Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive

The Role of Autophagy and Cell Communication in COPD Progression: Insights from Bioinformatics and scRNA-seq

Chronic obstructive pulmonary disease (COPD) is characterized by restricted airflow that leads to significant respiratory difficulties. This progressive disease often results in diminished pulmonary function and the onset of additional respiratory conditions. Autophagy, a critical cellular homeostasis mechanism, plays a significant role in the exacerbation of COPD. In this study, we utilized various bioinformatics tools to identify autophagy-related genes activated by smoking in individuals with COPD. Furthermore, we explored the immune landscape of COPD through these genes, analyzing cell communication patterns using scRNA-seq data. This analysis focused on key pathways between epithelial cells and other cellular subpopulations with different autophagy scores, essential for understanding the initiation and progression of COPD.

Systems biology of Haemonchus contortus - Advancing biotechnology for parasitic nematode control

Parasitic nematodes represent a substantial global burden, impacting animal health, agriculture and economies worldwide. Of these worms, Haemonchus contortus - a blood-feeding nematode of ruminants - is a major pathogen and a model for molecular and applied parasitology research. This review synthesises some key advances in understanding the molecular biology, genetic diversity and host-parasite interactions of H. contortus, highlighting its value for comparative studies with the free-living nematode Caenorhabditis elegans. Key themes include recent developments in genomic, transcriptomic and proteomic technologies and resources, which are illuminating critical molecular pathways, including the ubiquitination pathway, protease/protease inhibitor systems and the secretome of H. contortus. Some of these insights are providing a foundation for identifying essential genes and exploring their potential as targets for novel anthelmintics or vaccines, particularly in the face of widespread anthelmintic resistance. Advanced bioinformatic tools, such as machine learning (ML) algorithms and artificial intelligence (AI)-driven protein structure prediction, are enhancing annotation capabilities, facilitating and accelerating analyses of gene functions, and biological pathways and processes. This review also discusses the integration of these tools with cutting-edge single-cell sequencing and spatial transcriptomics to dissect host-parasite interactions at the cellular level. The discussion emphasises the importance of curated databases, improved culture systems and functional genomics platforms to translate molecular discoveries into practical outcomes, such as novel interventions. New research findings and resources not only advance research on H. contortus and related nematodes but may also pave the way for innovative solutions to the global challenges with anthelmintic resistance.

Akt mitigates ER stress-instigated cardiac dysfunction via regulation of ferroptosis and mitochondrial integrity in a DHODH-dependent manner

ER stress evokes various types of cell death and myocardial dysfunction. This study aimed to discern the involvement of ferroptosis in chronic Akt activation-offered benefit, if any, against ER stress-triggered cardiac remodeling and contractile anomalies. Cardiac-selective expression of active mutant of Akt (AktOE) and wild-type (WT) mice were challenged with the ER stress instigator tunicamycin (1 mg/kg, 48 h) prior to assessment of cardiac morphology and function. Tunicamycin insult prompted cardiac remodeling (interstitial fibrosis), deranged echocardiographic (higher LVESD, dropped ejection fraction and fractional shortening), cardiomyocyte mechanical and intracellular Ca2+ features alongside mitochondrial injury (collapsed mitochondrial membrane potential and ultrastructural change), oxidative stress, compromised Akt-GSK3β signaling, ER stress (upregulated GRP78 and Gadd153), carbonyl formation, apoptosis and ferroptosis (decreased GPX4, SLC7A11). Intriguingly, tunicamycin-evoked anomalies (except GRP78 and Gadd153) were abrogated by Akt activation. Chronic Akt activation negated tunicamycin-induced downregulation of ferric flavin enzyme dihydroorotate dehydrogenase (DHODH), which catalyzes the fourth step of pyrimidine ab initio biosynthesis, and conversion of dihydroorotic acid to orotate. ER stress-induced myocardial anomalies were reversed by the newly identified PI3K activator triptolide, DHODH activator menaquinone-4 and pyrimidine booster coenzyme Q. In vitro experiment revealed that Akt activation- or triptolide-evoked beneficial responses against tunicamycin-induced cardiomyocyte anomalies were cancelled off by DHODH inhibitor BAY2402234 or ferroptosis inducer erastin. These findings support that chronic Akt activation rescues ER stress-evoked myocardial derangements through DHODH-dependent control of ferroptosis and mitochondrial homeostasis.

Studying Noncovalent Interactions in Molecular Systems with Machine Learning

Noncovalent interactions (NCIs) is an umbrella term for a multitude of typically weak interactions within and between molecules. Despite the low individual energy contributions, their collective effect significantly influences molecular behavior. Accordingly, understanding these interactions is crucial across fields like catalysis, drug design, materials science, and environmental chemistry. However, predicting NCIs is challenging, requiring at least molecular mechanics-level pairwise energy contributions or efficient quantum mechanical electron correlation treatment. In this review, we investigate the application of machine learning (ML) to study NCIs in molecular systems, an emerging research field. ML excels at modeling complex nonlinear relationships, and is capable of integrating vast data sets from experimental and theoretical sources. It offers a powerful approach for analyzing interactions across scales, from small molecules to large biomolecular assemblies. Specifically, we examine data sets characterizing NCIs, compare molecular featurization techniques, assess ML models predicting NCIs explicitly, and explore inverse design approaches. ML enhances predictive accuracy, reduces computational costs, and reveals overlooked interaction patterns. By identifying current challenges and future opportunities, we highlight how ML-driven insights could revolutionize this field. Overall, we believe that recent proof-of-concept studies foreshadow exciting developments for the study of NCIs in the years to come.

DiffMC-Gen: A Dual Denoising Diffusion Model for Multi-Conditional Molecular Generation

The precise and efficient design of potential drug molecules with diverse physicochemical properties has long been a critical challenge. In recent years, the emergence of various deep learning-based de novo molecular generation algorithms offered new directions to this issue, among which denoising diffusion models have demonstrated significant potential. However, previous methods often fail to simultaneously optimize multiple properties of candidate compounds, which may stem from directly employing nongeometric graph neural networks (GNNs), rendering them incapable of accurately capturing molecular topologic and geometric information. In this study, a dual denoising diffusion model is developed for multi-conditional molecular generation (DiffMC-Gen), which integrates both discrete and continuous features to enhance its ability to perceive 3D molecular structures. Additionally, it involves a multi-objective optimization strategy to simultaneously optimize multiple properties of the target molecule, including binding affinity, drug-likeness, synthesizability, and toxicity. From the perspectives of both 2D and 3D molecular generation, the molecules generated by DiffMC-Gen exhibit state-of-the-art (SOTA) performance in terms of novelty and uniqueness, meanwhile achieving comparable results to previous methods in drug-likeness and synthesizability. Furthermore, the generated molecules have well-predicted biological activity and druglike properties for three target proteins-LRRK2, HPK1, and GLP-1 receptor, while also maintaining high standards of validity, uniqueness, and novelty. These results underscore its potential for practical applications in drug design.

GICL: A Cross-Modal Drug Property Prediction Framework Based on Knowledge Enhancement of Large Language Models

Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existing approaches often struggle with challenges such as low-quality data and structural complexity. Large language models (LLMs) excel in generating high-quality molecular representations due to their robust characterization capabilities. In this work, we introduce GICL, a cross-modal contrastive learning framework that integrates LLM-derived embeddings with molecular image representations. Specifically, LLMs extract feature representations from the SMILES strings of drug molecules, which are then contrasted with graphical representations of molecular images to achieve a holistic understanding of molecular features. Experimental results demonstrate that GICL achieves state-of-the-art performance on the ADMET task while offering interpretable insights into drug properties, thereby facilitating more efficient drug design and discovery.

Investigating the therapeutic potential of Ganoderma lucidum in treating optic nerve atrophy through network pharmacology and experimental validation

OBJECTIVE

The aim of this study is to employ network pharmacology to identify potential therapeutic targets for Ganoderma lucidum in the treatment of optic atrophy, and elucidate the underlying pharmacological mechanism.

METHODS

This study is mainly divided into two parts. In the first part, the chemical composition and Target of Ganoderma lucidum compound were predicted by TCMSP and Swiss Target Prediction, and the crossover gene between OA and Ganoderma lucidum target gene was screened based on GeneCards and OMIM database. Then, the target genes were enriched and the main pathways of action were analyzed to discover the possible mechanism of action for the treatment of optic atrophy. Finally, the selected core compounds and core targets were interfaced to understand the main binding patterns and affinity. The second part mainly verifies whether Ganoderma lucidum polysaccharide has protective effect on RGC. Firstly, CCK8 method was used to detect the proliferation and virulence analysis of RGC-5 cells with different concentrations of Ganoderma lucidum polysaccharide, and then RGC-5 cells were cultured in subgroups for 12 h, and then put into anaerobic encapsulation to make molds. After 24 h of continuous culture, cells were removed and collected for subsequent RT-PCR and WB detection.

