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Bioinformatics and 3D Structural Analysis of the Coronavirus Main Protease Active Site Diversity
Coronaviruses (Coronaviridae) such as SARS‐CoV‐2 (severe acute respiratory syndrome coronavirus) and MERS‐CoV (Middle East respiratory syndrome coronavirus) have been the source of recent outbreaks and global health concerns. While vaccines have been essential for controlling the SARS‐CoV‐2 (COVID‐19) pandemic, it is uncertain whether they will be effective against future coronavirus strains. Therefore, identification or design of a broad‐spectrum drug that targets highly conserved regions of the main protease of multiple coronavirus strains is essential in the long term. As part of a virtual summer research experience with the RCSB PDB, bioinformatics tools were employed to predict and construct 3D models of the coronavirus main protease (MPro) using SARS‐CoV‐2 as the template, with a focus on mutational trends and active sites. This study focused on the active sites of MPro, a cysteine protease essential for viral assembly and replication. Sequence alignments and structure modeling of MPro structures has identified conserved regions across multiple coronavirus strains. Inhibition of MPro halts coronavirus replication, making it an ideal drug target, and studies of MPro may foster and accelerate the discovery of high affinity broad‐spectrum drugs.
Update on Savannah River Site H-Canyon Exhaust Tunnel Erosion Analysis
H-Canyon Exhaust Tunnel • Nitric acid vapors • GPS denied • 11.176 -13.411 m/s Air Flow • Radioactivity • Concrete fines in the air • Uneven surfaces • Dark
Floating Wind Array Ontology and Modeling Framework
While there are many tools for designing and modeling a single floating turbine, array level design and modeling has much more to consider. Designing floating wind arrays requires a coupled approach considering many variables, from bathymetry to installation and maintenance to failure and risk analysis. With all of these considerations, an array-level modeling tool is needed to quickly evaluate array designs. The Floating Array Model (FAModel) tool developed at the National Renewable Energy Laboratory was created to fill this gap in low-fidelity array modeling. FAModel is a python framework created to streamline holistic low-fidelity floating wind modeling for array-level analysis. FAModel integrates site data and models with a variety of open-source modeling tools developed by NREL, including FLORIS, RAFT, MoorPy, and anchor capacity models. The integration of these tools allows users to quickly and holistically design an array by considering forces, area analysis, visualization, annual energy production, failure modeling, and component costs.
The Nature and State of Groundwater Contamination at the Nevada National Security Site: What Have We Learned from Decades of Groundwater Analysis? - 20337
The regulatory framework for remediating radionuclide contamination from underground nuclear testing at the Nevada National Security Site (NNSS) is based on a combination of characterization and modeling studies, monitoring, and institutional controls [1]. Currently, tritium is the largest contributor (∼90%) to the estimated 44.6 million-curie radionuclide inventory resulting from underground testing [2]. Because of its short half-life (12.32 years), its relative contribution reduces below 10% of the total radiologic inventory over the next 120 years as a result of radioactive decay. Although tritium levels are observed well above the Safe Drinking Water Act (SDWA) maximum contaminant levels (MCLs) in groundwater, other radionuclides are well below their MCLs except within the nuclear test near-field (nuclear test cavity and chimney) environment. In fact, most device-derived radionuclides are below their MCL in groundwater even in samples collected from this near-field environment. The distribution of radionuclides following the nuclear detonation greatly influences the availability of potential contaminants for groundwater transport. Tritium is initially distributed in the gas phase, later as tritiated water in steam, and finally as liquid water, and is available to groundwater transport away from the near-field environment. Other radionuclides that are mobile in groundwater are {sup 14}C, {sup 36}Cl, {sup 99}Tc, and {sup 129}I though their radiologic inventory is small when compared to tritium. Many radionuclides (e.g. U, Pu, Am) are incorporated to a significant extent into the melt glass at the bottom of the cavity and are accessible to groundwater primarily through the slow process of glass dissolution. These radionuclides are also adsorbed to the surfaces of the crushed rock within the cavity and chimney which limits their migration in groundwater. Although colloid facilitated transport of radionuclides at the NNSS has been observed [3], radionuclide concentrations decrease with time and migration distances due to desorption and colloid filtration processes. Current studies indicate that radionuclides associated with colloids are unlikely to migrate downgradient from NNSS underground nuclear tests at concentrations above the SDWA MCL [4][5]. The results of over 50 years of sampling, along with an understanding of these post detonation processes, indicate that tritium is the only contaminant of concern downgradient of testing and that even tritium will not exceed its MCL in groundwater after ∼120 years. Although other longer-lived radionuclides may continue to be released slowly from the near-field environment they will likely never reach levels exceeding their MCLs in groundwater downgradient of the NNSS. Groundwater monitoring will continue to verify these observations. (authors)
