Synthetic Cluster Research Articles

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks

The prevalence of depression has increased dramatically over the last several decades: it is frequently overlooked and can have a significant impact on both physical and mental health. Therefore, it is crucial to develop an automated detection system that can instantly identify whether a person is depressed. Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer-based systems to predict several mental health issues, such as depression. This study propose an ensemble of hybrid model-based techniques that aims to build a strong detection model that considers many psychological and sociodemographic characteristics of an individual to detect whether a person is depressed. Support vector machines (SVM) and multilayer perceptrons (MLP) are the two fundamental methods used to construct the suggested ensemble approach. The hybrid DeprMVM served as a meta-learner. In this study, the hybrid DeprMVM is a level-1 learner, whereas the SVM and MLP networks are level-0 learners. After the classifiers are trained and tested at level 0, their outputs are based on both the independent and dependent variables in the new data set that was used to train the meta-classifier. The training data class imbalance was reduced by applying the synthetic minority oversampling technique (SMOTE) and cluster sampling together, which improved the accuracy for detecting depression. Additionally, it can effectively reduce the risk of over-fitting from simply duplicating data points. To further confirm the effectiveness of the proposed method, various performance evaluation metrics were calculated and compared with previous studies conducted on this specific dataset. In conclusion, among all the techniques for identifying depression, the suggested ensemble approach had the best accuracy, at 99.39%, and an F1-score of 99.51%.

Anomalous Pressure-Induced Blue-Shifted Emission of Ionic Copper-Iodine Clusters: The Competitive Effect between Cuprophilic Interactions and Through-Space Interactions.

Developing ionic copper-iodine clusters with multiple emitting is crucial for enriching lighting and display materials with various colors. However, the luminescent properties of traditional ionic copper-iodine clusters are often closely associated with low-energy cluster-centered triplet emission, which will redshift further as the Cu⋅⋅⋅Cu bond length decreases. This article utilizes a pressure-treated strategy to achieve an anomalous pressure-induced blue-shifted luminescence phenomenon in ionic Cu4I6(4-dimethylamino-1-ethylpyridinium)2 crystals for the first time, which is based on dominant through-space charge-transfer (TSCT). Herein, we reveal that the more advantageous through-space interactions in the competition between cuprophilic interactions and through-space interactions can lead to a blue-shifted luminescence. High-pressure angle-dispersive X-ray diffraction and high-pressure infrared experiments show that the enhanced through-space interactions mainly originate from forming new intermolecular C-H⋅⋅⋅I hydrogen bonds and the enhancement of van der Waals interactions between organic cations and anionic clusters. Theoretical calculations and experimental studies of excited-state dynamics confirm that the blue-shifted emission is due to the increased energy gap between the excited triplet and ground states caused by the electron delocalization under stronger through-space interactions. This work deepens previous understanding and provides a new avenue to design and synthetic ionic copper-iodine clusters with high-energy TSCT emission.

Understanding flux switching in metabolic networks through an analysis of synthetic lethals

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.

Peering into the Heart of the Giant Molecular Cloud G148.24+00.41: A Deep Near-infrared View of the Newly Hatched Cluster FSR 655

We present a detailed near-infrared study of an embedded cluster located in the hub of the giant molecular cloud G148.24+00.41 of mass ∼105 M ⊙, with the TANSPEC instrument mounted on the 3.6 m Devasthal Optical Telescope. The hub is located near the geometric center of the cloud and represents its most massive clump. We studied the central 2 pc × 2 pc area of the hub with 5σ limiting magnitudes of 20.5, 20.1, and 18.6 mag in the J, H, and K s bands, respectively. Using the K s -band luminosity function and comparing it with the synthetic clusters, we obtained the age of the cluster as ∼0.5 Myr, which was found to corroborate well with the visual extinction versus the age of nearby embedded clusters. We find that the present mass of the cluster is around ∼180 M ⊙, and the cluster is currently forming stars at a rate of ∼330 M ⊙ Myr−1, with an efficiency of ∼20%. The cluster is connected to an extended gas reservoir through a filamentary network; thus, we hypothesize that the cluster has the potential to become a richer cluster in a few Myr of time.

