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GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization

Published on: October 24, 2011

A deep learning and generative modeling pipeline for mining and engineering alkaline-stsable xylanases.

Ruohan Zhang¹, Yiyang Zhang², Zhonghao Deng³

¹State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.

Bioresource Technology

|July 3, 2026

Summary

We developed AAEPre, a predictor for extremophilic proteins, to discover and engineer novel enzymes. This computational framework successfully identified a superior xylanase variant with enhanced alkaline activity for industrial applications.

Keywords:

AAEPre model Alkaline stability Alkalophilic and thermophilic xylanase Biological papermaking Extremozymes

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Area of Science:

Biotechnology
Enzyme Engineering
Computational Biology

Background:

Extremozymes are valuable biocatalysts for industrial biotechnology but are difficult to identify and optimize.
Current methods for enzyme discovery and engineering under extreme conditions are limited.

Purpose of the Study:

To develop a computational tool, AAEPre, for predicting acidophilic and alkalophilic proteins.
To establish an integrated pipeline for mining and engineering extremozymes.
To discover and optimize novel alkalophilic and thermophilic enzymes for industrial use.

Main Methods:

Developed AAEPre, a transfer learning-based predictor for acidophilic and alkalophilic proteins.
Integrated sequence-based prediction, generative modeling, and multi-parameter virtual screening.
Employed in silico diversification and systematic screening for enzyme optimization.

Main Results:

AAEPre achieved 0.80 average accuracy, outperforming conventional machine learning.
Discovered a novel xylanase (8E20) with activity at 55°C and pH 8.0.
Engineered variant 8E20-178 showed a 1.9-fold activity increase, shifted optimal pH to 10.0, and improved alkaline stability.

Conclusions:

The developed computational framework is generalizable and experimentally validated for enzyme discovery and optimization under extreme conditions.
The engineered xylanase variant 8E20-178 shows significant potential for industrial applications like pulp biobleaching.
The AAEPre model is publicly available for researchers.