RESULTS

Through screening target genes of Ganoderma lucidum and OA, 85 potential therapeutic targets were obtained by intersection. Through PPI network analysis of 85 potential targets, it was found that the degree values of TP53, TNF, CASP3, IL6, EGFR, MTOR, ESR1 and other targets were higher. (+)-Ganoderic acid Mf, (+)-Methyl ganolucidate A, epoxyganoderiol A, Ergosta-4,7, 22-Trien-3, 6-Dione and other compounds play a key role in the whole network. It may be the key compound of ganoderma lucidum in treating OA. Through enrichment pathway analysis, it was found that the number of genes was enriched in AGE-RAGE signaling pathway, cAMP signaling pathway, inflammation and cancer pathways, and the structure of TP53, TNF, CASP3, and IL6 binding to the above compounds was stable and the binding activity was high.

CONCLUSIONS

The findings suggest that Ganoderma lucidum may exert its therapeutic effects on optic atrophy by targeting TP53, TNF, CASP3, and IL6. Additionally, it may also be involved in the AGE-RAGE signaling pathway and cAMP signaling pathway. These results provide reference for the clinical application of ganoderma lucidum in the treatment of OA.

RegRNA 3.0: expanding regulatory RNA analysis with new features for motif, interaction, and annotation

Functional RNA molecules are crucial for biological processes from gene regulation to protein synthesis, and analyzing functional motifs and elements is essential for understanding RNA regulation. Building on RegRNA 1.0 and 2.0, we present RegRNA 3.0, a sophisticated meta-workflow that integrates 26 computational tools and 28 databases for annotation, enabling one-step and customizable RNA motif predictions. RegRNA streamlines multi-step analysis and enhances result interpretation with interactive visualizations and comprehensive reporting tools. When provided with an RNA sequence, RegRNA 3.0 generates predictions for RNA functional motifs, RNA interaction motifs, and comprehensive RNA annotations. Specifically, RNA functional motifs include core promoter elements, RNA decay, G-quadruplex, and 14 previous types. RNA interaction motifs include newly added RNA-ligand interactions and RNA-binding protein predictions, along with three previous types. RNA annotation includes RNA family classification, blood exosomes RNA, subcellular localizations, A-to-I editing events, modifications, and 3D structures, along with four previously supported features. RegRNA 3.0 accelerates gene regulation and RNA biology discoveries by offering a user-friendly platform for identifying and analyzing RNA motifs and interactions. The web interface has been improved for intuitive visualizations of predicted motifs and structures, with flexible download options in multiple formats. It is available at http://awi.cuhk.edu.cn/∼RegRNA/.

An integrative analysis combining bioinformatics, network pharmacology and experimental methods identified key genes of EGCG targets in Nasopharyngeal Carcinoma

BACKGROUND

Epigallocatechin gallate (EGCG), a frequently studied catechin in green tea, has been shown to be involved in the antiproliferation and apoptosis of human Nasopharyngeal carcinoma (NPC) cells. However, the pharmacological targets and mechanism by which EGCG can combat NPC patients remain to be studied in detail.

METHODS

Network pharmacology and bioinformatics were employed to investigate the molecular mechanisms underlying EGCG's therapeutic effects on NPC, with an emphasis on developing a prognostic risk model and identifying potential therapeutic targets.

RESULTS

A novel prognostic risk model was developed using univariate Cox regression, LASSO regression and multivariable Cox regression analyses, incorporating six genes to stratify patients into low- and highrisk groups. Kaplan-Meier analysis demonstrated significantly shorter progression-free survival in the high-risk group. The model's accuracy was further validated using time-dependent Receiver Operating Characteristic (ROC) curves. ESTIMATE analysis revealed significantly higher immune, stromal and overall ESTIMATE scores in the low-risk group compared to the high-risk group. Immune profiling indicated significant differences in five immune cell subtypes (memory B cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells and activated dendritic cells) between the two risk groups. Additionally, the low-risk group showed greater sensitivity to conventional chemotherapeutic agents. Immunohistochemistry and molecular docking analyses identified CYCS and MYL12B as promising targets for EGCG treatment.

CONCLUSION

This study utilised network pharmacology and bioinformatics to identify shared genes between EGCG and NPC, aiming to elucidate the molecular mechanisms through which EGCG inhibits NPC and to develop a prognostic model for assessing patient outcomes. The findings provide potential insights for the development of anti-NPC therapies and their clinical applications.

Machine learning assisted in Silico discovery and optimization of small molecule inhibitors targeting the Nipah virus glycoprotein

The Nipah virus (NiV), a lethal pathogen from the Paramyxoviridae family, presents a significant global health threat as a result of its high mortality rate and inter-human transmission. This investigation employed in silico methods that were assisted by machine learning to identify small-molecule inhibitors that target the NiV glycoprotein, a critical component of viral entry. Out of the 754 antiviral compounds that were screened using Lipinski's Rule of Five and DeepPurpose, 333 are identified. Five best hits were identified through molecular docking, each of which exhibited superior binding scores in comparison to the control. This was further refined to three compounds through density functional theory (DFT) analysis, with compound 138,567,123 exhibiting the highest electronic stability (DFT energy: -1976.74 Hartree; HOMO-LUMO gap: 0.83 eV). Its stability was verified by molecular dynamics (MD) simulations, which demonstrated consistent hydrogen bonding and minimal RMSD. Additionally, it possessed the highest docking score (-9.7 kcal/mol) and binding free energy (-24.04 kcal/mol, MM/GBSA). The results underscore ligand 138,567,123 as a promising antiviral candidate for NiV and illustrate the efficacy of machine learning-based in silico drug discovery.

A new age in structural S-layer biology - Experimental and in silico milestones

Surface (S-) layer proteins, considered as the most abundant proteins in nature, perform diverse and essential biological roles in many bacteria and most archaea. Their functions range from providing structural support, maintaining cell shape, and protecting against extreme environments to acting as a cell surface display matrix for biologically active molecules, such as S-layer protein-bound glycans, which facilitate interspecies interactions and cellular communication in both health and disease. The intricate, symmetric, nanometer-scale patterns of S-layer lattices have long fascinated structural biologists, yet only recent methodological advances have revealed detailed molecular insights. These advances include a deeper understanding of domain organization, cell wall anchoring mechanisms, and how nascent proteins are incorporated into existing lattices. Significant progress in sample preparation and high-resolution imaging has led to the precise structural characterization of S-layers across various bacterial and archaeal species. Furthermore, the advent of deep learning-based structure prediction has enabled modeling of S-layer proteins in several largely uncultured microbial lineages. This review summarizes major achievements in S-layer protein structural research over the past five years, presenting them with a typical workflow for the experimental structure determination. For the first time, it also explores recent breakthroughs in computational S-layer modelling and offers an outlook on how in silico methods may further advance our understanding of S-layer protein architecture.

Molecular characterization of doublesex and Mab-3 (DMRT) gene family in Ctenopharyngodon idella (grass carp)

Doublesex and Mab-3 (DMRT) gene family is a diverse group of transcriptional factors crucially involved in sex differentiation and biological processes such as body growth and differentiation in vertebrates. In this study, we analyzed DMRT genes structural characterization and physiochemical properties, and elucidated their functional role as a ligand of different gonadal receptors including androgen (AR), estrogen β (ER-β), estrogen γ (ER-γ), and progesterone (PR). All six genes of the DMRT gene family in grass carp (Ctenopharyngodon Idella Valenciennes, 1844) exhibited an acidic nature. These DMRT genes are primarily localized in the nucleus, where they play a role in DNA binding via doublesex DNA binding motif. All the DMRT gene pairs are under strong purifying selection with two segmental duplications envisaged about 18.30 (DMRT3a/DMRTA2) and 24.90 (DMRT2b/DMRT2a) million years ago (MYA). Recombination analysis revealed six potential recombinant breakpoints posing substantial evolutionary pressure for diverse cellular functioning of DMRT isoforms. Moreover, the DMRTA1 protein had a highest binding affinity of - 270.42 and - 267.16 for androgen receptors (AR) and progesterone receptors (PR), whereas, for estrogen receptors ER-β and ER-γ, the maximum binding affinity was observed with DMRT2a and DMRT2b proteins showing a docking score of - 254.22 and - 261.71, respectively. First time we studied the binding scores and interface residues of the DMRT genes as a ligand of gonadal receptors that play a crucial role in fish growth, sex development and differentiation, and spermatogenesis and oocyte maturation. The present study provides a molecular basis for DMRT genes in grass carp that may serve as a reference for in-depth phylogenomic study in other species.

NAE1 protein: a prognostic, immunomodulatory, and therapeutic biomarker associated with neddylation in hepatocellular carcinoma

Current predictive biomarkers for clinical outcomes and treatment in hepatocellular carcinoma (HCC) are not reliable enough. Neddylation, a novel post-translational modification, plays a crucial role in the immunomodulation, metabolism, and pathogenesis of HCC. However, whether it can function as a powerful predictive biomarker for HCC remains unknown. In current research, we first identified NAE1 as the most significant neddylation-related gene affecting the prognosis of HCC patients mainly through weighted gene co-expression network (WGCNA) and machine learning. Subsequently, we determined NAE1 expression as an independent risk factor for HCC using univariate and multivariate Cox regression and constructed a nomogram integrating NAE1 expression with clinical characteristics to predict survival probabilities in HCC patients. Bulk and single-cell RNA sequencing analyses revealed that NAE1 expression was primarily positively connected with immune cell infiltration in HCC, as assessed by the six latest immune algorithms. In addition, drug sensitivity and molecular docking collectively revealed the influence of NAE1 expression on the IC50 values of the four agents and the binding interactions between NAE1 protein and these drugs. Furthermore, we found that NAE1 depletion suppressed proliferation, migration, and invasion of HCC cells in vitro experiments. In conclusion, NAE1 protein holds considerable potential as a valuable biomarker for predicting clinical outcomes, immune landscapes, and drug sensitivity in HCC, as well as a promising therapeutic target.

