High-throughput Screening Strategy Research Articles

Utilizing 5' UTR Engineering Enables Fine-Tuning of Multiple Genes within Operons to Balance Metabolic Flux in Bacillus subtilis.

The application of synthetic biology tools to modulate gene expression to increase yield has been thoroughly demonstrated as an effective and convenient approach in industrial production. In this study, we employed a high-throughput screening strategy to identify a 5' UTR sequence from the genome of B. subtilis 168. This sequence resulted in a 5.8-fold increase in the expression level of EGFP. By utilizing the 5' UTR sequence to overexpress individual genes within the rib operon, it was determined that the genes ribD and ribAB serve as rate-limiting enzymes in the riboflavin synthesis pathway. Constructing a 5' UTR library to regulate EGFP expression resulted in a variation range in gene expression levels exceeding 100-fold. Employing the same 5' UTR library to regulate the expression of EGFP and mCherry within the operon led to a change in the expression ratio of these two genes by over 10,000-fold. So, employing a 5' UTR library to modulate the expression of the rib operon gene and construct a synthetic rib operon resulted in a 2.09-fold increase in riboflavin production. These results indicate that the 5' UTR sequence identified and characterized in this study can serve as a versatile synthetic biology toolkit for achieving complex metabolic network reconstruction. This toolkit can facilitate the fine-tuning of gene expression to produce target products.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry.

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Unraveling the Activity and Mechanism of TM@g-C4N3 Electrocatalysts in the Oxygen Reduction Reaction.

In this work, a high-throughput screening strategy and density functional theory (DFT) are jointly employed to identify high-performance TM@g-C4N3 (TM = 3d, 4d, 5d transition metals) single-atom catalysts (SACs) for the oxygen reduction reaction (ORR). Comprehensive studies demonstrated that Cu@, Zn@, and Ag@g-C4N3 show high ORR catalytic activities under both acidic and alkaline conditions with favorable overpotentials (ηORR) of 0.70, 0.89, and 0.89 V, respectively; among them, Cu@g-C4N3 is the best candidate. The ORR follows a four-electron mechanism with the final product H2O/OH-. Cu@, Zn@, and Ag@g-C4N3 catalysts also exhibit good thermal (500 K) and electrochemical (0.93-3.14 V) stabilities. Cu@, Zn@, and Ag@g-C4N3 demonstrate superior activities with low ηORR due to its moderate adsorption strength of *OH. The ηORR and the Gibbs free energy changes of *OH (ΔG4(acidic)/ΔG4(alkaline)) resemble a volcano-type relationship under acidic/alkaline conditions, respectively. Additionally, the O-O bond length in *OOH emerged as an effective structural descriptor for rapidly identifying the promising electrocatalysts. This research provides valuable insights into the origin of the ORR activity on TM@g-C4N3 and offers useful guidance for the efficient exploration of high-performance catalyst candidates.

Abstract 3884: Artificial intelligence (AI)-enabled discovery of GLX1546, a novel and potent inhibitor of ULK1/2 kinases and autophagy

