Biological production of chemicals often requires the use of cellular cofactors, such as nicotinamide adenine dinucleotide phosphate (NADP + ). These cofactors are expensive to use in vitro and difficult to control in vivo. We demonstrate the development of a noncanonical redox cofactor system based on nicotinamide mononucleotide (NMN + ). The key enzyme in the system is a computationally designed glucose dehydrogenase with a 10 7 -fold cofactor specificity switch toward NMN + over NADP + based on apparent enzymatic activity. We demonstrate that this system can be used to support diverse redox chemistries in vitro with high total turnover number (~39,000), to channel reducing power in Escherichia coli whole cells specifically from glucose to a pharmaceutical intermediate, levodione, and to sustain the high metabolic flux required for the Reprints and permissions information is available at www.nature.com/reprints.

The ability to biosynthetically produce chemicals beyond what is commonly found in Nature requires the discovery of novel enzyme function. Here we utilize two approaches to discover enzymes that enable specific production of longer-chain (C5–C8) alcohols from sugar. The first approach combines bioinformatics and molecular modelling to mine sequence databases, resulting in a diverse panel of enzymes capable of catalysing the targeted reaction. The median catalytic efficiency of the computationally selected enzymes is 75-fold greater than a panel of naively selected homologues. This integrative genomic mining approach establishes a unique avenue for enzyme function discovery in the rapidly expanding sequence databases. The second approach uses computational enzyme design to reprogramme specificity. Both approaches result in enzymes with >100-fold increase in specificity for the targeted reaction. When enzymes from either approach are integrated in vivo, longer-chain alcohol production increases over 10-fold and represents >95% of the total alcohol products.

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Keto acid decarboxylase (Kdc) is a key enzyme in producing keto acid derived higher alcohols, like isobutanol. The most active Kdc's are found in mesophiles; the only reported Kdc activity in thermophiles is 2 orders of magnitude less active. Therefore, the thermostability of mesophilic Kdc limits isobutanol production temperature. Here, we report development of a thermostable 2-ketoisovalerate decarboxylase (Kivd) with 10.5-fold increased residual activity after 1h preincubation at 60 °C. Starting with mesophilic Lactococcus lactis Kivd, a library was generated using random mutagenesis and approximately 8,000 independent variants were screened. The top single-mutation variants were recombined. To further improve thermostability, 16 designs built using Rosetta Comparative Modeling were screened and the most active was recombined to form our best variant, LLM4. Compared to wild-type Kivd, a 13 °C increase in melting temperature and over 4-fold increase in half-life at 60 °C were observed. LLM4 will be useful for keto acid derived alcohol production in lignocellulosic thermophiles. KEYWORDS: thermostability, keto acid decarboxylase, high-throughput screening, protein design, directed evolution, isobutyl alcohol K eto acid decarboxylases (Kdc) are an important group of

The rapidly growing appreciation of enzymes' catalytic and substrate promiscuity may lead to their expanded use in the fields of chemical synthesis and industrial biotechnology. Here, we explore the substrate promiscuity of enoyl-acyl carrier protein reductases (commonly known as FabI) and how that promiscuity is a function of inherent reactivity and the geometric demands of the enzyme's active site. We demonstrate that these enzymes catalyze the reduction of a wide range of substrates, particularly α,β-unsaturated aldehydes. In addition, we demonstrate that a combination of quantum mechanical hydride affinity calculations and molecular docking can be used to rapidly categorize compounds that FabI can use as substrates. The results here provide new insight into the determinants of catalysis for FabI and set the stage for the development of a new assay for drug discovery, organic synthesis, and novel biocatalysts.

To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of Escherichia coli class I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.

Enzymes have been through millions of years of evolution during which their active-site microenvironments are fine-tuned. Active-site residues are commonly conserved within protein families, indicating their importance for substrate recognition and catalysis. In this work, we systematically mutated active-site residues of l -threonine dehydrogenase from Thermoplasma volcanium and characterized the mutants against a panel of substrate analogs. Our results demonstrate that only a subset of these residues plays an essential role in substrate recognition and catalysis and that the native enzyme activity can be further enhanced roughly 4.6-fold by a single point mutation. Kinetic characterization of mutants on substrate analogs shows that l -threonine dehydrogenase possesses promiscuous activities toward other chemically similar compounds not previously observed. Quantum chemical calculations on the hydride-donating ability of these substrates also reveal that this enzyme did not evolve to harness the intrinsic substrate reactivity for enzyme catalysis. Our analysis provides insights into connections between the details of enzyme active-site structure and specific function. These results are directly applicable to rational enzyme design and engineering.

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