Machine-learning methods for ligand-protein molecular docking

To investigate the effect and mechanism of tetrahydrocurcumin on hepatocellular carcinoma based on phosphoinositide 3-kinases/AKT signaling pathway

BACKGROUND

Liver cancer has a high incidence and mortality worldwide, especially in China. Herein, we investigated the therapeutic effect and mechanism of tetrahydrocurcumin against hepatocellular carcinoma (HCC), with a focus on the of phosphoinositide 3-kinases (PI3K)/AKT signaling pathway.

AIM

To investigate the effects and mechanism of tetrahydrocurcumin in HCC cell lines HepG2 and Huh7.

METHODS

Using Metascape, we analyzed the potential targets of tetrahydrocurcumin in HCC. Molecular docking validation was performed using SYBYL2.0. Cell Counting Kit-8, wound healing, and transwell assays were performed to evaluate the effects of tetrahydrocurcumin on HepG2 and Huh7 cell migration, invasion, and apoptosis. The expression of PI3K/AKT signaling pathway-related proteins was detected by western blotting.

RESULTS

Network pharmacology and molecular docking showed that tetrahydrocurcumin has high binding affinity for phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha. In vitro experiments demonstrated that tetrahydrocurcumin suppressed the migration and invasion of liver cancer cells, promoted their apoptosis, and downregulated the expression of p-PI3K, p-AKT, and B cell leukemia/lymphoma 2, while upregulating caspase-3, p53, and B cell leukemia/lymphoma 2 associated X.

CONCLUSION

In summary, tetrahydrocurcumin suppresses PI3K/AKT signaling, promotes apoptosis, and prevents the migration and invasion of liver cancer cells.

Association between intraoperative electroencephalograph complexity index and postoperative delirium in elderly patients undergoing orthopedic surgery: a prospective cohort study

PURPOSE

The primary method for predicting POD (postoperative confusion) relies on the analysis of clinical features. Brain activity complexity is a promising factor associated with the state of consciousness. The aim of this study was to investigate the role of EEG (electroencephalography) complexity changes in predicting POD in elderly patients undergoing orthopedic surgery.

METHODS

From January 2024 to August 2024, 289 elderly patients undergoing orthopedic surgery were recruited at the Second Affiliated Hospital of Nanjing Medical University. Intraoperative EEG data from patients were collected and then EEG nonlinear features were extracted by MATLAB custom scripts. The logistic regression and CNN (convolutional neural networks) were used to explore the predictive effect of nonlinear features on POD from both static and dynamic perspectives.

RESULTS

Low permutation Lempel-Ziv complexity (PLZC) among the EEG nonlinear features emerged as an independent risk factor for POD [OR = 0.210; 95% CI (0.050-0.850); p = 0.029]. Receiver operating characteristic curve (ROC) analysis revealed a poor area under the curve of 0.615 (95% CI 0.517-0.711) for PLZC in predicting POD. After the inclusion of temporal factors, the ROC analysis indicated that the EEG nonlinear indices had a moderate predictive effect on POD [AUC = 0.701; (95% CI 0.541-0.862)].

CONCLUSIONS

EEG nonlinear feature indices may be effective biomarkers for POD and could help predict POD in elderly patients undergoing orthopedic surgery.

Advances in bioinformatic methods for the acceleration of the drug discovery from nature

BACKGROUND

Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE

This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN

It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS

A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS

Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION

Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

Investigating the possible mechanism of Cornus officinalis in the therapy of ischemic stroke by UHPLC-Q-TOF-MS, network pharmacology, molecular docking, and experimental verification

ETHNOPHARMACOLOGICAL RELEVANCE

Cornus officinalis is a conventional Chinese medicine for tonifying liver and kidney in ancient China. The active ingredients from Cornus officinalis can delay the progression of cerebral aneurysms, alleviate experimental autoimmune encephalomyelitis, and show a good intervention effect on brain diseases. Loganin, the active ingredient of Cornus officinalis, has a neuroprotective effect on cerebral ischemia-reperfusion injury in mice. It is yet unknown, nevertheless, how Cornus officinalis works to treat ischemic stroke.

AIM OF THE STUDY

Based on ultra-high performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UHPLC-Q-TOF-MS), network pharmacology and molecular docking, Cornus officinalis's mechanism of intervention in ischemic stroke is explored and verified by experiments.

MATERIALS AND METHODS

To examine the chemical components of Cornus officinalis, UHPLC-Q-TOF-MS was used. The network pharmacology was used to construct the "active ingredient-core target-main pathway" network of Cornus officinalis. Then, the link between the main active components and the key protein targets, as determined by network pharmacology, was verified through the application of molecular docking. The middle cerebral artery occlusion/reperfusion (MCAO/R) rat model used in this study was created using the suture technique. The pharmacological effects of Cornus officinalis were explored by neurological function score, behavior, TTC staining, ultrasound and flow cytometry. Western blot and qPCR were used to confirm the core target.

RESULTS

The outcomes of the investigation demonstrated that Cornus officinalis had a potent anti-ischemic stroke effect. UHPLC-Q-TOF-MS method was used to determine 24 chemical constituents in Cornus officinalis, of which 22 components had a close relationship with protein targets relevant to ischemic stroke. The 27 protein targets screened by "active ingredient-core target-main pathway" may be the possible targets of Cornus officinalis in the therapy of ischemic stroke. Most of the 27 protein targets had to do with the inflammatory response, apoptosis and energy metabolism. KEGG enrichment analysis showed that AGE/RAGE ranked high and was closely related to inflammatory response. Molecular docking predicted that the top 10 components in the network diagram had good binding with inflammatory factors IL6, IL-1β and TNF-α protein targets. Western blot research outcomes stated that Cornus officinalis could firmly impede the production of AGE, RAGE, and P-NFκB P65. Cornus officinalis had the potential to prevent ischemic stroke by drastically inhibiting the production of TNF-α, IL-1β, and IL-6, according to the results of qPCR study.

CONCLUSION

This study found that Cornus officinalis can improve the brain injury, motor ability and blood flow velocity of MCAO/R rats and suppress the inflammatory reaction through the AGE/RAGE/NFκB pathway to exert the therapeutic effect on ischemic stroke.

Interaction of major tea polyphenols with bovine milk proteins and its effect on in vitro bioaccessibility of tea polyphenols

Adding bovine milk into tea and tea-containing beverages has been popular nowadays, while this dietary system may affect the digestion and absorption of tea polyphenols. In this work, the interactions of tea polyphenols with bovine milk proteins and their impacts on the bioaccessibility of tea polyphenols were investigated. The results indicate that tea polyphenols interact with amino acid residues of bovine milk proteins through hydrogen bonds and van der Waals forces spontaneously. Tea polyphenols cause static fluorescence quenching of bovine milk proteins, with different interaction types through one binding site. The interaction between tea polyphenols and bovine milk proteins forms a complex, which reduces the contents of α-helix, β-turn, and random coil in the secondary structure of bovine milk proteins while increasing the β-sheet content. Tea polyphenol-bovine milk protein interaction can enhance the bioaccessibility of tea polyphenols, with esterified tea polyphenols epicatechin gallate and epigallocatechin gallate showing better improvement effects.

Integrating lipid metabolomics, serum medicinal chemistry, network pharmacology and experimental validation to explore the mechanism of Sanmiao wan in the treatment of rheumatoid arthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Rheumatoid arthritis (RA) is a common autoimmune disease with a high clinical morbidity and leads to persistent chronic inflammation. Sanmiao wan is a classic formula for the treatment of RA, and the results of clinical and experimental studies have shown its therapeutic effect on RA. However, its mechanism of action remains unclear.

AIM OF THE STUDY

The aim of this study was to evaluate the effect of Sanmiao wan on RA rats and to further explore its protective mechanism.

MATERIALS AND METHODS

Research was conducted using RA models induced by Freund's adjuvant complete, and the degree of arthritis, bone destruction, histopathological and clinical chemical indexes of RA model rats were used to evaluate the animal model and the therapeutic effect of Sanmiao wan. A combination of lipid metabolomics, serum medicinal chemistry, network pharmacology, molecular docking and experimental validation was used to systematically elucidate the potential mechanism of action of Sanmiao wan in the treatment of RA.

