BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.

Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.

Kalium 3.0 is a comprehensive depository of natural, artificial, and labeled polypeptides acting on potassium channels.

Here, we introduce the third release of Kalium database (http://kaliumdb.org/), a manually curated comprehensive depository that accumulates data on polypeptide ligands of potassium channels. The major goal of this amplitudinous update is to summarize findings for natural polypeptide ligands of K+ channels, as well as data for the artificial derivatives of these substances obtained over the decades of exploration. We manually analyzed more than 700 original manuscripts and systematized the information on mutagenesis, production of radio- and fluorescently labeled derivatives, and the molecular pharmacology of K+ channel ligands. As a result, data on more than 1200 substances were processed and added enriching the database content fivefold. We also included the electrophysiological data obtained on the understudied and neglected K+ channels including the heteromeric and concatenated channels. We associated target channels in Kalium with corresponding entries in the official database of the International Union of Basic and Clinical Pharmacology. Kalium was supplemented with an adaptive Statistics page, where users are able to obtain actual data output. Several other improvements were introduced, such as a color code to distinguish the range of ligand activity concentrations and advanced tools for filtration and sorting. Kalium is a fully open-access database, crosslinked to other databases of interest. It can be utilized as a convenient resource containing ample up-to-date information about polypeptide ligands of K+ channels.

A Molecular Modeling Investigation of the Therapeutic Potential of Marine Compounds as DPP-4 Inhibitors.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated levels of blood glucose due to insulin resistance or insulin-secretion defects. The development of diabetes is mainly attributed to the interaction of several complex pathogenic, genetic, environmental and metabolic processes. Dipeptidyl peptidase-4 (DPP-4) is a serine protease that cleaves X-proline dipeptides from the N-terminus of several polypeptides, including natural hypoglycemic incretin hormones. Inhibition of this enzyme restores and maintains glucose homeostasis, making it an attractive drug target for the management of T2DM. Natural products are important sources of bioactive agents for anti-T2DM drug discovery. Marine ecosystems are a rich source of bioactive products and have inspired the development of drugs for various human disorders, including diabetes. Here, structure-based virtual screening and molecular docking were performed to identify antidiabetic compounds from the Comprehensive Marine Natural Products Database (CMNPD). The binding characteristics of two shortlisted compounds, CMNPD13046 and CMNPD17868, were assessed using molecular dynamics simulations. Thus, this study provides insights into the potential antidiabetic activity and the underlying molecular mechanism of two compounds of marine origin. These compounds could be investigated further for the development of potent DPP-4 inhibitors.

Mechanisms of Danggui Buxue Tang on Hematopoiesis via Multiple Targets and Multiple Components: Metabonomics Combined with Database Mining Technology.

This study aimed to explore the mechanism of action of Danggui Buxue Tang (DBT) with its multiple components and targets in the synergistic regulation of hematopoiesis. Mouse models of hematopoiesis were established using antibiotics. Metabolomics was used to detect body metabolites and enriched pathways. The active ingredients, targets, and pathways of DBT were analyzed using system pharmacology. The results of metabolomics and system pharmacology were integrated to identify the key pathways and targets. A total of 515 metabolites were identified using metabolomics. After the action of antibiotics, 49 metabolites were markedly changed: 23 were increased, 26 were decreased, and 11 were significantly reversed after DBT administration. Pathway enrichment analysis showed that these 11 metabolites were related to bile secretion, cofactor biosynthesis, and fatty acid biosynthesis. The results of the pharmacological analysis showed that 616 targets were related to DBT-induced anemia, which were mainly enriched in biological processes, such as bile secretion, biosynthesis of cofactors, and cholesterol metabolism. Combined with the results of metabolomics and system pharmacology, we found that bile acid metabolism and biotin synthesis were the key pathways for DBT. Forty-two targets of DBT were related to these two metabolic pathways. PPI analysis revealed that the top 10 targets were CYP3A4, ABCG2, and UGT1A8. Twenty-one components interacted with these 10 targets. In one case, a target corresponds to multiple components, and a component corresponds to multiple targets. DBT acts on multiple targets of ABCG2, UGT1A8, and CYP3A4 through multiple components, affecting the biosynthesis of cofactors and bile secretion pathways to regulate hematopoiesis.

