Browse Articles

Motif-driven dynamics and intermediates during unfolding of multi-domain BphC enzyme

Understanding the folding mechanisms of multi-domain proteins is crucial for gaining insights into protein folding dynamics. The BphC enzyme, a key player in the degradation of polychlorinated biphenyls consists of eight identical subunits, each containing two domains, with each domain comprising two “βαβββ” motifs. In this study, we employed high-temperature molecular dynamics simulations to systematically analyze the unfolding dynamics of a BphC subunit. Our results reveal that the unfolding process of BphC is a complex, multi-intermediate, and multi-phased event. Notably, we identified a thermodynamically stable partially unfolded intermediate. The unfolding sequences, pathways, and rates of the motifs differ significantly. Motif D unfolds first and most rapidly, while Motif C initiates unfolding before Motifs A and B but completes it slightly later. The unfolding behavior of the motifs strongly influences the domain unfolding, leading to the early initiation of Domain 2 unfolding compared to Domain 1, although at a slower rate. The motifs and domains exhibit both independence and cooperativity during the unfolding process, which we interpret through proposed cascading effects. We hypothesize that the folding mechanism of BphC begins with local folding, which propagates through cooperative interactions across structural hierarchies to achieve the folded state. These findings provide new insights into the folding and unfolding mechanisms of multi-domain proteins.

Glyphosate exposure and GM seed rollout unequally reduced perinatal health

The advent of herbicide-tolerant genetically modified (GM) crops spurred rapid and widespread use of the herbicide glyphosate throughout US agriculture. In the two decades following GM-seeds’ introduction, the volume of glyphosate applied in the United States increased by more than 750%. Despite this breadth and scale, science and policy remain unresolved regarding the effects of glyphosate on human health. We identify the causal effect of glyphosate exposure on perinatal health by combining 1) county-level variation in glyphosate use driven by 2) the timing of the GM technology and 3) differential geographic suitability for GM crops. Our results suggest the introduction of GM seeds and glyphosate significantly reduced average birthweight and gestational length. While we find effects throughout the birthweight distribution, low expected-weight births experienced the largest reductions: Glyphosate’s birthweight effect for births in the lowest decile is 12 times larger than that in the highest decile. Together, these estimates suggest that glyphosate exposure caused previously undocumented and unequal health costs for rural US communities over the last 20 years.

Selected configuration interaction for high accuracy and compact wave functions: Propane as a case study

Traditionally, because of the limit of full configuration interaction, complete active space (CAS) theory is most often used to model bond dissociation and other dynamical processes where the multi-reference character becomes important. Inconveniently, the CAS method is highly dependent on the choice of active space and, therefore, inherently non-black-box, in addition to the exponential scaling with respect to electrons and orbitals. This illustrates the need for methods that can accurately treat multi-reference electronic structure problems without significant dependence on input parameters. Selected configuration interaction (SCI) methods have experienced a revival in recent years because of their independence of these predicaments. SCI methods aim to exploit the sparsity of the full configuration interaction space to identify all relevant electronic configurations and, therefore, keep the wave function as compact as possible while still representing the total multi-reference electronic structure accurately. In this work, we take the recent achievement by Gao et al. to run full configuration interaction on the propane molecule in a minimal basis set (23 electrons in 26 orbitals) as an occasion to demonstrate that our SCI methods implemented in the GeneralSCI program package can achieve high energetic accuracy in conjunction with very compact wave functions, which considerably alleviate computational cost. Furthermore, we show the good performance of our SCI methods in reproducing a propane bond dissociation surface and energy. This illustrates that SCI methods can be readily applied to problems in chemical reactivity.

Cooperation between symbiotic partners through protein trafficking

Collision integrals within the Chapman–Enskog theory for a generalized Lennard-Jones potential

We report the values of the collision integrals, needed for the calculation of the macroscopic transport properties such as viscosity (η) and diffusion coefficient (D) of gases within the Chapman–Enskog kinetic gas theory, for a generalized Lennard-Jones potential (gLJ), a more general potential with an adjustable long range 1/r dependence that can describe a wide range of intermolecular interactions.

