Search

Nanoparticle-mediated mRNA delivery has emerged as a promising therapeutic modality, but its growth is still limited by the discovery and optimization of effective and well-tolerated delivery strategies. Lipid nanoparticles containing charged or ionizable lipids are an emerging standard for in vivo mRNA delivery, so creating facile, tunable strategies to synthesize these key lipid-like molecules is essential to advance the field. Here, we generate a library of N-substituted glycine oligomers, peptoids, and undertake a multistage down-selection process to identify lead candidate peptoids as the ionizable component in our Nutshell nanoparticle platform. First, we identify a promising peptoid structural motif by clustering a library of >200 molecules based on predicted physical properties and evaluate members of each cluster for reporter gene expression in vivo. Then, the lead peptoid motif is optimized using design of experiments methodology to explore variations on the charged and lipophilic portions of the peptoid, facilitating the discovery of trends between structural elements and nanoparticle properties. We further demonstrate that peptoid-based Nutshells leads to expression of therapeutically relevant levels of an anti-respiratory syncytial virus antibody in mice with minimal tolerability concerns or induced immune responses compared to benchmark ionizable lipid, DLin-MC3-DMA. Through this work, we present peptoid-based nanoparticles as a tunable delivery platform that can be optimized toward a range of therapeutic programs.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) represent a significant potential for novel therapeutic applications because of their bioactive properties, stability, and specificity. RiPPs are synthesized on ribosomes, followed by intricate post-translational modifications (PTMs), crucial for their diverse structures and functions. PTMs, such as cyclization, methylation, and proteolysis, play crucial roles in enhancing RiPP stability and bioactivity. Advances in synthetic biology and bioinformatics have significantly advanced the field, introducing new methods for RiPP production and engineering. These methods encompass strategies for heterologous expression, genetic refactoring, and exploiting the substrate tolerance of tailoring enzymes to create novel RiPP analogs with improved or entirely new functions. Furthermore, the introduction and implementation of cutting-edge screening methods, including mRNA display, surface display, and two-hybrid systems, have expedited the identification of RiPPs with significant pharmaceutical potential. This comprehensive review not only discusses the current advancements in RiPP research but also the promising opportunities that leveraging these bioactive peptides for therapeutic applications presents, illustrating the synergy between traditional biochemistry and contemporary synthetic biology and genetic engineering approaches.

Whole-organism-based screen holds promise for discovering biologically active compounds. However, high-content imaging is challenging due to the difficulty of positioning live animals and individual variability of neuron counts. Here, we present a protocol to identify neuroprotective compounds for dopaminergic neurons in zebrafish using an image-based small-molecule screen. We describe steps for raising larvae, agarose embedding, and treatment to induce neurodegeneration. We then detail procedures for live confocal imaging, image processing, and data analysis. For complete details on the use and execution of this protocol, please refer to Kim et al. (2021).1.

Strategies in genetic and pharmacological modulation of innate immunity to enhance oncolytic virotherapy (OV) efficacy are being explored. We have recently characterized the ability for vanadium-based compounds, a class of pan-phosphatase (PP) inhibitors, to potentiate OVs. We next sought to identify PPs that could be targeted to enhance OVs, akin to vanadium. By conducting a high-throughput screen of a library of silencing RNA (siRNA) targeting human PPs, we uncovered several PPs that robustly enhanced infectivity and oncolysis of the oncolytic vesicular stomatitis virus (VSV∆51). Knockdown of our top validated hit, lysosomal acid phosphatase 2 (ACP2), increased VSV∆51 viral titers by over 20-fold. In silico analysis by RNA sequencing revealed ACP2 to regulate antiviral type I interferon (IFN-1) signaling pathways, similar to vanadium. To further exploit this mechanism for therapeutic gain, we encoded a short-hairpin RNA (shRNA) against ACP2 into oncolytic vesicular stomatitis virus (VSV∆51) under a miR-30 promoter. This bioengineered OV demonstrated expression of the miR-30 promoter, knockdown of ACP2, repression and ultimately, showed markedly enhanced viral VSV∆51 particle production compared to its non-targeting control counterpart. Altogether, this study identifies IFN-1 regulating PP targets, namely ACP2, that may prove instrumental in increasing the therapeutic efficacy of OVs. [ABSTRACT FROM AUTHOR]

Numerous diseases, including age‐related macular degeneration (AMD), arise from the blood‐retinal barrier and blood vessel abnormalities in the eye; unfortunately, there is a lack of reliable in vitro models for their systematic study. This study describes a high‐throughput microphysiological system (MPS) designed to model the outer Blood‐Retinal Barrier (oBRB). The MPS platform is engineered to integrate seamlessly with high‐content screening technologies, utilizing a design with a single oBRB model incorporating RPE (retina pigment epithelial cells) and endothelial cell co‐culture to fit within a single 96‐well. Arranged in the standard 96‐well plate format, the platform allows high‐throughput assessment of barrier integrity through 3D confocal imaging (ZO‐1 staining), Trans Epithelial Electrical Resistance (TEER), and permeability measurements. The oBRB model enables the investigation of crosstalk among different cell types in co‐culture. This includes assessing changes in the barrier integrity of the Retinal Pigment Epithelium (RPE) monolayer and investigating neovascularization events resulting from endothelial cell remodeling. The platform is positioned for utility in drug discovery and development efforts targeting diseases involving oBRB damage and choroidal neovascularization, such as age‐related macular degeneration. [ABSTRACT FROM AUTHOR]

Background: Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results: We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion: This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5's HydraScreen and Strateos' automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution: We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery. [ABSTRACT FROM AUTHOR]

Adoptive T cell therapy is a pivotal strategy in cancer immunotherapy, demonstrating potent clinical efficacy. However, its limited durability often results in primary resistance. High-throughput screening technologies, which include both genetic and non-genetic approaches, facilitate the optimization of adoptive T cell therapies by enabling the selection of biologically significant targets or substances from extensive libraries. In this review, we examine advancements in high-throughput screening technologies and their applications in adoptive T cell therapies. We highlight the use of genetic screening for T cells, tumor cells, and other promising combination strategies, and elucidate the role of non-genetic screening in identifying small molecules and targeted delivery systems relevant to adoptive T cell therapies, providing guidance for future research and clinical applications. [ABSTRACT FROM AUTHOR]

