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Comparing plant litter molecular diversity assessed from proximate analysis and 13 C NMR spectroscopy
Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using 13 C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.
Mapping structural and dynamic divergence across the MBOAT family
Membrane-bound O-acyltransferases (MBOATs) are membrane-embedded enzymes that catalyze acyl chain transfer to a diverse group of substrates, including lipids, small molecules, and proteins. MBOATs share a conserved structural core, despite wide-ranging functional specificity across both prokaryotes and eukaryotes. The structural basis of catalytic specificity, regulation and interactions with the surrounding environment remain uncertain. Here, we combine comparative molecular dynamics (MD) simulations with bioinformatics to assess molecular and interactional divergence across the family. In simulations, MBOATs differentially distort the bilayer depending on their substrate type. Additionally, we identify lipid binding sites surrounding reactant gates in the surrounding membrane. Complementary bioinformatic analyses reveal a conserved role for re-entrant loop-2 in MBOAT fold stabilization and a key hydrogen bond bridging DGAT1 dimerization. Finally, we predict differences in MBOAT solvation and water gating properties. These data are pertinent to the design of MBOAT-specific inhibitors that encompass dynamic information within cellular mimetic environments.
Qubit Condensation for Assessing Efficacy of Molecular Simulation on Quantum Computers
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Machine learning reveals key ion selectivity mechanisms in polymeric membranes with subnanometer pores
Designing single-species selective membranes for high-precision separations requires a fundamental understanding of the molecular interactions governing solute transport. Here, we comprehensively assess molecular-level features that influence the separation of 18 different anions by nanoporous cellulose acetate membranes. Our analysis identifies the limitations of bulk solvation characteristics to explain ion transport, highlighted by the poor correlation between hydration energy and the measured permselectivity (R 2 = 0.37). Entropy-enthalpy compensation, spanning 40 kilojoules per mole, leads to a free-energy barrier (ΔG‡) variation of only ~8 kilojoules per mole across all anions. We apply machine learning to elucidate descriptors for energetic barriers from a set of 126 collected features. Notably, electrostatic features account for 75% of the overall features used to describe ΔG‡, despite the relatively uncharged state of cellulose acetate. Our work presents an approach for studying ion transport across nanoporous membranes that could enable the design of ion-selective membranes.
Assessing the Impact of Molecular Architecture on the Radiation Durability of Diglycolamide Minor Actinide Extractants
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Assessing the viability of K-Mo2C for reverse water-gas shift scale-up: molecular to laboratory to pilot scale
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Molecular Geometry and Electronic Structure of Copper Corroles
Copper corroles are known for their unique multiconfigurational electronic structures in the ground state, which arise from the transfer of electrons from the π orbitals of the corrole to the d-orbital of copper. While density functional theory (DFT) provides reasonably good molecular geometries, the determination of the ground spin state and the associated energetics is heavily influenced by functional choice, particularly the percentage of the Hartree–Fock exchange. Using extended multireference perturbation theory methods (XMS-CASPT2), the functional choice can be assessed. The molecular geometries and electronic structures of both the unsubstituted and the meso-triphenyl copper corroles were investigated. A minimal active space was employed for structural characterization, while larger active spaces are required to examine the electronic structure. The XMS-CASPT2 investigations conclusively identify the ground electronic state as a multiconfigurational singlet (S o ) with three dominant electronic configurations in its lowest energy and characteristic saddled structure. In contrast, the planar geometry corresponds to the triplet state (T o ), which is approximately 5 kcal/mol higher in energy compared to the S o state for both the bare and substituted copper corroles. Notably, the planarity of the T o geometry is reduced in the substituted corrole compared with that in the unsubstituted one. By analyzing the potential energy surface (PES) between the S o and T o geometries using XMS-CASPT2, the multiconfigurational electronic structure is shown to transition toward a single electron configuration as the saddling angle decreases (i.e., as one approaches the planar geometry). Despite the ability of the functionals to reproduce the minimum energy structures, only the TPSSh-D 3 PES is reasonably close to the XMS-CASPT2 surface. Significant deviations along the PES are observed with other functionals.
