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bioRxiv · 10.64898/2026.09.24.753989

AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification

QIAO, J.·Ma, X.·Song, Y.·Deng, Q.·Ni, X.·Liu, G.·Meng, S.·Shi, F.·Deng, L.·Cui, H.·Li, X.
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Abstract

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 (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

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QIAO, J., Ma, X., Song, Y., Deng, Q., Ni, X., Liu, G., Meng, S., Shi, F., Deng, L., Cui, H., Li, X.. 2026-09-24. AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification. https://doi.org/10.64898/2026.09.24.753989

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