Structural insights at acidic pH of dye-decolorizing peroxidase from Bacillus subtilis

Engineering A-type Dye-Decolorizing Peroxidases by Modification of a Conserved Glutamate Residue

Dye-decolorizing peroxidases (DyPs) are recently identified microbial enzymes that have been used in several Biotechnology applications from wastewater treatment to lignin valorization. However, their properties and mechanism of action still have many open questions. Their heme-containing active site is buried by three conserved flexible loops with a putative role in modulating substrate access and enzyme catalysis. Here, we investigated the role of a conserved glutamate residue in stabilizing interactions in loop 2 of A-type DyPs. First, we did site saturation mutagenesis of this residue, replacing it with all possible amino acids in bacterial DyPs from Bacillus subtilis (BsDyP) and from Kitasatospora aureofaciens (KaDyP1), the latter being characterized here for the first time. We screened the resulting libraries of variants for activity towards ABTS and identified variants with increased catalytic efficiency. The selected variants were purified and characterized for activity and stability. We furthermore used Molecular Dynamics simulations to rationalize the increased catalytic efficiency and found that the main reason is the electron channeling becoming easier from surface-exposed tryptophans. Based on our findings, we also propose that this glutamate could work as a pH switch in the wild-type enzyme, preventing intracellular damage.

Structural insights, biocatalytic characteristics, and application prospects of lignin-modifying enzymes for sustainable biotechnology

Lignin modifying enzymes (LMEs) have gained widespread recognition in depolymerization of lignin polymers by oxidative cleavage. LMEs are a robust class of biocatalysts that include lignin peroxidase (LiP), manganese peroxidase (MnP), versatile peroxidase (VP), laccase (LAC), and dye-decolorizing peroxidase (DyP). Members of the LMEs family act on phenolic, non-phenolic substrates and have been widely researched for valorization of lignin, oxidative cleavage of xenobiotics and phenolics. LMEs implementation in the biotechnological and industrial sectors has sparked significant attention, although its potential future applications remain underexploited. To understand the mechanism of LMEs in sustainable pollution mitigation, several studies have been undertaken to assess the feasibility of LMEs in correlating to diverse pollutants for binding and intermolecular interactions at the molecular level. However, further investigation is required to fully comprehend the underlying mechanism. In this review we presented the key structural and functional features of LMEs, including the computational aspects, as well as the advanced applications in biotechnology and industrial research. Furthermore, concluding remarks and a look ahead, the use of LMEs coupled with computational framework, built upon artificial intelligence (AI) and machine learning (ML), has been emphasized as a recent milestone in environmental research.