Calcium oscillations in primary neuronal cultures and iPSCs have been employed to investigate arrhythmogenicity and epileptogenicity in drug development. Previous studies have demonstrated that Ca2+ influx via NMDA and nicotinic acetylcholine receptors (nAChRs) modulates Ca2+ oscillations. Nevertheless, there has been no comprehensive investigation into the impact of ischemia or nAChR-positive allosteric modulators (PAM) drugs on Ca2+ oscillations at a level that would facilitate high-throughput screening. We investigated the effects of ischemia and nAChR subtypes or nAChR PAM agonists on Ca2+ oscillations in high-density 2D and 3D-sphere primary neuronal cultures using 384-well plates with FDSS-7000. Ischemia for 1 and 2 h resulted in an increase in the frequency of Ca2+ oscillations and a decrease in their amplitude in a time-dependent manner. The NMDA and AMPA receptor inhibition significantly suppressed Ca2+ oscillation. Inhibition of NR2A or NR2B had the opposite effect on Ca oscillations. The potentiation of ischemia-induced Ca2+ oscillations was significantly inhibited by the NMDA receptor antagonist, MK-801, and the frequency of these oscillations was suppressed by the NR2B inhibitor, Ro-256981. In the 3D-neurosphere, the application of an α7nAChR agonist increased the frequency of Ca2+ oscillations, whereas the activation of α4β2 had no effect. The combination of nicotine and PNU-120596 (type II PAM) affected the frequency and amplitude of Ca2+ oscillations in a manner distinct from that of type I PAM. These systems may be useful not only for detecting epileptogenicity but also in the search for neuroprotective agents against cerebral ischemia. [ABSTRACT FROM AUTHOR]

Ceria‐supported precious metal catalysts have undergone extensive investigation for the catalytic methane combustion. However, it remains a significant challenge to achieve both highly synergistic oxidation activity and efficient atom utilization remains a challenge for commonly used supported nanoparticles and single‐atom catalysts. Dual‐atom catalysts (DACs) emerges as a frontier of advanced catalysts, presenting unique catalytic properties that benefit from the synergy of neighboring metal sites. In this study, 361 ceria‐supported DACs (M1M2/CeO2) encompassing combinations of 19 transition metals are systematically explored. Using high‐throughput density functional theory calculations, the structures, stability as well as activity of M1M2/CeO2 are assessed. Notably, Au1Ga1/CeO2 is identified as a promising DAC exhibiting high activity for methane total oxidation, substantiated by comprehensive DFT‐calculated reaction pathways. Furthermore, employing six machine‐learning algorithms, the structure‐properties relationship is explored within ceria‐based DACs and highlight the importance of oxidation states and atomic radii of doped metals as the descriptors. The trained model by computational dataset exhibits high accuracy and predict a more active Mn1Au1/CeO2 than those screened using only DFT datasets. The high‐throughput strategy demonstrated in this work not only provides insights into the rational design of methane oxidation catalysts, but also paves the way for exploring DACs for diverse applications. [ABSTRACT FROM AUTHOR]

Electrocatalytic CO2 reduction (CO2RR) offers a promising avenue to address rising atmospheric CO2 levels by producing high‐value chemicals. However, the development of efficient, long‐lasting catalysts remains challenging. In this study, particle swarm optimization is employed to design a novel bimetallic boronene structure, thereby enhancing CO2RR activity through precise tuning of the metal‐substrate microenvironment. Through high‐throughput screening, seven CuMB4 (M = V, Zn, Nb, Ag, Cd, Ta, Au) monolayers are identified as promising CO2RR catalysts based on stability, conductivity, catalytic activity, and selectivity. These materials, characterized by hyper‐coordination and neighboring bimetallic effects, are proposed for synthesis via self‐assembled surface growth. Notably, CuZnB4 exhibited exceptional theoretical catalytic activity, characterized by remarkably low limiting potentials (UL) of −0.16 V for CH4 and −0.27 V for C2H4, as well as low kinetic barriers of 0.65 and 0.53 eV, respectively. The enhanced activity results from neighboring effects optimizing densely populated boron active sites and the ability to suppress hydrogen evolution reactions. Additionally, this study explored intrinsic properties influencing catalytic activity using volcano plots, descriptor analysis, and electron density evaluation. Unlike traditional catalysts prone to oxidation, CuMB4 varieties possess self‐activating properties, facilitating the conversion of *O/*OH to H2O and enhancing CO2 conversion. This research introduces robust CO2RR catalysts and provides insights into manipulating neighboring effects, thus guiding future catalyst design. [ABSTRACT FROM AUTHOR]

Background: Microalgae have emerged as sustainable alternatives to fossil fuels and high-value petrochemicals. Despite the commercial potential of microalgae, their low biomass productivity is a significant limiting factor for large-scale production. In the photoautotrophic cultivation of microalgae, achievable cell density levels depend on the light transmittance of the production system, which can significantly decrease the photosynthetic rate and biomass production. In contrast, the mixotrophic cultivation of microalgae using heterotrophic carbon sources enables high-density cultivation, which significantly enhances biomass productivity. The identification of optimal production conditions is crucial for improving biomass productivity; however, it is typically time- and resource-consuming. To overcome this problem, high-throughput screening (HTS) system presents a practical approach to maximize biomass and lipid production and enhance the industrial applicability of microalgae. Results: In this study, we proposed a two-step HTS assay that allows effective screening of heterotrophic conditions compatible with new microalgal isolates. To confirm the effectiveness of the HTS assay, three microalgal isolates with distinctive morphological and genetic traits were selected. Suitable cultivation conditions, including various heterotrophic carbon sources, substrate concentrations, and temperatures, were investigated using a two-step HTS assay. The optimized conditions were validated at the flask scale, which confirmed a significant enhancement in the biomass and lipid productivity of each isolate. Moreover, the two-step HTS assay notably enhanced economic and temporal efficiency compared to conventional flask-based optimization. Conclusions: These results suggest that our two-step HTS assay is an efficient strategy for investigating and optimizing microalgal culture conditions to maximize biomass and lipid productivity. This approach has the potential to enhance the industrial applicability of microalgae and facilitate the seamless transition from laboratory to field applications. [ABSTRACT FROM AUTHOR]

Rieske dioxygenase enzymes can perform the cis‐dihydroxylation of aliphatic olefins, representing a potential green alternative to established methods of performing this important transformation. However, the activity of the natural enzymes in this context is low relative to their more well‐known activity in the cis‐dihydroxylation of aromatics. To enable the engineering of dioxygenase enzymes for improved activity in the dihydroxylation of aliphatic olefins, we have developed an assay system to detect the relevant diol metabolites produced from whole‐cell fermentation cultures. Optimization studies were carried out to maximize the sensitivity of the assay system, and its utility in the in vitro screening of enzyme variant libraries was demonstrated. The assay system was utilized in screening studies that identified Rieske dioxygenase variants with significantly improved activity in the dihydroxylation of aliphatic olefins relative to the wild‐type enzyme. [ABSTRACT FROM AUTHOR]

