ArticleChemical science2026
Decoding cryptic defluorinases through a latent generative sequence landscape.
Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
The special nature of the fluorine atom imparts remarkable strength and unique physical properties to chemical bonds. Unlike man-made fluorochemicals, fluorinated natural products remain rare due to low bioavailability and toxicity of fluoride. Despite this, defluorinases have evolved in nature to cleave carbon-fluorine bonds, with the hydrolytic fluoroacetate dehalogenase being one of the most well-characterized examples. These enzymes are of fundamental interest and hold unrealized biotechnological potential, yet the scope of this unique chemistry remains underexplored in the biosphere. Here, we trained and applied a machine learning-based framework, termed latent generative landscapes (LGLs), to map the functional sequence space of the α/β-hydrolase superfamily. This approach identified 3014 putative defluorinases that were previously not annotated or plausibly misannotated. Experimental validation of selected candidates led to the reclassification of five novel defluorinases, all exhibiting high thermal stability (
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