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Epidermal growth factor receptor (EGFR) exon 20 insertion mutations (ex20ins) account for ≤ 12% of all EGFR‐mutant nonsmall cell lung cancers. We analysed real‐world datasets to determine the frequency of ex20ins variants, and the ability of polymerase chain reaction (PCR) and next‐generation sequencing (NGS) to identify them. Three real‐world United States NGS databases were used: GENIE, FoundationInsights, and GuardantINFORM. Mutation profiles consistent with in‐frame EGFR ex20ins were summarized. GENIE, FoundationInsights, and GuardantINFORM datasets identified 180, 627, and 627 patients with EGFR ex20ins respectively. The most frequent insertion region of exon 20 was the near loop (~ 70%), followed by the far loop (~ 30%) and the helical (~ 3–6%) regions. GENIE, FoundationInsights, and GuardantINFORM datasets identified 41, 102, and 96 unique variants respectively. An analysis of variants projected that ~ 50% of EGFR ex20ins identified by NGS would have been missed by PCR‐based assays. Given the breadth of EGFR ex20ins identified in the real‐world US datasets, the ability of PCR to identify these mutations is limited. NGS platforms are more appropriate to identify patients likely to benefit from EGFR ex20ins‐targeted therapies. Three next‐generation sequencing (NGS) databases (GENIE, FoundationInsights and GuardantINFORM) were analyzed to estimate the frequency of exon 20 insertion (ex20ins) variants in epidermal growth factor receptor (EGFR)–mutant non‐small cell lung cancer (NSCLC). Results indicate ~ 50% of ex20ins identified by NGS would have been missed by PCR and the near loop is the most frequent insertion site.

The interactions of water with solid surfaces govern their apparent hydrophobicity/hydrophilicity, influenced at the molecular scale by surface coverage of chemical groups of varied nonpolar/polar character. Recently, it has become clear that the precise patterning of surface groups, and not simply average surface coverage, has a significant impact on the structure and thermodynamics of hydration layer water, and, in turn, on macroscopic interfacial properties. Here we show that patterning also controls the dynamics of hydration water, a behavior frequently thought to be leveraged by biomolecules to influence functional dynamics, but yet to be generalized. To uncover the role of surface heterogeneities, we couple a genetic algorithm to iterative molecular dynamics simulations to design the patterning of surface functional groups, at fixed coverage, to either minimize or maximize proximal water diffusivity. Optimized surface configurations reveal that clustering of hydrophilic groups increases hydration water mobility, while dispersing them decreases it, but only if hydrophilic moieties interact with water through directional, hydrogen-bonding interactions. Remarkably, we find that, across different surfaces, coverages, and patterns, both the chemical potential for inserting a methane-sized hydrophobe near the interface and, in particular, the hydration water orientational entropy serve as strong predictors for hydration water diffusivity, suggesting that these simple thermodynamic quantities encode the way surfaces control water dynamics. These results suggest a deep and intriguing connection between hydration water thermodynamics and dynamics, demonstrating that subnanometer chemical surface patterning is an important design parameter for engineering solid−water interfaces with applications spanning separations to catalysis.

The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, 𝓠, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.

An in-depth knowledge of the interaction of water with amorphous silica is critical to fundamental studies of interfacial hydration water, as well as to industrial processes such as catalysis, nanofabrication, and chromatography. Silica has a tunable surface comprising hydrophilic silanol groups and moderately hydrophobic siloxane groups that can be interchanged through thermal and chemical treatments. Despite extensive studies of silica surfaces, the influence of surface hydrophilicity and chemical topology on the molecular properties of interfacial water is not well understood. In this work, we controllably altered the surface silanol density, and measured surface water diffusivity using Overhauser dynamic nuclear polarization (ODNP) and complementary silica–silica interaction forces acrosswater using a surface forces apparatus (SFA). The results show that increased silanol density generally leads to slower water diffusivity and stronger silica–silica repulsion at short aqueous separations (less than ∼4 nm). Both techniques show sharp changes in hydration properties at intermediate silanol densities (2.0–2.9 nm−2). Molecular dynamics simulations of model silica–water interfaces corroborate the increase in water diffusivity with silanol density, and furthermore show that even on a smooth and crystalline surface at a fixed silanol density, adjusting the spatial distribution of silanols results in a range of surface water diffusivities spanning ∼10%. We speculate that a critical silanol cluster size or connectivity parameter could explain the sharp transition in our results, and can modulate wettability, colloidal interactions, and surface reactions, and thus is a phenomenon worth further investigation on silica and chemically heterogeneous surfaces.