Arctic soil carbon insulation averts large spring cooling from surface–atmosphere feedbacks

The insulative properties of soil organic carbon (SOC) and surface organic layers (moss, lichens, litter) regulate surface–atmosphere energy exchanges in the Arctic through a coupling with soil temperatures. However, a physical description of this process is lacking in many climate models, potentially biasing their high-latitude climate predictions. Using a coupled surface–atmosphere model, we identified a strong feedback loop between soil insulation, surface air temperature, and snowfall. Without insulation, the latent heat needed for soil ice thawing leads to a late spring and summer cold bias in surface air temperature (above 2 °C) over Arctic regions. The integration of soil insulation eliminates this bias and significantly improves the simulation of permafrost dynamics. Our findings, including the potential consequences of large perturbations (e.g., fires), highlight the importance of combining soil water freezing with a physical representation of SOC and surface organic layer insulation in Earth system models, to improve Arctic climate predictions.

H2O trimer: Rigorous 12D quantum calculations of intermolecular vibrational states, tunneling splittings, and low-frequency spectrum

The water trimer, as the smallest water cluster in which the three-body interactions can manifest, is arguably the most important hydrogen-bonded trimer. Accurate, fully coupled quantum treatment of its excited intermolecular vibrations has long been an elusive goal. Here, we present the methodology that for the first time allows rigorous twelve-dimensional (12D) quantum calculation of the intermolecular vibration-tunneling eigenstates of the water trimer, with the monomers treated as rigid. These 12D eigenstates are used to simulate the low-frequency absorption spectrum of the trimer for direct comparison with the measured far-infrared (FIR) spectrum of the water trimer in helium nanodroplets. The 12D calculations reveal weak coupling between the large-amplitude torsional and intermolecular stretching vibrations. The calculated torsional tunneling splittings are in excellent agreement with spectroscopic results. There are visible differences between the spectrum simulated using the 12D eigenstates and that based on our earlier 9D calculations where the stretching vibrations are not included. The peaks in the 12D spectrum are generally shifted to slightly lower energies relative to those in the 9D spectrum, as well as the measured FIR spectrum, and are often split by intermolecular stretch–bend Fermi resonances that the 9D treatment cannot capture.

Expansion of a conserved architecture drives the evolution of the primate visual cortex

Human brain evolution is marked by a disproportionate expansion of cortical regions associated with advanced perceptual and cognitive functions. While this expansion is often attributed to the emergence of novel specialized brain areas, modifications to evolutionarily conserved cortical regions also have been linked to species-specific behaviors. Distinguishing between these two evolutionary outcomes has been limited by the ability to make direct comparisons between species. Here, we addressed this limitation by examining the expansion of the human visual cortex relative to macaques using a common functional architecture: retinotopy. Our findings revealed that human visual cortex expansion is primarily driven by increases in the surface area of a visual map architecture present in macaques rather than an increase in the number of individual areas. This expansion was not uniform, with higher-order areas, particularly in the parietal cortex, exhibiting the largest growth. Comparisons between neonate and adult humans revealed that these relative areal size differences were already established at birth. A meta-analysis of neuroimaging studies indicated that the most expanded areas are associated with advanced cognitive functions beyond visual processing. These results suggest that human perceptual and cognitive adaptations may be rooted in the expansion of evolutionarily conserved cortical architecture, with modifications even in the sensory cortex contributing to the broader cognitive functions characteristic of human behavior.

Enhanced sampling of robust molecular datasets with uncertainty-based collective variables

Generating a dataset that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine-learned interatomic potentials. However, the complexity of molecular systems, characterized by intricate potential energy surfaces, with numerous local minima and energy barriers, presents a significant challenge. Traditional methods of data generation, such as random sampling or exhaustive exploration, are either intractable or may not capture rare, but highly informative configurations. In this study, we propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically relevant data points, focusing on regions of configuration space where ML model predictions are most uncertain. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations. The effectiveness of our approach in overcoming energy barriers and exploring unseen energy minima, thereby enhancing the dataset in an active learning framework, is demonstrated on alanine dipeptide and bulk silica.

A room temperature rechargeable Li–LiNO ₃ battery with high capacity

Lithium-ion batteries (LIBs) have become advanced energy storage technologies; however, specific capacity remains limited by the active materials in cathodes. Here, we report Li–LiNO 3 batteries (LNBs) where LiNO 3 in electrolyte serves as both active materials and ion conductor at room temperature. LNBs operate on a highly reversible redox between NO 3 − and NO 2 − , which results in an impressive areal capacity of 19 mAh cm −2 at a plateau voltage of 1.75 V. Furthermore, the pouch cell exhibits stable cycling at a capacity of 100 mAh. This research underscores the potential of LNBs for high-capacity energy storage.