RESULT

Pharmacodynamic results showed that Sanmiao reduced joint swelling and improved immunity, and the results of non-targeted lipid metabolomics showed a total of 6 lipid core markers, which were hypothesised to play a therapeutic role in RA by modulating the glycerophospholipid metabolism and sphingolipid metabolism pathways. Using serum medicinal chemistry, we identified 19 blood components and predicted the targets related to RA, and combined with network pharmacology, we screened a total of 59 components and disease-cross-cutting targets, and the enrichment analysis and network pharmacology and KEGG results indicated that the core targets were TNF, IL6, MMP3, and the key metabolic pathways were TNF signaling pathway, lipid and The key metabolic pathways are TNF signaling pathway, lipid and atherosclerosis, rheumatoid arthritis, IL-17 signaling pathway and sphingolipid signaling pathway, etc. It was verified by molecular docking and ELISA experiments that palmatine, cyasterone, atractylenolide I, atractylenolide III, wogonoside, wogonin, phellodendrine, and berberine in Sanmiao could reduce the activity of these targets, thereby inhibiting the expression of inflammatory factors TNF-α, IL6, IL17, RF, MMP3, STAT3.

CONCLUSIONS

Sanmiao has a good therapeutic effect on RA, and for the first time, it was found that its potential mechanism of action may be to treat RA by decreasing the activities of TNF, IL6, MMP3 and modulating glycerophospholipid metabolism and sphingolipid metabolism.It provides a solid basis for the clinical application of Sanmiao wan.

Exploring the therapeutic potential of Xiangsha Liujunzi Wan in Crohn's disease: from network pharmacology approach to experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Xiangsha Liujunzi Wan (LJZW) is a traditional Chinese medicine (TCM) formula containing a variety of traditional Chinese herb components. Its principal components are often used in the treatment of gastrointestinal diseases and contribute to the treatment of Crohn's disease (CD).

AIM OF THE STUDY

To explore the therapeutic potential of LJZW in CD through network pharmacology, bioinformatics, molecular docking, and experimental verification.

METHODS

The principal bioactive components and corresponding targets of LJZW were ascertained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential targets for CD were identified in GeneCards, OMIM, DrugBank, DisGeNET, CTD, and Gene Expression Omnibus (GEO) databases. Intersection targets of LJZW and CD were identified using a Venn diagram and visualized using Cytoscape 3.8.0 to construct a protein-protein interaction (PPI) network. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to assess the function of intersection targets. AutoDockTools and PyMOL were used for molecular docking to recognize the association between the core ingredients of LJZW and the core targets of CD. Subsequently, a series of experiments were conducted for validation.

RESULTS

The network pharmacology results indicated that there were 156 bioactive components and 268 corresponding targets for LJZW, 3023 primary relevant targets for CD, and 169 intersection targets for LJZW and CD. The PPI network was employed to identify five hub genes and six clusters. The GO functional analysis indicated that intersection targets are primarily correlated with oxidative stress and inflammatory responses. KEGG pathway analysis revealed that these targets were primarily associated with the phosphotylinosital 3 kinase (PI3K)-protein kinase B (AKT) and mitogen-activated protein kinase (MAPK) signaling pathways. The molecular docking results showed that the core ingredients of LJZW had good binding ability with the core targets of CD. A series of experiments demonstrated that LJZW could effectively attenuate TNBS-induced colitis symptoms, inhibit the inflammatory response, and protect intestinal barrier function by inhibiting the PI3K-AKT and MAPK signaling pathways, thus preventing and treating CD.

CONCLUSION

LJZW has the characteristics of multi-component, multi-target, and multi-pathway treatment, which helps to improve the treatment of CD, protect the intestinal barrier, and exert the effect of anti-inflammatory therapy by inhibiting PI3K-AKT and MAPK signaling pathways.

PPDock: Pocket Prediction-Based Protein-Ligand Blind Docking

Predicting the docking conformation of a ligand in the protein binding site (pocket), i.e., protein-ligand docking, is crucial for drug discovery. Traditional docking methods have a long inference time and low accuracy in blind docking (when the pocket is unknown). Recently, blind docking techniques based on deep learning have significantly improved inference efficiency and achieved good docking results. However, these methods often use the entire protein for docking, which makes it difficult to identify the correct pocket and results in poor generalization performance. In this study, we propose a two-stage docking paradigm, where pocket prediction is followed by pocket-based docking. Following this paradigm, we design a new blind docking method based on pocket prediction (PPDock). Through extensive experiments on benchmark data sets, our proposed PPDock outperforms existing methods in nearly all evaluation metrics, demonstrating strong docking accuracy, generalization ability, and efficiency.

Signaling lymphocytic activation molecule family 8 disrupts epithelial barrier in chronic rhinosinusitis with nasal polyps through M1 macrophage polarization

BACKGROUND

Recent studies reveal that M1 macrophages accumulate predominantly in noneosinophilic chronic rhinosinusitis with nasal polyps (neCRSwNP). However, the precise mechanisms regulating M1 macrophages and their impact on the epithelial barrier remain unclear.

OBJECTIVE

To investigate the expression and regulatory role of signaling lymphocytic activation molecule family (SLAMF)8, a molecule exclusively expressed in myeloid cells, in M1 macrophage polarization and its potential contribution to neCRSwNP development.

METHODS

We evaluated SLAMF8 expression and its correlation with clinical variables using real-time quantitative polymerase chain reaction and Western blot in sinonasal mucosa samples from CRSwNP and control subjects. Immunofluorescence staining confirmed the co-expression of SLAMF8 with macrophages. After SLAMF8 knockdown, we explored the influence on macrophage M1 polarization and the effect on epithelial-mesenchymal transition (EMT) process and tight junction integrity in epithelial cells through an indirect co-culture system of M1 macrophages with human nasal epithelial cells.

RESULTS

SLAMF8 was highly expressed on M1 macrophages in polyp tissues, notably in neCRSwNP, and correlated with disease severity indices only in neCRSwNP. SLAMF8 knockdown in THP-1 cells reduced M1 macrophage markers (CD86, iNOS, and NLRP3) and decreased secretion of inflammatory cytokines (interleukin-1 beta, interleukin-6, and tumor necrosis factor alpha). Co-culture with M1 macrophage supernatant after SLAMF8 knockdown enhanced epithelial viability, reduced EMT and apoptosis, and up-regulated tight junction markers, occludin and claudin-4, in nasal epithelial cells.

CONCLUSION

SLAMF8 elevation correlates with the EMT, epithelial tight junction, and disease severity in neCRSwNP. SLAMF8 up-regulation promotes M1 macrophage polarization, which facilitates EMT and impairs nasal epithelial barrier function. SLAMF8 may represent a novel therapeutic target for neCRSwNP.

Prognostic values of intracellular cell-related genes in esophageal cancer and their regulatory mechanisms

Esophageal cancer is a grave malignant condition. While radiotherapy, often in conjunction with chemotherapy, serves as a cornerstone in the management of locally advanced or metastatic cases, patient tolerance and treatment resistance frequently hinder its efficacy. Cell-in-cell structures, prevalent in various tumors, have been linked to prognosis. Hence, investigating the prognostic significance and regulatory mechanisms of genes related to these intracellular structures in esophageal cancer is imperative. The Cancer Genome Atlas (TCGA) Esophageal Cancer (ESCA) dataset served as the training set for the analysis. Differentially expressed genes (DEGs) in ESCA samples were identified, with those related to intercellular structures designated cell-in-cell-related differential expression genes (CIC-related DEGs). Cox regression analysis was employed to identify prognostic genes, categorizing samples into high- and low-risk groups based on median risk scores. Validation was conducted using the GSE53624 risk model. Established methodologies included morphological mapping, enrichment analysis, immune infiltration analysis, prognostic gene expression validation, molecular docking, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) validation. Thirty-eight intersecting genes were identified between the disease and normal groups in ESCA samples. Stepwise multivariate Cox analysis pinpointed three prognostic genes: androgen receptor (AR), C-X-C motif chemokine ligand 8 (CXCL8), and epidermal growth factor receptor (EGFR). The risk model's applicability was confirmed in the GSE53624 dataset, revealing eight significantly different immune-related gene sets. Prognostic gene expression validation demonstrated significant differences between the disease and normal groups in both datasets. The proteins corresponding to the three prognostic genes interacted with gefitinib and osimertinib. RT-PCR results corroborated the differential expression of prognostic genes in esophageal cancer tissues. This study identified AR, CXCL8, and EGFR as prognostic genes and demonstrated their molecular interactions with gefitinib and osimertinib, providing a foundation for ESCA diagnosis and treatment.

