N20D/N116E Combined Mutant Downward Shifted the pH Optimum of Bacillus subtilis NADH Oxidase

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Enhancing Acid Resistance of Aspergillus niger Pectin Lyase through Surface Charge Design for Improved Application in Juice Clarification

Pectin lyases (PNLs) can enhance juice clarity and flavor by degrading pectin in highly esterified fruits, but their inadequate acid resistance leads to rapid activity loss in juice. This study aimed to improve the acid resistance of Aspergillus niger PNL pelA through surface charge design. A modification platform was established by fusing pelA with a protein tag and expressing the fusion enzyme in Escherichia coli. Four single-point mutants were identified to increase the surface charge using computational tools. Moreover, the combined mutant M6 (S514D/S538E) exhibited 99.8% residual activity at pH 3.0. The M6 gene was then integrated into the A. niger genome using a multigene integration system to obtain the recombinant PNL AM6. Notably, AM6 improved the light transmittance of orange juice to 45.3%, which was 8.39 times higher than that of pelA. In conclusion, AM6 demonstrated the best-reported acid resistance, making it a promising candidate for industrial juice clarification.

Shifting the pH profiles of Staphylococcus epidermidis lipase (SEL) and Staphylococcus hyicus lipase (SHL) through generating chimeric lipases by DNA shuffling strategy

Enzymes are often required to function in a particular reaction condition by the industrial procedure. In order to identify critical residues affecting the optimum pH of Staphylococcal lipases, chimeric lipases from homologous lipases were generated via a DNA shuffling strategy. Chimeric 1 included mutations of G166S, K212E, T243A, H271Y. Chimeric 2 consisted of substitutions of K212E, T243A, H271Y. Chimeric 3 contained substitutions of K212E, R359L. From the screening results, the pH profiles for chimeric 1 and 2 lipases were shifted from pH 7 to 6. While the pH of chimeric 3 was shifted to 8. It seems the mutation of K212E in chimeric 1 and 2 decreased the pH to 6 by changing the electrostatic potential surface. Furthermore, chimeric 3 showed 10 ˚C improvement in the optimum temperature due to the rigidification of the catalytic loop through the hydrophobic interaction network. Moreover, the substrate specificity of chimeric 1 and 2 was increased towards the longer carbon length chains due to the mutation of T243A adjacent to the lid region through increasing the flexibility of the lid. Current study illustrated that directed evolution successfully modified lipase properties including optimum pH, temperature and substrate specificity through mutations, especially near catalytic and lid regions.