Symmetry breaking and structural polymorphism in a bacterial microcompartment shell protein for chol
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Ochoa, Jessica M.↗
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Molecular dynamics analysis of the binding of human interleukin-6 with interleukin-6 α-receptor
Human interleukin-6 (hIL-6) is a multifunctional cytokine that regulates immune and inflammatory responses in addition to metabolic and regenerative processes and cancer. hIL-6 binding to the IL-6 receptor (IL-6Rα) induces homodimerization and recruitment of the glycoprotein (gp130) to form a hexameric signaling complex. Anti-IL-6 and IL-6R antibodies are clinically approved inhibitors of IL-6 signaling pathway for treating rheumatoid arthritis and Castleman's disease, respectively. There is a potential to develop novel small molecule IL-6 antagonists derived from understanding the structural basis for IL-6/IL-6Rα interactions. In this work, we combine homology modeling with extensive molecular dynamics (MD) simulations to examine the association of hIL-6 with IL-6Rα. A comparison with MD of apo hIL-6 reveals that the binding of hIL-6 to IL-6Rα induces structural and dynamic rearrangements in the AB loop region of hIL-6, disrupting intraprotein contacts and increasing the flexibility of residues 48 to 58 of the AB loop. In contrast, due to the involvement of residues 59 to 78 in forming contacts with the receptor, these residues of the AB loop are observed to rigidify in the presence of the receptor. The binary complex is primarily stabilized by two pairs of salt bridges, Arg181 (hIL-6)- Glu182 (IL-6Rα) and Arg184 (hIL-6)- Glu183 (IL-6Rα) as well as hydrophobic and aromatic stacking interactions mediated essentially by Phe residues in both proteins. An interplay of electrostatic, hydrophobic, hydrogen bonding, and aromatic stacking interactions facilitates the formation of the hIL-6/IL-6Rα complex.
59 BASIC BIOLOGICAL SCIENCES↗
Adsorption space for microporous polymers with diverse adsorbate species
Abstract The enormous number of combinations of adsorbing molecules and porous materials that exist is known as adsorption space. The adsorption space for microporous polymers has not yet been systematically explored, especially when compared with efforts for crystalline adsorbents. We report molecular simulation data for the adsorptive and structural properties of polymers of intrinsic microporosity with a diverse set of adsorbate species with 345 distinct adsorption isotherms and over 240,000 fresh and swollen structures. These structures and isotherms were obtained using a sorption-relaxation technique that accounts for the critical role of flexibility of the polymeric adsorbents. This enables us to introduce a set of correlations that can estimate adsorbent swelling and fractional free volume dilation as a function of adsorbate uptake based on readily characterized properties. The separation selectivity of the 276 distinct binary molecular pairs in our data is reported and high-performing adsorbent systems are identified.
36 MATERIALS SCIENCE↗
Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity
Abstract Flexible metal–organic materials that exhibit stimulus-responsive switching between closed (non-porous) and open (porous) structures induced by gas molecules are of potential utility in gas storage and separation. Such behaviour is currently limited to a few dozen physisorbents that typically switch through a breathing mechanism requiring structural contortions. Here we show a clathrate (non-porous) coordination network that undergoes gas-induced switching between multiple non-porous phases through transient porosity, which involves the diffusion of guests between discrete voids through intra-network distortions. This material is synthesized as a clathrate phase with solvent-filled cavities; evacuation affords a single-crystal to single-crystal transformation to a phase with smaller cavities. At 298 K, carbon dioxide, acetylene, ethylene and ethane induce reversible switching between guest-free and gas-loaded clathrate phases. For carbon dioxide and acetylene at cryogenic temperatures, phases showing progressively higher loadings were observed and characterized using in situ X-ray diffraction, and the mechanism of diffusion was computationally elucidated.
