Markov State Models: To Optimize or Not to Optimize.

Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins including protein folding. With all statistical and machine learning (ML) models, choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or hyperparameters, are often evaluated by expert judgment or, in the case of MSMs, by maximizing variational scores such as the VAMP-2 score. Modern ML and statistical pipelines often use automatic hyperparameter selection techniques ranging from the simple, choosing the best score from a random selection of hyperparameters, to the complex, optimization via, e.g., Bayesian optimization. In this work, we ask whether it is possible to automatically select MSM models this way by estimating and analyzing over 16,000,000 observations from over 280,000 estimated MSMs. We find that differences in hyperparameters can change the physical interpretation of the optimization objective, making automatic selection difficult. In addition, we find that enforcing conditions of equilibrium in the VAMP scores can result in inconsistent model selection. However, other parameters that specify the VAMP-2 score (lag time and number of relaxation processes scored) have only a negligible influence on model selection. We suggest that model observables and variational scores should be only a guide to model selection and that a full investigation of the MSM properties should be undertaken when selecting hyperparameters.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

Aerosol mass and size-resolved metal content in urban Bangkok, Thailand.

Inhalable particulate matter (PM) is a health concern, and people living in large cities such as Bangkok are exposed to high concentrations. This exposure has been linked to respiratory and cardiac diseases and cancers of the lung and brain. Throughout 2018, PM was measured in northern Bangkok near a toll road (13.87°N, 100.58°E) covering all three seasons (cool, hot and rainy). PM10 was measured in 24- and 72-h samples. On selected dates aerodynamic size and mass distribution were measured as 3-day samples from a fixed 5th floor inlet. Particle number concentration was measured from the 5th floor inlet and in roadside survey measurements. There was a large fraction of particle number concentration in the sub-micron range, which showed the greatest variability compared with larger fractions. Metals associated with combustion sources were most found on the smaller size fraction of particles, which may have implications for associated adverse health outcomes because of the likely location of aerosol deposition in the distal airways of the lung. PM10 samples varied between 30 and 100 µg m-3, with highest concentrations in the cool season. The largest metal fractions present in the PM10 measurements were calcium, iron and magnesium during the hot season with average airborne concentrations of 13.2, 3.6 and 2.0 µg m-3, respectively. Copper, zinc, arsenic, selenium, molybdenum, cadmium, antimony and lead had large non-crustal sources. Principal component analysis (PCA) identified likely sources of the metals as crustal minerals, tailpipe exhaust and non-combustion traffic. A health risk analysis showed a higher risk of both carcinogenic and non-carcinogenic health effects in the drier seasons than the wet season due to ingestion of nickel, arsenic, cadmium and lead.

Reduced Antibacterial Drug Resistance and blaCTX-M ß-Lactamase Gene Carriage in Cattle-Associated Escherichia coli at Low Temperatures, at Sites Dominated by Older Animals, and on Pastureland: Implications for Surveillance.

Little is known about the drivers of critically important antibacterial resistance in species with zoonotic potential present on farms (e.g., CTX-M ß-lactamase-positive Escherichia coli). We collected samples monthly between January 2017 and December 2018 on 53 dairy farms in South West England, along with data for 610 variables concerning antibacterial usage, management practices, and meteorological factors. We detected E. coli resistant to amoxicillin, ciprofloxacin, streptomycin, and tetracycline in 2,754/4,145 (66%), 263/4,145 (6%), 1,475/4,145 (36%), and 2,874/4,145 (69%), respectively, of samples from fecally contaminated on-farm and near-farm sites. E. coli positive for blaCTX-M were detected in 224/4,145 (5.4%) of samples. Multilevel, multivariable logistic regression showed antibacterial dry cow therapeutic choice (including use of cefquinome or framycetin) to be associated with higher odds of blaCTX-M positivity. Low average monthly ambient temperature was associated with lower odds of blaCTX-ME. coli positivity in samples and with lower odds of finding E. coli resistant to each of the four test antibacterials. This was in addition to the effect of temperature on total E. coli density. Furthermore, samples collected close to calves had higher odds of having E. coli resistant to each antibacterial, as well as E. coli positive for blaCTX-M Samples collected on pastureland had lower odds of having E. coli resistant to amoxicillin or tetracycline, as well as lower odds of being positive for blaCTX-MIMPORTANCE Antibacterial resistance poses a significant threat to human and animal health and global food security. Surveillance for resistance on farms is important for many reasons, including tracking impacts of interventions aimed at reducing the prevalence of resistance. In this longitudinal survey of dairy farm antibacterial resistance, we showed that local temperature-as it changes over the course of a year-was associated with the prevalence of antibacterial-resistant E. coli We also showed that prevalence of resistant E. coli was lower on pastureland and higher in environments inhabited by young animals. These findings have profound implications for routine surveillance and for surveys carried out for research. They provide important evidence that sampling at a single time point and/or single location on a farm is unlikely to be adequate to accurately determine the status of the farm regarding the presence of samples containing resistant E. coli.

Transient cavity dynamics and divergence from the Stokes-Einstein equation in organic aerosol.

The diffusion of small molecules through viscous matrices formed by large organic molecules is important across a range of domains, including pharmaceutical science, materials chemistry, and atmospheric science, impacting on, for example, the formation of amorphous and crystalline phases. Here we report significant breakdowns in the Stokes-Einstein (SE) equation from measurements of the diffusion of water (spanning 5 decades) and viscosity (spanning 12 decades) in saccharide aerosol droplets. Molecular dynamics simulations show water diffusion is not continuous, but proceeds by discrete hops between transient cavities that arise and dissipate as a result of dynamical fluctuations within the saccharide lattice. The ratio of transient cavity volume to solvent volume increases with size of molecules making up the lattice, increasing divergence from SE predictions. This improved mechanistic understanding of diffusion in viscous matrices explains, for example, why organic compounds equilibrate according to SE predictions and water equilibrates more rapidly in aerosols.