Enhancing the secretion of recombinant proteins, particularly non-enzymatic proteins that predominate in food and pharmaceutic protein products, remains a significant challenge due to limitations in high-throughput screening methods. This study addresses this bottleneck by establishing a yeast surface display system in the food-grade microorganism Kluyveromyces lactis, enabling efficient display of model target proteins on the yeast cell surface. To assess its potential as a universal high-throughput screening tool for enhanced non-enzymatic protein secretion, we evaluated the consistency between protein display levels and secretion efficiency under the influence of various genetic factors. Our results revealed a strong correlation between these two properties. Furthermore, screening in a random mutagenesis library successfully identified a mutant with improved secretion. These findings demonstrate the potential of the K. lactis surface display system as a powerful and universal tool for high-throughput screening of strains with superior non-enzymatic protein secretion capacity. We believe this study could pave the way for efficient large-scale production of heterologous food and therapeutic proteins in industries. Key points: • A YSD (yeast surface display) system was established in Kluyveromyces lactis • This system enables high-throughput screening of non-enzymatic protein secretion • This technology assists industrial production of food and therapeutic proteins [ABSTRACT FROM AUTHOR]

Background: The efficacy of mRNA-based vaccines and therapies relies on lipid nanoparticles (LNPs) as carriers to deliver mRNA into cells. The chemical structure of ionizable lipids (ILs) within LNPs is crucial in determining their delivery efficiency. Results: In this study, we synthesized 623 alkyne-bearing ionizable lipids using the A3 coupling reaction and assessed their effectiveness in mRNA delivery. ILs with specific structural features—18-carbon alkyl chains, a cis-double bond, and ethanolamine head groups—demonstrated superior mRNA delivery capabilities. Variations in saturation, double bond placement, and chain length correlated with decreased efficacy. Alkynes positioned adjacent to nitrogen atoms in ILs reduced the acid dissociation constant (pKa) of LNPs, thereby hindering mRNA delivery efficiency. Conversion of alkynes to alkanes significantly enhanced mRNA delivery of ILs both in vitro and in vivo. Moreover, combining optimized ILs with cKK-E12 yields synergistic LNPs that showed markedly augmented mRNA expression levels in vivo. Conclusions: Overall, our study provides insights into the structure–function relationships of ILs, providing a foundation for the rational design of ILs to enhance the efficacy of LNPs in mRNA delivery. [ABSTRACT FROM AUTHOR]

Pregnane X receptor (PXR) is a xenobiotic-sensing nuclear receptor with a well-established role in regulating drug metabolism and clearance. Recent studies have shown that PXR is involved in cell proliferation, apoptosis, immune response, and energy homeostasis. It is important to identify compounds that may modulate PXR activity to prevent drug-drug interactions, distinguish chemicals which could potentially generate toxicity, and identify compounds for further development towards therapeutic usage. In this study, we have screened the National Center for Advancing Translational Sciences (NCATS) Pharmacologically Active Chemical Toolbox (NPACT) library, which consists of 5,099 unique pharmacologically active synthetic and naturally derived small molecules to identify PXR antagonists. Ninety-four compounds were identified as potential PXR antagonists through a primary screen and 66 were confirmed in a confirmation study. Of these compounds, twenty potential PXR antagonists, including gamma-secretase modulator 2 (GSM2) and fusidic acid, were selected for further study based on their efficacy, potency, and novelty. Their PXR inhibition abilities were assessed by examining their effects on cytrochrome P450 (CYP) 3A4 mRNA expression using metabolically competent HepaRG cells. Additionally, a pharmacological inhibition assay using various concentrations of rifampicin as a stimulator was performed in HepG2- CYP3A4 -hPXR cells to confirm the activity of the 20 selected compounds against PXR. Finally, HepaRG cells were used to confirm PXR antagonism by verification of a concentration-dependent decrease of CYP3A4 when co-treated with the known PXR agonist, rifampicin. Additionally, the potent actives were further investigated using molecular docking to find the potential interactions of the novel ligands with the active sites of hPXR. To our knowledge from the current study, GSM2 and fusidic acid have been identified as novel PXR antagonists, which provides useful information for further investigation regarding possible drug-drug interactions, as well as the detection of potential therapeutic effects or other toxic consequences. [ABSTRACT FROM AUTHOR]

Drosophila melanogaster usage has provided substantial insights into the pathogenesis of several nucleotide repeat expansion diseases (NREDs), a group of genetic diseases characterized by the abnormal expansion of DNA repeats. Leveraging the genetic simplicity and manipulability of Drosophila, researchers have successfully modeled close to 15 NREDs such as Huntington's disease (HD), several spinocerebellar ataxias (SCA), and myotonic dystrophies type 1 and 2 (DM1/DM2). These models have been instrumental in characterizing the principal associated molecular mechanisms: protein aggregation, RNA toxicity, and protein function loss, thus recapitulating key features of human disease. Used in chemical and genetic screenings, they also enable us to identify promising small molecules and genetic modifiers that mitigate the toxic effects of expanded repeats. This review summarizes the close to 150 studies performed in this area during the last seven years. The relevant highlights are the achievement of the first fly-based models for some NREDs, the incorporation of new technologies such as CRISPR for developing or evaluating transgenic flies containing repeat expanded motifs, and the evaluation of less understood toxic mechanisms in NREDs such as RAN translation. Overall, Drosophila melanogaster remains a powerful platform for research in NREDs. [ABSTRACT FROM AUTHOR]

The market size of biosurfactants (BSs) has been expanding at an extremely fast pace due to their broad application scope. Therefore, the re-construction of cell factories with modified genomic and metabolic profiles for desired industrial performance has been an intriguing aspect. Typical mutagenesis approaches generate huge mutant libraries, whereas a battery of specific, robust, and cost-effective high-throughput screening (HTS) methods is requisite to screen target strains for desired phenotypes. So far, only a few specialized HTS assays have been developed for BSs that were successfully applied to obtain anticipated mutants. The most important milestones to reach, however, continue to be: specificity, sensitivity, throughput, and the potential for automation. Here, we discuss important colorimetric and fluorometric HTS approaches for possible intervention on automated HTS platforms. Moreover, we explain current bottlenecks in developing specialized HTS platforms for screening high-yielding producers and discuss possible perspectives for addressing such challenges. [ABSTRACT FROM AUTHOR]

