Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology

Therapeutic potential of IKK-β inhibitors from natural phenolics for inflammation in cardiovascular diseases

Cardiovascular disease (CVDs) is a chronic disease with the highest morbidity and mortality in the world. Previous studies have suggested that preventing inflammation serves an efficient role in protection against cardiovascular diseases. Modulation of IKK-β activity can be used to treat and control CVDs associated with chronic inflammation, which targets the phosphorylation of IκB following the release of the RelA complex, and then translocates to the nucleus, eventually triggering the transcription of several genes that induce chemokines, cytokines, and adhesion molecules. Most importantly, the IκB kinase (IKK) complex is involved in transcriptional activation by phosphorylating the inhibitory molecule IkBα, enabling activation of NF-κB. Phenolic compounds possess cardioprotective potential that may be related to modulating inflammatory responses involved in CVDs. The SystemsDock analysis was used to explore whether 38 active compounds inhibit IKK-β activity based on literature. Docking results showed that the top docking score of three chemical compounds were icariin, salvianolic acid B, and plantainoside D in all compounds. Icariin, salvianolic acid B, and plantainoside D are the most promising IKKβ inhibitors. These phytochemicals could be helpful to find the lead compounds on designing and developing novel cardioprotective agents.

Research on the potential mechanism of Chuanxiong Rhizoma on treating Diabetic Nephropathy based on network pharmacology

Background: Chuanxiong Rhizoma is one of the traditional Chinese medicines which have been used for years in the treatment of diabetic nephropathy (DN). However, the mechanism of Chuanxiong Rhizoma in DN has not yet been fully understood. Methods: We performed network pharmacology to construct target proteins interaction network of Chuanxiong Rhizoma. Active ingredients were acquired from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. DRUGBANK database was used to predict target proteins of Chuanxiong Rhizoma. Gene ontology (GO) biological process analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed for functional prediction of the target proteins. Molecular docking was applied for evaluating the drug interactions between hub targets and active ingredients. Results: Twenty-eight target genes fished by 6 active ingredients of Chuanxiong Rhizoma were obtained in the study. The top 10 significant GO analyses and 6 KEGG pathways were enriched for genomic analysis. We also acquired 1366 differentially expressed genes associated with DN from GSE30528 dataset, including five target genes: KCNH2, NCOA1, KDR, NR3C2 and ADRB2. Molecular docking analysis successfully combined KCNH2, NCOA1, KDR and ADRB2 to Myricanone with docking scores from 4.61 to 6.28. NR3C2 also displayed good docking scores with Wallichilide and Sitosterol (8.13 and 8.34, respectively), revealing good binding forces to active compounds of Chuanxiong Rhizoma. Conclusions: Chuanxiong Rhizoma might take part in the treatment of DN through pathways associated with steroid hormone, estrogen, thyroid hormone and IL-17. KCNH2, NCOA1, KDR, ADRB2 and NR3C2 were proved to be the hub targets, which were closely related to corresponding active ingredients of Chuanxiong Rhizoma.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Study on the Multitarget Mechanism and Key Active Ingredients of Herba Siegesbeckiae and Volatile Oil against Rheumatoid Arthritis Based on Network Pharmacology

BACKGROUND

Herba Siegesbeckiae (HS, Xixiancao in Chinese) is widely used to treat inflammatory joint diseases such as rheumatoid arthritis (RA) and arthritis, and its molecular mechanisms and active ingredients have not been completely elucidated.

METHODS

In this study, the small molecule ligand library of HS was built based on Traditional Chinese Medicine Systems Pharmacology (TCMSP). The essential oil from HS was extracted through hydrodistillation and analyzed by Gas Chromatography-Mass Spectrometer (GC-MS). The target of RA was screened based on Comparative Toxicogenomics Database (CTD). The key genes were output by the four algorithms' maximum neighborhood component (MNC), degree, maximal clique centrality (MCC), and stress in cytoHubba in Cytoscape, while biological functions and pathways were also analyzed. The key active ingredients and mechanism of HS and essential oil against RA were verified by molecular docking technology (Sybyl 2.1.1) in treating RA. The interaction between 6 active ingredients (degree ≥ 5) and CSF2, IL1β, TNF, and IL6 was researched based on the software Ligplot.

