Nicolas Blöchliger

Kinetic response of a photoperturbed allosteric protein

By covalently linking an azobenzene photoswitch across the binding groove of a PDZ domain, a conformational transition, similar to the one occurring upon ligand binding to the unmodified domain, can be initiated on a picosecond timescale by a laser pulse. The protein structures have been characterized in the two photoswitch states through NMR spectroscopy and the transition between them through ultrafast IR spectroscopy and molecular dynamics simulations. The binding groove opens on a 100-ns timescale in a highly nonexponential manner, and the molecular dynamics simulations suggest that the process is governed by the rearrangement of the water network on the protein surface. We propose this rearrangement of the water network to be another possible mechanism of allostery.

Fully automated disc diffusion for rapid antibiotic susceptibility test results: a proof-of-principle study

Background Antibiotic resistance poses a significant threat to patients suffering from infectious diseases. Early readings of antibiotic susceptibility test (AST) results could be of critical importance to ensure adequate treatment. Disc diffusion is a well-standardized, established and cost-efficient AST procedure; however, its use in the clinical laboratory is hampered by the many manual steps involved, and an incubation time of 16-18 h, which is required to achieve reliable test results. Methods We have evaluated a fully automated system for its potential for early reading of disc diffusion diameters after 6-12 h of incubation. We assessed availability of results, methodological precision, categorical agreement and interpretation errors as compared with an 18 h standard. In total, 1028 clinical strains (291 Escherichia coli , 272 Klebsiella pneumoniae , 176 Staphylococcus aureus and 289 Staphylococcus epidermidis ) were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLab TM automation system. Results and conclusions Our results demonstrate that: (i) early AST reading is possible for important pathogens; (ii) methodological precision is not hampered at early timepoints; and (iii) species-specific reading times must be selected. As inhibition zone diameters change over time and are phenotype/drug combination dependent, specific cut-offs and expert rules will be essential to ensure reliable interpretation and reporting of early susceptibility testing results.

Peptide Binding to a PDZ Domain by Electrostatic Steering via Nonnative Salt Bridges

We have captured the binding of a peptide to a PDZ domain by unbiased molecular dynamics simulations. Analysis of the trajectories reveals on-pathway encounter complex formation, which is driven by electrostatic interactions between negatively charged carboxylate groups in the peptide and positively charged side chains surrounding the binding site. In contrast, the final stereospecific complex, which matches the crystal structure, features completely different interactions, namely the burial of the hydrophobic side chain of the peptide C-terminal residue and backbone hydrogen bonds. The simulations show that nonnative salt bridges stabilize kinetically the encounter complex during binding. Unbinding follows the inverse sequence of events with the same nonnative salt bridges in the encounter complex. Thus, in contrast to protein folding, which is driven by native interactions, the binding of charged peptides can be steered by nonnative interactions, which might be a general mechanism, e.g., in the recognition of histone tails by bromodomains.

A scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systems

a b s t r a c t Advances in IT infrastructure have enabled the generation and storage of very large data sets describing complex systems continuously in time. These can derive from both simulations and measurements. Analysis of such data requires the availability of scalable algorithms. In this contribution, we propose a scalable algorithm that partitions instantaneous observations (snapshots) of a complex system into kinetically distinct sets (termed basins). To do so, we use a combination of ordering snapshots employing the method’s only essential parameter, i.e., a definition of pairwise distance, and annotating the resultant sequence, the so-called progress index, in different ways. Specifically, we propose a combination of cutbased and structural annotations with the former responsible for the kinetic grouping and the latter for diagnostics and interpretation. The method is applied to an illustrative test case, and the scaling of an approximate version is demonstrated to be O(N log N) with N being the number of snapshots. Two realworld data sets from river hydrology measurements and protein folding simulations are then used to highlight the utility of the method in finding basins for complex systems. Both limitations and benefits of the approach are discussed along with routes for future research.

High-Resolution Visualisation of the States and Pathways Sampled in Molecular Dynamics Simulations

We have recently developed a scalable algorithm for ordering the instantaneous observations of a dynamical system evolving continuously in time. Here, we apply the method to long molecular dynamics trajectories. The procedure requires only a pairwise, geometrical distance as input. Suitable annotations of both structural and kinetic nature reveal the free energy basins visited by biomolecules. The profile is supplemented by a trace of the temporal evolution of the system highlighting the sequence of events. We demonstrate that the resultant SAPPHIRE (States And Pathways Projected with HIgh REsolution) plots provide a comprehensive picture of the thermodynamics and kinetics of complex, molecular systems exhibiting dynamics covering a range of time and length scales. Information on pathways connecting states and the level of recurrence are quickly inferred from the visualisation. The considerable advantages of our approach are speed and resolution: the SAPPHIRE plot is scalable to very large data sets and represents every single snapshot. This minimizes the risk of missing states because of overlap or prior coarse-graining of the data.