New Approach for Targeted Treatment of Mild COVID-19 by Honeysuckle through Network Pharmacology Analysis.

OBJECTIVE: To investigate the potential pharmacological value of extracts from honeysuckle on patients with mild coronavirus disease 2019 (COVID-19) infection. METHODS: The active components and targets of honeysuckle were screened by Traditional Chinese Medicine Database and Analysis Platform (TCMSP). SwissADME and pkCSM databases predict pharmacokinetics of ingredients. The Gene Expression Omnibus (GEO) database collected transcriptome data for mild COVID-19. Data quality control, differentially expressed gene (DEG) identification, enrichment analysis, and correlation analysis were implemented by R toolkit. CIBERSORT evaluated the infiltration of 22 immune cells. RESULTS: The seven active ingredients of honeysuckle had good oral absorption and medicinal properties. Both the active ingredient targets of honeysuckle and differentially expressed genes of mild COVID-19 were significantly enriched in immune signaling pathways. There were five overlapping immunosignature genes, among which RELA and MAP3K7 expressions were statistically significant (P < 0.05). Finally, immune cell infiltration and correlation analysis showed that RELA, MAP3K7, and natural killer (NK) cell are with highly positive correlation and highly negatively correlated with hematopoietic stem cells. CONCLUSION: Our analysis suggested that honeysuckle extract had a safe and effective protective effect against mild COVID-19 by regulating a complex molecular network. The main mechanism was related to the proportion of infiltration between NK cells and hematopoietic stem cells.

Mechanism of action of Tripterygium wilfordii for treatment of idiopathic membranous nephropathy based on network pharmacology.

BACKGROUND: Although thunder god vine (Tripterygium wilfordii) has been widely used for treatment of idiopathic membranous nephropathy (IMN), the pharmacological mechanisms underlying its effects are still unclear. This study investigated potential therapeutic targets and the pharmacological mechanism of T. wilfordii for the treatment of IMN based on network pharmacology. METHODS: Active components of T. wilfordii were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. IMN-associated target genes were collected from the GeneCards, DisGeNET, and OMIM databases. VENNY 2.1 was used to identify the overlapping genes between active compounds of T. wilfordii and IMN target genes. The STRING database and Cytoscape 3.7.2 software were used to analyze interactions among overlapping genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the targets were performed using Rx64 4.0.2 software, colorspace, stringi, DOSE, clusterProfiler, and enrichplot packages. RESULTS: A total of 153 compound-related genes and 1485 IMN-related genes were obtained, and 45 core genes that overlapped between both categories were identified. The protein-protein interaction network and MCODE results indicated that the targets TP53, MAPK8, MAPK14, STAT3, IFNG, ICAM1, IL4, TGFB1, PPARG, and MMP1 play important roles in the treatment of T. wilfordii on IMN. Enrichment analysis showed that the main pathways of targets were the AGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. CONCLUSION: This study revealed potential multi-component and multi-target mechanisms of T. wilfordii for the treatment of IMN based on network pharmacological, and provided a scientific basis for further experimental studies.

Virtual Screening and Molecular Docking to Study the Mechanism of Chinese Medicines in the Treatment of Coronavirus Infection.

BACKGROUND Heat-clearing and detoxifying herbs (HDHs) play an important role in the prevention and treatment of coronavirus infection. However, their mechanism of action needs further study. This study aimed to explore the anti-coronavirus basis and mechanism of HDHs. MATERIAL AND METHODS Database mining was performed on 7 HDHs. Core ingredients and targets were screened according to ADME rules combined with Neighborhood, Co-occurrence, Co-expression, and other algorithms. GO enrichment and KEGG pathway analyses were performed using the R language. Finally, high-throughput molecular docking was used for verification. RESULTS HDHs mainly acts on NOS3, EGFR, IL-6, MAPK8, PTGS2, MAPK14, NFKB1, and CASP3 through quercetin, luteolin, wogonin, indirubin alkaloids, ß-sitosterol, and isolariciresinol. These targets are mainly involved in the regulation of biological processes such as inflammation, activation of MAPK activity, and positive regulation of NF-kappaB transcription factor activity. Pathway analysis further revealed that the pathways regulated by these targets mainly include: signaling pathways related to viral and bacterial infections such as tuberculosis, influenza A, Ras signaling pathways; inflammation-related pathways such as the TLR, TNF, MAPK, and HIF-1 signaling pathways; and immune-related pathways such as NOD receptor signaling pathways. These pathways play a synergistic role in inhibiting lung inflammation and regulating immunity and antiviral activity. CONCLUSIONS HDHs play a role in the treatment of coronavirus infection by regulating the body's immunity, fighting inflammation, and antiviral activities, suggesting a molecular basis and new strategies for the treatment of COVID-19 and a foundation for the screening of new antiviral drugs.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets.

