Advancing Enzyme’s Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning
importance 4/10
ai assisted
Protein Structure and Dynamics industrial biocatalysis stage: research
Problem
Industrial enzymes require thermostability, but designing stable variants of haloalkane dehalogenase DhaA115 is challenging.
Why it matters
Enzyme instability limits industrial biocatalysis applications.
What the AI did
Automated platforms FireProt and PROSS designed mutations; machine learning filtered out destabilizing mutations, improving prediction accuracy and yielding stable enzymes.
Results
Highly stable haloalkane dehalogenases with enhanced catalytic properties; no numeric metrics stated
Impact
ML curation improved automated enzyme stabilization designs for a challenging target.
Record
| Venue | ACS Catalysis |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Antonín Kunka, Sérgio M. Marques, Martin Havlásek, Michal Vasina, Nikola Velatova, Lucia Cengelova, David R. Kovar, Jiřı́ Damborský et al. |
| Citations | 75 (OpenAlex, at import time) |
| References | 43 |
| Model or method | FireProt, PROSS; machine learning (unnamed) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1021/acscatal.3c02575 |
| Open access | hybrid |
| Keywords | enzyme stabilization, machine learning, FireProt, PROSS, haloalkane dehalogenase, thermostability |
Importance rating
4
real but narrower advance
Thermostability is called essential for bioindustry enzymes, but abstract addresses one enzyme (DhaA115) and method improvement; no scale stated.