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At least 397 records · Page 22

Structural Insights into Mechanisms Underlying Mitochondrial and Bacterial Cytochrome c Synthases

Mitochondrial holocytochrome c synthase (HCCS) is an essential protein in assembling cytochrome c (cyt c) of the electron transport system. HCCS binds heme and covalently attaches the two vinyls of heme to two cysteine thiols of the cyt c CXXCH motif. Human HCCS recognizes both cyt c and cytochrome c1 of complex III (cytochrome bc1). HCCS is mutated in some human diseases and it has been investigated recombinantly by mutational, biochemical, and reconstitution studies in the past decade. Here, we employ structural prediction programs (e.g., AlphaFold 3) on HCCS and its two substrates, heme and cytochrome c. The results, when combined with spectroscopic and functional analyses of HCCS and variants, provide insights into the structural basis for heme binding, apocyt c binding, covalent attachment, and release of the holocyt c product. Results from in vitro reconstitution of purified human HCCS using cyt c and cyt c1 peptides as acceptors are consistent with the structural modeling of substrate binding. Reconstitution of HCCS and cyt c1 provides an approach to studying cyt c1 assembly, which has been refractile to recombinant in vivo reconstitution (unlike HCCS and cyt c). We propose a structural basis for release of the holocyt c product from HCCS based on in vitro studies and on cryoEM structures of the bacterial cyt c synthase (CcsBA) active site. We analyze the kinetoplastid mitochondrial synthase (KCCS), and hypothesize a molecular evolutionary path from mitochondrial endosymbiosis to the current HCCS.

Biochemistry & Molecular Biology↗

Parameter identification methods for low-order gray box building energy models: A critical review

The body of knowledge on gray box building energy modeling (GBBEM) has been developed over the past few decades and has undergone some important changes recently. Starting with simple methods and simple buildings, the science of GBBEM has grown to encompass complex and more computationally intensive techniques and complex commercial buildings. Numerous works including a recent review have considered model structure and inputs in a nearly systematic way, but no extant work systematically reviews the approaches for GBBEM parameter search initialization and final identification, despite this being arguably the most difficult and impactful part of the modeling process. To this end, we critically review 55 extant works describing advantages, limitations, and domain of applicability of several classes of parameter initialization and optimization techniques specifically for GBBEM. We categorize the classes of methods and analyze the evidence of their applicability for different applications within the field of GBBEM. We find an emerging consensus that initialization of parameter searches for anything other than the simplest building elements is often challenging and sometimes requires a stochastic approach to begin the parameter identification process. After this initial process, faster methods have been used in some cases but often the nature of the problem requires stochastic methods for this portion of the process as well. For less complex systems, more deterministic and efficient methods have been shown to be effective. Finally, we draw conclusions as to the domain of applicability of different classes of initialization and optimization techniques for GBBEEM and offer suggestions for research directions that are likely to prove fruitful.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Mysterious SiB 3 : Identifying the Relation between α- and β-SiB 3

