Mechanistic Origins of Enhanced PET Hydrolase Activity from Molecular Dynamics and Deep Learning-Assisted Network Analysis

Deep Learning–driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization

Enzymatic depolymerization of polyethylene terephthalate (PET) offers a promising route to mitigate the increasingly severe problem of plastic pollution. However, the development of highly efficient PET hydrolases capable of processing post-consumer PET remains a critical challenge. Recent advances in artificial intelligence (AI) provide new opportunities to accelerate the enzyme engineering of PET hydrolases. Here, we report a systematic computational redesign of the PET hydrolase NI (ThcCut1-AICCG-H185N/F189I) using the deep-learning framework, EITLEM-Kinetics. By integrating mutation free-energy constraints with kinetic parameter prediction, the framework enables simultaneous optimization of catalytic activity and thermostability. A total of nine beneficial substitution sites were identified and experimentally validated that overcoming the activity–stability trade-off. Combinatorial iteration yielded an optimal variant, NI-E65K/H107Y/A2R/L33F (NI-KYRF), which exhibited an 80% and 90% increase in depolymerization activity toward Gf-PET film and one-step pretreated post-consumer PET (pc-PET powder), respectively, along with a 2.61 °C increase in melting temperature (Tm). NI-KYRF displayed a specific activity of 665 μmolTPAeq h−1 mgenzyme−1, representing 2.0- and 1.48-fold improvements over representative high-performance PET hydrolases ICCG and TurboPETase. The depolymerization performance of NI-KYRF towards various post-consumer PET wastes and polyester plastics was significantly improved. At 70 °C, it achieved a 95.5% depolymerization conversion of pc-PET powder within 24 h, and for untreated post-consumer PET film, the conversion reached 80.1%, representing a 2.1-fold improvement in depolymerization efficiency over the parental NI. In addition, enhanced activity toward polyesters plastics, including PBAT and PBT, indicates an expanded substrate scope. Molecular dynamics simulations and structural analysis reveal that these mutations enhance enzyme performance through synergistic mechanisms. Overall, this study establishes a deep learning–guided computational design framework based on EITLEM-Kinetics and demonstrates its effectiveness and broad potential for engineering high-performance PET hydrolases.

TRACER navigates rearrangement-driven sesterterpene chemical space via multimodal enzyme-product representation learning

Deep Learning‐Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions

ABSTRACT Microplastics (MPs) accumulation in ecosystem and human organs poses urgent environmental and health risks, yet few enzymes efficiently degrade polyethylene terephthalate (PET) under physiological conditions. We leveraged deep learning to mine unexplored sequence space across 246 million proteins, discovering AhPETase, an evolutionarily distinct hydrolase with low homology (<50% sequence identity) to known PET‐degrading enzymes. This noncanonical biocatalyst efficiently depolymerizes PET at 37°C, outperforming all typical PETases and achieving a 7.76‐fold enhancement over IsPETase, one of the most representative mesophilic PETases. Additionally, engineered variant AhPETaseM1 retains functional activity for over 20 days under physiological conditions and can degrade post‐consumer PET MPs 34‐fold faster than recombinant human‐derived enzyme MG8 (rMG8) under equal enzyme loading. Critically, it reversed PET‐induced toxicity in human lung and colon cells, establishing the first proof‐of‐concept for enzymatic MPs detoxification.

Machine learning-assisted discovery of AdMysD for enhanced porphyra-334 biosynthesis.

