Boosting the Electrical Performance of PLA-Based Triboelectric Nanogenerators for Sustainable Power Sources and Self-Powered Sensing.

Triboelectric nanogenerators (TENGs) have emerged as a promising technology for harvesting mechanical energy from the ambient environment. However, developing tribopositive materials with strong piezoelectric effects and high electron-donating ability still remains a challenge. Herein, poly(ethylene glycol) monomethyl ether (mPEG) to soft poly(lactic acid) (PLA) is adopted, then PLA/mPEG nanofibers are fabricated under electrospinning and used as the tribopositive material for fabricating robust power density TENGs. The crystallinity and dynamic mechanical properties of PLA/mPEG nanofibers are investigated. The results revealed that the incorporation of mPEG provided an effective approach to elevate the electron-donating ability and charge transfer efficiency in PLA. The PLA/mPEG-based TENGs achieved a high open-circuit voltage of 342.8 V, a short-circuit current of 38.5 µA, and a maximum power density of 116.21 W m-2 over a 2 cm2 contact area at an external load of 106 Ω, respectively. Strikingly, excellent stability and durability are demonstrated after continuous cycles up to 104 cycles. Noteworthy, the TENGs are explored for self-powered sensing applications, with seven TENG units integrated to act as self-powered sensors playing music through buzzers when pressed by fingers. Eventually, this work provides new insights into tuning the structures and properties of electrospun polymers to reinforce the TENG output and self-powered systems.

Glycosylation of Secondary Metabolites: A Multifunctional UDP-Glycosyltransferase, CsUGT74Y1, Promotes the Growth of Plants.

Camellia sinensis contains numerous glycosylated secondary metabolites that provide various benefits to plants and humans. However, the genes that catalyze the glycosylation of multitype metabolites in tea plants remain unclear. Here, 180 uridine diphosphate-dependent glycosyltransferases that may be involved in the biosynthesis of glycosylated secondary metabolites were identified from the National Center for Biotechnology Information public databases. Subsequently, CsUGT74Y1 was screened through phylogenetic analysis and gene expression profiling. Compositional and induced expression analyses revealed that CsUGT74Y1 was highly expressed in tea tender shoots and was induced under biotic and abiotic stress conditions. In vitro enzymatic assays revealed that rCsUGT74Y1 encoded a multifunctional UGT that catalyzed the glycosylation of flavonoids, phenolic acids, lignins, and auxins. Furthermore, CsUGT74Y1-overexpressing Arabidopsis thaliana exhibited enhanced growth and accumulation of flavonol and auxin glucosides. Our findings provide insights into identifying specific UGTs and demonstrate that CsUGT74Y1 is a multifunctional UGT that promotes plant development.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Structure-Based Design of 4-Hydroxyphenylpyruvate Dioxygenase Inhibitor as a Potential Herbicide for Cotton Fields.

4-Hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) is one of the most promising herbicide targets for the development of agricultural chemicals owing to its unique mechanism of action in plants. We previously reported on the co-crystal structure of Arabidopsis thaliana (At) HPPD complexed with methylbenquitrione (MBQ), an inhibitor of HPPD that we previously discovered. Based on this crystal structure, and in an attempt to discover even more effective HPPD-inhibiting herbicides, we designed a family of triketone-quinazoline-2,4-dione derivatives featuring a phenylalkyl group through increasing the interaction between the substituent at the R1 position and the amino acid residues at the active site entrance of AtHPPD. Among the derivatives, 6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethyl-3-(1-phenylethyl)quinazoline-2,4(1H,3H)-dione (23) was identified as a promising compound. The co-crystal structure of compound 23 with AtHPPD revealed that hydrophobic interactions with Phe392 and Met335, and effective blocking of the conformational deflection of Gln293, as compared with that of the lead compound MBQ, afforded a molecular basis for structural modification. 3-(1-(3-Fluorophenyl)ethyl)-6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethylquinazoline-2,4(1H,3H)-dione (31) was confirmed to be the best subnanomolar-range AtHPPD inhibitor (IC50 = 39 nM), making it approximately seven times more potent than MBQ. In addition, the greenhouse experiment showed favorable herbicidal potency for compound 23 with a broad spectrum and acceptable crop selectivity against cotton at the dosage of 30-120 g ai/ha. Thus, compound 23 possessed a promising prospect as a novel HPPD-inhibiting herbicide candidate for cotton fields.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

miR-155-1 as a positive factor for novel duck reovirus replication by regulating SOCS5-mediated interferons.

