Alterations at the peptidyl transferase centre of the ribosome induced by the synergistic action of the streptogramins dalfopristin and quinupristin

BACKGROUND: The bacterial ribosome is a primary target of several classes of antibiotics. Investigation of the structure of the ribosomal subunits in complex with different antibiotics can reveal the mode of inhibition of ribosomal protein synthesis. Analysis of the interactions between antibiotics and the ribosome permits investigation of the specific effect of modifications leading to antimicrobial resistances. Streptogramins are unique among the ribosome-targeting antibiotics because they consist of two components, streptogramins A and B, which act synergistically. Each compound alone exhibits a weak bacteriostatic activity, whereas the combination can act bactericidal. The streptogramins A display a prolonged activity that even persists after removal of the drug. However, the mode of activity of the streptogramins has not yet been fully elucidated, despite a plethora of biochemical and structural data. RESULTS: The investigation of the crystal structure of the 50S ribosomal subunit from Deinococcus radiodurans in complex with the clinically relevant streptogramins quinupristin and dalfopristin reveals their unique inhibitory mechanism. Quinupristin, a streptogramin B compound, binds in the ribosomal exit tunnel in a similar manner and position as the macrolides, suggesting a similar inhibitory mechanism, namely blockage of the ribosomal tunnel. Dalfopristin, the corresponding streptogramin A compound, binds close to quinupristin directly within the peptidyl transferase centre affecting both A- and P-site occupation by tRNA molecules. CONCLUSIONS: The crystal structure indicates that the synergistic effect derives from direct interaction between both compounds and shared contacts with a single nucleotide, A2062. Upon binding of the streptogramins, the peptidyl transferase centre undergoes a significant conformational transition, which leads to a stable, non-productive orientation of the universally conserved U2585. Mutations of this rRNA base are known to yield dominant lethal phenotypes. It seems, therefore, plausible to conclude that the conformational change within the peptidyl transferase centre is mainly responsible for the bactericidal activity of the streptogramins and the post-antibiotic inhibition of protein synthesis

L11 domain rearrangement upon binding to RNA and thiostrepton studied by NMR spectroscopy

Ribosomal proteins are assumed to stabilize specific RNA structures and promote compact folding of the large rRNA. The conformational dynamics of the protein between the bound and unbound state play an important role in the binding process. We have studied those dynamical changes in detail for the highly conserved complex between the ribosomal protein L11 and the GTPase region of 23S rRNA. The RNA domain is compactly folded into a well defined tertiary structure, which is further stabilized by the association with the C-terminal domain of the L11 protein (L11(ctd)). In addition, the N-terminal domain of L11 (L11(ntd)) is implicated in the binding of the natural thiazole antibiotic thiostrepton, which disrupts the elongation factor function. We have studied the conformation of the ribosomal protein and its dynamics by NMR in the unbound state, the RNA bound state and in the ternary complex with the RNA and thiostrepton. Our data reveal a rearrangement of the L11(ntd), placing it closer to the RNA after binding of thiostrepton, which may prevent binding of elongation factors. We propose a model for the ternary L11–RNA–thiostrepton complex that is additionally based on interaction data and conformational information of the L11 protein. The model is consistent with earlier findings and provides an explanation for the role of L11(ntd) in elongation factor binding

Massive X-ray screening reveals two allosteric drug binding sites of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous health problems and economical challenges for mankind. To date, no effective drug is available to directly treat the disease and prevent virus spreading. In a search for a drug against COVID-19, we have performed a massive X-ray crystallographic screen of repurposing drug libraries containing 5953 individual compounds against the SARS-CoV-2 main protease (Mpro), which is a potent drug target as it is essential for the virus replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds binding to Mpro. In subsequent cell-based viral reduction assays, one peptidomimetic and five non-peptidic compounds showed antiviral activity at non-toxic concentrations. Interestingly, two compounds bind outside the active site to the native dimer interface in close proximity to the S1 binding pocket. Another compound binds in a cleft between the catalytic and dimerization domain of Mpro. Neither binding site is related to the enzymatic active site and both represent attractive targets for drug development against SARS-CoV-2. This X-ray screening approach thus has the potential to help deliver an approved drug on an accelerated time-scale for this and future pandemics

X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous human suffering. To date, no effective drug is available to directly treat the disease. In a search for a drug against COVID-19, we have performed a high-throughput X-ray crystallographic screen of two repurposing drug libraries against the SARS-CoV-2 main protease (M^(pro)), which is essential for viral replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds that bind to M^(pro). In subsequent cell-based viral reduction assays, one peptidomimetic and six non-peptidic compounds showed antiviral activity at non-toxic concentrations. We identified two allosteric binding sites representing attractive targets for drug development against SARS-CoV-2