Mass Conservative Time-Series GAN for Synthetic Extreme Flood-Event Generation: Impact on Probabilistic Forecasting Models

The lack of data on flood events poses challenges in flood management. In this paper, we propose a novel approach to enhance flood-forecasting models by utilizing the capabilities of Generative Adversarial Networks (GANs) to generate synthetic flood events. We modified a time-series GAN by incorporating constraints related to mass conservation, energy balance, and hydraulic principles into the GAN model through appropriate regularization terms in the loss function and by using mass conservative LSTM in the generator and discriminator models. In this way, we can improve the realism and physical consistency of the generated extreme flood-event data. These constraints ensure that the synthetic flood-event data generated by the GAN adhere to fundamental hydrological principles and characteristics, enhancing the accuracy and reliability of flood-forecasting and risk-assessment applications. PCA and t-SNE are applied to provide valuable insights into the structure and distribution of the synthetic flood data, highlighting patterns, clusters, and relationships within the data. We aimed to use the generated synthetic data to supplement the original data and train probabilistic neural runoff model for forecasting multi-step ahead flood events. t-statistic was performed to compare the means of synthetic data generated by TimeGAN with the original data, and the results showed that the means of the two datasets were statistically significant at 95% level. The integration of time-series GAN-generated synthetic flood events with real data improved the robustness and accuracy of the autoencoder model, enabling more reliable predictions of extreme flood events. In the pilot study, the model trained on the augmented dataset with synthetic data from time-series GAN shows higher NSE and KGE scores of NSE = 0.838 and KGE = 0.908, compared to the NSE = 0.829 and KGE = 0.90 of the sixth hour ahead, indicating improved accuracy of 9.8% NSE in multistep-ahead predictions of extreme flood events compared to the model trained on the original data alone. The integration of synthetic training datasets in the probabilistic forecasting improves the model’s ability to achieve a reduced Prediction Interval Normalized Average Width (PINAW) for interval forecasting, yet this enhancement comes with a trade-off in the Prediction Interval Coverage Probability (PICP).

A CRISPR-Cas9-Mediated Large-Fragment Assembly Method for Cloning Genomes and Biosynthetic Gene Cluster.

The ability to clone large DNA fragments from genomes is valuable for both basic and applied research, such as the construction of synthetic genomes, and the expression of biosynthetic gene clusters (BGCs) for natural product discovery. Here, we report a fast and efficient platform for the direct capture of genome DNAs, by combining CRISPR and Gibson assembly. We demonstrate this method with the ability of cloning large DNA fragments ranging from 30 to 77 kb from various host genomes, achieving a near 100% cloning fidelity for DNA fragments below 50 kb. We next demonstrate this method by the cloning of a 40 kb fragment from Streptomyces ceruleus A3(2), which is rich in BGCs for natural products; and used this method cloning the 40 kb fengycin synthetic gene cluster from B. subtilis 168, encoding for a class of peptides with bioactivity. This method provides efficient and simple opportunities for assembling large DNA constructs from distant sources.

Efficiently manufacturing ectoine via metabolic engineering and protein engineering of L-2,4-diaminobutyrate transaminase

Ectoine, so-called tetrahydropyrimidine, is an important osmotic adjustment solute and widely applied in cosmetics and protein protectant. Some attempts have been made to improve the ectoine productivity. However, the strains with both high ectoine production capacity and high glucose conversion were still absent so far. Aim to construct a strain for efficiently producing ectoine, ectoine synthetic gene cluster ectABC from Pseudomonas stutzeri was overexpressed in E. coli BL21 (DE3). The ection production was improved by 382 % (ectoine titer increased from 1.73 g/L to 8.33 g/L) after the rational design of rate-limiting enzyme L-2,4-diaminobutyrate transaminase EctBps (protein engineering) combined with the metabolic engineering that focused on the enrichment and conversion of precursors. The final strain YW20 was applied to overproduce ectoine in fed-batch fermentation and yield 68.9 g/L of ectoine with 0.88 g/L/h of space-time yield and the highest glucose conversion reported [34 % (g/g)]. From the fermentation broth, ectoine was purified with 99.7 % purity and 79.8 % yield. This study successfully provided an engineered strain as well as an efficient method for the industrial bio-synthesis and preparation of ectoine.