Chemistry↗
Er-doped anatase TiO 2 thin films on LaAlO 3 (001) for quantum interconnects (QuICs)
Rare-earth ions (REIs) doped into solid-state crystal hosts offer an attractive platform for realizing quantum interconnects that can function as quantum memories and quantum repeaters. The 4f valence electrons of REIs are shielded by 5s and 5p electrons and undergo highly coherent transitions even when embedded in host crystals. In particular, Er 3+ has an optical transition in the telecom band that is suitable for low-loss communication. Recently, REIs in thin film systems have gained interest due to potential advantages in providing a flexible host crystal environment, enabling scalable on-chip integration with other quantum devices. Here, we investigate the structural and optical properties of Er-doped anatase TiO 2 thin films on LaAlO 3 (001) substrates. By choosing a system with minimal lattice mismatch and adjusting Er-dopant concentration, we achieve optical inhomogeneous linewidths of 5 GHz at 4.5 K. Furthermore, we show that 9 nm-thick buffer and capping layers can reduce the linewidth by more than 40%, suggesting a pathway to further narrowing linewidths in this system. We also identify that Er 3+ ions mainly incorporate into substitutional Ti 4+ sites with non-polar D 2d symmetry, which makes Er dopants insensitive to the first order to local electric fields from impurities and is desirable for coherence properties of Er 3+ spins.
42 ENGINEERING↗
Enhancing ACPF Analysis: Integrating Newton-Raphson Method with Gradient Descent and Computational Graphs
This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy sources in power systems introduces variability and unpredictability, and this method addresses these challenges. It leverages the robustness of NR for accurate approximations and the flexibility of GD for handling variable conditions, all without requiring Jacobian matrix inversion. Furthermore, computational graphs provide a structured and visual framework that simplifies and systematizes the application of these methods. The goal of this fusion is to overcome the limitations of traditional ACPF methods and improve the resilience, adaptability, and efficiency of modern power grid analyses. We validate the effectiveness of our advanced algorithm through comprehensive testing on established IEEE benchmark systems. Furthermore, our findings demonstrate that our approach not only speeds up the convergence process but also ensures consistent performance across diverse system states, representing a significant advancement in power flow computation.
24 POWER TRANSMISSION AND DISTRIBUTION↗
SoLID Program at JLab
An overview of the Solenoidal Large Intensity Device (SoLID) and its scientific program will be given in this talk. SoLID is a spectrometer/detector system proposed to exploit the full potential of the Jefferson Lab (JLab) 12 GeV energy upgrade. SoLID will push the limit of luminosity frontier in hadronic physics with its unique capability to handle very high rates with large acceptance under high luminosity (1037-39/cm2/s). A rich and vibrant scientific program has been developed for SoLID, including but not limited to the precision study of the 3d nucleon structure in both momentum space using Semi-Inclusive Deep Inelastic Scattering (SIDIS) and coordinate space using Deep Virtual Exclusive Reactions (DVER), probing physics beyond the Standard Model with Parity Violating Deep Inelastic Scattering (PVDIS), and investigating the gluonic field contribution to the proton structure and proton mass via J/¿ threshold production. The SoLID collaboration has developed a robust, low risk and flexible conceptual design, with a base line design capable of accomplishing its scientific goals and flexibility to adopt the cutting-edge technology. Detector subsystems have been tested with prototypes in realistic high luminosity conditions and are demonstrated to function well under extremely challenging environment to satisfy the requirements of planned experiments.
Chen, Jian-Ping [Thomas Jefferson National Acceler↗
The differing effects of a dual acting regulator on SIRT1
SIRT1 is an NAD + -dependent protein deacetylase that has been shown to play a significant role in many biological pathways, such as insulin secretion, tumor formation, lipid metabolism, and neurodegeneration. There is great interest in understanding the regulation of SIRT1 to better understand SIRT1-related diseases and to better design therapeutic approaches that target SIRT1. There are many known protein and small molecule activators and inhibitors of SIRT1. One well-studied SIRT1 regulator, resveratrol, has historically been regarded as a SIRT1 activator, however, recent studies have shown that it can also act as an inhibitor depending on the identity of the peptide substrate. The inhibitory nature of resveratrol has yet to be studied in detail. Understanding the mechanism behind this dual behavior is crucial for assessing the potential side effects of STAC-based therapeutics. Here, we investigate the detailed mechanism of substrate-dependent SIRT1 regulation by resveratrol. We demonstrate that resveratrol alters the substrate recognition of SIRT1 by affecting the K M values without significantly impacting the catalytic rate k cat ). Furthermore, resveratrol destabilizes SIRT1 and extends its conformation, but the conformational changes differ between the activation and inhibition scenarios. We propose that resveratrol renders SIRT1 more flexible in the activation scenario, leading to increased activity, while in the inhibition scenario, it unravels the SIRT1 structure, compromising substrate recognition. Our findings highlight the importance of substrate identity in resveratrol-mediated SIRT1 regulation and provide insights into the allosteric control of SIRT1. This knowledge can guide the development of targeted therapeutics for diseases associated with dysregulated SIRT1 activity.
