Robust Approach for Quantifying Glucocorticoid Binding to the Anti-Cortisol Fab Fragment via Native Mass Spectrometry

In the development of proteins, aptamers, and molecular imprints for diagnostic purposes, a major goal is to obtain a molecule with both a high binding affinity and specificity for the target ligand. Cushing syndrome or Addison's disease can be diagnosed by cortisol level tests. We have previously characterized and solved the crystal structure of an anti-cortisol (17) Fab fragment having a high affinity to cortisol but also significant cross-reactivity to other glucocorticoids, especially the glucocorticoid drug prednisolone. We used native mass spectrometry (MS) to determine the binding affinities of nine steroid hormones to anti-cortisol (17) Fab, including steroidogenic precursors of cortisol. Based on the results, the number of hydroxyl groups in the structure of a steroid ligand plays a key role in the antigen recognition by the Fab fragment as the ligands with three hydroxyl groups, cortisol and prednisolone, had the highest affinities. The antibody affinity toward steroid hormones often decreases with a decrease in the number of hydroxyl groups in the structure. The presence of the hydroxyl group at position C11 increased the affinity more than did the other hydroxyl groups at positions C17 or C21. The binding affinities obtained by native MS were compared to the values determined by surface plasmon resonance (SPR), and the affinities were found to correlate well between these two techniques. Our study demonstrates that native MS with a large dynamic range and high sensitivity is a versatile tool for ligand binding studies of proteins.

Calculation and Visualization of Binding Equilibria in Protein Studies

A set of simulation applets has been developed for visualizing the behavior of the association and dissociation reactions in protein studies. These reactions are simple equilibrium reactions, and the equilibrium constants, most often dissociation constant K _D, are useful measures of affinity. Equilibria, even in simple systems, may not behave intuitively, which can cause misconceptions and mistakes. These applets can be utilized for planning experiments, for verifying experimental results, and for visualization of the equilibria in education. The considered reactions include protein homodimerization, ligand binding to a receptor (or heterodimerization), and competitive ligand binding. The latter one can be considered as either a ligand binding to two receptors or a binding of two ligands to a single receptor. In general, the user is required to input the total concentrations of all proteins and ligands and the dissociation constants of all complexes, and the applets output the equilibrium concentrations of all protein species graphically as functions of concentration and as numerical values at a specified point. Also, a curve fitting tool is provided which roughly estimates the concentrations or the dissociation constants based on the experimental data. The applets are freely available online (URL: https://protsim.github.io/protsim) and readily hackable for custom purposes if necessary.

Three-dimensional structure of xylonolactonase from Caulobacter crescentus: A mononuclear iron enzyme of the 6-bladed β-propeller hydrolase family

Xylonolactonase Cc XylC from Caulobacter crescentus catalyzes the hydrolysis of the intramolecular ester bond of d‐xylonolactone. We have determined crystal structures of Cc XylC in complex with d‐xylonolactone isomer analogues d‐xylopyranose and (r)‐(+)‐4‐hydroxy‐2‐pyrrolidinone at high resolution. Cc XylC has a 6‐bladed β‐propeller architecture, which contains a central open channel having the active site at one end. According to our previous native mass spectrometry studies, Cc XylC is able to specifically bind Fe²⁺. The crystal structures, presented here, revealed an active site bound metal ion with an octahedral binding geometry. The side chains of three amino acid residues, Glu18, Asn146, and Asp196, which participate in binding of metal ion are located in the same plane. The solved complex structures allowed suggesting a reaction mechanism for intramolecular ester bond hydrolysis in which the major contribution for catalysis arises from the carbonyl oxygen coordination of the xylonolactone substrate to the Fe²⁺. The structure of Cc XylC was compared with eight other ester hydrolases of the β‐propeller hydrolase family. The previously published crystal structures of other β‐propeller hydrolases contain either Ca²⁺, Mg²⁺, or Zn²⁺ and show clear similarities in ligand and metal ion binding geometries to that of Cc XylC. It would be interesting to reinvestigate the metal binding specificity of these enzymes and clarify whether they are also able to use Fe²⁺ as a catalytic metal. This could further expand our understanding of utilization of Fe²⁺ not only in oxidative enzymes but also in hydrolases.

