FAZER+/ILIND/IPLuso/1/2026

Duration of the project - 18 months

Economic Value - 20000 €

This project aims to develop a computational platform for the discovery and engineering of enzymes capable of recognizing and degrading polyurethane (PU) microplastics, a major but relatively understudied contributor to environmental pollution. While enzymatic plastic degradation research has largely focused on PET and catalytic hydrolysis mechanisms, this work addresses the critical knowledge gap surrounding the early molecular recognition events that precede degradation, including enzyme adsorption, binding, stabilization, and adaptation at complex PU interfaces. Given the structural diversity of PU materials, which exhibit substantial variation in hydrophobicity, flexibility, crystallinity, and chemical composition, understanding how enzymes interact with these heterogeneous surfaces is essential for improving biodegradation efficiency.

The project focuses initially on environmentally relevant polyester-based PU systems containing methylene diphenyl diisocyanate hard segments and polyester polyol soft segments, selected through a rational evaluation framework based on environmental prevalence, societal relevance, computational tractability, and biodegradation potential. A key innovation is the integration of structural bioinformatics, sequence analysis, molecular modeling, interface biophysics, physicochemical characterization, and artificial intelligence to identify novel enzymes with enhanced polymer recognition capabilities. By combining physics-based simulations with AI-driven approaches, the project seeks to establish a robust enzyme discovery pipeline capable of rapidly prioritizing promising candidates beyond those traditionally identified through literature-based methods.

In parallel, computational biomolecular engineering strategies will be employed to optimize enzyme recognition interfaces by identifying key structural determinants of effective polymer binding and introducing targeted mutations to improve enzyme affinity, stability, and performance at PU surfaces. Promising engineered variants will subsequently undergo experimental validation through protein expression, purification, and degradation assays. Ultimately, this research will deliver new insights into enzyme-polymer recognition mechanisms and establish a multidisciplinary framework that combines computational enzyme discovery, molecular simulation, AI, and protein engineering to support the development of sustainable biotechnological solutions for polyurethane microplastic remediation and environmental cleanup.