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

Mutations01:39

Mutations

83.4K
Overview
83.4K
Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

10.7K
The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
10.7K
Allosteric Proteins-ATCase01:19

Allosteric Proteins-ATCase

5.8K
Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
Aspartate transcarbamoylase (ATCase) is a cytosolic enzyme that catalyzes the condensation of L-aspartate and carbamoyl phosphate to  N-carbamoyl-L-aspartate. This reaction is the first step in pyrimidine biosynthesis. UTP and CTP, the end products of the pyrimidine synthesis...
5.8K
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

30
Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
30
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.5K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.5K

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

Reinforcement learning control of quantum error correction.

Nature·2026
Same author

Accelerating scientific discovery with Co-Scientist.

Nature·2026
Same author

Advancing conversational diagnostic AI with multimodal reasoning.

Nature medicine·2026
Same author

Advancing regulatory variant effect prediction with AlphaGenome.

Nature·2026
Same author

Novel Interventional Techniques for Lumbar and Cervical Pain.

International anesthesiology clinics·2025
Same author

Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2025

関連する実験動画

Updated: Jul 16, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.0K

AlphaMissenseでプロテオーム全体のミスセンスの変異効果を正確に予測する

Jun Cheng1, Guido Novati1, Joshua Pan1

  • 1Google DeepMind, London, UK.

Science (New York, N.Y.)
|September 21, 2023
PubMed
まとめ

AlphaMissenseは,進化的および構造的データを用いて,人間のミスセンスの多様性の臨床的重要性を予測します. このツールは変異の89%を分類し,遺伝子研究と遺伝子の本質を理解するのに役立ちます.

さらに関連する動画

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

10.6K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.7K

関連する実験動画

Last Updated: Jul 16, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.0K
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

10.6K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.7K

科学分野:

  • ゲノミクス
  • バイオ情報学
  • コンピュータ生物学

背景:

  • ヒトのミセンス変異のほとんどは 臨床的意義が不明で 遺伝疾患の研究を妨げています
  • 変異の病原性を正確に予測することは,臨床的解釈と遺伝的変異の理解に不可欠です.

研究 の 目的:

  • ミッセンスの多様性の病原性を予測するための新しい計算ツールであるAlphaMissenseの開発と検証.
  • ヒトゲノムの多様性予測に関する包括的なデータベースを作成する.
  • 変異性の病原性と遺伝子の本質性との関係を調べる

主な方法:

  • AlphaFoldの適応であるAlphaMissenseは,ヒトと霊長類の変異集団の周波数データベースを使用して微調整されました.
  • このモデルは病原性を予測するために構造的文脈と進化的保存を統合しています.
  • 性能は様々な遺伝的および実験的基準で評価された.

主要な成果:

  • アルファミッセンスは,ベンチマークデータに関する明確なトレーニングなしにミッセンスの変異の病原性を予測する最先端の性能を達成しています.
  • 遺伝子の平均病原性スコアは細胞の本質性を予測し,他の方法では見逃された重要な遺伝子を特定します.
  • すべての可能なヒト単一のアミノ酸置換のための予測のデータベースが提供されています.

結論:

  • アルファミッセンスはミッセンスの変異の病原性を効果的に予測し,変異の89%をおそらく良性または病原性として分類します.
  • このツールは科学界にとって貴重なリソースであり,遺伝子変異の解釈を進めています.
  • 予測的病原性スコアは,遺伝子の本質性と細胞機能の理解を高める.