Integrating complementary biological information for multi-objective enzyme engineering
- 1Duke University
Abstract
Enzyme catalysts are increasingly used for sustainable pharmaceutical manufacturing, but engineering industrial biocatalysts remains challenging as multiple catalytic and developability properties must be optimized simultaneously from limited experimental data. Here, we develop a machine learning-guided multi-objective design framework that integrates sparse functional measurements with complementary evolutionary and structural information to engineer the ketoreductase Gre2. The resulting designs achieved simultaneous improvements in catalytic performance, protein yield, and thermal stability, with selected designs retaining improved performance under process-relevant conditions. More broadly, our results demonstrate that integrating complementary biological information enables efficient multi-objective enzyme engineering from sparse experimental data, providing a general strategy for accelerating industrial biocatalyst development.
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BibTeX
@misc{blalock2026integrating,
title = {{Integrating complementary biological information for multi-objective enzyme engineering}},
author = {N. Blalock and Y. Sosa and J. Heuschkel and R. Li and X. Ma and S. Radomkit and H. Wu and F. Buono and J. Song and N. Pefaur and L. J. Kingsley and P. A. Romero},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.15.751856},
url = {https://www.biorxiv.org/content/10.64898/2026.09.15.751856v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Integrating complementary biological information for multi-objective enzyme engineering
AU - N. Blalock
AU - Y. Sosa
AU - J. Heuschkel
AU - R. Li
AU - X. Ma
AU - S. Radomkit
AU - H. Wu
AU - F. Buono
AU - J. Song
AU - N. Pefaur
AU - L. J. Kingsley
AU - P. A. Romero
PY - 2026
DA - 2026/09/15
PB - bioRxiv
DO - 10.64898/2026.09.15.751856
UR - https://www.biorxiv.org/content/10.64898/2026.09.15.751856v1
AB - Enzyme catalysts are increasingly used for sustainable pharmaceutical manufacturing, but engineering industrial biocatalysts remains challenging as multiple catalytic and developability properties must be optimized simultaneously from limited experimental data. Here, we develop a machine learning-guided multi-objective design framework that integrates sparse functional measurements with complementary evolutionary and structural information to engineer the ketoreductase Gre2. The resulting designs achieved simultaneous improvements in catalytic performance, protein yield, and thermal stability, with selected designs retaining improved performance under process-relevant conditions. More broadly, our results demonstrate that integrating complementary biological information enables efficient multi-objective enzyme engineering from sparse experimental data, providing a general strategy for accelerating industrial biocatalyst development.
ER -