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相关概念视频

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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Single-pass Transmembrane Proteins01:25

Single-pass Transmembrane Proteins

4.8K
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...
4.8K
Insertion of Multi-pass Transmembrane Proteins in the RER01:29

Insertion of Multi-pass Transmembrane Proteins in the RER

7.7K
The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
The multipass transmembrane proteins are the type IV integral membrane proteins with multiple topogenic sequences determining their spatial arrangement in the ER membrane. Nearly all multipass proteins lack a cleavable signal sequence and use...
7.7K
Insertion of Single-pass Transmembrane Proteins in the RER01:26

Insertion of Single-pass Transmembrane Proteins in the RER

6.4K
Integral membrane proteins are proteins adhered to the lipid bilayer of a cell organelle or membrane. They can be of two types: transmembrane integral proteins that span the lipid bilayer and monotopic proteins that are attached to either side of the membrane but do not pass through it.
Integral transmembrane proteins possess transmembrane and extra membrane domains. The transmembrane domains are primarily made of 20-25 hydrophobic amino acids arranged in a helical secondary confirmation. These...
6.4K
Introduction to Membrane Proteins01:16

Introduction to Membrane Proteins

65.8K
The cell membrane, or plasma membrane, is an ever-changing landscape. It is described as a fluid mosaic where various macromolecules are embedded in the phospholipid bilayer. Among the macromolecules are proteins. The protein content varies across cell types. For example, mitochondrial inner membranes contain ~76% protein content, while myelin contains ~18% protein content. Individual cells contain many types of membrane proteins—red blood cells contain over 50—and different cell...
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相关实验视频

Updated: May 17, 2025

Transmembrane Domain Oligomerization Propensity determined by ToxR Assay
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Transmembrane Domain Oligomerization Propensity determined by ToxR Assay

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跨膜同质体接口识别:利用序列和结构特征预测alpha-helical跨膜蛋白同质体中的接口残留物.

Bander Almalki1, 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 14, 2025
PubMed
概括

识别蛋白界面残留物是理解细胞功能的关键. 我们的新机器学习方法通过整合序列和结构来准确预测这些残留物,优于现有的计算方法.

关键词:
分化二元化是指二元化的过程.接口位于预测预测.机器学习是机器学习.分子动力学分子动力学跨膜类同质聚合物.

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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 结构生物学 结构生物学

背景情况:

  • 比托皮性跨膜蛋白通过接口残留物形成二元体,对细胞功能至关重要.
  • 准确识别这些接口残留是关键的,但在计算上具有挑战性.
  • 现有的方法要么是通用的二元化,要么是专门用于接口残留物.

研究的目的:

  • 开发一种新的机器学习方法,用于准确预测蛋白界面残留物.
  • 整合顺序和结构特征,以提高预测性能.
  • 在识别接口残留方面超越最先进的计算方法.

主要方法:

  • 开发了一种集序和结构特征的机器学习模型.
  • 从预测的蛋白质结构和各种域中提取特征.
  • 在基准数据集上使用交叉验证验证模型.

主要成果:

  • 拟议的方法比现有的最先进的方法获得了更高的F1分数.
  • 超越了接口残留预测的一般和专用计算方法.
  • 与RoseTTAFold2和AlphaFold2Multimer等领先的多重体结构预测器相比,表现出卓越的性能.

结论:

  • 结合序列和结构特征的综合方法非常有效.
  • 开发的方法在预测蛋白界面残留物方面取得了重大进展.
  • 这项工作为了解蛋白质-蛋白质相互作用和细胞功能提供了更准确的工具.