バイオメディカルの限界を打ち破る: 先進光子源がタンパク質設計を可能にする(Breaking boundaries in biomedicine: Advanced Photon Source enables protein design)

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2025-03-11 アルゴンヌ国立研究所

バイオメディカルの限界を打ち破る: 先進光子源がタンパク質設計を可能にする(Breaking boundaries in biomedicine: Advanced Photon Source enables protein design)

The crystal structure of CHD_r1 (gray) is very similar to the computational design model (colored). (Image by Linna An, et al., Science.)

ワシントン大学のデビッド・ベイカー教授らは、AIを活用して特定の小分子と高い効果で結合するタンパク質を設計する手法を開発した。米国エネルギー省アルゴンヌ国立研究所の先進光子源(APS)でその構造が解析され、科学誌『Science』に発表された。既存のセンサーは検出できる分子が限られていたが、新手法では多様なリガンドに対応可能。肝疾患のバイオマーカーや抗がん剤、ホルモンの迅速な検出が可能となり、家庭での検査や環境毒素の検出にも応用が期待される。AIとX線技術の組み合わせにより、バイオメディシンや環境科学の発展が見込まれる。

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形状補完擬似環を用いた多様な小分子の結合と検知 Binding and sensing diverse small molecules using shape-complementary pseudocycles

Linna An, Meerit Said, Long Tran, Sagardip Majumder, Inna Goreshnik, Gyu Rie Lee, David Juergens, Justas Dauparas, Ivan Anishchenko, […], and David Baker +15 authorsAuthors Info & Affiliations
Science 18 Jul 2024 Vol 385, Issue 6706 pp. 276-282
DOI: 10.1126/science.adn3780

Editor’s summary

A major current challenge in protein design is the de novo creation of selective and high-affinity small-molecule–binding proteins. One promising concept is a small domain that could be incorporated into other applications with ease. An et al. designed small, pseudocyclic peptides with a central cavity that can be expanded or reduced by changing the number of repeating units to match the size of a target ligand. High-throughput screening and computational resampling enabled the creation of second-generation binders with substantially higher affinities for four example target molecules. The authors demonstrate two applications of such binders that rely on incorporating them as a domain into a larger, de novo–designed protein: a nanopore fusion for small-molecule sensing and a split protein for chemically induced dimerization. —Michael A. Funk

Abstract

We describe an approach for designing high-affinity small molecule–binding proteins poised for downstream sensing. We use deep learning–generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.

生物工学一般
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