Plastic materials are essential in modern life, serving various sectors such as energy, technology, construction, health, and transportation. Annually, the global plastic polymers market exceeds 500 million metric tons, with plastic production growing by 35% over the past decade and projected to reach 700 million metric tons by 2030. Single-use plastics, such as Poly(ethylene terephthalate) (PET), are widely used in the manufacture of fabrics and containers for beverages and food, commonly discarded after use. Unfortunately, the global recycling rate is low (<30%), with the majority of plastics estimated to be incinerated, landfilled, or leaked into the environment worldwide [1, 2].

Enzyme-based depolymerization offers a promising alternative to current technologies for chemical or mechanical recycling of PET polymer [2, 3, 4]. Significant advances have been made in enzyme engineering to optimize natural PET hydrolases, resulting in improved variants such as DuraPETase [6], FAST-PETase [7], PHL7-L210T [8], LCC-ICCG [9], TurboPETase [10], and LCC-A2 [11], to name a few. Many of these improved variants were developed, to varying extents, through computer-aided strategies, demonstrating the potential synergy between computational and experimental approaches. Machine learning is also unlocking new avenues in the identification of novel enzymes from natural diversity [12]. A significant challenge in the coming years is understanding, rationalizing, and predicting the performance of enzyme-based PET depolymerization efficiencies by integrating advanced molecular simulations with experimental techniques and deep learning. 

While industrial deployment of enzyme-based PET depolymerization appears at the horizon [2], continuous improvements are required to establish and sustain a circular economy for PET and plastics in general. Today, we are living in an exciting time where the convergence of experimental and computational techniques allows us to raise new questions and open up novel directions: what strategies are most effective in improving both the thermostability and catalytic efficiency of PET hydrolases? How can we make these enzymes resistant to lower pH values? What is the detailed mechanism of action at the solid/liquid interface where PET depolymerization occurs? How does the enzyme interact with crystalline and amorphous regions of PET? Is depolymerization of the crystalline region of PET achievable? How can the effects of product inhibition on enzyme activity be reduced during PET depolymerization? Additionally, what are the technical and economic challenges in scaling up enzyme-based PET depolymerization to an industrial level? How can the process be made cost-competitive with traditional PET manufacturing methods? What potential new PET hydrolases can be discovered from natural diversity or designed through human intervention? How can detailed structural and functional characterizations of enzyme-substrate interactions improve our understanding and facilitate the rational design of more effective enzymes? Furthermore, we all keep on wondering to what extent what we are learning about PET degradation can be extended and applied to the degradation of other plastics, such as PBS (Polybutylene Succinate) or PBT (Polybutylene Terephthalate). 

With this spirit, we aim to bring together leading experts in computational and experimental approaches for enzymatic plastic depolymerization to discuss open questions in the field, foster collaborations, and tackle pressing challenges in the computer-aided design of next-generation plastic-degrading enzymes. Come to join our discussions!