Diseases caused by novel duck reovirus (NDRV) have brought considerable economic losses to the poultry industry. MicroRNAs (miRNAs) have an impact on virus replication and antiviral immunity. However, the miRNA profile upon NDRV infection in duck embryo fibroblasts (DEFs) remains to be discovered. In this study, small RNA (sRNA) sequencing was performed to decipher the cellular miRNA response to NDRV infection. Based on 26 differentially expressed miRNAs (19 upregulated and 7 downregulated miRNAs) obtained from sequencing data and their target genes predicted by software, GO and KEGG analyses were performed to elucidate the functions of miRNAs in NDRV invasion, replication, and virus spread. "FoxO signaling pathway", "autophagy", and "Toll-like receptor signaling pathway" might participate in NDRV replication as revealed by KEGG enrichment analysis. The miR-155-1 sequence was found to be identical to rno-miR-155-5p and was sharply increased with the progression of NDRV infection. Moreover, NDRV-induced miR-155-1 could act as a positive factor for virus replication in DEFs, which inhibited type I interferon (IFN-I) production. Luciferase assay confirmed that miR-155-1 disturbed the abundance of suppressor of cytokine signaling (SOCS) 5 by targeting 3'-UTR. SOCS5, which is linked to increased IRF7 expression, restricts IFN expression and promotes NDRV replication in DEFs. Therefore, this study proposed that miR-155-1 was used by NDRV to restrict SOCS5 expression, attenuating the production of IFN-I and creating a favorable environment for virus replication.

Determination of Characteristic Volatile Component Fingerprint of Peucedanum Praeruptorum Dunn at Different Harvest Periods Based on HS-GC-IMS.

BACKGROUND: Peucedanum praeruptorum Dunn (PPD) is a Chinese herbal medicine with medicinal value. Clinical studies have shown that PPD has protective effects against wind-heat, cough, asthma, cardiovascular diseases, and cancer. Therefore, cultivation of PPD is becoming more common. However, it has been difficult to determine the optimal harvest period for botanical Chinese medicines such as PPD. OBJECTIVES: Odor characteristics are directly related to the chemical components contained in traditional Chinese medicines. In particular, for traditional Chinese medicines such as PPD that are rich in volatile components, higher quality usually means more beneficial volatile components. The purpose of this study was to analyze changes in PPD volatile components across different harvest periods, and provide the basis for the identification of the ideal harvest period to ensure PPD quality. METHODS: We measured the volatile components of PPD at different harvest periods using HS-GC-IMS to characterize its volatile component fingerprint at different harvest periods. RESULTS: We identified 80 volatile components in PPD across five harvest periods, and combined complex heatmap and PCA methods distinguish the characteristics of the different harvest periods, and used ion mobility spectrometry to determine the volatile organic compounds (VOCs), which mainly included compounds such as olefins, esters, alcohols, aldehydes, and ketones, and determined that the abundance of volatile components reached a peak in December. CONCLUSIONS: The fingerprint determination of characteristic volatile components based on HS-GC-IMS can distinguish PPD in different harvest periods. HIGHLIGHTS: We used HS-GC-IMS to determine the characteristic fingerprint of volatile components from PPD across different harvest periods. This approach differs from past studies, which have determined the optimal harvest time of medicinal materials based on only the content of a single active ingredient.

Unveiling toxicity profile for food risk components: A manually curated toxicological databank of food-relevant chemicals.

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry.

Distribution- and Metabolism-Based Drug Discovery: A Potassium-Competitive Acid Blocker as a Proof of Concept.

