High-throughput Screening Strategy Research Articles

High-throughput screening strategies for plastic-depolymerizing enzymes.

A multitude of plastic-depolymerizing microorganisms and enzymes have been discovered in the plastisphere. Identifying and engineering such microbial strains and enzymes necessitate robust and high-throughput screening strategies for developing effective microbial solutions to counter the plastic accumulation problem and decouple the reliance on fossil resources. This review covers new methods and approaches for the effective high-throughput screening of depolymerizing enzymes for various plastics, such as polyethylene terephthalate (PET), polyurethane (PU), and polylactic acid (PLA). We discuss the application scope of the existing methods, as well as potential developments and integration of screening techniques to identify and enhance plastic depolymerases. The prospects for screening a wider range of plastic depolymerases with the advances in biotechnology tools such as droplet microfluidics and biosensors are highlighted.

High-Throughput Screening Strategy and Metal-Organic Framework-Based Multifunctional Controlled-Release Nanomaterial for Osteoarthritis Therapy.

Osteoarthritis (OA) is a prevalent degenerative disease that lacks effective therapy. Oxidative stress is one of the major factors contributing to OA; however, treatments targeting oxidative stress are still lacking. In the current study, we established an oxidative stress-induced cell death model in chondrocytes in vitro and screened drugs that may suppress oxidative stress-induced cell death. Ethyl gallate (EG) was identified as the most potent drug against oxidative stress-induced cell death out of more than 600 drugs in the natural product library. Application of drugs without an appropriate delivery system for OA therapy may have drawbacks such as low bioavailability, short action time, and poor efficacy. Herein, poly-His6-zinc assembly (PZA), a pH-responsive metal-organic framework (MOF) loaded with EG (EG@PZA) was designed for OA therapy. It was demonstrated that EG@PZA may have the lysosome escape property, which dramatically increases the utilization of EG. Furthermore, EG@PZA showed enhanced release capability of EG in the acidic microenvironment. In vitro and in vivo studies demonstrated that EG@PZA effectively suppresses oxidative stress-induced extracellular matrix degradation, ferroptosis, and senescence in chondrocytes and also ameliorates OA in the destabilization of the medial meniscus (DMM) mouse model in vivo. Together, the current study showed that EG@PZA may become a potential controlled-release nanomaterial for effective OA therapy.

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature of UC symptoms, current pharmacological treatments fail to meet patients' expectations. This study presents a machine learning-assisted high-throughput screening strategy to expedite the discovery of efficient nanozymes for UC treatment. Therapeutic requirements, including antioxidant property, acid stability, and zeta potential, are quantified and predicted by using a machine learning model. Non-quantifiable attributes, including intestinal barrier repair efficacy and biosafety, are assessed via high-throughput screening. Feature significance analysis, sure independence screening, and sparsifying operator symbolic regression reveal the high-dimensional structure-activity relationships between material features and therapeutic needs. SrDy2O4 with high stability, low toxicity, targeting ability, and reactive oxygen species (ROS) scavenging capability is identified, which reduces ROS production, lowers cytochrome C levels in cytoplasm, and inhibits apoptosis in intestinal epithelial cells by stabilizing the mitochondrial membrane potential. Mice treated with SrDy2O4 show improvements in colon length and body weight compared with dextran sodium sulfate salt-treated model group. Transcriptomic and 16S rRNA sequencing analyses show that SrDy2O4 boosts beneficial gut bacteria, and decreases pathogenic bacteria, thereby effectively restoring gut microbiota balance. Moreover, SrDy2O4 offers the advantage of X-ray imaging without side effects.

Single-molecule studies reveal the off-pathway early paused state intermediates as a target of streptolydigin inhibition of RNA polymerase and its dramatic enhancement by Gre factors.