Abstract Background: Autophagy is an evolutionarily conserved process, critical for the recycling of damaged or excess cellular components, which promotes cell survival and growth under stress conditions. Cancer cells, especially those with RAS mutations, can activate the ULK1/2 (UNC-51-like kinase 1/2)-initiated autophagy pathway for survival and diminish RAS pathway inhibitors’ efficacy. The combination of RAS pathway and ULK1/2 kinase inhibitors may provide synergistic or additive effects for RAS mutated solid tumors. We have identified a structurally small molecule, GLX1546, which shows strong inhibition against ULK1/2, as well as autophagy induced by RAS pathway inhibitors in cells. Methods: Multiple dual-target activity predictive models were constructed and trained using extensive protein-ligand interaction data. These models, in conjunction with in-house built physicochemical property predictive models, were integrated into an AI-driven computational pipeline to perform the in silico screening on both ULK1/2 inhibitory activities and drug-like properties. ULK1/2 in vitro activities were assessed by enzymatic assays and cellular assays based on the phosphorylation of ULK substrate. The in vivo anti-tumor efficacy of GLX1546 was evaluated in A549 CDX mouse model both as monotherapy and in combination with trametinib. Results: An AI-driven high-throughput virtual screening strategy using deep learning models was implemented for the discovery of novel and potent ULK1/2 small-molecule inhibitors. GLX1546 was developed as a potent ULK1/2 inhibitor with IC50 values of 1.13 nM for ULK1 and 4.75 nM for ULK2. Moreover, to address trametinib-induced activation of ULK1/2 in KRAS and BRAF mutated solid tumors, GLX1546 provided IC50 values of 54 nM in A549, 21 nM in HCT 116, 67 nM in MIA PaCa-2, and 76 nM in A375 cells against both basal and trametinib-induced pATG13. Furthermore, GLX1546 exhibited good exposure, sound safety, and high oral bioavailability (~100%) in mice. The combination of trametinib (PO, QD) with GLX1546 (PO, BID) in 21-day cycles also led to inhibition of ATG13 phosphorylation and greater tumor growth inhibition than trametinib alone in A549 CDX mouse model. Conclusions: These results indicate that GLX1546 can inhibit ULK-mediated autophagy induced by trametinib. The combination of GLX1546 with RAS pathway inhibitors may offer a novel cancer therapy for RAS mutated cancers, highlighting AI as an effective tool for drug discovery. Keywords: AI-driven drug discovery, autophagy, small-molecule inhibitors, ULK Citation Format: Guonan Cui, Xulun Sun, Wenyu Zhu, Qi Wang, Jinchen Li, Zhongzong Pan, Chengtao Li. Artificial intelligence (AI)-enabled discovery of GLX1546, a novel and potent inhibitor of ULK1/2 kinases and autophagy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3884.

Challenges and advancements in high-throughput screening strategies for cancer therapeutics

Drug discovery relies on high-throughput screening (HTS) methods incorporating both target- and cell-based assays. This comprehensive review delves into the challenges and benefits associated with these assays within the context of HTS. The strategies for developing screening assays, spanning both primary and secondary screens for target identification, are discussed. Furthermore, we review the methods of identifying the most efficacious drugs through these approaches for the treatment of cancer in detail. While various drugs have been identified for cancer treatment, there remains a pressing need for more relevant phenotypic assays. These assays aim to produce the desired disease phenotype, with a specific emphasis on highlighting targets rather than off-targets. The ultimate goal is to pave the way for innovative drug development strategies that can effectively treat cancer patients, thereby reducing the mortality rate.

Accelerate the design of new superhard carbon allotropes in Pca21 space group: High-throughput screening and machine learning strategies

As an efficient calculation and screening method, high-throughput can discover and optimize new materials, shorten the development cycle and cost of new materials. However, using high throughput for material screening and computation, a large amount of computing resources and storage space are indispensable. To accelerate the design of novel superhard carbon materials, we combined machine learning methods with high-throughput computing to construct three machine learning models: support vector machine regression, random forests, and artificial neural networks, and mined data from existing material databases to select 1276 structures as datasets for the model to predict the volume modulus and shear modulus. Through comparative analysis, the optimal model was selected to predict the bulk modulus and shear modulus of the structures obtained by high-throughput calculations, and the prediction results of the model were verified by density functional theory (DFT) calculations, and 8 superhard carbon allotropes in the Pca21 space group were eventually found.

Attenuation of phytofungal pathogenicity of Ascomycota by autophagy modulators

Autophagy in eukaryotes functions to maintain homeostasis by degradation and recycling of long-lived and unwanted cellular materials. Autophagy plays important roles in pathogenicity of various fungal pathogens, suggesting that autophagy is a novel target for development of antifungal compounds. Here, we describe bioluminescence resonance energy transfer (BRET)-based high-throughput screening (HTS) strategy to identify compounds that inhibit fungal ATG4 cysteine protease-mediated cleavage of ATG8 that is critical for autophagosome formation. We identified ebselen (EB) and its analogs ebselen oxide (EO) and 2-(4-methylphenyl)−1,2-benzisothiazol-3(2H)-one (PT) as inhibitors of fungal pathogens Botrytis cinerea and Magnaporthe oryzae ATG4-mediated ATG8 processing. The EB and its analogs inhibit spore germination, hyphal development, and appressorium formation in Ascomycota pathogens, B. cinerea, M. oryzae, Sclerotinia sclerotiorum and Monilinia fructicola. Treatment with EB and its analogs significantly reduced fungal pathogenicity. Our findings provide molecular insights to develop the next generation of antifungal compounds by targeting autophagy in important fungal pathogens.

