Current Computer - Aided Drug Design - Current Issue

Network pharmacology is a novel approach that uses bioinformatics to predict multitarget drugs and ingredient-target interactions in various diseases. A thorough search of previously published studies revealed that Hedyotis diffusa Willd (HDW) and Astragalus membranaceus (AM) possess anticancer activity. Colon cancer (CC) is one of the most common malignant tumors of the digestive tract and occurs in the colon. Herein, we explored the effect of two drugs in the treatment of CC.

The present study aimed to predict and verify the effect of these two drugs in the treatment of CC.

To explore the molecular mechanisms of the “HDW-AM” drug in the treatment of CC, we analyzed its principal efficiency in terms of ingredients, target spots, and pathways via network pharmacology, molecular docking, and experimental verification. The ingredients and their gene target sites were searched and screened through the TCMSP platform according to specific filtering conditions. Subsequently, components corresponding to the gene targets were chosen to construct the drug component-target network. The GEO (Gene Expression Omnibus) dataset was used to collect and screen for gene chips under CC and normal conditions, obtain differential genes, and construct a volcano map. The intersection genes between drug and disease targets were screened, the “.tsv” file was downloaded from the STRING platform and imported into Cytoscape 3.8.0 for visualization, a protein-protein interaction (PPI) network was constructed, the core targets were identified, and the common components with core targets were docked through Autodock Tools-1.5.6. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were carried out through the Metascape platform to determine the major pathways. The CCK-8 (Cell Counting Kit-8) assay verified the effect of AKT1 on cell proliferation after treatment with quercetin.

After the screening, 3658 DEGs (1841 downregulated and 1817 upregulated) were obtained from the GSE75970 gene chip; 21 active components and 220 targets were identified from the drugs. Subsequently, ten core genes (including AKT1, P53, and CASP3) and six major components were screened. GO functional analysis and KEGG analysis revealed that “HDW-AM” regulates cell migration and motility through the combination of a transcription regulator complex, membrane rafts, vesicle lumen, and protein kinases via the MAPK, PI3K-Akt, and IL-17 signaling pathways. The molecular docking results suggested that quercetin binds to AKT1, TP53, TNF, and CASP3. HDW-AM may exert a therapeutic effect on CC by modulating AKT1, TP53, TNF, and CASP3 and through signaling pathways. A CCK-8 cytotoxicity assay verified that quercetin affects cell viability through AKT1.

The current study provides a theoretical basis for an in-depth investigation into the molecular mechanism of the “HDW-AM” drug in CC treatment via network pharmacology, molecular docking, and experimental verification.

Gushukang (GSK), a traditional Chinese medical prescription, has made a great and extensive contribution to the treatment of different forms of osteoporosis, but polypharmacology studies of its mechanism of action are lacking. This study investigates the pharmacological mechanism of osteoporosis using network pharmacology and molecular docking. Experimental verification was carried out to confirm the efficacy of GSK on RANKL-induced osteoclast differentiation in RAW264.7 cells to verify the network pharmacology studies.

The effective chemical components and corresponding targets of osteoporosis with oral bioavailability of more than 30% and drug-like properties greater than 0.18 were searched in the TCMSP and TCM-ID databases. DrugBank, GeneCards, OMIM, TTD, and other databases were examined for targets related to osteoporosis. Using Cytoscape software, a network of possible TCM-active ingredient-osteoporosis targets was created. STRING software was used to create the networks of protein-protein interactions. The DAVID program was carried out to conduct GO and KEGG pathway enrichment analyses of the targets. Molecular docking and pattern of action analysis were carried out using software like AutoDock Vina and Discovery Studio Visualizer. The growth media for RAW264.7 cells contained varying doses of GSK serum and 50 ng/mL RANKL. The activity of TRAP was altered. Additionally, genes related to osteoclasts were examined using an RT-PCR assay.