ENVnet provides a global molecular resource of dissolved organic matter
Dissolved organic matter (DOM) is an important component of Earth's carbon cycle and one of the planet's most chemically diverse pools, yet the molecular structures of its constituents remain largely unresolved. This limitation has hindered our ability to link DOM composition to microbial processes and ecosystem function. Here we present ENVnet, a global molecular repository built from tandem mass spectrometry data collected across 13 terrestrial and aquatic environment types, including 419 newly generated samples that expand publicly available DOM metabolomics data and cover previously underrepresented environments. By computationally deconvolving chimeric mass spectra, a longstanding challenge in environmental metabolomics, we recover high-quality fragmentation data for >22,000 distinct molecular features (defined by a specific precursor mass and fragmentation pattern). Using ENVnet, we uncover conserved and environment-specific molecular patterns in DOM composition and underlying biogeochemical processes. We also use molecular features encoded in ENVnet to train predictive models of DOM persistence, allowing molecular-level assessment of microbial turnover in independent systems.
Accelerating strain phenotyping with desorption electrospray ionization-imaging mass spectrometry and untargeted analysis of intact microbial colonies
Significance Synthetic biology has entered an era in which reading and writing DNA sequences are no longer rate-limiting steps in microbial strain engineering. Indeed, analytical methods measuring the resulting metabolic outcomes of specific gene edits have lagged behind the ability to generate new recombinant strains. Herein, we report a mass spectrometry strategy to accelerate these analytical workflows by directly analyzing metabolites and molecules produced from engineered microorganisms in a multiplexed process. Using untargeted acquisitions and unsupervised analytics, we assess the molecular features that change across discrete strains including primary target species, secondary products, and species outside the engineered fatty acid biosynthesis pathway.
Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection
Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662–C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662–C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662–C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.
Environmental DNA as a tool for hydropower impact assessments: current status, special considerations, and future integration
Globally there is an urgent need to find sustainable solutions to balance energy production with the protection of vulnerable species and conservation of biodiversity. This is particularly critical for freshwater ecosystems, habitats, and species that may be impacted by hydropower development and operations needed to meet energy grid demands. Reliable and accurate environmental impact assessments (EIAs) that identify the biological, physical, or social impacts of hydropower are key to ensure biodiversity, ecosystem, and societal sustainability. The analysis of environmental DNA (eDNA) has the potential to transform hydropower EIAs, management and mitigation planning, and decision-making procedures. Further, the incorporation of eDNA surveys into EIAs during both hydropower planning and continued operations may streamline regulatory processes by improving our understanding of potentially impacted biota and habitats and evaluating environmental impacts mitigation. Here, we: (i) highlight current understanding and use of eDNA in freshwater environments; (ii) examine critical considerations for eDNA integration into hydropower EIAs and biological monitoring; (iii) identify knowledge gaps in eDNA analysis and applications unique to hydropower-regulated systems; and (iv) discuss future opportunities to bolster the incorporation of eDNA into hydropower research including regulatory acceptance and public engagement. While we acknowledge that there are several factors that may complicate the broad adoption of eDNA as a tool for assessing the impacts of hydropower, we anticipate that growing confidence in eDNA through hydropower-specific protocols, calibrations, and validations will overcome these inherent uncertainties.
Molecular Dynamics Modeling and Experimental Assessment of Helium Bubble Growth and Surface Morphology Evolution
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Development of Composite Photocatalyst Materials that are Highly Selective for Solar Hydrogen Production and their Evaluation in Z-Scheme Reactor Designs
The key technology gap preventing a vertically stacked dual-bed particle suspension reactor from achieving the DOE MYRD&D ultimate cost target for H 2 production remains the lack of materials in particle form factor that exhibit ≥10% solar-to-H 2 energy conversion (STH) efficiency as a suspension. Therefore, our project goals centered around strategies to increase the STH efficiency by enhancing photophysical properties of perovskite oxide particles including increased visible-light absorption, increased selectivity for electrocatalysis of the H 2 evolution reaction (HER) and the O 2 evolution reaction (OER) through development of ultrathin oxide coatings, correlating composition and structure to function, and improving understanding of multiscale transport and kinetic processes.
Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE
The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.