Antibiotic streptolydigin (Stl) inhibits bacterial transcription by blocking the trigger loop folding in the active center of RNA polymerase (RNAP), which is essential for catalysis. We use acoustic force spectroscopy to characterize the dynamics of transcription elongation in ternary elongation complexes (ECs) of RNAP in the presence of Stl at a single-molecule level. We found that Stl induces long-lived stochastic pauses while the instantaneous velocity of transcription between the pauses is unaffected. Stl enhances the short-lived pauses associated with an off-pathway early paused state intermediates of the RNAP nucleotide addition cycle. Unexpectedly, we found that transcript cleavage factors GreA and GreB, which were thought to be Stl competitors, do not alleviate the Stl-induced pausing; instead, they synergistically increase transcription inhibition by Stl. This is the first known instance of a transcriptional factor enhancing antibiotic activity. We propose a structural model of the EC-Gre-Stl complex that explains the observed Stl activities and provides insight into possible cooperative action of secondary channel factors and other antibiotics binding at the Stl pocket. These results offer a new strategy for high-throughput screening for prospective antibacterial agents.

Identification of chemical inhibitors targeting long noncoding RNA through gene signature-based high throughput screening.

Scalable methods for functionally high-throughput screening of RNA-targeting small molecules are currently limited. Here, an RNA knockdown gene signature and high-throughput sequencing-based high-throughput screening (HTS2) were integrated to identify RNA-targeting compounds. We first generated a gene signature characterizing the knockdown of the long non-coding RNA LINC00973. Then, screening of 8199 compounds by HTS2 assay identified that treatments of Hesperadin and GSK1070916 significantly mimic the expression pattern of the LINC00973 knockdown gene signature. Functionally, cell phenotype changes after treatments of these two compounds also mimic the losing function of LINC00973 in multiple types of cancer cells. Mechanistically, the inhibitory action of these two compounds on LINC00973 primarily operates via the AURKB-mediated MAPK signaling pathway, resulting in reduced expression of the transcription factor c-Jun. Consequently, this leads to the suppression of LINC00973 transcription. Moreover, these two compounds significantly inhibit xenograft tumor growth in vivo. Clinically, we further found that breast tumors with high expression of LINC00973 also show relatively high expression of AURKB or JUN, and vice versa. In summary, we established a novel high-throughput screening strategy to identify small molecules capable of targeting RNA, provided two promising compounds targeting LINC00973 and further shed light on the underlying transcriptional upregulation mechanism of LINC00973 within cancer cells.

(Invited) the Use of Metal-Organic Frameworks in Adsorption Cooling/Heating: From the Perspective of Computational Screening

With the severe climate change, there is a growing energy demand for cooling and heating, which takes up 20% of the global energy consumption. Adsorption cooling/heating driven by low-grade heat sources including waste heat and solar energy has been a promising alternative owing to its reduced electricity consumption, low carbon emission and easy operation. However, the coefficient of performance for cooling/heating (COPC/ COPH) and specific cooling/heating effects(SCE/SHE) is still unsatisfactory due to the low uptake and high regeneration cost of conventional adsorbents such as activated carbon and zeolites. Metal-organic frameworks (MOFs) have been recognized as the potential adsorbents for adsorption cooling/heating due to their outstanding adsorption capacity resulted from ultrahigh surface area. Nevertheless, given the huge number of MOFs, it is a challenging task to quickly identify the potential candidates. Besides, the relationship between structure properties, adsorption behaviors and the cooling/heating performance is still elusive. In this study, high-throughput computational screening strategy based on integrated grand canonical Monte Carlo (GCMC) simulation and numerical simulation for efficiently evaluating the cooling/heating performance of a large number of MOFs was developed for both alcohol and water working fluids. The extracted structure-performance relationship highlighted the importance of both structure properties of MOFs and their adsorption characteristics. Machine learning algorithm was also carried out to accelerate the cooling/heating performance prediction of MOFs. To be consistent with experimental measurement, the adsorption dynamics was also taken into consideration. Multiscale modeling combining the molecular simulation and the mathematical simulation was proposed to obtain the COP and specific cooling power (SCP) for a vast number of MOF-based working pairs with high efficiency. This study provides the theoretical approaches to quickly identify high-efficient MOF adsorbents for high-performing adsorption cooling/heating.

