Data-driven enzyme engineering to identify function-enhancing enzymes
importance 7/10
ai assisted
Enzyme Catalysis and Immobilization biocatalysis stage: research
Problem
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science.
Why it matters
Enhanced enzymes could enable drug functionalization, plastic/pollutant degradation, and food allergy treatment.
What the AI did
Reviews how data-driven models (statistical modeling, machine learning, deep learning) have advanced prediction and design of efficiency-enhancing enzyme mutants.
Results
not stated in source
Impact
Data-driven models have enhanced understanding of enzyme sequence-structure-function relationships; challenges remain.
Record
| Venue | Protein Engineering Design and Selection |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Yaoyukun Jiang, Xinchun Ran, Zhongyue John Yang |
| Citations | 22 (OpenAlex, at import time) |
| References | 111 |
| Model or method | machine learning/deep learning (no specific model named) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1093/protein/gzac009 |
| Open access | green |
| Keywords | enzyme engineering, machine learning, deep learning, enzyme variants, biocatalysis |
Importance rating
7
significant — thousands to millions affected
'Holy grail' protein science challenge; abstract cites drug functionalization, plastic/pollutant degradation, food allergy treatment, but gives no scale.