Shedding light on the undiscovered pathways of yeast metabolism

IF 48.3 1区化学 Q1 CHEMISTRY, PHYSICAL

Nature Catalysis Pub Date : 2026-04-28 DOI:10.1038/s41929-026-01529-4

Vikas D. Trivedi, Steve Mathew D A

引用次数: 0

Abstract

What if substantial biochemical information remains absent from current genome-scale metabolic models? By merging retrobiosynthesis with deep learning, Yeast-MetaTwin uncovers promiscuous reactions and predicts degradation routes with tangible engineering consequences.

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揭示酵母未被发现的代谢途径

如果目前的基因组尺度代谢模型中仍然缺少大量的生化信息，那该怎么办？通过将逆转录生物合成与深度学习相结合，Yeast-MetaTwin发现了混杂反应，并预测了具有实际工程后果的降解途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Catalysis Chemical Engineering-Bioengineering

CiteScore

52.10

自引率

1.10%

发文量

140

期刊介绍： Nature Catalysis serves as a platform for researchers across chemistry and related fields, focusing on homogeneous catalysis, heterogeneous catalysis, and biocatalysts, encompassing both fundamental and applied studies. With a particular emphasis on advancing sustainable industries and processes, the journal provides comprehensive coverage of catalysis research, appealing to scientists, engineers, and researchers in academia and industry. Maintaining the high standards of the Nature brand, Nature Catalysis boasts a dedicated team of professional editors, rigorous peer-review processes, and swift publication times, ensuring editorial independence and quality. The journal publishes work spanning heterogeneous catalysis, homogeneous catalysis, and biocatalysis, covering areas such as catalytic synthesis, mechanisms, characterization, computational studies, nanoparticle catalysis, electrocatalysis, photocatalysis, environmental catalysis, asymmetric catalysis, and various forms of organocatalysis.