concentrate specific lipids and proteins at one place within the membrane, which helps in cell signaling, adhesion, and other critical cellular processes. These domains can differ in size, composition, function, and lifespan.
Protein Domains
The membrane comprises a group of distinct proteins responsible for carrying out a cell's specific function. For example, the plasma membrane of the human sperm, or a single germ cell, contains a unique set of proteins in the...
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相关实验视频

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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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E2EDA:基于端到端深度学习的蛋白质域组合.

Hai-Tao Zhu1, Yu-Hao Xia1, Gui-Jun Zhang1

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Journal of chemical information and modeling
|October 3, 2023
PubMed
概括

本研究介绍了E2EDA,这是一种用于高效和准确的多域蛋白质结构预测的深度学习方法. E2EDA通过预测域间方向来改进全链建模,在准确性和速度上优于现有方法.

科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物学是结构生物学.
  • 深度学习应用程序深度学习应用程序

背景情况:

  • 深度学习已经推进了单域蛋白质结构预测.
  • 预测多域蛋白质结构,特别是域间导向,仍然是一个重大挑战.
  • 对多域蛋白质的准确建模对于基于结构的药物发现至关重要.

研究的目的:

  • 为蛋白质域组装开发一个端到端的深度学习方法.
  • 为了提高全链蛋白质结构建模的准确性和效率.
  • 通过增强的蛋白质建模,提供对基于结构的药物发现的见解.

主要方法:

  • 开发了基于EfficientNetV2的RMNet模型,使用注意力机制来预测域间刚性运动.
  • 将预测的刚性运动转化为空间转换,用于直接的全链模型组装.
  • 设计的RMscore用于从多个组装的候选人中选择最佳模型.

主要成果:

  • 在基准测试中,E2EDA的TM平均得分为0.827,超过了SADA (0.792) 和DEMO (0.730).
  • 使用E2EDA重新组装的模型显示,与定制数据集上的AlphaFold2预测相比,TM得分高出7.0%.
  • 与SADA相比,E2EDA显著提高了效率,运行时间减少了64.7%,与AlphaFold相比减少了19.2%.

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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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结论:

  • E2EDA为多域蛋白质结构预测提供了一个高度准确和高效的端到端方法.
  • 该方法有效地捕捉了域间的方向,改进了现有的最先进的模型,如AlphaFold2.2.
  • E2EDA的速度和准确性为基于结构的药物发现研究提供了宝贵的工具.