The activity of hyaD contributed to the virulence of avian Pasteurella multocida

Fowl cholera is an infectious disease that affects both poultry and wild birds, characterized by hemorrhagic and septicemic symptoms, caused by Pasteurella multocida (P. multocida), and leading to substantial economic losses in the poultry sector. The development of genetic engineering vaccines against avian P. multocida encountered early-stage challenges due to the limited availability of effective gene editing tools. Presently, NgAgoDM-enhanced homologous recombination stands as a potent technique for achieving efficient gene knockout in avian P. multocida. Hence, this study employed NgAgoDM-enhanced homologous recombination to target and knockout hyaE (239-359aa), hyaD, hexABC, and hexD, denoted as ΔhyaE (239-359aa), ΔhyaD, ΔhexABC, and ΔhexD, respectively. Additionally, we generated a hyaD recovery strain with two point mutations, designated as mhyaD. Thus, this study systematically examined the impact of capsular synthetic gene clusters on the pathogenicity of P. multocida. Moreover, the study demonstrated the critical role of hyaD activity in the virulence of avian P. multocida. This study offers novel insights for enhancing attenuated vaccines further.

Lipopeptides from Bacillus velezensis ZLP-101 and their mode of action against bean aphids Acyrthosiphon pisum Harris

BackgroundNatural products are important sources for the discovery of new biopesticides to control the worldwide destructive pests Acyrthosiphon pisum Harris. Here, insecticidal substances were discovered and characterized from the secondary metabolites of the bio-control microorganism Bacillus velezensis strain ZLP-101, as informed by whole-genome sequencing and analysis.ResultsThe genome was annotated, revealing the presence of four potentially novel gene clusters and eight known secondary metabolite synthetic gene clusters. Crude extracts, prepared through ammonium sulfate precipitation, were used to evaluate the effects of strain ZLP-101 on Acyrthosiphon pisum Harris aphid pests via exposure experiments. The half lethal concentration (LC50) of the crude extract from strain ZLP-101 against aphids was 411.535 mg/L. Preliminary exploration of the insecticidal mechanism revealed that the crude extract affected aphids to a greater extent through gastric poisoning than through contact. Further, the extracts affected enzymatic activities, causing holes to form in internal organs along with deformation, such that normal physiological activities could not be maintained, eventually leading to death. Isolation and purification of extracellular secondary metabolites were conducted in combination with mass spectrometry analysis to further identify the insecticidal components of the crude extracts. A total of 15 insecticidal active compounds were identified including iturins, fengycins, surfactins, and spergualins. Further insecticidal experimentation revealed that surfactin, iturin, and fengycin all exhibited certain aphidicidal activities, and the three exerted synergistic lethal effects.ConclusionsThis study improved the available genomic resources for B. velezensis and serves as a foundation for comprehensive studies of the insecticidal mechanism by Bacillus velezensis ZLP-101 in addition to the active components within biological control strains.

The spin, expansion, and contraction of open star clusters

Empirical constraints on the internal dynamics of open clusters are important for understanding their evolution and evaporation. High-precision astrometry from Gaia DR3 is thus useful to observe aspects of the cluster dynamics. This work aims to identify dynamically peculiar clusters such as spinning and expanding clusters. We also quantify the spin frequency and expansion rate and compare them with $N$-body models to identify the origins of the peculiarities. We used the latest Gaia DR3 and archival spectroscopic surveys to analyse the radial velocities and proper motions of the cluster members in 1379 open clusters. A systematic analysis of synthetic clusters was performed to demonstrate the observability of the cluster spin along with effects of observational uncertainties. $N$-body simulations were used to understand the evolution of cluster spin and expansion for initially non-rotating clusters. We identified spin signatures in ten clusters (and 16 candidates). Additionally, we detected expansion in 18 clusters and contraction in three clusters. The expansion rate is compatible with previous theoretical estimates based on the expulsion of residual gas. The orientation of the spin axis is independent of the orbital angular momentum. The spin frequencies are much larger than what was expected from simulated, initially non-rotating clusters. This indicates that $>1$<!PCT!> of the clusters are born rotating and/or they have undergone strong interactions. Higher precision observations are required to increase the sample of such dynamically peculiar clusters and to characterise them.

Highly Activated Terminal Carbon Monoxide Ligand in an Iron-Sulfur Cluster Model of FeMco with Intermediate Local Spin State at Fe.

Nitrogenases, the enzymes that convert N2 to NH3, also catalyze the reductive coupling of CO to yield hydrocarbons. CO-coordinated species of nitrogenase clusters have been isolated and used to infer mechanistic information. However, synthetic FeS clusters displaying CO ligands remain rare, which limits benchmarking. Starting from a synthetic cluster that models a cubane portion of the FeMo cofactor (FeMoco), including a bridging carbyne ligand, we report a heterometallic tungsten-iron-sulfur cluster with a single terminal CO coordination in two oxidation states with a high level of CO activation (νCO = 1851 and 1751 cm-1). The local Fe coordination environment (2S, 1C, 1CO) is identical to that in the protein making this system a suitable benchmark. Computational studies find an unusual intermediate spin electronic configuration at the Fe sites promoted by the presence the carbyne ligand. This electronic feature is partly responsible for the high degree of CO activation in the reduced cluster.