Discovery of novel DdlA inhibitors in multidrug-resistant Pseudomonas aeruginosa using virtual screening, molecular docking, and dynamics simulations

Pseudomonas aeruginosa is a gram-negative, opportunistic pathogen that represents a serious risk factor in healthcare services due to its natural resistance mechanisms and the increasing prevalence of multi-drug resistant strains. This study utilized in silico computational approaches to identify the novel inhibitors for D-alanine-D-alanine ligase A (DdlA), an essential enzyme for the bacterial peptidoglycan biosynthesis pathway necessary for cell wall integrity. A structure-based virtual screening of The Medicinal Fungi Secondary Metabolites and Therapeutics (MeFSAT) chemical library was conducted, followed by molecular docking to evaluate the binding affinity of small molecules to the DdlA active site. MSID000191, MSID000200, and MSID000102 were recognized as the leading candidates in the preliminary docking data due to their low binding energy values. These compounds exhibited binding energies markedly superior to the control drug (D-cycloserine), suggesting a substantial potential for inhibiting the DdlA enzyme. Detailed interaction analyses revealed significant salt bridges and hydrogen bonds with active site residues, which enhance the stability of the complex. Density Functional Theory (DFT) analysis and MMPBSA calculations also provided insights into electronic properties and binding free energy, respectively. These findings highlight the potential of these inhibitors as therapeutic candidates and showcase the effectiveness of computational methods in accelerating drug discovery against multidrug-resistant P. aeruginosa. Future research should incorporate more in-silico techniques and experimental validations to confirm these results.

Enhanced prediction of antigen and antibody binding interface using ESM-2 and Bi-LSTM

The binding interface between antigens and antibodies is pivotal in humoral immune responses and provides crucial effective defense against pathogens and exogenous threats. Existing predictive computational methodologies, including structure-based and sequence-based approaches, offer valuable insights but face challenges such as unknown antigen structures and reliance on manually curated features. Most current methods primarily predict antigen epitope, often neglecting the specific molecular epitope-paratope interactions essential for immune efficacy. In this study, we introduce a novel approach EPP (Epitope-Paratope Predictor), using the ESM-2 protein language model as a feature encoder and a Bi-LSTM network to predict epitope-paratope interactions. Our method processes antigen and antibody sequences as inputs, leveraging a novel dataset strategy and encoding protein representations to enhance prediction accuracy. The results demonstrate a significant improvement in prediction accuracy compared to existing methods, highlighting the importance of protein feature encoder and temporal dependencies within sequences. The model's performance in different antigen clusters is analyzed, while those predictions are compared with that from AlphaFold3 and Dock method. Our method validation shows superior performance in recognizing distinctive epitopes of the same antigen when bound to different antibodies. This approach offers a new strategy for an in-depth understanding of antigen-antibody interactions, essential for an array of pioneer projects, such as structure-guided design and affinity maturation for precision antibodies targeting a given epitope.

Assessment of Sedative Activity of Lonicerin: In Vivo Approach With Pharmacokinetics and Molecular Docking

BACKGROUND

Lonicerin (LON) has been identified to have different biological properties, such as anticancer, anti-inflammatory, immunomodulatory, antibacterial, antimicrobial, and neuroprotective. This study aims to assess the sedative effect of LON in Swiss albino mice, which is yet to be discovered.

MATERIALS AND METHODS

Mice were treated with two different doses of LON (5 and 10 mg/kg) and 2 mg/kg of diazepam (DZP), which is the referral GABAergic medication, and the latency time and sleeping duration of animals were observed. A computational study was also conducted to evaluate the docking scores and display the binding sites of LON and receptor (GABAA α1 and β2 subunits). The study also investigated the pharmacokinetics and drug-likeness properties of LON along with toxicological analysis by using SwissADME and Protox-3 software, respectively.

RESULTS

Findings revealed that the higher concentration of LON reduced the latency (9.86 ± 1.44 min) and increased the sleep duration (191.29 ± 7.43 min) compared to the lower concentration. Besides, the combination group of LON and DZP showed the lowest latency (6.17 ± 0.82 min) and highest sleeping time (219.00 ± 6.39 min). In the in silico study, LON exhibited a strong docking score (-8.1 kcal/mol) with the macromolecules, which is closer to the binding affinity of DZP (-8.3 kcal/mol), indicating that LON could show strong sedative activity by binding with the GABAA receptor. Computational toxicity analysis revealed that LON is non-hepatotoxic, non-neurotoxic, noncarcinogenic, noncytotoxic, non-ecotoxic, and non-mutagenic.

CONCLUSION

Therefore, LON may be effective for the treatment of insomnia in the near future.

Inhibition Mechanism of Mulberry Prenylated Flavonoids Sanggenone D/Kuwanon G Against α-Glucosidase and the Regulation of Glucose via GLUT4 Pathway

BACKGROUND

Inhibition of α-glucosidase activity is recognized as an effective strategy for managing type 2 diabetes.

METHODS

The inhibitory mechanisms of two kinds of mulberry flavonoids, namely sanggenone D and kuwanon G, on α-glucosidase were investigated and the hypoglycemic pathways were explored in the current study.

RESULTS

The outcomes indicate that sanggenone D (IC50: 4.51 × 10-5 mol/L) and kuwanon G (IC50: 3.83 × 10-5 mol/L) inhibited α-glucosidase activity by non-competition/anti-competition mixed inhibition and competitive inhibition, respectively. Moreover, the secondary structure of α-glucosidase was altered by static quenching and exhibited a decrease in α-helix and β-antiparallel content, and an increase in β-sheet content. Furthermore, the interaction forces between sanggenone D/kuwanon G and α-glucosidase were hydrophobic interactions and hydrogen bonds, as evidenced by molecular docking. The binding affinity, stability, and binding energy aligned with the results of IC50. Notably, the cyclization in sanggenone D structure resulted in a decrease in the number of phenolic hydroxyl groups and thus a reduction in the formation of hydrogen bonds, which ultimately diminished the binding affinity of sanggenone D to α-glucosidase. In addition, Western blot analysis further indicated that sanggenone D and kuwanon G regulated glucose metabolism by activating the GLUT4 pathway.

CONCLUSIONS

The results provided useful reference for the application of sanggenone D and kuwanon G in hypoglycemic functional components.

Phytochemical Screening and Computational Insights into VP26 Inhibitors for Mitigating White Spot Syndrome Virus in Shrimp Aquaculture

White spot syndrome disease (WSSD) is a contagious disease caused by white spot syndrome virus (WSSV) in shrimp aquaculture. Given its high degree of contagiousness, controlling its rapid spread has proven to be challenging, causing significant economic loss to farmers. To prevent these losses, farmers often resort to the use of large doses of general aquaculture antibiotics. However, prolonged exposure to such antibiotics can lead to adverse effects for both the shrimp and the humans consuming them. Additionally, this practice contributes to the global issue of antimicrobial resistance. Currently, there are no vaccines or antibiotics that specifically target this virus. Therefore, exploring potential compounds through virtual screening offers a promising avenue for finding effective solutions. A total of 1683 phytochemical metabolites from 40 medicinal plants were screened against the target VP26, which plays a pivotal role in virus maturation. Initial screening was performed via ADMET and molecular docking analysis. Furthermore, we evaluated the binding affinity via machine learning-based scoring schemes. Importantly, the compounds displayed applicable toxicity properties during testing with ECOSAR. The binding ability of the compounds was validated with 150 ns of MD simulation. Overall, isocolumbin and urolithin A 3-O-glucuronide had significant effects on the outcomes of all the analyses. Therefore, we believe that this compound could be an alternative therapeutic option to the WSS virus in shrimp aquaculture.

Unlocking the potential of Bavachin in vitamin D receptor cascade modulation for rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint damage and disrupted vitamin D signaling, leading to calcium deposition in joints. This study explores the therapeutic potential of Bavachin (BVN), a phytoestrogen from Psoralea corylifolia, in modulating vitamin D signaling in RA.

METHODS

Vitamin D receptor (VDR) structure was modeled using SWISS-MODEL, AlphaFold, and I-TASSER, followed by docking with BVN and Estradiol (E2) via AutoDock Vina. BVN's effects on VDR mRNA and protein levels in RA-FLS were assessed by qRT-PCR and Western blot(WB). VDR-related BVN targets in RA were explored through protein-protein interaction (PPI) network and pathway analysis in Cytoscape. BVN's impact on RXRα expression and VDR-RXRα interaction was examined by WB and immunofluorescence. Alizarin staining evaluated calcium deposition, while qRT-PCR analyzed BVN's regulation of calcium-binding proteins.

RESULTS

In silico analysis revealed a strong interaction of VDR with BVN with Gibbs-free energy of -7.2 Kcal/mol with prominent H-bonds. Further, in vitro study in RA-FLS revealed that BVN treatment increased VDR mRNA and protein expression. PPI and pathway enrichment analysis retrieved RXRα as the prominent protein to be targeted by BVN in Vitamin D signaling. BVN treatment also significantly upregulated RXRα expression and enhanced the interaction between VDR and RXRα. Further, BVN modulated associated calcium signaling, reduced calcium deposition in RA-FLS and significantly downregulated calcium-binding proteins CALB1, CALB2, NCX1, TRPV5, and TRPV6.

CONCLUSION

Collectively, this study depicted a prominent therapeutic efficacy of BVN in targeting Vitamin D signaling and associated calcium deposition to alleviate RA pathogenesis.

Chromene flavanones from Dalea boliviana as xanthine oxidase inhibitors: in vitro biological evaluation and molecular docking studies

Background

Prenylated flavanones represent a structurally diverse class of natural compounds with significant biological potential. Among them, chromene flavanones (CFs) constitute a rare and specialized subgroup with promising therapeutic applications. These molecules have gained attention due to their potential to inhibit xanthine oxidase (XO), a key enzyme involved in oxidative stress-related disorders such as gout and hyperuricemia. Their distinctive structural features, combined with notable bioactivity, make them compelling candidates for further pharmacological exploration. Given their potential relevance, this study focuses on the in vitro and in silico evaluation of three CFs isolated from Dalea boliviana Britton [Fabacea], assessing their capacity to inhibit XO and elucidating key structure-activity relationships (SARs) that contribute to their biological effectiveness.

Purpose

This study aims to investigate the in vitro and in silico interactions of the chromene flavanones, namely, (2S) 5,2'-dihydroxy-6″,6″-dimethylchromeno-(7,8:2″,3″)-flavanone (1), (2S) 5,2'-dihydroxy-6″,6″-dimethylchromeno-(7,8:2″,3″)-3'-prenylflavanone (2), and obovatin (3), obtained from D. boliviana, with XO, in order to explore their potential as XO inhibitors and their potential therapeutic applications for hyperuricemic diseases.

Material and Methods

XO inhibition by the three chromene flavanones was measured spectroscopically. The relationships between their structures and inhibitory activities were evaluated. Moreover, molecular docking studies were performed to propose the binding modes of the most active natural compounds.

Results and discussion

Compounds 1 and 2 exhibited potent inhibition, with IC50 values in the nanomolar range (0.5 ± 0.01 nM and 1.7 ± 0.46 nM, respectively), demonstrating significantly higher activity than allopurinol (AL), the reference inhibitor (IC50 = 247 ± 4 nM). In contrast, compound 3 displayed only weak inhibition. SAR analysis revealed that the presence of a chromene moiety in the A-ring, combined with hydroxyl and prenyl groups in the B-ring, played a crucial role in enhancing inhibitory activity. Molecular docking studies confirmed the strong binding affinities of compounds 1 and 2 within the active site of XO (PDB ID: 3NVY), with binding energies of -6.1687 kcal/mol and -6.7820 kcal/mol, respectively. Key stabilizing interactions involved π-π interactions with Phe914 and hydrogen bonding with residues such as Leu873 and Leu1014. These findings highlight the structural features essential for potent XO inhibition and suggest that chromene flavanones represent a valuable scaffold for the development of novel inhibitors. Further molecular dynamics simulations could provide deeper insights into their stability and interaction dynamics, aiding in the rational design of more effective XO inhibitors.

Conclusion

Our findings lead us to propose these chromene flavanones as lead compounds for the design and development of novel XO inhibitors for treating diseases in which exacerbated activity of this enzyme is involved.

Structure-Based Analysis of Semisynthetic Anti-TB Rufomycin Analogues

This study employed structural information from cocrystals of rufomycin 4 (1a) and caseinolytic protein C1 (ClpC1)-NTD-wt to guide design and semisynthesis of rufomycin analogues, evaluate their antituberculosis (TB) biological profiles, and establish structure-activity relationships (SAR). Covering three regions of interest (ROIs, A-C) as modification sites, 14 of the 30 semisynthetic analogues (2-31) showed similar or improved MICs relative to the main natural precursors, rufomycins 4/6 (1a/b). Compounds 5 and 27 exhibited up to 10-fold enhanced potency against Mycobacterium tuberculosis (Mtb) in vitro, with MIC values of 1.9 and 1.4 nM, respectively. Evaluation of ClpC1-binding properties used existing ClpC1-NTD complexes with rufomycin 4 (PDB: 6cn8) and ecumicin (PDB: 6pbs) as references. The newly reported X-ray ClpC1-NTD cocrystal structure of 11 (syn. But4-Cl) revealed significant conformational effects involving the side chains of certain amino acids of the heptapeptide and confirmed the importance of ROIs A-C for medicinal chemistry efforts. Observed interactions of the N-terminal tail of ClpC1 with the rufomycin analogues vs ecumicin explains their different modes of inactivating the ClpC1/P1/P2 homeostatic machinery. Collectively, the observations inform further SAR optimization strategies for the rufomycin class of antibiotics and complement our understanding of their mode of action.

Exploring the mechanism of Cynanchum paniculatum (Bunge) Kitag's therapeutic strategy for rheumatoid arthritis: integrating network pharmacology, molecular docking and in vivo experiments

Rheumatoid arthritis (RA) is a chronic inflammatory disorder characterized by joint swelling, cartilage degradation, and joint deformity. The traditional Chinese herb Cynanchum paniculatum (Bunge) Kitag has been utilized in the management of RA, but the underlying mechanisms are unknown. This study utilized network pharmacology analysis to identify 26 active compounds associated with RA treatment and elucidate their interactions with 23 critical targets linked to RA. Subsequently, molecular docking studies revealed eight compounds with the capacity to bind to multiple key targets, with butyl isobutyl phthalate and geranyl acetone emerging as the most promising candidates based on their drug-likeness properties. To validate these findings, a rat model of adjuvant-induced arthritis was employed. Oral administration of geranyl acetone led to a significant reduction in paw swelling and pro-inflammatory markers, including TNF-α, IL-6, IL-1β, and MPO. Furthermore, it resulted in histological improvements in ankle tissues, all without adverse effects on weight or immune organs. Mechanistically, geranyl acetone was found to impede the progression of RA by modulating the TLR4/MyD88/NF-κB signaling pathway. In conclusion, C. paniculatum demonstrates substantial therapeutic potential for RA due to its multi-target and multi-pathway activities. Moreover, geranyl acetone, when used as a standalone agent, exhibits significant promise in alleviating RA symptoms, offering a compelling avenue for further research and potential clinical applications.

Unique and overlapping mechanisms of valbenazine, deutetrabenazine, and vitamin E for tardive dyskinesia

In 2017, the Food and Drug Administration (FDA) approved valbenazine and deutetrabenazine, two vesicular monoamine transporter 2 (VMAT2) inhibitors, as treatments for tardive dyskinesia (TD). Additionally, some trials have suggested that vitamin E may benefit TD patients. However, the mechanistic basis for these treatments remains unclear. The objective of this study was to analyze and compare the mechanisms of valbenazine, deutetrabenazine, and vitamin E in TD treatment utilizing network pharmacology and molecular docking approaches. Putative target genes associated with valbenazine, deutetrabenazine, and vitamin E were retrieved from the PharmMapper, CTD, GeneCards, SwissTargetPrediction, and DrugBank databases. TD-related targets were identified using the GeneCards, DisGeNET, OMIM, and TTD databases. A protein-protein interaction (PPI) network was created to identify core targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via DAVID, and Cytoscape was used to build a drug-pathway-target-disease network. Molecular docking evaluated drug-target interactions. A total of 32, 36, and 62 targets relevant to the treatment of TD were identified for valbenazine, deutetrabenazine, and vitamin E, respectively. PPI and KEGG pathway analyses suggested that valbenazine and deutetrabenazine may influence TD through the dopaminergic synapse signaling pathway via common core targets (e.g., Dopamine Receptor D1 (DRD1), DRD2, Monoamine Oxidase B (MAOB), Solute Carrier Family 6 Member 3 (SLC6A3), SLC18A2) and specific targets (DRD3 for valbenazine, MAOA for deutetrabenazine). Vitamin E may affect TD by targeting the PI3K-Akt pathway through AKT Serine/Threonine Kinase 1 (AKT1), Brain-Derived Neurotrophic Factor (BDNF), Insulin (INS), Nitric Oxide Synthase 3 (NOS3), and Toll-Like Receptor 4 (TLR4). This study provides insights into the common and unique molecular mechanisms by which valbenazine, deutetrabenazine, and vitamin E may treat TD. Pharmacological experiments should be conducted to verify and further explore these results. The findings offer a theoretical basis for further pharmacological investigation and a resource for TD drug screening.

Identification of Therapeutic Targets for Hyperuricemia: Systematic Genome-Wide Mendelian Randomization and Colocalization Analysis

Background: At present, there are still limitations and challenges in the treatment of hyperuricemia (HUA). Mendelian randomization (MR) has been widely used to identify new therapeutic targets. Therefore, we conducted a systematic druggable genome-wide MR to explore potential therapeutic targets and drugs for HUA. Methods: We integrated druggable genome data; blood, kidney, and intestinal expression quantitative trait loci (eQTLs); and HUA-associated genome-wide association study (GWAS) data to analyze the potential causal relationships between drug target genes and HUA using the MR method. Summary-data-based MR (SMR) analysis and Bayesian colocalization were used to assess causality. In addition, we conducted phenome-wide association studies, protein network construction, and enrichment analysis of significant targets to evaluate their biological functions and potential side effects. Finally, we performed drug prediction and molecular docking to identify potential drugs targeting these genes for HUA treatment. Results: Overall, we identified 22 druggable genes significantly associated with HUA through MR, SMR, and colocalization analyses. Among them, two prior druggable genes (ADORA2B and NDUFC2) reached statistically significant levels in at least two tissues in the blood, kidney, and intestine. Further results from phenome-wide studies revealed that there were no potential side effects of ADORA2B or NDUFC2. Moreover, we screened 15 potential drugs targeting the 22 druggable genes that could serve as candidates for HUA drug development. Conclusions: This study provides genetic evidence supporting the potential benefits of targeting 22 druggable genes for HUA treatment, offering new insights into the development of targeted drugs for HUA.

Unveiling polyphenol-protein interactions: a comprehensive computational analysis

Our study investigates polyphenol-protein interactions, analyzing their structural diversity and dynamic behavior. Analysis of the entire Protein Data Bank reveals diverse polyphenolic structures, engaging in various noncovalent interactions with proteins. Interactions observed across crystal structures among diverse polyphenolic classes reveal similarities, underscoring consistent patterns across a spectrum of structural motifs. On the other hand, molecular dynamics (MD) simulations of polyphenol-protein complexes unveil dynamic binding patterns, highlighting the influx of water molecules into the binding site and underscoring limitations of static crystal structures. Water-mediated interactions emerge as crucial in polyphenol-protein binding, leading to variable binding patterns observed in MD simulations. Comparison of high- and low-resolution crystal structures as starting points for MD simulations demonstrates their robustness, exhibiting consistent dynamics regardless of the quality of the initial structural data. Additionally, the impact of glycosylation on polyphenol binding is explored, revealing its role in modulating interactions with proteins. In contrast to synthetic drugs, polyphenol binding seems to exhibit heightened flexibility, driven by dynamic water-mediated interactions, which may also facilitate their promiscuous binding. Comprehensive dynamic studies are, therefore essential to understand polyphenol-protein recognition mechanisms. Overall, our study provides novel insights into polyphenol-protein interactions, informing future research for harnessing polyphenolic therapeutic potential through rational drug design.Scientific contribution: In this study, we present an analysis of (natural) polyphenol-protein binding conformations, leveraging the entirety of the Protein Data Bank structural data on polyphenols, while extending the binding conformation sampling through molecular dynamics simulations. For the first time, we introduce experimentally supported large-scale systematization of polyphenol binding patterns. Moreover, our insight into the significance of explicit water molecules and hydrogen-bond bridging rationalizes the polyphenol promiscuity paradigm, advocating for a deeper understanding of polyphenol recognition mechanisms crucial for informed natural compound-based drug design.

Protein structure prediction via deep learning: an in-depth review

The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.

Ligustilide covalently binds to Cys254 of the creatine kinase, M-type protein, ameliorating acute myocardial ischemia by enhancing the creatine phosphate level

BACKGROUND

Myocardial ischemia (MI) threatens the health of middle-aged and older adults by reducing cardiac oxygen supply and function. Current therapies, including vasodilation, thrombolysis, and interventions, focus on relieving symptoms and improving blood flow but do not adequately address underlying energy metabolism issues. Ligustilide exerts a protective effect on the cardiovascular system and holds the potential for ameliorating MI; however, there is currently no systematic elucidation of ligustilide's target and action mechanism for MI.

PURPOSE

This study aimed to comprehensively assess ligustilide's potential targets for improving acute MI and elucidate its underlying mechanism.

METHODS

The therapeutic effects of ligustilide were evaluated at doses of 30, 15, and 7.5 mg/kg over 7 days in a murine model of acute MI induced by isoproterenol hydrochloride. The putative target protein was identified through target fishing, in-gel imaging, and thermal shift assay (TSA), followed by tissue and cell localization studies via a ligustilide probe. The interaction sites between ligustilide and the target protein were elucidated using protein profiling, molecular docking, and TSA at the protein level. Subsequently, knockdown and reconstruction tests were employed at the cellular level to identify the functionally active sites where ligustilide binds to the target protein. Finally, molecular docking and molecular dynamics simulations were conducted to elucidate the underlying mechanism by which ligustilide enhances creatine kinase, M-type (CKMM) protein activity.

RESULTS

The covalent bonding of ligustilide in cardiac tissue enhances the therapeutic effect on acute MI in mice. For the first time, we found ligustilide specifically targets Cys254 of the CKMM protein following epoxidation. This irreversible binding effectively reduces the proximity between creatine and ATP, promoting creatine phosphorylation and ultimately increasing the creatine phosphate (CP) level by 9.50 % to 19.31 %. The accumulation of CP alleviates MI by enhancing energy metabolism, mitigating oxidative stress, and suppressing inflammatory responses.

CONCLUSIONS

Our study unveiled ligustilide as a CKMM activator, which effectively enhances the content of CP and mitigates acute MI. The findings significantly contribute to advancing our understanding of ligustilide's function for myocardial protection while proposing a novel activation mechanism of CKMM to improve MI. And the insight into the covalent regulation of the active pocket on CKMM may lead to an alternative therapeutic strategy against acute MI.

[Nobel Prize in chemistry 2024: David Baker, Demis Hassabis et John M. Jumper. The revolution of artificial intelligence in structural biology]

The 2024 Nobel Prize in chemistry has been awarded to Demis Hassabis and John M. Jumper (Google DeepMind) for the development of artificial intelligence-guided protein structure prediction and to David Baker (University of Washington, Seattle, USA) for the development of computational protein design. This event marks a revolution in the field of structural biology that has led, among other things, to the generation of a library of nearly 200 million predicted protein structures designed to speed up research. This revolution in AI has also led to the design of several artificial proteins of medical interest presented in this review.

Molecular simulations guided drugs repurposing to inhibit human GPx1 enzyme for cancer therapy

Overexpression of the antioxidant enzyme glutathione peroxidase-1 (GPx1) is associated with different cancer types. Inhibitors of GPx1, including mercaptosuccinic acid and pentathiepins derivatives, have been proposed previously and investigated as potent drugs to combat cancer. However, these compounds often lack specificity and demonstrate off-target effects, which necessitates the need for more targeted, non-toxic, and effective GPx1 inhibitors. This study utilized molecular docking and dynamic simulations based computational pipeline to repurpose drugs, approved by The Food and Drug Administration [1], as potent GPx1 inhibitors from a library containing 1615 synthetic compounds. The drug suitability and stability of the selected compounds were further investigated using ADMET, bioactivity probability, Molecular Mechanics-Generalized Born Surface Area (MM-GBSA), and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) analyses. Initially, 13 compounds were virtually screened based on the Triangle Matcher algorithm, docking modules, and GBVI/WSA dG scoring function. Of these 13 screened compounds, three compounds, including dronedarone, nilotinib, and thonzonium, were rigorously selected based on their ADMET profiles, physicochemical properties, drug suitability, and stability and were subjected to Molecular Dynamic (MD) simulations. MD simulations further validated the stability of the dronedarone, nilotinib, and thonzonium complexes with GPx1 and provided further insights into the mechanism of their interaction. The in-silico approaches used herein revealed thonzonium, dronedarone, and nilotinib as potent GPx1 inhibitors.

Data on the docking of millet-derived secondary metabolites as multi-target ligands for diabetes

The deterioration of human health due to unhealthy lifestyle and dietary habits has led to a worldwide increase in various metabolic diseases that significantly affect public health. Diabetes is one of the most serious health problems, is caused by abnormal metabolic processes and is becoming increasingly common. According to World Health Organisation (WHO) reports, a significant proportion of the world's population suffers from these diseases and their incidence continues to rise at an alarming rate. These metabolic disorders are characterised by elevated blood sugar levels, which serve as a warning sign for a variety of other health problems. Factors contributing to these diseases include a high-fat diet, insufficient physical activity, genetic predisposition, lack of exercise and underlying diseases. Diabetes mellitus, a fast-growing chronic metabolic disease, is characterised by insufficient insulin production by the pancreas or the body's inability to use insulin action. Various strategies are recommended by health and nutrition experts to manage this condition, including lifestyle changes, exercise, low-carbohydrate and low-fat diets and intermittent fasting. In cases where these measures prove insufficient, medication may be prescribed. However, the development of multi-drug therapies for metabolic disorders has proven to be an attractive field for pharmacists as they address several diseases simultaneously. Despite the promising effects of multi-drug therapies, the high costs and potential side effects associated with recently developed drugs necessitate alternative approaches. The utilisation of natural bioactive compounds from plant extracts represents a promising high-throughput strategy. This approach utilises network pharmacology and screening methods to identify potential ligands that act as inhibitors for the treatment of complex, interconnected diseases. In the current investigation, we used a molecular docking approach to investigate secondary metabolites from millet as potential multi-target ligands for the treatment of diabetes and obesity.

Computational discovery of natural inhibitors targeting enterovirus D68 3C protease using molecular docking pharmacokinetics and dynamics simulations

Enterovirus D68 (EV-D68) is a significant global health threat, responsible for severe respiratory and neurological complications, with no FDA-approved antiviral treatments currently available. The 3C protease of EV-D68, an essential enzyme involved in viral replication, represents a key target for therapeutic development. In this study, we employed a comprehensive computational approach, including molecular docking, pharmacokinetic predictions, and molecular dynamics simulations, to identify potential natural inhibitors of the EV-D68 3C protease. Screening a library of natural compounds, Withaferin-A (CID: 265,237) and Baicalin (CID: 64,982) emerged as top candidates due to their favorable pharmacokinetic profiles, high binding affinities (-10.7 kcal/mol for Withaferin-A and -9.5 kcal/mol for Baicalin), and interactions with key residues in the protease's active site. The molecular dynamics simulations demonstrated the stability of the protein-ligand complexes, with low root mean square deviation (RMSD) and fluctuation (RMSF) values over a 100-ns trajectory. Free energy calculations further supported the superior binding efficiency of Withaferin-A. These findings suggest that Withaferin-A and Baicalin have significant potential as natural inhibitors of EV-D68 3C protease, offering a promising foundation for future experimental validation and the development of targeted antiviral therapies against EV-D68.

PinMyMetal: a hybrid learning system to accurately model transition metal binding sites in macromolecules

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural, catalytic, or regulatory roles. Modeling transition metal ions, especially in transient, reversible, and concentration-dependent regulatory sites, remains challenging. We present PinMyMetal (PMM), a hybrid machine learning system designed to accurately predict transition metal localization and environment in macromolecules, tailored to tetrahedral and octahedral geometries. PMM outperforms other predictors, achieving high accuracy in ligand and coordinate predictions. It excels in predicting regulatory sites (median deviation 0.36 Å), demonstrating superior accuracy in locating catalytic sites (0.33 Å) and structural sites (0.19 Å). Each predicted site is assigned a certainty score based on local structural and physicochemical features, independent of homologs. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validate diverse functional metal sites from different structure sources (predicted structures, cryo-EM, and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top . The PMM workflow can interrogate with protein sequence to characterize the localization of the most probable transition metals, which is often interchangeable and hard to differentiate by nature.

Green Tea Pressurized Hot Water Extract in Atherosclerosis: A Multi-Approach Study on Cellular, Animal, and Molecular Mechanisms

Atherosclerosis is a persistent inflammatory disorder influenced by oxidative stress and lipid imbalances, and it continues to be a major contributor to cardiovascular diseases. Rich in catechins and flavonoids, green tea pressurized hot water extract (GPHWE) demonstrated potent antioxidant activity through DPPH, ABTS, hydroxyl, and nitric oxide scavenging assays. In vitro, GPHWE protected RAW264.7 macrophages from oxidized LDL (Ox-LDL)-induced cytotoxicity and apoptosis by mitigating oxidative stress and enhancing cell survival. Animal studies using mice fed a high-fat diet (HFD) revealed notable improvements in lipid profiles, including decreases in total cholesterol, LDL, the atherosclerosis index (AI), the coronary risk index (CRI), and triglycerides, as well as lower levels of malondialdehyde (MDA), an indicator of oxidative stress. These results were comparable to those achieved with Simvastatin. Molecular docking studies indicated strong binding affinities of catechins to essential targets such as LOX-1, HMG-CoA reductase, caspase-3, and Nrf2, implying that the mechanisms of GPHWE involve antioxidant properties, regulation of lipids, and stabilization of plaques. The catechins of GPHWE, including epigallocatechin gallate (EGCG), epicatechin gallate (ECG), and epigallocatechin (EGC), were tentatively identified through qualitative analysis performed by UHPLC-QTOF-MS. This comprehensive approach positions GPHWE as a promising natural remedy for preventing atherosclerosis and reducing cardiovascular risk.

KLX ameliorates liver cancer progression by mediating ZBP1 transcription and ubiquitination and increasing ZBP1-induced PANoptosis

Liver cancer is a highly aggressive malignancy with poor survival rates. Current treatments, including liver transplantation, immunotherapy, and gene therapy, are often limited by late-stage diagnosis and significant side effects, highlighting the urgent need for novel therapeutic agents. In this study, we evaluated the therapeutic potential of Kanglexin (KLX), a novel anthraquinone derivative, in the treatment of liver cancer. In vitro, KLX inhibited the proliferation and migration of HepG2 and Hep3B cells in a dose-dependent manner. Mechanistically, KLX upregulated Z-DNA binding protein 1 (ZBP1) expression, inducing PANoptosis by directly binding to ZBP1, altering its conformation, and reducing its affinity for the E3 ubiquitin ligase ring finger protein 180 (RNF180). This interaction decreased ZBP1 ubiquitination, thereby increasing its stability. Additionally, KLX upregulated the expression of the transcription factor homeobox D10 (HOXD10), which further increased ZBP1 expression. Elevated ZBP1 levels significantly suppressed liver cancer cell proliferation and migration, whereas the inhibitory effects of KLX were reversed upon ZBP1 knockdown. In a xenograft model, KLX significantly inhibited tumor growth with a lower toxicity than oxaliplatin (OXA). In conclusion, KLX promoted PANoptosis in liver cancer cells by upregulating ZBP1 and preventing its degradation, thereby inhibiting liver cancer progression and migration. These findings suggest that KLX is a promising therapeutic agent for liver cancer.

Probing nanopores: molecular dynamics insights into the mechanisms of DNA and protein translocation through solid-state and biological nanopores

Nanopore sequencing technology has revolutionized single-molecule analysis through its unique capability to detect and characterize individual biomolecules with unprecedented precision. This perspective provides a comprehensive analysis of molecular dynamics (MD) simulations in nanopore research, with particular emphasis on comparing molecular transport mechanisms between biological and solid-state platforms. We first examine how MD simulations at atomic resolution reveal distinct characteristics: biological nanopores exhibit sophisticated molecular recognition through specific amino acid interactions, while solid-state nanopores demonstrate advantages in structural stability and geometric control. Through detailed analysis of simulation methodologies and their applications, we show how computational approaches have advanced our understanding of critical phenomena such as ion selectivity, conformational dynamics, and surface effects in both nanopore types. Despite computational challenges including limited simulation timescales and force field accuracy constraints, recent advances in high-performance computing and artificial intelligence integration have significantly improved simulation capabilities. By synthesizing perspectives from physics, chemistry, biology, and computational science, this perspective provides both theoretical insights and practical guidelines for developing next-generation nanopore platforms. The integration of computational and experimental approaches discussed here offers promising directions for advancing nanopore technology in applications ranging from DNA/RNA sequencing and protein post-translational modification analysis to disease diagnosis and drug screening.

Exploring deleterious non-synonymous SNPs in FUT2 gene, and implications for norovirus susceptibility and gut microbiota composition

Fucosyltransferase 2 (FUT2) gene has been extensively reported to play its role in potential gut microbiota changes and norovirus susceptibility. The normal activity of FUT2 has been found to be disrupted by non-synonymous single nucleotide polymorphisms (nsSNPs) in its gene. To explore the possible mutational changes and their deleterious effects, we employed state-of-the-art computational strategies. Firstly, nine widely-used bioinformatics tools were utilized for initial screening of possibly deleterious nsSNPs. Subsequently, the structural and functional effects of screened nsSNPs on FUT2 were evaluated by utilizing relevant computational tools. Following this, the two shortlisted nsSNPs, including G149S (rs200543547) and V196G (rs367923363), were further validated by their molecular docking with norovirus capsid protein, VP1. As compared to wild-type, the higher stability and lower binding energy scores of the both the mutants indicated their stable binding with VP1, which ultimately leads to norovirus implications. These docking results were further verified by a comprehensive computational approach, molecular dynamic simulation, which gave results in the form of lower RMSD, RMSF, RoG, and hydrogen bond values of both the mutants, depicted in relevant graphs. Overall, this research explores and validated the two FUT2 nsSNPs (G146S and V196G), which may possibly linked with the norovirus susceptibility and gut microbiota changes. Moreover, our findings highlights the value of computational strategies in mutational analysis and welcomes any further experimental validation.

Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a substantial economic burden on society.

OBJECTIVE

This study aimed to elucidate the molecular mechanisms underlying SLE by analyzing glucocorticoid-related genes (GRGs) expression profiles.

METHODS

We examined the expression profiles of GRGs in SLE and performed consensus clustering analysis to identify stable patient clusters. We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to investigate biological functional differences, and we also conducted CIBERSORTx to estimate the number of immune cells. Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. Moreover, we employed single-sample Gene Set Enrichment Analysis (ssGSEA) to analyze immune infiltration. We also constructed an RNA-binding protein (RBP)-mRNA network and conducted drug sensitivity analysis along with molecular docking studies.

RESULTS

Patients with SLE were divided into two subclusters, revealing a total of 2,681 DEGs. Among these, 1,458 genes were upregulated, while 1,223 were downregulated in cluster_1. GSVA showed significant changes in the pathways associated with cluster_1. Immune infiltration analysis revealed high levels of monocyte in all samples, with greater infiltration of various immune cells in cluster_1. A comparison of SLE patients to control subjects identified 269 DEGs, which were enriched in several pathways. Hub genes, including PTX3, DYSF and F2R, were selected through LASSO and RF methods, resulting in a well-performing diagnostic model. Drug sensitivity and docking studies suggested F2R as a potential new therapeutic target.

CONCLUSION

PTX3, DYSF and F2R are potentially linked to SLE and are proposed as new molecular markers for its onset and progression. Additionally, monocyte infiltration plays a crucial role in advancing SLE.

UPLC-Q-TOF-MS and network pharmacology to reveal the mechanism of Guizhi Gegen decoction against type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease. Clinical studies have shown that the incidence and prevalence of T2DM has been on the rise globally in recent years, and the mortality rate is also increasing. Chinese herbs is multiple target for disease. Guizhi Gegen decoction (GZGGD) is one of the most alternative treatment for T2DM. However, the treatment mechanism is unclear. The composition of the GZGGD was determined by ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. The key targets and pathways were predicted by network pharmacology and molecular docking. In vivo experiments were performed to further verify and reveal the potential mechanism of action. We identified 44 active components of GZGGD (genistein, 26-hydroxyporicoic acid DM, puerarin, eugenol, and gentiobiose). Network pharmacology predicted key targets such as TNF, AKT1, TP53, EGFR, and STAT3, and AGE-RAGE, IL-17 signaling pathways were enriched. Molecular docking showed that the active components of GZGGD have good binding activity with the potential targets of T2DM. In vivo animal experiments showed improvement in white blood, fasting blood glucose, and inflammatory factor levels (INS, TC, TNF-α, and IL-6). This study clarifies the potential role of GZGGD in T2DM, which can help in the study of T2DM.

Unveiling Interaction Signatures Across Viral Pathogens through VASCO: Viral Antigen-Antibody Structural COmplex dataset

Viral antigen-antibody (Ag-Ab) interactions shape immune responses, drive pathogen neutralization, and inform vaccine strategies. Understanding their structural basis is crucial for predicting immune recognition, optimizing immunogen design to induce broadly neutralizing antibodies (bnAbs), and developing antiviral therapeutics. However, curated structural benchmarks for viral Ag-Ab interactions remain scarce. To address this, we present VASCO (Viral Antibody-antigen Structural COmplex dataset), a high-resolution, non-redundant collection of ~1225 viral Ag-Ab complexes sourced from the Protein Data Bank (PDB) and refined via energy minimization. Spanning Coronaviruses, Influenza, Ebola, HIV, and others, VASCO provides a comprehensive structural reference for viral immune recognition. By comparing VASCO against general protein-protein interactions (GPPI), we identify distinct sequence and structural features that define viral Ag-Ab binding. While conventional descriptors show broad similarities across datasets, deeper analyses reveal key sequence-space interactions, secondary structure preferences, and manifold-derived latent features that distinguish viral complexes. These insights highlight the limitations of GPPI-trained predictive models and the need for specialized computational frameworks. VASCO serves as a critical resource for advancing viral immunology, improving predictive modeling, and guiding immunogen design to elicit protective antibody responses. By bridging sequence and structural immunological datasets, VASCO should enable better docking, affinity prediction, and antiviral therapeutic development-key to pandemic preparedness and emerging pathogen response.

MedKG: enabling drug discovery through a unified biomedical knowledge graph

Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.

Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-Directed Molecular Generation

Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence (AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates improved performance over existing approaches in generating molecules having the desired properties, including penalized LogP, QED, and celecoxib similarity, without any prior knowledge. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.

Anxiogenic-like effects of coumarin, possibly through the GABAkine interaction pathway: Animal studies with in silico approaches

BACKGROUND

Anxiety disorder is the most common mental illness and a major contributor to impairment. Thus, there is an urgent need to find novel lead compounds to mitigate anxiety. It is widely recognized that the neurobiology of anxiety-related behavior involves GABAergic systems.

OBJECTIVES

This research aimed to examine the anxiogenic action of coumarin (CMN), a natural benzopyrone derived from plants, and determine its underlying mechanism through in vivo and in silico investigations.

METHODS

This was accomplished by using a variety of behavioral procedures, including open field, swing, hole cross, and light-dark tests, on male and female Swiss albino mice that had been orally administered three experimental doses of CMN (1, 2, and 4 mg/kg). The CMN group was also examined with the GABAA receptor agonist diazepam (DZP, 2 mg/kg) and flumazenil antagonist (FLU, 0.1 mg/kg). Furthermore, CMN and standards were subjected to a molecular docking analysis to determine their binding affinities for the GABAA receptor subunits (α1, α4, β2, γ2, and δ). Several software programs were used to visualize the ligand-receptor interaction and analyze the pharmacokinetic profile.

RESULTS

Compared to typical treatments, our results show that CMN (1 mg/kg) significantly (p < 0.05) increases the locomotor activity of animals. Furthermore, CMN exerted the highest binding affinity (-6.5 kcal/mol) with the GABA-α1 receptor compared to conventional DZP. Along with FLU, CMN displayed several hydrophobic and hydrogen bonds with GABAA receptor subunits. The pharmacokinetic and drug-like properties of CMN are also remarkable. In animal studies, CMN worked synergistically with FLU to provide anxiogenic-like effects.

CONCLUSION

We conclude that, based on in vivo and in silico data, CMN, alone or in combination with FLU, may be employed in future neurological clinical studies. However, further research is needed to confirm this behavioral activity and elucidate the possible mechanism of action.

Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction

Computational systems biology employs computational algorithms and integrates diverse data sources, such as gene expression profiles, molecular interactions, and network modeling, to identify promising drug candidates through repurposing existing compounds in response to urgent healthcare needs. This study tackles the urgent need for rapid therapeutic development against emerging infectious diseases. We introduce a novel analytic expression for sensitivity analysis based on the Kronecker product and enhance model prediction performance using Graph Convolutional Networks (GCNs) with sensitivity-based graph reduction. Our algorithm refines prediction performance by leveraging sensitivity-based graph reduction. By integrating RNA-seq data, molecular interactions, and GCNs, we identify disease-related genes and pathways, construct heterogeneous graph models, and predict potential drugs. This approach involves novel analytical expressions that assess sensitivity to model loss, employing the Kronecker product approach. Subgraph analysis identifies nodes for removal, leading to a refined graph used for model retraining. This cost-effective pipeline focuses on computational methods for drug repurposing, targeting infectious diseases such as Zika virus and COVID-19 infection. Applied to these infections, our methodology integrates 659 proteins and 703 drugs for Zika virus, and 495 proteins and 468 drugs for COVID-19, along with their interactions derived from gene expression profiles. Top candidate drugs, such as Betamethasone phosphate and Bizelesin for Zika virus, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19, were validated through literature review or docking analysis. This scalable approach demonstrates promise in repurposing drugs for urgent healthcare challenges.

Predictive performance of a centrosome-associated prognostic model in prognosis and immunotherapy of lung adenocarcinoma

In recent years, mounting investigations have highlighted the pivotal role of centrosomes in cancer progression. In this study, we employed bioinformatics and statistics to establish a 13-centrosome-associated gene prognostic model for lung adenocarcinoma (LUAD) utilizing transcriptomic data from TCGA. Based on the Riskscore, patients were stratified into high- and low-risk groups. Through survival analysis and receiver operating characteristic curve analysis, our model demonstrated a consistent and robust prognostic capacity, which was further validated using the GEO database. Univariate/multivariate Cox regression analyses identified our model as an independent prognostic factor for LUAD patients. Subsequently, immunoinfiltration analysis showed that immune cell infiltration levels of aDCs, iDCs, Mast cells, and Neutrophils, as well as immune functionalities such as HLA, Type I IFN Response and Type II IFN Response, were markedly reduced in the high-risk group compared to the low-risk group. Finally, we conducted a drug screening to identify potential treatments for patients with different prognoses. We utilized the GDSC database and molecular docking techniques to identify small molecule compounds targeting the prognostic genes. In conclusion, our prognostic model exhibits robust and reliable predictive capability, and it may have important clinical implications in guiding treatment decisions for LUAD patients.

ProS2Vi: A Python tool for visualizing proteins secondary structure

The Protein Secondary Structure Visualizer (ProS2Vi) is a novel Python-based visualization tool designed to enhance the analysis and information accessibility of protein secondary structures, calculated and identified using the Dictionary of Secondary Structure of Proteins (DSSP) algorithm. Leveraging robust Python libraries such as "Biopython" for data handling, "Flask" for Graphical User Interface (GUI), "Jinja2", and "wkhtmltopdf" for visualization, ProS2Vi offers a modern and intuitive representation for visualization of the DSSP assigned secondary structures to each residue of any proteins' amino acid sequence. Significant features of ProS2Vi include customizable icon colors, the number of residues per line, and the ability to export visualizations as scalable PDFs, enhancing both visual appeal and functional versatility through a user-friendly GUI. We have designed ProS2Vi specifically for secure and local operation, which significantly increases security when working with novel protein data.

Integrated UPLC-ESI-MS/MS, network pharmacology, and transcriptomics to reveal the material basis and mechanism of Schisandra chinensis Fruit Mixture against diabetic nephropathy

Backgrounds

It has been regarded as an essential treatment option for diabetic nephropathy (DN) in Traditional Chinese medicine. Previous studies have demonstrated the anti-DN efficacy of Schisandra chinensis Fruit Mixture (SM); however, a comprehensive chemical fingerprint is still uncertain, and its mechanism of action, especially the potential therapeutic targets of anti-DN, needs to be further elucidated.

Objective

Potential mechanisms of SM action on DN were explored through network pharmacology and experimental validation.

Methods

The chemical composition of SM was analyzed using UPLC-ESI-MS/MS technology. Active bioactive components and potential targets of SM were identified using TCMSP, SwissDrugDesign, and SymMap platforms. Differentially expressed genes were determined using microarray gene data from the GSE30528 dataset. Related genes for DN were obtained from online databases, which include GeneCards, OMIM and DisGeNET. PPI networks and compound-target-pathway networks were constructed using Cytoscape. Functional annotation was performed using R software for GO enrichment and KEGG pathway analysis. The DN model was built for experimental validation using a high-sugar and high-fat diet combined with STZ induction. Hub targets and critical signaling pathways were detected using qPCR, Western blotting and immunofluorescence.

Results

Utilizing the UPLC-ESI-MS/MS coupling technique, a comprehensive analysis identified 1281 chemical components of SM's ethanol extract, with 349 of these components recognized as potential bioactive compounds through network pharmacology. Through this analysis, 126 shared targets and 15 HUB targets were pinpointed. Of these, JAK2 is regarded as the most critical gene. Enrichment analysis revealed that SM primarily operates within the PI3K/AKT signaling pathway. In vivo experiments confirmed that SM improved pathological injury and renal function in rats with DN while improving mitochondrial morphology and function and modulating the expression of proteins linked to apoptosis (cleaved-caspase-3, Bax, and Bcl-2) and pro-inflammatory factors (IL-6 and TNF-α). Mechanistically, SM alleviates DN primarily by suppressing the PI3K/AKT/mTOR and JAK2/STAT3 signaling pathways to fulfill the energy needs of renal tissues. Furthermore, molecular docking analysis provided direct validation of these findings.

Conclusion

The findings of this study offer initial indications of the active component and robust anti-inflammatory and anti-apoptotic characteristics of SM in the mitigation of DN, along with its capacity to safeguard the integrity and functionality of mitochondria. This research unequivocally validates the favorable anti-DN effects of SM, indicating its potential as a viable pharmaceutical agent for the management of DN.

Three STEPs forward: A trio of unexpected structures of PTPN5

Protein tyrosine phosphatases (PTPs) play pivotal roles in myriad cellular processes by counteracting protein tyrosine kinases. Striatal-enriched protein tyrosine phosphatase (STEP, PTPN5) regulates synaptic function and neuronal plasticity in the brain and is a therapeutic target for several neurological disorders. Here, we present three new crystal structures of STEP, each with unexpected features. These include high-resolution conformational heterogeneity at multiple sites, and a highly coordinated citrate molecule in the active site, a previously unseen conformational change at an allosteric site, an intramolecular disulfide bond that was characterized biochemically but had never been visualized structurally, and two serendipitous covalent ligand binding events at surface-exposed cysteines that are nearly or entirely unique to STEP among human PTPs. Together, our results offer new views of the conformational landscape of STEP that may inform structure-based design of allosteric small molecules to specifically inhibit this biomedically important enzyme.

In situ and in vitro cryo-EM reveal structures of mycobacterial encapsulin assembly intermediates

Prokaryotes rely on proteinaceous compartments such as encapsulin to isolate harmful reactions. Encapsulin are widely expressed by bacteria, including the Mycobacteriaceae, which include the human pathogens Mycobacterium tuberculosis and Mycobacterium leprae. Structures of fully assembled encapsulin shells have been determined for several species, but encapsulin assembly and cargo encapsulation are still poorly characterised, because of the absence of encapsulin structures in intermediate assembly states. We combine in situ and in vitro structural electron microscopy to show that encapsulins are dynamic assemblies with intermediate states of cargo encapsulation and shell assembly. Using cryo-focused ion beam (FIB) lamella preparation and cryo-electron tomography (CET), we directly visualise encapsulins in Mycobacterium marinum, and observed ribbon-like attachments to the shell, encapsulin shells with and without cargoes, and encapsulin shells in partially assembled states. In vitro cryo-electron microscopy (EM) single-particle analysis of the Mycobacterium tuberculosis encapsulin was used to obtain three structures of the encapsulin shell in intermediate states, as well as a 2.3 Å structure of the fully assembled shell. Based on the analysis of the intermediate encapsulin shell structures, we propose a model of encapsulin self-assembly via the pairwise addition of monomers.

Effect of Rhei Radix Et Rhizome on treatment of polycystic ovary syndrome by regulating PI3K/AKT pathway and targeting EGFR/ALB in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Abnormal endocrine metabolism caused by polycystic ovary syndrome (PCOS) poses a serious risk to reproductive health in females. According to Traditional Chinese Medicine (TCM) theories, the leading causes of PCOS include turbid phlegm, blood stasis and stagnation of liver Qi. Rhei Radix Et Rhizome is widely used in TCM to attack stagnation, clear damp heat, relieve fire. Rhei Radix Et Rhizome is an important part of the TCM formulas for the treatment of PCOS, which has a long history of medicinal use. However, the specific effect and mechanisms of Rhei Radix Et Rhizome on PCOS have yet to be elucidated.

AIM OF THE STUDY

The object of this study aimed to investigate the effect and its pharmacological mechanism of Rhei Radix Et Rhizome on the treatment of polycystic ovary syndrome.

METHODS

PCOS was induced in female Sprague Dawley (SD) rats by administering letrozole (1 mg/kg, per orally, p.o.) for 21 days, then treated with Rhei Radix Et Rhizome at doses of 0.6 g/kg or 1.2 g/kg. Rats weight, blood glucose and estrus period are measured, and serum hormone include free testosterone (T), luteinizing hormone (LH), follicle-stimulating hormone (FSH) and ovarian lesions were observed to determine the effects of Rhei Radix Et Rhizome. Network pharmacology and molecular docking predicted the targets of Rhei Radix Et Rhizome on PCOS. Epidermal growth factor receptor (EGFR), albumin (ALB), PI3K and P-AKT/AKT protein expression levels in ovarian tissues were assessed by Western blot.

RESULTS

Rhei Radix Et Rhizome reduce abnormal weight and fasting blood glucose induced by letrozole (n = 5, p < 0.01), and improve the disturbed estrus cycle, reduce T, LH levels and LH/FSH ratio of PCOS rats (n = 4, p < 0.01). In addition, it alleviates the polycystic changes of ovaries in PCOS rats and reduces ovarian histopathological damage (n = 4, p < 0.01). Additionally, the core active components of Rhei Radix Et Rhizome for PCOS include Sennoside D_qt, Procyanidin B-5,3'-O-gallate, and Mutatochrome, which strongly bind to core therapeutic targets ALB and EGFR. Furthermore, the treatment reduces the increase of EGFR and ALB induced by letrozole (n = 4, p < 0.01). KEGG pathway enrichment analysis highlights endocrine resistance and prolactin signaling pathway, in both of which the PI3K/AKT pathway plays a crucial role. Our results show Rhei Radix Et Rhizome rescue the abnormal expression of PI3K/AKT pathway in PCOS rats (n = 4, p < 0.01). However, no significant dose-dependent relationship was observed in the tested dose range for the above experiments.

CONCLUSION

These findings suggest that Rhei Radix Et Rhizome can regulate the PI3K/AKT pathway and target EGFR and ALB to treat polycystic ovary syndrome in rats. This study provides a scientific basis for the use of Rhei Radix Et Rhizome in the treatment of PCOS and highlights its potential mechanism through modulation of the PI3K/AKT pathway.

Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR

The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, including cancers, genetic disorders, and hereditary diseases. CRISPR-Cas9 based genome editing is a multi-step process such as designing a precise gRNA, selecting the appropriate Cas protein, and thoroughly evaluating both on-target and off-target activity of the Cas9-gRNA complex. To ensure the accuracy and effectiveness of CRISPR-Cas9 system, after the targeted DNA cleavage, the process requires careful analysis of the resultant outcomes such as indels and deletions. Following the success of artificial intelligence (AI) in various fields, researchers are now leveraging AI algorithms to catalyze and optimize the multi-step process of CRISPR-Cas9 system. To achieve this goal AI-driven applications are being integrated into each step, but existing AI predictors have limited performance and many steps still rely on expensive and time-consuming wet-lab experiments. The primary reason behind low performance of AI predictors is the gap between CRISPR and AI fields. Effective integration of AI into multi-step CRISPR-Cas9 system demands comprehensive knowledge of both domains. This paper bridges the knowledge gap between AI and CRISPR-Cas9 research. It offers a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process. Furthermore, it provides details of 80 available CRISPR-Cas9 system-related datasets that can be utilized to develop AI-driven applications. Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. In the context of representation learning methods and classifiers/regressors, a thorough analysis of existing predictive pipelines is utilized for recommendations to develop more robust and precise predictive pipelines.