Binary silicon boride SiB 3 has been reported to occur in two forms, as disordered and nonstoichiometric α-SiB 3–x , which relates to the α-rhombohedral phase of boron, and as strictly ordered and stoichiometric β-SiB 3 . Similar to other boron-rich icosahedral solids, these SiB 3 phases represent potentially interesting refractory materials. However, their thermal stability, formation conditions, and thermodynamic relation are poorly understood. Here, we map the formation conditions of α-SiB 3–x and β-SiB 3 and analyze their relative thermodynamic stabilities. α-SiB 3–x is metastable (with respect to β-SiB 3 and Si), and its formation is kinetically driven. Pure polycrystalline bulk samples may be obtained within hours when heating stoichiometric mixtures of elemental silicon and boron at temperatures 1200–1300 °C. At the same time, α-SiB 3–x decomposes into SiB6 and Si, and optimum time-temperature synthesis conditions represent a trade-off between rates of formation and decomposition. The formation of stable β-SiB 3 was observed after prolonged treatment (days to weeks) of elemental mixtures with ratios Si/B = 1:1–1:4 at temperatures 1175–1200 °C. The application of high pressures greatly improves the kinetics of SiB 3 formation and allows decoupling of SiB 3 formation from decomposition. Quantitative formation of β-SiB 3 was seen at 1100 °C for samples pressurized to 5.5–8 GPa. β-SiB 3 decomposes peritectoidally at temperatures between 1250 and 1300 °C. The highly ordered nature of β-SiB 3 is reflected in its Raman spectrum, which features narrow and distinct lines. In contrast, the Raman spectrum of α-SiB 3–x is characterized by broad bands, which show a clear relation to the vibrational modes of isostructural, ordered B 6 P. The detailed composition and structural properties of disordered α-SiB 3–x were ascertained by a combination of single-crystal X-ray diffraction and 29 Si magic angle spinning NMR experiments. Notably, the compositions of polycrystalline bulk samples (obtained at T ≤ 1200 °C) and single crystal samples (obtained from Si-rich molten Si–B mixtures at T > 1400 °C) are different, SiB 2.93(7) and SiB 2.64(2) , respectively. The incorporation of Si in the polar position of B 12 icosahedra results in highly strained cluster units. This disorder feature was accounted for in the refined crystal structure model by splitting the polar position into three sites. The electron-precise composition of α-SiB 3–x is SiB 2.5 and corresponds to the incorporation of, on average, two Si atoms in each B 12 icosahedron. Accordingly, α-SiB 3–x constitutes a mixture of B 10 Si 2 and B 11 Si clusters. The structural and phase stability of α-SiB 3–x were explored using a first-principles cluster expansion. The most stable composition at 0 K is SiB 2.5 , which however is unstable with respect to the decomposition β-SiB 3 + Si. Modeling of the configurational and vibrational entropies suggests that α-SiB 3–x only becomes more stable than β-SiB 3 at temperatures above its decomposition into SiB 6 and Si. Hence, we conclude that α-SiB 3–x is metastable at all temperatures. Density functional theory electronic structure calculations yield band gaps of similar size for electron-precise α-SiB 2.5 and β-SiB 3 , whereas α-SiB 3 represents a p-type conductor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modelling Development in Radical (Co)Polymerization of Multivinyl Monomers

Radical polymerization (RP) of multivinyl monomers (MVMs) provides a facile solution for manipulating polymer topology and has received increasing attention due to their industrial and academic significance. Continuous efforts have been made to understand their mechanism, which is the key to regulating materials structure. Modelling techniques have become a powerful tool that can provide detailed information on polymerization kinetics which is inaccessible by experiments. Many publications have reported the combination of experiments and modelling for free radical polymerization (FRP) and reversible-deactivation radical polymerizations (RDRP) of MVMs. Herein, a minireview is presented for the most important modelling techniques and their applications in FRP/RDRP of MVMs. This review hopes to illustrate that the combination of modelling and wet experiments can be a great asset to polymer researchers and inspire new thinking for the future MVMs experiment optimization and product design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Nearly hyperuniform, nonhyperuniform, and antihyperuniform density fluctuations in two-dimensional transition metal dichalcogenides with defects

