Regioselective enzymatic depolymerization of aromatic-aliphatic polyester revealed by computational modelling.

Poly(butylene adipate-co-terephthalate) (PBAT) is widely utilized in the production of food packaging and mulch films. Its extensive application has contributed significantly to global solid waste, posing numerous environmental challenges. Recently, enzymatic recycling has emerged as a promising eco-friendly solution for the management of plastic waste. Here, we systematically investigate the depolymerization mechanism of PBAT catalyzed by cutinase TfCutSI with molecular docking, molecular dynamics simulations, and quantum mechanics/molecular mechanics calculations. Although the binding affinities for acid ester and terephthalic acid ester bonds are similar, a regioselective depolymerization mechanism and a "chain-length" effect on regioselectivity were proposed and evidenced. The regioselectivity is highly associated with specific structural parameters, namely Substrate@O4-Met@H7 and Substrate@C1-Ser@O1 distances. Notably, the binding mode of BTa captured by X-ray crystallography does not facilitate subsequent depolymerization. Instead, a previously unanticipated binding mode, predicted through computational analysis, is confirmed to play a crucial role in BTa depolymerization. This finding proves the critical role of computational modelling in refining experimental results. Furthermore, our results revealed that both the hydrogen bond network and enzyme's intrinsic electric field are instrumental in the formation of the final product. In summary, these novel molecular insights into the PBAT depolymerization mechanism offer a fundamental basis for enzyme engineering to enhance industrial plastic recycling.

Novel polyurethane-degrading cutinase BaCut1 from Blastobotrys sp. G-9 with potential role in plastic bio-recycling.

Environmental pollution caused by plastic waste has become global problem that needs to be considered urgently. In the pursuit of a circular plastic economy, biodegradation provides an attractive strategy for managing plastic wastes, whereas effective plastic-degrading microbes and enzymes are required. In this study, we report that Blastobotrys sp. G-9 isolated from discarded plastic in landfills is capable of depolymerizing polyurethanes (PU) and poly (butylene adipate-co-terephthalate) (PBAT). Strain G-9 degrades up to 60% of PU foam after 21 days of incubation at 28 â„ƒ by breaking down carbonyl groups via secretory hydrolase as confirmed by structural characterization of plastics and degradation products identification. Within the supernatant of strain G-9, we identify a novel cutinase BaCut1, belonging to the esterase family, that can reproduce the same effect. BaCut1 demonstrates efficient degradation toward commercial polyester plastics PU foam (0.5 mg enzyme/25 mg plastic) and agricultural film PBAT (0.5 mg enzyme/10 mg plastic) with 50% and 18% weight loss at 37 â„ƒ for 48 h, respectively. BaCut1 hydrolyzes PU into adipic acid as a major end-product with 42.9% recovery via ester bond cleavage, and visible biodegradation is also identified from PBAT, which is a beneficial feature for future recycling economy. Molecular docking, along with products distribution, elucidates a special substrate-binding modes of BaCut1 with plastic substrate analogue. BaCut1-mediated polyester plastic degradation offers an alternative approach for managing PU plastic wastes through possible bio-recycling.

Impacts of QM region sizes and conformation numbers on modelling enzyme reactions: a case study of polyethylene terephthalate hydrolase.