FDA compound library screening Baicalin upregulates TREM2 for the treatment of cerebral ischemia-reperfusion injury

Acute ischemic stroke (AIS) is a leading cause of global incidence and mortality rates. Oxidative stress and inflammation are key factors in the pathogenesis of AIS neuroinjury. Therefore, it is necessary to develop drugs that target neuroinflammation and oxidative stress in AIS. The Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), primarily expressed on microglial cell membranes, plays a critical role in reducing inflammation and oxidative stress in AIS. In this study, we employed a high-throughput screening (HTS) strategy to evaluate 2625 compounds from the (Food and Drug Administration) FDA library in vitro to identify compounds that upregulate the TREM2 receptor on microglia. Through this screening, we identified Baicalin as a potential drug for AIS treatment. Baicalin, a flavonoid compound extracted and isolated from the root of Scutellaria baicalensis, demonstrated promising results. Next, we established an in vivo mouse model of cerebral ischemia-reperfusion injury (MCAO/R) and an in vitro microglia cell of oxygen-glucose deprivation reperfusion (OGD/R) to investigate the role of Baicalin in inflammation injury, oxidative stress, and neuronal apoptosis. Our results showed that baicalin effectively inhibited microglia activation, reactive oxygen species (ROS) production, and inflammatory responses in vitro. Additionally, baicalin suppressed neuronal cell apoptosis. In the in vivo experiments, baicalin not only improved neurological functional deficits and reduced infarct volume but also inhibited microglia activation and inflammatory responses. Overall, our findings demonstrate the efficacy of Baicalin in treating MCAO/R by upregulating TREM2 to reduce inflammatory responses and inhibit neuronal apoptosis.

Natural compound library screening to identify berberine as a treatment for rheumatoid arthritis.

Fibroblast-like synoviocytes (FLS) play a critical role on the exacerbation and deterioration of rheumatoid arthritis (RA). Aberrant activation of FLS pyroptosis signaling is responsible for the hyperplasia of synovium and destruction of cartilage of RA. This study investigated the screened traditional Chinese medicine berberine (BBR), an active alkaloid extracted from the Coptis chinensis plant, that regulates the pyroptosis of FLS and secretion of inflammatory factors in rheumatoid arthritis. First, BBR was screened using a high-throughput drug screening strategy, and its inhibitory effect on RA-FLS was verified by in vivo and in vitro experiments. Second, BBR was intraperitoneally administrated into the collagen-induced arthritis rat model, and the clinical scores, arthritis index, and joint HE staining were evaluated. Third, synovial tissues of CIA mice were collected, and the expression of NLRP3, cleaved-caspase-1, GSDMD-N, Mst1, and YAP was detected by Western blot. The administration of BBR dramatically alleviated the severity of collagen-induced arthritis rat model with a decreased clinical score and inflammation reduction. In addition, BBR intervention significantly attenuates several pro-inflammatory cytokines (interleukin-1β, interleukin-6, interleukin-17, and interleukin-18). Moreover, BBR can reduce the pyroptosis response (caspase-1, NLR family pyrin domain containing 3, and gasdermin D) of the RA-FLS in vitro, activating the Hippo signaling pathway (Mammalian sterile 20-like kinase 1, yes-associated protein, and transcriptional enhanced associate domains) so as to inhibit the pro-inflammatory effect of RA-FLS. These results support the role of BBR in RA and may have therapeutic implications by directly repressing the activation, migration of RA-FLS, which contributing to the attenuation of the progress of CIA. Therefore, targeting PU.1 might be a potential therapeutic approach for RA. Besides, BBR inhibited RA-FLS pyroptosis by downregulating of NLRP3 inflammasomes (NLRP3, caspase-1) and eased the pro-inflammatory activities via activating the Hippo signaling pathway, thereby improving the symptom of CIA.

Sulfate transport and metabolism: strategies to improve the seed protein quality.

