Modeling‐Assisted Design of Thermostable Benzaldehyde Lyases from Rhodococcus erythropolis for Continuous Production of α‐Hydroxy Ketones

Abstract

Enantiopure α-hydroxy ketones are important building blocks of active pharmaceutical ingredients (APIs), which can be produced by thiamine-diphosphate-dependent lyases, such as benzaldehyde lyase. Here we report the discovery of a novel thermostable benzaldehyde lyase from Rhodococcus erythropolis R138 (ReBAL). While the overall sequence identity to the only experimentally confirmed benzaldehyde lyase from Pseudomonas fluorescens Biovar I (PfBAL) was only 65 %, comparison of a structural model of ReBAL with the crystal structure of PfBAL revealed only four divergent amino acids in the substrate binding cavity. Based on rational design, we generated two ReBAL variants, which were characterized along with the wild-type enzyme in terms of their substrate spectrum, thermostability and biocatalytic performance in the presence of different co-solvents. We found that the new enzyme variants have a significantly higher thermostability (up to 22 °C increase in T 50 ) and a different co-solvent-dependent activity. Using the most stable variant immobilized in packed-bed reactors via the SpyCatcher/SpyTag system, (R)-benzoin was synthesized from benzaldehyde over a period of seven days with a stable spacetime-yield of 9.3 mmol • L -1 • d À 1 . Our work expands the important class of benzaldehyde lyases and therefore contributes to the development of continuous biocatalytic processes for the production of α-hydroxy ketones and APIs.

Rhodococcus strains as a good biotool for neutralizing pharmaceutical pollutants and obtaining therapeutically valuable products: Through the past into the future

“…It is necessary to create and maintain collections of rhodococcal strains, pan-genome, and metagenome databases, which will entail the acquisition of a huge array of biological data-their analysis and interpretation will assist in obtaining completely new systemic knowledge about bacterial phenomena 10.3389/fmicb.2022.967127 Frontiers in Microbiology 21 frontiersin.org and processes. As for biological big data processing, difficult to process and interpret, metabolic engineering, metabolic flux determination, enzyme design, etc., it seems reasonable to involve artificial intelligence (machine learning, neural networks, and deep learning; Nagaraja et al, 2020;Kim et al, 2021;Peng et al, 2021;Jang et al, 2022). This will allow us, firstly, to identify previously unexplored therapeutically valuable substances; secondly, to establish detailed pathways of pharmaceutical biotransformation and biodegradation; thirdly, to optimize the conditions (media composition, additional growth substrates, etc.)…”

“…Currently, studies are underway using machine learning to search for genes encoding pharmaceutically useful enzymes. Thus, for example, the computational prediction tool Tome enabled the identification of a thermostable benzaldehyde lyase ( Re BAL) from R. erythropolis R138 ( Peng et al, 2021 ). To expand the substrate spectrum of the enzyme, the authors used site-directed mutagenesis in the binding site of the gene encoding for Re BAL to form two variants of Re BAL mat and Re BAL wid .…”

A Magnetosome-Based Platform for Flow Biocatalysis

“…For such approaches, the SpyTag–SpyCatcher system has recently been established. , The system consists of a 13 aa peptide tag (SpyTag, ST) and a 116 aa peptide (SpyCatcher, SC), which autocatalytically form an intermolecular isopeptide bond between an aspartate and lysine residue under a wide range of temperatures, pH values, and buffers and can genetically be fused to the protein of interest. The system has been employed for a large variety of applications ranging from materials science, molecular engineering, live-cell imaging, and protein purification to synthetic biology. ,,− …”