RESULTS

There were 31 small molecule constituents of HS and 16 main chemical components of essential oil (relative content >1%) of HS. There were 47 chemical components in HS. Networks showed that 9 core targets (TNF, IL1β, CSF2, IFNG, CTLA4, IL18, CD26, CXCL8, and IL6) of RA were based on Venn diagrams. In addition, molecular docking simulation indicated that CSF2, IL1β, TNF, and IL6 had good binding activity with the corresponding compounds (degree > 10).The 6 compounds (degree ≥ 5) of HS and essential oil had good interaction with 5 or more targets.

CONCLUSION

This study validated and predicted the mechanism and key active ingredients of HS and volatile oil in treating RA. Additionally, this study provided a good foundation for further experimental studies.

Multi-dimensional computational pipeline for large-scale deep screening of compound effect assessment: an in silico case study on ageing-related compounds

Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions-structural similarity, binding profiles and their network effects across pathways and molecular interaction maps-to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline.

Unraveling the action mechanism of polygonum cuspidatum by a network pharmacology approach

As a popular Chinese herbal medicine (CHM), polygonum cuspidatum is widely used to treat various diseases in China. However, its biological function and action mechanism have yet to be systematically explored. In the present study, we first identified 14 potential active ingredients of polygonum cuspidatum using the TCMSP server and then conducted an in silico target prediction for these ingredients using PharmMapper. The subsequent KEGG pathway enrichment analysis of the 57 identified potential targets revealed that they were closely associated with cancer and gynecological disorders. Furthermore, a protein-protein interaction network of these targets was constructed using STRING and Cytoscape, through which 11 core targets were excavated according to degree, a key topological parameter. Meanwhile, we developed a novel formula, in which the "R value" is determined by average shortest path length and closeness centrality, two other key topological parameters, to evaluate the reliability of these predicted core targets. Intriguingly, among the top 10 core targets excavated using this new formula, 7 overlapped with the former 11 core targets, showing a good consistency in these core targets between the different prediction algorithms. Next, 7 ingredients were identified/validated from the crude extract of polygonum cuspidatum using UPLC-MS/MS. Noteworthy, 6 potential targets predicted for these 7 ingredients overlapped with the 7 core targets excavated from the previous in silico analyses. Further molecular docking and druggability analyses suggested that polydatin may play a pivotal role in manifesting the therapeutic effects of polygonum cuspidatum. Finally, we carried out a series of cell functional assays, which validated the anti-proliferative effects of polygonum cuspidatum on gynecological cancer cells, thus demonstrating our network pharmacology approach is reliable and powerful enough to guide the CHM mechanism study.

Integrated network pharmacology and targeted metabolomics to reveal the mechanism of nephrotoxicity of triptolide

Triptolide (TP) is one of the important active components in Tripterygium wilfordii Hook. F., which shows strong anti-inflammatory and immunomodulatory effects. However, a large number of literature studies have reported that TP is the main component causing nephrotoxicity, and the mechanism of nephrotoxicity has not yet been revealed. Therefore, it is of great practical significance to clarify the toxicity mechanism of TP. This study integrated network pharmacology and targeted metabolomics to reveal the nephrotoxicity mechanism of TP. Firstly, network pharmacology screening of 61 action targets related to TP induced nephrotoxicity, with 39 direct targets and 22 indirect targets, was performed. Subsequently, based on a large-scale protein-protein interaction (PPI) and molecular docking validation, the core targets were identified. Based on the above targets and enrichment analysis, the purine metabolism, Toll-like receptor signaling pathway and NF-κB signaling pathway were found play a pivotal role in TP-induced nephrotoxicity. Literature investigation showed that purine and pyrimidine metabolism pathways were closely related to kidney diseases. Therefore, by using the quantitative method of determining endogenous purine and pyrimidine previously established in the laboratory, a targeted metabolomic analysis of TP was carried out. Finally, six nephrotoxicity biomarkers, dihydroorotate, thymidine, 2-deoxyinosine, uric acid, adenosine and xanthine, were found. Combining the above results, the mechanisms underlying the nephrotoxicity of TP were speculated to be due to the over-consumption of xanthine and uric acid, which would result in enormous ROS being released in response to oxidative stress in the body. Furthermore, activation of the Toll-like receptor signalling pathway can promotes the phosphorylation of the downstream protein NF-κB and causes an inflammatory response that ultimately leads to nephrotoxicity.