Sulfur-containing amino acids (SAA), namely methionine, and cysteine are crucial essential amino acids (EAA) considering the dietary requirements of humans and animals. However, a few crop plants, especially legumes, are characterized with suboptimal levels of these EAA thereby limiting their nutritive value. Hence, improved comprehension of the mechanistic perspective of sulfur transport and assimilation into storage reserve, seed storage protein (SSP), is imperative. Efforts to augment the level of SAA in seed storage protein form an integral component of strategies to balance nutritive quality and quantity. In this review, we highlight the emerging trends in the sulfur biofortification approaches namely transgenics, genetic and molecular breeding, and proteomic rebalancing with sulfur nutrition. The transgenic 'push and pull strategy' could enhance sulfur capture and storage by expressing genes that function as efficient transporters, sulfate assimilatory enzymes, sulfur-rich foreign protein sinks, or by suppressing catabolic enzymes. Modern molecular breeding approaches that adopt high throughput screening strategies and machine learning algorithms are invaluable in identifying candidate genes and alleles associated with SAA content and developing improved crop varieties. Sulfur is an essential plant nutrient and its optimal uptake is crucial for seed sulfur metabolism, thereby affecting seed quality and yields through proteomic rebalance between sulfur-rich and sulfur-poor seed storage proteins.

Design and screening of multifunctional NOx electroreduction catalysts for ammonia production

Multifunctional single atomic catalysts (SACs) are advantageous for the co-reduction of NOx mixtures. In this work, we used a strategy for high-throughput screening of SACs of NOxRR based on density functional theory. SACs were designed based on the B and N defects of hexagonal BN, and its hydrogenation in NOxRR was studied. The stability of SACs is found to be regulated by the spatial radius of the metal atoms and the electronic structure of the surface. Electron enrichment in the metal center facilitates activation of the intermediate *NHO and reduces the limiting potential. The activity of SACs can be briefly described by ΔG*NO. Ultimately, Mn-BN-B is identified as a SAC with excellent performance for NO3RR, NO2RR and NORR, because of the limiting potential of − 0.26 V, and Cu, Co-BN-B also showed good NORR activity. Our work provides a procedure for efficiently screening multifunctional electrocatalysts to simultaneously reduce all NOx species to synthesize NH3.

Bionic inspired multifunctional modular energetic materials: an exploration of new generation of application-oriented energetic materials

Aiming to balance the pertinence and universality of energetic materials, this study proposes a new concept of bionic inspired multifunctional modular energetic materials and seeks out potential monomers via a high-throughput screening strategy.

Advances in Droplet-Based Microfluidic High-Throughput Screening of Engineered Strains and Enzymes Based on Ultraviolet, Visible, and Fluorescent Spectroscopy

Genetic engineering and directed evolution are effective methods for addressing the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well-plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields, such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as those involving materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions as well as improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved the high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase as well as the high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

CD49f and CD146: A Possible Crosstalk Modulates Adipogenic Differentiation Potential of Mesenchymal Stem Cells.

The lack of appropriate mesenchymal stem cells (MSCs) selection methods has given the challenges for standardized harvesting, processing, and phenotyping procedures of MSCs. Genetic engineering coupled with high-throughput proteomic studies of MSC surface markers arises as a promising strategy to identify stem cell-specific markers. However, the technical limitations are the key factors making it less suitable to provide an appropriate starting material for the screening platform. A more accurate, easily accessible approach is required to solve the issues. This study established a high-throughput screening strategy with forward versus side scatter gating to identify the adipogenesis-associated markers of bone marrow-derived MSCs (BMSCs) and tonsil-derived MSCs (TMSCs). We classified the MSC-derived adipogenic differentiated cells into two clusters: lipid-rich cells as side scatter (SSC)-high population and lipid-poor cells as SSC-low population. By screening the expression of 242 cell surface proteins, we identified the surface markers which exclusively found in lipid-rich subpopulation as the specific markers for BMSCs and TMSCs. High-throughput screening of the expression of 242 cell surface proteins indicated that CD49f and CD146 were specific for BMSCs and TMSCs. Subsequent immunostaining confirmed the consistent specific expression of CD49f and CD146 and in BMSCs and TMSCs. Enrichment of MSCs by CD49f and CD146 surface markers demonstrated that the simultaneous expression of CD49f and CD146 is required for adipogenesis and osteogenesis of mesenchymal stem cells. Furthermore, the fate decision of MSCs from different sources is regulated by distinct responses of cells to differentiation stimulations despite sharing a common CD49f+CD146+ immunophenotype. We established an accurate, robust, transgene-free method for screening adipogenesis associated cell surface proteins. This provided a valuable tool to investigate MSC-specific markers. Additionally, we showed a possible crosstalk between CD49f and CD146 modulates the adipogenesis of MSCs.

