無秩序なタンパク質を利用した設計:物理ベースのAI活用(Order from disordered proteins)

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2025-10-06 ハーバード大学

ハーバード大学とノースウェスタン大学の研究者は、物理モデルを基盤とした機械学習手法により、「天然変性タンパク質(IDP)」を設計できるアルゴリズムを開発した。自動微分を利用し、アミノ酸配列を望ましい性質に最適化することで、従来AI(例:AlphaFold)が扱えなかった構造不定のタンパク質を精密に設計可能にした。成果は疾患研究や創薬に新たな展望を開くもので、『Nature Computational Science』誌に掲載。

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本質的に無秩序なタンパク質の配列-アンサンブル-機能関係の一般化設計 Generalized design of sequence–ensemble–function relationships for intrinsically disordered proteins

Ryan K. Krueger,Michael P. Brenner & Krishna Shrinivas
Nature Computational Science  Published:06 October 2025
DOI:https://doi.org/10.1038/s43588-025-00881-y

無秩序なタンパク質を利用した設計:物理ベースのAI活用(Order from disordered proteins)

Abstract

The design of folded proteins has advanced substantially in recent years. However, many proteins and protein regions are intrinsically disordered and lack a stable fold, that is, the sequence of an intrinsically disordered protein (IDP) encodes a vast ensemble of spatial conformations that specify its biological function. This conformational plasticity and heterogeneity makes IDP design challenging. Here we introduce a computational framework for de novo design of IDPs through rational and efficient inversion of molecular simulations that approximate the underlying sequence–ensemble relationship. We highlight the versatility of this approach by designing IDPs with diverse properties and arbitrary sequence constraints. These include IDPs with target ensemble dimensions, loops and linkers, highly sensitive sensors of physicochemical stimuli, and binders to target disordered substrates with distinct conformational biases. Overall, our method provides a general framework for designing sequence–ensemble–function relationships of biological macromolecules.

生物化学工学
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