Core fucosylation is catalyzed by α‐1,6‐fucosyltransferase (FUT8), which fucosylates the innermost GlcNAc of N‐glycans. Given the association of FUT8 with various diseases, including cancer, selective FUT8 inhibitors applicable to in vivo or cell‐based systems are highly sought‐after. Herein, we report the discovery of a compound that selectively inhibits FUT8 in cell‐based assays. High‐throughput screening revealed a FUT8‐inhibiting pharmacophore, and further structural optimization yielded an inhibitor with a KD value of 49 nM. Notably, this binding occurs only in the presence of GDP (a product of the enzymatic reaction catalyzed by FUT8). Mechanistic studies suggested that this inhibitor generates a highly reactive naphthoquinone methide derivative at the binding site in FUT8, which subsequently reacts with FUT8. Furthermore, prodrug derivatization of this inhibitor improved its stability, enabling suppression of core fucose expression and subsequent EGFR and T‐cell signaling in cell‐based assays, paving the way for the development of drugs targeting core fucosylation. [ABSTRACT FROM AUTHOR]

Individuals are exposed to a wide arrays of hazardous chemicals on a daily basis through various routes, many of which have not undergone comprehensive toxicity assessments. While traditional developmental toxicity tests involving pregnant animals are known for their reliability, they are also associated with high costs and time requirements. Consequently, there is an urgent demand for alternative, cost-efficient, and rapid in vitro testing methods. This study aims to address the challenges related to automating and streamlining the screening of early developmental toxicity of chemicals by introducing a mouse embryoid body test (EBT) model in a 384-ultra low attachment well format. Embryoid bodies (EBs) generated in this format were characterized by a spontaneous differentiation trajectory into cardiac mesoderm by as analyzed by RNA-seq. Assessing prediction accuracy using reference compounds suggested in the ICH S5(R3) guideline and prior studies resulted in the establishment of the acceptance criteria and applicability domain of the EBT model. The results indicated an 84.38% accuracy in predicting the developmental toxicity of 23 positive and 9 negative reference compounds, with an optimized cutoff threshold of 750 µM. Overall, the developed EBT model presents a promising approach for more rapid, high-throughput chemical screening, thereby facilitating well-informed decision-making in environmental management and safety assessments. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purification of polymer‐grade (99.9%) C2H4 from C2 hydrocarbon mixture is industrially important but challenging. Cryogenic distillation is energy intensive. Recently, pressure/vacuum swing adsorption (P/VSA) processes using metal‐organic frameworks (MOFs) as adsorbents have been attracting increasing attention as an alternative technology. A multiscale hierarchical framework is proposed to select promising MOFs and design suitable P/VSA processes by combining property‐based material screening and model‐based process optimization. Using this framework, we successfully identified seven promising one‐step C2H4 purification MOFs that outperformed the benchmark TJT‐100 in both C2H4 purity and recovery for the 5/90/5 C2H2/C2H4/C2H6 feed mixture. Among them, OFUCAV exhibited the highest C2H4 productivity of 0.158 mol/m3/s while JAVTAC demonstrated the lowest energy consumption of 42.76 kWh/tonne. The latter can potentially save 80% of the energy consumption compared to cryogenic distillation. Moreover, these two P/VSA processes show very high robustness in response to composition fluctuations in the feed stream. [ABSTRACT FROM AUTHOR]

Chronic bronchitis (CB) is a challenging condition that worsens over time. Patients continue to experience an abundance of symptoms despite their best attempts to manage them. The etiology of CB is goblet cell overproduction and mucus hypersecretion, which aggravates airflow obstruction by luminal blockage of small airways, epithelial remodeling and modification of airway surface tension that predisposes to collapse. Larger randomized controlled trials are required to validate the pilot data that are currently accessible and assess the treatment’s durability as therapies are still in the early stages of clinical development. To overcome the prevailing increase in CB with the rise in air pollution globally, this study proposes a de novo drug discovery candidate for the effective treatment of CB. With the aid of cutting-edge technology of high-throughput screening, a phytochemical, apigenin, has been administered to target the Beta-2 adrenergic receptor protein and utilized as an exemplary ligand to target the discovery of a suitable drug candidate for Beta-2 adrenergic receptor protein. The drug likeness scores, no evident toxicity and binding affinity –9.092 kcal/mol the “CHEMBL294878” proves to be a potential drug candidate discovered for CB. A therapeutic drug specifically meant to treat CB can be developed with the help of the computational data provided by the research presented in this study. [ABSTRACT FROM AUTHOR]

Developing economical non-platinum oxygen reduction reaction (ORR) catalysts is crucial for the continuous development of fuel cell technology. Transition metal phthalocyanines (TM-Pc) are recognized as ideal catalysts for ORR due to their high turnover frequency and structural tunability. However, the catalytic performance of TM-Pc monolayers with different central atom compositions in ORR has not been sufficiently investigated and explored so far. In this study, a series of TM-Pc monolayers with different central atom compositions were systematically screened to explore their interaction law and catalytic mechanism in ORR based on density functional theory, cleverly using a combination of high-throughput screening and experiment. Our calculations show that the change of the d-band center avoids the strong adsorption of intermediates, and the interaction strength between intermediates and TM-Pc determines the catalytic activities for the ORR. Among the 30 candidate materials, we identified two promising candidates, Fe-Pc and Mn-Pc, which not only have low overpotentials of 0.45 and 0.47 V but also have high thermodynamic and electrochemical stability. The results guide the rational design of high-performance ORR electrocatalysts and are also of reference significance for elucidating the catalytic mechanism of TM-Pc materials for ORR. [Display omitted] • TM-PC electrocatalysts for ORR are systematically screened. • Fe-Pc and Mn-Pc, two highly promising materials, are established. • The ORR mechanism of TM-Pc materials is thoroughly investigated. [ABSTRACT FROM AUTHOR]