Update on Savannah River Site H-Canyon Exhaust Tunnel Erosion Analysis – 24099
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Integrated Methane Monitoring Platform Extension, Volume I: Final Technical Report
The IMMPE project, DE-FE0032284, was to enhance methane monitoring technologies and their applications across various natural gas asset classes. The scope included deploying advanced methane detection and monitoring technologies to identify and mitigate fugitive methane emissions, measuring emission rates, and assessing impacts. The findings included the successful mitigation of identified emissions and quantification of emission rates. A key outcome was the development of a comprehensive template and summary of recommendations for methane emissions monitoring, which is replicable for both upstream and downstream applications. Furthermore, the project emphasized the importance of education by providing training opportunities for technicians and regulators, thereby fostering awareness and promoting the adoption of cost-effective methane emissions monitoring and management techniques.
MASTODON: An Open-Source Software for Seismic Analysis and Risk Assessment of Critical Infrastructure
Seismic analysis and risk assessment of safety-critical infrastructure like hospitals, nuclear power plants, dams, and facilities handling radioactive materials involve computationally intensive numerical models and coupled multiphysics scenarios. They are also performed in a strict regulatory environment that requires high software quality assurance standards, and in the case of safety-related nuclear facilities, a conformance to the American Society of Mechanical Engineers Nuclear Quality Assurance (NQA-1) standard. This paper introduces the open-source finite-element software, MASTODON (Multi-hazard Analysis of Stochastic Time-Domain Phenomena), which implements state-of-the-art seismic analysis and risk assessment tools in a quality-controlled environment. MASTODON is built on MOOSE (Multi-physics Object-Oriented Simulation Environment), which is a highly parallelizable, NQA-1 conforming, coupled multiphysics, finite-element framework developed at Idaho National Laboratory. MASTODON is capable of fault rupture and source-to-site wave propagation using the domain reduction method, nonlinear site response, and soil-structure interaction analysis, implicit and explicit time integration, automated stochastic simulations, and seismic probabilistic risk assessment. When coupled with other MOOSE applications, MASTODON can also solve strongly and weakly coupled multiphysics problems. This paper presents a summary of the capabilities of MASTODON and some demonstrative examples.
Thermodynamic modeling of the Pd–Zn system with uncertainty quantification and its implication to tailor catalysts
Pd–Zn intermetallic catalysts show encouraging combinations of activity and selectivity on well-defined active site ensembles. Thermodynamic description of the Pd–Zn system, delineating phase boundaries and enumerating site occupancies within intermediate alloy phases, is essential to determining the ensembles of Pd–Zn atoms as a function of composition and temperature. Combining the present extensive first-principles calculations based on density functional theory (DFT) and available experimental data, the Pd–Zn system was remodeled using the CALculation of PHAse Diagrams (CALPHAD) approach. High throughput modeling tools with uncertainty quantification, i.e., ESPEI and PyCalphad, were incorporated in the phase analysis. Here, the site occupancies across the γ phase composition region were given special attention. A four-sublattice model was used for the γ phase owing to its four Wyckoff positions, i.e., the outer tetrahedral (OT) site 8c, the inner tetrahedral (IT) site 8c, the octahedral (OH) site 12e, and the cuboctahedral (CO) site 24g. The site fractions of Pd and Zn calculated from the present thermodynamic model show the occupancy preference of Pd in the OT and OH sublattices in agreement with experimental observations. The force constants obtained from DFT-based phonon calculations further supports the tendency of Pd occupying the OH sublattice compared with the IT and CO sublattice. The catalytic ensembles changing from Pd monomers (Pd 1 ) to trimers (Pd 3 ) on the surface of γ phase are attributed to the increase of Pd occupancy in the OH sublattice.