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients' Targets covering PubMed literatures 2000-2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into 'My-target' system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

In Silico Exploration of Novel Tubulin Inhibitors: A Combination of Docking and Molecular Dynamics Simulations, Pharmacophore Modeling, and Virtual Screening.

Microtubules play a critical role in mitosis and cell division and are regarded as an excellent target for anticancer therapy. Although microtubule-targeting agents have been widely used in the clinical treatment of different human cancers, their clinical application in cancer therapy is limited by both intrinsic and acquired drug resistance and adverse toxicities. In a previous work, we synthesized compound 9IV-c, ((E)-2-(3,4-dimethoxystyryl)-6,7,8-trimethoxy-N-(3,4,5-trimethoxyphenyl)quinoline-4-amine) that showed potent activity against multiple human tumor cell lines, by targeting spindle formation and/or the microtubule network. Accordingly, in this study, to identify potent tubulin inhibitors, at first, molecular docking and molecular dynamics studies of compound 9IV-c were performed into the colchicine binding site of tubulin; then, a pharmacophore model of the 9IV-c-tubulin complex was generated. The pharmacophore model was then validated by Güner-Henry (GH) scoring methods and receiver operating characteristic (ROC) analysis. The IBScreen database was searched by using this pharmacophore model as a screening query. Finally, five retrieved compounds were selected for molecular docking studies. These efforts identified two compounds (b and c) as potent tubulin inhibitors. Investigation of pharmacokinetic properties of these compounds (b and c) and compound 9IV-c displayed that ligand b has better drug characteristics compared to the other two ligands.

Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma.

OBJECTIVE: To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. METHODS: The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. RESULTS: 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. CONCLUSION: The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

A Network Pharmacology-Based Study on Irritable Bowel Syndrome Prevention and Treatment Utilizing Shenling Baizhu Powder.

METHODS: Metabolomics was used to detect the secondary metabolites in SLBZP; the target protein was acquired by target fishing according to the compound's structure. The SymMap database was used to search herbal medicines for the target protein. The target gene of IBS gave rise to the common gene protein which is the potential target of SLBZP in IBS therapy. The interactions between target proteins were analyzed in a STRING database, the protein relationship network was analyzed using Cytoscape software, and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the core target gene group was carried out in a DAVID database in order to construct the "compound-traditional Chinese medicine/molecule-target-pathway" network. Molecular docking was used to verify the core protein and its related small molecular compounds. RESULT: There were 129 types of secondary metabolites in SLBZP. 80 target proteins of these metabolites were potential core targets for IBS treatment including acetylcholinesterase (AChE), arachidonate-5-lipoxygenase (ALOX5), B-cell lymphoma-2 (BCL2), recombinant cyclin D1 (CCND1), and catenin-ß1 (CTNNB1), among others. Results from these targets indicated that the most enriched pathway was the tumor necrosis factor (TNF) signaling pathway (p < 0.001) and that the most abundant pathway was signal transduction. In the network nodes of the TNF signaling pathway, the Chinese medicines with the highest aggregation were Lablab semen album and Glycyrrhizae radix et rhizoma (degree = 11). The small molecules with the highest aggregation were oxypeucedanin and 3,5,6,7,8,3',4'-heptamethoxyflavone (degree = 4). Molecular docking results confirmed that daidzein 7-O-glucoside (daidzin) had the highest degree of binding to TNF proteins in the TNF signaling pathway. CONCLUSION: This study shows that SLBZP can treat IBS by influencing multiple targets and pathways, of which the TNF signaling pathway may be the most significant. This typifies the pharmacological characteristics of traditional Chinese medicine, i.e., multiple targets, numerous pathways, and specific therapeutic effects on diseases. SLBZP can therefore be used as a candidate drug for clinical IBS by intervening in human signal transduction.

