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関連する概念動画

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
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...
10.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Leaky Scanning02:28

Leaky Scanning

5.1K
During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
5.1K
Conservation of Protein Domains02:26

Conservation of Protein Domains

3.1K
3.1K

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関連する実験動画

Updated: Jun 9, 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

17.0K

リラックスした配列空間での最適化を使用してスケーラブルなタンパク質設計

Christopher Frank1,2, Ali Khoshouei1,2, Lara Fuβ1,2

  • 1Laboratory for Biomolecular Nanotechnology, Department of Biosciences, School of Natural Sciences Technical University of Munich, 85748 Garching, Germany.

Science (New York, N.Y.)
|October 24, 2024
PubMed
まとめ
この要約は機械生成です。

この研究は新しい"幻覚"ベースのタンパク質設計方法を導入し 再訓練なしに高品質のタンパク質の背骨と相互作用を効率的に作成します このアプローチは,様々なタンパク質設計の課題に対する幅広い適用性とスケーラビリティを示しています.

さらに関連する動画

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

Published on: July 14, 2015

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

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関連する実験動画

Last Updated: Jun 9, 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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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

7.2K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.7K

科学分野:

  • 計算生物学
  • タンパク質工学

背景:

  • 機械学習 (ML) 方法,特に拡散モデルは,デノボのタンパク質設計を進めている.
  • 既存の方法はしばしば異なる設計作業のための再訓練を必要とします.

研究 の 目的:

  • リラックスされたシーケンス空間での"幻覚"を用いた新しい,再訓練のないタンパク質設計アプローチを開発する.
  • 様々なスケールで高品質のタンパク質の背骨とタンパク質相互作用の効率的な設計を可能にします.

主な方法:

  • リラックスされたシーケンス空間で動作する"幻覚"ベースの生成アプローチ.
  • 100以上の設計されたタンパク質の実験生産と特徴付け.
  • 高解像度結晶構造と冷凍電子顕微鏡 (cryo-EM) を用いた検証

主要な成果:

  • 1000アミノ酸までの単鎖タンパク質を成功裏に設計し,検証した.
  • ヘテロダイマーを含む合成タンパク質の相互作用の正確な設計を証明した.
  • 設計性,範囲,スケーラビリティの高い性能を達成しました.

結論:

  • "幻覚"に基づいた方法は,現在のタンパク質設計パイプラインに効率的で汎用的な代替案を提供します.
  • リラックスした配列の最適化は,新しいタンパク質の設計と工学のための強力な戦略を提供します.
  • このアプローチはスケーラブルで,様々なタンパク質設計の問題に適用できます.