Jiawei Foshou San Induces Apoptosis in Ectopic Endometrium Based on Systems Pharmacology, Molecular Docking, and Experimental Evidence

Foshou San is a typical gynaecological formula with wild usage in traditional Chinese medicine. Jiawei Foshou San (JFS) is a novel ingredient prescription from Foshou San with antiendometriosis effect in unclear mechanisms. To uncover the potential application and proapoptotic mechanisms of JFS, JFS ingredient-drug target-disease networks, GO enrichment, and pathway analysis were established for potential application prediction. Molecular docking and validation in vivo were investigated by the proapoptotic mechanisms of JFS. In this study, 99 common targets were related to 108 diseases. 484 biological processes, 44 cell components, 59 molecular functions, and 37 pathways were significantly identified in GO enrichment and pathway analysis. In molecular docking, ligustrazine showed binding activity with Bcl-2, Bax, caspase-9, caspase-3, and PARP. In vivo, JFS elevated the shrink rate of ectopic endometrium, by suppressing E2 and PROG. An in-depth study showed that apoptosis was activated through diminishing Bcl-2, rising Bax and Bad, and expressing more caspase-3 and caspase-9 using JFS. JFS promoted the protein level of cleaved-PARP. In brief, JFS might be applied for various diseases through multiple targets and pathways, especially endometriosis by Bcl-2 pathway. These findings reveal the potential application for further evaluation and uncover the proapoptotic mechanism of JFS.

Key Topics in Molecular Docking for Drug Design

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Aberrant Regulation of RAD51 Promotes Resistance of Neoadjuvant Endocrine Therapy in ER-positive Breast Cancer

Breast cancer is one of the most common malignant cancers affecting females. Estrogen receptor (ER)-positive breast cancer is responsive to endocrine therapy. Although current therapies offer favorable prospects for improving survival, the development of resistance remains a severe problem. In this study, we explored the resistance mechanisms of ER-positive breast cancer to neoadjuvant endocrine therapy. Microarray data of GSE87411 contained 109 pairs of samples from Z1031 trial, including untreated samples and post-treated samples with neoadjuvant aromatase inhibitor (AI) therapy. The differentially expressed genes (DEGs) were obtained from two different comparisons: untreated samples versus post-treated samples with AIs, and post-treated samples sensitive versus resistant to AIs. Multiple bioinformatic methods were applied to evaluate biological function, protein-protein network and potential binding between target protein and aromatase inhibitor. Then, regulation of gene expression, DNA methylation and clinicopathological factors of breast cancer were further analyzed with TCGA data. From GSE87411 dataset, 30 overlapped DEGs were identified. Cell division was found to be the main function of overlapped DEGs by functional enrichment and gene ontology (GO) analysis. RAD51 recombinase (RAD51), a key protein of homologous recombination, was detected to interact with BReast CAncer genes 2 (BRCA2). Moreover, according to the docking simulation, RAD51 might potentially bind to AIs. Overexpressed RAD51 was associated with hypermethylation of BRCA2, resistance to AIs and poor overall survival of patients with ER-positive breast cancer. Furthermore, RAD51 was found to be a better indicator than MKI67 for predicting resistance in neoadjuvant setting. The results indicated that methylation of BRCA2 led to incomplete suppression on RAD51, which caused an increased expression of RAD51, subsequently AI-resistance and poor prognosis in ER-positive breast cancer. RAD51 could be a new candidate used as a predicative marker and therapeutic target in neoadjuvant endocrine treatment.

Integrative analysis of the contribution of mRNAs and long non‑coding RNAs to the pathogenesis of asthma

Asthma, a common but poorly controlled disease, is one of the most serious health problems worldwide; however, the mechanisms underlying the development of asthma remain unknown. Long non-coding RNAs (lncRNAs) and mRNAs serve important roles in the initiation and progression of various diseases. The present study aimed to investigate the role of differentially expressed lncRNAs and mRNAs associated with asthma. Differentially expressed lncRNAs and mRNAs were screened between the expression data of 62 patients with asthma and 43 healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the biological functions and pathways associated with the lncRNAs and mRNAs identified. Protein-protein interaction (PPI) networks were subsequently generated. In addition, lncRNA-mRNA weighted co-expression networks were obtained. In total, 159 differentially expressed lncRNAs and 1,261 mRNAs were identified. GO and KEGG analyses revealed that differentially expressed mRNAs regulated asthma by participating in the ‘vascular endothelial (VEGF) signaling pathway’, ‘oxidative phosphorylation’, ‘Fc ε RI signaling pathway’, ‘amino sugar and nucleotide sugar metabolism’, ‘histidine metabolism’, ‘β-alanine metabolism’ and ‘extracellular matrix-receptor interaction’ (P<0.05). Furthermore, protein kinase B 1 had the highest connectivity degree in the PPI network, and was significantly enriched in the ‘VEGF signaling pathway’ and ‘Fc ε RI signaling pathway’. A total of 8 lncRNAs in the lncRNA-mRNA co-expression network were reported to interact with 52 differentially expressed genes, which were enriched in asthma-associated GO and KEGG pathways. The results obtained in the present study may provide insight into the profile of differentially expressed lncRNAs associated with asthma. The identification of a cluster of dysregulated lncRNAs and mRNAs may serve as a potential therapeutic strategy to reverse the progression of asthma.

