Enzymes for Synthesis and Degradation of Bio-Based Polyesters: Automatic In Silico Screening and Experimental...

Area of Science:

• Polymer Science
• Biocatalysis
• Computational Chemistry

Background:

• Developing sustainable polymers is crucial for eco-design.
• Efficient synthesis and degradation of polyesters require effective biocatalysts.
• In silico screening offers a promising approach to identify suitable enzymes.

Purpose of the Study:

• To develop and validate computational workflows for in silico generation and screening of biocatalysts for polyester synthesis and degradation.
• To enable the rational eco-design of tailored, sustainable polymers.
• To provide a cost-effective and efficient alternative to experimental screening methods.

Main Methods:

• Integration of molecular dynamics simulations (Gromacs) and docking (AutoDock) into an automated computational workflow.
• Implementation of the pipeline in modeFRONTIER for sequential execution of software tools.
• Screening of bio-based monomers and enzymes, correlating computational predictions with experimental synthesis and hydrolysis data.

Main Results:

• The computational pipeline successfully predicted enzyme-substrate matches for both polyester synthesis and hydrolysis.
• The method was validated against experimental data for 6 proteins and 20 substrates, confirming prediction reliability.
• The workflow demonstrated efficient prioritization and ranking of potential biocatalysts.

Conclusions:

• Integrating computational tools allows for the rational prediction of enzyme activity toward polyesters.
• This in silico approach provides a cost-effective and time-saving method for identifying optimal biocatalysts for sustainable polymer development.
• The study validates the use of computational methods in accelerating the discovery of enzymes for eco-friendly polymer synthesis and degradation.