Background: Microalgae are potential sustainable resources for the production of value-added chemicals that can be used as biofuels, pharmaceuticals, and nutritional supplements. Arachidonic acid (ARA), a omega-6 fatty acid, plays a crucial role in infant development and immune response, and can be used in cosmetics and pharmaceuticals. Demand for industrial-scale ARA production is continuously increasing because of its broad applicability. To address this demand, there has been a significant shift towards microorganism-based ARA production. To accelerate large-scale ARA production, it is crucial to select suitable strains and establish optimal culture conditions. Results: Here, we isolated a novel microalga Lobosphaera incisa CFRC-1, a valuable strain that holds promise as a feedstock for ARA production. Optimal cultivation conditions were investigated using a high-throughput screening method to enhance ARA production in this novel strain. Out of 71 candidates, four organic carbon substrates were identified that could be utilized by L. incisa CFRC-1. Through flask-scale verification, fructose was confirmed as the optimal organic carbon substrate for promoting microalgal growth, total lipid accumulation, and ARA production. Subsequently, we investigated appropriate substrate concentration and cultivation temperature, confirming that the optimal conditions were 30 g L− 1 of fructose and 27 ℃ of temperature. Under these optimized conditions, biomass and ARA production reached 13.05 ± 0.40 g L− 1 and 97.98 ± 7.33 mg L− 1, respectively, representing 9.6-fold and 5.3-fold increases compared to the conditions before optimization conditions. These results achieved the highest biomass and ARA production in flask-scale cultivation, indicating that our approach effectively improved both production titer and productivity. Conclusions: This study presents a novel microalgae and optimized conditions for enhancing biomass and ARA production, suggesting that this approach is a practical way to accelerate the production of valuable microalgae-based chemicals. These findings provide a basis for large-scale production of ARA-utilizing microalgae for industrial applications. [ABSTRACT FROM AUTHOR]

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students' problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions. [ABSTRACT FROM AUTHOR]

The development of oxygen‐dependent fuel cells is frequently hindered by the high cost of cathode materials and the sluggish kinetics of the oxygen reduction reaction (ORR). It is thus crucial to explore low‐cost and high‐activity catalysts with accelerated mass transport. Here, a high‐throughput screening method is developed by integrating an explicit solvation model with machine learning potential‐based molecular dynamics simulations, which enables the efficient evaluation of a vast array of diverse diatomic combinations and ultimately identifying Fe–Co dual‐atomic sites as the optimal ORR electrocatalysts. The superior electrocatalytic performance of the diatomic Fe/Co sites loaded on 3D‐interconnected ordered macroporous carbon (FeCo‐3DMNC) is experimentally verified, achieving high ORR activity with half‐wave potentials of 0.806 V in acidic and 0.905 V in alkaline environments. Additionally, innovative hybrid acid/alkali aluminum–air fuel cells (hA/A‐AAFCs) incorporating FeCo‐3DMNC as the cathode catalyst demonstrate a notably high open‐circuit voltage of 2.72 V and a record‐breaking power density of 827 mW cm−2, significantly outperforming conventional alkaline aluminum–air fuel cells. This work marks a significant advancement by combining cutting‐edge computational screening with rigorous experimental validation to develop promising electrocatalysts, potentially paving the way for the advanced energy storage and conversion technologies. [ABSTRACT FROM AUTHOR]

Dual‐atom catalysts (DACs) often exhibit superior electrocatalytic activity, due to their versatile combinations and synergistic effects. However, the neglect of both dynamic axial adsorption of the active site upon working potential and the reactant adsorption as a rate‐determining step hinders the establishment of an accurate high‐throughput screening strategy. Here, the oxygen reduction reaction (ORR) of 42 kinds of 3d–3d metal DACs by density functional theory (DFT) calculations are systematiclly investigated and demonstrated that the ORR kinetics can be limited by O2* adsorption besides the proton–electron transfer step and the active center of DACs may be reconstructed by axial pre‐adsorption of intermediates under working potential. Therefore, the ORR volcano plot is proposed by using both the O2* and OH* adsorption as activity descriptors. Then, a high‐throughput screening method is constructed and 38 promising ORR DACs are screened out from 267 DACs containing 3d, 4d, or 5d metals. Importantly, the previously unexplored MnCoN6 DAC is also experimentally synthesized, and exhibits ultrahigh ORR activity outperforming Pt/C, perfectly matching with theoretical prediction. In short, this work not only proposes a volcano plot‐based high‐throughput screening method but also provides a proof‐of‐concept of experimental verification of theoretical prediction to heuristically design electrocatalysts for other reactions. [ABSTRACT FROM AUTHOR]

ABSTRACT Bioactive peptides (BAPs) have attracted considerable interest in scientific research due to their heterogeneity in sequence and structure, which underpins various biological functionalities. In this context, goat casein, an abundant by‐product of the dairy industry, emerges as a valuable source of BAPs. The present study undertook a meticulous evaluation of the bioactive potentials of goat casein‐derived peptides through an integrated approach combining computational simulations, high‐throughput screening, and molecular docking techniques. The initial phase involved the enzymatic digestion of goat milk casein using trypsin, followed by the identification of peptides via liquid chromatography–tandem mass spectrometry (LC–MS/MS), uncovering a total of 597 peptides. Subsequent prioritization using the PeptideRanker algorithm identified 70 peptides exhibiting potential bioactivity, as denoted by scores above 0.8. Advanced screening employing the BIOPEP database and the AutoDock and CAMPR4 tools facilitated the elucidation of 16 antioxidant, 59 hypotensive, 63 hypoglycemic, 70 hypolipidemic, and 25 antimicrobial peptides. Molecular docking studies further elucidated the spontaneous nature of the interactions between the peptides and their respective receptors, predominantly mediated by hydrogen bonding and hydrophobic interactions. Four peptides specifying all activities simultaneously were synthesized, and their activities were verified by in vitro experiments. These results not only highlight an effective strategy for the high‐throughput screening of goat casein‐derived peptides but also underscore the potential of utilizing casein as a viable source of functional food ingredients. This study thereby contributes significantly to the expanding field of functional food research, suggesting a sustainable approach to explore the potential of dairy by‐products. [ABSTRACT FROM AUTHOR]

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Separating propylene (C3H6) from propylene/propane (C3H6/C3H8) mixture using energy‐efficient adsorption is industrially important, but due to the lack of universal pore features, the rational selection of a suitable adsorbent in the ocean of porous materials is a tough task. In this study, a comprehensive work on the discovery of high‐performance C3H6/C3H8 separation adsorbents is carried out by utilizing the advantages of high‐throughput computational screening (HTCS). First, based on the HTCS data mining in the CoRE MOF 2019 and Tobacco 3.0 database, the target material, Cd‐HFDPA, is screened out. Second, the pore electrostatic potential (ESP) analysis shows that Cd‐HFDPA has obvious pore characteristics and high affinity for C3H6 according to ESP matching, which is further confirmed by adsorption isotherms, Ideal Adsorbed Solution Theory selectivity, adsorption enthalpy analyses, and breakthrough experiments. Finally, an industrial two‐bed pressure swing adsorption process for Cd‐HFDPA adsorbent is proposed and its productivity and energy consumption are compared with other benchmark materials. [ABSTRACT FROM AUTHOR]

Background: Building extensive drug candidate libraries as early in the development pipeline as possible, with high-throughput in vitro absorption, distribution, metabolism, and excretion (ADME) profiling, is crucial for the selection of lead compounds to guide subsequent research and production phases. Traditionally, the analysis of metabolic stability assays heavily relies on high-throughput LC-MS/MS (liquid chromatography tandem mass spectrometry) techniques to meet with the lead profiling demands. Laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) is a quick and efficient technique for characterizing complex biological samples without laborious sample preparation. Objective: In this study, using an automated LA-REIMS well plate reader, achieving an 8 s per sample measurement time, the oxidative metabolic stability of active drug agents was assessed using biomimetic metalloporphyrin-based oxidative model reactions. Results: The results obtained using the novel LA-REIMS-based protocol were compared to and corroborated by those obtained using conventional HPLC-UV-MS (high performance liquid chromatography with ultra-violet detection coupled with mass spectrometry) measurements. Conclusions: LA-REIMS emerges as a promising technique, demonstrating potential suitability for semi-quantitative high-throughput metabolic stability in an optimized solvent environment. [ABSTRACT FROM AUTHOR]

Computer‐aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain. [ABSTRACT FROM AUTHOR]

Magnetic refrigeration based on magnetocaloric effect (MCE) has become a promising cooling technology to replace the traditional vapor compression refrigeration. However, traditional methods for searching MCE materials require producing many different compositions, causing unbearable workload and long experimental periods. Here, 3D printed La0.7Ce0.3Fe11.65Si1.35–Fe compositionally gradient alloys (CGAs) are successfully prepared using laser powder bed fusion equipped with a powder hopper with dual‐bin structure. This CGAs accelerate the high‐throughput screening for the best composition of La(Fe, Si)13/Fe with both high MCE and mechanical properties. The good interfacial compatibility between brittle 1:13 phase and reinforcing α‐Fe improves the mechanical properties significantly. Even after hydrogenation, the compressive strength and ultimate strain of the La(Fe, Si)13/Fe hydrides are ≈220% and ≈150% higher than those of stoichiometric La(Fe, Si)13. Meanwhile, the hydrogenated composite exhibits a large MCE under low magnetic field, e.g., the magnetic entropy change |ΔSM|max of 7.6 J kg−1 K−1 under 2 T is 52% higher than that of the benchmark Gd (5.0 J kg−1 K−1). Furthermore, this La(Fe, Si)13/Fe is 3D printed into various complex shapes suitable for heat exchangers. This study provides an innovative strategy for high‐throughput screening of new materials. [ABSTRACT FROM AUTHOR]

[Display omitted] • A human iPSCs-based high-throughput anti-seizure compounds screening platform was built. • The human iPSCs-derived neurons can effectively and efficiently predict positive compounds. • This integrated anti-seizure drug discovering approach combines the advantages of human genetic background, HTS, and animal brain network. • Piperine, magnolol, α-asarone, and osthole can reduce epileptiform activity and seizures. • Natural compounds are promising candidates used for anti-seizure drug discovery. One-third of people with epilepsy continue to experience seizures despite treatment with existing anti-seizure medications (ASMs). The failure of modern ASMs to substantially improve epilepsy prognosis has been partly attributed to overreliance on acute rodent models in preclinical drug development as they do not adequately recapitulate the mechanisms of human epilepsy, are labor-intensive and unsuitable for high-throughput screening (HTS). There is an urgent need to find human-relevant HTS models in preclinical drug development to identify novel anti-seizure compounds. This paper developed high-throughput preclinical screening models to identify new ASMs. 14 natural compounds (α-asarone, curcumin, vinpocetine, magnolol, ligustrazine, osthole, tanshinone IIA, piperine, gastrodin, quercetin, berberine, chrysin, schizandrin A and resveratrol) were assessed for their ability to suppress epileptiform activity as measured by multi-electrode arrays (MEA) in neural cultures derived from human induced pluripotent stem cells (iPSCs). In parallel, they were tested for anti-seizure effects in zebrafish and mouse models, which have been widely used in development of modern ASMs. The effects of the compounds in these models were compared. Two approved ASMs were used as positive controls. Epileptiform activity could be induced in iPSCs-derived neurons following treatment with 4-aminopyridine (4-AP) and inhibited by standard ASMs, carbamazepine, and phenytoin. Eight of the 14 natural compounds significantly inhibited the epileptiform activity in iPSCs-derived neurons. Among them, piperine, magnolol, α-asarone, and osthole showed significant anti-seizure effects both in zebrafish and mice. Comparative analysis showed that compounds ineffective in the iPSCs-derived neural model also showed no anti-seizure effects in the zebrafish or mouse models. Our findings support the use of iPSCs-derived human neurons for first-line high-throughput screening to identify compounds with anti-seizure properties and exclude ineffective compounds. Effective compounds may then be selected for animal evaluation before clinical testing. This integrated approach may improve the efficiency of developing novel ASMs. [ABSTRACT FROM AUTHOR]

The development of new crystal materials for sodium-ion batteries is considered one of the most exciting fields in solid-state electrochemistry. To search for new sodium-ion conductors, we selected more than 200 structures from the Inorganic Crystal Structure Database (version 2023/1), consisting of four, five, and six elements with several oxygen-anionic groups. After the selection of 45 unique structures through geometrical analysis with the periodic 1D, 2D, and 3D migration maps, we calculated diffusion barriers and the crystal chemical stability index for each within the bond valence site energy method. Thus, we selected 21 different compounds to determine the room-temperature ionic conductivity and obtained 7 substances that have conductivity greater than 10−5 S cm−1. Among these prospective conductors, we identified a new class of Na2Ln(MO4)(PO4) (Ln = Er, Y, Tb, Ho; M = Mo, W) compounds with the layered sodium diffusion, which were not previously considered as possible conductors. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) are pivotal in mediating diverse physiological and pathological processes, rendering them promising targets for drug discovery. GPCRs account for about 40% of FDA-approved drugs, representing the most successful drug targets. However, only approximately 15% of the 800 human GPCRs are targeted by market drugs, leaving numerous opportunities for drug discovery among the remaining receptors. Cell expression systems play crucial roles in the GPCR drug discovery field, including novel target identification, structural and functional characterization, potential ligand screening, signal pathway elucidation, and drug safety evaluation. Here, we discuss the principles, applications, and limitations of widely used cell expression systems in GPCR-targeted drug discovery, GPCR function investigation, signal pathway characterization, and pharmacological property studies. We also propose three strategies for constructing genome-wide pan-GPCR cell libraries, which will provide a powerful platform for GPCR ligand screening, and facilitate the study of GPCR mechanisms and drug safety evaluation, ultimately accelerating the process of GPCR-targeted drug discovery. The genome-wide pan-GPCR cell line libraries, which utilize overexpression, PRESTO-Tango, and CRISPRa/i technologies, provide a powerful platform for GPCR-targeted drug discovery, which will facilitate the study of GPCR function, drug safety evaluation, and accelerate GPCR drug discovery. [Display omitted] [ABSTRACT FROM AUTHOR]

