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関連する概念動画

Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

5.9K
Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
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Structural Protein Function01:56

Structural Protein Function

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Conserved Binding Sites01:49

Conserved Binding Sites

4.3K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

13.0K
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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Neural Circuits01:25

Neural Circuits

1.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Updated: Sep 4, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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ディープラーニングを用いたプロテインの機能部位

Jue Wang1,2, Sidney Lisanza1,2,3, David Juergens1,2,4

  • 1Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.

Science (New York, N.Y.)
|July 21, 2022
PubMed
まとめ
この要約は機械生成です。

ディープラーニングは機能的な部位の配列を最適化することで 新しいタンパク質の構造を設計します これらの方法は 酵素や免疫因子のような 多様なタンパク質を作り 計算や実験で検証されます

さらに関連する動画

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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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

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関連する実験動画

Last Updated: Sep 4, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

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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

318

科学分野:

  • タンパク質工学
  • 計算生物学
  • バイオテクノロジー

背景:

  • タンパク質の機能は 安定した構造内の特定の残留物に依存しています
  • 望ましい機能を持つ新しいタンパク質を設計することは大きな課題です.
  • 現在の方法では 既定のタンパク質の折りたたみや構造が必要です

研究 の 目的:

  • 新しいタンパク質の設計のための ディープラーニングの方法の開発
  • 機能的なタンパク質サイトを作るために 架空のアーキテクチャを事前に指定することなく
  • 免疫原体,酵素,結合タンパク質を含む多様なタンパク質を生成する.

主な方法:

  • "制限された幻覚"を導入し, 望ましい機能的な場所のシーケンスを最適化します.
  • RoseTTAFoldを使って機能的な場所の周りに"インペインティング"を開発しました.
  • 様々な機能的なタンパク質を設計するために これらの方法を適用しました

主要な成果:

  • 免疫因子候補,受容体トラップ,金属タンパク質,酵素,タンパク質結合タンパク質を成功裏に設計した.
  • インシリコ予測と実験テストの組み合わせで検証された設計.
  • 既定の折りたたみなしに 機能的なタンパク質の構造を作り出す 能力を示した.

結論:

  • ディープラーニングは 新しいタンパク質の設計に 強力なツールを提供します
  • これらの方法により,特異な機能を備えた新種のタンパク質が作られます.
  • 開発されたアプローチは,バイオテクノロジーとタンパク質工学において広範な応用がある.