Enhancing the Hydrolysis and Acyl Transfer Activity of Carboxylesterase DLFae4 by a Combinational Mutagenesis and In-Silico Method

Development of DeepPQK and DeepQK sequence-based deep learning models to predict protein-ligand affinity and application in the directed evolution of ferulic esterase DLfae4

Affinity plays an essential role in the rate and stability of enzyme-catalyzed reactions, thus directly impacting the catalytic activity. In general, the predictive method for protein-ligand binding affinity mainly relies on high-resolution protein crystal structure data; however, some protein crystals are difficult to culture, time-consuming, and expensive to obtain. In this study, two sequence-based neural network deep learning models - DeepPQK and DeepQK, were constructed to predict the protein-ligand binding affinity. DeepPQK was developed by integrating local and global contextual features using convolutional neural networks(CNN) with protein sequences, pocket amino acids, and ligands as input. In particular, the protein-binding pocket, which possesses special properties for directly binding the ligand, was used as the local input feature for predicting protein-ligand binding affinity. DeepQK, consisting of a protein sequence module and a ligand module, utilizes these features for its predictions, enabling the identification of the intrinsic relationship between protein sequence and affinity. Specifically, dilated convolution was used to capture multiscale long-range interactions and the special sequence-level features of a protein and ligand. When tested on the 2016 core dataset, the Pearson correlation coefficient of DeepPQK and DeepQK reached 0.805 and 0.804 respectively, which is a significant accuracy improvement compared with the recent state-of-art methods. Both models, once trained, can learn the two- and three-dimensional structural properties of proteins, and the relative position relationship between proteins and ligands. Based on the results, a series of variants of feruloyl esterase DLFae4 were designed using DeepPQK and DeepQK, and the enzyme activity of these mutations was verified by experiments, among which the optimal mutant I149G/W237H/M297C improved 5.6-fold enzyme activity and 10.1-fold catalytic efficiency than the wild-type enzyme. In conclusion, DeepPQK and DeepQK deep learning models overcome the limitations of traditional methods that depend on protein crystal structures and have been successfully applied to guide the directed evolution of enzymes, providing a new approach to studying enzyme-directed evolution. The resource codes are available at https://github.com/KK-SW1207/DeepPQK_QK.

Abridgement of Microbial Esterases and Their Eminent Industrial Endeavors

Esterases are hydrolases that contribute to the hydrolysis of ester bonds into both water-soluble acyl esters and emulsified glycerol-esters containing short-chain acyl groups. They have garnered significant attention from biotechnologists and organic chemists due to their immense commercial value. Esterases, with their diverse and significant properties, have become highly sought after for various industrial applications. Synthesized ubiquitously by a wide range of living organisms, including animals, plants, and microorganisms, these enzymes have found microbial esterases to be the preferred choice in industrial settings. The cost-effective production of microbial esterases ensures higher yields, unaffected by seasonal variations. Their applications span diverse sectors, such as food manufacturing, leather tanneries, paper and pulp production, textiles, detergents, cosmetics, pharmaceuticals, biodiesel synthesis, bioremediation, and waste treatment. As the global trend shifts toward eco-friendly and sustainable practices, industrial processes are evolving with reduced waste generation, lower energy consumption, and the utilization of biocatalysts derived from renewable and unconventional raw materials. This review explores the background, structural characteristics, thermostability, and multifaceted roles of bacterial esterases in crucial industries, aiming to optimize and analyze their properties for continued successful utilization in diverse industrial processes. Additionally, recent advancements in esterase research are overviewed, showcasing novel techniques, innovations, and promising areas for further exploration.

Enhancing β-Galactosidase Performance for Galactooligosaccharides Preparation via Strategic Glucose Re-Tunneling

This study focuses on the characterization and re-engineering of glucose transport in β-galactosidase (BglD) to enhance its catalytic efficiency. Computational prediction methods were employed to identify key residues constituting access tunnels for lactose and glucose, revealing distinct pockets for both substrates. In silico simulated saturation mutagenesis of residues T215 and T473 led to the identification of eight mutant variants exhibiting potential enhancements in glucose transport. Site-directed mutagenesis at T215 and T473 resulted in mutants with consistently enhanced specific activities, turnover rates, and catalytic efficiencies. These mutants also demonstrated improved galactooligosaccharide (GOS) synthesis, yielding an 8.1-10.6% enhancement over wild-type BglD yield. Structural analysis revealed that the mutants exhibited transformed configurations and localizations of glucose conduits, facilitating expedited glucose release. This study's findings suggest that the re-engineered mutants offer promising avenues for enhancing BglD's catalytic efficiency and glucose translocation, thereby improving GOS synthesis. By-product (glucose) re-tunneling is a viable approach for enzyme tunnel engineering and holds significant promise for the molecular evolution of enzymes.

Tuning Thermostability and Catalytic Efficiency of Aflatoxin-Degrading Enzyme by Error-prone PCR

In our previous work, a recombinant aflatoxin-degrading enzyme derived from Myxococcus fulvus (MADE) was reported. However, the low thermal stability of the enzyme had limitations for its use in industrial applications. In this study, we obtained an improved variant of recombinant MADE (rMADE) with enhanced thermostability and catalytic activity using error-prone PCR. Firstly, we constructed a mutant library containing over 5000 individual mutants. Three mutants with T₅₀ values higher than the wild-type rMADE by 16.5 °C (rMADE-1124), 6.5 °C (rMADE-1795), and 9.8 °C (rMADE-2848) were screened by a high-throughput screening method. Additionally, the catalytic activity of rMADE-1795 and rMADE-2848 was improved by 81.5% and 67.7%, respectively, compared to the wild-type. Moreover, structural analysis revealed that replacement of acidic amino acids with basic amino acids by a mutation (D114H) in rMADE-2848 increased the polar interactions with surrounding residues and resulted in a threefold increase in the t_1/2 value of the enzyme and made it more thermaltolerate. KEY POINTS: • Mutant libraries construction of a new aflatoxins degrading enzyme by error-prone PCR. • D114H/N295D mutant improved enzyme activity and thermostability. • The first reported enhanced thermostability of aflatoxins degrading enzyme better for its application.