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相关概念视频

Protein Organization01:24

Protein Organization

6.5K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.5K
Protein and Protein Structure02:15

Protein and Protein Structure

79.5K
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...
79.5K
Protein and Protein Structures02:15

Protein and Protein Structures

10.4K
10.4K
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
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
Protein Families02:47

Protein Families

15.3K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
15.3K

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相关实验视频

Updated: Jun 30, 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

Published on: November 3, 2011

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通过整合蛋白质语言模型来预测单个序列蛋白质结构.

Xiaoyang Jing1, Fandi Wu1,2, Xiao Luo3,4

  • 1MoleculeMind Ltd., Beijing 100084, China.

Proceedings of the National Academy of Sciences of the United States of America
|March 20, 2024
PubMed
概括

一种名为RaptorX-Single的新方法仅使用单个序列来预测蛋白质结构,其性能优于对抗体和具有有限同类蛋白质的现有工具. 这种深度学习方法可以在没有多个序列对齐的情况下推进蛋白质结构预测.

关键词:
抗体结构预测 抗体结构预测蛋白质语言模型蛋白质结构预测 蛋白质结构预测单个突变效应的单一突变效应.单个序列蛋白质结构的修订

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An Integrated Approach for Microprotein Identification and Sequence Analysis
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相关实验视频

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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:

  • 深度学习已经显著提升了蛋白质结构预测.
  • 目前的领先方法,如AlphaFold2,需要多重序列对齐 (MSA),这在生物学上并不代表天然蛋白质折叠.
  • 需要使用无MSA蛋白质结构预测方法.

研究的目的:

  • 开发和评估RaptorX-Single,一种基于单个序列的新型蛋白质结构预测方法.
  • 为了比较RaptorX-Single与基于MSA和其他无MSA方法的性能.
  • 研究蛋白质语言模型对预测准确性的影响.

主要方法:

  • 多种蛋白质语言模型与结构生成模块的集成.
  • 开发RaptorX-Single,这是一个深度学习框架,用于无MSA蛋白质结构预测.
  • 在各种蛋白质数据集上对AlphaFold2和其他无MSA预测器进行比较分析.

主要成果:

  • 与基于MSA的方法相比,RaptorX-Single显示了显著更快的计算时间.
  • 该方法实现了对抗体,少数同类蛋白质和单一突变效应的卓越预测准确性.
  • 性能受到底层蛋白质语言模型的规模和训练数据的影响.
  • 即使与基于MSA的AlphaFold2相比,RaptorX-Single也显示出具有丰富同类蛋白质的竞争性结果.

结论:

  • RaptorX-Single为蛋白质结构预测提供了可行和高效的替代方案,特别是在MSA无法或有限的场景中.
  • 该研究强调了蛋白质语言模型特征对预测性能的重要性.
  • 这种没有MSA的方法扩大了深度学习在结构生物学中的适用性.