Ancestral Sequence Reconstruction Synergized with Deep Learning Significantly Enhances the Thermophilicity and Thermostability of the Highly Active Alkaline Protease AprE
- DOI
- 10.1021/acs.jafc.6c05012
- Published
- 2026-09-15
- Container
- Journal of Agricultural and Food Chemistry
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jafc.6c05012,
title = {Ancestral Sequence
Reconstruction Synergized with
Deep Learning Significantly Enhances the Thermophilicity and Thermostability
of the Highly Active Alkaline Protease AprE},
author = {Yian Cai and Yarui Bian and Yuying Chen and Fenghua Wang and Lei Zhao and Chong Peng and Yu Li and Fuping Lu},
year = {2026},
journal = {Journal of Agricultural
and Food Chemistry},
doi = {10.1021/acs.jafc.6c05012},
url = {https://doi.org/10.1021/acs.jafc.6c05012}
}RIS
TY - JOUR TI - Ancestral Sequence Reconstruction Synergized with Deep Learning Significantly Enhances the Thermophilicity and Thermostability of the Highly Active Alkaline Protease AprE AU - Yian Cai AU - Yarui Bian AU - Yuying Chen AU - Fenghua Wang AU - Lei Zhao AU - Chong Peng AU - Yu Li AU - Fuping Lu PY - 2026 JO - Journal of Agricultural and Food Chemistry DO - 10.1021/acs.jafc.6c05012 UR - https://doi.org/10.1021/acs.jafc.6c05012 ER -
APA
Cai, Y., Bian, Y., Chen, Y., Wang, F., Zhao, L., Peng, C., Li, Y., & Lu, F. (2026). Ancestral Sequence Reconstruction Synergized with Deep Learning Significantly Enhances the Thermophilicity and Thermostability of the Highly Active Alkaline Protease AprE. Journal of Agricultural and Food Chemistry. https://doi.org/10.1021/acs.jafc.6c05012
Source records
- crossref · retrieved 2026-11-05T10:04:11.863Z