Harnessing computational and experimental approaches to identify potent hits against Leishmania donovani sterol C-24 methyltransferase from ChemBridge library

Leishmaniasis is a neglected tropical disease and is one of the major causes of mortality in poverty-stricken areas. A limited chemotherapeutics arsenal is available to tackle this deadly infection. Thus, identifying novel potent scaffolds using innovative strategies is the need of the hour. High-throughput screening (HTS) is a critical technique that can accelerate the process of drug discovery by evaluating millions of drug-like molecules using various automation tools and biological assays. In the present study, we have employed the HTS strategy to identify potent hits against Leishmania donovani sterol C-24 methyltransferase (LdSMT) from the in-house ChemBridge library. Firstly, a robust dataset was prepared with previously reported sterol C-24 methyltransferase inhibitors, belonging to diverse structural classes. Then, ligand-based virtual screening using similarity search was performed to screen the ChemBridge library having ∼20,000 molecules. This computational approach yielded 81 candidate compounds, which were selected for further molecular docking and biological evaluation. Anti-leishmanial assays revealed that out of 81 molecules, seven showed potential parasitic killing. Three molecules namely IIIM-CB-14, IIIM-CB-29, and IIIM-CB-45 were the most potent ones with 50 % inhibitory concentration (IC50) of 5.76, 8.08, and 10.64 µg/mL, respectively. SEM analyses suggest that these potent hits cause considerable morphological alterations. ADME studies of the potent hit molecules indicate that all the hits have considerable drug-likeness properties. Further, molecular dynamics studies were also performed to check the stable confirmation of LdSMT protein with the top two hits (IIIM-CB-14 and IIIM-CB-45). Thus, the present study harnesses computational and experimental approaches to unravel potent anti-leishmanial scaffolds.

Comprehensive Understanding of Laboratory Evolution for Food Enzymes: From Design to Screening Innovations.

In the field of food processing, enzymes play a pivotal role in improving product quality and flavor, and extending shelf life. However, the exposure of traditional food enzymes to high temperatures during processing often leads to a decrease in activity or even inactivation, limiting the effectiveness of their application under high-temperature conditions. Therefore, the modification of thermostability and activity of enzymes to adapt to extreme conditions through protein engineering has become a key way to improve the efficiency and economic benefits of industrial production. Directed evolution and semirational design strategies in the laboratory have proven to be broadly applicable frameworks for biochemical researchers in the food field, including those who are beginners. In this review, we systematically summarize semirational design strategies and high-throughput screening strategies, and introduce some intuitive computer simulation software to improve the thermostability and enzyme activity of food enzymes. The application of these strategies and techniques provides a comprehensive guide for the optimization of food enzymes. In addition, the latest hot topics of genetically engineered food enzymes in the field of application are discussed.

Designing a whole-cell biosensor applicable for S-adenosyl-l-methionine-dependent methyltransferases

This study was undertaken to develop a high-throughput screening strategy using a whole-cell biosensor to enhance methyl-group transfer, a rate-limiting step influenced by intracellular methyl donor availability and methyltransferase efficiency. An l-homocysteine biosensor was designed based on regulatory protein MetR from Escherichia coli, which rapidly reported intracellular l-homocysteine accumulation resulted from S-adenosyl-l-homocysteine (SAH) formation after methyl-group transfer. Using S-adenosyl-l-methionine (SAM) as a methyl donor, this biosensor was applied to caffeic acid 3-O-methyltransferase derived from Arabidopsis thaliana (AtComT). After several rounds of directed evolution, the modified enzyme achieved a 13.8-fold improvement when converting caffeic acid to ferulic acid. The best mutant exhibited a 5.4-fold improvement in catalytic efficiency. Characterization of beneficial mutants showed that improved O-methyltransferase dimerization greatly contributed to enzyme activity. This finding was verified when we switched and compared the N-termini involved in dimerization across different sources. Finally, with tyrosine as a substrate, the evolved AtComT mutant greatly improved ferulic acid biosynthesis, yielding 3448 mg L−1 with a conversion rate of 88.8%. These results have important implications for high-efficiency O-methyltransferase design, which will greatly benefit the biosynthesis of a wide range of natural products. In addition, the l-homocysteine biosensor has the potential for widespread applications in evaluating the efficiency of SAM-based methyl transfer.