Hyperuniform many-body systems in d-dimensional Euclidean space R d are characterized by completely suppressed (normalized) infinite-wavelength density fluctuations, and appear to be endowed with novel exotic physical properties. Recently, hyperuniform systems of disordered varieties have been observed in the context of various atomic-scale two-dimensional (2D) materials. Here, we analyze the effects of localized defects on the density fluctuations across length scales and on the hyperuniformity property of experimental samples of two-dimensional transition metal dichalcogenides. In particular, we extract atomic coordinates from time series annular dark field-scanning transmission electron microscopy (ADF-STEM) imaging data of 2D tungsten chalcogenides with the 2H structure (Te-doped 2H-WSe 2 ) showing continuous development and evolution of electron-beam induced defects, and construct the corresponding chemical-bonding informed coordination networks between the atoms. We then compute a variety of pair statistics and bond-orientational statistics to characterize the samples. At low defect concentrations, the corresponding materials are nearly hyperuniform, characterized by significantly suppressed scattering at the zero wave-number limit (omitting forward/ballistic scattering). As more defects are introduced, the (approximate) hyperuniformity of the materials is gradually destroyed, and the system becomes non-hyperuniform even when the material still contains a significant amount of crystalline regions. At high defect concentrations, the structures become antihyperuniform with diverging (normalized) large-scale density fluctuations, mimicking those typically observed at the thermal critical points associated with phase transitions. Overall, the defected materials possess varying degrees of orientation order, and there is apparently no intermediate hexatic phase emerging. To understand the observed nearly hyperuniform density fluctuations in the slightly defected materials, we construct a minimalist structural model and demonstrate that the experimental samples can be essentially viewed as perturbed honeycomb crystals with small correlated displacements and double chalcogen vacancies. Moreover, the small correlated displacements alone can significantly degrade hyperuniformity of the perfect honeycomb structure. Therefore, even a small amount of vacancies, when coupled with correlated displacements, can completely destroy hyperuniformity of the system.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

PORFLOW modeling of Vadose zone flow and transport for the E-area intermediate level vault

In support of the E-Area Performance Assessment, a two-dimensional model of water flow and radionuclide transport through the E-Area Intermediate Level Vault (ILV) and local vadose zone has been developed using the PORFLOWTM software. The purpose of the model is to calculate flux to the water table for radionuclides eluted from the ILV during its operational life, the period of institutional control, and times following site closure. Results of model calculations will be used by a three-dimensional PORFLOW model of transport through the aquifer to determine radionuclide concentrations at a hypothetical 100 meter well and at the site boundary where contaminated groundwater could be assessed by members of the public following site closure. While newly developed, the model structure closely follows that used in the 2008 PA while incorporating a refined computational mesh, updated material properties for the vadose zone soil and vault concrete, revised infiltration rates, and new solid-liquid distribution coefficients. The model also addresses degradation of concrete hydraulic properties by blending soil and concrete water retention curves over a 500 year period. That is, the hydraulic properties of ILV concrete start out as fresh concrete at the time of site closure and degrade to soil properties over a 500 year time period. This approach has not been used previously in E-Area PA’s. The model has been used to calculate water flow through the ILV vadose zone and, on a trial basis, transport of a limited number of radionuclides from the waste region inside the ILV to the water table. Figure ES-1 provides a view of the model and an example of water flow after the concrete has substantially degraded. Figure dimensions are in centimeters. The red area in the left hand side figure is the waste disposal region within the ILV. Colored bands in the figures indicate different soil and concrete regions. Figure ES-2 shows a calculation of flux to the water table (mol/yr) for U-234 and its one-year half-life decay chain (U-234 → Th-230 → Ra-226 → Pb-210). Pb-210 flux is too small to show on the same scale used for Th-230 and Ra-226 in Figure ES-2. The model has also been used to make a preliminary evaluation of the release of tritium from TPBAR disposal containers that will be place in the ILV. A separately calculated tritium release rate as a function of time was used as a source in the PORFLOW model. The TPBAR disposal schedule and tritium release rate differ from the values used in the 2008 PA. Results from these initial trial runs are compared to calculations made for the 2008 PA ILV analysis. In brief, a new PORFLOW model of flow and transport through the ILV vadose zone has been developed and tested. The model is ready to be used for PA calculations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Efficient derivative computation for unsteady fatigue-constrained nonlinear aero-structural wind turbine blade optimization

Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especially when fatigue constraints are considered. In this work, we systematically compare and evaluate four differentiation techniques, namely algorithmic differentiation, implicit differentiation, sparsity exploitation, and parallelization, to determine their effectiveness in computing accurate gradients through time-domain aero-structural simulations. By integrating these techniques with unsteady nonlinear aerodynamic and structural models, we develop software designed for accurate gradient computation. We show that combining these techniques addresses memory and runtime challenges associated with long simulations required by design load cases. Specifically, the most effective combination reduces derivative computation wall time by over an order of magnitude compared to finite differencing while maintaining superior accuracy. We demonstrate this approach in a proof-of-concept aero-structural optimization of a wind turbine blade that improves the cost of energy by 12.78 %. This comparative study establishes a viable approach for fatigue-aware blade design that balances computational efficiency with modeling accuracy.