Exploring the Cardioprotective Mechanisms of Ligusticum wallichii in Myocardial Infarction Through Network Pharmacology and Experimental Validation

Background

Myocardial infarction represents a coronary artery ailment with the highest incidence and fatality rates among cardiovascular conditions. However, effective pharmacological interventions remain elusive. This study seeks to elucidate the molecular mechanisms underlying the effects of Ligusticum wallichii on myocardial infarction through network pharmacology and experimental validation.

Methods

Initially, potential targets of Ligusticum wallichii's active ingredients and myocardial infarction-related targets were retrieved from databases. Subsequently, core targets of Ligusticum wallichii on myocardial infarction were identified via the PPI network analysis and subjected to GO and KEGG pathway enrichment analyses. Molecular docking was employed to validate the binding affinities between the core targets and the bioactive components. The findings from network pharmacology analysis were corroborated through in vitro and in vivo experiments.

Results

Seven active ingredients from Ligusticum wallichii were identified, corresponding to 122 targets. Molecular docking revealed robust binding affinities of Myricanone, Senkyunone, and Sitosterol to key target proteins (EGFR, STAT3, and SRC). In vitro, experiments demonstrated that pretreatment with the active components of Ligusticum wallichii protected myocardial cells from OGD exposure and modulated the expression of their key target genes. In vivo, experiments showed that the active components of Ligusticum wallichii significantly improved myocardial infarction via alleviating myocardial fibrosis and oxidative stress and did not elicit toxic effects in mice.

Conclusion

The collective findings suggest that Ligusticum wallichii shows promising potential for myocardial infarction treatment by regulating key target proteins (EGFR, STAT3, and SRC), which play roles in oxidative stress and myocardial fibrosis.

Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity

Although the antiallergic properties of compounds such as CAPE, Melatonin, Curcumin, and Vitamin C have been poorly discussed by experimental studies, the antiallergic properties of these famous molecules have never been discussed with calculations. The histamine-1 receptor (H1R) belongs to the family of rhodopsin-like G-protein-coupled receptors expressed in cells that mediate allergies and other pathophysiological diseases. In this study, pharmacological activities of FDA-approved second generation H1 antihistamines (Levocetirizine, desloratadine and fexofenadine) and molecules such as CAPE, Melatonin, Curcumin, Vitamin C, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, density functional theory (DFT), molecular docking, biological targets and activities were compared by calculating. Since drug development is an extremely risky, costly and time-consuming process, the data obtained in this study will facilitate and guide future studies. It will also enable researchers to focus on the most promising compounds, providing an effective design strategy. Their pharmacological activity was carried out using computer-based computational techniques including DFT, molecular docking, ADMET analysis, biological targeting, and activity methods. The best binding sites of Desloratadine, Levocetirizine, Fexofenadine, CAPE, Quercetin, Melatonin, curcumin, Vitamin C ligands to Desmoglein 1, Human Histamine H1 receptor, IgE and IL13 protons were determined by molecular docking method and binding energy and interaction states were analyzed. Fexofenadine and Quercetin ligand showed the most effective binding affinity. Melatonin had the best Caco-2 permeability PPB values of Quercetin, CAPE and Curcumin were at optimal levels. On the OATP1B1 and OATP1B3 of curcumin and CAPE, Quercetin was found to have strong inhibition effects on BCRP. Melatonin and CAPE were found to have the highest inhibition values on CYP1A2, while CAPE had the highest inhibition values on CYP2C19 and CYP2C9. Vitamin C and Quercetin were found to be safer in terms of cardiac toxicity and mutagenic risks, while Desloratadine and Levocetirizine carried high risks of neurotoxicity and hematotoxicity, while CAPE was noted for its high enzyme inhibitory activities and low toxicity profiles, while the hERG blockade, DILI, and cytotoxicity values of other compounds pointed to various safety concerns. This study demonstrated the potential of machine learning methods in understanding and discovering H1 receptor blockers. The results obtained provide important clues in the development of important strategies in the clinical use of H1 receptor blockers. In the light of these data, CAPE and Quercetin are remarkable molecules.

Study on Interaction Between 5-(4 Methoxyphenyl)-1-Phenyl-1H-1,2,3-Triazole with High-Abundant Blood Proteins and Identification of Low-Abundant Proteins by Serum Proteomics

A comprehensive strategy, including spectroscopic, molecular simulation, proteomics, and bioinformatics techniques, was employed to investigate a novel triazole, 5-(4-methoxyphenyl)-1-phenyl-1H-1,2,3-triazole, its interactions with high-abundance blood proteins, and identification of low-abundance proteins. The binding constants and thermodynamic parameters of the triazole to two high-abundance blood globular proteins, human serum albumin, and human immunoglobulin G (HIgG), were obtained by spectroscopic techniques and computational chemistry. The two-dimensional gel electrophoresis in combination with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was employed to isolate and identify differentially expressed low-abundance proteins in human blood serum samples following exposure to the triazole. The results indicated that there is strong binding of the triazole to human serum albumin/HIgG and hydrophobic interaction plays a main role in the system. There were 21 highly expressed proteins identified from blood serum samples intervened by the triazole. By bioinformatics analysis, one of the differential proteins, kininogen-1 protein, was to explore the mechanism of action of 5-(4-methoxyphenyl)-1-phenyl-1H-1,2,3-triazole intervention on the kallikrein-kinin signaling pathways related to HeLa cervical cancer cells. The triazole displayed antiproliferative activity and significantly altered a kallikrein-10 expression, suggesting a possible antitumor mechanism involving the kallikrein-kinin system. These research findings provide scientific insights for further development and application of the 1,2,3-triazole compound. The study highlights the potential of the compound as a multifunctional pharmaceutical agent, particularly in cancer therapies, and lays the foundation for its future clinical applications in targeting drug-protein interactions.

Computational approach based on freely accessible tools for antimicrobial drug design

Antimicrobial drug development is crucial for public health, especially with the emergence of pandemics and drug resistance that prompts the search for new therapeutic resources. In this context, in silico assays consist of a valuable approach in the rational drug design because they enable a faster and more cost-effective identification of drug candidates compared to in vitro screening. However, once a potential drug is identified, in vitro and in vivo assays are essential to verify the expected activity of the compound and advance it through the subsequent stages of drug development. This work aims to outline an in silico protocol that utilizes only freely available computational tools for identifying new potential antimicrobial agents, which is also suitable in the broad spectrum of drug design. Additionally, this paper reviews relevant computational methods in this context and provides a summary of information concerning the protein-ligand interaction.

Aristolochic acids-hijacked p53 promotes liver cancer cell growth by inhibiting ferroptosis

Aristolochic acids (AAs) have been identified as a significant risk factor for hepatocellular carcinoma (HCC). Ferroptosis is a type of regulated cell death involved in the tumor development. In this study, we investigated the molecular mechanisms by which AAs enhanced the growth of HCC. By conducting bioinformatics and RNA-Seq analyses, we found that AAs were closely correlated with ferroptosis. The physical interaction between p53 and AAs in HepG2 cells was validated by bioinformatics analysis and SPR assays with the binding pocket sites containing Pro92, Arg174, Asp207, Phe212, and His214 of p53. Based on the binding pocket that interacts with AAs, we designed a mutant and performed RNA-Seq profiling. Interestingly, we found that the binding pocket was responsible for ferroptosis, GADD45A, NRF2, and SLC7A11. Functionally, the interaction disturbed the binding of p53 to the promoter of GADD45A or NRF2, attenuating the role of p53 in enhancing GADD45A and suppressing NRF2; the mutant did not exhibit the same effects. Consequently, this event down-regulated GADD45A and up-regulated NRF2, ultimately inhibiting ferroptosis, suggesting that AAs hijacked p53 to down-regulate GADD45A and up-regulate NRF2 in HepG2 cells. Thus, AAs treatment resulted in the inhibition of ferroptosis via the p53/GADD45A/NRF2/SLC7A11 axis, which led to the enhancement of tumor growth. In conclusion, AAs-hijacked p53 restrains ferroptosis through the GADD45A/NRF2/SLC7A11 axis to enhance tumor growth. Our findings provide an underlying mechanism by which AAs enhance HCC and new insights into p53 in liver cancer. Therapeutically, the oncogene NRF2 is a promising target for liver cancer.

