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関連する概念動画

Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
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...
12.6K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

11.2K
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...
11.2K
Proteomics01:33

Proteomics

7.8K
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...
7.8K
Protein Families02:47

Protein Families

15.6K
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...
15.6K
Protein Organization01:24

Protein Organization

6.8K
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....
6.8K

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Updated: Aug 28, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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ProteinMPNNを使用して,ディープラーニングベースの堅牢なタンパク質配列設計

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
まとめ
この要約は機械生成です。

ProteinMPNNという新しい ディープラーニング・メソッドは タンパク質の配列設計に優れ 配列回復においてロゼッタを上回っています この高度なツールは,ナノ粒子や結合タンパク質を含む様々なタンパク質の構造を再設計し,実験研究によって検証されました.

さらに関連する動画

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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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

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関連する実験動画

Last Updated: Aug 28, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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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

Published on: January 26, 2024

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

305

科学分野:

  • コンピュータ生物学
  • タンパク質工学
  • 深層学習

背景:

  • ロゼッタのような 物理的な方法に頼っています
  • ディープラーニングは タンパク質構造の予測を 変えましたが 配列設計は まだ変わっていません

研究 の 目的:

  • ディープラーニングベースのタンパク質配列設計方法であるProteinMPNNを紹介する.
  • 既存の方法と比較して優れた性能を示します.
  • 多様なタンパク質の設計上の課題に 多様なタンパク質の設計上の課題に 多様なタンパク質の設計上の課題に 多様なタンパク質の設計上の課題に 多様なタンパク質の設計上の課題に

主な方法:

  • タンパク質配列設計のためのディープラーニングモデルであるProteinMPNNを開発した.
  • ロゼッタとシーケンスの復元を比較して,ネイティブのタンパク質の背骨の性能を評価した.
  • モノマー,オリゴマー,ナノ粒子,結合タンパク質を含む様々な複雑なタンパク質構造にこの方法を適用した.

主要な成果:

  • プロテインMPNNは,ローゼッタの32.9%よりも,ネイティブの脊椎で52.4%のシーケンス回復を達成しました.
  • この方法は,単一のチェーンや複数のチェーンで配列設計を可能にします.
  • X線結晶学,冷凍電子顕微鏡,機能研究を使用して,以前失敗した設計を成功裏に救出し,新しい設計を検証しました.

結論:

  • ProteinMPNNは,新しいタンパク質の配列設計のための強力で正確なディープラーニングアプローチを提供します.
  • 複雑で多鎖のデザインを扱う能力は タンパク質工学の範囲を広げています
  • 実験的な検証により,タンパク質設計の様々な用途において,この方法の高い有用性と正確性が確認されています.