A quantum mechanics/molecular mechanics (QM/MM) approach is a broadly used tool in computational enzymology. Treating the QM region with a high-level DFT method is one of the important branches. Here, taking leaf-branch compost cutinase-catalyzed polyethylene terephthalate depolymerization as an example, the convergence behavior of energy barriers as well as key structural and charge features with respect to the size of the QM region (up to 1000 atoms) is systematically investigated. BP86/6-31G(d)//CHARMM and M06-2X/6-311G(d,p)//CHARMM level of theories were applied for geometry optimizations and single-point energy calculations, respectively. Six independent enzyme conformations for all the four catalytic steps (steps (i)-(iv)) were considered. Most of the twenty-four cases show that at least 500 QM atoms are needed while only two rare cases show that ∼100 QM atoms are sufficient for convergence when only a single conformation was considered. This explains why most previous studies showed that 500 or more QM atoms are required while a few others showed that ∼100 QM atoms are sufficient for DFT/MM calculations. More importantly, average energy barriers and key structural/charge features from six conformations show an accelerated convergence than that in a single conformation. For instance, to reach energy barrier convergence (within 2.0 kcal mol-1) for step (ii), only ∼100 QM atoms are required if six conformations are considered while 500 or more QM atoms are needed with a single conformation. The convergence is accelerated to be more rapid if hundreds and thousands of conformations were considered, which aligns with previous findings that only several dozens of QM atoms are required for convergence with semi-empirical QM/MM MD simulations.

Regioselective oxidation of heterocyclic aromatic hydrocarbons catalyzed by cytochrome P450: A case study of carbazole.

Recently there are increasing interests in accurately evaluating the health effects of heterocyclic PAHs. However, the activation mechanism and possible metabolites of heterocyclic PAHs catalyzed by human CYP1A1 is still elusive to a great extent. Here, leveraged to high level QM/MM calculations, the corresponding activation pathways of a representative heterocyclic PAHs, carbazole, were systematically explored. The first stage is electrophilic addition or hydrogen abstraction from N-H group. Electrophilic addition was evidenced to be more feasible and regioselectivity at C3 and C4 sites were identified. Correlations between energy barriers and key structural/electrostatic parameters reveal that O-Cα distance and Fe-O-Cα angle are the main origin for the catalytic regioselectivity. Electrophilic addition was determined as the rate-determining step and the subsequent possible reactions include epoxidation, NIH shift (the hydrogen migration from the site of hydroxylation to the adjacent carbon) and proton shuttle. The corresponding products are epoxides, ketones and hydroxylated carbazoles, respectively. The main metabolites (hydroxylated carbazoles) are estimated to be more toxic than carbazole. The regioselectivity of carbazole activated by CYP1A1 is different from the environmental processes (gas and aqueous phase). Collectively, these results will inform the in-depth understanding the metabolic processes of heterocyclic PAHs and aid the accurate evaluation of their health effects.

Enantioselectivity and origin of enhanced efficiency in polyethylene terephthalate hydrolases catalyzed depolymerization.

Biotechnology is one of the most promising strategies to resolve the global crisis of plastic pollution. A clear understanding of the core enzyme mechanisms in the biotransformation process is critical for rational enzyme engineering and for practical, industrial-scale applications. Herein, we systematically examined and evidenced a largely unexplored piece in the depolymerization mechanism catalyzed by polyethylene terephthalate (PET) hydrolases: their enantioselectivity. We found that all the short-lived tetrahedron intermediates (IM3 and IM8) possess S-type chirality in six representative PET hydrolases. For instance, the binding percentage ratio of pro-S:pro-R is 57:21 in FAST-PETase, while pro-S binding leads to a much lower average energy barrier (5.2 kcal/mol) than pro-R binding (33.1 kcal/mol). Key structural features (e.g. the angle for Ser@H1-His@N1-PET@O2 and distance for His@N1-PET@O2) that significantly modulate the enantioselectivity were identified. The origin of the energy landscape variation between wild-type IsPETase and mutant FAST-PETase was also unveiled via analysis of key features, the distortion/interaction energy, and non-covalent bond interactions. This study supplies the missing piece in the mechanism for depolymerization catalyzed by PET hydrolases, and will aid in the rational design of enzymes for industrial recycling of PET plastic waste.

Computational biotransformation of polyethylene terephthalate by depolymerase: A QM/MM approach.

