Protein Engineering

Protein engineering enables the redesign of natural biocatalysts toward improved stability, activity, selectivity, and compatibility with industrial processes. In our group, this approach is applied to enzymes involved in the degradation and modification of both natural and synthetic polymers, supporting sustainable solutions for biomass valorization, plastic recycling, and circular bioeconomy.

Our workflow integrates bioinformatics, protein structure prediction, molecular modelling, molecular dynamics simulations, machine learning, and rational design with recombinant expression, site-directed mutagenesis and biochemical characterization. This combination allows us to identify promising enzyme scaffolds, understand the molecular basis of their function, and generate variants with improved properties.

Current research includes the stabilization of DmPETase for PET depolymerization, engineering the substrate specificity of a thermotolerant Zhizhongheella caldifontis esterase toward PET-derived intermediates, tuning the pH-dependent activity of the GH30 xylanase TtXyn30A, investigating decoration-dependent substrate recognition by a GH44 xyloglucanase from Abortiporus biennis, and developing CNN-based machine learning models for predicting polymer-binding carbohydrate-binding modules. Together, these projects establish a computationally guided and experimentally validated platform for designing enzymes and binding modules for sustainable biocatalysis.