Conventional methods of drug design require compromise in the form of side effects to achieve sufficient efficacy because targeting drugs to specific organs remains challenging. Thus, new strategies to design organ-specific drugs that induce little toxicity are needed. Based on characteristic tissue niche-mediated drug distribution (TNMDD) and patterns of drug metabolism into specific intermediates, we propose a strategy of distribution- and metabolism-based drug design (DMBDD); through a physicochemical property-driven distribution optimization cooperated with a well-designed metabolism pathway, SH-337, a candidate potassium-competitive acid blocker (P-CAB), was designed. SH-337 showed specific distribution in the stomach in the long term and was rapidly cleared from the systemic compartment. Therefore, SH-337 exerted a comparable pharmacological effect but a 3.3-fold higher no observed adverse effect level (NOAEL) compared with FDA-approved vonoprazan. This study contributes a proof-of-concept demonstration of DMBDD and provides a new perspective for the development of highly efficient, organ-specific drugs with low toxicity.

Functional analysis of the dihydroflavonol 4-reductase family of Camellia sinensis: exploiting key amino acids to reconstruct reduction activity.

Anthocyanins and proanthocyanidins (PAs) are important types of flavonoids, plant secondary metabolites with a wide range of industrial and pharmaceutical applications. DFR (dihydroflavonol 4-reductase) is a pivotal enzyme that plays an important role in the flavonoid pathway. Here, four CsDFR genes were isolated from Camellia sinensis, and their overexpression was analyzed in vitro and in vivo. Based on transcription and metabolic analyses, CsDFR expression was closely consistent with catechins and PAs accumulation. Moreover, enzyme activity analyses revealed that the two recombinant proteins CsDFRa and CsDFRc exhibited DFR activity, converting dihydroflavonols into leucoanthocyanins in vitro, but CsDFRb1 and CsDFRb3 did not. CsDFRa and CsDFRc overexpression in AtDFR mutants (tt3) revealed that CsDFRs are involved in the biosynthesis of anthocyanins and PAs, as CsDFRa and CsDFRc restored not only the purple petiole phenotype but also the seed coat color. Site-directed mutagenesis revealed that the two amino acid residues S117 and T123 of CsDFRa play a prominent role in controlling DFR reductase activity. Enzymatic assays indicated that CsDFRa and CsDFRc exhibited a higher affinity for DHQ and DHK, respectively, whereas CsDFRb1N120S and CsDFRb1C126T exhibited a higher affinity for DHM. Our findings comprehensively characterize the DFRs from C. sinensis and shed light on their critical role in metabolic engineering.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations.

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

Two UDP-Glycosyltransferases Catalyze the Biosynthesis of Bitter Flavonoid 7-O-Neohesperidoside through Sequential Glycosylation in Tea Plants.

Flavonoid glycosides are typical bitter and astringent tasting compounds that contribute to the taste of tea beverages. However, the genes that contribute to the biosynthesis of bitter compounds (e.g., flavanone 7-O-neohesperidoside) in tea plants have yet to be identified. In this study, we identified 194 UDP-glycosyltransferases (UGTs) from the tea transcriptome database. Among them, two genes, CsUGT75L12 and CsUGT79B28, encoding flavonoid 7-O-glycosyltransferase and 7-O-glucoside(1→2)rhamnosyltransferase, respectively, were identified from Camellia sinensis. In vitro, the purified recombinant enzyme rCsUGT75L12 specifically transports the glucose unit from UDP-glucose to the 7-OH position of the flavonoid to produce the respective 7-O-glucoside. rCsUGT79B28 regiospecifically transfers a rhamnose unit from UDP-rhamnose to the 2″-OH position of flavonoid 7-O-glucosides to produce flavonoid 7-O-di-glycosides. Additionally, the expression profiles of the two CsUGTs were correlated with the accumulation patterns of 7-O-glucoside and 7-O-neohesperidoside, respectively, in tea plants. These results indicated that the two CsUGTs are involved in the biosynthesis of bitter flavonoid 7-O-neohesperidoside through the sequential glucosylation and rhamnosylation of flavonoids in C. sinensis. Taken together, our findings provided not only molecular insights into flavonoid di-glycoside metabolism in tea plants but also crucial molecular markers for controlling the bitterness and astringent taste of tea.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Ultrasound-activable piezoelectric membranes for accelerating wound healing.

