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Currently, thirty-one dysprosium, thirty-two holmium, thirty-two erbium, thirty-three thulium, and thirty-one ytterbium isotopes have been observed and the discovery of these isotopes is described here. For each isotope a brief synopsis of the first refereed publication, including the production and identification method, is presented.
Currently, 31 rubidium, 35 strontium, 35 molybdenum, and 38 rhodium isotopes have been observed and the discovery of these isotopes is described here. For each isotope a brief synopsis of the first refereed publication, including the production and identification method, is presented.
Currently, 1917 nuclear isomers with half-lives longer than 100 ns have been discovered in 1310 different nuclides and 103 different elements. Here, while the physical properties of isomers have been compiled before, this is the first compilation of the isomer discoveries. For each isomer the reference, year, laboratory and country are documented.
Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.
Peptidyl prolyl cis/trans isomerization by Pin1 regulates various oncogenic signals during cancer progression, and its inhibition through multiple approaches has established Pin1 as a therapeutic target. However, lack of simplified screening systems has limited the discovery of potent Pin1 inhibitors. We utilized phosphorylation-dependent binding of Pin1 to its specific substrate to develop a screening system for Pin1 inhibitors. Using this system, we screened a chemical library, and identified a novel selenium derivative as Pin1 inhibitor. Based on structure-activity guided chemical synthesis, we developed more potent Pin1 inhibitors that inhibited cancer cell proliferation. -- Highlights: •Novel screening for Pin1 inhibitors based on Pin1 binding is developed. •A novel selenium compound is discovered as Pin1 inhibitor. •Activity guided chemical synthesis of selenium derivatives resulted potent Pin1 inhibitors.
The Andes-endemic Barnadesioideae lineage is the oldest surviving and phylogenetically basal subfamily of the Asteraceae (Compositae), a prolific group of flowering plants with world-wide distribution (∼24,000 species) marked by a rich diversity of sesquiterpene lactones (STLs). Intriguingly, there is no evidence that members of the Barnadesioideae produce STLs, specialized metabolites thought to have contributed to the adaptive success of the Asteraceae family outside South America. The biosynthesis of STLs requires the intimate expression and functional integration of germacrene A synthase (GAS) and germacrene A oxidase (GAO) to sequentially cyclize and oxidize farnesyl diphosphate into the advanced intermediate germacrene A acid leading to diverse STLs. Our previous discovery of GAO activity conserved across all major subfamilies of Asteraceae, including the phylogenetically basal lineage of Barnadesioideae, prompted further investigation of the presence of the gateway GAS in Barnadesioideae. Herein we isolated two terpene synthases (BsGAS1/BsGAS2) from the basal Barnadesia spinosa (Barnadesioideae) that displayed robust GAS activity when reconstituted in yeast and characterized in vitro. Despite the apparent lack of STLs in the Barnadesioideae, this work unambiguously confirms the presence of GAS in the basal genera of the Asteraceae. Phylogenetic analysis reveals that the two BsGASs fall into two distinct clades of the Asteraceae's GASs, and BsGAS1 clade is only retained in the evolutionary closer Cichorioideae subfamily, implicating BsGAS2 is likely the ancestral base of most GASs found in the lineages outside the Barnadesioideae. Taken together, these results show the enzymatic capacities of GAS and GAO emerged prior to the subsequent radiation of STL-producing Asteraceae subfamilies. - Highlights: • Sesquiterpene lactones are characteristic metabolites in Asteraceae family. • Barnadesioideae is the basal lineage of all Asteraceae plants, producing sesquiterpene lactones. • Two germacrene A synthases (GASs) were identified and characterized from Barnadesia spinosa. • A phylogenetic analysis showed two sub-clades of GASs have evolved in Asteraceae. • GAS enzymes evolved prior to the divergence and global dispersal of the modern Asteraceae.