PIM1, CYP1B1, and HSPA2 Targeted by Quercetin Play Important Roles in Osteoarthritis Treatment by Achyranthes bidentata

Aim

Achyranthes bidentata is one of the most commonly used Chinese herbal medicines (CHM) that is currently considered for the treatment of osteoarthritis. The purpose of this study was to reveal the mechanism of Achyranthes bidentata in osteoarthritis treatment based on the network pharmacology.

Methods

The effective components of Achyranthes bidentata were firstly screened out from the TCMSP database with ADME property parameters. Then, osteoarthritis-related proteins targeted by the effective components were predicted based on the DrugBank and CTD databases. Subsequently, enrichment analysis and interaction network between targets of effective components and pathways were also studied. In addition, the differentially expressed genes (DEGs) of GSE55457 were used for validation of the osteoarthritis-related target proteins. Finally, the effective components-target molecular docking models were predicted.

Results

A total of 10 effective components were identified, of which kaempferol and quercetin had 1 and 29 targets, respectively. There were 26 target proteins of quercetin related to the osteoarthritis. These targets were mainly enriched in mitochondrial ATP synthesis coupled proton transport, cellular response to estradiol stimulus, and nitric oxide biosynthetic process. In addition, there were three common proteins, PIM1, CYP1B1, and HSPA2 based on the DEGs of GSE55457, which were considered as the key targeted proteins of the quercetin.

Conclusion

The docking of PIM1-quercetin, CYP1B1-quercetin, and HSPA2-quercetin may play important roles during the treatment of osteoarthritis by Achyranthes bidentata.

Chemical profliling of Dingkun Dan by ultra High performance liquid chromatography Q exactive orbitrap high resolution mass spectrometry

Dingkun Dan (DKD) has been widely used for a variety of gynecological disease. However, the systematic analysis of the chemical constituents of DKD has not been well established because of the complexity of the formula and confidentiality. In this paper, liquid chromatography Q Exactive high resolution accurate mass spectrometry (UHPLC-QE-HRMS) with automated MetaboLynx analysis was established to characterize the chemical constituents of DKD. The analysis was performed on a Water Acquity UPLC® HSS T3 using a gradient elution system. Full scan ranged 100-1500 m/z in positive and negative ion mode combined with MS/MS fragmentation for top 5 ions was proposed for aiding the structural identification of the components. All of the peaks were tentatively characterized by not only comparing the retention time and MS data with those from reported literature and database, but also summarizing the fragmentation pathways and promoting to other ingredients identification. Additionally, the network pharmacology study had been used to analysis the identified ingredients and DKD's clinical diseases. In this work, a total of 121 components and isomers were characterized, including amino acids, phenolic acids, lactones, terpenoids, alkaloids, saponins, flavonoids, and other compounds. Network pharmacology analysis showed that identified compounds, such as ginsenosides and notoginsenosides, crocin I, echinacoside, rutin and verbascoside, could be responsible for the pharmacological activity of DKD by regulating the hormone with related metabolism pathways, estrogen signaling pathways and serotonergic synapse pathways. It could indicate that UHPLC-MS showed obvious superiority used to find the potential bioactive compounds of complicated TCM formula without the process of extraction and isolation.

Research on the Potential Mechanism of Gypenosides on Treating Thyroid-Associated Ophthalmopathy Based on Network Pharmacology

