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Composite metal-organic frameworks (MOFs) tend to possess complex interfaces that prevent facile and rational design. Here we present a joint computational/experimental workflow that screens thousands of MOFs and identifies the optimal MOF pairs that can seamlessly connect to one another by taking advantage of the fact that the metal nodes of one MOF can form coordination bonds with the linkers of the second MOF. Six MOF pairs (HKUST-1@MOF-5, HKUST-1@IRMOF-18, UiO-67@HKUST-1, PCN-68@MOF-5, UiO-66@MIL-88B(Fe) and UiO-67@MIL-88C(Fe)) yielded from our theoretical predictions were successfully synthesized, leading to clean single crystalline MOF@MOF, demonstrating the power of our joint workflow. Our work can serve as a starting point to accelerate the discovery of novel MOF composites that can potentially be used for many different applications. Composite metal-organic framework materials can display useful synergetic properties, but typically suffer from disordered interfaces. Here the authors computationally identify optimal MOF pairings, looking at specific interactions between linkers and nodes, and synthesize six single crystal MOF@MOF composites.

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.

In the development of metal-organic frameworks (MOFs), secondary building units (SBUs) have been utilized as molecular modules for the construction of nanoporous materials with robust structures. Under solvothermal synthetic conditions, dynamic changes in the metal coordination environments and ligand coordination modes of SBUs determine the resultant product structures. Alternatively, MOF phases with new topologies can also be achieved by post-synthetic treatment of as-synthesized MOFs via the introduction of acidic or basic moieties that cause the simultaneous cleavage/reformation of coordination bonds in the solid state. In this sense, we studied the solid-state transformation of two ndc-based Zn-MOFs (ndc = 1,4-naphthalene dicarboxylate) with different SBUs but the same pcu topology to another MOF with sev topology. One of the chosen MOFs with pcu nets is [Zn 2 (ndc) 2 (bpy)] n (bpy = 4,4′-bipyridine), (6C bpy -MOF) consisting of a 6-connected pillared-paddlewheel SBU, and the other is IRMOF-7 composed of 6-connected Zn 4 O(COO) 6 SBUs and ndc. Upon post-structural modification, these pcu MOFs were converted into the same MOF with sev topology constructed from the uncommon 7-connected Zn 4 O(COO) 7 SBU (7C-MOF). The appropriate post-synthetic conditions for the transformation of each SBUs were systematically examined. In addition, the effect of the pillar molecules in the pillared-paddlewheel MOFs on the topology conversion was studied in terms of the linker basicity, which determines the inertness during the solid-state phase transformation. This post-synthetic modification approach is expected to expand the available methods for designing and synthesizing MOFs with controlled topologies.

We introduce an advanced model for predicting protein–ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein–ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein–ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein–ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model’s efficiency and generalizability. The model’s efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery. Scientific contribution Our work introduces a novel training strategy for a protein–ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.

Structure-based virtual screening (SBVS) is a pivotal computational approach in drug discovery, enabling the identification of potential drug candidates within vast chemical libraries by predicting their interactions with target proteins. The SBVS relies on the receptor protein structures, making it sensitive to structural variations. Kinase, one of the major drug targets, is known as one of the typical examples of an active site conformation change caused by the type of binding inhibitors. Examination of human kinase structures shows that the majority of conformations have the DFGin state. Thus, SBVS using the structures might cause a favor of type of ligand type I inhibitors, bind to the DFGin state, rather than finding the diverse scaffolds. Recent advances in protein structure prediction, such as AlphaFold2 (AF2), offer promising solutions but may still be possibly influenced by the structural bias in existing templates. To address these challenges, we introduce a multi-state modeling (MSM) protocol for kinase structures. We apply MSM to AF2 by providing state-specific templates, allowing us to overcome structural biases and thus apply them to kinase SBVS. We benchmarked our MSM models in three categories: quality of predicted models, reproducibility of ligand binding poses, and identification of hit compounds by ensemble SBVS. The results demonstrate that MSM-generated models exhibit comparable or improved structural accuracy compared to standard AF2 models. We also show that MSM models enhance the accuracy of cognate docking, effectively capturing the interactions between kinases and their ligands. In virtual screening experiments using DUD-E compound libraries, our MSM approach consistently outperforms standard AF2 modeling. Notably, MSM-based ensemble screening excels in identifying diverse hit compounds for kinases with structurally diverse active sites, surpassing standard AF2 models. We highlight the potential of MSM in broadening the scope of kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors. One of the main problems with structure-based virtual screening is structural flexibility. Ensemble screening is one of the conventional approaches to solving the issue. Gathering experimental structures or molecular simulations could be used to compile the receptor structures. Recent developments in algorithms for predicting protein structures, like AlphaFold2, suggest that different receptor conformations could be produced. However, the prediction approaches produce biased structures because of the bias in the structure database. In order to solve the problem, we developed a protocol called multi-state modeling for kinases. Rather than supplying multiple sequence alignments as an input, we gave the AlphaFold2 a specific template structure and the sequence alignment between the template and query. Our findings imply that our technique can yield a particular structural state of interest with an enhanced or comparable structural quality to AlphaFold2 and predict highly accurate protein-ligand complex structures. Lastly, compared to the typical AlphaFold2 models, ensemble screening using the multi-state modeling approach improves the structure-based virtual screening performance, particularly for diverse active molecular scaffolds.

