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
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.
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.
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