Thyroid-associated ophthalmopathy is the commonest orbital disease in adults. However, shortcomings still exist in treatments. The aim of this study was to identify the efficacy and potential mechanism of gypenosides in the treatment of thyroid-associated ophthalmopathy. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was screened for active compounds of gypenosides, and targets were predicted using Swiss Target Prediction. The targets of thyroid-associated ophthalmopathy were obtained from Online Mendelian Inheritance in Man, Comparative Toxicogenomic Database and GeneCards Human gene database. Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome Pathways were determined based on the common targets. Protein-protein interaction (PPI) network was constructed to further understand of relationship among target genes, compounds and proteins. Molecular docking was performed to investigate the binding ability between gypenosides and hub genes. A total of 70 targets for gypenosides and 804 targets for thyroid-associated ophthalmopathy were obtained with 8 common targets identified. GO analysis and KEGG pathway analysis revealed that the hub genes were enriched in JAK-STAT, while Reactome pathways analysis indicated genes enriched in interleukin pathways. PPI network showed STAT1, STAT3, and STAT4 were at the center. Additionally, molecular docking indicated that STAT1 and STAT3 display good binding forces with gypenosides. This study indicates that target genes mainly enriched in JAK-STAT signaling pathway, particularly in STATs, which can be combined with gypenosides. This may suggest that gypenosides have curative effect on thyroid-associated ophthalmopathy via the JAK-STAT pathway.

A structure-function approach identifies L-PGDS as a mediator responsible for glucocorticoid-induced leptin expression in adipocytes

Leptin is an adipokine predominantly secreted by adipocytes and has many physiological roles, including in energy homeostasis. We identified that AM630, a cannabinoid receptor 2 (CB2) antagonist, down-regulated leptin expression in mature adipocytes differentiated from either stromal vascular fractions isolated from inguinal fat pads of C57BL/6J mice or 3T3-L1 preadipocytes. However, the leptin-suppressive effects of AM630 preserved in CB2-deficient adipocytes indicated the off-target activity of AM630 in leptin expression. Pharmacological and genetic studies, cheminformatics, and docking simulation were applied to identify the potential protein target of AM630 that modulates leptin expression in differentiated primary preadipocytes. Screening of the reported off-targets of AM630 identified a synthetic cannabinoid WIN55212-2 exerting the same function. Target deconvolution and docking simulation suggested that AM630 and WIN55212-2 were both inhibitors of lipocalin-type prostaglandin D2 synthase (L-PGDS). Further studies showed that L-PGDS positively regulates leptin expression. Although glucocorticoid and aldosterone were previously reported to induce expression of both L-PGDS and leptin, our data demonstrated that L-PGDS mediates only glucocorticoid-induced leptin expression in differentiated primary preadipocytes. No effect was observed after aldosterone treatment. This newly discovered glucocorticoid - L-PGDS - leptin pathway may provide insights into current clinical use of glucocorticoid and management of their undesired effects such as obesity.

Mechanisms of Compound Kushen Injection for the Treatment of Lung Cancer Based on Network Pharmacology

Background

Compound Kushen Injection (CKI) is a Chinese patent drug that shows good efficacy in treating lung cancer (LC). However, its underlying mechanisms need to be further clarified.

Methods

In this study, we adopted a network pharmacology method to gather compounds, predict targets, construct networks, and analyze biological functions and pathways. Moreover, molecular docking simulation was employed to assess the binding potential of selected target-compound pairs.

Results

Four networks were established, including the compound-putative target network, protein-protein interaction (PPI) network of LC targets, compound-LC target network, and herb-compound-target-pathway network. Network analysis showed that 8 targets (CHRNA3, DRD2, PRKCA, CDK1, CDK2, CHRNA5, MMP1, and MMP9) may be the therapeutic targets of CKI in LC. In addition, molecular docking simulation indicated that CHRNA3, DRD2, PRKCA, CDK1, CDK2, MMP1, and MMP9 had good binding activity with the corresponding compounds. Furthermore, enrichment analysis indicated that CKI might exert a therapeutic role in LC by regulating some important pathways, namely, pathways in cancer, proteoglycans in cancer, PI3K-Akt signaling pathway, non-small-cell lung cancer, and small cell lung cancer.

Conclusions

This study validated and predicted the mechanism of CKI in treating LC. Additionally, this study provides a good foundation for further experimental studies and promotes the reasonable application of CKI in the clinical treatment of LC.

