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Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast

The need to mitigate and substitute the use of fossil fuels as the main energy matrix has led to the study and development of biofuels as an alternative. Second-generation (2G) ethanol arises as one biofuel with great potential, due to not only maintaining food security, but also as a product from economically interesting crops such as energy-cane. One of the main challenges of 2G ethanol is the inefficient uptake of pentose sugars by industrial yeast Saccharomyces cerevisiae, the main organism used for ethanol production. Understanding the main drivers for xylose assimilation and identify novel and efficient transporters is a key step to make the 2G process economically viable.

Fiamenghi, M. B., Bueno, J. G. R., Camargo, A. P., Borelli, G., Carazzolle, M. F., Pereira, G. A. G., … & José, J. (2022). Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast. Biotechnology for biofuels and bioproducts, 15(1), 1-15.
https://link.springer.com/article/10.1186/s13068-022-02153-7
 
 

 

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