PDB Code(s): 7PLB, 7PLC and 7PLD;

Xylonolactonase from Caulobacter crescentus Is a Mononuclear Nonheme Iron Hydrolase

Caulobacter crescentus xylonolactonase (Cc XylC, EC 3.1.1.68) catalyzes an intramolecular ester bond hydrolysis over a nonenzymatic acid/base catalysis. Cc XylC is a member of the SMP30 protein family, whose members have previously been reported to be active in the presence of bivalent metal ions, such as Ca²⁺, Zn²⁺, and Mg²⁺. By native mass spectrometry, we studied the binding of several bivalent metal ions to Cc XylC and observed that it binds only one of them, namely, the Fe²⁺ cation, specifically and with a high affinity (K_d = 0.5 μM), pointing out that Cc XylC is a mononuclear iron protein. We propose that bivalent metal cations also promote the reaction nonenzymatically by stabilizing a short-lived bicyclic intermediate on the lactone isomerization reaction. An analysis of the reaction kinetics showed that Cc XylC complexed with Fe²⁺ can speed up the hydrolysis of d-xylono-1,4-lactone by 100-fold and that of d-glucono-1,5-lactone by 10-fold as compared to the nonenzymatic reaction. To our knowledge, this is the first discovery of a nonheme mononuclear iron-binding enzyme that catalyzes an ester bond hydrolysis reaction.

Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods

In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde. KEY POINTS: • DERA aldolases are promiscuous enzymes. • Synthetic utility of DERA aldolase was improved by protein engineering approaches. • Machine learning methods aid the protein engineering of DERA.

The meaningfulness of open data in Public Health and Healthcare

Background

The growing challenges and opportunities of Big Data for Public Health have revealed the potential to improve the efficiency and cost-effectiveness of public policy, for example through better targeting of resources with regard to General Practice (GP) prescribing. Open data has an important role due to its easy access and potential to complement proprietary data sources from, e.g., regional hospitals, and also itself be complemented with social data acquired by specialized approaches.

Methods

MIDAS pipeline of open source tools aiming integrating, analysing and visualising Open Data enabling health professionals and decision-makers to: (i) improve the usability of open data in combination with proprietary data through combining multiple visualisation tools in an integrated dashboard (ii) to explore the meaning of data in a global/local context based on new information using tone analysis and natural language techniques; and (iii) to have better informed decision-making based on evidence from trusted knowledge-bases. Specific data sources used have included information extracted from the biomedical database MEDLINE, worldwide news and government open data. Social media sources have also been used to gather information from the general public.

Results

Results include a strong correlation between antidepressant prescribing and economic deprivation, and a wide variation in how individual GP practices respond to demographic conditions. Automated anomaly detection based on the Local Outlier Probability has also been shown to be an easily understood and controllable approach to identifying prescribing outliers.

Conclusions

MIDAS demonstrates the significant value of open data from heterogeneous sources as basis decision-making in public health and healthcare, particularly when it is combined with proprietary or closed datasets. A key challenge in this regard is the ability to integrate and utilize data from diverse sources in a variety of formats and standards.

Key messages

MIDAS is exemplar on tackling the need for improved standards of open data, and new software architectures, tools and platforms addressing a complex ecosystem of heterogenous data sources and formats. MIDAS demonstrates the significant value of open data from heterogeneous sources as basis decision-making in public health and healthcare, particularly when combined with proprietary datasets.

Removal of F-specific RNA bacteriophages in artificial recharge of groundwater--a field study

Artificial recharge of groundwater offers a semi-natural means to produce raw water for drinking-water plants. Surface water works are increasingly being replaced by artificial groundwater works in Finland. Two municipalities, one serving 30,000 and the other 170,000 inhabitants, have considered filtering river water through eskers for the production of potable water. In this study the removal of bacteriophages during infiltration of river water was estimated, for the evaluation of treatment adequacy in a field study. A 5-m-deep column of sand was constructed and used to mimic the percolating phase in infiltration. An artificial esker was constructed on the riverbank by isolating a 2-m-wide, 2-m-deep and 18-m-long bed of coarse sand with plastic. The sand bed represented the saturated zone. River water was pumped at a rate of 40 L/h to the sand column. The river water was spiked with F+ specific RNA phage MS2 by adding phage suspension during one week at an average concentration of 4.3 x 10(9) PFU/mL. Samples for phage assays were taken during one month, from four sampling sites, on the basis of detention time as estimated by a tracer experiment with sodium chloride. The median count of MS2 for percolated water was 2.4 x 10(5) PFU/mL, representing a 96.7% reduction. During the passage of 6 m in the saturated zone, a further reduction of 98.5% occurred. During the passage from 6 m to 12 m the additional reduction was 99.97%. The overall reduction was between 6 and 7 log10 units. The removal of MS2 phages was rather efficient, although the esker material was coarse, mainly sandy, gravel.