Phosphates and polyphosphates play ubiquitous roles in biology as integral structural components of cell membranes and bone, or as vehicles of energy storage via adenosine triphosphate and phosphocreatine. The solution phase space of phosphate species appears more complex than previously known. We present nuclear magnetic resonance (NMR) and cryogenic transmission electron microscopy (cryo-TEM) experiments that suggest phosphate species including orthophosphates, pyrophosphates, and adenosine phosphates associate into dynamic assemblies in dilute solutions that are spectroscopically “dark.” Cryo-TEM provides visual evidence of the formation of spherical assemblies tens of nanometers in size, while NMR indicates that a majority population of phosphates remain as unassociated ions in exchange with spectroscopically invisible assemblies. The formation of these assemblies is reversibly and entropically driven by the partial dehydration of phosphate groups, as verified by diffusion-ordered spectroscopy (DOSY), indicating a thermodynamic state of assembly held together by multivalent interactions between the phosphates. Molecular dynamics simulations further corroborate that orthophosphates readily cluster in aqueous solutions. This study presents the surprising discovery that phosphate-containing molecules, ubiquitously present in the biological milieu, can readily form dynamic assemblies under a wide range of commonly used solution conditions, highlighting a hitherto unreported property of phosphate’s native state in biological solutions.

Although the Drosophila melanogaster (fly) model is a popular platform for investigating diet-related phenomena, it can be challenging to measure the volume of agar-based food media flies consume. We addressed this challenge by developing a dye-based method called Consumption-Excretion (Con-Ex). In Con-Ex studies, flies consume solid food labeled with dye, and the volume of food consumed is reflected by the sum of the dye inside of and excreted by flies. Flies consumed-excreted measurable amounts of FD&C Blue No. 1 (Blue 1) and other dyes in Con-Ex studies, but only Blue 1 was readily detectable at concentrations that had no discernable effect on consumption-excretion. In studies with Blue 1, consumption-excretion (i) increased linearly with feeding duration out to 24 h at two different laboratory sites, (ii) was sensitive to starvation, mating status and strain, and (iii) changed in response to alteration of media composition as expected. Additionally, the volume of liquid Blue 1 consumed from capillary tubes was indistinguishable from the volume of Blue 1 excreted by flies, indicating that excreted Blue 1 reflects consumed Blue 1. Our results demonstrate that Con-Ex with Blue 1 as a food tracer is a useful method for assessing ingestion of agar-based food media in adult flies.

Performance of membranes for water purification is highly influenced by the interactions of solvated species with membrane surfaces, including surface adsorption of solutes upon fouling. Current efforts toward fouling-resistant membranes often pursue surface hydrophilization, frequently motivated by macroscopic measures of hydrophilicity, because hydrophobicity is thought to increase solute–surface affinity. While this heuristic has driven diverse membrane functionalization strategies, here we build on advances in the theory of hydrophobicity to critically examine the relevance of macroscopic characterizations of solute–surface affinity. Specifically, we use molecular simulations to quantify the affinities to model hydroxyl- and methyl-functionalized surfaces of small, chemically diverse, charge-neutral solutes represented in produced water. We show that surface affinities correlate poorly with two conventional measures of solute hydrophobicity, gas-phase water solubility and oil–water partitioning. Moreover, we find that all solutes show attraction to the hydrophobic surface and most to the hydrophilic one, in contrast to macroscopically based hydrophobicity heuristics. We explain these results by decomposing affinities into direct solute interaction energies (which dominate on hydroxyl surfaces) and water restructuring penalties (which dominate on methyl surfaces). Finally, we use an inverse design algorithm to show how heterogeneous surfaces, with multiple functional groups, can be patterned to manipulate solute affinity and selectivity. These findings, importantly based on a range of solute and surface chemistries, illustrate that conventional macroscopic hydrophobicity metrics can fail to predict solute–surface affinity, and that molecular-scale surface chemical patterning significantly influences affinity—suggesting design opportunities for water purification membranes and other engineered interfaces involving aqueous solute–surface interactions.

