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In response, we developed iExtreme, a comprehensive database containing 1,030 genomes of extremophiles across three categories, alongside a deep learning method for their identification. This method achieved an accuracy of up to 0.99 in predicting extremophile living conditions. Through **iExtreme**, we identified 520 previously unknown extremophilic species and 4,419 extremophilic genomes from various databases. Additionally, we utilized structure-based clustering to discover novel D-psicose 3-epimerases (DPEase) and α-amylases. To further enhance enzyme activity, we developed a directed evolution method using phage-assisted non-continuous evolution in droplets. The evolved DPEase demonstrated a 3-fold increase in activity, a 5.4-fold extension in half-life, and achieved the highest reported yield of 243 g/L D-allulose.
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