Docking‑based virtual screening of BRD4 (BD1) inhibitors: assessment of docking methods, scoring functions and in silico molecular properties

To enhance the accuracy of virtual screening for bromodomain-containing protein 4 (BRD4) inhibitors, two docking protocols and seven scoring functions were compared. A total of 73 crystal structures of BRD4 (BD1) complexes were selected for analysis. Firstly, docking was carried out using both the LibDock and CDOCKER methods. The CDOCKER protocol was shown to be more effective based on the root mean square deviation (RMSD) values (in Å) between the docking positions and the co-crystal structures, achieving a docking accuracy rate of 86.3%. Then, among the various scoring functions (LigScore1, LigScore2, PLP1, PLP2, PMF, PMF04 and Ludi3), PMF showed the highest correlation with inhibition constants (r2 = 0.614), while Ludi3 scored lowest (r2 = 0.266). Finally, using ligand descriptors from PubChem, a strong correlation (r2 > 0.5) with inhibition constants for heavy atom count was found. Based on these comprehensive evaluations, the PMF scoring function emerged as the best tool for docking-based virtual screening of potential BRD4 (BD1) inhibitors. And the correlation between molecular properties and BRD4 (BD1) ligands also provided information for future design strategies.

Integrating network pharmacology and in vivo model to reveal the cardiovascular protective effects of kaempferol-3-O-rutinoside on heart failure

Kaempferol-3-O-rutinoside (KR) has an excellent cardioprotective effect, but its mechanism of action is not clear. Network pharmacology was used to predict the signaling pathways, whereas molecular docking was used for preliminary validation of KR binding to targets. AMI model rats with ligated left anterior descending coronary arteries were established. HE staining was used to detect pathological changes, and ELISA was used to detect the expression of TNF-α and IL-6. Network pharmacology results showed PI3K-AKT signaling pathway may be the main mechanism, and molecular docking predicted that KR could bind strongly to the PI3K and AKT. KR could significantly reduce cardiac pathological changes, decrease the level of TNF-α and IL-6, and enhance the mRNA and protein expressions of PI3K and AKT. KR ameliorates HF after AMI by enhancing the expressions of PI3K and AKT, which will be helpful in elucidating the mechanism of KR through multiple techniques.

Immunostimulatory activity of sea buckthorn polysaccharides via TLR2/4-mediated MAPK and NF-κB signaling pathways in vitro and in vivo

SP0.1-1, derived from Sea buckthorn (Hippophae rhamnoides L.), has been discovered to exhibit unique antioxidant activity. In this study, we investigated the immunomodulatory activity and mechanisms of SP0.1-1 on macrophage RAW 264.7 cells in vitro and immunosuppressive mice induced by cyclophosphamide in vivo. The results indicated SP0.1-1 strengthened the immune functions via promoting the proliferation of RAW264.7 cells and phagocytic activity, along with stimulating the release of NO, ROS and cytokines including TNF-α, IL-6, IL-1β and IFN-γ. Western blot and molecular docking analysis demonstrated that SP0.1-1 attached to the prime receptors TLR2 and TLR4 in RAW264.7 cells, and triggered the activation of MyD88-mediated MAPK and NF-κB signaling pathways, thereby exerting the immune response in RAW264.7 cells. However, the intervention of specific inhibitors against TLR2, TLR4, JNK, ERK, p38 and NF-κB blocked the TLR-mediated MAPK and NF-κB signaling pathways and downregulated the levels of NO and the aforementioned cytokines, thus suppressing the activation of macrophages. Therefore, it can be speculated that SP0.1-1 activated the macrophages principally via the TLR2/4-MyD88-mediated MAPK and NF-κB signaling pathways. Additionally, SP0.1-1 could protect against the cyclophosphamide-induced immunosuppression in mice, manifested by the improvement of body weight, immune organ indices, phagocytic index, and the relievement of spleen damage, along with the enhancement of cytokines TNF-α, IL-6, IFN-γ and immunoglobulin IgG and IgM. These findings will shed light on the molecular mechanism of SP0.1-1 on the immunoregulatory effect, and lay the foundation for exploiting a potential immunostimulatory agent of SP0.1-1.

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

LC-MS/MS analysis and α-glucosidase inhibitory effect of majonoside R2 in Vietnamese ginseng (Panax vietnamensis Ha & Grushv.)

Majonoside R2 (MR2), the principal saponin of Vietnamese ginseng (Panax vietnamensis Ha & Grushv.), has the unique structure of ocotillol-type dammarane and showed remarkable biological activities. This paper deals with the new findings in the chemical analysis MR2 by the tandem LC-MS/MS and, especially, its inhibitory activities on α-glucosidase for diabetic management. The developed LC-MS/MS method revealed advantages of high selectivity with specific mass transition from precursor [M + H]+ ion (m/z 784.4) into product ion (m/z 475.1), high sensitivity (calibration range: 5-250 ppb; LOD: 1.5 ppb; LOQ: 5.0 ppb), and high accuracy to support further pharmaceutical analysis of MR2. MR2 and its aglycone ocotillol relatively showed certain inhibitory effects on α-glucosidase in vitro with the IC50 values of 353.05 and 219.64 µg/mL and supported by molecular docking analysis, in which MR2 and ocotillol could play as allosteric inhibitors with high binding affinity (-7.8 and -8.1 kcal/mol) evidenced by bonding interactions.

A Computational Approach to Characterize the Protein S-Mer Tyrosine Kinase (PROS1-MERTK) Protein-Protein Interaction Dynamics

Protein S (PROS1) has recently been identified as a ligand for the TAM receptor MERTK, influencing immune response and cell survival. The PROS1-MERTK interaction plays a role in cancer progression, promoting immune evasion and metastasis in multiple cancers by fostering a tumor-supportive microenvironment. Despite its importance, limited structural insights into this interaction underscore the need for computational studies to explore their binding dynamics, potentially guiding targeted therapies. In this study, we investigated the PROS1-MERTK interaction using advanced computational analyses to support immunotherapy research. High-resolution structural models from ColabFold, an AlphaFold2 adaptation, provided a baseline structure, allowing us to examine the PROS1-MERTK interface with ChimeraX and map residue interactions through Van der Waals criteria. Molecular dynamics (MD) simulations were conducted in GROMACS over 100 ns to assess stability and conformational changes using RMSD, RMSF, and radius of gyration (Rg). The PROS1-MERTK interface was predicted to contain a heterogeneous mix of amino acid contacts, with lysine and leucine as frequent participants. MD simulations demonstrated prominent early structural shifts, stabilizing after approximately 50 ns with small conformational shifts occurring as the simulation completed. In addition, there are various regions in each protein that are predicted to have greater conformational fluctuations as compared to others, which may represent attractive areas to target to halt the progression of the interaction. These insights deepen our understanding of the PROS1-MERTK interaction role in immune modulation and tumor progression, unveiling potential targets for cancer immunotherapy.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

A comprehensive review of new small molecule drugs approved by the FDA in 2022: Advance and prospect

In 2022, the U.S. Food and Drug Administration approved a total of 16 marketing applications for small molecule drugs, which not only provided dominant scaffolds but also introduced novel mechanisms of action and clinical indications. The successful cases provide valuable information for optimizing efficacy and enhancing pharmacokinetic properties through strategies like macrocyclization, bioequivalent group utilization, prodrug synthesis, and conformation restriction. Therefore, gaining an in-depth understanding of the design principles and strategies underlying these drugs will greatly facilitate the development of new therapeutic agents. This review focuses on the research and development process of these newly approved small molecule drugs including drug design, structural modification, and improvement of pharmacokinetic properties to inspire future research in this field.

