IQSPred-PLM:タンパク質言語モデルに基づく解釈可能なクオラムセンシングペプチド予測モデル
Yusen Su1, Qingyang Guo1, Taigang Liu2
1College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China.
Interdisciplinary sciences, computational life sciences
|August 26, 2025
まとめ
この研究では,クオラムセンシングペプチド (QSP) の予測のための新しいモデルであるIQSPred-PLMを導入し,タンパク質言語モデルとコンボリューションニューラルネットワークを使用しています. このモデルは 重要な細菌のシグナル伝達分子を 高い精度で特定します
科学分野:
- 微生物学
- バイオ情報学
- コンピューター生物学
背景:
- クオラムセンシング (QS) は,細菌の協同行動を制御する細胞間通信メカニズムです.
- 定数感知ペプチド (QSP) は,特にグラム陽性細菌における重要なシグナル伝達分子であり,毒性やバイオフィルム形成などの機能に影響を与えます.
- 既存のQSP予測ツールは,パフォーマンスと解釈性の強化を必要とする.
研究 の 目的:
- QSPを予測するための新しい高性能モデルを開発する.
- QSPの識別の正確性と解釈性を向上させる.
- 細菌のシグナル伝達分子の予測に 最先端のディープラーニング技術を活用する
主な方法:
- タンパク質言語モデル (PLM) の統合,特にペプチド配列のエンコーディングのためのESM-2.
- 特徴抽出のための多スケール残留コンボリューションニューラルネットワーク (MSRes-CNN) の適用.
- アダプティブ・ウェイト・モジュール (AWM) を使ったダイナミックな機能統合で,その後に完全に接続された分類ネットワークが使用されます.
主要な成果:
- IQSPred-PLMはベンチマークデータセットで優れた予測性能を達成しました.
- 主要な性能指標には97.50%の精度 (ACC),0.951のマシューズ相関係数 (MCC),0.990のROC曲線下の面積 (AUC) が含まれる.
- ケーススタディと解釈性分析はモデルの有効性を検証した.
結論:
- IQSPred-PLMは,QSP予測の正確性と解釈性において大きな進歩を示しています.
- このモデルの性能は,生物学的配列分析のためのPLMとCNNの統合の可能性を強調しています.
- このツールはバクテリアの伝達を理解し,標的を絞った介入策を策定するのに役立ちます.
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