Three-Coordinate Nickel and Metal-Metal Interactions in a Heterometallic Iron-Sulfur Cluster.

Biological multielectron reactions often are performed by metalloenzymes with heterometallic sites, such as anaerobic carbon monoxide dehydrogenase (CODH), which has a nickel-iron-sulfide cubane with a possible three-coordinate nickel site. Here, we isolate the first synthetic iron-sulfur clusters having a nickel atom with only three donors, showing that this structural feature is feasible. These have a core with two tetrahedral irons, one octahedral tungsten, and a three-coordinate nickel connected by sulfide and thiolate bridges. Electron paramagnetic resonance (EPR), Mössbauer, and superconducting quantum interference device (SQUID) data are combined with density functional theory (DFT) computations to show how the electronic structure of the cluster arises from strong magnetic coupling between the Ni, Fe, and W sites. X-ray absorption spectroscopy, together with spectroscopically validated DFT analysis, suggests that the electronic structure can be described with a formal Ni1+ atom participating in a nonpolar Ni-W σ-bond. This metal-metal bond, which minimizes spin density at Ni1+, is conserved in two cluster oxidation states. Fe-W bonding is found in all clusters, in one case stabilizing a local non-Hund state at tungsten. Based on these results, we compare different M-M interactions and speculate that other heterometallic clusters, including metalloenzyme active sites, could likewise store redox equivalents and stabilize low-valent metal centers through metal-metal bonding.

InSAR and machine learning reveal new understanding of coastal subsidence risk in the Yellow River Delta, China

Coastal subsidence is a geological disaster that has devastating consequences. However, an accurate understanding of its risks involves more than simply assessing the amount or rate of land subsidence. The existing methods used to evaluate geological disaster risks depend on extensive data collection, entail substantial workloads, suffer from error estimation challenges, and lack regional adaptability. These limitations prevent us from fully understanding coastal subsidence risks in estuarine deltas. Therefore, in this study, we propose a new subsidence risk assessment method that addresses the challenges of traditional geological risk assessments in terms of spatial coverage, spatiotemporal resolution, and data collection difficulty. First, Sentinel-1 multitemporal interferometric synthetic aperture radar (MT-InSAR) and cluster analysis were used to estimate the subsidence hazards. Subsequently, Landsat-8 imagery and a random forest (RF) classifier were used to obtain land use and land cover (LULC), and the analytic hierarchy process (AHP) was used to obtain settlement vulnerability. Thereafter, subsidence susceptibility was derived from the sediment layer thickness. By combining subsidence hazard, vulnerability, and susceptibility, the first subsidence risk map with a 30 m resolution was generated. The results showed that 4.54 % of the Yellow River Delta (YRD) area was high-risk, 8.75 % was medium-risk, and 10.14 % was low-risk. Notably, the risk map shows a clear overlap between high-risk and saltwater mining areas in the YRD. The proposed method is expected to improve our understanding of the coastal subsidence risk in estuarine deltas. Considering that the risk in high-value economic areas in the YRD is increasing, whereas the risk in low-value economic areas may change owing to human activity, early preventive measures are required.

Divergent catalytic behaviors of assembled organogold(i) clusters derived from enyne cyclization.

Homogeneous gold catalysis has attracted much recent attention due to diverse activation modes of gold(i) towards unsaturated organic groups. Because of attractive aurophilic interaction, structural transformations of metalated species into high nuclear clusters are often proposed in gold catalysis, while to date little is known about their assembly behaviors and catalytic activity. In this work, based on stoichiometric Au(i)-mediated enyne cyclization reactions, we achieve a discrete vicinal dicarbanion-centered Au4 intermediate and three assembled Au11, Au28, and Au14 clusters held together by several aryl dicarbanions. Spectral monitoring, kinetic and theoretical investigations confirm that these discrete and assembled intermediates display four different pathways upon catalyzing the cyclization reaction of the same 1,5-enyne substrate. The discrete Au4 cluster undergoes a full protodeauration process to generate active [Au(PPh3)]+ species for catalytic use. In contrast, the net-like Au11 cluster experiences a substrate-induced dissociation to generate a semi-stable Au10 unit and an active [alkyne-Au(PPh3)]+ fragment for further transformation. The dumbbell-like Au28 cluster is prone to cleavage of the central Au-Au linkage and each Au14 moiety exposes a coordination unsaturated site to activate a substrate molecule. However, the synthetic closed-Au14 cluster with full ligand protection is no longer catalytically active.

Cu site differentiation in tetracopper(i) sulfide clusters enables biomimetic N2O reduction.

Copper clusters feature prominently in both metalloenzymes and synthetic nanoclusters that mediate catalytic redox transformations of gaseous small molecules. Such reactions are critical to biological energy conversion and are expected to be crucial parts of renewable energy economies. However, the precise roles of individual metal atoms within clusters are difficult to elucidate, particularly for cluster systems that are dynamic under operating conditions. Here, we present a metal site-specific analysis of synthetic Cu4(μ4-S) clusters that mimic the Cu Z active site of the nitrous oxide reductase enzyme. Leveraging the ability to obtain structural snapshots of both inactive and active forms of the synthetic model system, we analyzed both states using resonant X-ray diffraction anomalous fine structure (DAFS), a technique that enables X-ray absorption profiles of individual metal sites within a cluster to be extracted independently. Using DAFS, we found that a change in cluster geometry between the inactive and active states is correlated to Cu site differentiation that is presumably required for efficient activation of N2O gas. More precisely, we hypothesize that the Cu δ+⋯Cu δ- pairs produced upon site differentiation are poised for N2O activation, as supported by computational modeling. These results provide an unprecedented level of detail on the roles of individual metal sites within the synthetic cluster system and how those roles interplay with cluster geometry to impact the reactivity function. We expect this fundamental knowledge to inform understanding of metal clusters in settings ranging from (bio)molecular to nanocluster to extended solid systems involved in energy conversion.

3D scaling laws and projection effects in The300-NIKA2 Sunyaev-Zeldovich Large Program Twin Samples

The abundance of galaxy clusters with mass and redshift is a wellknown cosmological probe. The cluster mass is a key parameter for studies that aim to constrain cosmological parameters using galaxy clusters, making it critical to understand and properly account for the errors in its estimates. Subsequently, it becomes important to correctly calibrate scaling relations between observables like the integrated Compton parameter and the mass of the cluster. The NIKA2 Sunyaev-Zeldovich Large program (LPSZ) enables one to map the intracluster medium profiles in the mm–wavelength band with great details (resolution of 11 & 17″ at 1.2 & 2 mm, respectively) and hence, to estimate the cluster hydrostatic mass more precisely than previous SZ observations. However, there are certain systematic effects which can only be accounted for with the use of simulations. For this purpose, we employ The Three Hundred simulations which have been modelled with a range of physics modules to simulate galaxy clusters. The so-called twin samples are constructed by picking synthetic clusters of galaxies with properties close to the observational targets of the LPSZ. In particular, we use the Compton parameter maps and projected total mass maps of these twin samples along 29 different lines of sight. We investigate the scatter that projection induces on the total masses. Eventually, we consider the statistical values along different lines of sight to construct a kind of 3D scaling law between the integrated Compton parameter, total mass, and overdensity of the galaxy clusters to determine the overdensity that is least impacted by the projection effect.

Synthesis and characterization of iron clusters with an icosahedral [Fe@Fe12]16+ Core.

Iron-metal clusters are crucial in a variety of critical biological and material systems, including metalloenzymes, catalysts, and magnetic storage devices. However, a synthetic high-nuclear iron cluster has been absent due to the extreme difficulty in stabilizing species with direct iron-iron bonding. In this work, we have synthesized, crystallized, and characterized a (Tp*)4W4S12(Fe@Fe12) cluster (Tp*=tris(3,5-dimethyl-1-pyrazolyl)borate(1-)), which features a rare trideca-nuclear, icosahedral [Fe@Fe12] cluster core with direct multicenter iron-iron bonding between the interstitial iron (Fei) and peripheral irons (Fep), as well as Fep···Fep ferromagnetic coupling. Quantum chemistry studies reveal that the stability of the cluster arises from the 18-electron shell-closing of the [Fe@Fe12]16+ core, assisted by its bonding interactions with the peripheral tridentate [(Tp*)WS3]4- ligands which possess both S→Fe donation and spin-polarized Fe-W σ bonds. The ground-state electron spin is theoretically predicted to be S=32/2 for the cluster. The existence of low oxidation-state (OS ∼+1.23) iron in this compound may find interesting applications in magnetic storage, spintronics, redox chemistry, and cluster catalysis.