Computational Insight into Transition Metal Atoms Anchored on B2C3P as Single‐Atom Electrocatalysts for Nitrogen Reduction Reaction

AbstractNH3 is not only an important chemical raw material but also a high‐energy storage chemical with zero carbon. Electrocatalytic nitrogen reduction reaction (NRR), which can be driven by clean electric energy under ambient conditions, has become a promising technology for NH3 synthesis due to its environmentally friendly properties. Because of the limitations of low yield and high overpotential, efficient catalysts are urgently needed to solve this problem. In this study, based on density functional theory method and high throughput screening strategy, the NRR was investigated on transition metal single atom anchored to 2D B2C3P surface (TM@B2C3P) as single‐atom catalysts (SACs). The results showed that V@B2C3P and Ti@B2C3P have good catalytic properties, and the limiting potentials were −0.10 and −0.24 V, respectively. Furthermore, the charge density difference and crystal orbital Hamilton population calculations demonstrated that the high catalytic activity can be attributed to the obvious charge transfer between TM@B2C3P and the adsorption intermediates. It is hoped that this work can play a certain role in exploring the application of SACs in NRR.

High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor

Abstract2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit. However, these materials exhibit comparatively lower charge carrier mobility and higher contact resistance than 3D semiconductors, making it challenging to realize high-performance devices at scale. In this work, we search for high-mobility 2D materials by combining a high-throughput screening strategy with state-of-the-art calculations based on the ab initio Boltzmann transport equation. Our analysis singles out a known transition metal dichalcogenide, monolayer WS2, as the most promising 2D semiconductor, with the potential to reach ultra-high room-temperature hole mobilities in excess of 1300 cm2/Vs should Ohmic contacts and low defect densities be achieved. Our work also highlights the importance of performing full-blown ab initio transport calculations to achieve predictive accuracy, including spin–orbital couplings, quasiparticle corrections, dipole and quadrupole long-range electron–phonon interactions, as well as scattering by point defects and extended defects.

Novel Drug-like HsrA Inhibitors Exhibit Potent Narrow-Spectrum Antimicrobial Activities against Helicobacter pylori.

Helicobacter pylori infection constitutes a silent pandemic of global concern. In the last decades, the alarming increase in multidrug resistance evolved by this pathogen has led to a marked drop in the eradication rates of traditional therapies worldwide. By using a high-throughput screening strategy, in combination with in vitro DNA binding assays and antibacterial activity testing, we identified a battery of novel drug-like HsrA inhibitors with MIC values ranging from 0.031 to 4 mg/L against several antibiotic-resistant strains of H. pylori, and minor effects against both Gram-negative and Gram-positive species of human microbiota. The most potent anti-H. pylori candidate demonstrated a high therapeutic index, an additive effect in combination with metronidazole and clarithromycin as well as a strong antimicrobial action against Campylobacter jejuni, another clinically relevant pathogen of phylum Campylobacterota. Transcriptomic analysis suggests that the in vivo inhibition of HsrA triggers lethal global disturbances in H. pylori physiology including the arrest of protein biosynthesis, malfunction of respiratory chain, detriment in ATP generation, and oxidative stress. The novel drug-like HsrA inhibitors described here constitute valuable candidates to a new family of narrow-spectrum antibiotics that allow overcoming the current resistome, protecting from dysbiosis, and increasing therapeutic options for novel personalized treatments against H. pylori.

