Silica@lipase hybrid biocatalysts with superior activity by mimetic biomineralization in oil/water two-phase system for hydrolysis of soybean oil

Molecular simulations guide immobilization of lipase on nest-like ZIFs with regulatable hydrophilic/hydrophobic surface

The catalytic performance of immobilized lipase is greatly influenced by functional support, which attracts growing interest for designing supports to achieve their promotive catalytic activity. Many lipases bind strongly to hydrophobic surfaces where they undergo interfacial activation. Herein, the behavioral differences of lipases with distinct lid structures on interfaces of varying hydrophobicity levels were firstly investigated by molecular simulations. It was found that a reasonable hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation. Building on these findings, a novel "nest"-like superhydrophobic ZIFs (ZIFN) composed of hydrophobic ligands was prepared for the first time and used to immobilize lipase from Aspergillus oryzae (AOL@ZIFN). The AOL@ZIFN exhibited 2.0-folds higher activity than free lipase in the hydrolysis of p-Nitrophenyl palmitate (p-NPP). Especially, the modification of superhydrophobic ZIFN with an appropriate amount of hydrophilic tannic acid can significantly improve the activity of the immobilized lipase (AOL@ZIFN-TA). The AOL@ZIFN-TA exhibited 30-folds higher activity than free lipase, and still maintained 82% of its initial activity after 5 consecutive cycles, indicating good reusability. These results demonstrated that nanomaterials with rational arrangement of the hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation and improve its activity, displaying the potential of the extensive application.

Bioremediation of organic pollutants by laccase-metal-organic framework composites: A review of current knowledge and future perspective

Immobilized laccases are widely used as green biocatalysts for bioremediation of phenolic pollutants and wastewater treatment. Metal-organic frameworks (MOFs) show potential application for immobilization of laccase. Their unique adsorption properties provide a synergic effect of adsorption and biodegradation. This review focuses on bioremediation of wastewater pollutants using laccase-MOF composites, and summarizes the current knowledge and future perspective of their biodegradation and the enhancement strategies of enzyme immobilization. Mechanistic strategies of preparation of laccase-MOF composites were mainly investigated via physical adsorption, chemical binding, and de novo/co-precipitation approaches. The influence of architecture of MOFs on the efficiency of immobilization and bioremediation were discussed. Moreover, as sustainable technology, the integration of laccases and MOFs into wastewater treatment processes represents a promising approach to address the challenges posed by industrial pollution. The MOF-laccase composites can be promising and reliable alternative to conventional techniques for the treatment of wastewaters containing pharmaceuticals, dyes, and phenolic compounds. The detailed exploration of various immobilization techniques and the influence of MOF architecture on performance provides valuable insights for optimizing these composites, paving the way for future advancements in environmental biotechnology. The findings of this research have the potential to influence industrial wastewater treatment and promoting cleaner treatment processes and contributing to sustainability efforts.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Saponification Value of Fats and Oils as Determined from 1H-NMR Data: The Case of Dairy Fats

The saponification value of fats and oils is one of the most common quality indices, reflecting the mean molecular weight of the constituting triacylglycerols. Proton nuclear magnetic resonance (1H-NMR) spectra of fats and oils display specific resonances for the protons from the structural patterns of the triacylglycerols (i.e., the glycerol backbone), methylene (-CH2-) groups, double bonds (-CH=CH-) and the terminal methyl (-CH3) group from the three fatty acyl chains. Consequently, chemometric equations based on the integral values of the 1H-NMR resonances allow for the calculation of the mean molecular weight of triacylglycerol species, leading to the determination of the number of moles of triacylglycerol species per 1 g of fat and eventually to the calculation of the saponification value (SV), expressed as mg KOH/g of fat. The algorithm was verified on a series of binary mixtures of tributyrin (TB) and vegetable oils (i.e., soybean and rapeseed oils) in various ratios, ensuring a wide range of SV. Compared to the conventional technique for SV determination (ISO 3657:2013) based on titration, the obtained 1H-NMR-based saponification values differed by a mean percent deviation of 3%, suggesting the new method is a convenient and rapid alternate approach. Moreover, compared to other reported methods of determining the SV from spectroscopic data, this method is not based on regression equations and, consequently, does not require calibration from a database, as the SV is computed directly and independently from the 1H-NMR spectrum of a given oil/fat sample.