A curated list of targeted optimized promiscuous ketoreductases (TOP-K).

Enzymes are either specific or promiscuous catalysts in nature. The latter is portrayed by protein families like CYP450Es, Aldo-ketoreductases and short/medium-chain dehydrogenases which participate in detoxification or secondary metabolite production. However, enzymes are evolutionarily 'blind' to an ever-increasing synthetic substrate library. Industries and laboratories have circumvented this by high-throughput screening or site-specific engineering to synthesize the product of interest. However, this paradigm entails cost and time-intensive one-enzyme, one-substrate catalysis model. One of the superfamilies regularly used for chiral alcohol synthesis are short-chain dehydrogenases/reductases (SDRs). Our objective is to determine a superset of promiscuous SDRs that can catalyze multiple ketones. They are typically classified into shorter 'Classical' and longer 'Extended' type ketoreductases. However, current analysis of modelled SDRs reveals a length-independent conserved N-terminus Rossmann-fold and a variable substrate-binding C-terminus substrate-binding region for both categories. The latter is recognized to influence the enzyme's flexibility and substrate promiscuity and we hypothesize these properties are directly linked with each other. We tested this by catalyzing ketone intermediates with the essential and specific enzyme: FabG_E, as well as non-essential SDRs such as UcpA and IdnO. The experimental results confirmed this biochemical-biophysical association, making it an interesting filter for ascertaining promiscuous enzymes. Hence, we created a dataset of physicochemical properties derived from the protein sequences and employed machine learning algorithms to examine potential candidates. This resulted in 24 targeted optimized ketoreductases (TOP-K) from 81 014 members. The experimental validation of select TOP-Ks demonstrated the correlation between the C-terminal lid-loop structure, enzyme flexibility and turnover rate on pro-pharmaceutical substrates.

Industrial light at the end of the iron-containing (group III) alcohol dehydrogenase tunnel.

There are three prominent alcohol dehydrogenases superfamilies: short-chain, medium-chain, and iron-containing alcohol dehydrogenases (FeADHs). Many members are valuable catalysts for producing industrially relevant products such as active pharmaceutical intermediates, chiral synthons, biopolymers, biofuels, and secondary metabolites. However, FeADHs are the least explored enzymes among the superfamilies for commercial tenacities. They portray a conserved structure having a "tunnel-like" cofactor and substrate binding site with particular functions, despite representing high sequence diversity. Interestingly, phylogenetic analysis demarcates enzymes catalyzing distinct native substrates where closely related clades convert similar molecules. Further, homologs from various mesophilic and thermophilic microbes have been explored for designing a solvent and temperature-resistant enzyme for industrial purposes. The review explores different iron-containing alcohol dehydrogenases potential engineering of the enzymes and substrates helpful in manufacturing commercial products.

Validated In Silico Population Model of Escherichia coli.

Flux balance analysis (FBA) and ordinary differential equation models have been instrumental in depicting the metabolic functioning of a cell. Nevertheless, they demonstrate a population's average behavior (summation of individuals), thereby portraying homogeneity. However, living organisms such as Escherichia coli contain more biochemical reactions than engaging metabolites, making them an underdetermined and degenerate system. This results in a heterogeneous population with varying metabolic patterns. We have formulated a population systems biology model that predicts this degeneracy by emulating a diverse metabolic makeup with unique biochemical signatures. The model mimics the universally accepted experimental view that a subpopulation of bacteria, even under normal growth conditions, renders a unique biochemical state, leading to the synthesis of metabolites and persister progenitors of antibiotic resistance and biofilms. We validate the platform's predictions by producing commercially important heterologous (isobutanol) and homologous (shikimate) metabolites. The predicted fluxes are tested in vitro resulting in 32- and 42-fold increased product of isobutanol and shikimate, respectively. Moreover, we authenticate the platform by mimicking a bacterial population in the presence of glyphosate, a metabolic pathway inhibitor. Here, we observe a fraction of subsisting persisters despite inhibition, thus affirming the signature of a heterogeneous populace. The platform has multiple uses based on the disposition of the user.

Validated In Silico Model for Biofilm Formation in Escherichia coli.

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.

Structure and function relationship of OqxB efflux pump from Klebsiella pneumoniae.

OqxB is an RND (Resistance-Nodulation-Division) efflux pump that has emerged as a factor contributing to the antibiotic resistance in Klebsiella pneumoniae. OqxB underwent horizontal gene transfer and is now seen in other Gram-negative bacterial pathogens including Escherichia coli, Enterobacter cloacae and Salmonella spp., further disseminating multi-drug resistance. In this study, we describe crystal structure of OqxB with n-dodecyl-ß-D-maltoside (DDM) molecules bound in its substrate-binding pocket, at 1.85 Å resolution. We utilize this structure in computational studies to predict the key amino acids contributing to the efflux of fluoroquinolones by OqxB, distinct from analogous residues in related transporters AcrB and MexB. Finally, our complementation assays with mutated OqxB and minimum inhibitory concentration (MIC) experiments with clinical isolates of E. coli provide further evidence that the predicted structural features are indeed involved in ciprofloxacin efflux.