Methoden zur Bestimmung stadtklimatischer Parameter und deren Belastbarkeit

Die für die Bestimmung stadtklimatischer Parameter wie Wind, Temperatur, Feuchte, Niederschlag und Luftbelastung geeigneten Messmethoden (in-situ, mobil, fernerkundet) und Modelltypen (statistisch, physikalisch, numerisch) werden in diesem Kapitel mit ihren Vor- und Nachteilen kurz vorgestellt. Ihre Eignung für die Bestimmung stadtklimatischer Parameter in Szenarien der Stadtentwicklung und des Klimawandels wird erläutert. Methods for determining urban parameters and their suitability: Measurement methods (in-situ, mobile, remote sensing) and types of models (statistical, physical, numerical) suitable for determining urban climate parameters such as wind, temperature, humidity, precipitation and air pollution concentration are briefly presented in this chapter, together with their advantages and disadvantages. Their suitability for determining urban climate parameters in scenarios of urban development and of climate change is explaine

Interactive analysis notebooks on DESY batch resources

Batch scheduling systems are usually designed to maximise fair resource utilisation and efficiency, but are less well designed for demanding interactive processing, which requires fast access to resources while low upstart latency is only of secondary significance for high throughput of high performance computing scheduling systems. The computing clusters at DESY are intended as batch systems for end users to run massive analysis and simulation jobs enabling fast turnaround systems, in particular when processing is expected to feed back to operation of instruments in near real-time. The continuously increasing popularity of Jupyter Notebooks for interactive and online processing made an integration of this technology into the DESY batch systems indispensable. We present here our approach to utilise the HTCondor and SLURM backends to integrate Jupyter Notebook servers and the techniques involved to provide fast access. The chosen approach offers a smooth user experience allowing users to customize resource allocation tailored to their computational requirements. In addition, we outline the differences between the HPC and the HTC implementations and give an overview of the experience of running Jupyter Notebook services

Scaling the U-net: Segmentation of biodegradable bone implants in high-resolution synchrotron microtomograms

Highly accurate segmentation of large 3D volumes is a demanding task. Challenging applications like the segmentation of synchrotron microtomograms (SRuCT) at high-resolution, which suffer from low contrast, high spatial variability and measurement artifacts, readily exceed the capacities of conventional segmentation methods, including the manual segmentation by human experts. The quantitative characterization of the osseointegration and spatio-temporal biodegradation process of bone implants requires reliable, and very precise segmentation. 2D U-net based segmentation has been a natural choice. To obtain optimal results, we investigated the scaling of 2D U-net for high resolution grayscale volumes, the impact of most crucial model hyper-parameters (i.e., the model width, depth, and input size) and multi-axes prediction fusing. To leverage the 3D information of high-resolution SRuCT, common three axes prediction fusing is extended, investigating the effect of adding more than three axes prediction. In a systematic evaluation we compare the performance of scaling the U-net by intersection over union and quantitative measurements of osseointegration and degradation parameters. Overall, we observe that a compound scaling of the U-net and multi-axes prediction fusing with soft voting yields the best results. The quantitative analysis shows that the trained model is better and significantly faster than the current semi-manual segmentation

Abstract: Verbesserung des 2D U-Nets für die 3D Mikrotomographie mit Synchrotronstrahlung mittels Multi-Axes Fusing

Die genaue Segmentierung großer 3D-Volumina ist eine sehr zeitaufwendige und für die Analyse und Interpretation unabdingbare Aufgabe. Die am Synchrotron gemessene Mikrotomogramme (SRμCT) zu segmentieren, ist besonders anspruchsvoll, sowohl für algorithmische Lösungen, als auch für die Experten, da sich die Daten durch geringen Kontrast, hohe räumliche Variabilität und Messartefakte auszeichnen. Am Beispiel von 3D Tomogrammen zu Biodegradationsprozessen von Knochenimplantaten untersuchten wir die Skalierung des 2D U-Nets für hochaufgelöste Graustufenvolumina unter Verwendung von drei wichtigen Modellhyperparametern (d. h. Modellbreite, -tiefe und Eingabegröße) [1]. Um die 3D-Informationen der SRμCTs zu nutzen, wird die Vorhersage der Segmentierung aus drei orthogonalen Blickrichtungen gemacht und anschließendem Fusionieren derselben. Wir haben diese Fusionierung erweitert und den Effekt der Nutzung von mehr als drei Achsen untersucht. In der Auswertung vergleichen wir die Ergebnisse der Skalierung des U-Nets durch Intersection over Union (IoU) und quantitative Messungen von Osseointegrations- und Degradationsparametern. Zusammenfassend lässt sich feststellen, dass eine kombinierte Skalierung des U-Netzes (d.h. alle drei Modellparameter werden gemeinsam geändert) und eine Mehrachsenvorhersage, die mit Soft Voting fusioniert wird, den höchsten IoU für die Klasse „Degradationsschicht“ von 0.813 gegenüber der Baseline von 0.801 ergibt. Abschließend zeigte die quantitative Analyse, dass die auf der Grundlage der Modellsegmentierung berechneten Parameter weniger von den Ground-Truth-Ergebnissen abwichen als die auf der Grundlage der halbautomatischen Segmentierungsmethode berechneten