Network pharmacological analysis revealed that the primary efficacy targets of osteoporosis were PTGS2, PTGS1, HSP90AA1, NCOA2, ADRB2, ESR1, NCOA1, and AR. The pharmacological targets of osteoporosis may be mediated by substances including quercetin, kaempferol, luteolin, naringenin, icariin, anthocyanin, tanshinone IIA, and cryptotanshinone. GSK markedly inhibited RANKL-induced TRAP activity. qRT-PCR results revealed decreased expression of the PTGS2 and ADRB2 genes upon GSK treatment.

The findings of network pharmacology, molecular docking, as well as experimental verification provide a new further study for elucidating the pharmacodynamic substance basis and polypharmacology mechanism of GSK in treating osteoporosis.

In recent years, analyzing complex biological networks to predict future links in such networks has attracted the attention of many medical and computer science researchers. The discovery of new drugs is one of the application cases for predicting future connections in biological networks. The operation of drug-target interactions prediction (DTIP) can be considered a fundamental step in identifying potential interactions between drug and target to identify new drugs.

The previous studies reveal that predictions are made based on known interactions using computational methods to solve the cost problem and avoid blind study of all interactions. But, there seem to be challenges such as the lack of confirmed negative samples and the low accuracy in some computational methods. Thus, we have proposed an efficient and hybrid approach called MKPUL-BLM to manage some of the aforementioned challenges for predicting drug-target interactions.

The MKPUL-BLM combins multi-kernel and positive unlabeled learning (PUL) approaches. Our method uses more information to increase accuracy, in addition to minimizing small similarities using network information. Also, potential negative samples are produced using a PUL approach because of lacking negative laboratory samples. Finally, labels are expanded via a semi-supervised.

Our method improved to 0.98 and 0.94 in the old interactions set for the ROCAUC and AUPR criteria, respectively. Also, this method enhanced ROCAUC and AUPR criteria by 0.89 and 0.77 for the new interactions set.

The MKPUL-BLM can be considered an efficient alternative to achieve more reliable predictions in the field of DTIP.

Shenfu injection was derived from the classical Chinese medicine formula ‘Shenfu decoction’, which was widely used in the treatment of cardiovascular and cerebrovascular diseases in clinical practice.

Predict the main active ingredients, core targets, and related signaling pathways of Shenfu injection in the treatment of ischemic stroke.

Databases were used to collect the active ingredients and target information of Shenfu injection; GO and KEGG pathway enrichment analyses were performed using the David database. The effects of Shenfu injection on core targets were verified using molecular docking and in vivo experiments.

The predicted results identified 44 active ingredients and 635 targets in Shenfu injection, among which 418 targets, including TNF, IL-6, MAPK1, and MAPK14, were potential targets for the treatment of ischemic stroke. Molecular docking revealed that the active ingredients had good binding to IL-6, MAPK1, and MAPK14. In vivo experiments demonstrated that Shenfu injection significantly improved the pathological damage due to ischemic stroke, promoted the expression of tight junction proteins, and inhibited MMP-2 and MMP-9 expressions, thereby reducing BBB permeability. Animal experiments revealed that Shenfu injection could inhibit p38、JNK and ERK phosphorylation.

Mechanism of Shenfu injection in treating ischemic stroke may be via inhibition of the inflammatory factors levels and protecting the BBB, thereby warranting subsequent studies and highlighting its potential as a reference for new drug development.

The design of an epitope-based vaccine against diphtheria toxin (DTx) originated from the idea that many strong binder epitopes may be structurally located in the depth of DTx. Subsequently, many ineffective antibodies may be produced by the presentation of those epitopes to T and B lymphocytes. The other critical issue is the population coverage of a vaccine that has been neglected in traditional vaccines.

Given the issues above, our study aimed to design a peptide-based diphtheria vaccine, considering the issues of unwanted epitopes and population coverage.

The frequencies of pre-determined HLA alleles were listed. A country in which almost all HLA alleles had been determined in almost all geographical distribution was selected. The epitopes within the sequence of diphtheria toxin were predicted by the NetMHCIIPan server based on the selected HLA alleles. Strong binder epitopes on the surface of diphtheria toxin were selected by structural epitope mapping. The epitopes, which cover almost all the human population for each of the HLA alleles in the candidate country, were then selected as epitope-based vaccines.

