Systematic profiling of ale yeast protein dynamics across fermentation and repitching

Studying the genetic and molecular characteristics of brewing yeast strains is crucial for understanding their domestication history and adaptations accumulated over time in fermentation environments, and for guiding optimizations to the brewing process itself. Saccharomyces cerevisiae (brewing yeast) is among the most profiled organisms on the planet, yet the temporal molecular changes that underlie industrial fermentation and beer brewing remain understudied. Here, we characterized the genomic makeup of a Saccharomyces cerevisiae ale yeast widely used in the production of Hefeweizen beers, and applied shotgun mass spectrometry to systematically measure the proteomic changes throughout 2 fermentation cycles which were separated by 14 rounds of serial repitching. The resulting brewing yeast proteomics resource includes 64,740 protein abundance measurements. We found that this strain possesses typical genetic characteristics of Saccharomyces cerevisiae ale strains and displayed progressive shifts in molecular processes during fermentation based on protein abundance changes. We observed protein abundance differences between early fermentation batches compared to those separated by 14 rounds of serial repitching. The observed abundance differences occurred mainly in proteins involved in the metabolism of ergosterol and isobutyraldehyde. Our systematic profiling serves as a starting point for deeper characterization of how the yeast proteome changes during commercial fermentations and additionally serves as a resource to guide fermentation protocols, strain handling, and engineering practices in commercial brewing and fermentation environments. Finally, we created a web interface (https://brewing-yeast-proteomics.ccbb.utexas.edu/) to serve as a valuable resource for yeast geneticists, brewers, and biochemists to provide insights into the global trends underlying commercial beer production.

Elucidating molecular mechanisms of protoxin-II state-specific binding to the human NaV1.7 channel

Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells, and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-II (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.

Geometry for low-inertia aerosol capture: Lessons from fog-basking beetles

Water in the form of windborne fog droplets supports life in many coastal arid regions, where natural selection has driven nontrivial physical adaptation toward its separation and collection. For two species of Namib desert beetle whose body geometry makes for a poor filter, subtle modifications in shape and texture have been previously associated with improved performance by facilitating water drainage from its collecting surface. However, little is known about the relevance of these modifications to the flow physics that underlies droplets' impaction in the first place. We find, through coupled experiments and simulations, that such alterations can produce large relative gains in water collection by encouraging droplets to "slip" toward targets at the millimetric scale, and by disrupting boundary and lubrication layer effects at the microscopic scale. Our results offer a lesson in biological fog collection and design principles for controlling particle separation beyond the specific case of fog-basking beetles.

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

Particulate matter (PM) is a ubiquitous component of air pollution that is epidemiologically linked to human pulmonary diseases. PM chemical composition varies widely, and the development of high-throughput experimental techniques enables direct profiling of cellular effects using compositionally unique PM mixtures. Here, we show that in a human bronchial epithelial cell model, exposure to three chemically distinct PM mixtures drive unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability, DNA damage responses, and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Finally, we observed that PM mixtures with higher cadmium content induced increased DNA damage and drove redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of individual cellular morphologies provides a robust, high-throughput approach to gauge the effects of environmental stressors on biological systems and score cellular susceptibilities to pollution.

Electrolytes with Solvating Inner Sheath Engineering for Practical Na-S Batteries

Sodium-sulfur (Na-S) batteries with durable Na-metal stability, shuttle-free cyclability, and long lifespan are promising to large-scale energy storages. However, meeting these stringent requirements poses huge challenges with the existing electrolytes. Herein, a localized saturated electrolyte (LSE) is proposed with 2-methyltetrahydrofuran (MeTHF) as an inner sheath solvent, which represents a new category of electrolyte for Na-S system. Unlike the traditional high concentration electrolytes, the LSE is realized with a low salt-to-solvent ratio and low diluent-to-solvent ratio, which pushes the limit of localized high concentration electrolyte (LHCE). The appropriate molecular structure and solvation ability of MeTHF regulate a saturated inner sheath, which features a reinforced coordination of Na⁺ to anions, enlarged Na⁺ -solvent distance, and weakened anion-diluent interaction. Such electrolyte configuration is found to be the key to build a sustainable interphase and a quasi-solid-solid sulfur redox process, making a dendrite-inhibited and shuttle-free Na-S battery possible. With this electrolyte, pouch cells with decent cycling performance under rather demanding conditions are demonstrated.

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

A "cation-anion regulation" synergistic anode host for dendrite-free lithium metal batteries

Dendritic Li deposition has been "a Gordian knot" for almost half a century, which significantly hinders the practical use of high-energy lithium metal batteries (LMBs). The underlying mechanisms of this dendrite formation are related to the preferential lithium deposition on the tips of the protuberances of the anode surface and also associated with the concentration gradient or even depletion of anions during cycling. Therefore, a synergistic regulation of cations and anions at the interface is vital to promoting dendrite-free Li anodes. An ingenious molecular structure is designed to realize the "cation-anion regulation" with strong interactions between adsorption sites and ions at the molecular level. A quaternized polyethylene terephthalate interlayer with a "lithiophilic" ester building block and an "anionphilic" quaternary ammonium functional block can guide ions to form dendrite-free Li metal deposits at an ultrahigh current density of 10 mA cm^-2, enabling stable LMBs.