Droplet microfluidic-based technology is a powerful tool for biotechnology, and it is also expected that it will be applied to culturing and screening methods. Using this technology, a new high-throughput screening method for lactic acid bacteria was developed. In this study, the conventional culture of lactic acid bacteria that form clear zones on an agar medium was reproduced in water-in-oil droplets, and only the droplets in which lactic acid bacteria grew were collected one by one. Using this method, the specific recovery of Lactiplantibacillus plantarum from a mixture of L. plantarum and Escherichia coli and the acquirement of lactic acid bacteria from an environmental sample were successful. This method could be applied to various conventional screening methods using the clear zone as a microbial growth indicator. This has expanded the possibilities of applying droplet microfluidic-based technology to microbial cultivations. [ABSTRACT FROM AUTHOR]

The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts (SACs) is crucial for catalyzing the CO2 reduction reaction (CO2RR). However, it remains a major challenge. While density-functional theory calculations serve as a powerful tool for catalyst screening, their time-consuming nature poses limitations. This paper presents a machine learning (ML) model based on easily accessible intrinsic descriptors to enable rapid, cost-effective, and high-throughput screening of efficient SACs in complex systems. Our ML model comprehensively captures the influences of interactions between 3 and 5d metal centers and 8 C, N-based coordination environments on CO2RR activity and selectivity. We reveal the electronic origin of the different activity trends observed in early and late transition metals during coordination with N atoms. The extreme gradient boosting regression model shows optimal performance in predicting binding energy and limiting potential for both HCOOH and CO production. We confirm that the product of the electronegativity and the valence electron number of metals, the radius of metals, and the average electronegativity of neighboring coordination atoms are the critical intrinsic factors determining CO2RR activity. Our developed ML models successfully predict several high-performance SACs beyond the existing database, demonstrating their potential applicability to other systems. This work provides insights into the low-cost and rational design of high-performance SACs. [ABSTRACT FROM AUTHOR]

Biosensors are valuable tools in accelerating the test phase of the design‐build‐test‐learn cycle of cell factory development, as well as in bioprocess monitoring and control. G protein‐coupled receptor (GPCR)‐based biosensors enable cells to sense a wide array of molecules and environmental conditions in a specific manner. Due to the extracellular nature of their sensing, GPCR‐based biosensors require compartmentalization of distinct genotypes when screening production levels of a strain library to ensure that detected levels originate exclusively from the strain under assessment. Here, we explore the integration of production and sensing modalities into a single Saccharomyces cerevisiae strain and compartmentalization using three different methods: (1) cultivation in microtiter plates, (2) spatial separation on agar plates, and (3) encapsulation in water‐in‐oil‐in‐water double emulsion droplets, combined with analysis and sorting via a fluorescence‐activated cell sorting machine. Employing tryptamine and serotonin as proof‐of‐concept target molecules, we optimize biosensing conditions and demonstrate the ability of the autocrine screening method to enrich for high producers, showing the enrichment of a serotonin‐producing strain over a nonproducing strain. These findings illustrate a workflow that can be adapted to screening for a wide range of complex chemistry at high throughput using commercially available microfluidic systems. [ABSTRACT FROM AUTHOR]

Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2'-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by (i) quantifying colorimetric SA-β-Gal via optical density; (ii) implementing staining background controls; and (iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C12FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical. [ABSTRACT FROM AUTHOR]

Comprehending early amyloidogenesis is essential for the development of effective therapeutic strategies. In tauopathies like Alzheimer's disease (AD), the abnormal accumulation of tau protein is initiated by pathological tau seeds. Mounting evidence implies that the microtubule binding domain, consisting of three to four repeats, plays a pivotal role in this process, yet the exact region driving the formation of pathogenic species needs to be further scrutinized. Here, we chemically synthesized individual tau repeats to identify those exhibiting pathogenic prion‐like characteristics. Notably, repeat 3 (R3) displayed a remarkable propensity to polymerize, form toxic filaments, and induce cognitive impairment, even in the absence of an aggregation‐promoting inducer, highlighting its physiological relevance. Additionally, oligomeric R3 was identified as a particularly pathological form, prompting the establishment of a screening platform. Through screening, tolcapone was found to possess therapeutic efficacy against pathological tau aggregates in PS19 transgenic mice. This screening platform provides a valuable avenue for identifying compounds that selectively interact with peptides implicated in the progression of tauopathies. [ABSTRACT FROM AUTHOR]

During the deamination and amination processes of meso -diaminopimelate dehydrogenase (meso -DAPDH) from Symbiobacterium thermophilum (StDAPDH), residue R71 was observed to display distinct functions. H154 has been proposed as a basic residue that facilitates water molecules to attack the D-chiral carbon of meso -DAP during deamination. Inspired by the phenomenon of R71, the effects of H154 during deamination and amination were investigated in this study with the goal of enhancing the amination activities of StDAPDH. Single site saturation mutagenesis indicated that almost all of the H154 mutants completely lost their deamination activity towards meso -DAP. However, some H154 variants showed enhanced k cat / K m values towards pyruvic acid and other bulky 2-keto acids, such as 2-oxovaleric acid, 4-methyl-2-oxopentanoic acid, 2-ketobutyric acid, and 3-methyl-2-oxobutanoic acid. When combined with the previously reported W121L/H227I mutant, triple mutants with significantly improved k cat / K m values (2.4-, 2.5-, 2.5-, and 4.0-fold) towards these 2-keto acids were obtained. Despite previous attempts, mutations at the H154 site did not yield the desired results. Moreover, this study not only recognizes the distinctive impact of H154 on both the deamination and amination reactions, but also provides guidance for further high-throughput screening in protein engineering and understanding the catalytic mechanism of StDAPDH. • H154 was first experimentally identified to be the fatal residue for deamination. • H154 mutation boosted the amination towards pyruvic acid and bulky 2-keto acids. • Coupling H154 variants with W121L/H227I resulted in enhanced amination activities. • The first report on the contrary effects of H154 during deamination and amination. • A reminder to update the high-throughput screening for DAPDH amination activity. [ABSTRACT FROM AUTHOR]