Articulated object recognition -- the task of identifying both the geometry and kinematic joints of objects with movable parts -- is essential for enabling robots to interact with everyday objects such as doors and laptops. However, existing approaches often rely on strong assumptions, such as a known number of articulated parts; require additional inputs, such as depth images; or involve complex intermediate steps that can introduce potential errors -- limiting their practicality in real-world settings. In this paper, we introduce ScrewSplat, a simple end-to-end method that operates solely on RGB observations. Our approach begins by randomly initializing screw axes, which are then iteratively optimized to recover the object's underlying kinematic structure. By integrating with Gaussian Splatting, we simultaneously reconstruct the 3D geometry and segment the object into rigid, movable parts. We demonstrate that our method achieves state-of-the-art recognition accuracy across a diverse set of articulated objects, and further enables zero-shot, text-guided manipulation using the recovered kinematic model. See the project website at: https://screwsplat.github.io.

A Co 3 O 4 /ordered mesoporous carbon (OMC) nanocomposite, in which Co 3 O 4 nanoparticles (NPs), with an average size of about 10 nm homogeneously embedded in the OMC framework, are prepared for use as an anode material in Li-ion batteries. The composite is prepared by a one-pot synthesis based on the solvent evaporation-induced co-self-assembly of a phenolic resol, a triblock copolymer F127, and Co(NO 3 ) 2 ·6H 2 O, followed by carbonization and oxidation. The resulting material has a high reversible capacity of ~1,025 mA h g −1 after 100 cycles at a current density of 0.1 A g −1 . The enhanced cycling stability and rate capability of the composite can be attributed to the combined mesoporous nanostructure which provides efficient pathways for Li-ion transport and the homogeneous distribution of the Co 3 O 4 NPs in the pore wall of the OMC, which prevents aggregation. These findings suggest that the OMC has promise for use as a carbon metric for metals and metal oxides as an anode material in high performance Li-ion batteries.

Background DNA methylation is an epigenetic regulatory mechanism that plays an essential role in mediating biological processes and determining phenotypic plasticity in organisms. Although the horse reference genome and whole transcriptome data are publically available the global DNA methylation data are yet to be known. Results We report the first genome-wide DNA methylation characteristics data from skeletal muscle, heart, lung, and cerebrum tissues of thoroughbred (TH) and Jeju (JH) horses, an indigenous Korea breed, respectively by methyl-DNA immunoprecipitation sequencing. The analysis of the DNA methylation patterns indicated that the average methylation density was the lowest in the promoter region, while the density in the coding DNA sequence region was the highest. Among repeat elements, a relatively high density of methylation was observed in long interspersed nuclear elements compared to short interspersed nuclear elements or long terminal repeat elements. We also successfully identified differential methylated regions through a comparative analysis of corresponding tissues from TH and JH, indicating that the gene body regions showed a high methylation density. Conclusions We provide report the first DNA methylation landscape and differentially methylated genomic regions (DMRs) of thoroughbred and Jeju horses, providing comprehensive DMRs maps of the DNA methylome. These data are invaluable resource to better understanding of epigenetics in the horse providing information for the further biological function analyses.

ObjectivesThe job of firefighting can cause lumbar burden and low back pain. This study aimed to identify the association between age and lumbar intervertebral disc degeneration and whether the association differs between field and administrative (non-field) firefighters.MethodsSubjects were selected using a stratified random sampling method. Firefighters were stratified by geographic area, gender, age and type of job. First, 25 fire stations were randomly sampled considering regional distribution. Then firefighters were stratified by gender, age and their job and randomly selected among the strata. A questionnaire survey and MRI scans were performed, and then four radiologists used Pfirrmann classification methods to determine the grade of lumbar intervertebral disc degeneration.ResultsPfirrmann grade increased with lumbar intervertebral disc level. Analysis of covariance showed that age was significantly associated with lumbar intervertebral disc degeneration (p<0.05). The value of β (parameter estimate) was positive at all lumbar intervertebral disc levels and was higher in the field group than in the administrative group at each level. In logistic regression analysis, type of job was statistically significant only with regard to the L4–5 intervertebral disc (OR 3.498, 95% CI 1.241 to 9.860).ConclusionsLumbar intervertebral disc degeneration is associated with age, and field work such as firefighting, emergency and rescue may accelerate degeneration in the L4–5 intervertebral disc. The effects of field work on lumbar intervertebral disc degeneration were not clear in discs other than at the level L4–5.

A Co sub(3)O sub(4)/ordered mesoporous carbon (OMC) nanocomposite, in which Co sub(3)O sub(4) nanoparticles (NPs), with an average size of about 10 nm homogeneously embedded in the OMC framework, are prepared for use as an anode material in Li-ion batteries. The composite is prepared by a one-pot synthesis based on the solvent evaporation-induced co-self-assembly of a phenolic resol, a triblock copolymer F127, and Co(NO sub(3)) sub(2).6H sub(2)O, followed by carbonization and oxidation. The resulting material has a high reversible capacity of ~1,025 mA h g super(-1) after 100 cycles at a current density of 0.1 A g super(-1). The enhanced cycling stability and rate capability of the composite can be attributed to the combined mesoporous nanostructure which provides efficient pathways for Li-ion transport and the homogeneous distribution of the Co sub(3)O sub(4) NPs in the pore wall of the OMC, which prevents aggregation. These findings suggest that the OMC has promise for use as a carbon metric for metals and metal oxides as an anode material in high performance Li-ion batteries.