Identification of potential therapeutic target genes in mouse mesangial cells associated with diabetic nephropathy using bioinformatics analysis

The aim of the present study was to identify genes under the effect of transforming growth factor-β (TGF-β1), high glucose (HG) and glucosamine (GlcN) in MES-13 mesangial cells and elucidate the molecular mechanisms of diabetic nephropathy (DN). The gene expression datasets GSE2557 and GSE2558 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were independently screened using the GEO2R online tool. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape software. The hub genes were identified by the NetworkAnalyzer plugin. Overlapping genes were subjected to molecular docking analysis using SystemsDock. A total of 202 upregulated and 158 downregulated DEGs from the HG-treated groups, 138 upregulated and 103 downregulated DEGs from the GlcN-treated groups, and 81 upregulated and 44 downregulated DEGs from the TGF-β1-treated groups were identified. The majority of the DEGs were independently enriched in 'nucleosome assembly', 'chromatin silencing' and 'xenobiotic glucuronidation'. In addition, KEGG pathways were significantly enriched in 'systemic lupus erythematosus', 'protein processing in endoplasmic reticulum' and 'aldarate metabolism pathway', and 'TNF signaling pathway' intersected in the TGF-β1-treated and HG-treated groups. In total, eight hub genes, Jun, prostaglandin-endoperoxide synthase 2 (Ptgs2), fibronectin 1 (Fn1), cyclin-dependent kinase (Cdk)2, Fos, heat shock protein family A (Hsp70) member 5 (Hspa5), Hsp90b1 and homo sapiens hypoxia upregulated 1 (Hyou1), and three overlapping genes, Ras homolog gene family, member B (RHOB), complement factor H (CFH) and Krüppel-like factor 15 (KLF15), were selected. Valsartan with RHOB, and fosinopril with CFH and KLF15 had preferential binding activity. In conclusion, Jun, Ptgs2, Fn1, Cdk2, Fos, Hspa5, Hsp90b1, Hyou1, RHOB, CFH and KLF15 may be potential therapeutic targets for mesangial cells associated with DN, which may provide insight into DN treatment strategies.

Ligand fishing with cellular membrane-coated cellulose filter paper: a new method for screening of potential active compounds from natural products

Ligand fishing is a widely used approach for screening active compounds from natural products. Recently, cell membrane (CM) as affinity ligand has been applied in ligand fishing, including cell membrane chromatography (CMC) and CM-coated magnetic bead. However, these methods possess many weaknesses, including complicated preparation processes and time-consuming operation. In this study, cheap and easily available cellulose filter paper (CFP) was selected as carrier of CM and used to fabricate a novel CM-coated CFP (CMCFP) for the first time. The type of CFP was optimized according to the amount of immobilized protein, and the immobilization of CM onto CFP by the insertion and self-fusion process was verified by confocal imaging. The CMCFP exhibited good selectivity and stability and was used for fishing potentially active compounds from extracts of Angelica dahurica. Three potentially active compounds, including bergapten, pabulenol, and imperatorin, were fished out and identified. The traditional Chinese medicine systems pharmacology database and analysis platform was used to build an active compound-target protein network, and accordingly, the gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1) was deduced as potential target of CM for the active compounds of Angelica dahurica. Molecular docking was performed to evaluate the interaction between active compounds and GABRA1, and bergapten was speculated as a new potentially active compound. Compared with other methods, the fishing assay based on CMCFP was more effective, simpler, and cheaper.

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log₂ fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.

Systems pharmacology reveals the unique mechanism features of Shenzhu Capsule for treatment of ulcerative colitis in comparison with synthetic drugs

In clinic, both synthetic drugs and Shenzhu Capsule (SZC), one kind of traditional Chinese medicines (TCMs), are used to treat ulcerative colitis (UC). In our study, a systems pharmacology approach was employed to elucidate the chemical and mechanism differences between SZC and synthetic drugs in treating UC. First, the compound databases were constructed for SZC and synthetic drugs. Then, the targets of SZC were predicted with on-line tools and validated using molecular docking method. Finally, chemical space, targets, and pathways of SZC and synthetic drugs were compared. Results showed that atractylenolide I, atractylone, kaempferol, etc., were bioactive compounds of SZC. Comparison of SZC and synthetic drugs showed that (1) in chemical space, the area of SZC encompasses the area of synthetic drugs; (2) SZC can act on more targets and pathways than synthetic drugs; (3) SZC can not only regulate immune and inflammatory reactions but also act on ulcerative colitis complications (bloody diarrhea) and prevent UC to develop into colorectal cancer whereas synthetic drugs mainly regulate immune and inflammatory reactions. Our study could help us to understand the compound and mechanism differences between TCM and synthetic drugs.