KPC-2 β-lactamase enables carbapenem antibiotic resistance through fast deacylation of the covalent intermediate
Serine active-site β-lactamases hydrolyze β-lactam antibiotics through the formation of a covalent acyl-enzyme intermediate followed by deacylation via an activated water molecule. Carbapenem antibiotics are poorly hydrolyzed by most β-lactamases owing to slow hydrolysis of the acyl-enzyme intermediate. However, the emergence of the KPC-2 carbapenemase has resulted in widespread resistance to these drugs, suggesting it operates more efficiently. Here, we investigated the unusual features of KPC-2 that enable this resistance. We show that KPC-2 has a 20,000-fold increased deacylation rate compared with the common TEM-1 β-lactamase. Furthermore, kinetic analysis of active site alanine mutants indicates that carbapenem hydrolysis is a concerted effort involving multiple residues. Substitution of Asn170 greatly decreases the deacylation rate, but this residue is conserved in both KPC-2 and non-carbapenemase β-lactamases, suggesting it promotes carbapenem hydrolysis only in the context of KPC-2. X-ray structure determination of the N170A enzyme in complex with hydrolyzed imipenem suggests Asn170 may prevent the inactivation of the deacylating water by the 6α-hydroxyethyl substituent of carbapenems. In addition, the Thr235 residue, which interacts with the C3 carboxylate of carbapenems, also contributes strongly to the deacylation reaction. In contrast, mutation of the Arg220 and Thr237 residues decreases the acylation rate and, paradoxically, improves binding affinity for carbapenems. Thus, the role of these residues may be ground state destabilization of the enzyme-substrate complex or, alternatively, to ensure proper alignment of the substrate with key catalytic residues to facilitate acylation. These findings suggest modifications of the carbapenem scaffold to avoid hydrolysis by KPC-2 β-lactamase.
Mitigating Algorithmic Bias in Cancer Site Classification Models
Purpose Integrating artificial intelligence in cancer diagnostics has improved tumor classification beyond rule-based systems. Despite these advancements, these models may still encode demographic biases. We conducted a large-scale, applied bias-probing study of a deep learning–based cancer site classifier to quantify race information encoded in document embeddings. We then evaluated how performance changes when race-correlated embedding dimensions are removed in a post-training sensitivity analysis. Methods The cancer site classifier was trained using 3.5 million electronic cancer pathology reports from six of the National Cancer Institute's SEER registries. We trained a hierarchical self-attention network to generate 400-dimensional document embeddings. These embeddings were used to train two downstream, gradient-boosted decision tree classifiers: one to classify the cancer sites and another to predict racial categories. We identified overlapping features by intersecting the top 50 feature-importance rankings from the site and race models and computed their cumulative feature importance in each model. As a post hoc sensitivity analysis, we progressively pruned these overlapping dimensions, retrained the site model, and compared overall macro-F1 and accuracy, race-stratified macro-F1, and group fairness metrics on the basis of demographic parity and equalized odds before and after pruning. Results The analysis revealed minimal feature overlap between the cancer site and race prediction models, and the cumulative importance scores indicated a negligible influence of racial information on clinical predictions. Post-training pruning of overlapping features did not compromise the models' diagnostic accuracy, with a 0.07% loss in accuracy. Conclusion Our findings demonstrate that HiSAN-generated embeddings from SEER data can be used effectively in cancer site classification without significant demographic bias influencing the outcomes. Post-training pruning therefore functions as a practical audit and sensitivity check.