A high-throughput RapidFire mass spectrometry assay is described for elongation of very long-chain fatty acids family 6 (Elovl6). Elovl6 is a microsomal enzyme that regulates the elongation of C12-16 saturated and monounsaturated fatty acids. Elovl6 may be a new therapeutic target for fat metabolism disorders such as obesity, type 2 diabetes, and nonalcoholic steatohepatitis. To identify new Elovl6 inhibitors, we developed a high-throughput fluorescence screening assay in 1536-well format. However, a number of false positives caused by fluorescent interference have been identified. To pick up the real active compounds among the primary hits from the fluorescence assay, we developed a RapidFire mass spectrometry assay and a conventional radioisotope assay. These assays have the advantage of detecting the main products directly without using fluorescent-labeled substrates. As a result, 276 compounds (30%) of the primary hits (921 compounds) in a fluorescence ultra-high-throughput screening method were identified as common active compounds in these two assays. It is concluded that both methods are very effective to eliminate false positives. Compared with the radioisotope method using an expensive {sup 14}C-labeled substrate, the RapidFire mass spectrometry method using unlabeled substrates is a high-accuracy, high-throughput method. In addition, some of the hit compounds selected from the screening inhibited cellular fatty acid elongation in HEK293 cells expressing Elovl6 transiently. This result suggests that these compounds may be promising lead candidates for therapeutic drugs. Ultra-high-throughput fluorescence screening followed by a RapidFire mass spectrometry assay was a suitable strategy for lead discovery against Elovl6. - Highlights: • A novel assay for elongation of very-long-chain fatty acids 6 (Elovl6) is proposed. • RapidFire mass spectrometry (RF-MS) assay is useful to select real screening hits. • RF-MS assay is proved to be beneficial because of its high-throughput and accuracy. • A combination of fluorescent and RF-MS assays is effective for Elovl6 inhibitors.
Keap1 constitutively binds to the transcription factor Nrf2 to promote its degradation, resulting in negative modulation of genes involved in cellular protection against oxidative stress. Keap1 is increasingly recognized as an attractive target for treating diseases involving oxidative stress, including cancer, atherosclerosis, diabetes, arthritis, and neurodegeneration. We used phage-display peptide screening to identify a tetrapeptide showing moderate binding affinity, which inhibits the interaction between Nrf2 and Keap1. The tetrapeptide does not include an ETGE motif, which is a commonly found consensus sequence in known peptidic inhibitors. In addition to affinity parameters, IC{sub 50}, K{sub D}, and thermodynamic parameters, the crystal structure of the complex was determined to elucidate the binding conformation. The binding interactions resemble those of known small-molecule inhibitors as opposed to those of substrates and peptidic inhibitors. Although the tetrapeptide's affinity is not very high, our results may help facilitate the designing of small-molecule inhibitors during lead generation in drug discovery. - Highlights: • Keap1 inhibitory tetrapeptide with moderate affinity was discovered. • Crystal structure of the complex showed the unique binding mode. • Structural information gives a valuable insight for design of therapeutic compounds.
The use of ensemble-based docking for the exploration of biochemical pathways and toxicity prediction of drug candidates is described. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.
Previous efforts by our group have established pyrazolo[1,5-a]pyrimidine as a viable core for the development of potent and selective CDK inhibitors. As part of an effort to utilize the pyrazolo[1,5-a]pyrimidine core as a template for the design and synthesis of potent and selective kinase inhibitors, we focused on a key regulator in the cell cycle progression, CHK1. Continued SAR development of the pyrazolo[1,5-a]pyrimidine core at the C5 and C6 positions, in conjunction with previously disclosed SAR at the C3 and C7 positions, led to the discovery of potent and selective CHK1 inhibitors.
Interleukin receptor-associated kinase 4 (IRAK4) is a critical element of the Toll-like/interleukin-1 receptor inflammation signaling pathway. A screening campaign identified a novel diaminopyrimidine hit that exhibits weak IRAK4 inhibitory activity and a ligand efficiency of 0.25. Hit-to-lead activities were conducted through independent SAR studies of each of the four pyrimidine substituents. Optimal activity was observed upon removal of the pyrimidine C-4 chloro substituent. The intact C-6 carboribose is required for IRAK4 inhibition. Numerous heteroaryls were tolerated at the C-5 position, with azabenzothiazoles conferring the best activities. Aminoheteroaryls were preferred at the C-2 position. These studies led to the discovery of inhibitors 35, 36, and 38 that exhibit nanomolar inhibition of IRAK4, improved ligand efficiencies, and modest kinase selectivities.
