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Zn(II)-Driven Impact of Monomeric Transthyretin on Amyloid-beta Amyloidogenesis
No full textExtracellular accumulation of amyloid-beta (Abeta) peptides in the brain plays a significant role in the development of Alzheimer\u27s disease (AD). While the co-localization and interaction of proteins and metal ions with Abeta in extracellular milieu are established, their precise pathogenic associations remain unclear. Here we report the impact of Zn(II) on the anti-amyloidogenic properties of monomeric transthyretin (M-TTR), which coexists spatially with Abeta and Zn(II) in extracellular fluids. Our findings demonstrate the Zn(II)-promoted ternary complex formation involving M-TTR, Abeta40, and Zn(II) as well as M-TTR\u27s proteolytic activity towards Abeta40. These interactions alter the inhibitory effect of M-TTR on Abeta40 amyloidogenesis, particularly affecting the primary nucleation process, and mitigate the cytotoxicity induced by Abeta40. This study unveils the variable activities of M-TTR towards Abeta40, driven by Zn(II), providing insights into how metal ions influence the entanglement of M-TTR in the Abeta-related pathology linked to AD
AI-guided biorefinery optimization for the production of lignin-carbohydrate complexes with tailored properties
No full textLignin-carbohydrate complexes (LCCs) present a unique prospect for exploiting the synergy between lignin and carbohydrates in high-value products. To date, the production of LCCs in high yields is still an open challenge. Herein, we address this challenge with a novel approach for the targeted production of LCCs. With the help of artificial intelligence (AI), we optimized our AquaSolv Omni (AqSO) biorefinery toward the synthesis of LCCs with high carbohydrate content (up to 60/100 Ar) and high yields (up to 15 wt%). Our AI approach was essential for biorefinery fine-tuning toward maximum performance, while keeping the number of experiments within reasonable limits. More specifically, we followed a Bayesian Optimization approach that allowed us to iteratively collect data and explore the effect on yield and carbohydrate content of selected processing conditions: temperature, process severity, and liquid-to-solid ratio. By means of a Pareto front analysis, we identified optimal trade-offs between the LCC yield and carbohydrate content. We discovered sizeable regions of processing conditions that yield LCCs in 8-15 wt% with carbohydrate content in the range of 10-40/100 Ar. To evaluate the utility of the produced LCCs for future high-value applications, we measured key properties: the glass transition temperature (Tg), the surface tension, and the antioxidant activity. Intriguingly, we found that LCCs with high carbohydrate content are generally related with low Tg and surface tension. The presented biorefinery concept, in conjunction with its AI-guided optimization, is a first step toward the scalable production of LCCs tailor-made for high-value applications
CNSMolGen: a bidirectional recurrent neural networks based generative model for de novo central nervous system drug design
No full textCentral nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug discovery, reducing costs and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a bidirectional recurrent neural networks (Bi-RNNs) system for de novo molecular design of CNS drugs by learning from compounds with CNS drug properties. Result shown that the pre-trained model was able to generate more than 90% of completely new molecular structures, and these new molecules possessed the properties of CNS drug molecules and synthesizable. In addition, transfer learning was performed on small datasets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned dataset and generated a Focused database against the target protein. The potential biological activities of the generated molecules were verified using the physics-based induced fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development
HCat-GNet: An Interpretable Graph Neural Network for Catalysis Optimization
No full textHomogeneous catalysts enable faster conversions of molecules with higher selectivities (stereo- and regioselectivity) in chemical reactions. Traditionally, catalyst improvements are made through empirical trials, where the catalyst is functionalised by adding, removing or modifying groups within its structure and, subsequently, reevaluating the new catalytic activity. This procedure is not efficient and leads to unsuccessful trials that waste resources. Machine learning (ML) approaches have been proposed to accelerate homogeneous asymmetric catalyst optimization. However, these often lack a general descriptor generation procedure to allow encoding of molecules from a broad region of chemical space. To overcome this, we propose a homogeneous catalyst graph neural network (HCat-GNet) for the prediction of selectivity of catalysts given the SMILES of participant molecules. We demonstrate its use in rhodium-catalyzed asymmetric 1,4-addition (RhCAA), a reaction of major importance in organic synthesis. We benchmark HCat-GNet against traditional ML methods for its ability to predict RhCAA stereoselectivity from two chiral diene ligand two datasets; one for learning and one for final testing. For the learning dataset, both traditional ML and HCat-GNet methods give comparable results. However, when presented with the new unseen test dataset, traditional ML models perform poorly, while HCat-GNet retains a general ability to accurately predict product absolute stereochemistry and reaction stereoselectivity. Furthermore, HCat-GNet allows model interpretability, permitting analysis of the effect of ligand substituents in determining reaction selectivity. HCat-GNet shows greater potential for catalyst optimization than traditional ML, as it allows the use of a non-fixed number of participant molecules to train the model, only requiring the SMILES of the molecules to create graph representations. HCat-GNet allows more general models that accurately extrapolate into unseen regions of chemical space
Bifunctional and Recyclable Polyesters by Chemoselective Ring-Opening Polymerization of a δ-Lactone Derived from CO2 and Butadiene
No full textWhen aiming at the direct use of CO2 for the preparation of advanced/value-added materials, the synthesis of CO2/olefin copolymers is very appealing but challenging. The δ-lactone 3-ethylidene-6-vinyltetrahydro-2H-pyran-2-one (EVP), synthesized by telomerization of CO2 with 1,3-butadiene, is a promising intermediate. However, chemoselective ring-opening polymerization (ROP) of EVP is hampered by unfavorable thermodynamics and the competitive polymerization of highly reactive C=C double bonds. Herein, we report the first chemoselective ROP of EVP using a phosphazene/urea binary catalyst, affording exclusively a linear unsaturated polyester poly(EVP)ROP, with a molar mass (Mn) up to 6.5 kg·mol-1 and narrow distribution (Ð = 1.24), which can be fully recycled back to the pristine monomer, thus establishing a monomer-polymer-monomer closed-loop life cycle. Remarkably, poly(EVP)ROP features two pendent C=C double bonds per repeating unit, which show distinct reactivity and thus can be properly engaged in sequential functionalizations towards the synthesis of bifunctional polyesters. This methodology provides a facile access to bifunctional and recyclable polyesters from readily available feedstocks. In these polyesters, the carbon dioxide content reaches 33 mol% (29 wt%). The reasons for the remarkable chemoselectivity observed were investigated by Density-functional theory (DFT) calculations
A Selective and Sensitive Hg2+ Aminonaphthalimide-aza-crown-ether Cellular Chemosensor
No full textCrown ether ionophores linked to napthalimide fluorophores provide a powerful and versatile class of fluorescent chemosensors. Here we demonstrate the unusual Hg2+ ion selectivity of an aza-crown ether ionophore linked to a 4-aminonapthalimide by 1,4-phenylenediamine. Binding, computational, and fluorescence studies reveal an intramolecular charge transfer mechanism. The sensor demonstrates exceptional selectivity for Hg2+ in aqueous ethanol and detects both Hg2+and Zn2+ in aqueous acetonitrile. The sensor\u27s Hg2+ sensitivity is retained in live cells at biologically relevant concentrations of Hg2+, making it a potentially versatile and convenient tool for environmental and biological assay and monitoring applications
Unravelling guest dynamics in crystalline molecular organics using solid-state NMR and molecular dynamics simulation
No full textSolid-state NMR and atomistic molecular dynamics (MD) simulations are used to understand the disorder of guest solvent molecules in two cocrystal solvates of the pharmaceutical furosemide. Traditional approaches to interpreting the NMR data fail to provide a coherent model of molecular behaviour and indeed give misleading kinetic data. In contrast, direct prediction of the NMR properties from MD simulation trajectories allows the NMR data to be correctly interpreted in terms of combined jump-type and libration-type motions. Time-independent component analysis of the MD trajectories provides additional insight, particularly for motions that are invisible to NMR. This allows a coherent picture of the dynamics of molecules restricted in molecular-sized cavities to be determined
Frataxin Traps Low Abundance Quaternary Structure to Stimulate Human Fe-S Cluster Biosynthesis
No full textIron-sulfur clusters are essential protein cofactors synthesized in human mitochondria by an NFS1-ISD11-ACP-ISCU2-FXN assembly complex. Surprisingly, researchers have discovered three distinct quaternary structures for cysteine desulfurase subcomplexes, which display similar interactions between NFS1-ISD11-ACP protomeric units but distinct dimeric interfaces between the protomers. Although the role of these different architectures is unclear, possible functions include regulating activity and promoting the biosynthesis of distinct sulfur-containing biomolecules. Here, crystallography, native ion-mobility mass spectrometry, and chromatography methods reveal the Fe-S assembly subcomplex exists as an equilibrium mixture of these different quaternary structures. Our results suggest Friedreich\u27s ataxia (FRDA) protein frataxin (FXN) functions as a "molecular lock" and shifts the equilibrium towards one of the architectures to stimulate the cysteine desulfurase activity and promote iron-sulfur cluster biosynthesis. An NFS1-designed variant similarly shifts the equilibrium and partially replaces FXN in activating the complex. These results suggest that eukaryotic cysteine desulfurases are unusual members of the morpheein class of enzymes that control their activity through their oligomeric state. Overall, the findings support architectural switching as a regulatory mechanism linked to FXN activation of the human Fe-S cluster biosynthetic complex and provide new opportunities for therapeutic interventions of the fatal neurodegenerative disease FRDA
High Throughput Methodology for Investigating Green Hydrogen Generating Processes using Colorimetric Detection Films and Machine Vision
No full textThe generation of hydrogen from abundant and renewable precursors driven by sunlight will be a cornerstone of a future, sustainable hydrogen infrastructure. Current methods to monitor the evolution of hydrogen in such photocatalytic systems such as gas chromatography, mass spectrometry, manometry or Raman spectroscopy are either expensive and low throughput or lack sensitivity and selectivity over other gasses. This impediment hinders the generation of photo-driven hydrogen evolution data necessary for machine learning and artificial intelligence-based protocols. This work presents an open-source approach for studying solar-driven hydrogen evolution reactions (HERs) in parallel that uses colorimetric hydrogen detection films in tandem with an image analysis software capable of providing metrics such as hydrogen amount, hydrogen evolution rates, incubation times, and plateau times, and more. The sensing medium is composed of 0.05 % (w/w) Pt impregnated molybdenum (VI) oxide or tungsten (VI) oxide which was incorporated into poly(vinyl alcohol) films placed under clear, gas impermeable septa. To conduct experiments, users require only blue reaction-driving high intensity LEDs, a camera, and uniform lighting to take pictures as the septa darken. This work introduces a sample configuration in which nine samples in hydrogen sensitive septa-capped vials were illuminated and the gas evolution is monitored using a RaspberryPi for image capture and storage. Two calibration methods are presented, one uses a gravimetric hydrogen evolution with Zn/HCl that is compared to a direct hydrogen injection. Both methods allow the accurate correlation of normalized intensity values of film photographs to mole fractions of H2 ranging from 0 to 50%. Four light-driven HERs are described that highlight the capabilities of the detection method, two of which were conducted using the novel septa-based instrumentation while the other two experiments used the films on a 108 multiwell plate using a previously discussed photoreactor
Time-resolved multi-omics illustrates host and gut microbe interactions during Salmonella infection.
No full textSalmonella infection, also known as Salmonellosis, is one of the most common food-borne illnesses. Salmonella infection can trigger host defensive functions, including an inflammatory response. The provoked-host inflammatory response has a significant impact on the bacterial population in the gut. In addition, Salmonella competes with other gut microorganisms for survival and growth within the host. Compositional and functional alterations in gut bacteria occur because of the host immunological response and competition between Salmonella and the gut microbiome. Host variation and the inherent complexity of the gut microbial community make understanding commensal and pathogen interactions particularly difficult during a Salmonella infection. Here we present metabolomics and lipidomics analyses along with 16s rRNA sequence analysis, revealing a comprehensive view of the metabolic interactions between the host and the gut microbiota during Salmonella infection in a CBA/J mouse model. We found that different metabolic pathways were altered over the four investigated time points of Salmonella infection (days -2, +2, +6, and +13). Furthermore, metatranscriptomics analysis integrated with metabolomics and lipidomics analysis facilitated an understanding of the heterogeneous response of mice depending on the degree of dysbiosis