絶対的拘束自由エネルギー計算によるリガンド選択性の予測
Matteo Aldeghi1, Alexander Heifetz2, Michael J Bodkin2
1Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford , South Parks Road, Oxford OX1 3QU, U.K.
Journal of the American Chemical Society
|December 24, 2016
まとめ
薬剤結合選択性を予測することは,安全な薬剤開発に不可欠です. 分子ダイナミクスを用いた自由エネルギー計算は,複数のタンパク質標的間の阻害剤結合親和性を正確に予測する見込みを示しています.
科学分野:
- コンピュータ化学
- 薬物の発見
- 構造生物学
背景:
- 薬物選択性は安全性と有効性にとって不可欠です.
- 結合選択性を計算的に予測することは依然として重要な課題です.
- 現在の方法論は薬剤設計において一般的適用性が欠けている.
研究 の 目的:
- 結合選択性を予測するための分子動力学に基づく自由エネルギー計算の性能を評価する.
- 異なるブロモドメインファミリーにおけるこれらの計算の正確性を評価する.
- 計算上の予測と実験的同熱定位カロメトリーデータを比較する.
主な方法:
- アルケミカル経路を用いた絶対結合自由エネルギー計算.
- リガンド-タンパク質の親和性を推定するための分子動力学シミュレーション.
- 多重標的におけるブロモドメイン阻害剤の結合プロフィールの分析
主要な成果:
- 最初のケーススタディにおける7つのブロモドメインの実験データと (0. 81 kcal/ mol誤差,0. 75の相関) の優れた一致性.
- リガンドの結合方向を正確に推定する.
- 22のブロモドメインの広範囲阻害剤に対する適度な精度 (1.76 kcal/ mol誤差,0. 48の相関),再パラメトリゼーションにより改善された.
結論:
- 自由エネルギー計算は,薬物結合選択性を予測するための厳格な枠組みを提供します.
- 方法論は合理的な薬剤設計と標的の検証に 大きな可能性を秘めている.
- リパラメトリゼーション戦略は,計算上の予測の精度を高めることができます.
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