AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification
TL;DR
An artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA) and folding energy guided recombination to achieve simultaneous multi property optimization is developed.
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 °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 °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. Entry for the Table of Contents We present a computational workflow combining property specific machine learning with folding energy guided recombination to achieve simultaneous multi property optimization. By efficiently navigating vast sequence spaces, this rational design pipeline successfully tailors biocatalysts for stringent manufacturing realities, ultimately enabling a completely streamlined industrial saccharification process that bypasses bulk acid addition.