Acid-active proteases to optimize dietary protein digestibility: a step towards sustainable nutrition.

Introduction: Historically, prioritizing abundant food production often resulted in overlooking nutrient quality and bioavailability, however, environmental concerns have now propelled sustainable nutrition and health efficacy to the forefront of global attention. In fact, increasing demand for protein is the major challenge facing the food system in the 21st century with an estimation that 70% more food is needed by 2050. This shift has spurred interest in plant-based proteins for their sustainability and health benefits, but most alternative sources of protein are poorly digestible. There are two approaches to solve digestibility: improve the digestibility of food proteins or improve the digestive capacity of consumers. Enhancing nutrient digestibility and bioavailability across diverse protein sources is crucial, with proteases presenting a promising avenue. Research, inspired by the proteases of human breast milk, has demonstrated that exogenous microbial proteases can activate within the human digestive tract and substantially increase the digestion of targeted proteins that are otherwise difficult to fully digest. Methods: Here, we introduce the use of an acid-active family of bacterial proteases (S53) to improve the digestibility and nutritional quality of a variety of protein sources, evaluated using the INFOGEST 2.0 protocol. Results: Results from in vitro digestibility indicate that the most effective protease in the S53 family substantially improves the digestibility of an array of animal and plant-derived proteins-soy, pea, chickpea, rice, casein, and whey. On average, this protease elevated protein digestibility by 115% during the gastric phase and by 15% in the intestinal phase, based on the degree of hydrolysis. Discussion: The widespread adoption of these proteases has the potential to enhance nutritional value and contribute to food security and sustainability. This approach would complement ongoing efforts to improve proteins in the food supply, increase the quality of more sustainable protein sources and aid in the nourishment of patients with clinically compromised, fragile intestines and individuals like older adults and high-performance athletes who have elevated protein needs.

The pyruvate decarboxylase activity of IpdC is a limitation for isobutanol production by Klebsiella pneumoniae.

BACKGROUND: Klebsiella pneumoniae contains an endogenous isobutanol synthesis pathway. The ipdC gene annotated as an indole-3-pyruvate decarboxylase (Kp-IpdC), was identified to catalyze the formation of isobutyraldehyde from 2-ketoisovalerate. RESULTS: Compared with 2-ketoisovalerate decarboxylase from Lactococcus lactis (KivD), a decarboxylase commonly used in artificial isobutanol synthesis pathways, Kp-IpdC has an 2.8-fold lower Km for 2-ketoisovalerate, leading to higher isobutanol production without induction. However, expression of ipdC by IPTG induction resulted in a low isobutanol titer. In vitro enzymatic reactions showed that Kp-IpdC exhibits promiscuous pyruvate decarboxylase activity, which adversely consume the available pyruvate precursor for isobutanol synthesis. To address this, we have engineered Kp-IpdC to reduce pyruvate decarboxylase activity. From computational modeling, we identified 10 amino acid residues surrounding the active site for mutagenesis. Ten designs consisting of eight single-point mutants and two double-point mutants were selected for exploration. Mutants L546W and T290L that showed only 5.1% and 22.1% of catalytic efficiency on pyruvate compared to Kp-IpdC, were then expressed in K. pneumoniae for in vivo testing. Isobutanol production by K. pneumoniae T290L was 25% higher than that of the control strain, and a final titer of 5.5 g/L isobutanol was obtained with a substrate conversion ratio of 0.16 mol/mol glucose. CONCLUSIONS: This research provides a new way to improve the efficiency of the biological route of isobutanol production.

Novel Binding Partners for CCT and PhLP1 Suggest a Common Folding Mechanism for WD40 Proteins with a 7-Bladed Beta-Propeller Structure.

This study investigates whether selected WD40 proteins with a 7-bladed ß-propeller structure, similar to that of the ß subunit of the G protein heterotrimer, interact with the cytosolic chaperonin CCT and its known binding partner, PhLP1. Previous studies have shown that CCT is required for the folding of the Gß subunit and other WD40 proteins. The role of PhLP1 in the folding of Gß has also been established, but it is unknown if PhLP1 assists in the folding of other Gß-like proteins. The binding of three Gß-like proteins, TBL2, MLST8 and CDC20, to CCT and PhLP1, was demonstrated in this study. Co-immunoprecipitation assays identified one novel binding partner for CCT and three new interactors for PhLP1. All three of the studied proteins interact with CCT and PhLP1, suggesting that these proteins may have a folding machinery in common with that of Gß and that the well-established Gß folding mechanism may have significantly broader biological implications than previously thought. These findings contribute to continuous efforts to determine common traits and unique differences in the folding mechanism of the WD40 ß-propeller protein family, and the role PhLP1 has in this process.

Discovery, Design, and Structural Characterization of Alkane-Producing Enzymes across the Ferritin-like Superfamily.

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.

Engineering a nicotinamide mononucleotide redox cofactor system for biocatalysis.

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 107-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 central carbon metabolism to support growth. Overall, this work demonstrates efficient use of a noncanonical cofactor in biocatalysis and metabolic pathway design.

Elucidating Substrate Promiscuity within the FabI Enzyme Family.

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,ß-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.

Engineering a Thermostable Keto Acid Decarboxylase Using Directed Evolution and Computationally Directed Protein Design.

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.

Systematic Functional Analysis of Active-Site Residues in l-Threonine Dehydrogenase from Thermoplasma volcanium.

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.

Integrative genomic mining for enzyme function to enable engineering of a non-natural biosynthetic pathway.

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.

Computational enzyme design: transitioning from catalytic proteins to enzymes.

The widespread interest in enzymes stem from their ability to catalyze chemical reactions under mild and ecologically friendly conditions with unparalleled catalytic proficiencies. While thousands of naturally occurring enzymes have been identified and characterized, there are still numerous important applications for which there are no biological catalysts capable of performing the desired chemical transformation. In order to engineer enzymes for which there is no natural starting point, efforts using a combination of quantum chemistry and force-field based protein molecular modeling have led to the design of novel proteins capable of catalyzing chemical reactions not catalyzed by naturally occurring enzymes. Here we discuss the current status and potential avenues to pursue as the field of computational enzyme design moves forward.