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相关概念视频

Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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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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Conservation of Protein Domains Over Different Proteins02:26

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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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Proteomics01:33

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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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...
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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....
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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使用ProteinMPNN进行基于深度学习的稳健蛋白序列设计

J Dauparas1,2, I Anishchenko1,2, N Bennett1,2,3

  • 1Department of Biochemistry, University of Washington, Seattle, WA, USA.

Science (New York, N.Y.)
|September 15, 2022
PubMed
概括
此摘要是机器生成的。

新的深度学习方法ProteinMPNN在蛋白质序列设计方面表现出色, 在序列恢复方面超过了Rosetta. 这种先进的工具成功地重新设计了各种蛋白质结构,包括纳米粒子和结合蛋白,经过实验研究验证.

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科学领域:

  • 计算生物学
  • 蛋白质工程
  • 深度学习

背景情况:

  • 传统的新型蛋白质设计依赖于基于物理的方法,
  • 深度学习已经改变了蛋白质结构的预测,

研究的目的:

  • 介绍基于深度学习的蛋白质序列设计方法ProteinMPNN.
  • 与现有方法相比,证明其优越的性能.
  • 在各种蛋白质设计挑战中展示它的多功能性.

主要方法:

  • 开发了ProteinMPNN,一种用于蛋白序列设计的深度学习模型.
  • 对原生蛋白骨进行性能评估,与罗塞塔的序列恢复进行比较.
  • 将该方法应用于各种复杂的蛋白质结构,包括单体,寡体,纳米粒子和结合蛋白.

主要成果:

  • 在本地脊椎上,ProteinMPNN实现了52.4%的序列恢复,明显高于Rosetta的32.9%.
  • 该方法可以在单个和多个链中进行合序列设计.
  • 使用X射线结晶学,冷电子显微镜和功能研究成功挽救了以前失败的设计,并验证了新的设计.

结论:

  • ProteinMPNN提供了一种强大而准确的深度学习方法,用于新的蛋白序列设计.
  • 它处理复杂的多链设计的能力扩大了蛋白质工程的范围.
  • 实验验证证了该方法在各种蛋白质设计应用中的高效性和准确性.