17 WIND ENERGY↗

Evaluation of Composite Structural Materials for Heliostat Cost Reduction

Structures manufactured from steel comprise up to 40% of a CSP heliostat's cost. Composite structures represent a potential opportunity to reduce this cost. A reference heliostat structural model has been created with a reflector area of 25m 2 . The design, constructed of low-carbon steel, pro-vides baseline deflection and stiffness under a 21 m/s operating wind speeds. Wind loads on the tracker structure are determined for both operating and stow conditions. An established roster of suitable metal alternative materials is considered including: glass, basalt, and carbon reinforced polymer (GFRP, BFRP, and CFRP respectively). Three heliostat components are investigated: the pylon, torque tube, and the purlin-strut assembly. Composite material properties are substituted for those of steel, and the beams are re-sized to match the original steel components' deflection under given wind loads. Weight and cost changes resulting from this resizing are evaluated. It is found that GFRP and BFRP represent a 3X–6X cost premium for the same operating deflection character-istics as steel across all three investigated component classes; with weight reduction only achieved for the purlin-strut assembly. While CFRP components can achieve approximately 25–75% weight savings depending on the application, this comes with a 9X–14X cost increase over the steel base-line for tube-type structures and roughly 5X cost increase when replacing c-channel structures. This work does not rule out the possibility of cost savings when the heliostat design and kinematics to take advantage of composites' specific properties.

14 SOLAR ENERGY↗

MoO x S y /Ni 3 S 2 Microspheres on Ni Foam as Highly Efficient, Durable Electrocatalysts for Hydrogen Evolution Reaction

Hydrogen energy derived from water splitting is the cleanest renewable energy source, but it is also very challenging to achieve because the hydrogen evolution reaction (HER) requires highly efficient and low-cost electrocatalysts. Here, we have fabricated a novel hierarchical system of amorphous molybdenum oxy/sulfide microspheres with crystalline Ni 3 S 2 intergrown in situ on Ni foam (MoO x S y /Ni 3 S 2 /NF) as an outstanding electrocatalyst for HER. The MoO x S y /Ni 3 S 2 /NF demonstrates an ultra-low overpotential of 58 mV at a current density of 10 mA cm -2 and extremely durable stability (>200 h), suggesting superior performance comparable to that of Pt-C/NF under acidic conditions. The X-ray absorption fine structure (XAFS) determines the average valence state of Mo to be +(5 + δ), with a coordination motif by O and S. To explain such high HER activity, a [Mo 2 O 2 (S,O) 4 ] dimer-based periodic model structure with the average composition of [Mo 4 O 8 S 4 ] interfaced with the Ni 3 S 2 (101) surface is proposed. The interactions between the Ni of Ni 3 S 2 and bridging S/O of [Mo 4 O 8 S 4 ] result in an average formal Mo charge state between +5 and +6, and significant charge transfer from Ni 3 S 2 to [Mo 4 O 8 S 4 ] activates the Mo = O bonds. The calculated |ΔG H* | of less than 50 meV suggests that the double-bonded O is the most active site. This work points to the importance of oxy/sulfides with Mon+ (+5 < n < +6) as exceptional electrochemical catalysts for HER.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The protein structurome of Orthornavirae and its dark matter