Bioinformatics Analysis and Experimental Findings Reveal the Therapeutic Actions and Targets of Cyathulae Radix Against Type 2 Diabetes Mellitus

Objective: This study elucidated the mechanistic role of Cyathulae Radix (CR) in type 2 diabetes mellitus (T2DM) through bioinformatics analysis and experimental validation. Methods: Components and targets of CR were retrieved from the traditional Chinese medical systems pharmacology, while potential T2DM targets were obtained from GeneCards and Online Mendelian Inheritance in Man databases. Intersecting these datasets yielded target genes between CR and T2DM. Differential genes were used for constructing a protein-protein interaction network, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking and dynamics simulations were performed using AutoDock and GROMACS, respectively, and in vitro experiments validated the results. Experiments evaluated the effect of CR on T2DM pancreatic β-cells. Results: Bioinformatics analysis identified four active compounds of CR, 157 related genes, and 5431 T2DM target genes, with 141 shared targets. Key targets such as JUN, MAPK1, and MAPK14 were identified through topological analysis of the PPI network. GO analysis presented 2663 entries, while KEGG analysis identified 161 pathways. The molecular docking results demonstrated favorable binding energy between the core components and the core proteins. Among them, JUN-rubrosterone, MAPK1-rubrosterone, and MAPK14-rubrosterone deserved further investigation. Molecular dynamics results indicated that all of them can form stable binding interactions. CR could inhibit the expression of JUN, MAPK1, and MAPK14, promote insulin secretion, alleviate apoptosis, and regulate autophagy in INS-1 cells. Conclusion: This study suggests CR approach to T2DM management by multitarget and multipathway provides a scientific basis for further research on the hypoglycemic effect of CR.

The stripe rust effector Pst3180.3 inhibits the transcriptional activity of TaMYB4L to modulate wheat immunity and analyzes the key active sites of the interaction conformation

Puccinia striiformis f. sp. tritici (Pst) has a wide range and serious damage, which severely threatens global wheat production. In this study, we focused on an effector protein Pst3180.3, which was induced to be highly expressed during the Pst infection stage. The N-terminal 19 amino acid of Pst3180.3 was verified to function as a signal peptide and transferred to cytoplasm and nucleus of wheat following Pst infection. Transient overexpression of Pst3180.3 in Nicotiana benthamiana inhibited programmed cell death triggered via BAX. The instantaneous silencing of Pst3180.3 by BSMV- HIGS significantly reduced the number of uredinia and increased accumulation of reactive oxygen species. Those results indicated that Pst3180.3 is an important pathogenic factor of Pst. Interaction of Pst3180.3 with a transcription factor TaMYB4L in host was confirmed through yeast two-hybrid, luciferase complementation, and co-immunoprecipitation. Virus-induced gene silencing of TaMYB4L weakened the resistance to Pst, indicated that TaMYB4L may be involved in the positive regulation of plant immunity. Dual-luciferase assays revealed that Pst3180.3 inhibited the transcriptional activity of TaMYB4L. Meanwhile, molecular docking analysis identified the key residue sites for the interaction and binding between Pst3180.3 and MYB4L. Those results demonstrated that Pst3180.3 binds to TaMYB4L and interacts to inhibit wheat resistance to Pst infection.

Novel and efficient techniques in the discovery of antioxidant peptides

As a research hotspot in food science and nutrition, antioxidant peptides can function by scavenging free radicals, inhibiting peroxides, and chelating metal ions. Therefore, how to efficiently discover and screen antioxidant peptides has become a key issue in research and production. Traditional discovery methods are time-consuming and costly, but also challenging to resolve the quantitative structure-activity relationship of antioxidant peptides. Several novel techniques, including artificial intelligence, molecular docking, bioinformatics, quantum chemistry, phage display, switchSENSE, surface plasmon resonance, and fluorescence polarization, are emerging rapidly as solutions. These techniques possess efficient capability for the discovery of antioxidant peptides, even with the potential for high-throughput screening. In addition, the quantitative structure-activity relationship can be resolved. Notably, combining these novel techniques can overcome the drawbacks of a single one, thus improving efficiency and expanding the discovery horizon. This review has summarized eight novel and efficient techniques for discovering antioxidant peptides and the combination of techniques. This review aims to provide scientific evidence and perspectives for antioxidant peptide research.

Bioinformatics and network pharmacology discover the molecular mechanism of Liuwei Dihuang pills in treating cerebral palsy

A collection of chronic central motor, postural, and activity restriction symptoms are referred to as cerebral palsy (CP). Previous research suggests that a number of perinatal variables, including hypoxia, may be linked to CP. And the pathophysiological process that causes brain injury in growing fetuses is mostly caused by amniotic fluid infection and intra-amniotic inflammation. Still, there is still much to learn about the molecular mechanism of CP. The goal of this study was to identify the molecular mechanism of Liuwei Dihuang pill (LWDHP) in the treatment of CP using network pharmacology and bioinformatics. The Chinese medicine database provided the LWDHP components and targets, the CP illness gene data set was gathered from a disease, and the expression profile of children with CP was chosen from anther database. Using the Kyoto Encyclopedia of Genes and Genomes and gene ontology databases, a network of interactions between proteins was created, and functional enrichment analysis was carried out. Analysis of traditional Chinese medicine found that the key active ingredients of LWDHP are quercetin, Stigmasterol and kaempferol. Through enrichment analysis, it was found that the hub genes for LWDHP treatment of CP are CXCL8, MMP9, EGF, PTGS2, SPP1, BCL2L1, MMP1, and AR. K EGG analysis found that LWDHP treatment of CP mainly regulates PI3K-Akt signaling pathway, IL-17 signaling pathway, Jak-STAT signaling pathway, NF-kappa B signaling pathway, etc. To summarize, LWDHP regulates immunological and inflammatory variables through a variety of components, targets, and signaling pathways, which plays a significant role in the development and management of CP.

Recent strategies in target identification of natural products: Exploring applications in chronic inflammation and beyond

Natural products are a treasure trove for drug discovery, especially in the areas of infection, inflammation and cancer, due to their diverse bioactivities and complex, and varied structures. Chronic inflammation is closely related to many diseases, including complex diseases such as cancer and neurodegeneration. Improving target identification for natural products contributes to elucidating their mechanism of action and clinical progress. It also facilitates the discovery of novel druggable targets and the elimination of undesirable ones, thereby significantly enhancing the productivity of drug discovery and development. Moreover, the rise of polypharmacological strategies, considered promising for the treatment of complex diseases, will further increase the demand for target deconvolution. This review underscores strategies for identifying natural product targets (NPs) in the context of chronic inflammation over the past 5 years. These strategies encompass computational methodologies for early target discovery and the anticipation of compound binding sites, proteomics-driven approaches for target delineation and experimental biology techniques for target validation and comprehensive mechanistic exploration.

Recent advances from computer-aided drug design to artificial intelligence drug design

Computer-aided drug design (CADD), a cornerstone of modern drug discovery, can predict how a molecular structure relates to its activity and interacts with its target using structure-based and ligand-based methods. Fueled by ever-increasing data availability and continuous model optimization, artificial intelligence drug design (AIDD), as an enhanced iteration of CADD, has thrived in the past decade. AIDD demonstrates unprecedented opportunities in protein folding, property prediction, and molecular generation. It can also facilitate target identification, high-throughput screening (HTS), and synthetic route prediction. With AIDD involved, the process of drug discovery is greatly accelerated. Notably, AIDD offers the potential to explore uncharted territories of chemical space beyond current knowledge. In this perspective, we began by briefly outlining the main workflows and components of CADD. Then through showcasing exemplary cases driven by AIDD in recent years, we describe the evolving role of artificial intelligence (AI) in drug discovery from three distinct stages, that is, chemical library screening, linker generation, and de novo molecular generation. In this process, we attempted to draw comparisons between the features of CADD and AIDD.

Dammarane-Type Saponins from the Leaves of Vietnamese Ginseng (Panax vietnamensis Ha & Grushv.) and Their Acetylcholinesterase Inhibition In Vitro and In Silico

Vietnamese ginseng (Panax vietnamensis Ha & Grushv.) represents one of the famous Panax spp. for valuable applications in both traditional and modern medicine; in which, its rhizome part has mainly been used as the medicinal materials based on the bioactive ginsenosides such as ginsenoside Rb1, ginsenoside Rg1, ginsenoside Rd, and majonoside R2. In modern medicine, the development of medicinal materials and utilization of medicinal plants are crucially based on standard bioactive ingredients, so this study to evaluate the leaves of Vietnamese ginseng as source of bioactive ginsenoside led to the identification of seven ginsenosides (1-7). Of them, ginsenoside Rd (2) and pseudoginsenoside RS1 (5) showed inhibitory effects on acetylcholinesterase in vitro with the IC50 values of 47.13 and 79.58 μM and supported by molecular docking analysis, in which ginsenoside Rd (2) and pseudoginsenoside RS1 (5) could play as allosteric inhibitors with high binding affinity (-8.5 and -9.4 kcal/mol) as evidenced by hydrogen bonding and hydrophobic interactions. The findings provided the scientific evidence for using the leaves of Vietnamese ginseng as an alternative source to the roots to enhance memory in traditional medicine as well as for further research on the anti-dementia effects of 2 and 5.

