Journal article

Publication year: 2021

Pages: 223-

Journal: Communications Biology

Volume number: 4

Issue number: 1

eISSN: 2399-3642

Open access status: Gold

DOI Link: https://doi.org/10.1038/s42003-021-01715-z

Publisher: Nature Research

Abstract: 
Enzymes empower chemical industries and are the keystone for metabolic engineering. For example, linalool synthases are indispensable for the biosynthesis of linalool, an important fragrance used in 60-80% cosmetic and personal care products. However, plant linalool synthases have low activities while expressed in microbes. Aided by bioinformatics analysis, four linalool/nerolidol synthases (LNSs) from various Agaricomycetes were accurately predicted and validated experimentally. Furthermore, we discovered a linalool synthase (Ap.LS) with exceptionally high levels of selectivity and activity from Agrocybe pediades, ideal for linalool bioproduction. It effectively converted glucose into enantiopure (R)-linalool in Escherichia coli, 44-fold and 287-fold more efficient than its bacterial and plant counterparts, respectively. Phylogenetic analysis indicated the divergent evolution paths for plant, bacterial and fungal linalool synthases. More critically, structural comparison provided catalytic insights into Ap.LS superior specificity and activity, and mutational experiments validated the key residues responsible for the specificity. Zhang et al. identified four linalool/nerolidol synthases from fungi using bioinformatics and phylogenetic analysis and validated their functions with in vitro and in vivo methods. One of them is a rare and highly specific monoterpene synthase and responsible for impressive titres of the commercially sought-after fragrance (R)-linalool.

Harvard Citation style: Zhang, C., Chen, X., Lee, R., Rehka, T., Maurer-Stroh, S. and Rühl, M. (2021) Bioinformatics-aided identification, characterization and applications of mushroom linalool synthases, Communications Biology, 4(1), Article 223. p. 223. https://doi.org/10.1038/s42003-021-01715-z

APA Citation style: Zhang, C., Chen, X., Lee, R., Rehka, T., Maurer-Stroh, S., & Rühl, M. (2021). Bioinformatics-aided identification, characterization and applications of mushroom linalool synthases. Communications Biology. 4(1), Article 223, 223. https://doi.org/10.1038/s42003-021-01715-z