Thermodynamic Stability and Site‐Specific Distribution of Graphitic and Pyridinic Nitrogen in Graphene Moiré on Ru(0001)
Abstract Graphene‐like materials are of interest for large‐scale hydrogen storage applications due to their lightweight, durable, and scalable properties. Nitrogen‐doping minimizes kinetic limitations in diffusion and recombination on surfaces, however, the role of graphitic nitrogen (GN) and pyridinic nitrogen (PN) is not well understood. Nitrogen‐doped graphene is synthesized on Ru(0001) using chemical vapor deposition (CVD) of pyridine and ion irradiation. Scanning tunneling microscopy (STM), x‐ray photoelectron spectroscopy (XPS), and density functional theory (DFT) are used to identify the structure, location, and thermodynamic stability of nitrogen species within the graphene moiré. CVD of pyridine results in a low nitrogen concentration (<0.1at%), while the post‐growth nitrogen ion irradiation allows us to increase the concentration further. The concentration of GN and PN is controlled by varying the ion dose and annealing temperature. Comparison of measured and simulated STM images of GN and PN yield an excellent agreement, allowing us to confidently establish that GN is preferentially located near the center of the Atop region, while PN is located in the valley region of the graphene moiré. This report explicitly confirms the site assignments and provides a foundation for the site synthesis and analysis of structural and electronic properties that drive the reactivity of N‐doped graphene.
Primary donor triplet states of Photosystem I and II studied by Q-band pulse ENDOR spectroscopy
The photoexcited triplet state of the “primary donors” in the two photosystems of oxygenic photosynthesis has been investigated by means of electron-nuclear double resonance (ENDOR) at Q-band (34 GHz). The data obtained represent the first set of 1 H hyperfine coupling tensors of the 3 P700 triplet state in PSI and expand the existing data set for 3 P680. We achieved an extensive assignment of the observed electron-nuclear hyperfine coupling constants (hfcs) corresponding to the methine α-protons and the methyl group β-protons of the chlorophyll (Chl) macrocycle. The data clearly confirm that in both photosystems the primary donor triplet is located on one specific monomeric Chl at cryogenic temperature. In comparison to previous transient ENDOR and pulse ENDOR experiments at standard X-band (9–10 GHz), the pulse Q-band ENDOR spectra demonstrate both improved signal-to-noise ratio and increased resolution. The observed ENDOR spectra for 3 P700 and 3 P680 differ in terms of the intensity loss of lines from specific methyl group protons, which is explained by hindered methyl group rotation produced by binding site effects. Contact analysis of the methyl groups in the PSI crystal structure in combination with the ENDOR analysis of 3 P700 suggests that the triplet is located on the Chl a' (P A ) in PSI. The results also provide additional evidence for the localization of 3 P680 on the accessory Chl D1 in PSII.
Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations
Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practical steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.
A catalytic dyad modulates conformational change in the CO 2 -fixing flavoenzyme 2-ketopropyl coenzyme M oxidoreductase/carboxylase
2-Ketopropyl-coenzyme M oxidoreductase/carboxylase (2-KPCC) is a member of the flavin and cysteine disulfide containing oxidoreductase family (DSOR) that catalyzes the unique reaction between atmospheric CO 2 and a ketone/enolate nucleophile to generate acetoacetate. However, the mechanism of this reaction is not well understood. Here, we present evidence that 2-KPCC, in contrast to the well-characterized DSOR enzyme glutathione reductase, undergoes conformational changes during catalysis. Using a suite of biophysical techniques including limited proteolysis, differential scanning fluorimetry, and native mass spectrometry in the presence of substrates and inhibitors, we observed conformational differences between different ligand-bound 2-KPCC species within the catalytic cycle. Analysis of site-specific amino acid variants indicated that 2-KPCC-defining residues, Phe501-His506, within the active site are important for transducing these ligand induced conformational changes. We propose that these conformational changes promote substrate discrimination between H + and CO 2 to favor the metabolically preferred carboxylation product, acetoacetate.