59 BASIC BIOLOGICAL SCIENCES↗
Modularity-at-scale for advanced reactors presentation
Modularity options have been limited for traditional nuclear energy deployment due to the conventional light water reactor (LWR) safety requirements, such as high pressure retaining heavy and robust containment structures. However, a relatively new regulatory approach called ‘Functional Containment’ has potential to allow less expensive and more flexible designs for non-LWRs. Functional containment provides flexibility in design and deployment based on risk-informed and performance-based criteria, so that reactors are not over-designed. Non-nuclear industry has successfully used modular design approaches in automotive, aerospace, chemical processing, building construction, and ship building. These industries have shown that modular construction reduces construction time by around 30% - 50% compared to the conventional stick-built approach. The nuclear industry can use similar approaches to reduce construction time and costs, balanced with safety requirements, using the functional containment approach. This paper discussing the background of modularity in nuclear energy, examples of less learned, modularity approaches in non-nuclear industries and the potential of cost and schedule savings through the emerging regulatory design flexibilities potentially enabling combination of modular deployment at different scales.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
MODULARITY-AT-SCALE FOR COST COMPETITIVE DEPLOYMENT OF NUCLEAR ENERGY
Modularity options have been limited for traditional nuclear energy deployment due to the conventional light water reactor (LWR) safety requirements, such as high pressure retaining heavy and robust containment structures. However, a relatively new regulatory approach called ‘Functional Containment’ has potential to allow less expensive and more flexible designs for non LWRs. Functional containment provides flexibility in design and deployment based on risk informed and performance-based criteria, so that reactors are not over-designed. Non-nuclear industry has successfully used modular design approaches in automotive, aerospace, chemical processing, building construction, and ship building. These industries have shown that modular construction reduces construction time by around 30% - 50% compared to the conventional stick built approach. The nuclear industry can use similar approaches to reduce construction time and costs, balanced with safety requirements, using the functional containment approach. This paper discussing the background of modularity in nuclear energy, examples of less learned, modularity approaches in non-nuclear industries and the potential of cost and schedule savings through the emerging regulatory design flexibilities potentially enabling combination of modular deployment at different scales.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Structural Characterization of the Human Cytosolic Malate Dehydrogenase I
The first crystal structure of the human cytosolic malate dehydrogenase I (MDH1) is described. Structure determination at a high resolution (1.65 Å) followed production, isolation, and purification of human MDH1 using a bacterial expression system. The structure is a binary complex of MDH1 with only a bound malonate molecule in the substrate binding site. Comparisons of this structure with malate dehydrogenase enzymes from other species confirm that the human enzyme adopts similar secondary, tertiary, and quaternary structures and that the enzyme retains a similar conformation even when nicotinamide adenine dinucleotide (NAD + ) is not bound. A comparison to the highly homologous porcine (sus scrofa) MDH1 ternary structures leads to the conclusion that only small conformational differences are needed to accommodate binding by NAD + or other NAD + mimetics. Conformational differences observed in the second subunit show that the NAD + binding elements are nevertheless quite flexible. Comparison of hMDH1 to the human mitochondrial malate dehydrogenase (hMDH2) reveals some key differences in the α7–α8 loop, which lies directly beneath the substrate binding pocket. These differences might be exploited in the structure-assisted design of selective small molecule inhibitors of hMDH1, an emerging target for the development of anticancer therapeutics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
RNA target highlights in CASP15 : Evaluation of predicted models by structure providers
Abstract The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA‐only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x‐ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA–protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.