Parameter Estimation for Open Clusters using an Artificial Neural Network with a QuadTree-based Feature Extractor

With the unprecedented increase in the number of known star clusters, quick and modern tools are needed for their analysis. In this work, we develop an artificial neural network (ANN) trained on synthetic clusters to estimate the age, metallicity, extinction, and distance of Gaia open clusters. We implement a novel technique to extract features from the color–magnitude diagram of clusters by means of the QuadTree tool, and we adopt a multiband approach. We obtain reliable parameters for ∼5400 clusters. We demonstrate the effectiveness of our methodology in accurately determining crucial parameters of Gaia open clusters by performing a comprehensive scientific validation. In particular, with our analysis we have been able to reproduce the Galactic metallicity gradient as it is observed by high-resolution spectroscopic surveys. This demonstrates that our method reliably extracts information on metallicity from color–magnitude diagrams (CMDs) of stellar clusters. For the sample of clusters studied, we find an intriguing systematic older age compared to previous analyses present in the literature. This work introduces a novel approach to feature extraction using a QuadTree algorithm, effectively tracing sequences in CMDs despite photometric errors and outliers. The adoption of ANNs, rather than convolutional neural networks, maintains the full positional information and improves performance, while also demonstrating the potential for deriving cluster parameters from simultaneous analysis of multiple photometric bands, beneficial for upcoming telescopes like the Vera Rubin Observatory. The implementation of ANN tools with robust isochrone fit techniques could provide further improvements in the quest for open cluster parameters.

Decorated Vesicles as Prebiont Systems (a Hypothesis).

Decorated vesicles in deep, seafloor basalts form abiotically, but show at least four life-analogous features, which makes them a candidate for origin of life research. These features are a physical enclosure, carbon-assimilatory catalysts, semi-permeable boundaries, and a source of usable energy. The nanometer-to-micron-sized spherules on the inner walls of decorated vesicles are proposed to function as mineral proto-enzymes. Chemically, these structures resemble synthetic FeS clusters shown to convert CO2, CO and H2 into methane, formate, and acetate. Secondary phyllosilicate minerals line the vesicles' inner walls and can span openings in the vesicles and thus can act as molecular sieves between the vesicles' interior and the surrounding aquifer. Lastly, basalt glass in the vesicle walls takes up protons, which replace cations in the silicate framework. This results in an inward proton flux, reciprocal outward flux of metal cations, more alkaline pH inside the vesicle than outside, and production of more phyllosilicates. Such life-like features could have been exploited to move decorated vesicles toward protolife systems. Decorated vesicles are proposed as study models of prebiotic systems that are expected to have existed on the early Earth and Earth-like exoplanets. Their analysis can lead to better understanding of changes in planetary geocycles during the origin of life.

Structure-guided design of a trivalent nanobody cluster targeting SARS-CoV-2 spike protein

Nanobodies are natural anti-SARS-CoV-2 drug candidates. Engineering multivalent nanobodies is an effective way to improve the functional binding affinity of natural nanobodies by simultaneously targeting multiple sites on viral proteins. However, multivalent nanobodies have usually been engineered by trial and error, and rational designs are still lacking. Here, we describe a structure-guided design of a self-assembled trivalent nanobody cluster targeting the SARS-CoV-2 spike protein. Using the nanobody Nb6 as a monovalent binder, we first selected a human-derived trimerization scaffold evaluated by molecular dynamics simulations, then selected an optimal linker according to the minimum distance between Nb6 and the trimerization scaffold, and finally successfully engineered a trivalent nanobody cluster called Tribody. Compared with the low-affinity monovalent counterpart (Nb6), Tribody showed much higher target binding affinity (KD < 1 pM) and thus had a 900-fold increase in antiviral neutralization against SARS-CoV-2 pseudovirus. We determined the cryo-EM structure of the Tribody-spike complex and confirmed that all three Nb6 binders of Tribody collectively bind to the three receptor-binding domains (RBDs) of the spike and lock them in a 3-RBD-down conformation, fully consistent with our structure-guided design. This study demonstrates that synthetic nanobody clusters with human-derived self-assembled scaffolds are potential protein drugs against SARS-CoV-2 coronaviruses.