Harnessing catalytic knowledge to guide enzyme discovery and evolution with AlphaEnzyme
- 1Shanghai Jiao Tong University
Abstract
Retrieving an enzyme for a target reaction provides a starting point for evolution, but predicting how to improve it remains difficult. This requires connecting evolutionary information with the structural and chemical context of catalysis. Here we present AlphaEnzyme, an enzyme foundation model built on a structure-grounded representation of an enzyme paired with its reaction, from which catalytic compatibility, kinetics and mutational response are read out together. On a time-separated benchmark, AlphaEnzyme achieves a top-1 catalyst retrieval success rate of 30.1%, against 17.2% for the strongest baseline. Michaelis constant predictions achieve the highest rank correlations among the compared methods, reaching 0.64 for enzymes sharing at most 40% identity with the kinetic training set. The model also enriches high-fitness TrpB variants at a budget of 16 candidates without target-specific fitness training. In prospective transaminase engineering, several prioritized variants exceed the parent, with the best design yielding a 30.4-fold increase in product signal in a lysate-based assay while retaining (R)-selectivity. Together, AlphaEnzyme provides a common molecular basis for selecting starting enzymes and prioritizing sequence changes for their improvement.
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BibTeX
@misc{liu2026harnessing,
title = {{Harnessing catalytic knowledge to guide enzyme discovery and evolution with AlphaEnzyme}},
author = {Y. Liu and S. Zheng},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.04.764416},
url = {https://www.biorxiv.org/content/10.64898/2026.11.04.764416v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Harnessing catalytic knowledge to guide enzyme discovery and evolution with AlphaEnzyme
AU - Y. Liu
AU - S. Zheng
PY - 2026
DA - 2026/11/04
PB - bioRxiv
DO - 10.64898/2026.11.04.764416
UR - https://www.biorxiv.org/content/10.64898/2026.11.04.764416v1
AB - Retrieving an enzyme for a target reaction provides a starting point for evolution, but predicting how to improve it remains difficult. This requires connecting evolutionary information with the structural and chemical context of catalysis. Here we present AlphaEnzyme, an enzyme foundation model built on a structure-grounded representation of an enzyme paired with its reaction, from which catalytic compatibility, kinetics and mutational response are read out together. On a time-separated benchmark, AlphaEnzyme achieves a top-1 catalyst retrieval success rate of 30.1%, against 17.2% for the strongest baseline. Michaelis constant predictions achieve the highest rank correlations among the compared methods, reaching 0.64 for enzymes sharing at most 40% identity with the kinetic training set. The model also enriches high-fitness TrpB variants at a budget of 16 candidates without target-specific fitness training. In prospective transaminase engineering, several prioritized variants exceed the parent, with the best design yielding a 30.4-fold increase in product signal in a lysate-based assay while retaining (R)-selectivity. Together, AlphaEnzyme provides a common molecular basis for selecting starting enzymes and prioritizing sequence changes for their improvement.
ER -