Molecular insights into the endoperoxide formation by Fe(II)/α-KG-dependent oxygenase NvfI

Endoperoxide-containing natural products are a group of compounds with structurally unique cyclized peroxide moieties. Although numerous endoperoxide-containing compounds have been isolated, the biosynthesis of the endoperoxides remains unclear. NvfI from Aspergillus novofumigatus IBT 16806 is an endoperoxidase that catalyzes the formation of fumigatonoid A in the biosynthesis of novofumigatonin. Here, we describe our structural and functional analyses of NvfI. The structural elucidation and mutagenesis studies indicate that NvfI does not utilize a tyrosyl radical in the reaction, in contrast to other characterized endoperoxidases. Further, the crystallographic analysis reveals significant conformational changes of two loops upon substrate binding, which suggests a dynamic movement of active site during the catalytic cycle. As a result, NvfI installs three oxygen atoms onto a substrate in a single enzyme turnover. Based on these results, we propose a mechanism for the NvfI-catalyzed, unique endoperoxide formation reaction to produce fumigatonoid A.

Chemical mapping exposes the importance of active site interactions in governing the temperature dependence of enzyme turnover

Uncovering the role of global protein dynamics in enzyme turnover is needed to fully understand enzyme catalysis. Recently, we have demonstrated that the heat capacity of catalysis can reveal links between the protein free energy landscape, global protein dynamics and enzyme turnover, suggesting that subtle changes molecular interactions at the active site can affect long range protein dynamics and link to enzyme temperature activity. Here we use a model promiscuous enzyme (Glucose dehydrogenase from Sulfolobus Solfataricus) to chemically map how individual substrate interactions affect the temperature dependence of enzyme activity and the network of motions throughout the protein. Utilizing a combination of kinetics, REES spectroscopy and computational simulation we explore the complex relationship between enzyme-substrate interactions and the global dynamics of the protein. We find that changes in the heat capacity of catalysis and protein dynamics can be mapped to specific substrate-enzyme interactions. Our study reveals how subtle changes in substrate binding affect global changes in motion and flexibility extending throughout the protein.

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo kcats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.

A Fluorogenic Heparan Sulfate Disaccharide for the Measurement of Heparanase Activity

<p>The endo-β-glucuronidase heparanase mediates mammalian heparan sulfate catabolism, and is of considerable medical interest due to its prominent role in cancer aggression and metastasis. Biochemical studies of heparanase are currently hampered by a lack of suitable chromogenic or fluorogenic assay substrates, instead relying on lengthy multistep procedures to measure activity. Herein, we demonstrate that N’,6-O’-bis-sulfated 4-methylumbelliferyl heparan sulfate disaccharide is a competent fluorogenic heparanase substrate. Despite somewhat slow turnover, the high sensitivity of 4-methylumbelliferyl fluorescence provides a wide signal window that enables both enzyme turnover and inhibition kinetics measurements. Crystal structures with heparanase also provide the first ever observation of a substrate in an activated ¹S₃ conformation, highlighting previously unknown interactions involved in turnover. Our results pave the way for the design of further improved HPSE substrates that may enable rapid assessment of enzyme activity, which in turn will drive development of new heparanase inhibitors for therapeutic use.</p>

A Fluorogenic Heparan Sulfate Disaccharide for the Measurement of Heparanase Activity

<p>The endo-β-glucuronidase heparanase mediates mammalian heparan sulfate catabolism, and is of considerable medical interest due to its prominent role in cancer aggression and metastasis. Biochemical studies of heparanase are currently hampered by a lack of suitable chromogenic or fluorogenic assay substrates, instead relying on lengthy multistep procedures to measure activity. Herein, we demonstrate that N’,6-O’-bis-sulfated 4-methylumbelliferyl heparan sulfate disaccharide is a competent fluorogenic heparanase substrate. Despite somewhat slow turnover, the high sensitivity of 4-methylumbelliferyl fluorescence provides a wide signal window that enables both enzyme turnover and inhibition kinetics measurements. Crystal structures with heparanase also provide the first ever observation of a substrate in an activated ¹S₃ conformation, highlighting previously unknown interactions involved in turnover. Our results pave the way for the design of further improved HPSE substrates that may enable rapid assessment of enzyme activity, which in turn will drive development of new heparanase inhibitors for therapeutic use.</p>

A Capillary-Perfused, Nanocalorimeter Platform for Thermometric Enzyme-Linked Immunosorbent Assay with Femtomole Sensitivity

Enzyme-catalyzed chemical reactions produce heat. We developed an enclosed, capillary-perfused nanocalorimeter platform for thermometric enzyme-linked immunosorbent assay (TELISA). We used catalase as enzymes to model the thermal characteristics of the micromachined calorimeter. Model-assisted signal analysis was used to calibrate the nanocalorimeter and to determine reagent diffusion, enzyme kinetics, and enzyme concentration. The model-simulated signal closely followed the experimental signal after selecting for the enzyme turnover rate (kcat) and the inactivation factor (InF), using a known label enzyme amount (Ea). Over four discrete runs (n = 4), the minimized model root mean square error (RMSE) returned 1.80 ± 0.54 fmol for the 1.5 fmol experiments, and 1.04 ± 0.37 fmol for the 1 fmol experiments. Determination of enzyme parameters through calibration is a necessary step to track changing enzyme kinetic characteristics and improves on previous methods to determine label enzyme amounts on the calorimeter platform. The results obtained using model-system signal analysis for calibration led to significantly improved nanocalorimeter platform performance.

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, non-physiological, inconsistent, and labor-intensive to obtain.We use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.