8-Amino-imidazo[1,5-a]pyrazine-based Bruton’s tyrosine kinase (BTK) inhibitors, such as 6, exhibited potent inhibition of BTK but required improvements in both kinase and hERG selectivity (Liu et al., 2016; Gao et al., 2017). In an effort to maintain the inhibitory activity of these analogs and improve their selectivity profiles, we carried out SAR exploration of groups at the 3-position of pyrazine compound 6. This effort led to the discovery of the morpholine group as an optimized pharmacophore. Compounds 13, 23 and 38 displayed excellent BTK potencies, kinase and hERG selectivities, and pharmacokinetic profiles.
Small molecules encoded by biosynthetic pathways mediate cross-species interactions and harbor untapped potential, which has provided valuable compounds for medicine and biotechnology. Since studying biosynthetic gene clusters in their native context is often difficult, alternative efforts rely on heterologous expression, which is limited by host-specific metabolic capacity and regulation. Here, in this work, we describe a computational-experimental technology to redesign genes and their regulatory regions with hybrid elements for cross-species expression in Gram-negative and -positive bacteria and eukaryotes, decoupling biosynthetic capacity from host-range constraints to activate silenced pathways. These synthetic genetic elements enabled the discovery of a class of microbiome-derived nucleotide metabolites—tyrocitabines—from Lactobacillus iners. Tyrocitabines feature a remarkable orthoester-phosphate, inhibit translational activity, and invoke unexpected biosynthetic machinery, including a class of “Amadori synthases” and “abortive” tRNA synthetases. Our approach establishes a general strategy for the redesign, expression, mobilization, and characterization of genetic elements in diverse organisms and communities.
Here, research on direct air capture (DAC) of CO 2 at Oak Ridge National Laboratory (ORNL) over the past five years is highlighted, starting with the initial discovery, continuing with fundamental and applied research, and concluding with commercialization by teaming up with industry.
Entanglement is an energetically favorable occurrence, pervasive in both nature and synthetic systems, where networks prefer to entwine with one another rather than remain open and accessible. As a result of entanglement, valuable internal pore space, useful for applications such as catalysis, storage, and sensing, is lost. Here, we detail our discovery of a phenomenon termed “charged point-point repulsion,” which energetically favors dis-entanglement, transforming a densely packed uranium-based framework with 14.2 and 19.8 Å pores into an open material with 40.7 Å pores and record-high void fraction and pore volume. We anticipate that the phenomenon described in this work could be widely generalized for the direct and systematic production of non-entangled structures with an abundance of valuable pore space and far-reaching practical importance. More fundamentally, this study compels a re-evaluation of the thermodynamics of porosity.
Traditionally, slide-ring gels are stretchable but soft as a result of an elasticity-stretchability trade-off. Herein, we introduce a new approach to breaking this trade-off and creating reinforced slide-ring networks with mobile crosslinkers. Our approach involves the construction of a polyethylene glycol double-threaded γ-cyclodextrin-based pro-slide-ring crosslinker that serves as a modular component for 3D printing and copolymerization. The resulting crystalline-domain-reinforced slide-ring hydrogels, or CrysDoS-gels, exhibit both high elasticity and high stretchability. The modular synthesis allows for high-throughput synthesis of CrysDoS-gels, generating a large amount of data for structure-property analysis. Here, by employing data science techniques, such as machine learning and linear regression, not only were we able to identify which chemical components influence the mechanical properties of CrysDoS-gels, but this analysis also aided in the discovery of better-performing CrysDoS-gels. Finally, we demonstrate the potential application of the newly discovered CrysDoS-gels as sensing devices by 3D printing them as stress sensors with high sensitivity and a broad detection range.
The traditional development of novel metal–organic frameworks (MOFs) is often hindered by challenges such as synthetic accessibility and time- and resource-intensive experimentation. High-throughput, automated experimental and computational techniques have enabled rapid chemical space exploration and theoretical MOF design. When combined with artificial intelligence (AI), these methods can be used to lead autonomous laboratories to new frontiers for MOF discovery, where these materials can be designed for a specific application, efficiently synthesized, characterized, and evaluated. Here, this perspective highlights the role of AI in advancing automated MOF synthesis and characterization, computational MOF design and screening, and the integration of these approaches within autonomous workflows to ultimately enable the MOF laboratories of the future.
The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.