At first, 793 strong binder epitopes were predicted, of which 82 were surface epitopes. Nine surface epitopes whose amino acids had extruding side chains were selected. Finally, 2 epitopes had the most population coverage and were suggested as a di-epitope diphtheria vaccine. The population coverage of the di-epitope vaccine in France and the world was 100 and 99.24%, respectively. HLA-DP had the most roles in epitope presentation.

Our results indicated that 97.75% of unwanted antibodies (791 epitopes) have been reduced. Achieving two immunodominant surface epitopes confirmed our rational filtration strategy for sequential reduction of unwanted epitopes. Our novel insight may pave a new way to designing novel peptide-based vaccines to avoid producing non-specific antibodies.

To investigate the therapeutic effect of Yiqi Jianpi Tongqiao (YJT) formula (Hedysarum Multijugum Maxim, Magnoliae Flos, Xanthii Fructus, Notopterygii Rhizoma Et Radix, Kaempferiae Rhizoma, Acoritataninowii Rhizoma, Saposhnikoviae Radix) on an allergic rhinitis mouse model, and to explore the active ingredients, key targets, and molecular mechanisms of this formula using network pharmacology and molecular docking methods.

An allergic rhinitis mouse model was established to observe changes in rhinitis symptoms, nasal mucosal morphology, and serum indicators after administering the YJT formula. The TCMSP, GeneCards, OMIM, and DisGeNET databases were used to screen for the active ingredients, action targets, and disease targets of the YJT formula. The Cytoscape software was used to construct a network of the active ingredients and action targets. The protein-protein interaction (PPI) network was used to predict hub genes. The corresponding active compounds with the hub genes' highest oral bioavailability (OB) values were identified, followed by molecular docking analysis.

Animal experiments demonstrated that the YJT formula reduced rhinitis symptoms (nasal itching, runny nose, and face scratching) in allergic rhinitis mice, as well as decreased nasal mucosal inflammatory reactions and serum inflammatory indicators (histamine, OVA-specific IgE, IL-1β levels). Furthermore, 63 active components and 101 potential indicator targets of the YJT formula were identified, along with 5 hub genes (IL6, AKT1, IL1B, VEGFA, and PTGS2), and the corresponding active compounds with the highest OB values were quercetin, aloe-emodin, and denudanolide b. Molecular docking results revealed the binding energy between quercetin, aloe-emodin, denudanolide b and 5 hub genes (IL6, AKT1, IL1B, VEGFA, and PTGS2) were -5.78 to -10.22 kcal/mol, the binding energy between dexamethasone and 5 hub genes were -6.3 to -9.7 kcal/mol. In addition, GO and KEGG analysis suggested significant enrichment of these genes in biological processes such as response to lipopolysaccharide, response to molecule of bacterial origin, and response to reactive oxygen species, as well as signaling pathways like AGE-RAGE signaling pathway in diabetic complications, Lipid and atherosclerosis, and IL-17 signaling pathway.

The YJT formula has therapeutic effects in an allergic rhinitis mouse model, with the main active components being quercetin, aloe-emodin, and denudanolide b, and the key targets being IL6, AKT1, IL1B, VEGFA, and PTGS2, involving multiple signaling pathways.

Exogenous substances modulate metabolism by regulating the expression and function of UDP-glycosyltransferases (UGTs). However, the exact mechanism in the intestine was rarely understood. Herein, we explored the effects of representative flavonoids and organic acids on the regulation of UGT1A1.

MTT assays and western blot analysis were used to explore the effect of polyphenols. X-ray diffraction was used to reveal the catalytic mechanisms of UGTs.

MTT assays showed that these compounds basically had almost no cytotoxicity, even in concentrations up to 200 μM. However, through western blot assays, UGT1A1 expression was increased after being treated with liquiritigenin and caffeic acid. Furthermore, liquiritigenin and caffeic acid enhanced the nuclear translocation of Nrf2. Moreover, a 2.5-Å crystal structure of the complex containing UGTs C-terminal domain and organic acid was solved, and the UDPGA binding pocket could be occupied by organic acid, suggesting the enzyme activity might be impaired by organic acid.