Efficient screening of single phase forming low-activation high entropy alloys

Screening of chemical space constituting a palette of seven low-activation metallic elements (Ti, V, Cr, Mn, Fe, Ta, and W) is performed to find single-phase equiatomic/non-equiatomic body-centered cubic (BCC) quaternary/quinary high entropy alloys (HEA). A high throughput screening using ab-initio computations-assisted thermodynamic modeling strategy is implemented. Benchmarking the accuracies of the empirical models for predicting simple solid solutions HEAs is performed considering experimentally synthesized thirty-six low-activation HEAs from the literature. Based on the most efficient empirical model, compositional ranges of several new HEAs are suggested where the formation of single-phase HEA is most preferable. A thorough comparison of high-throughput screening results with CALPHAD data and experiments establishes the efficiency of the high-throughput screening strategy.

Microarray-Based CD38 Peptide Probe Screening for Multiple Myeloma Imaging.

Assessing CD38 expression in vivo has become a significant element in multiple myeloma (MM) therapy, as it can be used to detect lesions and forecast the effectiveness of treatment. Accurate diagnosis requires a multifunctional, high-throughput probe screening platform to develop molecular probes for tumor-targeted multimodal imaging and treatment. Here, we investigated a microarray chip-based strategy for high-throughput screening of peptide probes for CD38. We obtained two new target peptides, CA-1 and CA-2, from a 105 peptide library with a dissociation constant (KD) of 10-7 M. The specificity and affinity of the target peptides were confirmed at the molecular and cellular levels. Peptide probes were labeled with indocyanine green (ICG) dye and 68Ga-DOTA, which were injected into a CD38-positive Ramos tumor-bearing mouse via its tail vein, and small animal fluorescence and positron emission tomography (PET) imaging showed that the peptide probes could show specific enrichment in the tumor tissue. Our study shows that a microchip-based screening of peptide probes can be used as a promising imaging tool for MM diagnosis.

High-throughput droplet microfluidics screening and genome sequencing analysis for improved amylase-producing Aspergillus oryzae

BackgroundThe exceptional protein secretion capacity, intricate post-translational modification processes, and inherent safety features of A. oryzae make it a promising expression system. However, heterologous protein expression levels of existing A. oryzae species cannot meet the requirement for industrial-scale production. Therefore, establishing an efficient screening technology is significant for the development of the A. oryzae expression system.ResultsIn this work, a high-throughput screening method suitable for A. oryzae has been established by combining the microfluidic system and flow cytometry. Its screening efficiency can reach 350 droplets per minute. The diameter of the microdroplet was enlarged to 290 µm to adapt to the polar growth of A. oryzae hyphae. Through enrichment and screening from approximately 450,000 droplets within 2 weeks, a high-producing strain with α-amylase increased by 6.6 times was successfully obtained. Furthermore, 29 mutated genes were identified by genome resequencing of high-yield strains, with 15 genes subjected to editing and validation. Two genes may individually influence α-amylase expression in A. oryzae by affecting membrane-associated multicellular processes and regulating the transcription of related genes.ConclusionsThe developed high-throughput screening strategy provides a reference for other filamentous fungi and Streptomyces. Besides, the strains with different excellent characteristics obtained by efficient screening can also provide materials for the analysis of genetic and regulatory mechanisms in the A. oryzae expression system.

Isolation and Characterization of a Serratia rubidaea from a Shallow Water Hydrothermal Vent.

Microbial life present in the marine environment has to be able to adapt to rapidly changing and often extreme conditions. This makes these organisms a putative source of commercially interesting compounds since adaptation provides different biochemical routes from those found in their terrestrial counterparts. In this work, the goal was the identification of a marine bacterium isolated from a sample taken at a shallow water hydrothermal vent and of its red product. Genomic, lipidomic, and biochemical approaches were used simultaneously, and the bacterium was identified as Serratia rubidaea. A high-throughput screening strategy was used to assess the best physico-chemical conditions permitting both cell growth and production of the red product. The fatty acid composition of the microbial cells was studied to assess adaptation at the lipid level under stressful conditions, whilst several state-of-the-art techniques, such as DSC, FTIR, NMR, and Ultra-High Resolution Qq-Time-of-Flight mass spectrometry, were used to characterize the structure of the pigment. We hypothesize that the pigment, which could be produced by the cells up to 62 °C, is prodigiosin linked to an aliphatic compound that acts as an anchor to keep it close to the cells in the marine environment.

Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning

The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods.