A deep learning and generative modeling pipeline for mining and engineering alkaline-stsable xylanases.
TL;DR
A transfer learning-based predictor for acidophilic and alkalophilic proteins, trained on a curated non-redundant dataset, and an integrated pipeline for mining and engineering alkalophilic and thermophilic enzymes, combining sequence-based prediction, generative modeling, and multi-parameter virtual screening are developed.
Abstract
Extremozymes offer substantial potential as biocatalysts in industrial biotechnology, yet their identification and optimization remain challenging. Here, we developed AAEPre, a transfer learning-based predictor for acidophilic and alkalophilic proteins, trained on a curated non-redundant dataset. AAEPre achieved an average accuracy of 0.80 and outperformed conventional machine learning approaches. Based on this model, we developed an integrated pipeline for mining and engineering alkalophilic and thermophilic enzymes, combining sequence-based prediction, generative modeling, and multi-parameter virtual screening. This strategy enabled the discovery of a novel xylanase, 8E20, with optimal activity at 55 °C and pH 8.0, followed by large-scale in silico diversification to generate 1000,000 variants. Systematic screening identified the superior variant 8E20-178, which exhibits a 1.9-fold increase in catalytic activity, a shift in optimal pH from 8.0 to 10.0, and improved alkaline stability. Structural analysis suggests that strengthened hydrophobic interactions and charge redistribution contribute to its improved alkali tolerance. Notably, 8E20-178 has strong potential for practical use, including pulp biobleaching and beating. The AAEPre model now is available at http://106.8.105.46:10152/, and is free for users. Collectively, our work presents a generalizable and experimentally validated computational framework for enzyme discovery and optimization under extreme conditions.