High-throughput screening identifies ibuprofen as an sEV PD-L1 inhibitor for synergistic cancer immunotherapy

Programmed death-ligand 1 (PD-L1) on tumor-derived small extracellular vesicles (sEVs) limits therapeutic effectiveness by interacting with the PD-1 receptor on host immune cells. Targeting the secretion of sEV PD-L1 has emerged as a promising strategy to enhance immunotherapy. However, the lack of small-molecule inhibitors poses a challenge for clinical translation. In this study, we developed a target and phenotype dual-driven high-throughput screening (TAP-HTS) strategy that combined virtual screening with nanoflow-based experimental verification. We identified ibuprofen (IBP) as a novel inhibitor that effectively targeted sEV PD-L1 secretion. IBP disrupted the biogenesis and secretion of PD-L1+ sEVs in tumor cells by physically interacting with a critical regulator of sEV biogenesis, hepatocyte growth factor-regulated tyrosine kinase substrate (HRS). Notably, the mechanism of action of IBP is distinct from its commonly known targets, cyclooxygenases (COX1 or COX2). Administration of IBP stimulated antitumor immunity and enhanced the efficacy of anti-PD-1 therapy in melanoma and oral squamous cell carcinoma (OSCC) mouse models. To address potential adverse effects, we further developed an IBP gel for topical application, which demonstrated remarkable therapeutic efficacy when combined with anti-PD-1 treatment. The discovery of this specific small inhibitor provides a promising avenue for establishing durable, systemic antitumor immunity.

High-Throughput screening of TM1TM2/N6–1 (TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Mo) as effective electrocatalysts for nitrogen fixation

The electrochemical nitrogen reduction reaction (NRR) is an emerging nitrogen fixation method in recent years. Double-atom catalysts compensate for the disadvantage of single-atom catalysts having only one active site, improving catalytic reaction activity. Herein, using a four-step high-throughput screening strategy with density functional theory (DFT) calculations, 45 homonuclear and heteronuclear catalysts, namely, transition metal dimers anchored on nitrogen-doped graphene (TM1TM2/N6–1, TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Mo), were systematically studied to evaluate their catalytic performance for NRR. Our results showed that 16 catalysts stand out, among them, ScMo/N6–1 has an ultralow limiting potential of -0.09 V and a Faradaic efficiency of 100 %. This work provides a group of promising candidates and a reasonable high-throughput design strategy for NRR catalysts.

High-throughput characterization of the influence of Streptococcus sanguinis genes on the interaction between Streptococcus sanguinis and Porphyromonas gingivalis.

Porphyromonas gingivalis is a keystone pathogen in periodontitis, and Streptococcus sanguinis is an abundant oral commensal bacterium associated with periodontal health. However, the interaction between P. gingivalis and S. sanguinis remains obscure. Here, we established a strategy for high-throughput measurement of the cell number of P. gingivalis in the coculture with S. sanguinis by detecting the concentration of hydrogen sulfate. The interaction between P. gingivalis and over 2000 S. sanguinis single-gene mutants was characterized using this strategy, and several interaction-associated genes in S. sanguinis were determined by detecting more P. gingivalis cells in the coculture with matched S. sanguinis mutants. Three S. sanguinis interaction-associated genes were predicted to be responsible for cysteine metabolism, and the supplementation of exogenous L-cysteine promoted the cell number of P. gingivalis in the coculture with S. sanguinis. Thus, exogenous L-cysteine and the compromised cysteine metabolism in S. sanguinis enhanced the growth of P. gingivalis in the existence of S. sanguinis. Additionally, the interaction between P. gingivalis and other Streptococcus spp. was examined, and S. pneumoniae was the only streptococci that had no inhibition on the cell number of P. gingivalis. In total, this study established a new strategy for high-throughput screening of the interaction between Streptococcus and P. gingivalis and discovered a set of genes in S. sanguinis that impacted the interaction. The influence of exogenous L-cysteine on the interaction between P. gingivalis and S. sanguinis in the oral cavity needs further investigation.