Novel Elongator Protein 2 Inhibitors Mitigating Tumor Necrosis Factor-α Induced Osteogenic Differentiation Inhibition.

Tumor necrosis factor-α is a common cytokine that increases in inflammatory processes, slows the differentiation of bone formation, and induces osteodystrophy in the long-term inflammatory microenvironment. Our previous study confirmed that the Elongation protein 2 (ELP2) plays a significant role in osteogenesis and osteogenic differentiation, which is considered a drug discovery target in diseases related to bone formation and differentiation. In this study, we applied an in silico virtual screening method to select molecules that bind to the ELP2 protein from a chemical drug molecule library and obtained 95 candidates. Then, we included 11 candidates by observing the docking patterns and the noncovalent bonds. The binding affinity of the ELP2 protein with the candidate compounds was examined by SPR analysis, and 5 out of 11 compounds performed good binding affinity to the mouse ELP2 protein. After in vitro cell differentiation assay, candidates 2# and 5# were shown to reduce differentiation inhibition after tumor necrosis factor-α stimulation, allowing further optimization and development for potential clinical treatment of inflammation-mediated orthopedic diseases.

Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.

Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the 'Autocorrelation' module contributed more while the 'Atom type electrotopological state' contributed the least to the model.

IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.

The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information-Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj = caj and cdataj = cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj = cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon's entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.

Molecular Insights into Binding Mode and Interactions of Structure-Based Virtually Screened Inhibitors for Pseudomonas aeruginosa Multiple Virulence Factor Regulator (MvfR).

Multi-drug resistance (MDR) bacterial pathogens pose a threat to global health and warrant the discovery of new therapeutic molecules, particularly those that can neutralize their virulence and stop the evolution of new resistant mechanisms. The superbug nosocomial pathogen, Pseudomonas aeruginosa, uses a multiple virulence factor regulator (MvfR) to regulate the expression of multiple virulence proteins during acute and persistent infections. The present study targeted MvfR with the intention of designing novel anti-virulent compounds, which will function in two ways: first, they will block the virulence and pathogenesis P. aeruginosa by disrupting the quorum-sensing network of the bacteria, and second, they will stop the evolution of new resistant mechanisms. A structure-based virtual screening (SBVS) method was used to screen druglike compounds from the Asinex antibacterial library (~5968 molecules) and the comprehensive marine natural products database (CMNPD) (~32 thousand compounds), against the ligand-binding domain (LBD) of MvfR, to identify molecules that show high binding potential for the relevant pocket. In this way, two compounds were identified: Top-1 (4-((carbamoyloxy)methyl)-10,10-dihydroxy-2,6-diiminiodecahydropyrrolo[1,2-c]purin-9-yl sulfate) and Top-2 (10,10-dihydroxy-2,6-diiminio-4-(((sulfonatocarbamoyl)oxy)methyl)decahydropyrrolo[1,2-c]purin-9-yl sulfate), in contrast to the co-crystallized M64 control. Both of the screened leads were found to show deep pocket binding and interactions with several key residues through a network of hydrophobic and hydrophilic interactions. The docking results were validated by a long run of 200 ns of molecular dynamics simulation and MM-PB/GBSA binding free energies. All of these analyses confirmed the presence of strong complex formation and rigorous intermolecular interactions. An additional analysis of normal mode entropy and a WaterSwap assay were also performed to complement the aforementioned studies. Lastly, the compounds were found to show an acceptable range of pharmacokinetic properties, making both compounds potential candidates for further experimental studies to decipher their real biological potency.

SuperTCM: A biocultural database combining biological pathways and historical linguistic data of Chinese Materia Medica for drug development.

AIM OF THE STUDY: Botanicals used in Traditional Chinese Medicine (TCM) are a rich source for drug discovery and provide models for multi-component drug development. To facilitate the studies of the actions of TCM drugs and expand their applications, a comprehensive database is urgently required. METHODS: One online resource connects all the relevant data from multiple scientific sources and languages. Drug information from published TCM databases and the official Chinese Pharmacopoeia as well as specialized meta-websites such as Kew's Medicinal Plant Names Service was integrated on a higher level. RESULTS: Our database, SuperTCM, covers the aspects of TCM derived from medicinal plants, encompassing pharmacological recipes up to chemical compounds. It provides the information for 6516 TCM drugs (or "herbs") with 5372 botanical species, 55,772 active ingredients against 543 targets in 254 KEGG pathways associated with 8634 diseases. SuperTCM is freely available at http://tcm.charite.de/supertcm.

