Reengineered carbonyl reductase for reducing methyl-substituted cyclohexanones

The carbonyl reductase from Candida parapsilosis (CPCR2) is a versatile biocatalyst for the production of optically pure alcohols from ketones. Prochiral ketones like 2-methyl cyclohexanone are, however, only poorly accepted, despite CPCR2's large substrate spectrum. The substrate spectrum of CPCR2 was investigated by selecting five amino positions (55, 92, 118, 119 and 262) and exploring them by single site-saturation mutagenesis. Screening of CPCR2 libraries with poor (14 compounds) and well-accepted (2 compounds) substrates showed that only position 55 and position 119 showed an influence on activity. Saturation of positions 92, 118 and 262 yielded only wild-type sequences for the two well-accepted substrates and no variant converted one of the 14 other compounds. Only the variant (L119M) showed a significantly improved activity (7-fold on 2-methyl cyclohexanone; vmax = 33.6 U/mg, Km = 9.7 mmol/l). The L119M substitution exhibited also significantly increased activity toward reduction of 3-methyl (>2-fold), 4-methyl (>5-fold) and non-substituted cyclohexanone (>4-fold). After docking 2-methyl cyclohexanone into the substrate-binding pocket of a CPCR2 homology model, we hypothesized that the flexible side chain of M119 provides more space for 2-methyl cyclohexanone than branched L119. This report represents the first study on CPCR2 engineering and provides first insights how to redesign CPCR2 toward a broadened substrate spectru

A high-throughput screening and computation platform for identifying synthetic promoters with enhanced cell-state specificity (SPECS)

Cell state-specific promoters constitute essential tools for basic research and biotechnology because they activate gene expression only under certain biological conditions. Synthetic Promoters with Enhanced Cell-State Specificity (SPECS) can be superior to native ones, but the design of such promoters is challenging and frequently requires gene regulation or transcriptome knowledge that is not readily available. Here, to overcome this challenge, we use a next-generation sequencing approach combined with machine learning to screen a synthetic promoter library with 6107 designs for high-performance SPECS for potentially any cell state. We demonstrate the identification of multiple SPECS that exhibit distinct spatiotemporal activity during the programmed differentiation of induced pluripotent stem cells (iPSCs), as well as SPECS for breast cancer and glioblastoma stem-like cells. We anticipate that this approach could be used to create SPECS for gene therapies that are activated in specific cell states, as well as to study natural transcriptional regulatory networks