菌類におけるIII型ポリケチド合成体の機能的多様性を解明する
Nika Sokolova1, Stepan S Denisov2, Thomas Hackl3
1Department of Chemical and Pharmaceutical Biology, University of Groningen, Antonius Deusinglaan 1, Groningen, 9713AV, The Netherlands.
Angewandte Chemie (International ed. in English)
|September 4, 2025
まとめ
菌類の3型ポリケチド合成酵素 (T3PKS) を特徴づける新しい方法を開発し 新しい酵素特異性を発見し 活性性を予測する機械学習モデルを作成しました
科学分野:
- 生物化学と分子生物学
- 酵素学
- バイオ情報学
背景:
- タイプIIIポリケチド合成酵素 (T3PKS) は,生態学的および臨床的に重要な多様な化合物を生成する重要な酵素です.
- 植物では広く研究されているが,キノコのT3PKSはほとんど特徴づけられていないため,その可能性に対する理解は限られている.
- 菌類のT3PKSの広大な酵素的可能性を調査するには,包括的なアプローチが必要である.
研究 の 目的:
- 菌類のタイプIIIポリケチド合成酵素 (T3PKSs) の特徴づけのための王国全体のワークフローを確立する.
- 菌類T3PKSの基板特異性と製品範囲を調査する.
- T3PKSの活性に対する予測的な機械学習モデルを開発し,新しい酵素を特定する.
主な方法:
- 公的な真菌ゲノムから1000以上の推定T3PKS酵素のバイオインフォマティック・マイニング.
- 酵素活性部位の構造とゲノム周辺の分析
- 細胞フリー表現と37の代表的なT3PKS候補のプロトタイプ化
- 特殊性の予測のために生成された酵素基板ペアデータを用いた機械学習モデル開発.
- 自然および非自然基板によるモデルの実験的検証.
主要な成果:
- 特徴づけられた真菌T3PKSにおける独特の基板とサイクル化の特異性の特定.
- 異なる酵素に対する好ましいマロニル-コエンザイムA拡張数の決定
- T3PKSの基質特異性を予測するための機械学習モデルの開発と実験的検証.
- 予測モデルを用いたキノコにおける2つの追加の乱交性T3PKSの発見.
結論:
- 開発されたワークフローは,キノコのT3PKSの包括的な特徴付けを可能にします.
- 機械学習モデルは,T3PKSのシリコンスクリーニングと発見のための強力なツールを提供します.
- T3PKS菌の製品範囲の洞察は,天然製品発見と合成生物学アプリケーションのための貴重な出発点を提供します.
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