A cardioviral 2C-ATP complex structure reveals the essential role of a conserved arginine in regulation of cardioviral 2C activity.

2C is a highly conserved picornaviral non-structural protein with ATPase activity and plays a multifunctional role in the viral life cycle as a promising target for anti-picornavirus drug development. While the structure-function of enteroviral 2Cs have been well studied, cardioviral 2Cs remain largely uncharacterized. Here, an endogenous ATP molecule was identified in the crystal structure of 2C from encephalomyocarditis virus (EMCV, Cardiovirus A). The ATP is bound into the ATPase active site with a unique compact conformation. Notably, the γ-phosphate of ATP directly interacts with Arg311 (conserved in cardioviral 2Cs), and its mutation significantly inhibits the ATPase activity. Unexpectedly, this mutation remarkably promotes 2C self-oligomerization and viral replication efficiency. Molecular dynamic simulations showed that the Arg311 side chain is highly dynamic, indicating it may function as a switch between the activation state and the inhibition state of ATPase activity. A hexameric ring model of EMCV 2C full length indicated that the C-terminal helix may get close to the N-terminal amphipathic helices to form a continuous positive region for RNA binding. The RNA-binding studies of EMCV 2C revealed that the RNA length is closely associated with the RNA-binding affinities and indicated that the substrate may wrap around the outer surface of the hexamer. Our studies provide a biochemical framework to guide the characterization of EMCV 2C and the essential role of arginine in cardioviral 2C functions. IMPORTANCE: Encephalomyocarditis virus (Cardiovirus A) is the causative agent of the homonymous disease, which may induce myocarditis, encephalitis, and reproductive disorders in various mammals. 2C protein is functionally indispensable and a promising target for drug development involving broad-spectrum picornaviral inhibitors. Here, an endogenous ATP molecule with a unique conformation was discovered by a combination of protein crystallography and high-performance liquid chromatography in the encephalomyocarditis virus (EMCV) 2C structure. Biochemical and structural characterization analysis of EMCV 2C revealed the critical role of conserved Arg311 in ATPase activity and self-oligomerization of EMCV 2C. The viral replication kinetics and infectivity study suggested that the residue negatively regulated the infectivity titer and virus encapsulation efficiency of EMCV and is, therefore, crucial for 2C protein to promote viral replication. Our systemic structure-function analysis provides unique insights into the function and regulation mechanism of cardioviral 2C protein.

Single-cell RNA sequencing illuminates the ontogeny, conservation and diversification of cartilaginous and bony fish lymphocytes.

Elucidating cellular architecture and cell-type evolution across species is central to understanding immune system function and susceptibility to disease. Adaptive immunity is a shared trait of the common ancestor of cartilaginous and bony fishes. However, evolutionary features of lymphocytes in these two jawed vertebrates remain unclear. Here, we present a single-cell RNA sequencing atlas of immune cells from cartilaginous (white-spotted bamboo shark) and bony (zebrafish and Chinese tongue sole) fishes. Cross-species comparisons show that the same cell types across different species exhibit similar transcriptional profiles. In the bamboo shark, we identify a phagocytic B cell population expressing several pattern recognition receptors, as well as a T cell sub-cluster co-expressing both T and B cell markers. In contrast to a division by function in the bony fishes, we show close linkage and poor functional specialization among lymphocytes in the cartilaginous fish. Our cross-species single-cell comparison presents a resource for uncovering the origin and evolution of the gnathostome immune system.

The Mitogenomic Landscape of Hexacorallia Corals: Insight into Their Slow Evolution.