Molecular Dynamics Simulation Study of the Protonation State Dependence of Glutamic Acid Transport through a Cyclic Peptide Nanotube
The effect of the protonation state of glutamic acid on its translocation through cyclic peptide nanotubes (CPNs) was assessed by using molecular dynamics (MD) simulations. Anionic (GLU–), neutral zwitterionic (GLU0), and cationic (GLU+) forms of glutamic acid were selected as three different protonation states for an analysis of energetics and diffusivity for acid transport across a cyclic decapeptide nanotube. Based on the solubility-diffusion model, permeability coefficients for the three protonation states of the acid were calculated and compared with experimental results for CPN-mediated glutamate transport through CPNs. Potential of mean force (PMF) calculations reveal that, due to the cation-selective nature of the lumen of CPNs, GLU–, so-called glutamate, shows significantly high free energy barriers, while GLU+ displays deep energy wells and GLU0 has mild free energy barriers and wells inside the CPN. The considerable energy barriers for GLU– inside CPNs are mainly attributed to unfavorable interactions with DMPC bilayers and CPNs and are reduced by favorable interactions with channel water molecules through attractive electrostatic interactions and hydrogen bonding. Unlike the distinct PMF curves, position-dependent diffusion coefficient profiles exhibit comparable frictional behaviors regardless of the charge status of three protonation states due to similar confined environments imposed by the lumen of the CPN. The calculated permeability coefficients for the three protonation states clearly demonstrate that glutamic acid has a strong protonation state dependence for its transport through CPNs, as determined by the energetics rather than the diffusivity of the protonation state. In addition, the permeability coefficients also imply that GLU– is unlikely to pass through a CPN due to the high energy barriers inside the CPN, which is in disagreement with experimental measurements, where a considerable amount of glutamate permeating through the CPN was detected. To resolve the discrepancy between this work and the experimental observations, several possibilities are proposed, including a large concentration gradient of glutamate between the inside and outside of lipid vesicles and bilayers in the experiments, the glutamate activity difference between our MD simulations and experiments, an overestimation of energy barriers due to the artifacts imposed in MD simulations, and/or finally a transformation of the protonation state from GLU– to GLU0 to reduce the energy barriers. Altogether, our study demonstrates that the protonation state of glutamic acid has a strong effect on the transport of the acid and suggests a possible protonation state change for glutamate permeating through CPNs.
Investigating Lignin Aggregation and Interactions with Solvents during γ-Valerolactone (GVL) Pretreatment: A Combined Small Angle Neutron Scattering and Molecular Simulations Study
The strong tendency of lignin to aggregate in solution, coupled with limited understanding of how its molecular structure governs this behavior, hinders its effective utilization in biorefineries. Here, in this study, we investigated the solution behavior of lignin extracted from poplar using γ-valerolactone/water (GVL/H2O, 9:1 wt/wt) through combined small-angle neutron scattering (SANS) and molecular dynamics (MD) simulations. Lignin samples obtained at 100 °C (L100) and 120 °C (L120) differed in β–O–4 content, hydroxyl distribution, and S/G ratio, enabling direct assessment of how molecular composition governs solvation and aggregation. SANS showed that L120 formed rigid and elongated cylindrical aggregates at 25 °C that transitioned to more flexible spheroidal structures by 50 °C and remained stable up to 80 °C, whereas L100 adopted globular aggregates that progressively collapsed with increasing temperature. MD simulations reinforced these observations by showing that S-rich (L120-like) oligomers had larger radii of gyration, stronger solvent coordination driven by methoxy groups, and fewer lignin–lignin contacts. In contrast, G-rich (L100-like) oligomers displayed persistent aggregation and lower solubility. Collectively, these results reveal that increased aromatic methoxylation enhances lignin–solvent interactions and suppresses self-association, whereas reduced methoxylation and higher β–O–4 content promote persistent aggregation into colloid-like structures with restricted solvent penetration into the aggregate interior.
Molecular Simulations of CH 4 and CO 2 Diffusion in Rigid Nanoporous Amorphous Materials
Molecular diffusion in nanoporous materials is important in determining the rate of equilibration of various adsorption processes and plays a pivotal role in kinetic separations and membrane-based separations. Because generating realistic structures of amorphous nanoporous materials is difficult, far less is known about diffusion in amorphous nanoporous materials than in their crystalline counterparts. Here, we present molecular dynamics simulations assessing the room-temperature self-diffusion of CH 4 and CO 2 in a wide range of rigid amorphous nanoporous materials, including porous carbons, kerogens, polymers of intrinsic microporosity, and hyper-cross-linked polymers. Our results are the largest collection of molecular diffusivities in amorphous nanoporous materials to date. In each material, the diffusivity increases with the adsorbate concentration at low and moderate adsorbate concentrations, reaching a maximum before decreasing due to steric effects at higher concentrations. The observed diffusivities are much slower than that would be expected based on standard descriptions of Knudsen diffusivity. Here we show that the observed diffusivities are not correlated in a simple way with scalar descriptors of the pore structures such as the pore limiting diameter.