A new paradigm in lignocellulolytic enzyme cocktail optimization: Free from expert-level prior knowledge and experimental datasets

Le Gao, Zhuohang Yu, Shengjie Wang, Yuejie Hou, Shouchang Zhang, Chichun Zhou, Xin Wu

Abstract:

Effectively pairing diverse lignocellulolytic enzyme cocktails with intricately structured lignocellulosic substrates
is an enduring challenge for science and technology. To date, extensive trial-and-error remains the primary
approach and no deep-learning methods were developed to address it due to limited experimental data and
incomplete expert-level knowledge of enzyme-cocktail-substrate structure-dynamics-function relationships.
Here, a novel model is developed to tackle this issue in efficient, cost-effective, and high-throughput manners. It
needs no pre-labeled datasets, instead utilizing simple features, eliminating the reliance on expert-level prior
knowledge of reaction mechanisms. Experimentally optimal combinations were found within predicted ranges of
tailor-made combinations with precision of 91.98%, covering 80.00% of overall top-100. Practical tests
demonstrated its effectiveness in narrowing down potential optimal combinations, speeding up targeted
screening, and enabling efficient degradation of lignocellulosic biomass. The method has good applications in
artificial proteins biosynthesis from low-value lignocellulosic straw, providing alternative solutions for biomass
biorefining challenges in complex enzyme-cocktail-substrate interactions.