The utility of the mitochondrial genomes (mitogenomes) in analyzing the evolutionary history of animals has been proven. Five deep-sea corals (Bathypathes sp.1, Bathypathes sp.2, Schizopathidae 1, Trissopathes sp., and Leiopathes sp.) were collected in the South China Sea (SCS). Initially, the structures and collinearity of the five deep-sea coral mitogenomes were analyzed. The gene arrangements in the five deep-sea coral mitogenomes were similar to those in the order Antipatharia, which evidenced their conservation throughout evolutionary history. Additionally, to elucidate the slow evolutionary rates in Hexacorallia mitogenomes, we conducted comprehensive analyses, including examining phylogenetic relationships, performing average nucleotide identity (ANI) analysis, and assessing GC-skew dissimilarity combining five deep-sea coral mitogenomes and 522 reference Hexacorallia mitogenomes. Phylogenetic analysis using 13 conserved proteins revealed that species clustered together at the order level, and they exhibited interspersed distributions at the family level. The ANI results revealed that species had significant similarities (identity > 85%) within the same order, while species from different orders showed notable differences (identity < 80%). The investigation of the Hexacorallia mitogenomes also highlighted that the GC-skew dissimilarity was highly significant at the order level, but not as pronounced at the family level. These results might be attributed to the slow evolution rate of Hexacorallia mitogenomes and provide evidence of mitogenomic diversity. Furthermore, divergence time analysis revealed older divergence times assessed via mitogenomes compared with nuclear data, shedding light on significant evolutionary events shaping distinct orders within Hexacorallia corals. Those findings provide new insights into understanding the slow evolutionary rates of deep-sea corals in all lineages of Hexacorallia using their mitogenomes.

Impacts of Matrix Metalloproteinase-2 Promoter Genotypes on Breast Cancer Risk.

BACKGROUND/AIM: Matrix metalloproteinase-2 (MMP-2) has been implicated in the pathogenesis of breast cancer (BC). However, there is limited research on the role of MMP-2 genotypes in BC risk. This study aimed to investigate the associations between two MMP-2 promoter polymorphisms, rs243865 and rs2285053, and BC risk. MATERIALS AND METHODS: MMP-2 genotypes were analyzed using PCR-based RFLP methodology in a cohort comprising 1,232 BC cases and 1,232 controls. RESULTS: Genotypic frequencies of MMP-2 rs243865 and rs2285053 in controls were consistent with Hardy-Weinberg equilibrium (p=0.3702 and 0.2036, respectively). There were no significant differences in the distribution of rs243865 and rs2285053 genotypes between BC cases and controls (p for trend=0.1602 and 0.2170, respectively). Variant genotypes at rs243865 and rs2285053 appeared to confer a protective effect, although not statistically significant (all p>0.05). Similarly, the variant T allele at rs243865 and rs2285053 showed a non-significant trend towards decreased BC risk (OR=0.84 and 0.89, 95%CI=0.69-1.02 and 0.78-1.02, p=0.0811 and 0.1043, respectively). There was no interaction observed between MMP-2 rs243865 or rs2285053 genotypes and age. Stratified analysis did not reveal significant associations between MMP-2 rs243865 or rs2285053 genotypes and triple-negative breast cancer (TNBC) (p=0.6458 and 0.8745, respectively). Among both TNBC and non-TNBC cases, none of the variant genotypes at rs243865 or rs2285053 showed significant associations with TNBC (all p>0.05). CONCLUSION: MMP-2 rs243865 and rs2285053 genotypes appear to have a minimal impact on individual susceptibility to BC or TNBC.

Pathophysiological impacts of 5-MeO-MiPT on zebrafish (Danio rerio) via the Gαq/11-PLCß signaling pathway.

Novel Psychoactive Substances (NPS) derived from tryptamines has been detected in aquatic environments, leading to environmental toxicology concerns. However, the specific toxicological mechanism, underlying these NPS, remains unclear. In our previous work, we used 5-Methoxy-N-isopropyl-N-methyltryptamine (5-MeO-MiPT) as the representative drug for NPS, and found that, 5-MeO-MiPT led to obvious behavioral inhibition and oxidative stress responses in zebrafishes model. In this study, Zebrafish were injected with varying concentrations of 5-MeO-MiPT for 30 days. RNA-seq, qPCR, metabolomics, and histopathological analyses were conducted to assess gene expression and tissue integrity. This study confirms that 5-MeO-MiPT substantially influences the transcription and expression of 13 selected genes, including ucp1, pet100, grik3, and grik4, mediated by the Gαq/11-PLCß signaling pathway. We elucidate the molecular mechanism that 5-MeO-MiPT can inhibit DAG-Ca2+/Pkc/Erk, Pkc/Pla2/PLCs and Ca2+/Camk Ⅱ/NMDA, while enhance Ca2+/Creb. Those secondary signaling pathways may be the mechanisms mediating 5-MeO-MiPT inhibiting normal behavior in zebrafish. These findings offer novel insights into the toxicological effects and addiction mechanisms of 5-MeO-MiPT. Moreover, it presents promising avenues for investigating other tryptamine-based NPS and offers a new direction for diagnosing and treating liver-brain pathway-related diseases.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

When time theft promotes performance: Measure development and validation of time theft motives.

The prevailing viewpoint has long depicted employee time theft as inherently detrimental. However, this perspective may stem from a limited understanding of the underlying motives that drive such behavior. Time theft can paradoxically be motivated by neutral and even laudable intentions, such as promoting work efficiency, thus rendering it potentially beneficial and constructive. Across three mixed-methods studies, we explore the motives behind employee time theft, develop and validate an instrument to assess these motives, and examine how they differentially predict time theft behavior. Specifically, in Study 1, we use a qualitative method and identify 11 types of time theft motives. Study 2 embarks on the development of measures of these motives, subsequently validating their factor structure. Study 3 examines their incremental variance in predicting time theft behavior by controlling for personality and demographic variables. Overall, these studies reveal that employees' engagement in time theft can be driven not solely by self-oriented motives but also by others- and work-oriented motives. Further, each of these motives provides incremental value in understanding time theft behavior. Implications for both research and practice emanating from these findings are also discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

VP0 Myristoylation Is Essential for Senecavirus A Replication.

Many picornaviruses require the myristoylation of capsid proteins for viral replication. Myristoylation is a site-specific lipidation to the N-terminal G residue of viral proteins, which is catalyzed by the ubiquitous eukaryotic enzyme N-myristoyltransferase (NMT) by allocating the myristoyl group to the N-terminal G residue. IMP-1088 and DDD85646 are two inhibitors that can deprive NMT biological functions. Whether Senecavirus A (SVA) uses NMT to modify VP0 and regulate viral replication remains unclear. Here, we found that NMT inhibitors could inhibit SVA replication. NMT1 knock-out in BHK-21 cells significantly suppressed viral replication. In contrast, the overexpression of NMT1 in BHK-21 cells benefited viral replication. These results indicated that VP0 is a potential NMT1 substrate. Moreover, we found that the myristoylation of SVA VP0 was correlated to the subcellular distribution of this protein in the cytoplasm. Further, we evaluated which residues at the N-terminus of VP0 are essential for viral replication. The substitution of N-terminal G residue, the myristoylation site of VP0, produced a nonviable virus. The T residue at the fifth position of the substrates facilitates the binding of the substrates to NMT. And our results showed that the T residue at the fifth position of VP0 played a positive role in SVA replication. Taken together, we demonstrated that SVA VP0 myristoylation plays an essential role in SVA replication.

Naringin Induces ROS-Stimulated G1 Cell-Cycle Arrest and Apoptosis in Nasopharyngeal Carcinoma Cells.

Naringin, a bioflavonoid compound from grapefruit or citrus, exerts anticancer activities on cervical, thyroid, colon, brain, liver, lung, thyroid, and breast cancers. The present investigation addressed exploring the anticancer effects of naringin on nasopharyngeal carcinoma (NPC) cells. Naringin exhibits a cytotoxic effect on NPC-TW 039 and NPC-TW 076 cells with IC50 372/328 and 394/307 µM for 24 or 48 h, respectively, while causing little toxicity toward normal gingival epithelial (SG) cells (>500/500 µM). We established that naringin triggered G1 arrest is achieved by suppressing cyclin D1, cyclin A, and CDK2, and upregulating p21 protein in NPC cells. Exposure of NPC cells to naringin caused a series of events leading to apoptosis including morphology change (cell shrinkage and membrane blebbing) and chromatin condensation. Annexin V and PI staining indicated that naringin treatment promotes necrosis and late apoptosis in NPC cells. DiOC6 staining showed a decline in the mitochondrial membrane potential by naringin treatment, which was followed with cytochrome c release, Apaf-1/caspase-9/-3 activation, PARP cleavage, and EndoG expression in NPC cells. Naringin upregulated proapoptotic Bax and decreased antiapoptotic Bcl-xL expression, and dysregulated Bax/Bcl-xL ratio in NPC cells. Notably, naringin enhanced death receptor-related t-Bid expression. Furthermore, an increased Ca2+ release by naringin treatment which instigated endoplasmic reticulum stress-associated apoptosis through increased IRE1, ATF-6, GRP78, GADD153, and caspase-12 expression in NPC cells. In addition, naringin triggers ROS production, and inhibition of naringin-induced ROS generation by antioxidant N-acetylcysteine resulted in the prevention of G1 arrest and apoptosis in NPC cells. Naringin-induced ROS-mediated G1 arrest and mitochondrial-, death receptor-, and endoplasmic reticulum stress-mediated apoptosis may be a promising strategy for treating NPC.

The changes of intestinal microbiota and metabolomics during the inhibition of bladder cancer by liquiritigenin.

Liquiritigenin is a natural medicine. However, its inhibitory effect and its potential mechanism on bladder cancer (BCa) remain to be explored. It was found that it could be visualized that the transplanted tumours in the low-dose liquiritigenin -treated group and the high-dose liquiritigenin -treated group were smaller than those in the model group. Liquiritigenin treatment led to alterations in Lachnoclostridium, Escherichia-Shigella, Alistipes and Akkermansia. Non-targeted metabolomics analysis showed that a total of multiple differential metabolites were identified between the model group and the high-dose liquiritigenin-treated group. This provides a new direction and rationale for the antitumour effects of liquiritigenin.

Biofilms in plastisphere from freshwater wetlands: Biofilm formation, bacterial community assembly, and biogeochemical cycles.

Microorganisms can colonize to the surface of microplastics (MPs) to form biofilms, termed "plastisphere", which could significantly change their physiochemical properties and ecological roles. However, the biofilm characteristics and the deep mechanisms (interaction, assembly, and biogeochemical cycles) underlying plastisphere in wetlands currently lack a comprehensive perspective. In this study, in situ biofilm formation experiments were performed in a park with different types of wetlands to examine the plastisphere by extrinsic addition of PVC MPs in summer and winter, respectively. Results from the spectroscopic and microscopic analyses revealed that biofilms attached to the MPs in constructed forest wetlands contained the most abundant biomass and extracellular polymeric substances. Meanwhile, data from the high-throughput sequencing showed lower diversity in plastisphere compared with soil bacterial communities. Network analysis suggested a simple and unstable co-occurrence pattern in plastisphere, and the null model indicated increased deterministic process of heterogeneous selection for its community assembly. Based on the quantification of biogeochemical cycling genes by high-throughput qPCR, the relative abundances of genes involving in carbon degradation, carbon fixation, and denitrification were significantly higher in plastisphere than those of soil communities. This study greatly enhanced our understanding of biofilm formation and ecological effects of MPs in freshwater wetlands.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Extraction, characterization and intestinal anti-inflammatory and anti-oxidative activities of polysaccharide from stems and leaves of Chuanminshen violaceum M. L. Sheh & R. H. Shan.

ETHNOPHARMACOLOGICAL RELEVANCE: Chuanminshen violaceum M. L. Sheh & R. H. Shan (CV) is used as a medicine with roots, which have the effects of benefiting the lungs, harmonizing the stomach, resolving phlegm and detoxifying. Polysaccharide is one of its main active components and has various pharmacological activities, but the structural characterization and pharmacological activities of polysaccharide from the stems and leaves parts of CV are still unclear. AIM OF THE STUDY: The aim of this study was to investigate the optimal extraction conditions for ultrasound-assisted extraction of polysaccharide from CV stems and leaves, and to carry out preliminary structural analyses, anti-inflammatory and antioxidant effects of the obtained polysaccharide and to elucidate the underlying mechanisms. MATERIALS AND METHODS: The ultrasonic-assisted extraction of CV stems and leaves polysaccharides was carried out, and the response surface methodology (RSM) was used to optimize the extraction process to obtain CV polysaccharides (CVP) under the optimal conditions. Subsequently, we isolated and purified CVP to obtain the homogeneous polysaccharide CVP-AP-I, and evaluated the composition, molecular weight, and structural features of CVP-AP-I using a variety of technical methods. Finally, we tested the pharmacological activity of CVP-AP-Ⅰ in an LPS-induced model of oxidative stress and inflammation in intestinal porcine epithelial cells (IPEC-J2) and explored its possible mechanism of action. RESULTS: The crude polysaccharide was obtained under optimal extraction conditions and subsequently isolated and purified to obtain CVP-AP-Ⅰ (35.34 kDa), and the structural characterization indicated that CVP-AP-Ⅰ was mainly composed of galactose, galactose, rhamnose and glucose, which was a typical pectic polysaccharide. In addition, CVP-AP-Ⅰ attenuates LPS-induced inflammation and oxidative stress by inhibiting the expression of pro-inflammatory factor genes and proteins and up-regulating the expression of antioxidant enzyme-related genes and proteins in IPEC-J2, by a mechanism related to the activation of the Nrf2/Keap1 signaling pathway. CONCLUSION: The results of this study suggest that the polysaccharide isolated from CV stems and leaves was a pectic polysaccharide with similar pharmacological activities as CV roots, exhibiting strong anti-inflammatory and antioxidant activities, suggesting that CV stems and leaves could possess the same traditional efficacy as CV roots, which is expected to be used in the treatment of intestinal diseases.

Identification of a linear B-cell epitope on the "puff" loop of the Senecavirus A VP2 protein involved in receptor binding.

Senecavirus A (SVA) is an important emerging swine pathogen that causes vesicular lesions in swine and acute death in newborn piglets. VP2 plays a significant role in the production of antibodies, which can be used in development of diagnostic tools and vaccines. Herein, the aim of the current study was to identify B-cell epitopes (BCEs) of SVA for generation of epitope-based SVA marker vaccine. Three monoclonal antibodies (mAbs), named 2E4, 1B8, and 2C7, against the SVA VP2 protein were obtained, and two novel linear BCEs, 177SLGTYYR183 and 266SPYFNGL272, were identified by peptide scanning. The epitope 177SLGTYYR183 was recognized by the mAb 1B8 and was fully exposed on the VP2 surface, and alanine scanning analysis revealed that it contained a high continuity of key amino acids. Importantly, we confirmed that 177SLGTYYR183 locates on "the puff" region within the VP2 EF loop, and contains three key amino acid residues involved in receptor binding. Moreover, a single mutation, Y182A, blocked the interaction of the mutant virus with the mAb 1B8, indicating that this mutation is the pivotal point for antibody recognition. In summary, the BCEs that identified in this study could be used to develop diagnostic tools and an epitope-based SVA marker vaccine.

Ultrasonic-assisted extraction of polysaccharide from Paeoniae Radix alba: Extraction optimization, structural characterization and antioxidant mechanism in vitro.

Paeoniae Radix alba is used in Traditional Chinese Medicine for the treatment of gastrointestinal disorders, immunomodulatory, cancer, and other diseases. In the current study, the yield of Paeoniae Radix alba polysaccharide (PRP) was significantly increased with optimal ultrasound-assisted extraction compared to hot water extraction. Further, an acidic polysaccharide (PRP-AP) was isolated from PRP after chromatographic separation and was characterized as a typical pectic polysaccharide with side chains of arabinogalactans types I and II. Moreover, it showed antioxidant effects on LPS-induced damage on IPEC-J2 cells determined by qRT-PCR and ELISA, including decreasing the pro-inflammatory factors' expressions and increasing the antioxidant enzymes activities, which was shown to be related to the Nrf2/Keap1 pathway modulated by PRP-AP. The metabolites change (such as itaconate, cholesterol sulfate, etc.) detected by untargeted metabolomic analysis in cells was also shown to be modulated by PRP-AP, and these metabolites were further utilized and protected cells damaged by LPS. These results revealed the cellular active mechanism of the macromolecular PRP-AP on protecting cells, and supported the hypothesis that PRP-AP has strong benefits as an alternative dietary supplement for the prevention of intestinal oxidative stress by modulating cellular metabolism.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

The compensation effect of competence frustration and its behavioral manifestations.

The frustration of competence, one of the three basic psychological needs proposed by self-determination theory, has been widely demonstrated to negatively influence one's motivation and well-being in both work and life. However, research on the recovery mechanism of competence is still in the nascent stage. In this study, a two-stage behavioral experiment was conducted to examine the restoration of competence and the potential moderating role of resilience. Results showed that individuals who were asked to recall experience of competence frustration performed better on subsequent tasks, manifesting their behavioral efforts of competence restoration. However, resilience does not play a significant moderating role in competence restoration. Through convergent behavioral evidence, findings of this study demonstrate the compensation effect of competence frustration.