Activation preference: A new descriptor to predict non-radical oxidation pathways

The peroxymonosulfate (PMS) activation was influenced strongly by active sites on a catalyst surface. To some extent, biochar characters, such as structure, elemental composition, and specific surface area (SSA), can reflect the composition of active sites. However, the understanding of active centers lacked accuracy in the structure–activity relationships of biochar-based catalysts (BBC) due to the consideration of one single feature. In this study, XGBoost (XGB), random forest regression (RF), and supporting vector regression (SVR) models were employed to construct descriptors to predict non-radical (NR) contribution in BBC/PMS systems. Consequently, the XGB model provided the best prediction with an R2 value of 0.81. Thereby, activation preference (γ), as a characteristic descriptor of BBC, was developed using the XGB model. Interestingly, the descriptor fully considered carbon content (C%), oxygen content (O%), defects (ID/IG), and SSA. Besides, O% played the most critical role in predicting NR contribution. Noticeably, the NR oxidation pathway could be promoted within the range of 50–60 % for C%, 30–50 % for O%, 0.4–1.2 for ID/IG, and 579–1259 m2/g for SSA in the BBC/PMS system. Herein, machine learning (ML) was applied to develop a comprehensive descriptor, further providing an innovative strategy for high-throughput screening of BBCs to activate PMS efficiently. The comprehensive descriptor offered a new perspective for elucidating the structure–activity relationship of BBC, facilitating the application of BBC/PMS systems in water treatment.

A High-Throughput Screening Strategy for Synthesizing Molecularly Imprinted Polymer Nanoparticles Selectively Targeting Tumors.

Molecularly imprinted polymers (MIPs) show significant promise as effective alternatives to antibodies in disease diagnosis and therapy. However, the challenging process of screening extensive libraries of monomer combinations and synthesis conditions to identify formulations with enhanced selectivity and affinity presents a notable time constraint. The need for expedient methods becomes clear in accelerating the strategic development of MIPs tailored for precise molecular recognition purposes. In this study, an innovative high-throughput screening methodology designed to identify the optimal MIP formulation for targeting tumors is presented. Employing a microtiter plate format, over 100 polymer syntheses are conducted, incorporating diverse combinations of functional monomers. Evaluation of binding performance utilizes fluorescence-based assays, focusing on an epitope of the epidermal growth factor receptor (EGFR). Through this meticulously structured screening process, synthesis conditions that produced MIP nanoparticles exhibiting substantial specificity for EGFR targeting (KD = 10-12m) are identified. These "bionic antibodies" demonstrate selective recognition of cancer cells in whole blood samples, even at concentrations as low as 5cellsmL-1. Further validation through fluorescent imaging confirms the tumor-specific localization of the MIPs in vivo. This highly efficient screening approach facilitates the strategic synthesis of imprinted polymers functioning as precision bioprobes.

Inhibitors of the Structural and Nonstructural Proteins of Alphaviruses.

The Alphavirus genus includes viruses that cause encephalitis due to neuroinvasion and viruses that cause arthritis due to acute and chronic inflammation. There is no approved therapeutic for alphavirus infections, but significant efforts are ongoing, more so in recent years, to develop vaccines and therapeutics for alphavirus infections. This review article highlights some of the major advances made so far to identify small molecules that can selectively target the structural and the nonstructural proteins in alphaviruses with the expectation that persistent investigation of an increasingly expanding chemical space through a variety of structure-based design and high-throughput screening strategies will yield candidate drugs for clinical studies. While most of the works discussed are still in the early discovery to lead optimization stages, promising avenues remain for drug development against this family of viruses.

A high-throughput assay identifies molecules with antimicrobial activity against persister cells.