Computationally efficient machine-learned model for GST phase change materials via direct and indirect learning

Phase change materials such as Ge2Sb2Te5 (GST) are ideal candidates for next-generation, non-volatile, solid-state memory due to the ability to retain binary data in the amorphous and crystal phases and rapidly transition between these phases to write/erase information. Thus, there is wide interest in using molecular modeling to study GST. Recently, a Gaussian Approximation Potential (GAP) was trained for GST to reproduce Density Functional Theory (DFT) energies and forces at a fraction of the computational cost [Zhou et al., Nat. Electron. 6, 746 (2023)]; however, simulations of large length and time scales are still challenging using this GAP model. Here, we present a machine-learned (ML) potential for GST implemented using the Atomic Cluster Expansion (ACE) framework. This ACE potential shows comparable accuracy to the GAP potential but performs orders of magnitude faster. We train the ACE potentials both directly from DFT and also using a recently introduced indirect learning approach where the potential is trained instead from an intermediate ML potential, in this case, GAP. Indirect learning allows us to consider a significantly larger training set than could be generated using DFT alone. We compare the directly and indirectly learned potentials and find that both reproduce the structure and thermodynamics predicted by the GAP and also match experimental measures of GST structure. The speed of the ACE model, particularly when using graphics processing unit acceleration, allows us to examine repeated transitions between crystal and amorphous phases in device-scale systems with only modest computational resources.

A divergent two-domain structure of the anti-Müllerian hormone prodomain

TGFβ family ligands are synthesized as precursors consisting of an N-terminal prodomain and C-terminal growth factor (GF) signaling domain. After proteolytic processing, the prodomain typically remains noncovalently associated with the GF, sometimes forming a high-affinity latent procomplex that requires activation. For the TGFβ family ligand anti-Müllerian hormone (AMH), the prodomain maintains a high-affinity interaction with its GF that does not render it latent. While the prodomain can be displaced by the type II receptor, AMHR2, the nature of the GF:prodomain interaction and the mechanism of prodomain displacement by AMHR2 are currently unknown. We show here that the AMH prodomain exhibits an atypical two-domain structure, containing a dimerizing and a GF-binding domain connected through a flexible linker. Cryo-EM and genomic analyses show that the distinctive GF-binding domain, the result of an exon insertion 450 Mya, comprises a helical bundle and a belt-like structure which interact with the GF at the type II and I receptor binding sites, respectively. The dimerizing domain, which adopts a TGFβ-like propeptide fold, covalently connects two prodomains through intermolecular disulfide bonds. Disease mutations map to both the GF-binding and dimerization domains. Our results support a model where AMHR2 displaces the helical bundle and induces a conformational change in the GF, followed by release of the prodomain and engagement of the type I receptor. Collectively, this study shows that the AMH prodomain has evolved an atypical binding interaction with the GF that favors, without disrupting signaling, the maintenance of a noncovalent complex until receptors are engaged.

Multiconfigurational short-range on-top pair-density functional theory

We present the theory and implementation of a fully variational wave function–density functional theory (DFT) hybrid model, which is applicable to many cases of strong correlation. We denote this model as the multiconfigurational self-consistent on-top pair-density functional theory (MC-srPDFT) model. We have previously shown how the multiconfigurational short-range DFT (MC-srDFT) hybrid model can describe many multiconfigurational cases of any spin symmetry and also state-specific calculations on excited states [Hedegård et al., J. Chem. Phys. 148(21), 214103 (2018)]. However, the srDFT part of the MC-srDFT has some deficiencies that it shares with Kohn–Sham DFT; in particular, (1) self-interaction errors (albeit reduced because of the range separation), (2) that different MS states incorrectly become non-degenerate, and (3) that singlet and non-singlet states dissociating to the same open-shell fragments incorrectly lead to different electronic energies at dissociation. The model that we present in this paper corrects these deficiencies by introducing the on-top pair density as an auxiliary variable replacing the spin density. Unlike other models in the literature, our model is fully variational and employs a long-range version of the on-top pair density. The implementation is a second-order optimization algorithm ensuring robust convergence to both ground and excited states. We show how MC-srPDFT solves the mentioned challenges by sample calculations on the ground state singlet curve of H2, N2, and Cr2 and the lowest triplet curves for N2 and Cr2. Furthermore, the rotational barrier for ethene is investigated for the S0 and T1 states. The calculations show correct degeneracy between the singlet and triplet curves at dissociation and the results are invariant to the choice of the MS value for the triplet curves.