Copyright of Science & Technology of Food Industry is the property of Science & Technology of Food Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision‐language models (VLMs) with chemical analysis techniques. A cutting‐edge VLM is unveiled, utilizing the expansive UMDFood‐90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro‐AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high‐throughput solution for nutritional analysis. [ABSTRACT FROM AUTHOR]

Finding birefringent material with giant optical anisotropy is urgent for photonic applications. However, the application of existing birefringent materials is limited by restricted transparency wavelength or optical anisotropy. The process of trial and error is time‐consuming and inefficient in terms of resources, especially when compared to the vast design spaces for materials. The immense chemical space necessitates a high‐throughput approach for giant optical anisotropy materials design. Here, a full‐applied wavelength map of birefringent materials is constructed, from conventional visible expanding to ultraviolet and deep‐ultraviolet region by high‐throughput screening methods. And highlighted 579 materials birefringence exceeds the one of available commercial birefringent materials in corresponding regions. Significantly, 54 materials possess giant birefringence above 0.5 @1064 nm. Via developing a high‐throughput screening technique for microscopic structures with given coordination number, the study further characterizes a comprehensive map of functional modules featuring birefringent activeness. According to the macroscopic and microscopic dual‐functional maps, and a novel crystal Li2(HC3N3S3)∙5H2O with birefringence difference 0.532 @546 nm is designed and synthesized successfully. This study identifies desired materials with high performance in unheeded chemical spaces. [ABSTRACT FROM AUTHOR]

Efficient drug development holds prime importance in the present era. Computational techniques offer potential solutions for efficacious drug design. The present review attempts to summarize the essentiality of the Quantitative Structure–Activity Relationship (QSAR) of Schiff bases and thiosemicarbazones for developing potent therapeutics. It provides an overview of recent QSAR computational studies conducted to develop Schiff bases, their derivatives as medicinal agents, and their activity alteration upon substitution and structural changes. Various recent research papers, primarily from leading indexing sources and databases like SCOPUS, Web of Science, PubMed, Medline, etc., have focused on the studies reported during the last five years. Software like HYPERCHEM, MatLaB, DRAGON and RECKON are generally used for the QSAR analysis. Analysis of Schiff bases using QSAR showed that complexes with high molecular weight exhibit antibacterial activity. Computer-aided technology channelizes drug development of potential lead compounds and considerably contributes to the discovery and expansion of drugs. However, certain aspects viz., accuracy for the prediction of drug-target binding affinity, conformational changes in protein, prediction of physical properties of novel drugs and allosteric sites, differences between around thousands of molecular descriptors, limited biological response and alignment protocol of training-set and test-set ligands need further exploration. [ABSTRACT FROM AUTHOR]

Genetically encoded circuits have been successfully utilized to assess and characterize target variants with desirable traits from large mutant libraries. Adenosylcobalamin is an essential coenzyme that is required in many intracellular physiological reactions and is widely used in the pharmaceutical and food industries. Highthroughput screening techniques capable of detecting adenosylcobalamin productivity and selecting superior adenosylcobalamin biosynthesis strains are critical for the creation of an effective microbial cell factory for the production of adenosylcobalamin at an industrial level. In this study, we developed an RNA-protein hybrid biosensor whose input part was an endogenous RNA riboswitch to specifically respond to adenosylcobalamin, the inverter part was an orthogonal transcriptional repressor to obtain signal inversion, and the output part was a fluorescent protein to be easily detected. The hybrid biosensor could specifically and positively correlate adenosylcobalamin concentrations to green fluorescent protein expression levels in vivo. This study also improved the operating concentration and dynamic range of the hybrid biosensor by systematic optimization. An individual cell harboring the hybrid biosensor presented over 20-fold higher fluorescence intensity than the negative control. Then, using such a biosensor combined with fluorescence-activated cell sorting, we established a highthroughput screening platform for screening adenosylcobalamin overproducers. This study demonstrates that this platform has significant potential to quickly isolate high-productive strains to meet industrial demand and that the framework is acceptable for various metabolites. [ABSTRACT FROM AUTHOR]

Nonlinear optical crystals are the core devices of all-solid-state lasers, and have extensive and important applications in information technology and national security. With the development of high-performance computing, the "top-down" computer-aided design methods have gradually become an important part of nonlinear optical materials design. In addition, the large-scale structural and properties information obtained based on high-throughput computing provides a solid data foundation for data mining and machine learning algorithm training, accelerating the development of the fourth paradigm of material design. This paper starts with the computational design of nonlinear optical materials, and then, discusses the new paradigm of data-driven nonlinear optical materials theoretical design. Finally, the recent research progress of our team in high-throughput screening, crystal structure prediction, and machine learning accelerated nonlinear optical materials are reviewed. [ABSTRACT FROM AUTHOR]

D‐amino acid oxidase (DAAO)‐catalyzed selective oxidative deamination is a very promising process for synthesizing l‐amino acids including l‐phosphinothricin (l‐PPT, a high‐efficiency and broad‐spectrum herbicide). However, the wild‐type DAAO's low activity toward unnatural substrates like d‐phosphinothricin (d‐PPT) hampers its application. Herein, a DAAO from Caenorhabditis elegans (CeDAAO) was screened and engineered to improve the catalytic potential on d‐PPT. First, we designed a novel growth selection system, taking into account the intricate relationship between the growth of Escherichia coli (E. coli) and the catalytic mechanism of DAAO. The developed system was used for high‐throughput screening of gene libraries, resulting in the discovery of a variant (M6) with significantly increased catalytic activity against d‐PPT. The variant displays different catalytic properties on substrates with varying hydrophobicity and hydrophilicity. Analysis using Alphafold2 modeling and molecular dynamic simulations showed that the reason for the enhanced activity was the substrate‐binding pocket with enlarged size and suitable charge distribution. Further QM/MM calculations revealed that the crucial factor for enhancing activity lies in reducing the initial energy barrier of the reductive half reaction. Finally, a comprehensive binding‐model index to predict the enhanced activity of DAAO toward d‐PPT, and an enzymatic deracemization approach was developed, enabling the efficient synthesis of l‐PPT with remarkable efficiency. [ABSTRACT FROM AUTHOR]