The Drosophila model is used to investigate the effects of diet on physiology as well as the effects of genetic pathways, neural systems and environment on feeding behavior. We previously showed that Blue 1 works well as a dye tracer to track consumption of agar-based media in Drosophila in a method called Con-Ex. Here, we describe Orange 4 as a novel dye for use in Con-Ex studies that expands the utility of this method. Con-Ex experiments using Orange 4 detect the predicted effects of starvation, mating status, strain, and sex on feeding behavior in flies. Orange 4 is consumed and excreted into vials linearly with time in Con-Ex experiments, the number of replicates required to detect differences between groups when using Orange 4 is comparable to that for Blue 1, and excretion of the dye reflects the volume of consumed dye. In food preference studies using Orange 4 and Blue 1 as a dye pair, flies decreased their intake of food laced with the aversive tastants caffeine and NaCl as determined using Con-Ex or a more recently described modification called EX-Q. Our results indicate that Orange 4 is suitable for Con-Ex experiments, has comparable utility to Blue 1 in Con-Ex studies, and can be paired with Blue 1 to assess food preference via both Con-Ex and EX-Q.

The self-assembly and phase separation of mixtures of polyelectrolytes and surfactants are important to a range of applications, from formulating personal care products to drug encapsulation. In contrast to systems of oppositely charged polyelectrolytes, in polyelectrolyte-surfactant systems the surfactants micellize into structures that are highly responsive to solution conditions. In this work, we examine how the morphology of micelles and degree of polyelectrolyte adsorption dynamically change upon varying the mixing ratio of charged and neutral surfactants. Specifically, we consider a solution of the cationic polyelectrolyte polydiallyldimethylammonium, anionic surfactant sodium dodecyl sulfate, neutral ethoxylated surfactants (C m EO n ), sodium chloride salt, and water. To capture the chemical specificity of these species, we leverage recent developments in constructing molecularly informed field theories via coarse-graining from all-atom simulations. Our results show how changing the surfactant mixing ratios and the identity of the nonionic surfactant modulates micelle size and surface charge, and as a result dictates the degree of polyelectrolyte adsorption. These results are in semi-quantitative agreement with experimental observations on the same system. Graphic abstract

Next-generation membranes for purification and reuse of highly contaminated water require materials with precisely tuned functionality to address key challenges, including the removal of small, charge-neutral solutes. Bioinspired multifunctional membrane surfaces enhance transport properties, but the combinatorically large chemical space is difficult to navigate through trial and error. Here, we demonstrate a computational inverse design approach to efficiently identify promising materials and elucidate design rules. We develop a combined evolutionary optimization, machine learning, and molecular simulation workflow to spatially design chemical functional group patterning in a model nanopore that enhances transport of water relative to solutes. The genetic optimization discovers nonintuitive functionalization strategies that hinder the transport of solutes through the pore, simply by patterning hydrophobic methyl and hydrophilic hydroxyl functional groups. Examining these patterns, we demonstrate that they exploit an unexpected diffusive solute hopping mechanism. This inverse design procedure and the identification of novel molecular mechanisms for pore chemical heterogeneity to impact solute selectivity demonstrate new routes to the design of membrane materials with novel functionalities. More broadly, this work illustrates how chemical design is a powerful strategy to modulate water-mediated surface–solute interactions in complex, soft material systems that are relevant to diverse technologies.