Polypharmacology Within the Full Kinome: a Machine Learning Approach

Protein kinases generate nearly a thousand different protein products and regulate the majority of cellular pathways and signal transduction. It is therefore not surprising that the deregulation of kinases has been implicated in many disease states. In fact, kinase inhibitors are the largest class of new cancer therapies. Understanding polypharmacology within the full kinome, how drugs interact with many different kinases, would allow for the development of safer and more efficacious cancer therapies. A full understanding of these interactions is not experimentally feasible making highly accurate computational predictions extremely useful and important. This work aims at making a machine learning model useful for investigating the full kinome. We evaluate many feature sets for our model and get better performance over molecular docking with all of them. We demonstrate that you can achieve a nearly 60% increase in success rate at identifying binding compounds using our model over molecular docking scores.

Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding

Drug discovery is an expensive, lengthy, and sometimes dangerous process. The ability to make accurate computational predictions of drug binding would greatly improve the cost-effectiveness and safety of drug discovery and development. This study incorporates ensemble docking, the use of multiple protein conformations extracted from a molecular dynamics trajectory to perform docking calculations, with additional biomedical data sources and machine learning algorithms to improve the prediction of drug binding. We found that we can greatly increase the classification accuracy of an active vs a decoy compound using these methods over docking scores alone. The best results seen here come from having an individual protein conformation that produces binding features that correlate well with the active vs. decoy classification, in which case we achieve over 99% accuracy. The ability to confidently make accurate predictions on drug binding would allow for computational polypharamacological networks with insights into side-effect prediction, drug-repurposing, and drug efficacy.

An automated framework for QSAR model building

Background

In-silico quantitative structure–activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community.

Results

In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62–99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection.

Conclusions

We developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems.

Electronic supplementary material

The online version of this article (10.1186/s13321-017-0256-5) contains supplementary material, which is available to authorized users.

How Can Synergism of Traditional Medicines Benefit from Network Pharmacology?

Many prescriptions of traditional medicines (TMs), whose efficacy has been tested in clinical practice, have great therapeutic value and represent an excellent resource for drug discovery. Research into single compounds of TMs, such as artemisinin from Artemisia annua L., has achieved great success; however, it has become evident that a TM prescription (which frequently contains various herbs or other components) has a synergistic effect in effecting a cure or reducing toxicity. Network pharmacology targets biological networks and analyzes the links among drugs, targets, and diseases in those networks. Comprehensive, systematic research into network pharmacology is consistent with the perspective of holisticity, which is a main characteristic of many TMs. By means of network pharmacology, research has demonstrated that many a TM show a synergistic effect by acting at different levels on multiple targets and pathways. This approach effectively bridges the gap between modern medicine and TM, and it greatly facilitates studies into the synergistic actions of TMs. There are different kinds of synergistic effects with TMs, such as synergy among herbs, effective parts, and pure compounds; however, for various reasons, new drug discovery should at present focus on synergy among pure compounds.

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.

Bufei Huoxue Capsule Attenuates PM2.5-Induced Pulmonary Inflammation in Mice

Atmospheric fine particulate matter 2.5 (PM 2.5) may carry many toxic substances on its surface and this may pose a public health threat. Epidemiological research indicates that cumulative ambient PM2.5 is correlated to morbidity and mortality due to pulmonary and cardiovascular diseases and cancer. Mitigating the toxic effects of PM2.5 is therefore highly desired. Bufei Huoxue (BFHX) capsules have been used in China to treat pulmonary heart disease (cor pulmonale). Thus, we assessed the effects of BFHX capsules on PM2.5-induced pulmonary inflammation and the underlying mechanisms of action. Using Polysearch and Cytoscape 3.2.1 software, pharmacological targets of BFHX capsules in atmospheric PM2.5-related respiratory disorders were predicted and found to be related to biological pathways of inflammation and immune function. In a mouse model of PM2.5-induced inflammation established with intranasal instillation of PM2.5 suspension, BFHX significantly reduced pathological response and inflammatory mediators including IL-4, IL-6, IL-10, IL-8, TNF-α, and IL-1β. BFHX also reduced keratinocyte growth factor (KGF), secretory immunoglobulin A (sIgA), and collagen fibers deposition in lung and improved lung function. Thus, BFHX reduced pathological responses induced by PM2.5, possibly via regulation of inflammatory mediators in mouse lungs.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

Metabolomics reveal the protective effect of Farfarae Flos against asthma using an OVA-induced rat model

A ¹H NMR based metabolomics approach combined with biochemical assay and histopathological inspection has been employed to study the protective effect of PEFF against asthma on a rat model.

systemsDock: a web server for network pharmacology-based prediction and analysis

We present systemsDock, a web server for network pharmacology-based prediction and analysis, which permits docking simulation and molecular pathway map for comprehensive characterization of ligand selectivity and interpretation of ligand action on a complex molecular network. It incorporates an elaborately designed scoring function for molecular docking to assess protein-ligand binding potential. For large-scale screening and ease of investigation, systemsDock has a user-friendly GUI interface for molecule preparation, parameter specification and result inspection. Ligand binding potentials against individual proteins can be directly displayed on an uploaded molecular interaction map, allowing users to systemically investigate network-dependent effects of a drug or drug candidate. A case study is given to demonstrate how systemsDock can be used to discover a test compound's multi-target activity. systemsDock is freely accessible at http://systemsdock.unit.oist.jp/.

vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available

Background

In drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1–10 % best ranked molecules.

