大規模な化学プロテオミクスは,リガンドの発見を加速し,細胞におけるリガンドの行動を予測する
Fabian Offensperger1, Gary Tin1, Miquel Duran-Frigola1,2
1CeMM, Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
まとめ
研究者は407個の小分子断片の タンパク質の相互作用をマッピングし 以前はターゲットにされていなかった タンパク質の薬剤を発見するための 資源を生み出しました これは化学プロテオミクスと 無薬プロテオームの薬剤発見を進めています
科学分野:
- 化学生物学
- プロテオミクス
- 薬物の発見
背景:
- タンパク質の化学的調節は 生物学を理解し 治療法を開発する鍵です
- 人間のプロテオームの80%は 機能的に結合していません 広範な研究にもかかわらずです
- 現在の化学プロテオミクスの方法は,スケーラブルな断片-タンパク質相互作用の発見のためにスループットの制限に直面しています.
研究 の 目的:
- 小分子断片の多様なセットのタンパク質結合傾向のプロテオーム全体のマップを作成します.
- 特定された断片タンパク質の相互作用の有用性を化学探査機の開発で実証する.
- 細胞環境内の断片の行動を予測するための機械学習を統合する.
主な方法:
- 407の構造的に多様な小分子断片をタンパク質に結合させるためのスクリーニング.
- 特定された相互作用をE3ユビキチンリガゼ,トランスポーター,キナーゼの活性化学プローブに先導することによって検証する.
- 断片-タンパク質の相互作用と細胞の行動を予測するために,機械学習のバイナリ分類器を使用します.
主要な成果:
- 407個の小分子断片に対するタンパク質結合傾向を詳細に示すプロテオーム全体のマップを生成した.
- 特定された断片-タンパク質の相互作用を成功裏に検証し,活性化学探査機の開発につながった.
- セルラーシステムにおける断片の行動を予測するための解釈可能な機械学習モデルを開発した.
結論:
- 断片-タンパク質の相互作用と予測モデルの開発されたリソースは,分子認識の研究を容易にする.
- この研究は,これまで未薬のタンパク質のリガンド発見を早め,ヒトタンパク質の大きなギャップを埋める.
- この発見は,化学プロテオミクスの進歩と,新しい治療法の開発のための基盤を提供します.
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