Exploration of the anti-insomnia mechanism of Ganoderma by central-peripheral multi-level interaction network analysis.

BACKGROUND: Ganoderma (Lingzhi in Chinese) has shown good clinical outcomes in the treatment of insomnia, restlessness, and palpitation. However, the mechanism by which Ganoderma ameliorates insomnia is unclear. We explored the mechanism of the anti-insomnia effect of Ganoderma using systems pharmacology from the perspective of central-peripheral multi-level interaction network analysis. METHODS: The active components and central active components of Ganoderma were obtained from the TCMIP and TCMSP databases, then screened to determine their pharmacokinetic properties. The potential target genes of these components were identified using the Swiss Target Prediction and TCMSP databases. The results were matched with the insomnia target genes obtained from the GeneCards, OMIM, DisGeNET, and TCMIP databases. Overlapping targets were subjected to multi-level interaction network analysis and enrichment analysis using the STRING, Metascape, and BioGPS databases. The networks analysed were protein-protein interaction (PPI), drug-component-target gene, component-target gene-organ, and target gene-extended disease; we also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. RESULTS: In total, 34 sedative-hypnotic components (including 5 central active components) were identified, corresponding to 51 target genes. Multi-level interaction network analysis and enrichment analysis demonstrated that Ganoderma exerted an anti-insomnia effect via multiple central-peripheral mechanisms simultaneously, mainly by regulating cell apoptosis/survival and cytokine expression through core target genes such as TNF, CASP3, JUN, and HSP90αA1; it also affected immune regulation and apoptosis. Therefore, Ganoderma has potential as an adjuvant therapy for insomnia-related complications. CONCLUSION: Ganoderma exerts an anti-insomnia effect via complex central-peripheral multi-level interaction networks.

COVIDrugNet: a network-based web tool to investigate the drugs currently in clinical trial to contrast COVID-19.

The COVID-19 pandemic poses a huge problem of public health that requires the implementation of all available means to contrast it, and drugs are one of them. In this context, we observed an unmet need of depicting the continuously evolving scenario of the ongoing drug clinical trials through an easy-to-use, freely accessible online tool. Starting from this consideration, we developed COVIDrugNet ( http://compmedchem.unibo.it/covidrugnet ), a web application that allows users to capture a holistic view and keep up to date on how the clinical drug research is responding to the SARS-CoV-2 infection. Here, we describe the web app and show through some examples how one can explore the whole landscape of medicines in clinical trial for the treatment of COVID-19 and try to probe the consistency of the current approaches with the available biological and pharmacological evidence. We conclude that careful analyses of the COVID-19 drug-target system based on COVIDrugNet can help to understand the biological implications of the proposed drug options, and eventually improve the search for more effective therapies.

Network Pharmacology Analysis of the Identification of Phytochemicals and Therapeutic Mechanisms of Paeoniae Radix Alba for the Treatment of Asthma.

BACKGROUND: Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall., has been suggested to play an important role for the treatment of asthma. A biochemical understanding of the clinical effects of Paeoniae Radix Alba is needed. Here, we explore the phytochemicals and therapeutic mechanisms via a systematic and comprehensive network pharmacology analysis. METHODS: Through TCMSP, PubChem, GeneCards database, and SwissTargetPrediction online tools, potential targets of active ingredients from PRA for the treatment of asthma were obtained. Cytoscape 3.7.2 was used to determine the target of active ingredients of PRA. Target protein interaction (PPI) network was constructed through the STRING database. The Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genes (KEGG) pathway enrichment analysis were analyzed through the biological information annotation database (DAVID). RESULTS: Our results indicate that PRA contains 21 candidate active ingredients with the potential to treat asthma. The enrichment analysis of GO and KEGG pathways found that the treatment of asthma by PRA may be related to the process of TNF (tumor necrosis factor) release, which can regulate and inhibit multiple signaling pathways such as ceramide signaling. CONCLUSIONS: Our work provides a phytochemical basis and therapeutic mechanisms of PRA for the treatment of asthma, which provides new insights on further research on PRA.

Ensemble learning application to discover new trypanothione synthetase inhibitors.

Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.