Biocatalytic oxyfunctionalization of unsaturated fatty acids to oxygenated chemicals via hydroxy fatty acids DOI

Deok‐Kun Oh,

Tae‐Eui Lee, Jin Lee

et al.

Biotechnology Advances, Journal Year: 2024, Volume and Issue: unknown, P. 108510 - 108510

Published: Dec. 1, 2024

Language: Английский

Machine learning meets enzyme engineering: examples in the design of polyethylene terephthalate hydrolases DOI

Rohan Ali,

Yifei Zhang

Frontiers of Chemical Science and Engineering, Journal Year: 2024, Volume and Issue: 18(12)

Published: Sept. 10, 2024

Language: Английский

Citations

3
Protein representations: Encoding biological information for machine learning in biocatalysis DOI Creative Commons
David Harding-Larsen, Jonathan Funk, Niklas Gesmar Madsen

et al.

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 77, P. 108459 - 108459

Published: Oct. 2, 2024

Language: Английский

Citations

3
Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity DOI Creative Commons

Zi-Lin Li,

Shuxin Pei, Ziying Chen

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 10, 2024

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving enantioselectivity biocatalyst towards target substrates often time resource intensive. Although machine learning has been used to reveal underlying relationship between protein sequences biocatalytic enantioselectivity, establishment substrate fitness space usually disregarded by chemists still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry geometry descriptors build random forest classification models predicting amidase new substrates. We further propose heuristic strategy based on these models, which rational engineering can be efficiently performed synthesize compounds with higher ee values, optimized variant results 53-fold E-value comparing wild-type amidase. This data-driven methodology expected broaden application biocatalysis research.

Language: Английский

Citations

3
Accelerating enzyme discovery and engineering with high-throughput screening DOI Creative Commons
Eray Ulaş Bozkurt, Emil C. Ørsted, Daniel C. Volke

et al.

Natural Product Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Recent progress in the DBTL cycle, including machine learning, facilitated enzyme mining for biocatalysis. Automation and standardization of library construction, coupled to high-throughput screening, further accelerates discovery process.

Language: Английский

Citations

2
Biocatalytic oxyfunctionalization of unsaturated fatty acids to oxygenated chemicals via hydroxy fatty acids DOI

Deok‐Kun Oh,

Tae‐Eui Lee, Jin Lee

et al.

Biotechnology Advances, Journal Year: 2024, Volume and Issue: unknown, P. 108510 - 108510

Published: Dec. 1, 2024

Language: Английский

Citations

2