AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification
Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum…
Glucoamylase plays a critical role in the industrial production of starch-derived sugars, but its performance is hindered by low thermostability and limited tolerance to slightly acidic conditions. Manufacturers typically have to cool down and acidify the reaction mixture to improve enzyme activity. Researchers have now developed an artificial intelligence-based approach to simultaneously enhance three key properties of glucoamylase derived from the fungus Penicillium oxalicum.
Two machine-learning models pinpointed specific amino acid substitutions that boost thermostability and pH stability. Screening identified beneficial variants within the candidate set. The optimal recombinant variant, PoGA T513E/Q305N, showed a 2.21-fold increase in catalytic efficiency compared to the parental enzyme. At elevated temperatures and alkaline pH, the engineered enzyme maintained activity for longer periods, leading to higher glucose production from starch without the need for acidification.
The study demonstrates a powerful method for jointly optimizing enzyme performance for industrial applications.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
