大型言語モデルを用いた抗原特異のペアチェーン抗体の生成
Perry T Wasdin1, Nicole V Johnson2, Alexis K Janke3
1Program in Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Center for Computational Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Center for Antibody Therapeutics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Cell
|November 5, 2025
まとめ
人工知能 (AI) は抗体発見を進めている. 新しいモデルであるMAGE (モノクローナル抗体生成器) は,SARS-CoV-2,H5N1,RSV-Aのような複数のウイルスに対する新しいヒト抗体を設計する.
科学分野:
- バイオテクノロジー
- 免疫学
- コンピュータ生物学
背景:
- 伝統的な抗体発見は非効率で費用がかかり,成功率は低い.
- 人工知能 (AI) は,抗体最適化と生成のための新しいアプローチを提供します.
- 既存のAI方法は,標的アグノスティックな方法で抗体配列を生成することができます.
研究 の 目的:
- 抗体配列生成のための新しいAIツールであるMAGE (モノクローナル抗体生成器) を紹介します.
- 配列ベースのタンパク質言語モデル (PLM) を精密に調整して,ペア化されたヒト変量重鎖および軽鎖抗体配列を生成する.
- 特定のウイルス標的に対する抗体を設計する MAGE の能力を実証する.
主な方法:
- 配列ベースのタンパク質言語モデル (PLM) のMAGEの開発.
- ヒトの重鎖と軽鎖の抗体配列をペアリングするためにPLMを微調整する.
- SARS-CoV-2,H5N1,RSV-Aに対する生成された抗体結合特異性の実験的検証.
主要な成果:
- MAGEは新規で多様な抗体配列を成功裏に生成した.
- SARS-CoV-2,鳥インフルエンザH5N1,RSV-Aに対するMAGE生成抗体の結合特異性は実験的に検証されました.
- MAGEの能力が証明された 複数の標的に対するヒトの抗体を設計する スタートテンプレートなしで
結論:
- MAGEは,複数の標的に対するヒトの抗体を設計するための,第一級のAIモデルです.
- このモデルは,従来の抗体発見方法の限界を克服しています.
- MAGEは,標的抗体の治療薬の急速な開発の可能性を示しています.
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