Polyethylene terephthalate (PET) is the most widely used polyester plastic, with applications in the textile and packaging industry. Currently, re-moulding is the main path for PET recycling, but this eventually leads to an unsustainable loss of quality; thus, other means of recycling are required. Enzymatic hydrolysis offers the possibility of monomer formation under mild conditions and opens up alternative and infinite recycling paths. Here, IsPETase, derived from the bacterium Ideonella sakaiensis, is considered to be the most active enzyme for PET degradation under mild conditions, and although several studies have demonstrated improvements to both the stability and activity of this enzyme, stability at even moderate temperatures is still an issue. In the present study, we have used sequence and structure-based bioinformatic tools to identify mutations to increase the thermal stability of the enzyme so as to increase PET degradation activity during extended hydrolysis reactions. We found that amino acid substitution S136E showed significant increases to activity and stability. S136E is a previously unreported variant that led to a 3.3-fold increase in activity relative to wild type.

PET hydrolases are an emerging class of enzymes that are being heavily researched for their use in bioprocessing polyethylene terephthalate (PET). While work has been done in studying the binding of PET oligomers to the active site of these enzymes, the dynamics of PET hydrolases binding to a bulk PET surface is an unexplored area. Here, methods were developed for total internal reflection fluorescence (TIRF) microscopy and fluorescence recovery after photobleaching (FRAP) microscopy to study the adsorption and desorption dynamics of these proteins onto a PET surface. TIRF microscopy was employed to measure both on and off rates of two of the most commonly studied PET hydrolases, PHL7 and LCC, on a PET surface. It was found that these proteins have a much slower off rates on the order of 10⁻³ s⁻¹, comparable to non‐productive binding in enzymes such as cellulose. In combination with FRAP microscopy, a dynamic model is proposed in which adsorption and desorption dominates over lateral diffusion over the surface. The results of this study could have implications for the future engineering of PET hydrolases, either to target them to a PET surface or to modulate interaction with their substrate.

Carbohydrate‐binding modules (CBMs) are noncatalytic domains that enhance enzyme binding to substrates. Type A CBMs show potential for engineering plastic‐degrading enzymes due to their affinity for synthetic polymers. This study presents a high‐throughput screening pipeline for characterizing the affinity and specificity of type A CBMs towards the synthetic polymers polyethylene terephthalate (PET), polystyrene (PS), and polyethylene (PE), and the polysaccharides cellulose, chitin, and starch. ≈800 CBMs from the families CBM2, CBM3, CBM10, and CBM64 are expressed as green fluorescent protein (GFP)‐fusion proteins and tested for binding using a modified holdup assay, which produced up to 10 000 data points per day. The screening identifies ≈150 binders for PET and PE, around 250 for PS, and demonstrates family‐specific binding patterns for avicel, chitin, and starch. To demonstrate practical utility, four CBMs with high PET affinity are fused to the PET hydrolase LCC ^ICCG , enhancing activity on PET powder by around 5‐fold. These CBM‐enzyme fusions mitigate competitive binding to plastic impurities, improving performance in mixed plastic assays. This work significantly expands the repertoire of CBMs binding to synthetic polymers, advances our understanding of CBM‐substrate interactions, and provides knowledge for engineering enzymes to tackle plastic pollution, particularly where mixed plastics pose significant challenges.

Understanding the mechanisms underlying substrate recognition and selectivity is essential for developing new biotechnological applications of carbohydrate-binding modules (CBMs). Many members of CBM family 2 (CBM2) prefer binding to crystalline substrates and show diverse substrate specificities despite sharing similar folds. We show here that CBM2 specificity cannot be rationalized solely based on sequence and fold. Rather, specific binding of CBM2s to crystalline surfaces depends on a balance of topological complementarity, hydrophobic dehydration, and hydrogen-bonding interactions. We compare the binding modes of Sc CBM2 and Ba CBM2 on cellulose, chitin, and polyethylene terephthalate (PET) with respect to protein and substrate properties. Using MD simulations and NMR, we explore the CBMs’ surface topology, hydration, solvent accessibility, and electrostatics. The results indicate that the two CBMs may bind to crystalline substrates in opposite orientations, and that the interplay between hydrophobic and π-stacking interactions along the aromatic triad and hydrogen-bonding interactions on either side determines their orientation. We further show that higher water density at the aromatic triad correlates with higher substrate affinity. Lastly, we probe the interaction dynamics of Ba CBM2 with PET nanoparticles and the monomer bis(2-hydroxyethyl) terephthalate (BHET) using NMR relaxation and solvent paramagnetic relaxation enhancement (sPRE) experiments, revealing a mixture of transient, low-population states involved in PET binding. These insights clarify the interplay among factors that drive CBM2 substrate recognition and highlight the importance of evaluating surface hydration and protein dynamics in future CBM2 studies.

Carbohydrate-binding modules (CBMs) are non-catalytic domains that enhance enzyme binding to substrates, crucial for polysaccharide degradation. Type A CBMs also show potential for engineering plastic-degrading enzymes due to their high affinity for synthetic polymers. This study presents a high-throughput screening pipeline for characterizing the affinity and specificity of Type A CBMs to the synthetic polymers polyethylene terephthalate (PET), polystyrene (PS), and polyethylene (PE), and the natural polysaccharides cellulose, chitin, and starch. Approximately 800 CBMs from the families CBM2, CBM3, CBM10, and CBM64 were expressed as enhanced green fluorescent protein (EGFP)-fusion proteins and tested for binding using a modified holdup assay, which could produce up to 10,000 data points per day. The screening identified approximately 150 binders for PET and PE, around 250 for PS, and demonstrated family-specific binding patterns for avicel, chitin, and starch. Distinct patterns of substrate affinity and specificity were observed, which allowed for rationalizations of binding at the structural and phylogenetic levels. To demonstrate practical utility, CBMs with high PET affinity were fused to the PET hydrolase LCC ^ICCG , enhancing enzymatic activity on crystalline PET powder by around 5-fold. Importantly, these CBM-enzyme fusions mitigated competitive binding to inert plastic impurities, improving performance in mixed plastic assays. This work significantly expands the known repertoire of CBMs capable of binding synthetic polymers, advances our understanding of CBM-substrate interactions, and provides knowledge for engineering enzymes to tackle plastic pollution, particularly in municipal solid waste where mixed plastics pose significant challenges. Significance Statement: Improper recycling of plastic poses a severe environmental threat, with synthetic polymers persisting in ecosystems for decades. Enzymatic recycling of plastic offers a promising solution, with optimization of the enzymes and processes ongoing. To expand the toolkit for engineering enzymes via substrate-binding module fusion, we developed a high-throughput screening platform to identify carbohydrate-binding modules (CBMs) that selectively bind synthetic plastics such as PET, polystyrene, and polyethylene, as well as natural polysaccharides. By screening 797 CBMs, we discovered binding domains with distinct specificities, enabling the design of enzyme fusions that enhance plastic degradation, even in mixed plastic environments. This approach provides insights into protein-substrate interactions that can be leveraged for both waste management and bioengineering innovations.