タンパク質の動態を予測する新技法、創薬に大きなブレークスルーをもたらす可能性(New technique for predicting protein dynamics may prove big breakthrough for drug discovery)

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2024-03-27 ブラウン大学

ブラウン大学の研究チームは、機械学習を使用してタンパク質の構造を迅速に予測する方法を開発し、タンパク質のダイナミクスと機能の理解を進めることができると述べています。この手法は正確で迅速、コスト効果が高く、新しい治療法のための多くのターゲットを明らかにする可能性があります。彼らはタンパク質の多様な構造を迅速に予測し、それらの構造がどれだけ頻繁に現れるかも予測できるようになりました。これにより、タンパク質の構造と機能に関する科学的理解が向上し、疾患の治療法開発を加速させることが可能となります。

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サブサンプリングAlphaFold2によるタンパク質のコンフォメーション分布のハイスループット予測 High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

Gabriel Monteiro da Silva,Jennifer Y. Cui,David C. Dalgarno,George P. Lisi & Brenda M. Rubenstein
Nature Communications  Published:27 March 2024
DOI:https://doi.org/10.1038/s41467-024-46715-9

タンパク質の動態を予測する新技法、創薬に大きなブレークスルーをもたらす可能性(New technique for predicting protein dynamics may prove big breakthrough for drug discovery)

Abstract

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins’ ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.

有機化学・薬学
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