Above all, liquiritigenin and caffeic acid maintained the metabolism balance by upregulating the expression of UGT1A1 via Nrf2 activation and inhibiting the enzyme activity in Caco-2 cells.

Estrogen alpha has been recognized as a perilous factor in breast cancer cell proliferation and has been proficiently treated in breast cancer chemotherapy with the development of selective estrogen receptor modulators (SERMs).

The major aim of this study was to identify the potential inhibitors against the most influential target ERα receptor by in silico studies of 115 phytochemicals from 17 medicinal plants using in silico molecular docking studies.

The molecular docking investigation was carried out by a genetic algorithm using the Auto Dock Vina program, and the validation of docking was also performed using molecular dynamic (MD) simulation by the Desmond tool of Schrödinger molecular modeling. The ADME(T) studies were performed by SWISS ADME and ProTox-II.

The top ten highest binding energy phytochemicals identified were amyrin acetate (-10.7 kcal/mol), uscharine (-10.5 kcal/mol), voruscharin (-10.0 kcal/mol), cyclitols (-10.0 kcal/mol), taraxeryl acetate (-9.9 kcal/mol), amyrin (-9.9 kcal/mol), barringtogenol C (-9.9 kcal/mol), calactin (-9.9 kcal/mol), 3-beta taraxerol (-9.8 kcal/mol), and calotoxin (-9.8 kcal/mol). A molecular docking study revealed that these phytochemical constituents showed higher binding affinity compared to the reference standard tamoxifen (-6.6 kcal/mol) towards the target protein ERα. The results of MD studies showed that all four tested compounds possess comparatively stable ligand-protein complexes with ERα target as compared to the tamoxifen-ERα complex.

Among the ten compounds, phytochemical amyrin acetate (triterpenoids) formed a more stable complex as well as exhibited greater binding affinity than standard tamoxifen. ADMET studies for the top ten phytochemicals showed a good safety profile. Additionally, these compounds are being reported for the first time in this study as possible inhibitors of ERα for the treatment of breast cancer by adopting the concept of drug repurposing. Hence, these phytochemicals can be further studied and can be used as a parent core molecule to develop novel lead molecules for breast cancer therapy.

The role of Forkhead Box D2 (FOXD2) in head and neck squamous cell carcinoma (HNSC) has never been studied.

Our object was to explore the role of FOXD2 in HNSC.

Clinical data for patients with HNSC was obtained from TCGA. Our study examined the atypical expression of FOXD2 in both HNSC and pan-cancer, along with its diagnostic and prognostic implications, as well as the association between FOXD2 expression and clinical characteristics, immune infiltration, immune checkpoint genes, and MSI. Gene set enrichment analysis (GESA) was used to investigate the potential regulation network of FOXD2 in HNSC. We analyze the genomic alterations of FOXD2 in HNSC. GSE13397 and qRT-PCR were used for the validation of FOXD2 expression.

FOXD2 was aberrantly expressed in 24 tumors. FOXD2 was significantly up-regulated in HNSC compared to normal head and neck tissue (p < 0.001). High FOXD2 expression was associated with the histologic grade of the patient with HNSC (p < 0.001), lymphovascular infiltration (p = 0.002) and lymph node neck dissection (p = 0.002). In HNSC, an autonomous correlation between FOXD2 expression and OS was observed (HR: 1.36; 95% CI: 1.04-1.78; p = 0.026). FOXD2 was associated with the neuronal system, neuroactive ligand-receptor interaction, and retinoblastoma gene in cancer. FOXD2 was associated with immune infiltration, immune checkpoints, and MSI. The somatic mutation rate of FOXD2 in HNSC was 0.2%. FOXD2 was significantly up-regulated in HNSC cell lines.

Our findings suggest that FOXD2 has the potential to serve as a prognostic biomarker and immunotherapeutic target for individuals with HNSC.

The machine learning-based QSAR modeling procedure, molecular generations, and molecular dynamic simulations were applied to virtually screen the DNA polymerase theta inhibitors.

