The live biotherapeutic SYNB1353 decreases plasma methionine via directed degradation in animal models and healthy volunteers.

Methionine is an essential proteinogenic amino acid, but its excess can lead to deleterious effects. Inborn errors of methionine metabolism resulting from loss of function in cystathionine ß-synthase (CBS) cause classic homocystinuria (HCU), which is managed by a methionine-restricted diet. Synthetic biotics are gastrointestinal tract-targeted live biotherapeutics that can be engineered to replicate the benefits of dietary restriction. In this study, we assess whether SYNB1353, an E. coli Nissle 1917 derivative, impacts circulating methionine and homocysteine levels in animals and healthy volunteers. In both mice and nonhuman primates (NHPs), SYNB1353 blunts the appearance of plasma methionine and plasma homocysteine in response to an oral methionine load. A phase 1 clinical study conducted in healthy volunteers subjected to an oral methionine challenge demonstrates that SYNB1353 is well tolerated and blunts plasma methionine by 26%. Overall, SYNB1353 represents a promising approach for methionine reduction with potential utility for the treatment of HCU.

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.

Thermal stability and kinetic constants for 129 variants of a family 1 glycoside hydrolase reveal that enzyme activity and stability can be separately designed.

Accurate modeling of enzyme activity and stability is an important goal of the protein engineering community. However, studies seeking to evaluate current progress are limited by small data sets of quantitative kinetic constants and thermal stability measurements. Here, we report quantitative measurements of soluble protein expression in E. coli, thermal stability, and Michaelis-Menten constants (kcat, KM, and kcat/KM) for 129 designed mutants of a glycoside hydrolase. Statistical analyses reveal that functional Tm is independent of kcat, KM, and kcat/KM in this system, illustrating that an individual mutation can modulate these functional parameters independently. In addition, this data set is used to evaluate computational predictions of protein stability using the established Rosetta and FoldX algorithms. Predictions for both are found to correlate only weakly with experimental measurements, suggesting improvements are needed in the underlying algorithms.

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.

Computational Introduction of Catalytic Activity into Proteins.

Recently, there have been several successful cases of introducing catalytic activity into proteins. One method that has been used successfully to achieve this is the theozyme placement and enzyme design algorithms implemented in Rosetta Molecular Modeling Suite. Here, we illustrate how to use this software to recapitulate the placement of catalytic residues and ligand into a protein using a theozyme, protein scaffold, and catalytic constraints as input.

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants.

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.