59 BASIC BIOLOGICAL SCIENCES↗
Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network
Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024
Zhang, Hengrui (ORCID:0000000231831654)↗
AuPd Nanoicosahedra: Atomic-Level Surface Modulation for Optimization of Electrocatalytic and Photocatalytic Energy Conversion
Modulation of bimetallic nanocatalysts with atomic precision would allow for significant increases in catalyst activity through the optimization of heteroatomic interplay. In practice, this level of control over homogeneously turning the heteroatomic states in the surface regions of catalysts remains a great challenge for practical applications requiring mass production. In this work, a one-step aqueous strategy for preparation of AuPd nanoicosahedra core–shell structures has been developed. By turning the molar ratio of Au and Pd precursors, the nanoicosahedra could be made with uniform core composition (Pd ~20%) and largely flexible surface compositions (Pd: 20–70%). Under model reactions of electrochemical ethanol oxidation (EOR) and 4-nitrophenol (4-NP) reduction, the Au 55 Pd 45 surface displayed superior activities. Here, x-ray absorption spectroscopy revealed that AuPd nanoicosahedra with Pd-poor surfaces (Au 79 Pd 21 and Au 60 Pd 40 ) maintain Au-like electronic structures while those with Pd-rich surfaces are more Pd-like. The Au 55 Pd 45 surface struck a delicate balance between the two extremes, leading to the optimal Au–Pd interplay for catalytic performance. Overall, the development of a one-step aqueous strategy for atomic-level control over bimetallic nanoparticle surface states is an important milestone for future advances in catalysis. Our findings in atomic-scale catalyst design will bring deep insights into the fields of nanosynthesis and heterogeneous catalysis, which will be beneficial for further scientific breakthroughs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A redefinition of the halo boundary leads to a simple yet accurate halo model of large-scale structure
ABSTRACT We present a model for the halo–mass correlation function that explicitly incorporates halo exclusion and allows for a redefinition of the halo boundary in a flexible way. We assume that haloes trace mass in a way that can be described using a single scale-independent bias parameter. However, our model exhibits scale-dependent biasing due to the impact of halo-exclusion, the use of a ‘soft’ (i.e. not infinitely sharp) halo boundary, and differences in the one halo term contributions to ξhm and ξmm. These features naturally lead us to a redefinition of the halo boundary that lies at the ‘by eye’ transition radius from the one-halo to the two-halo term in the halo–mass correlation function. When adopting our proposed definition, our model succeeds in describing the halo–mass correlation function with $\approx 2{{\ \rm per\ cent}}$ residuals over the radial range 0.1 h−1 Mpc < r < 80 h−1 Mpc, and for halo masses in the range 1013 h−1 M⊙ < M < 1015 h−1 M⊙. Our proposed halo boundary is related to the splashback radius by a roughly constant multiplicative factor. Taking the 87 percentile as reference we find rt/Rsp ≈ 1.3. Surprisingly, our proposed definition results in halo abundances that are well described by the Press–Schechter mass function with δsc = 1.449 ± 0.004. The clustering bias parameter is offset from the standard background-split prediction by $\approx 10{{\ \rm per\ cent}}\!-\!15{{\ \rm per\ cent}}$. This level of agreement is comparable to that achieved with more standard halo definitions.
79 ASTRONOMY AND ASTROPHYSICS↗
Progressive Tree-Based Compression of Large-Scale Particle Data
Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.
97 MATHEMATICS AND COMPUTING↗
Incorporating Flexibility Effects into Metal–Organic Framework Adsorption Simulations Using Different Models
High-throughput calculations based on molecular simulations to predict the adsorption of molecules inside metal–organic frameworks (MOFs) have become a useful complement to experimental efforts to identify promising adsorbents for chemical separations and storage. For computational convenience, all existing efforts of this kind have relied on simulations in which the MOF is approximated as rigid. In this paper, we use extensive adsorption–relaxation simulations that fully include MOF flexibility effects to explore the validity of the rigid framework approximation. We also examine the accuracy of several approximate methods to incorporate framework flexibility that are more computationally efficient than adsorption–relaxation calculations. We first benchmark various models of MOF flexibility for four MOFs with well-established CO 2 experimental consensus isotherms. We then consider a range of adsorption properties, including Henry’s constants, nondilute loadings, and adsorption selectivity, for seven adsorbates in 15 MOFs randomly selected from the CoRE MOF database. Our results indicate that in many MOFs adsorption–relaxation simulations are necessary to make quantitative predictions of adsorption, particularly for adsorption at dilute concentrations, although more standard calculations based on rigid structures can provide useful information. Finally, we investigate whether a correlation exists between the elastic properties of empty MOFs and the importance of including framework flexibility in making accurate predictions of molecular adsorption. Our results did not identify a simple correlation of this type.
36 MATERIALS SCIENCE↗
ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration
Abstract Motivation Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. Results This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementation ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.
Lai, Jhih-Siang (ORCID:0000000156775890)↗