Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Epistasis Analysis01:09

Epistasis Analysis

5.7K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.7K
Epistasis01:39

Epistasis

50.1K
In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
50.1K
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K
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
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

4.4K
4.4K
Protein Networks02:26

Protein Networks

4.5K
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,...
4.5K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

CYClones: A highly powered, fully genotyped, 8-parent yeast mapping population.

G3 (Bethesda, Md.)·2026
Same author

Functional profiling of 2,193 ASS1 missense variants: Insights into variant pathogenicity and epistatic interactions in citrullinemia type I.

PLoS genetics·2026
Same author

Pediatric liver transplantation for metabolic diseases: a single-center experience.

World journal of pediatrics : WJP·2026
Same author

Author Correction: De novo design of quasisymmetric two-component protein cages.

Nature·2026
Same author

Resources and care structures for management of urea cycle disorders in Japan and the United States: A system-level comparison.

Molecular genetics and metabolism·2026
Same author

De novo design of quasisymmetric two-component protein cages.

Nature·2026
Same journal

Chemotactic self-organization captures the dynamics of mammalian hair follicle patterning.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Tomographic imaging of superconducting order using particle-hole interference.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Analytical modeling for suction cup designs for skin-interfaced wearable devices.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Improving cell-free metabolism through direct integration of artificial respiratory chains.

Proceedings of the National Academy of Sciences of the United States of America·2026
関連記事をすべて見る

関連する実験動画

Updated: Jan 18, 2026

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

7.7K

タンパク質間における構造論理に基づいたエピスタシスの予測

Michelle Tang1, Gareth A Cromie1, Anowarul Kabir2

  • 1Pacific Northwest Research Institute, Seattle, WA 98122.

Proceedings of the National Academy of Sciences of the United States of America
|January 16, 2026
PubMed
まとめ
この要約は機械生成です。

遺伝子内相補は、エピスタシスの形態であり、機能喪失変異のペアからタンパク質の機能を回復させます。機械学習モデルは、この現象を正確に予測し、遺伝子変異の影響を理解することで個別化医療を支援します。

キーワード:
エピスタシス機械学習変異効果

さらに関連する動画

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

2.5K
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

949

関連する実験動画

Last Updated: Jan 18, 2026

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

7.7K
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

2.5K
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

949

科学分野:

  • 遺伝学および分子生物学
  • 計算生物学
  • 生化学

背景:

  • 遺伝子変異の表現型結果の予測は、個別化医療にとって重要です。
  • エピスタシス相互作用、特に遺伝子内相補のような正のエピスタシスは、これらの予測を複雑にします。
  • 遺伝子内相補は、タンパク質機能を回復させる機能喪失変異のペアを含みます。

主な方法:

  • ASLにおける遺伝子内相補相互作用を同定するために、酵母における変異スキャンを利用しました。
  • タンパク質言語モデル埋め込みを活用する機械学習アルゴリズムを採用しました。
  • フマル酸水和酵素のような関連酵素へのモデルの精度と一般化可能性を検証しました。

結論:

  • 遺伝子内相補は、活性部位の形成に関連する構造的基盤を持っています。
  • 機械学習フレームワークは、遺伝子内相補を正確に予測できます。
  • この予測フレームワークは、少なくとも4%の人タンパク質に適用可能であり、個別化医療を進歩させる可能性があります。