Bioinformatics analysis based on extracted ingredients combined with network pharmacology, molecular docking and molecular dynamics simulation to explore the mechanism of Jinbei oral liquid in the therapy of idiopathic pulmonary fibrosis

Objective

Jinbei oral liquid (JBOL), which is derived from a traditional hospital preparation, is frequently utilized to treat idiopathic pulmonary fibrosis (IPF) and has shown efficacy in clinical therapy. However, there are now several obstacles facing the mechanism inquiry, including target proteins, active components, and the binding affinity between crucial compounds and target proteins. To gain additional insight into the mechanisms underlying JBOL in anti-IPF, this study used bioinformation technologies, including network pharmacology, molecular docking, and molecular dynamic simulation, with a substantial amount of data based on realistic constituents.

Methods

Using network pharmacology, we loaded 118 realistic compounds into the SwissTargetPrediction and SwissADME databases and screened the active compounds and target proteins. IPF-related targets were collected from the OMIM, DisGeNET, and GeneCards databases, and the network of IPF-active constituents was built with Cytoscape 3.10.1. The GO and KEGG pathway enrichment analyses were carried out using Metascape, and the protein-protein interaction (PPI) network was constructed to screen the key targets with the STRING database. Finally, the reciprocal affinity between the active molecules and the crucial targets was assessed through the use of molecular docking and molecular dynamics simulation.

Results

A total of 122 targets and 34 tested active compounds were summarized in this investigation. Among these, kaempferol, apigenin, baicalein were present in high degree. PPI networks topological analysis identified eight key target proteins. AGE-RAGE, EGFR, and PI3K-Akt signaling pathways were found to be regulated during the phases of cell senescence, inflammatory response, autophagy, and immunological response in anti-IPF of JBOL. It was verified by molecular docking and molecular dynamics simulation that the combining way and binding energy between active ingredients and selected targets.

Conclusions

This work forecasts the prospective core ingredients, targets, and signal pathways of JBOL in anti-IPF, which has confirmed the multiple targets and pathways of JBOL in anti-IPF and provided the first comprehensive assessment with bioinformatic approaches. With empirical backing and an innovative approach to the molecular mechanism, JBOL is being considered as a potential new medication.

Screening of specific binding peptide for β-lactoglobulin using phage display technology

β-lactoglobulin (β-Lg) is a major food allergen, there is an urgent need to develop a rapid method for detecting β-Lg in order to avoid contact or ingestion by allergic patients. Peptide aptamers have high affinity, specificity, and stability, and have broad prospects in the field of rapid detection. Using β-Lg as the target, this study screened 11 peptides (P1-11) from a phage display library. Using molecular docking technology to predict binding energy and binding mode of proteins and peptides. Select the peptides with the best binding ability to β-Lg (P5, P7, P8) through ELISA. Combining them with whey protein, casein, and bovine serum protein, it was found that P7 has the best specificity for β-Lg, with an inhibition rate of 87.99%. Verified by molecular dynamics that P7 binds well with β-Lg. Therefore, this peptide can be used for the recognition of β-Lg, becoming a new recognition element for detecting β-Lg.

Calculation of solvation force in molecular dynamics simulation by deep-learning method

Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming. In this study, we developed a deep neural network to predict the decomposed solvation free energies and forces of all atoms in a molecule. To train the network, the internal coordinates of the molecule were used as the input data, and the solvation free energies along with transformed atomic forces from the Poisson-Boltzmann equation were used as labels. Both the training and prediction tasks were accelerated on GPU. Formal tests demonstrated that our method can provide reasonable predictions for small molecules when the network is well-trained with its simulation data. This method is suitable for processing lots of snapshots of molecules in a long trajectory. Moreover, we applied this method in the molecular dynamics simulation with enhanced sampling. The calculated free energy landscape closely resembled that obtained from explicit solvent simulations.

Regulation of optimized new Shengmai powder on cardiomyocyte apoptosis and ferroptosis in ischemic heart failure rats: The mediating role of phosphatidylinositol-3-kinase/protein kinase B/tumor protein 53 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Optimized New Shengmai Powder (ONSMP) is a sophisticated traditional Chinese medicinal formula renowned for bolstering vital energy, optimizing blood circulation, and mitigating fluid retention. After years of clinical application, ONSMP has shown a significant impact in improving myocardial injury and cardiac function and has a positive effect on treating heart failure. However, many unknowns exist about the molecular biological mechanisms of how ONSMP exerts its therapeutic effects, which require further research and exploration.

AIM OF THE STUDY

Exploring the potential molecular biological mechanisms by which ONSMP ameliorates cardiomyocyte apoptosis and ferroptosis in ischemic heart failure (IHF).

MATERIALS AND METHODS

First, we constructed a rat model of IHF by inducing acute myocardial infarction through surgery and using echocardiography, organ coefficients, markers of heart failure, antioxidant markers, and histopathological examination to assess the effects of ONSMP on cardiomyocyte apoptosis and ferroptosis in IHF rats. Next, we used bioinformatics analysis techniques to analyze the active components, signaling pathways, and core targets of ONSMP and calculated the interactions between core targets and corresponding elements. Finally, we detected the positive expression of apoptosis and ferroptosis markers and core indicators of signaling pathways by immunohistochemistry; detected the mean fluorescence intensity of core indicators of signaling pathways by immunofluorescence; detected the protein expression of signaling pathways and downstream effector molecules by western blotting; and detected the mRNA levels of p53 and downstream effector molecules by quantitative polymerase chain reaction.

RESULTS

ONSMP can activate the Ser83 site of ASK by promoting the phosphorylation of the PI3K/AKT axis, thereby inhibiting the MKK3/6-p38 axis and the MKK4/7-JNK axis signaling to reduce p53 expression, and can also directly target and inhibit the activity of p53, ultimately inhibiting p53-mediated mRNA and protein increases in PUMA, SAT1, PIG3, and TFR1, as well as mRNA and protein decreases in SLC7A11, thereby inhibiting cardiomyocyte apoptosis and ferroptosis, effectively improving cardiac function and ventricular remodeling in IHF rat models.

CONCLUSION

ONSMP can inhibit cardiomyocyte apoptosis and ferroptosis through the PI3K/AKT/p53 signaling pathway, delaying the development of IHF.

Leonurus japonicus Houtt. modulates neuronal apoptosis in intracerebral hemorrhage: Insights from network pharmacology and molecular docking

ETHNOPHARMACOLOGICAL RELEVANCE

Leonurus japonicus Houtt. (Labiatae), commonly known as Chinese motherwort, is a herbaceous flowering plant that is native to Asia. It is widely acknowledged in traditional medicine for its diuretic, hypoglycemic, antiepileptic properties and neuroprotection. Currently, Leonurus japonicus (Leo) is included in the Pharmacopoeia of the People's Republic of China. Traditional Chinese Medicine (TCM) recognizes Leo for its myriad pharmacological attributes, but its efficacy against ICH-induced neuronal apoptosis is unclear.

AIMS OF THE STUDY

This study aimed to identify the potential targets and regulatory mechanisms of Leo in alleviating neuronal apoptosis after ICH.

MATERIALS AND METHODS

The study employed network pharmacology, UPLC-Q-TOF-MS technique, molecular docking, pharmacodynamic studies, western blotting, and immunofluorescence techniques to explore its potential mechanisms.

RESULTS

Leo was found to assist hematoma absorption, thus improving the neurological outlook in an ICH mouse model. Importantly, molecular docking highlighted JAK as Leo's potential therapeutic target in ICH scenarios. Further experimental evidence demonstrated that Leo adjusts JAK1 and STAT1 phosphorylation, curbing Bax while augmenting Bcl-2 expression.

CONCLUSION

Leo showcases potential in mitigating neuronal apoptosis post-ICH, predominantly via the JAK/STAT mechanism.