Metatranscriptomics is uncovering more and more diverse families of viruses with RNA genomes comprising the viral kingdom Orthornavirae in the realm Riboviria. Thorough protein annotation and comparison are essential to get insights into the functions of viral proteins and virus evolution. In addition to sequence- and hmm profile-based methods, protein structure comparison adds a powerful tool to uncover protein functions and relationships. We constructed an Orthornavirae “structurome” consisting of already annotated as well as unannotated (“dark matter”) proteins and domains encoded in viral genomes. We used protein structure modeling and similarity searches to illuminate the remaining dark matter in hundreds of thousands of orthornavirus genomes. The vast majority of the dark matter domains showed either “generic” folds, such as single α-helices, or no high confidence structure predictions. Nevertheless, a variety of lineage-specific globular domains that were new either to orthornaviruses in general or to particular virus families were identified within the proteomic dark matter of orthornaviruses, including several predicted nucleic acid-binding domains and nucleases. In addition, we identified a case of exaptation of a cellular nucleoside monophosphate kinase as an RNA-binding protein in several virus families. Notwithstanding the continuing discovery of numerous orthornaviruses, it appears that all the protein domains conserved in large groups of viruses have already been identified. The rest of the viral proteome seems to be dominated by poorly structured domains including intrinsically disordered ones that likely mediate specific virus-host interactions.

59 BASIC BIOLOGICAL SCIENCES↗

Evolution of the Antarctic Ice Sheet from 2000–2300 and beyond: model sensitivity and uncertainty analysis using MPAS-Albany Land Ice

We present a description of the Antarctic Ice Sheet model configuration submitted to the ISMIP6-Antarctica-2300 experiment using the MPAS-Albany Land Ice model, along with three new sets of simulations: (1) a set of extended simulations to 2500 for three forced experiments and to 2775 for the control experiment; (2) a sensitivity analysis of our model configuration to parameters controlling basal sliding and sub-shelf melt, and to model structural choices including the choice of the energy and stress balances; and (3) a 72-member ensemble run on graphics processing units (GPUs) and analysis of variance to determine the primary sources of uncertainty in our ice-sheet model projections. Our extended simulations predict rapid retreat beginning after 2300 for SSP1-2.6 forcing and after 2500 for present-day (control) forcing, primarily in the Amundsen Sea Embayment. We find that varying the sub-shelf melt parameter between the 5th to 95th percentile values for a mean-Antarctic calibration target results in an up to ∼ ± 40 % change in sea-level contribution relative to our baseline simulations that used the median value. Using a linear basal sliding law reduces sea-level contribution by 51 %–73 % relative to our baseline nonlinear sliding law with an exponent of 1/5. When using basal sliding law exponents of 1/3 and 1/10, the overall difference from our baseline simulations at 2300 is on the order of 10 %. The Amundsen Sea Embayment region displays a strongly non-linear dependence of mass loss on the sliding law exponent, with no discernible relationship between the sliding law exponent and the mass loss by 2300, while the sectors feeding the Ross and Filchner-Ronne ice shelves exhibit more mass loss with a more-plastic sliding law. Our model fidelity sensitivity experiments reveal a 9 %–31 % increase in sea-level contribution when using a depth-integrated stress balance approximation relative to our three-dimensional solver, while using a fixed-in-time temperature field increases sea-level contribution by 14 %–88 % relative to two thermomechanically coupled configurations. Our 72-member ensemble and analysis of variance show that the uncertainty in long-term projections is dominated by the choice of Earth system model forcing and the presence or absence of hydrofracture forcing, rather than uncertainty in sliding and sub-shelf melt parameters.

58 GEOSCIENCES↗

Exploring the Links between Structural Distortions, Orbital Ordering, and Multipolar Magnetic Ordering in Double Perovskites Containing Re(VI) and Os(VII)

