ArticleNature chemistry2026
De novo chemo-optogenetics through the rational design of photoresponsive molecules and selection of their artificial protein binding pairs.
Article in Nature chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
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Authors and funding
12 authors.
Funding
Abstract
Optical manipulation of proteins is central to probing and engineering cellular functions. However, existing optogenetic tools based on natural photoreceptors and chemo-optogenetic tools based on natural protein-ligand pairs are difficult to reconfigure for the desired photochemical and binding properties. Here we introduce a de novo approach for creating chemo-optogenetic tools. Instead of modifying pre-existing ligands, we first design a synthetic photoswitch with defined properties and then use mRNA display to select for artificial protein binders that recognize a specific photoisomer-dependent conformation. This bottom-up framework yields artificial photoswitch-protein binder pairs that enable precise optical control of diverse biological activities in mammalian cells, such as kinase and lipid signalling, G-protein-coupled receptor activation, gene expression and cell differentiation. The regulatory mode (sustained, reversible or repeatable) is readily programmed by adjusting light inputs. This de novo approach provides customizable synthetic photoswitch-protein binder pairs, expanding opportunities for optical protein manipulation in biological and biomedical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.