Binding affinity screening of polyphenolic compounds in Stachys affinis extract (SAE) for their potential antioxidant and anti-inflammatory effects

Free radical is a marker in various inflammatory diseases. The antioxidant effect protects us from this damage, which also plays an essential role in preventing inflammation. Inflammation protects the body from biological stimuli, and pro-inflammatory mediators are negatively affected in the immune system. Inflammation caused by LPS is an endotoxin found in the outer membrane of Gram-negative bacteria, which induces immune cells to produce inflammatory cytokines such as cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase. Based on this, the antioxidant and anti-inflammatory effects of plant extracts were investigated. First, the main phenolic compounds for the five peaks obtained from Stachys affinis extract (SAE) were identified. The antioxidant effect of each phenolic compound was confirmed through HPLC analysis before and after the competitive binding reaction between DPPH and the extract. Afterward, the anti-inflammatory effect of each phenolic compound was confirmed through competitive binding between COX2 and the extract in HPLC analysis. Lastly, the anti-inflammatory effect of SAE was confirmed through in vitro experiments and also confirmed in terms of structural binding through molecular docking. This study confirmed that phenolic compounds in SAE extract have potential antioxidant and anti-inflammatory effects, and may provide information for primary screening of medicinal plants.

Analysis of environmental pollutant Bisphenol F elicited prostate injury targets and underlying mechanisms through network toxicology, molecular docking, and multi-level bioinformatics data integration

Bisphenol F (BPF) has gained prominence as an alternative to bisphenol A (BPA) in various manufacturing applications, yet being detected in diverse environments and posed potential public health risk. This research aims to elucidate the putative toxic targets and underlying molecular mechanisms of prostate injury induced by exposure to BPF through multi-level bioinformatics data, integrating network toxicology and molecular docking. Systematically leveraging multilevel databases, we determined 276 targets related to BPF and prostate injury. Subsequent screenings through STRING and Cytoscape tool highlighted 27 key targets, including BCL2, HSP90AA1, MAPK3, ESR1, and CASP3. GO and KEGG enrichment analyses demonstrated enrichment of targets involved in apoptosis, abnormal hormonal activities, as well as cancer-related signal transduction cascades, ligand-receptor interaction networks, and endocrine system signaling pathways. Molecular docking simulations conducted via Autodock corroborated high-affinity binding interaction between BPF and key targets. The results indicate that BPF exposure can contribute to the initiation and progression of prostate cancer and prostatic hyperplastic by modulating apoptosis and proliferation, altering nerve function in blood vessel endothelial cells, and disrupting androgen metabolism. This study offers theoretical underpinnings for comprehending the molecular mechanisms implicated in BPF-elicited prostatic toxicity, while concomitantly establishing foundational framework for the development of prophylactic and therapeutic strategies for prostatic injuries related to polycarbonate and epoxy resin plastics incorporated with BPF, as well as environments afflicted by elevated levels of these compounds.

Lysine lactylation in the regulation of tumor biology

Lysine lactylation (Kla), a newly discovered post-translational modification (PTM) of lysine residues, is progressively revealing its crucial role in tumor biology. A growing body of evidence supports its capacity of transcriptional regulation through histone modification and modulation of non-histone protein function. It intricately participates in a myriad of events in the tumor microenvironment (TME) by orchestrating the transitions of immune states and augmenting tumor malignancy. Its preferential modification of metabolic proteins underscores its specific regulatory influence on metabolism. This review focuses on the effect and the probable mechanisms of Kla-mediated regulation of tumor metabolism, the upstream factors that determine Kla intensity, and its potential implications for the clinical diagnosis and treatment of tumors.

The investigation of potential mechanism of Fuzhengkangfu Decoction against Diabetic myocardial injury based on a combined strategy of network pharmacology, transcriptomics, and experimental verification

BACKGROUND AND OBJECTIVES

Diabetic cardiomyopathy (DCM) is a cardiac condition resulting from myocardial damage caused by diabetes mellitus (DM), currently lacking specific therapeutic interventions. Fuzhengkangfu decoction (FZK) plays an important role in the prevention and treatment of various cardiovascular diseases. However, the efficacy and potential mechanisms of FZK are not fully understood. This study aims to investigate the protective effect and mechanisms of FZK against DCM.

METHODOLOGIES

Rats were given a high-calorie diet along with a low dosage of streptozotocin (STZ) to establish a rat model of DCM. The diabetic rats received FZK or normal saline subcutaneously for 12 weeks. Echocardiography was conducted to evaluate their heart function characteristics. Rat heart morphologies were assessed using Sirius Red staining and H&E staining. Transcriptome sequencing analysis and network pharmacology were used to reveal possible targets and mechanisms. Molecular docking was conducted to validate the association between the primary components of FZK and the essential target molecules. Finally, both in vitro and in vivo studies were conducted on the cardioprotective properties and mechanism of FZK.

RESULTS

According to the results of network pharmacology, FZK may prevent DCM by reducing oxidative stress and preventing apoptosis. Transcriptomics confirmed that FZK protected against DCM-induced myocardial fibrosis and remodelling, as predicted by network pharmacology, and suggested that FZK regulated the expression of oxidative stress and apoptosis-related proteins. Integrating network pharmacology and transcriptome analysis results revealed that the AGE-RAGE signalling pathway-associated MMP2, SLC2A1, NOX4, CCND1, and CYP1A1 might be key targets. Molecular docking showed that Poricoic acid A and 5-O-Methylvisammioside had the highest docking activities with these targets. We further conducted in vivo experiments, and the results showed that FZK significantly attenuated left ventricular remodelling, reduced myocardial fibrosis, and improved cardiac contractile function. And, our study demonstrated that FZK effectively reduced oxidative stress and apoptosis of cardiomyocytes. The data showed that Erk, NF-κB, and Caspase 3 phosphorylation was significantly inhibited, and Bcl-2/Bax was significantly increased after FZK treatment. In vitro, FZK significantly reduced AGEs-induced ROS increase and apoptosis in cardiomyocytes. Furthermore, FZK significantly inhibited the phosphorylation of Erk and NF-κB proteins and decreased the expression of MMP2. All the results confirmed that FZK inhibited the activation of the Erk/NF-κB pathway in AGE-RAGE signalling and alleviated oxidative stress and apoptosis of cardiomyocytes. In summary, we verified that FZK protects against DCM by inhibiting myocardial apoptotic remodelling through the suppression of the AGE-RAGE signalling pathway.

CONCLUSION

In conclusion, our research indicates that FZK demonstrates anti-cardiac dysfunction properties by reducing oxidative stress and cardiomyocyte apoptosis through the AGE-RAGE pathway in DCM, showing potential for therapeutic use.

Haloketones: A class of unregulated priority DBPs with high contribution to drinking water cytotoxicity

Although unregulated aliphatic disinfection byproducts (DBPs) had a much higher concentration and cytotoxicity than known aromatic DBPs, a recent study indicated that seven classes of regulated and unregulated priority DBPs (one and two-carbon-atom DBPs) just accounted for 16.2% of disinfected water cytotoxicity in the U.S., meaning some of the highly toxic aliphatic DBPs may be overlooked. Haloketones (HKs) are an essential class of priority DBPs with a 1-100 µg/L concentration in drinking water but lack cytotoxicity data. This study investigated the cytotoxicity of seven HKs using Chinese hamster ovary (CHO) cells. The order for cytotoxicity of HKs from most to least toxic was: 1,3-dichloroacetone (LC50: 1.0 ± 0.20 μM) ≈ 1,3-dibromoacetone (1.5 ± 0.19 μM) ≈ bromoacetone (1.9 ± 0.49 μM) > chloroacetone (4.3 ± 0.22 μM) > 1,1,3-trichloropropanone (6.6 ± 0.46 μM) > 1,1,1-trichloroacetone (222 ± 7.7 μM) > hexachloroacetone (3269 ± 344 μM). The cytotoxicity of HKs was higher than most regulated and priority aliphatic DBPs in mono-halogenated, di-halogenated, and tri-halogenated categories. A prediction model of HK cytotoxicity was developed based on the quantitative structure-activity relationship (QSAR), optimizing structures and computing descriptors with Gaussian 09 W. The average concentrations of HKs in representative drinking water samples from South Carolina (U.S.) and Suzhou (China) were 12.4 and 0.9 μg/L, respectively, accounting for 18.8% and 1.7% of their specific total DBPs measured (i.e. not TOX). For South Carolina drinking water, their contributions to total calculated additive cytotoxicity of aliphatic DBPs and overall drinking water cytotoxicity were 86.7% and 14.0%, respectively, demonstrating that HKs are an essential class of overlooked DBPs with a high contribution to drinking water cytotoxicity. Our study can help to explain the conflict that why regulated and priority DBPs (except HKs) just accounted for 16% of chlorinated drinking water cytotoxicity even enough they had much higher concentration and cytotoxicity than known aromatic DBPs.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Drug-Target Interaction Prediction Based on an Interactive Inference Network

Drug-target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug-target interactions (DTIs). In this study, a novel drug-target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug-target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug-target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing.