Weighted Distance Functions Improve Analysis of High-Dimensional Data: Application to Molecular Dynamics Simulations.

Data mining techniques depend strongly on how the data are represented and how distance between samples is measured. High-dimensional data often contain a large number of irrelevant dimensions (features) for a given query. These features act as noise and obfuscate relevant information. Unsupervised approaches to mine such data require distance measures that can account for feature relevance. Molecular dynamics simulations produce high-dimensional data sets describing molecules observed in time. Here, we propose to globally or locally weight simulation features based on effective rates. This emphasizes, in a data-driven manner, slow degrees of freedom that often report on the metastable states sampled by the molecular system. We couple this idea to several unsupervised learning protocols. Our approach unmasks slow side chain dynamics within the native state of a miniprotein and reveals additional metastable conformations of a protein. The approach can be combined with most algorithms for clustering or dimensionality reduction.

Whole genome sequencing for drug resistance profile prediction in Mycobacterium tuberculosis

Whole genome sequencing allows rapid detection of drug-resistant M. tuberculosis isolates. However, high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data have thus far been lacking. We determined drug resistance profiles of 176 genetically diverse clinical M. tuberculosis isolates from Democratic Republic of the Congo, Ivory Coast, Peru, Thailand and Switzerland by quantitative phenotypic DST for 11 antituberculous drugs using the BD BACTEC MGIT 960 system and 7H10 agar dilution to generate a cross-validated phenotypic DST readout. We compared phenotypic drug susceptibility results with predicted drug resistance profiles inferred by whole genome sequencing. Both phenotypic DST methods identically classified the strains into resistant/susceptible in 73-99% of the cases, depending on the drug. Changes in minimal inhibitory concentrations were readily explained by mutations identified by whole genome sequencing. Using the whole genome sequences we were able to predict quantitative drug resistance levels where wild type and mutant MIC distributions did not overlap. The utility of genome sequences to predict quantitative levels of drug resistance was partially limited due to incompletely understood mechanisms influencing the expression of phenotypic drug resistance. The overall sensitivity and specificity of whole genome-based DST were 86.8% and 94.5%, respectively. Despite some limitations, whole genome sequencing has high predictive power to infer resistance profiles without the need for time-consuming phenotypic methods. One sentence summary Whole genome sequencing of clinical M. tuberculosis isolates accurately predicts drug resistance profiles and may replace culture-based drug susceptibility testing in the future.

Combining forecast probabilities with graphical visualization for improved reporting of antimicrobial susceptibility testing

Sir, Antimicrobial susceptibility testing (AST) reports are used by clinicians to guide antibiotic treatment of patients suffering from infectious diseases. AST reports, such as those based on the Kirby– Bauer disc diffusion test, in general do not include raw data, but an interpretation of the data in clinical categories (resistant, intermediate, susceptible), which reflect the likelihood of therapeutic success. This practice is intended to provide clinicians with clear and unambiguous clinical information. However, it entails a major loss of data. In contrast to results from clinical chemistry or haematology, where methodological precision measurements and quantitative results are implemented in the reports, these are absent in AST reports, where a mere classification into clinical categories is performed on the basis of inhibition zone measurements or MIC determinations. As a consequence, AST reports do not allow estimation of the probabilities of miscategorization, especially for measurements close to the clinical breakpoints (CBPs), where the error probability is higher. Since 2014, AST categorization of most drug/species combinations has depended exclusively on MIC and/or inhibition zone measurements. However, AST methods still suffer from a notable methodological variability, which can lead to miscategorization of a clinical isolate. Different miscategorization types are defined on the basis of the therapeutic implications. Erratic classifications of true-susceptible isolates as resistant are considered major errors (MEs), misclassifications of true-resistant isolates as susceptible are referred to as very major errors (vMEs) and false assignments of bacterial isolates to adjacent interpretative categories (S!I, I!S, I!R) are considered minor errors (mEs). The rates of MEs, vMEs and mEs depend on a number of factors: (i) presence and width of an intermediate zone; (ii) position of a population relative to the CBP; and (iii) methodological variation. The latter parameter includes both the methodological imprecision (inoculum size, agar composition, incubation time, disc content, interand intra-person variability in the reading) and the biological variation. Here we report on Antibiotrust, a software that visualizes the antibiogram and the reliability of the categorization. Antibiotrust was developed with the aim of conveying a graphic report displaying AST data from Kirby–Bauer disc diffusion testing. However, this approach can also be used for data based on MIC determination. As shown in Figure 1, Antibiotrust reports display the inhibition zone diameters of antibiotic panels used for the various bacterial groups (e.g. Enterobacteriaceae). The rectangular boxes correspond to the various antibiotics and are partitioned into the interpretative categories [resistant (r) in red, intermediate (i) in yellow and susceptible (s) in green]. Inhibition zone diameter distributions within the susceptible WT population appear in green shades, which become darker with higher prevalences. The distributions are based on local data and are updated each year. This feature is particularly relevant for susceptible clinical isolates, as it visualizes the position of the tested clinical isolate relative to the distribution of the WT population as derived from local epidemiological data. Black boxes and error bars indicate the inhibition zone diameter along with the methodological variation. The latter significantly influences the classification reliability and thereby the rate of MEs and vMEs. The width of the error bars is continuously updated for each combination of antibiotic and species or bacterial group and is given by the 2-fold standard deviation of weekly repeated measurements of inhibition zones of a quality-control strain. The interpretative category is displayed on the left side of the antibiotic box and is accompanied by the reliability of the categorization, which is calculated separately for all antibiotics using a normal model. Intrinsic antibiotic resistances are displayed in blue (e.g. ampicillin for Klebsiella pneumoniae). Monochromatic boxes display interpretative categorizations that are deduced from other antibiotics (i.e. ciprofloxacin and levofloxacin from norfloxacin) in agreement with EUCAST-derived in-house expert interpretation rules. Reliabilities are not determined for intrinsic resistances, deduced interpretations and for antibiotic/species combinations classified as intermediate. Several studies have shown a good correlation between MIC values and inhibition zone diameters in several bacterial species. A prospective integration of MIC values inferred from disc diffusion assays by Antibiotrust may further advance the accuracy of the AST reports and allow a better estimate of the antimicrobial susceptibility patterns. The additional information provided by Antibiotrust will help in choosing the most appropriate antibiotic, as clinicians will be in the

