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Updated: Sep 9, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

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diPaRIS: ダイナミックで解釈可能なタンパク質-RNA相互作用の予測とU型ネットワークとノベル構造のエンコーディング

Lishen Zhang^1,2,3, Chengqian Lu⁴, Xiaoqing Peng⁵

¹School of Computer Science and Engineering, Central South University, Changsha, 410083, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|August 29, 2025

まとめ

ディープラーニングツールであるdiPaRISは,インビヴォのRNA構造を統合することで,ダイナミックなタンパク質-RNA相互作用を正確に予測します. この方法は遺伝子と病気のつながりや生物学的過程の理解を深めるものです

科学分野:

分子生物学
コンピュータ生物学
ゲノミクス

背景:

タンパク質とRNAの相互作用は,生物学的プロセスと病気にとって極めて重要です.
既存の計算方法はRNA構造内の核酸相関を捉えるのに苦労しています
これらの相互作用の正確な予測は,遺伝子機能と病気を理解するために不可欠です.

研究の目的:

ダイナミックなタンパク質とRNAの相互作用を予測するためのディープラーニング方法であるdiPaRISを開発する.
タンパク質とRNAの相互作用の予測の正確性と解釈性を向上させる.
予測力を高めるために,in vivo RNAの構造情報を統合する.

主な方法:

U型ネットワークアーキテクチャを利用したディープラーニングモデル diPaRISを開発した.
ニュクレオチド相関を捕捉するためのSHAPE-seqデータのための新しいエンコーディングスキームを導入した.
統合された in vivo RNA 構造を包括的に表現する.

主要な成果:

diPaRISは44のデータセットで優れたパフォーマンスを示し,高い精度,AUC,AUPR,F1スコアを達成しました.
このモデルは細胞間の予測に優れ,既存の方法を上回った.
シーケンス・バインディング・モチーフとアトリビューション・マップを含む解釈可能な分析を生成した.

結論:

diPaRISは,ダイナミックなタンパク質- RNAの相互作用を正確に予測し,解釈性を向上させます.
この方法は,保存された結合パターンと遺伝子変異の機能的解釈の洞察を提供します.
発見は複雑な疾患における遺伝子疾患関連性の理解を容易にする.

キーワード:

タンパク質とRNAの相互作用 RNA構造のエンコーディングディープラーニング解釈可能な分析

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