Ultrasonic energy harvesting technologies have gained much attention for biomedical applications due to their several desirable features including low-energy attenuation and strong penetration capability. In this work, flexible piezoelectric poly(vinylidenefluoride-co-trifluoroethylene) (P(VDF-TrFE))/barium titanate (BaTiO3, BT) membranes, capable of converting ultrasound energy to electric energy, were fabricated by an electrospinning process and their effects on the wound healing behaviors with/without ultrasonic stimulation were investigated. The piezoelectric membranes showed excellent electric outputs and can be used as a sustainable power source to quickly charge LEDs and capacitors. The penetration capability of ultrasound waves was investigated by implanting the membranes at different depths of porcine tissue. The membrane was able to generate a high voltage of 8.22 V even at a depth of 4.5 cm. Furthermore, ultrasonic stimulation on the piezoelectric membranes facilitated the proliferation and migration of the fibroblasts, and a cell migration rate of 92.6% was obtained after 24 h in the cell migration test. Under ultrasonic vibration, the electric field generated from the membranes accelerated the wound closure rate in an animal wound model. These results demonstrated the effectiveness of the flexible piezoelectric membranes in stimulating cellular behaviors, which may provide a new therapeutic strategy for wound care.

Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery.

Traditional drug discovery effectively contributes to the treatment of many diseases but is limited by high costs and long cycles. Quantitative structure-activity relationship (QSAR) methods were introduced to evaluate the activity of compounds virtually, which saves the significant cost of determining the activities of the compounds experimentally. Over the past two decades, many web tools for QSAR modeling with various features have been developed to facilitate the usage of QSAR methods. These web tools significantly reduce the difficulty of using QSAR and indirectly promote drug discovery. However, there are few comprehensive summaries of these QSAR tools, and researchers may have difficulty determining which tool to use. Hence, we systematically surveyed the mainstream web tools for QSAR modeling. This work may guide researchers in choosing appropriate web tools for developing QSAR models, and may also help develop more bioinformatics tools based on these existing resources. For nonprofessionals, we also hope to make more people aware of QSAR methods and expand their use.

Fragment-based drug design facilitates selective kinase inhibitor discovery.

Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase-fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase-fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase-fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked.

Precisely Engineered Photoreactive Titanium Nanoarray Coating to Mitigate Biofouling in Ultrafiltration.

To combat biofouling on membranes, diverse nanostructures of titanium dioxide (TiO2) have emerged as effective antimicrobial coatings due to TiO2's abilities to transport charge and photoinduce oxidation. However, TiO2 composite polymeric membranes synthesized using traditional methods of growing crystals have proven chemically unstable, with loss of coating and diminishing antimicrobial performance. Thus, new fabrication methods to enhance durability and efficacy should be considered. In this work, we propose a stepwise approach to construct a stable, uniform TiO2 nanoarray of regularly spaced, aligned crystals on the surface of a polytetrafluoroethylene ultrafiltration membrane using precisely controlled atomic layer deposition (ALD) followed by solvothermal deposition. We demonstrate that ALD can uniformly seed TiO2 nanoparticles on the membrane surface with atomic-scale precision. Subsequently, solvothermal deposition assembles and aligns a uniform TiO2 nanoarray forest. In the presence of sunlight, this TiO2 nanoarray effectively inactivates any deposited bacteria, increasing flux recovery after membrane cleaning. By systematically investigating this antimicrobial activity, we found that TiO2 both physically damages cell membranes as well as produces reactive oxygen species in the presence of sunlight that inactivate bacteria. Our study provides an effective bottom-up synthesis scheme to optimize and tailor antifouling TiO2 coatings for ultrafiltration and other surfaces for a wide range of applications.

Bioinformatics toolbox for exploring protein phosphorylation network.

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.