Floodplain forests drive fruit-eating fish diversity at the Amazon Basin-scale

Unlike most rivers globally, nearly all lowland Amazonian rivers have unregulated flow, supporting seasonally flooded floodplain forests. Floodplain forests harbor a unique tree species assemblage adapted to flooding and specialized fauna, including fruit-eating fish that migrate seasonally into floodplains, favoring expansive floodplain areas. Frugivorous fish are forest-dependent fauna critical to forest regeneration via seed dispersal and support commercial and artisanal fisheries. We implemented linear mixed effects models to investigate drivers of species richness among specialized frugivorous fishes across the ~6,000,000 km 2 Amazon Basin, analyzing 29 species from 9 families (10,058 occurrences). Floodplain predictors per subbasin included floodplain forest extent, tree species richness (309,540 occurrences for 2,506 species), water biogeochemistry, flood duration, and elevation, with river order controlling for longitudinal positioning along the river network. We observed heterogeneous patterns of frugivorous fish species richness, which were positively correlated with floodplain forest extent, tree species richness, and flood duration. The natural hydrological regime facilitates fish access to flooded forests and controls fruit production. Thus, the ability of Amazonian floodplain ecosystems to support frugivorous fish assemblages hinges on extensive and diverse seasonally flooded forests. Given the low functional redundancy in fish seed dispersal networks, diverse frugivorous fish assemblages disperse and maintain diverse forests; vice versa, diverse forests maintain more fish species, underscoring the critically important taxonomic interdependencies that embody Amazonian ecosystems. Effective management strategies must acknowledge that access to diverse and hydrologically functional floodplain forests is essential to ensure the long-term survival of frugivorous fish and, in turn, the long-term sustainability of floodplain forests.

Tailoring selenization dynamics: How heating rate manipulates nucleation and growth boosts efficiency in kesterite solar cells

Kesterite Cu2ZnSn(S,Se)4 (CZTSSe) has emerged as a promising photovoltaic material due to its low cost and high stability. The CZTSSe film for high-performance solar cells can be obtained by annealing the deposited CZTS precursor films with selenium (a process known as selenization). The design of the selenization process significantly affects the quality of the absorber layer. In this work, we systematically investigate the impact of heating rate on the selenization kinetics and the microstructural characteristics of the films using a two-step selenization method. The results indicate that a slow heating rate promotes surface crystallization, resulting in a thick and dense layer of large grains at the film surface that impedes the diffusion of Se vapor. Conversely, a rapid heating rate enhances the diffusion of Se into the interior of the film, synthesizing more low-melting-point intermediate compounds that facilitate grain growth and reduce the thickness of fine grains at the film bottom. Ultimately, a CZTSSe solar cell with an efficiency of 10.17% was fabricated at a heating rate of 200 °C/min. This research deepens the understanding of thin film growth mechanisms and advances the development of high-performance solar cells.

Exercise intensity and training alter the innate immune cell type and chromosomal origins of circulating cell-free DNA in humans

Exercising regularly promotes health, but these benefits are complicated by acute inflammation induced by exercise. A potential source of inflammation is cell-free DNA (cfDNA), yet the cellular origins, molecular causes, and immune system interactions of exercise-induced cfDNA are unclear. To study these, 10 healthy individuals were randomized to a 12-wk exercise program of either high-intensity tactical training (HITT) or traditional moderate-intensity training (TRAD). Blood plasma was collected pre- and postexercise at weeks 0 and 12 and after 4 wk of detraining upon program completion. Whole-genome enzymatic methylation sequencing (EM-seq) with cell-type proportion deconvolution was applied to cfDNA obtained from the 50 plasma samples and paired to concentration measurements for 90 circulating cytokines. Acute exercise increased the release of cfDNA from neutrophils, dendritic cells (DCs), and macrophages proportional to exercise intensity. Exercise training reduced cfDNA released in HITT participants but not TRAD and from DCs and macrophages but not neutrophils. For most participants, training lowered mitochondrial cfDNA at rest, even after detraining. Using a sequencing analysis approach we developed, we concluded that rapid ETosis, a process of cell death where cells release DNA extracellular traps, was the likely source of cfDNA, demonstrated by enrichment of nuclear DNA. Further, several cytokines were induced by acute exercise, such as IL-6, IL-10, and IL-16, and training attenuated the induction of only IL-6 and IL-17F. Cytokine levels were not associated with cfDNA induction, suggesting that these cytokines are not the main cause of exercise-induced cfDNA. Overall, exercise intensity and training modulated cfDNA release and cytokine responses, contributing to the anti-inflammatory effects of regular exercise.

