Learning deep representations of enzyme thermal adaptation
Journal article, 2022
deep neural networks
bioinformatics
enzyme catalytic temperatures
protein thermostability
transfer learning
optimal growth temperatures
Author
Gang Li
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Filip Buric
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Jan Zrimec
National Institute of Biology Ljubljana
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Sandra Viknander
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Jens B Nielsen
BioInnovation Institute
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Aleksej Zelezniak
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Faculty of Life Sciences & Medicine
Vilnius University
Martin Engqvist
Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Enginzyme AB
Protein Science
0961-8368 (ISSN) 1469896x (eISSN)
Vol. 31 12 e4480Using AI to unravel "DNA grammar" for synthetic biology applications
Swedish Research Council (VR) (2019-05356), 2020-01-01 -- 2024-12-31.
Subject Categories
Biochemistry and Molecular Biology
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
DOI
10.1002/pro.4480
PubMed
36261883