A combination of high-resolution powder diffraction techniques and solid-state NMR has been employed to explore the links between crystal structure, orbital ordering, and magnetism in three isostructural double perovskites containing transition metal ions with a 5d 1 configuration. In Ba 2 ZnReO 6 , both neutron and synchrotron X-ray powder diffraction data reveal a cubic-to-tetragonal transition at 23 K that breaks the degeneracy of the t 2g orbitals and leads to a pattern of orbital ordering that stabilizes magnetic ordering when the sample is cooled below 16 K. Similar behavior is observed in Ba 2 MgReO 6 , with an orbital ordering temperature of 33 K and a magnetic ordering temperature of 18 K. Prior theoretical works suggest that the pattern of orbital order seen in the P42/mnm space group is needed to stabilize the heavily canted antiferromagnetism of these compounds. Unfortunately, powder diffraction data is not sensitive enough to differentiate between the I4/mmm and P42/mnm structural models, as the distortions are too subtle to be unambiguously identified from either neutron or synchrotron X-ray powder diffraction methods. In contrast, both diffraction and 7 Li NMR data indicate that Ba 2 LiOsO 6 retains the cubic structure down to 1.7 K. The antiferromagnetic ground state and lack of any sign of orbital ordering in Ba 2 LiOsO 6 provide compelling evidence that the electronically driven tetragonal distortion seen in Ba2ZnReO6, and Ba2MgReO6 is intimately linked to the magnetic ordering seen in those compounds. The absence of magnetic reflections in high intensity neutron powder diffraction data collected on Ba 2 MgReO 6 strongly suggests ordering of multipolar moments on Re(VI), likely ferro-octupolar ordering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NSTXU Diagnostic Disruption Dynamic Loading Represented by Response Spectra

This article presents the results of transient dynamic simulations of loads due to disruption eddy currents on the NSTXU vacuum vessel. Dynamic loading at diagnostic mounting locations is expressed as response spectra derived from the time history results of the dynamic structural simulations of a variety of disruption scenarios. The disruption simulations draw on a history of the project assessments of worst case disruptions for specific components. Major efforts to assess disruption loading have included the vacuum vessel which is the major structural support for the machine, as well as the passive plates (PPs), high harmonic fast wave (HHFW) antenna, and centerstack casing. Each one of these efforts included transient electromagnetic simulations producing time-dependent eddy current Lorentz loads (and in some cases halo loads) which then were applied to time-dependent structural dynamic analyses intended to obtain the proper dynamic amplification factors. In some instances, the EM model and structural model were identical allowing direct transfer of EM forces to the structural model. In other cases, the EM and structural model were not identical and the vector potential (VP) transfer method was used. The results files from these analyses were available (or re-run) to post process in ANSYS Classic time history postprocessor. In conclusion, the ANSYS command is used to create response spectra from time history data at desired points on the vessel.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Exploring cryo-electron microscopy with molecular dynamics

Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.

Biochemistry & Molecular Biology↗

In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics

Conventional basin-by-basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present-day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid-cell using machine learning-based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid-cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small-to-medium-sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small-to-medium-sized catchments not used to derive the streamflow characteristics as well as five large basins. Comparisons indicated marked improvements in bias and Nash-Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof-of-concept for a new promising approach for fine-scale hydrologic model calibrations.

54 ENVIRONMENTAL SCIENCES↗

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon-climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool-based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool-based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. In this study, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

54 ENVIRONMENTAL SCIENCES↗

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide–MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell–based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2D b are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., Kd < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell–based vaccines.

60 APPLIED LIFE SCIENCES↗

Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning

Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detailed structural characterization and binding-affinity prediction. Typical structure-/physics-based methods of ligand virtual screening are designed for small, drug-like molecules, often rely on high-resolution, experimentally determined structures of the protein receptors, and rarely achieve sufficient sampling to obtain converged thermodynamic quantities with large ligands. In this study, we developed a hybrid molecular dynamics/machine learning (MD/ML) approach capable of predicting binding affinity rankings with high accuracy in systems involving large, flexible ligands, despite limited experimental structural information. Using coarse initial structural models, the predictions using the MD/ML workflow achieved strong alignment with experimental trends, particularly in the top-affinity tier for four legume LysM-RLKs (LYR3) binding to LCOs and a chitooligosaccharide. Furthermore, the MD-based conformation selection protocol provided critical structural insights into substrate specificity and binding mechanisms. This study demonstrates a powerful method to screen for challenging cognate ligand-receptors and advance our understanding of the molecular basis of microbial colonization in plants.

Lipo-chitooligosaccharides↗