Laboratory measurements of electrostatic solitary structures generated by electron beam injection

Electrostatic solitary structures are generated by injection of a suprathermal electron beam parallel to the magnetic field in a laboratory plasma. Electric microprobes with tips smaller than the Debye length ($\lambda_{De}$) enabled the measurement of positive potential pulses with half-widths 4 to 25$\lambda_{De}$ and velocities 1 to 3 times the background electron thermal speed. Nonlinear wave packets of similar velocities and scales are also observed, indicating that the two descend from the same mode which is consistent with the electrostatic whistler mode and result from an instability likely to be driven by field-aligned currents.Comment: 5 pages, 4 figures http://link.aps.org/doi/10.1103/PhysRevLett.105.11500

Structural variation of protein-ligand complexes of the first bromodomain of BRD4

The bromodomain-containing protein 4 (BRD4), a member of the bromodomain and extra-terminal domain (BET) family, plays a key role in several diseases, especially cancers. With increased interest in BRD4 as a therapeutic target, many X-ray crystal structures of the protein in complex with small molecule inhibitors are publicly available over the recent decade. In this study, we use this structural information to investigate the conformations of the first bromodomain (BD1) of BRD4. Structural alignment of 297 BRD4-BD1 complexes shows a high level of similarity between the structures of BRD4-BD1, regardless of the bound ligand. We employ WONKA, a tool for detailed analyses of protein binding sites, to compare the active site of over 100 of these crystal structures. The positions of key binding site residues show a high level of conformational similarity, with the exception of Trp81. A focused analysis on the highly conserved water network in the binding site of BRD4-BD1 is performed to identify the positions of these water molecules across the crystal structures. The importance of the water network is illustrated using molecular docking and absolute free energy perturbation simulations. 82% of the ligand poses were better predicted when including water molecules as part of the receptor. Our analysis provides guidance for the design of new BRD4-BD1 inhibitors and the selection of the best structure of BRD4-BD1 to use in structure-based drug design, an important approach for faster and more cost-efficient lead discovery

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets

Effects of rapid prey evolution on predator-prey cycles

We study the qualitative properties of population cycles in a predator-prey system where genetic variability allows contemporary rapid evolution of the prey. Previous numerical studies have found that prey evolution in response to changing predation risk can have major quantitative and qualitative effects on predator-prey cycles, including: (i) large increases in cycle period, (ii) changes in phase relations (so that predator and prey are cycling exactly out of phase, rather than the classical quarter-period phase lag), and (iii) "cryptic" cycles in which total prey density remains nearly constant while predator density and prey traits cycle. Here we focus on a chemostat model motivated by our experimental system [Fussmann et al. 2000,Yoshida et al. 2003] with algae (prey) and rotifers (predators), in which the prey exhibit rapid evolution in their level of defense against predation. We show that the effects of rapid prey evolution are robust and general, and furthermore that they occur in a specific but biologically relevant region of parameter space: when traits that greatly reduce predation risk are relatively cheap (in terms of reductions in other fitness components), when there is coexistence between the two prey types and the predator, and when the interaction between predators and undefended prey alone would produce cycles. Because defense has been shown to be inexpensive, even cost-free, in a number of systems [Andersson and Levin 1999, Gagneux et al. 2006,Yoshida et al. 2004], our discoveries may well be reproduced in other model systems, and in nature. Finally, some of our key results are extended to a general model in which functional forms for the predation rate and prey birth rate are not specified.Comment: 35 pages, 8 figure

Structurally diverse mitochondrial branched chain aminotransferase (BCATm) leads with varying binding modes identified by fragment screening

Inhibitors of mitochondrial branched chain aminotransferase (BCATm), identified using fragment screening, are described. This was carried out using a combination of STD-NMR, thermal melt (Tm), and biochemical assays to identify compounds that bound to BCATm, which were subsequently progressed to X-ray crystallography, where a number of exemplars showed significant diversity in their binding modes. The hits identified were supplemented by searching and screening of additional analogues, which enabled the gathering of further X-ray data where the original hits had not produced liganded structures. The fragment hits were optimized using structure-based design, with some transfer of information between series, which enabled the identification of ligand efficient lead molecules with micromolar levels of inhibition, cellular activity, and good solubility

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes. used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation; and screening technologies. This:compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia

The effectiveness of interventions to change six health behaviours: a review of reviews

Background: Several World Health Organisation reports over recent years have highlighted the high incidence of chronic diseases such as diabetes, coronary heart disease and cancer. Contributory factors include unhealthy diets, alcohol and tobacco use and sedentary lifestyles. This paper reports the findings of a review of reviews of behavioural change interventions to reduce unhealthy behaviours or promote healthy behaviours. We included six different health-related behaviours in the review: healthy eating, physical exercise, smoking, alcohol misuse, sexual risk taking (in young people) and illicit drug use. We excluded reviews which focussed on pharmacological treatments or those which required intensive treatments (e. g. for drug or alcohol dependency). Methods: The Cochrane Library, Database of Abstracts of Reviews of Effectiveness (DARE) and several Ovid databases were searched for systematic reviews of interventions for the six behaviours (updated search 2008). Two reviewers applied the inclusion criteria, extracted data and assessed the quality of the reviews. The results were discussed in a narrative synthesis. Results: We included 103 reviews published between 1995 and 2008. The focus of interventions varied, but those targeting specific individuals were generally designed to change an existing behaviour (e. g. cigarette smoking, alcohol misuse), whilst those aimed at the general population or groups such as school children were designed to promote positive behaviours (e. g. healthy eating). Almost 50% (n = 48) of the reviews focussed on smoking (either prevention or cessation). Interventions that were most effective across a range of health behaviours included physician advice or individual counselling, and workplace- and school-based activities. Mass media campaigns and legislative interventions also showed small to moderate effects in changing health behaviours. Generally, the evidence related to short-term effects rather than sustained/longer-term impact and there was a relative lack of evidence on how best to address inequalities. Conclusions: Despite limitations of the review of reviews approach, it is encouraging that there are interventions that are effective in achieving behavioural change. Further emphasis in both primary studies and secondary analysis (e.g. systematic reviews) should be placed on assessing the differential effectiveness of interventions across different population subgroups to ensure that health inequalities are addressed.</p

Blinded predictions and post-hoc analysis of the second solubility challenge data : exploring training data and feature set selection for machine and deep learning models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state-of-the-art, the American Chemical Society organised a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019, but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms, and were trained on a relatively small dataset of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility datasets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge datasets, with the best model, a graph convolutional neural network, resulting in a RMSE of 0.86 log units. Critical analysis of the models reveal systematic di↵erences between the performance of models using certain feature sets and training datasets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy, but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modelling complex chemical spaces from sparse training datasets

Microarray scanner calibration curves: characteristics and implications

BACKGROUND: Microarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear. RESULTS: By scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias. CONCLUSION: It is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy