Introduction: Inflammatory bowel disease (IBD) poses a major threat to human health. Current pharmacological therapies primarily manage symptoms and are often associated with adverse effects.
Objective: To develop targeted natural drugs with fewer side effects for IBD therapy by identifying potential agents from medicinal and edible Chinese herbs (MECHs) and clarifying their underlying molecular mechanisms.
Methods: An integrated approach was employed, combining single-cell analysis, transcriptomics, reverse network pharmacology, immunological infiltration assessment, molecular docking, ADMET evaluation, and molecular dynamics (MD) simulations.
Results: Multi-omic integration identified nine differentially infiltrating immune cell types and a CXCL8-CXCR2-driven neutrophil communication axis. Frequent intercellular communication was observed among neutrophils, epithelial cells, monocytes, B cells, and T cells. Topological screening yielded 15 hub targets and identified MMP2 and PTGS2 as key targets. Molecular docking, ADMET analyses, and 100-ns MD simulations converged on the natural product (NP) MOL009551 (isoprincepin) as a high-affinity, stable MMP2 binder (ΔG = -11.0 kcal/mol), supporting MMP2-directed isoprincepin as a novel therapeutic candidate for IBD.
Discussion: Bioinformatic analyses suggest that MMP2 may play an important role in IBD, and isoprincepin, identified from MECHs, may serve as a potential therapeutic agent by modulating MMP2 activity. However, experimental validation of their direct interaction and therapeutic efficacy remains necessary, along with further mechanistic and preclinical studies to clarify their potential for IBD treatment.
Conclusion: This study provides a comprehensive understanding of the molecular mechanisms underlying IBD, identifies MMP2 as a key target, and highlights isoprincepin as a promising natural product for IBD therapy.
Introduction: Prostate cancer is one of the most prevalent malignancies and a leading cause of cancer-related deaths among men. The androgen receptor (AR) plays a pivotal role in the development and progression of prostate cancer, making it a promising therapeutic target. This study aimed to evaluate the therapeutic potential of phytochemicals derived from the fruit of Ficus hispida in inhibiting the androgen receptor (PDB ID: 5T8E), thereby contributing to the treatment of prostate cancer.
Methods: Phytochemicals from Ficus hispida fruit were screened using molecular docking to assess their binding affinity to the androgen receptor. Subsequently, ADMET profiling and PASS online predictions were used to evaluate drug-likeness and anticancer potential. Molecular dynamics (MD) simulations (100 ns) were conducted to confirm the binding stability of the top candidates with the target protein.
Results: Five phytochemicals, Nodakenetin (CID: 26305), Isowigtheone hydrate (CID: 66728267), Methyl chlorogenate (CID: 6476139), 7-Hydroxycoumarin (CID: 5281426), and Gallic acid (CID: 370), were identified with high binding affinity and favorable binding free energy. The 100-ns MD simulations validated the structural stability of these phytochemical- AR complexes, indicating strong and stable interactions.
Conclusion: The identified phytochemicals from Ficus hispida demonstrate significant potential to inhibit androgen receptor activity and could serve as promising candidates for developing therapeutic agents against prostate cancer.
Introduction: The aim of the study was to investigate the mechanism of Qi Zhu Formula (QZF) against Metabolic-Associated Fatty Liver Disease (MAFLD) via network pharmacology and experimental validation.
Methods: Network pharmacology identified QZF components, targets, and pathways for MAFLD. Key predicted AMPK pathway targets (SREBP1C, FASN, ACC1) were validated. MAFLD was induced in rats with a 16-week high-fat/high-sugar diet. Low/medium/high QZF doses and positive control (YSF) were administered for 8 weeks. Serum parameters (liver function, lipids, glucose, cytokines, oxidative stress markers), liver histopathology (HE, Oil Red O), and hepatic mRNA/protein levels (SREBP1C, FASN, ACC1, p-AMPK) were assessed. In vitro, lipid accumulation and protein expression (p-AMPK, SREBP1C, FASN, ACC1) were measured in fatty AML12 cells treated with control/model/normal serum/QZF serum/AMPK inhibitor/ QZF serum + inhibitor.
Results: Network pharmacology identified 36 QZF components, 236 targets, and 138 intersecting MAFLD targets, enriching the AMPK pathway. QZF significantly reduced liver steatosis, inflammation, necrosis, serum liver enzymes, lipids, glucose, IL-6, IL-1β, TNF-α, FFA, MDA, and increased SOD in MAFLD rats. QZF upregulated hepatic p-AMPK protein and downregulated SREBP1C, FASN, and ACC1 mRNA/protein. QZF serum reduced lipid droplets in cells, most effectively at 24h, increasing p-AMPK and decreasing SREBP1C/FASN/ACC1 protein. AMPK inhibitor abolished QZF serum's effects.
Discussion: QZF's AMPK-mediated lipid suppression advances TCM mechanism validation, though unexamined pathways and compound synergies require exploration.
Conclusion: QZF ameliorates MAFLD by improving serum profiles, inhibiting lipid synthesis (via AMPK activation, suppressing SREBP1C/FASN/ACC1), reducing inflammation, and attenuating liver injury. Its "multi-target-multi-pathway" action supports its potential as a novel MAFLD treatment.
Introduction: This study aimed to investigate the therapeutic mechanism of coptisine in rotator cuff injury (RCI) through network pharmacology and experimental validation. This is the first study to examine the role of coptisine in rotator cuff injury (RCI), revealing a novel mechanism by which coptisine inhibits the PI3K/Akt/mTOR pathway, thereby coordinating inflammation resolution and tendon repair.
Methods: Network pharmacology was used to identify potential coptisine and RCI targets, which were then analyzed functionally to indicate critical pathways. A rat RCI model (right supraspinatus tendon transection) was used to validate the mechanism by detecting pathological changes, inflammatory factors, and mRNA expression related to the PI3K/Akt/mTOR pathway.
Results: Network pharmacology identified 29 overlapping coptisine and RCI targets, with an emphasis on the PI3K/Akt/mTOR pathway. Coptisine reduced tendon atrophy and inflammation in RCI rats, lowered blood TNF-α and IL-6 levels, elevated IL-10, and decreased PI3K, Akt, and mTOR mRNA expression in tendon tissues.
Conclusion: Coptisine improved RCI in rats by decreasing inflammation and the PI3K/Akt/ mTOR pathway, suggesting a possible therapeutic target for RCI.
Introduction: Protein-Protein Interactions (PPI) are crucial for cellular functions. Computational prediction of protein complexes from PPI networks is essential, yet traditional methods relying solely on network topology often lack biological features. Integrating topological and biological features can enhance prediction accuracy.
Methods: We proposed TOP-BIOCom, a machine learning-based approach that integrates feature fusion of novel topological, structural, and sequence-based features with the Embedding Lookup technique. The benchmark dataset was CYC2008, while the PPI network datasets were DIP and BioGrid. The performance evaluation measures precision, recall, and F-1 score were carried out to assess the efficiency of the TOP-BIOcom model and compared with the reported models.
Results: Our result with a novel feature fusion approach, demonstrated that the BioGrid PPI network dataset with Random Forest yielded an accuracy of 0.99, precision of 0.96, recall of 0.97, and an F1-score of 0.96. The model's validation accuracy was 0.99 and completed the task in 3.85 seconds. DIP dataset with LightGBM model achieved an accuracy of 0.95, with a precision of 0.88, a recall of 0.91, and an F1-score of 0.89. The validation accuracy matched the accuracy at 0.95.
Discussion: These results highlight the robustness of the proposed TOP-BIOcom model in predicting protein complexes from PPI networks with higher accuracy and faster execution. The proposed approach demonstrates superiority over existing methods, showing its effectiveness across different datasets and machine learning models.
Conclusion: These findings suggest that integrating topological and biological features can provide a holistic view of protein complexes enhancing prediction accuracy and aiding in drug discovery and understanding cellular mechanisms.
Introduction: Penicillin G Acylase (PGA) plays a central role in the synthesis of β- lactam antibiotics. While certain variants have been extensively studied, their catalytic efficiency remains suboptimal for industrial application, necessitating further enzyme engineering to enhance substrate binding and reaction kinetics. This study aims to rationally design and engineer PGA variants with improved catalytic efficiency and stability toward β-lactam antibiotics, using an integrated approach of 4D QSAR modeling and neural network-guided mutation prediction.
Method: A dataset of 30 enzyme-substrate complexes involving three PGA variants and diverse β-lactam substrates was compiled. Ten complexes were randomly selected for external validation. The binding conformation of Cefotaxime to a Bacillus thermotolerans PGA variant was used as a reference for molecular docking and structural alignment. Binding site analyses identified optimal substrate orientations, followed by 4D grid-based energy profiling, which revealed 15 high-energy hotspot residues per variant. These positions were systematically mutated in silico, generating 1130 variants through a neural network-based residue substitution algorithm.
Results: Subsequent docking studies with Cefotaxime showed a strong positive correlation between predicted docking energies and Ki values derived from the 4D QSAR model, validating the model's predictive capability. Molecular dynamics simulations (2 × 100 ns) for selected variants, particularly Sequence Id_0, Id_2, Id_5, and Id_7, demonstrated stable binding interactions and favourable atomic distances, indicative of improved substrate affinity.
Discussion: In Sequence Id_11, the hotspot is Phe148. Chain A showed the best results with Val and Leu as single mutants, followed by Met56 in Chain B with Leu, and Ser144 in Chain A with Glu, Ala, Ile, and Arg. In the case of Sequence Id_03, the hotspot is Phe147. Chain A showed good results with Ala, Lys, Thr, and Ser, whereas Tyr71 in Chain B showed good results with Glu, Lys, and Thr, and Arg266 in Chain B showed good results with Ala, Thr, and Val. Those that showed the highest sum of docking scores and Ki were chosen for further studies.
Conclusion: The study highlights the critical role of residue Phe148 in mediating stable interactions with Cefotaxime and other β-lactam substrates. The integrated computational strategy establishes a robust framework for engineering catalytically superior PGA variants, offering a valuable basis for further experimental validation and application in antibiotic biosynthesis.
Introduction: DNA methyltransferase 1 (DNMT1) has recently emerged as a potential therapeutic target for diabetic wound healing (DWH). Studies have shown that inhibition of DNMT1 may be valuable in accelerating DWH.
Method: Virtual screening of 3,646 phytochemicals derived from the IMPPAT database was performed against DNMT1. This was followed by exhaustive docking, ADMET analysis, and molecular dynamics simulation to identify potential phytochemical inhibitors of DNMT1.
Results: Out of the 17967 phytochemicals present in the database, 3646 of them were chosen for fast screening based on their drug-likeness properties. When compared with the reference compound, over 2500 compounds exhibited lower binding energies. The top 972 compounds having binding energies ≤ 8.7 kcal/mol were chosen, and 40 out of 972 compounds passed through the ADMET filters. These were then subjected to molecular docking, and the compound with the least binding energy and favourable hydrogen bonding was then selected for molecular dynamics simulation. The stability of the Oroxindin-DNMT1 complex was further validated by molecular dynamics simulation studies.
Discussion: Derived from the traditional Chinese remedy Huang-Qin, Oroxindin has been shown to possess a range of pharmacological effects, including anti-inflammatory, antitumor, and antioxidant properties. The wound-healing potential of Oroxindin has to be evaluated in vitro and in vivo for further validation.
Conclusion: Oroxindin emerged as the ideal phytochemical among the 3,646 screened. The ability of Oroxindin to accelerate DWH still needs to be evaluated in vitro and in vivo for further validation.
Introduction: Diabetes mellitus is an endocrine disorder characterized by metabolic abnormalities and chronic hyperglycemia, caused by insulin deficiency (Type I) or resistance (Type II). It affects various tissues differently, and its complications extend beyond classical targets, such as the kidneys and eyes, to lesser-studied organs, including the lungs. Understanding tissue-specific damage is crucial for effective disease management and the prevention of complications.
Objective: This study aims to evaluate the histopathological and immunohistochemical effects of diabetic lung fibrosis using a streptozotocin (STZ)-induced diabetes model. Additionally, it seeks to develop a high-performance image classification system based on deep neural networks to accurately classify tissue damage in diabetic models.
Methods: Lung tissue samples were collected from the STZ-induced diabetes model and analyzed through histopathological and immunohistochemical techniques. Image data were further processed using convolutional neural networks (CNNs), including pre-trained models, such as ResNet50, VGG16, and SqueezeNet. Classification was conducted in multiple color spaces (RGB, Grayscale, and HSV) and evaluated using performance metrics, including confusion matrix, precision, recall, F1 score, and accuracy.
Results and discussion: The use of color significantly enhanced image patch classification performance. Among the models tested, SqueezeNet in the RGB color space demonstrated the highest accuracy, achieving an F1 score of 93.49% ± 0.04 and an accuracy of 93.77% ± 0.04. These results indicated the efficacy of CNN-based classification in detecting lung damage associated with diabetes.
Conclusion: Our findings confirmed that diabetes induces histopathological changes in lung tissue, contributing to fibrosis and potential pulmonary complications. Deep learning-based classification methods, particularly when utilizing color space variations and advanced preprocessing techniques, provide a powerful tool for analyzing diabetic tissue damage and may aid in the development of diagnostic support systems.
Introduction: The study aims to explore selective potential inhibitors for the homologous BD1/BD2 domains of bromodomain-containing protein 4 (BRD4) and uncover the binding mechanisms between these inhibitors and BD1/BD2. Given BRD4's role as an epigenetic regulator and its potential in treating triple-negative breast cancer (TNBC), overcoming the challenge of domain-specific inhibition due to the structural similarity of BD1 and BD2 is crucial.
Methods: For comparison with experimental research, FL-411 was selected as a novel inhibitor for BD1/BD2. The AutoDock vina method was employed to screen potential lead compounds of BD1/BD2 from Traditional Chinese herbal medicines (TCMs) for nervous diseases. Molecular dynamics (MD) simulations were conducted to investigate the interaction mechanisms between BD1/BD2 and potential inhibitors (miltirone/FL-411).
Results: The analysis shows that the inhibitors stabilize the conformation of BD1/BD2 and enhance their hydrophobic and salt-bridge interactions. Notably, atomic interaction studies reveal that the oxygen atom of FL-411 binds with E85 of BD1, while the 1,1-Dimethylcyclohexane group of miltirone binds with H437 of BD2, indicating the selective characteristics of these potential inhibitors.
Discussion: The study reveals key structural determinants for BD1/BD2 selectivity, addressing a major challenge in BRD4-targeted drug design. MD simulations corroborate experimental data, validating the screening approach.
Conclusion: Based on conformational characters of FL-411/miltirone and atomic interaction mechanism of BD1/BD2 and inhibitors, the potential inhibitors with a new skeleton and lower binding energy were generated with artificial intelligence drug discovery (AIDD) methods.
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