Despite increasing environmental concerns on ever-lasting Polyethylene Terephthalate (PET), its global production is continuously growing. Effective strategies that can completely remove PET from environment are urgently desired. Here biotransformation processes of PET by one of the most effective enzymes, leaf-branch compost cutinase (LCC), were systematically explored with Molecular Dynamics and Quantum Mechanics/Molecular Mechanics approaches. We found that four concerted steps are required to complete the whole catalytic cycle. The last concerted step, deacylation, was determined as the rate-determining step with Boltzmann-weighted average barrier of 13.6 kcal/mol and arithmetic average of 16.1 ± 2.9 kcal/mol. Interestingly, unprecedented fluctuations of hydrogen bond length during LCC catalyzed transformation process toward PET were found. This fluctuation was also observed in enzyme IsPETase, indicating that it may widely exist in other catalytic triad (Ser-His-Asp) containing enzymes as well. In addition, possible features (bond, angle, dihedral angle and charge) that influence the catalytic reaction were identified and correlations between activation energies and key features were established. Our results present new insights into catalytic mechanism of hydrolases and shed light on the efficient recycling of the ever-lasting PET.

Denitrification performance of Pseudomonas fluorescens Z03 immobilized by graphene oxide-modified polyvinyl-alcohol and sodium alginate gel beads at low temperature.

Improving the effect of microbial denitrification under low-temperature conditions has been a popular focus of research in recent years. In this study, graphene oxide (GO)-modified polyvinyl-alcohol (PVA) and sodium alginate (SA) (GO/PVA-SA) gel beads were used as a heterotrophic nitrification-aerobic denitrification (HN-AD) bacteria (Pseudomonas fluorescens Z03) carrier to enhance nitrogen removal efficiency levels at low temperatures (6-8°C). The removal efficiency of N H 4 + -N and N O 3 - -N and the variations in concentrations of extracellular polymeric substances (EPS) under different GO doses (0.03-0.15 g l-1) were studied. The results indicated that the addition of GO can improve the efficiency of nitrogen removal, and the highest removal efficiency level and highest carbohydrate, protein, and total EPS content levels (50.28 mg, 132.78 mg and 183.06 mg (g GO/PVA-SA gel)-1, respectively) were obtained with 0.15 g l-1 GO. The simplified Monod model accurately predicted the nitrogen removal efficiency level. These findings suggested that the application of GO serves as an effective means to enhance nitrogen removal by stimulating the activity of HN-AD bacteria.

Developing a healthcare dataset information resource (DIR) based on Semantic Web.

BACKGROUND: The right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses of these datasets. While a number of efforts are underway to integrate the large amount and variety of datasets, the lack of an information resource that focuses on specific needs of target users of datasets has existed as a problem for years. To address this gap, we have developed a Dataset Information Resource (DIR), using a user-oriented approach, which gathers relevant dataset knowledge for specific user types. In the present version, we specifically address the challenges of entry-level data scientists in learning to identify, understand, and analyze major datasets in healthcare. We emphasize that the DIR does not contain actual data from the datasets but aims to provide comprehensive knowledge about the datasets and their analyses. METHODS: The DIR leverages Semantic Web technologies and the W3C Dataset Description Profile as the standard for knowledge integration and representation. To extract tailored knowledge for target users, we have developed methods for manual extractions from dataset documentations as well as semi-automatic extractions from related publications, using natural language processing (NLP)-based approaches. A semantic query component is available for knowledge retrieval, and a parameterized question-answering functionality is provided to facilitate the ease of search. RESULTS: The DIR prototype is composed of four major components-dataset metadata and related knowledge, search modules, question answering for frequently-asked questions, and blogs. The current implementation includes information on 12 commonly used large and complex healthcare datasets. The initial usage evaluation based on health informatics novices indicates that the DIR is helpful and beginner-friendly. CONCLUSIONS: We have developed a novel user-oriented DIR that provides dataset knowledge specialized for target user groups. Knowledge about datasets is effectively represented in the Semantic Web. At this initial stage, the DIR has already been able to provide sophisticated and relevant knowledge of 12 datasets to help entry health informacians learn healthcare data analysis using suitable datasets. Further development of both content and function levels is underway.