Mycosporine-like amino acids (MAAs) are functional secondary metabolites renowned for their exceptional UV protection, antioxidant properties, and environmental resilience. In the MAA biosynthetic pathway, MysD is the pivotal enzyme mediating the chemical transition of the cyclohexenone core into a cyclohexenimine-type scaffold. This MysD-catalyzed secondary amino acid modification not only dictates the chemical diversity of MAAs but also facilitates a crucial bathochromic shift, moving the UV absorption maximum from the UVB range into the high-penetration UVA region. Despite its significance, MysD remains the rate-limiting step in the biosynthesis of iminomycosporine-like amino acids. To date, only eight MysD enzymes have been heterologously validated, with even fewer subjected to biochemical characterization, which severely restricts the use of conventional supervised machine learning for enzyme discovery. In this study, we developed an integrated, data-driven screening framework combining Sequence Similarity Networks (SSN), deep representation learning (UniRep), and Positive-Unlabeled Bagging (PU Bagging) to explore the MysD functional landscape. This pipeline effectively compressed the search space from approximately 951 unannotated homologues to a prioritized 42 candidates. Experimental validation led to the discovery of AdMysD from Aphanothece hegewaldii, which exhibited a 3-fold increase in catalytic efficiency for porphyra-334 production relative to the previously established benchmark, NlMysD. Notably, while demonstrating a primary preference for l-Thr, AdMysD displayed significant substrate promiscuity by accepting l-Ser, l-Ala, and l-Cys to produce iminomycosporine derivatives. Our findings provide a biocatalytic tool for the efficient production of MAAs and demonstrate the potential of a PU-learning-based prioritization strategy for identifying rare enzyme families with sparse functional annotations.

Molecular Origin of the Enhanced PET Degradation Activity of LCC-ICCG Revealed by Computational Modeling

Poly(ethylene terephthalate) (PET) hydrolases have emerged as promising biocatalysts for closed-loop plastic recycling. Among the most efficient enzymes reported to date, LCC-ICCG exhibits exceptional PET-depolymerization performance under industrially relevant conditions. However, the molecular basis for its superior activity relative to engineered PETases such as FAST-PETase and HotPETase remains incompletely understood. Here, we combine microsecond-scale molecular dynamics simulations, quantum mechanical cluster calculations, pre-reaction-state analysis, noncovalent-interaction mapping, and distortion/interaction activation strain analysis to compare LCC-ICCG with FAST-PETase and HotPETase. The simulations show that LCC-ICCG samples catalytically competent pre-reaction-state geometries more frequently, mainly because V212 reshapes the local environment around the scissile ester. This residue relieves steric congestion, supports weak C–H···O guided substrate preorganization, and reinforces both the Asp-His catalytic dyad and the W190-associated pocket architecture. Density functional theory calculations further indicate that this preorganized active site lowers the acylation barrier to 15.5 kcal/mol by reducing substrate distortion and strengthening transition-state interactions. High-temperature simulations show that LCC-ICCG better preserves near-attack geometries at 350 K, linking thermal robustness to sustained catalytic preorganization. Moreover, reciprocal I208V mutations in IsPETase-derived enzymes enrich pre-reaction-state populations, supporting the transferability of the V212-centered design principle. Overall, these results establish pre-reaction-state stabilization as a key determinant of PET-hydrolase efficiency and provide mechanistic design rules for engineering next-generation PET depolymerases.

Machine Learning-Guided Enzyme Engineering Approaches for Enhanced Biocatalytic Efficiency: Concepts, Mechanisms, and Future Directions

Biocatalysis has emerged as a mainstay in the field of sustainable chemical synthesis owing to its high selectivity, mild reaction conditions, and reduced environmental impact. Traditional enzyme engineering approaches, such as rational design and directed evolution, are often associated with limited throughput and a limited understanding of sequence–structure–function relationships, despite high experimental costs. In recent years, the integration of machine learning (ML) into enzyme engineering has emerged as a transformative approach, enabling data-driven prediction, design, and optimization of biocatalysts, thereby enhancing performance and applications. This review provides a comprehensive overview of ML-guided strategies to improve key enzymatic parameters, including the turnover number (kcat), substrate affinity (Km), and catalytic efficiency (kcat/Km), with a focus on mechanistic insights and performance outcomes. The integration of ML models into design–build–test–learn (DBTL) cycles accelerated directed evolution, reduced screening efforts, and enabled targeted mutagenesis. Beyond applications, this review also discusses the current limitations of ML-guided approaches, including data scarcity, model interpretability, and challenges in predicting complex mutations and allosteric effects. The gap between computational predictions and experimental outcomes is identified, and the role of ML integration with enzyme kinetics, molecular dynamics, and high-throughput experimentation is emphasized. Future directions, such as generative AI, explainable ML, and autonomous laboratories, are discussed for next-generation biocatalytic applications.