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Protein Organization01:24

Protein Organization

8.9K
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....
8.9K
Protein Organization01:13

Protein Organization

155.4K
Overview
155.4K
Protein and Protein Structure02:15

Protein and Protein Structure

86.2K
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...
86.2K
Protein and Protein Structures02:15

Protein and Protein Structures

18.0K
18.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

13.9K
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...
13.9K

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関連する実験動画

Updated: Dec 30, 2025

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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ディープラーニングの潜在能力を利用したタンパク質構造の予測の改善

Andrew W Senior1, Richard Evans2, John Jumper2

  • 1DeepMind, London, UK. andrewsenior@google.com.

Nature
|January 17, 2020
PubMed
まとめ
この要約は機械生成です。

AlphaFoldは,残基対の距離を推定することによって,アミノ酸配列からタンパク質構造を予測するためにニューラルネットワークを使用します. この方法は,タンパク質構造の予測の精度を大幅に向上させ,タンパク質の機能を理解するのに役立ちます.

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: Dec 30, 2025

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

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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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科学分野:

  • 計算生物学
  • 構造生物学
  • バイオ情報学

背景:

  • タンパク質の構造の決定は,タンパク質の機能を理解するために不可欠です.
  • タンパク質の構造を決定する実験方法は困難で時間がかかります.
  • ホモログシーケンスの共変性などの遺伝情報を活用することで,構造の予測が改善されました.

研究 の 目的:

  • タンパク質の構造を正確に予測するための新しい方法を開発する.
  • ディープラーニングを用いた既存のタンパク質構造予測技術を改良する.

主な方法:

  • ニューラルネットワークは アミノ酸残留の距離を予測するために訓練されました
  • 予測された距離情報を用いて平均力のポテンシャルを構成した.
  • グラデント降下最適化はタンパク質構造を生成するために使用されました.

主要な成果:

  • アルファフォールドシステムは,タンパク質構造の予測において高い精度を達成しました.
  • AlphaFoldは,タンパク質構造予測の批判的評価 (CASP13) で他の方法を上回った.
  • この方法は,限られた同類データを持つ配列でも有効であることが示された.

結論:

  • アルファフォールドは 計算によるタンパク質構造の予測における 重要な進歩を表しています
  • 精度の向上により タンパク質の機能や機能不全の洞察が容易になります
  • このアプローチは,実験的に決定された同類構造がないタンパク質にとって特に価値があります.