The DNA polymerase theta (Polθ or POLQ) is an attractive target for treatments of homologous recombination deficient (such as BRCA deficient) cancers. There are no approved drugs for targeting POLQ, and only one inhibitor is in Phase II clinical trials; thus, it is necessary to develop novel POLQ inhibitors.

To build machine learning models that predict the bioactivities of POLQ inhibitors. To build molecular generation models that generate diverse molecules. To virtually screen the generated molecules by the machine learning models. To analyze the binding modes of the screening results by molecular dynamic simulations.

In the present work, 325 inhibitors with POLQ polymerase domain bioactivities were collected. Two machine learning methods, random forest and deep neural network, were used for building the ligand- and structure-based quantitative structure-activity relationship (QSAR) models. The substructure replacement-based method and transfer learning-based deep recurrent neural network method were used for molecular generations. Molecular docking and consensus QSAR models were carried out for virtual screening. The molecular dynamic simulations and MM/GBSA binding free energy calculation and decomposition were used to further analyze the screening results.

The MCC values of the best ligand- and structure-based consensus QSAR models reached 0.651 and 0.361 for the test set, respectively. The machine learning-based docking scores had better-predicted ability to distinguish the highly and weakly active poses than the original docking scores. The 96490 molecules were generated by both molecular generation methods, and 10 molecules were retained by virtual screening. Four favorable interactions were concluded by molecular dynamic simulations.

We hope that the screening results and the binding modes are helpful for designing the highly active POLQ polymerase inhibitors and the models of the molecular design workflow can be used as reliable tools for drug design.

The NLRP3 inflammasome mediates a range of inflammatory responses that are associated with an increasing number of pathological mechanisms. Over-activation of NLRP3 can exacerbate many diseases. However, NLRP3 antagonists have significant therapeutic potential. Moreover, NLRP3 plays an important role in limiting the growth and spread of some tumors, and NLRP3 agonists also have clinical value. MCC950 and BMS986299 are an antagonist and agonist of NLRP3, respectively. In light of the important clinical applications of NLRP3, especially for NLRP3 inhibitors, a computational method was used to investigate the interaction modes of MCC950 and BMS986299 with NLRP3 in order to design and develop more potent NLRP3 regulators.

In this study, the conformational behaviors of NLRP3 bound to the antagonist MCC950 in an inactive state and the agonist BMS986299 in an active state were investigated using 200 ns equilibrium all-atom molecular dynamics (MD) simulations, and then the analyses of the MD trajectories (RMSD, Rg, RMSF, SASA, PCA, and DCCM) were carried out to explore the mechanism of the antagonist and agonist on NLRP3 in the two different states.

The RMSD, RMSF, Rg, SASA, and PCA analyses indicated that NLRP3 was more dispersive and less energetically stable in the active state than in the inactive state and that MCC950 significantly reduced the fluctuations of the interactive residues while BMS986299 did not. The antagonist MCC950 interacted with residues mainly in the NBD, HD1, WHD, and HD2 domains of NLRP3, whereas the agonist BMS986299 mainly in the NBD and WHD of NLRP3. Additionally, both compounds did not interact with residues located in the FISNA domain. The conformation of the FISNA domain appeared to change significantly when NLRP3 was translated from an inactive state to an active state.

The antagonist may interact with residues mainly in the NBD, HD1, WHD, and HD2 domains, and the agonist may interact in the NBD and WHD domains. Our study provided new insights into the development of NLRP3 regulators.

Inguinal hernia in adults is a common and frequent disease in surgery, prone to occur in the elderly or in those with a weak abdominal wall. Despite its prevalence, Molecular mechanisms underlying inguinal hernia formation are unclear.

This study aims to identify potential gene markers for inguinal hernia and available drugs.

Pubmed2Ensembl text mining was used to identify genes related to “inguinal hernia” keywords. The GeneCodis system was used to specify GO biological process terms and KEGG pathways defined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The STRING tool was used to construct protein-protein interaction networks, which were then visualized using Cytoscape.CytoHubba and Molecular Complex Detection were utilized to analyze the module (MCODE). A GO and KEGG analysis of gene modules was conducted using the DAVID platform database. Hub genes are those that are concentrated in prominent modules. The drug-gene interaction database was also used to identify potential drugs for inguinal hernia patients based on their interactions between the hub genes. Finally, a Mendelian randomization study was conducted based on genome-wide association studies to determine whether hub genes cause inguinal hernias.

