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関連する概念動画

From DNA to Protein03:06

From DNA to Protein

22.6K
The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
22.6K
Single-Strand DNA Binding Proteins01:03

Single-Strand DNA Binding Proteins

16.8K
For successful DNA replication, the unwinding of double-stranded DNA must be accompanied by stabilization and protection of the separated single strands of the DNA. This crucial task is performed by single-strand DNA-binding (SSB) proteins. They bind to the DNA in a sequence-independent manner, which means that the nitrogenous bases of the DNA need not be present in a specific order for binding of SSB proteins to it. The binding of SSB proteins straightens single-stranded DNA (ssDNA) and makes...
16.8K
Mutations01:39

Mutations

94.7K
Overview
94.7K
Protein Families02:47

Protein Families

17.2K
Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
17.2K
Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

583
Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
The physicochemical properties of a drug play a significant role in its ability to bind to proteins. Lipophilic drugs, which dissolve in fats, oils, and lipids, can be...
583
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

9.7K
Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
9.7K

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関連する実験動画

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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iDLDDG:統合されたディープラーニング機能を使用して,DNA結合タンパク質の誤った変異によるタンパク質の安定性の変化を予測する.

Xuan Yu1, Fang Ge2,3, Dong-Jun Yu3

  • 1Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong SAR(HKG), 999077, China.

Briefings in bioinformatics
|February 13, 2026
PubMed
まとめ

DNA結合タンパク質の誤解変異を予測することは,病気を理解する上で極めて重要です. 私たちの新しいディープラーニングフレームワークであるiDLDDGは,DNA結合タンパク質の双鎖および単鎖への影響を正確に区別し,突然変異の予測を改善します.

キーワード:
DNA結合タンパク質はDNAを結合するタンパク質です.バイオインフォマティックスディープラーニングとは,ディープラーニングです.ミッセンセスの変異はミッセンセスの変異です.タンパク質とDNAの相互作用

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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A Protocol for Computer-Based Protein Structure and Function Prediction
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関連する実験動画

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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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科学分野:

  • ゲノミクスとバイオインフォマティクス
  • 分子生物学は分子生物学である.
  • コンピュータ生物学 コンピュータ生物学

背景:

  • タンパク質-DNA結合親和性に対するミスセンス変異の影響を正確に予測することは,疾患メカニズムの研究と治療開発に不可欠です.
  • 既存のモデルは,二重鎖のDNA結合タンパク質 (DSBs) と単一鎖のDNA結合タンパク質 (SSBs) の突然変異の特異的な特徴をしばしば説明できない.

研究 の 目的:

  • DSBとSSBの両方のタンパク質-DNA結合親和性に対するミスセンス変異の影響を正確に予測するためのコンピューティングフレームワークを開発する.
  • DSBとSSBの間の突然変異メカニズムを厳密に区別する方法を確立する.

主な方法:

  • 様々な情報源から包括的なデータセットを構築しました.
  • iDLDDGを開発し,シーケンスベースのエンブレディング (ESM2,ProtTrans,ESM1v) を複数スケールの構造的および進化的情報と統合したディープラーニングフレームワークである.
  • エントロピーベースのアルゴリズムを使用して,生体物理学的制約をモデル化するための181の最適な残留物を特定し,予測の正確性と効率性を向上させました.

主要な成果:

  • iDLDDGは,MPD276データセットで0.755の10倍クロス検証ピアソン相関係数 (PCC) を得て,最先端のパフォーマンスを達成しました.
  • DSBとSSBの両方をカバーする独立したテストセットで0.632のPCCを達成し,既存の方法を大幅に上回りました.
  • DSBとSSBの間の変異メカニズムを区別するフレームワークの能力を実証しました.

結論:

  • iDLDDGは,DNA結合タンパク質の病変学的変異の高精度予測のための基礎を提供します.
  • この研究は,DSBとSSBの変異メカニズムを厳密に区別できる最初のコンピューティングフレームワークを確立しています.
  • 予測精度と計算効率の向上により,DNA結合タンパク質における突然変異効果の大規模評価が可能です.