Results

The use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions.

Conclusions

SDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13–60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6–24 % over the 10 best-ranked molecules given by the individual docking programs.Graphical abstract

Actions of Huangqi decoction against rat liver fibrosis: a gene expression profiling analysis

Background

Huangqi decoction (HQD) is used for liver fibrosis and cirrhosis treatment in Chinese medicine. This study aims to investigate the pharmacological actions of HQD against liver fibrosis in rats by high-throughput gene expression profiling, network analysis and real-time qRT-PCR.

Methods

We analyzed the profiles of differentially expressed genes (DEGs) in dimethylnitrosamine (DMN)-induced liver fibrosis in rat. The liver tissue samples of control group (n = 3), model group (n = 3) and HQD group (n = 3) were examined by microarrays. Pathways were analyzed by KEGG. Pathway-gene and protein–protein interaction (PPI) networks were constructed with Cytoscape software. The expression of candidate genes was verified by qRT-PCR. P values less than 0.05 indicated statistical significance.

Results

Collagen deposition and hydroxyproline (Hyp) content were decreased in the HQD group compared with the model group (P < 0.001), while that of Hyp in the model group were increased compared with the control group (P < 0.001). In comparison with the model group, 1085 DEGs (all P < 0.05, |fold change| >1.5) and 52 pathways in the HQD group were identified. TGF-beta, ECM-receptor interaction, and the cell adhesion molecules pathways were significantly recovered by HQD (P < 0.001). A pathway-gene network was constructed, including 303 DEGs and 52 pathways, and 514 nodes and 2602 edges, among 142 genes with node degrees greater than 10. The expressions of PDGFra, PDGFrb, PDGFb, PDGFd, COL1A1, COL1A2, COL5A2, and THBS1 were significantly down-regulated by HQD (P < 0.001).

Conclusion

HQD down-regulated the expressions of PDGFra, PDGFrb, PDGFb, PDGFd, COL1A1, COL1A2, COL5A2 and THBS1, and TGF-β and PDGF signaling pathways in the DMN-induced liver fibrosis in rats.

Electronic supplementary material

The online version of this article (doi:10.1186/s13020-015-0066-5) contains supplementary material, which is available to authorized users.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

Can structural features of kinase receptors provide clues on selectivity and inhibition? A molecular modeling study

Cancer is a complex disease resulting from the uncontrolled proliferation of cell signaling events. Protein kinases have been identified as central molecules that participate overwhelmingly in oncogenic events, thus becoming key targets for anticancer drugs. A majority of studies converged on the idea that ligand-binding pockets of kinases retain clues to the inhibiting abilities and cross-reacting tendencies of inhibitor drugs. Even though these ideas are critical for drug discovery, validating them using experiments is not only difficult, but also in some cases infeasible. To overcome these limitations and to test these ideas at the molecular level, we present here the results of receptor-focused in-silico docking of nine marketed drugs to 19 different wild-type and mutated kinases chosen from a wide range of families. This investigation highlights the need for using relevant models to explain the correct inhibition trends and the results are used to make predictions that might be able to influence future experiments. Our simulation studies are able to correctly predict the primary targets for each drug studied in majority of cases and our results agree with the existing findings. Our study shows that the conformations a given receptor acquires during kinase activation, and their micro-environment, defines the ligand partners. Type II drugs display high compatibility and selectivity for DFG-out kinase conformations. On the other hand Type I drugs are less selective and show binding preferences for both the open and closed forms of selected kinases. Using this receptor-focused approach, it is possible to capture the observed fold change in binding affinities between the wild-type and disease-centric mutations in ABL kinase for Imatinib and the second-generation ABL drugs. The effects of mutation are also investigated for two other systems, EGFR and B-Raf. Finally, by including pathway information in the design it is possible to model kinase inhibitors with potentially fewer side-effects.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.