Utility of the Morgan Fingerprint in Structure-Based Virtual Ligand Screening

In modern drug discovery, virtual ligand screening (VLS) is frequently applied to identify possible hits before experimental testing and refinement due to its cost-effective nature for large compound libraries. For decades, efforts have been devoted to developing VLS methods with high accuracy. These include the state-of-the-art FINDSITE suite of approaches FINDSITEcomb2.0, FRAGSITE, and FRAGSITE2 and the meta version FRAGSITEcomb that were developed in our lab. These methods combine ligand homology modeling (LHM), traditional ligand similarity methods, and more recently machine learning approaches to rank ligands and have proven to be superior to most recent deep learning and large language model-based approaches. Here, we describe further improvements to our previous best methods by combining the Morgan fingerprint (MF) with the originally used PubChem fingerprint and FP2 fingerprint. We then benchmarked FINDSITEcomb2.0M, FRAGSITEM, FRAGSITE2M, and the composite meta-approach FRAGSITEcombM. On the 102 target DUD-E set, the 1% enrichment factor (EF1%) and area under the precision-recall curve (AUPR) of FRAGSITEcomb increased from 42.0/0.59 to 47.6/0.72. This 0.72 AUPR is significantly better than that of the state-of-the-art deep learning-based method DenseFS's AUPR of 0.443. An independent test on the 81 targets DEKOIS2.0 set shows that EF1%/AUPR increases from 18.3/0.520 to 23.1/0.683. An ablation investigation shows that the MF contributes to most of the improvement of all four approaches. Thus, the MF is a useful addition to structure-based VLS.

Enhanced optical imaging and fluorescent labeling for visualizing drug molecules within living organisms

The visualization of drugs in living systems has become key techniques in modern therapeutics. Recent advancements in optical imaging technologies and molecular design strategies have revolutionized drug visualization. At the subcellular level, super-resolution microscopy has allowed exploration of the molecular landscape within individual cells and the cellular response to drugs. Moving beyond subcellular imaging, researchers have integrated multiple modes, like optical near-infrared II imaging, to study the complex spatiotemporal interactions between drugs and their surroundings. By combining these visualization approaches, researchers gain supplementary information on physiological parameters, metabolic activity, and tissue composition, leading to a comprehensive understanding of drug behavior. This review focuses on cutting-edge technologies in drug visualization, particularly fluorescence imaging, and the main types of fluorescent molecules used. Additionally, we discuss current challenges and prospects in targeted drug research, emphasizing the importance of multidisciplinary cooperation in advancing drug visualization. With the integration of advanced imaging technology and molecular design, drug visualization has the potential to redefine our understanding of pharmacology, enabling the analysis of drug micro-dynamics in subcellular environments from new perspectives and deepening pharmacological research to the levels of the cell and organelles.

Computational analysis of curcumin-mediated alleviation of inflammation in periodontitis patients with experimental validation in mice

AIM

Using network pharmacology and experimental validation to explore the therapeutic efficacy and mechanism of curcumin (Cur) in periodontitis treatment.

MATERIALS AND METHODS

Network pharmacology was utilized to predict target gene interactions of Cur-Periodontitis. Molecular docking was used to investigate the binding affinity of Cur for the predicted targets. A mouse model with ligature-induced periodontitis (LIP) was used to verify the therapeutic effect of Cur. Microcomputed tomography (micro-CT) was used to evaluate alveolar bone resorption, while western blotting, haematoxylin-eosin staining and immunohistochemistry were used to analyse the change in immunopathology. SYTOX Green staining was used to assess the in vitro effect of Cur in a mouse bone marrow-isolated neutrophil model exposed to lipopolysaccharide.

RESULTS

Network pharmacology identified 114 potential target genes. Enrichment analysis showed that Cur can modulate the production of neutrophil extracellular traps (NETs). Molecular docking experiments suggested that Cur effectively binds to neutrophil elastase (ELANE), peptidylarginine deiminase 4 (PAD4) and cathepsin G, three enzymes involved in NETs. In LIP mice, Cur alleviated alveolar bone resorption and reduced the expression of ELANE and PAD4 in a time-dependent but dose-independent manner. Cur can directly inhibit NET formation in the cell model.

CONCLUSIONS

Our research suggested that Cur may alleviate experimental periodontitis by inhibiting NET formation.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Screening Antioxidant Components in Different Parts of Dandelion Using Online Gradient Pressure Liquid Extraction Coupled with High-Performance Liquid Chromatography Antioxidant Analysis System and Molecular Simulations

Utilizing online gradient pressure liquid extraction (OGPLE) coupled with a high-performance liquid chromatography antioxidant analysis system, we examined the antioxidative active components present in both the aerial parts and roots of dandelion. By optimizing the chromatographic conditions, we identified the ferric reducing-antioxidant power system as the most suitable for online antioxidant reactions in dandelion. Compared to offline ultrasonic extraction, the OGPLE method demonstrated superior efficiency in extracting chemical components with varying polarities from the samples. Liquid chromatography-mass spectrometry revealed twelve compounds within the dandelion samples, with nine demonstrating considerable antioxidant efficacy. Of these, the aerial parts and roots of dandelion contained nine and four antioxidant constituents, respectively. Additionally, molecular docking studies were carried out to investigate the interaction between these nine antioxidants and four proteins associated with oxidative stress (glutathione peroxidase, inducible nitric oxide synthase, superoxide dismutase, and xanthine oxidase). The nine antioxidant compounds displayed notable binding affinities below -5.0 kcal/mol with the selected proteins, suggesting potential receptor-ligand interactions. These findings contribute to enhancing our understanding of dandelion and provide a comprehensive methodology for screening the natural antioxidant components from herbs.

Protective effect of esculentoside A against myocardial infarction via targeting C-X-C motif chemokine receptor 2

Myocardial infarction (MI) is the primary cause of cardiac mortality. Esculentoside A (EsA), a triterpenoid saponin, has anti-inflammatory and antioxidant activities. However, its effect on MI remains unknown. In this study, the protective effect and mechanisms of EsA against MI were investigated. EsA significantly alleviated hypoxia-induced HL-1 cell injury, including increasing cell viability, inhibiting reactive oxygen species (ROS) production, mitochondrial membrane potential (MMP) and lactate dehydrogenase (LDH) leakage. In mouse MI model by left coronary artery (LAD) ligating, EsA obviously restored serum levels of creatine kinase isoenzymes (CK-MB), cardiac troponin I (cTnI), superoxide dismutase (SOD) and malondialdehyde (MDA). In addition, the cardioprotective effect of EsA was further confirmed by infarct size, electrocardiogram and echocardiography. Mechanistically, the targeted binding relationship between EsA and C-X-C motif chemokine receptor 2 (CXCR2) was predicted by molecular docking and dynamics, and validated by small molecule pull-down and surface plasmon resonance tests. EsA inhibited CXCR2 level both in vitro and in vivo, correspondingly alleviated oxidative stress by suppressing NOX1 and NOX2 and relieved inflammation through inhibiting p65 and p-p65. It demonstrated that EsA could play a cardioprotective role by targeting CXCR2. However, the effect of EsA against MI was abolished in combination with CXCR2 overexpression both in vitro and in vivo. This study revealed that EsA showed excellent cardioprotective activities by targeting CXCR2 to alleviate oxidative stress and inflammation in MI. EsA may function as a novel CXCR2 inhibitor and a potent candidate for the prevention and intervention of MI in the future.

The Development of Aptamer-Based Gold Nanoparticle Lateral Flow Test Strips for the Detection of SARS-CoV-2 S Proteins on the Surface of Cold-Chain Food Packaging

The COVID-19 pandemic over recent years has shown a great need for the rapid, low-cost, and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild-type SARS-CoV-2 spike proteins (SPs) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S proteins of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real-world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.