Universality in diffusion-controlled nucleation and growth

Nucleation and growth are studied in a system that undergoes diffusion-controlled condensation under gradual changes in parameters, such as cooling. It is demonstrated that when the Gibbs–Thompson effect becomes negligible, the system falls into a universal regime. i.e., the final droplet size distribution remains invariant under certain rescaling of system parameters. An approximate yet very accurate analytic form is obtained for the final droplet size distribution in this regime.

Codon bias, nucleotide selection, and genome size predict in situ bacterial growth rate and transcription in rewetted soil

In soils, the first rain after a prolonged dry period represents a major pulse event impacting soil microbial community function, yet we lack a full understanding of the genomic traits associated with the microbial response to rewetting. Genomic traits such as codon usage bias and genome size have been linked to bacterial growth in soils—however, often through measurements in culture. Here, we used metagenome-assembled genomes (MAGs) with 18 O-water stable isotope probing and metatranscriptomics to track genomic traits associated with growth and transcription of soil microorganisms over one week following rewetting of a grassland soil. We found that codon bias in ribosomal protein genes was the strongest predictor of growth rate. We also found higher growth rates in bacteria with smaller genomes, suggesting that reduced genome size enables a faster response to pulses in soil bacteria. Faster transcriptional upregulation of ribosomal protein genes was associated with high codon bias and increased nucleotide skew. We found that several of these relationships existed within phyla, indicating that these associations between genomic traits and activity could be generalized characteristics of soil bacteria. Finally, we used publicly available metagenomes to assess the distribution of codon bias across a pH gradient and found that microbial communities in higher pH soils—which are often more water limited and pulse driven—have higher codon usage bias in their ribosomal protein genes. Together, these results provide evidence that genomic characteristics affect soil microbial activity during rewetting and pose a potential fitness advantage for soil bacteria where water and nutrient availability are episodic.

Corresponding-states framework for classical and quantum fluids—Beyond Feynman–Hibbs

Effective potential methods, obtained by applying a quantum correction to a classical pair potential, are widely used for describing the thermophysical properties of fluids with mild nuclear quantum effects. In case of strong nuclear quantum effects, such as for liquid hydrogen and helium, the accuracy of these quantum corrections deteriorates significantly, but at present no simple alternatives are available. In this work, we solve this issue by developing a new, three-parameter corresponding-states principle that remains applicable in the regions of the phase diagram where quantum effects become significant. The new principle emerges from a mapping procedure, which shows that quantum-corrected pair potentials can be made conformal to their underlying classical pair potential by modifying the latter’s repulsive range. This mapping enables an accurate description of fluids with quantum-corrected interactions based on off-the-shelf methods for classical fluids (e.g., equations of state, classical density functional theory, and entropy scaling) using effective, mapped intermolecular-potential parameters. These effective parameters depend on temperature and molecular mass; simple analytic equations in case of a classical Mie potential with Feynman–Hibbs quantum corrections are presented. Using Mie Feynman–Hibbs force fields from the literature, we show that this procedure provides accurate predictions for the properties of fluids with mild nuclear quantum effects, such as neon or hydrogen at moderate temperatures. Moreover, by adjusting the functional form of the effective intermolecular-potential parameters to experimental data for helium and hydrogen, we are able to apply the corresponding-states principle for optimal quantum-corrected pair potentials that far surpass the accuracy of the Feynman–Hibbs correction.

The androgen clock is an epigenetic predictor of long-term male hormone exposure

Aging is a complex process characterized by biological decline and a wide range of molecular alterations to cells, including changes to DNA methylation. In this study, we used a male-specific epigenetic marker of aging to build an epigenetic predictor that measures long-term androgen exposure in sheep and mice (median absolute error of 4.3 and 1.4 mo, respectively). We term this predictor the androgen clock and show its “tick” is mediated by the androgen receptor and can be accelerated beyond that in normal male mice by supplementing females with dihydrotestosterone. Conversely, the removal of androgens by castration in sheep completely halted the androgen clock. In addition to potential applications in medicine and agriculture, we predict the androgen clock will prove a useful model to understand the mechanisms and processes of age-associated DNA methylation change because it can be precisely enhanced and halted using small molecule manipulation with few additional effects on the cell.