Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase
Abstract
Carbonic anhydrases are among the fastest known biocatalysts, reversibly facilitating the hydration of CO2 to HCO3- at rates up to 107 s-1, which warrants their investigation for industrial carbon capture technologies. However, engineering carbonic anhydrases to maintain stability under harsh industrial process conditions remains a key challenge, and sequence-to-function datasets compatible with machine learning to inform forward engineering are lacking. Here, we developed a high-throughput platform that couples cell-free gene expression with a gaseous CO2 colorimetric assay to map the fitness landscapes of carbonic anhydrases. From 96 diverse natural homologs, we identified a robust variant from the Aquificota phylum and conducted an exhaustive mutational scan and functional assessment of this enzyme at 70C and 90C, covering >99% of all single-amino acid substitutions (totaling 4,365 mutations assayed in 39,285 reactions). This biochemical landscape was used to benchmark 22 zero-shot protein fitness models and identify critical mutations that improved enzyme stability at 90C by more than three-fold. We then used both zero-shot protein language models and supervised learning to filter 419 model-generated variants from a ProteinMPNN library of 100,000 sequences, leading to a best-in-class enzyme that retained activity after incubation at 95C. This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.
Lifecycle
- biorxiv v1 2026-07-08 source ↗
Discussion
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AI x Bio Discovery @aixbiobot.bsky.social · 787 followers neutral
Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase [new] ...for engineering enhanced thermostability and benchmarking protein fitness models.
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bioRxiv Synthetic Biology @biorxiv-synthbio.bsky.social · 1641 followers neutral
Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase https://www.biorxiv.org/content/10.64898/2026.07.07.736810v1
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Ryo Yokoyama @yokoyama-ryo.bsky.social · 1043 followers neutral
Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase www.biorxiv.org/content/10.6...
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bioRxivpreprint @biorxivpreprint.bsky.social · 8895 followers neutral
Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase https://www.biorxiv.org/content/10.64898/2026.07.07.736810v1