Introduction. Persister cells are transiently non-growing antibiotic-tolerant bacteria that cause infection relapse, and there is no effective antibiotic therapy to tackle these infections.Gap statement. High-throughput assays in drug discovery are biased towards detecting drugs that inhibit bacterial growth rather than killing non-growing bacteria. A new and simple assay to discover such drugs is needed.Aim. This study aims to develop a simple and high-throughput assay to identify compounds with antimicrobial activity against persister cells and use it to identify molecular motifs with such activity.Methodology. We quantified Staphylococcus aureus persister cells by enumeration of colony forming units after 24 h ciprofloxacin treatment. We first quantified how the cell concentration, antibiotic concentration, growth phase and presence/absence of nutrients during antibiotic exposure affected the fraction of persister cells in a population. After optimizing these parameters, we screened the antimicrobial activity of compound fragments to identify molecular structures that have activity against persister cells.Results. Exponential- and stationary-phase cultures transferred to nutrient-rich media displayed a bi-phasic time-kill curve and contained 0.001-0.07% persister cells. A short rifampicin treatment resulted in 100% persister cells for 7 h, after which cells resumed activity and became susceptible. Stationary-phase cultures displayed a low but constant death rate but ultimately resulted in similarly low survival rates as the exponential-phase cultures after 24 h ciprofloxacin treatment. The persister phenotype was only maintained in most of the population for 24 h if cells were transferred to a carbon-free minimal medium before exposure to ciprofloxacin. Keeping cells starved enabled the generation of high concentrations of S. aureus cells that tolerate 50× MIC ciprofloxacin, and we used this protocol for rapid screening for biocidal antibiotics. We identified seven compounds from four structural clusters with activity against antibiotic-tolerant S. aureus. Two compounds were moderately cytotoxic, and the rest were highly cytotoxic.Conclusion. Transferring a stationary-phase culture to a carbon-free minimal medium for antimicrobial testing is a simple strategy for high-throughput screening for new antibiotics that kill persister cells. We identified molecule fragments with such activity, but further screening is needed to identify motifs with lower general cytotoxicity.

Thermal shift assay (TSA)-based drug screening strategy for rapid discovery of inhibitors against the Nsp13 helicase of SARS-CoV-2

The pandemic of coronavirus disease 2019 (COVID-19) is no longer a global health emergency, but variants of the causative agent (severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2) are still circulating today. However, only three partially effective antiviral drugs have been approved or authorized by the United States Food and Drug Administration (FDA) to treat COVID-19 (i.e., remdesivir, Paxlovid and molnupiravir). This study focuses on the discovery of small molecular inhibitors targeting Nsp13, which is an RNA helicase essential to SARS-CoV-2 replication and transcription. A cost-effective thermal shift assay (TSA) was employed in primary high-throughput screening (HTS) of 1970 compounds (10 μM), by which 26 (1.32%) hits showed high binding affinity to the recombinant Nsp13 (ΔTm > 3 °C). Among them, seven and four compounds displayed low micromolar inhibitory kinetics on the ATPase (ATP hydrolysis) and helicase (RNA unwinding) activities of Nsp13, respectively. The seven ATPase inhibitors include benidipine (IC50 = 1.07 μM), butylparaben (IC50 = 2.87 μM), diclofenac (IC50 = 6.55 μM), dyclonine HCl (IC50 = 6.54 μM), methyl salicylate (IC50 = 4.53 μM), olsalazine (IC50 = 4.91 μM) and verteporfin (IC50 = 5.01 μM). The four helicase inhibitors include curcumin (IC50 = 12.23 μM), efaproxiral (IC50 = 12.47 μM), fenretinide (IC50 = 13.63 μM) and wedelolactone (IC50 = 14.91 μM). These biochemically validated hits are worth being pursued further for antiviral efficacies and for potential development of leads. The cost-effectively TSA-based HTS strategy can be employed to screen larger compound libraries and adapted to the drug discovery for other protein targets.