Evaluating the bond strength and fracture mechanisms of cold-sprayed zinc coating on AZ91 magnesium substrate via a combined experimental and computational approach
Magnesium (Mg) alloys are ideal candidates for automotive applications due to their high strength to weight ratio, castability, recyclability etc., however, they lack corrosion and oxidation resistance. Solid-state deposition techniques, such as cold spray, have been demonstrated to enhance their corrosion resistance as it relies on the severe plastic deformation of powder particles upon impact with the substrate to form a metallurgical bond with the substrate and within the coating. At cold sprayed interfaces, a heterogeneous microstructure is formed that includes some porosity, oxides and intermetallics which can significantly affect coating performance. Thus, establishing a direct correlation between the interface microstructure and its properties can aid in designing optimal cold spray parameters. In this study, we investigated the microstructure and mechanical properties of a zinc (Zn) coating deposited on a high pressure die cast (HPDC) AZ91 Mg substrate via high resolution scanning transmission electron microscopy, in situ micro-tensile testing, and finite element method (FEM) modeling. Micro-tensile pillars fabricated using the plasma focused ion beam (PFIB) successfully isolates the coating-substrate interface within the gauge length. The average bond strength of Zn-Mg interface was determined to be ∼140 MPa with failure occurring partially at the interface and mostly into the coatings. A detailed microstructural characterization revealed evidence of a strong metallurgical bonding at the Zn-Mg interface and formation of the C14 MgZn 2 laves phase interlayer resulting in a mixed mode of fracture during the micro-tensile experiments. FEM modeling reveals the stress distribution along the interfaces and suggests that a MgZn 2 layer thickness between 200–400 nm is optimum to increase the bond strength and minimize the triaxiality. Such a site-specific interfacial analysis with correlative computational modeling provides crucial insight into the overall performance of cold spray interfaces.
Recent Developments in Revealing the Impact of Complex Metal Oxide Reconstruction on Catalysis
Surface reconstruction of complex metal oxides refers to the alterations of the topmost catalyst surface, relative to the bulk structure, following exposure to pretreatment or reaction conditions during heterogeneous catalysis. (1) This structural and electronic transformation may involve (1) changes of the exposed crystallographic facets, (2) changes in chemical composition, such as the enrichment of specific elements, and, (3) morphological variations, including surface roughness and defects (Scheme 1). Beyond relaxation, surface reconstruction here also encompasses disruptions to the periodicity of the sublayers and changes to its stoichiometry. This change significantly influences the chemical properties of the catalyst surface, such as redox sites, (2) acid/base pairs, hydroxyl groups, (3) and surface defects. Surface reconstruction occurs due to the thermodynamic drive to lower the surface energy of the catalyst under the surrounding environment (e.g., temperature, chemical potential of species present). In addition to the treatment or reaction conditions, the extent of reconstruction also depends on the elements in the metal oxide structure. For instance, annealing ABO 3 perovskites (SrTiO 3 , BaTiO 3 , and BaZrO 3 ) at high temperatures (500 °C) in O 2 has a very different impact on the A/B ratio at the surface (ranging from 0.9 to 2.5), depending on the identity of A and B. (4) Metal oxide surface reconstruction under an electric field and exposure to acidic and alkaline media has recently gained increased attention in electrocatalysis, such as in the oxygen evolution reaction (OER); (5,6) however, in this Viewpoint we focus on the impact of surface reconstruction on thermal catalysis, a critical enduring research theme with ongoing challenges. Here, we start showcasing approaches to characterize surface reconstruction at the topmost surface layer, such as low-energy ion scattering (LEIS), discussing advantages and limitations. Next, we connect characterization of surface reconstruction to site-specific kinetic analysis, enabled via steady-state isotopic transient kinetic analysis (SSITKA), and we express our viewpoint on building structure–performance relationships by deeply understanding reconstructed surfaces. Later, we comment on the potential of tuning surface reconstruction to enhance catalytic performance. Finally, we explore the interplay between surface reconstruction and the “intelligent behavior”.
Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies
The Biosphere-Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land-surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah-MP LSM framework through evaluation and optimization of the Noah-MP BATS ground snow albedo formulation using 2-band (visible and near-infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah-MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh-snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central-southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh-snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh-snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh-snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time-varying formulation for fresh-snow albedo.