The identification of 96 genes associated with inguinal hernia was carried out using text mining techniques. It was constructed using PPI networks with 80 nodes and 476 edges, and the sequence of the genes was performed using CytoHubba. MCODE analysis identified three gene modules. Three modules contain 37 genes clustered as hub candidate genes associated with inguinal hernia patients. The PI3K-Akt, MAPK, AGE-RAGE, and HIF-1 pathways were found to be enriched in signaling pathways. Sixteen of the 37 genes were found to be targetable by 30 existing drugs. The relationship between hub genes and inguinal hernia was examined using Mendelian randomization. The research revealed nine genes that may be connected with inguinal hernia, such as POMC, CD40LG, TFRC, VWF, LOX, IGF2, BRCA1, TNF, and HGF in the plasma. By inverse variance weighting, ALB was associated with an increased risk of inguinal hernia with an OR of 1.203 (OR [95%] = 1,04 [1.012 to 1.089], p = 0.008).

We identified potential hub genes for inguinal hernia, predicted potential drugs for inguinal hernia, and reverse-validated potential genes by Mendelian randomization. This may provide further insights into asymptomatic pre-diagnostic methods and contribute to studies to understand the molecular mechanisms of risk genes associated with inguinal hernia.

The coronavirus E ion channel has previously been studied as a potential target for antiviral therapy, with several compounds found to bind to the channel. Since, these compounds have low activity, searching for effective E ion channel inhibitors of great importance.

This study aimed to develop a computational approach for designing ligands for the coronaviral E ion channel and identify potential inhibitors based on this approach.

The structure of the E-ion channel was refined using molecular dynamics, and the pore responsible for binding cage compounds was selected as the inhibitor-binding site. Potential inhibitor structures were identified using molecular docking, and their binding was confirmed using molecular dynamics simulations.

A number of potential SARS E ion channel inhibitors have been identified, and the binding modes and possible mechanisms of action of these inhibitors have been clarified.

This study presents a computational approach that can be used to design ligands for E ion channels and identify potential inhibitors, providing valuable insights into the development of new antiviral therapies. The behavior of the E protein pentamer of SARS-CoV-2 in its native environment was investigated using Molecular Dynamics (MD), resulting in an equilibrated structure that could be used to develop new inhibitors through molecular docking. Simulation of the MD of E-channel complexes with amantadine analogues allowed for the identification of the main types of ligand-protein interactions that are responsible for the good binding of ligands within the channel's inner chamber.

Thermolabile hemolysin (TLH) is a key virulent protein of Vibrio alginolyticus, known for its hemolytic and phospholipase activities, leading to shrimp vibriosis disease. It has been suggested as a potential therapeutic candidate for vibriosis therapy.

Computational studies, including molecular docking, toxicity analysis, and molecular dynamics (MD) simulations, were conducted to investigate the inhibition of the phospholipase activity of TLH by phytochemicals from Emblica officinalis.

Out of the twenty-nine compounds, the top three, including Ellagic acid (CID 5281855), Quercetin (CID 5280343), and Kaempferol (CID 5280863), were sorted based on their highest molecular docking scores of -9.2, -8.9, and -8.8, respectively. Subsequently, molecular dynamics (MD) simulations of these selected leads were performed to observe the structural stability of these compounds in the binding sites of TLH protein. The MD simulation outcomes indicated that all three compounds demonstrated superior stability throughout 100 nanoseconds compared to the control compound Resveratrol. The molecular simulation results suggest stable interactions, with average root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) values of 1-2 Å and 0-3 Å. Pharmacokinetic and toxicity analyses were conducted to evaluate the suitability and toxicity of these selected compounds. All top three compounds passed the Lipinski rule, and toxicity criteria.

Therefore, these compounds have the potential to serve as effective therapeutics for controlling Vibrio alginolyticus infection in shrimp.