Rapid disc diffusion antibiotic susceptibility testing for Pseudomonas aeruginosa, Acinetobacter baumannii and Enterococcus spp.

Abstract Background We investigated the feasibility of rapid disc diffusion antibiotic susceptibility testing (rAST) with reading of inhibition zones after 6 and/or 8 h of incubation for Enterococcus faecalis, Enterococcus faecium, Pseudomonas aeruginosa and Acinetobacter baumannii. In addition, we evaluated discrimination of resistant populations from the WT populations at early timepoints and the requirement for clinical breakpoint adaptations for proper interpretation of rAST data. Methods In total, 815 clinical strains [E. faecalis (n = 135), E. faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158)] were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLabTM automation system. WT populations and non-WT populations were defined using epidemiological cut-offs. Results and conclusions rAST at 6 and 8 h was possible for A. baumannii and enterococci with readability of inhibition zones >90%. Overall categorical agreement of rAST at 6 h with AST at 18 h was 97.2%, 97.4% and 95.3% for E. faecalis, E. faecium and A. baumannii, respectively. With few exceptions, major categorization error rates were <1% for A. baumannii, and vancomycin-resistant E. faecium were clearly separated from the WT at 6 h. For P. aeruginosa the average readability of inhibition zones was 68.9% at 8 h and we found an overall categorical agreement of 94.8%. Adaptations of clinical breakpoints and/or introduction of technical buffer zones, preferably based on aggregated population data from various epidemiological settings, are required for proper interpretation of rAST.

MASTER: a model to improve and standardize clinical breakpoints for antimicrobial susceptibility testing using forecast probabilities

Objectives: The procedure for setting clinical breakpoints (CBPs) for antimicrobial susceptibility has been poorly standardized with respect to population data, pharmacokinetic parameters and clinical outcome. Tools to standardize CBP setting could result in improved antibiogram forecast probabilities. We propose a model to estimate probabilities for methodological categorization errors and defined zones of methodological uncertainty (ZMUs), i.e. ranges of zone diameters that cannot reliably be classified. The impact of ZMUs on methodological error rates was used for CBP optimization. Methods: The model distinguishes theoretical true inhibition zone diameters from observed diameters, which suffer from methodological variation. True diameter distributions are described with a normal mixture model. The model was fitted to observed inhibition zone diameters of clinical Escherichia coli strains. Repeated measurements for a quality control strain were used to quantify methodological variation. Results: For 9 of 13 antibiotics analysed, our model predicted error rates of <0.1% applying current EUCAST CBPs. Error rates were >0.1% for ampicillin, cefoxitin, cefuroxime and amoxicillin/clavulanic acid. Increasing the susceptible CBP (cefoxitin) and introducing ZMUs (ampicillin, cefuroxime, amoxicillin/clavulanic acid) decreased error rates to <0.1%. ZMUs contained low numbers of isolates for ampicillin and cefuroxime (3% and 6%), whereas the ZMU for amoxicillin/clavulanic acid contained 41% of all isolates and was considered not practical. Conclusions: We demonstrate that CBPs can be improved and standardized by minimizing methodological categorization error rates. ZMUs may be introduced if an intermediate zone is not appropriate for pharmacokinetic/pharmacodynamic or drug dosing reasons. Optimized CBPs will provide a standardized antibiotic susceptibility testing interpretation at a defined level of probability.