Target-target Interactions Research Articles

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions. However, these methods cannot fully utilize the node information from heterogeneous networks. Therefore, we propose a method based on heterogeneous graph convolutional neural network for drug-target interaction prediction, GCHN-DTI (Predicting drug-target interactions by graph convolution on heterogeneous net-works), to predict potential DTIs. GCHN-DTI integrates network information from drug-target interactions, drug-drug interactions, drug-similarities, target-target interactions, and target-similarities. Then, the graph convolution operation is used in the heterogeneous network to obtain the node embedding of the drugs and the targets. Furthermore, we incorporate an attention mechanism between graph convolutional layers to combine node embedding from each layer. Finally, the drug-target interaction score is predicted based on the node embedding of the drugs and the targets. Our model uses fewer network types and achieves higher prediction performance. In addition, the prediction performance of the model will be significantly improved on the dataset with a higher proportion of positive samples. The experimental evaluations show that GCHN-DTI outperforms several state-of-the-art prediction methods.

Splice-switch oligonucleotide-based combinatorial platform prioritizes synthetic lethal targets CHK1 and BRD4 against MYC-driven hepatocellular carcinoma.

Deregulation of MYC is among the most frequent oncogenic drivers in hepatocellular carcinoma (HCC). Unfortunately, the clinical success of MYC-targeted therapies is limited. Synthetic lethality offers an alternative therapeutic strategy by leveraging on vulnerabilities in tumors with MYC deregulation. While several synthetic lethal targets of MYC have been identified in HCC, the need to prioritize targets with the greatest therapeutic potential has been unmet. Here, we demonstrate that by pairing splice-switch oligonucleotide (SSO) technologies with our phenotypic-analytical hybrid multidrug interrogation platform, quadratic phenotypic optimization platform (QPOP), we can disrupt the functional expression of these targets in specific combinatorial tests to rapidly determine target-target interactions and rank synthetic lethality targets. Our SSO-QPOP analyses revealed that simultaneous attenuation of CHK1 and BRD4 function is an effective combination specific in MYC-deregulated HCC, successfully suppressing HCC progression in vitro. Pharmacological inhibitors of CHK1 and BRD4 further demonstrated its translational value by exhibiting synergistic interactions in patient-derived xenograft organoid models of HCC harboring high levels of MYC deregulation. Collectively, our work demonstrates the capacity of SSO-QPOP as a target prioritization tool in the drug development pipeline, as well as the therapeutic potential of CHK1 and BRD4 in MYC-driven HCC.

A network pharmacology-based exploration of the active compounds and potential drug targets of Si-Jun-Zi decoction in the treatment of cutaneous squamous cell carcinoma.

BackgroundCutaneous squamous cell carcinoma (cSCC), a kind of skin cancer with high rates of morbidity and mortality, occurs frequently in the clinic. Although early surgical treatment can achieve good results, there is no effective prevention and treatment for the recurrence and metastasis of cSCC. As a useful resource to protect humans from disease, traditional Chinese medicine (TCM) has been adopted by clinicians for thousands of years.MethodsIn this study, we collected a Chinese medicine formula and then employed a data mining method to analyze drug combinations of Si-Jun-Zi (SJZ) decoction. Multiple databases were used in this study to predict various ingredients, compounds, and their targets in the decoction. The potential targets of cSCC were also obtained from the database in the same way. In addition, as bioinformatics analysis methods, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used in our research as supplementary means to network pharmacology. Finally, we used ultra-performance liquid chromatography (UPLC) fingerprinting to analyze the effective components of the TCM decoction.ResultsWe detected 559 active compounds from Ginseng, Largehead Atractylodes, India Bread, and Glycyrrhiza Inflata, and selected 136 molecules under specific conditions. The mechanisms of the TCM formula were illustrated by the network pharmacology, such as compounds-herb network, compounds-target network, disease-target network, and target-target interaction network, as well as characteristics of the TCM. Then, GO analysis and KEGG analysis were performed on the compounds in the network using multiple methods of data mining and bioinformatics, and 10 candidate targets were identified. In addition, the UPLC fingerprinting method was used to analyze the components of SJZ decoction.ConclusionsNetwork pharmacology was performed to investigate the characteristics and mechanism of SJZ decoction, and a bioinformatics method was used to analyze the relationship between the effective compounds of the SJZ TCM decoction and cSCC-related specific targets and pathways, to find a variety of candidate compounds with multi-target activity.

Targeted Therapies in Lung Cancers: Current Landscape and Future Prospects.

Lung cancer is the most common malignant cancer worldwide. Targeted therapies have emerged as a promising treatment strategy for lung cancers. To evaluate the current landscape of targets and find promising targets for future new drug discovery for lung cancers, this research identified the science-technology-clinical development pattern and mapped the interaction network of targets. Targets for cancers were classified into 3 groups based on a paper published in Nature. We searched for scientific pieces of literature, patent documents and clinical trials of targets in Group 1 and Group 2 for lung cancers. Then, a target-target interaction network of Group 1 was constructed, and the science-technology-clinical (S-T-C) development patterns of targets in Group 1 were identified. Finally, based on the cluster distribution and the development pattern of targets in Group 1, interactions between the targets were employed to predict potential targets in Group 2 for drug development. The target-target interaction (TTI) network of group 1 resulted in 3 clusters with different developmental stages. The potential targets in Group 2 are divided into 3 ranks. Level-1 is the first priority and level-3 is the last. Level-1 includes 16 targets, such as STAT3, CRKL, and PTPN11, that are mostly involved in signaling transduction pathways. Level-2 and level-3 contain 8 and 6 targets, respectively, related to various biological functions. This study will provide references for drug development in lung cancers, emphasizing that priorities should be given to targets in Level-1, whose mechanisms are worth further exploration.

Asymmetric Blockade and Multiqubit Gates via Dipole-Dipole Interactions

Because of their strong and tunable interactions, Rydberg atoms can be used to realize fast two-qubit entangling gates. We propose a generalization of a generic two-qubit Rydberg-blockade gate to multiqubit Rydberg-blockade gates that involve both many control qubits and many target qubits simultaneously. This is achieved by using strong microwave fields to dress nearby Rydberg states, leading to asymmetric blockade in which control-target interactions are much stronger than control-control and target-target interactions. The implementation of these multiqubit gates can drastically simplify both quantum algorithms and state preparation. To illustrate this, we show that a 25-atom Greenberger-Horne-Zeilinger state can be created using only three gates with an error of 5.8%.

SmartGraph: a network pharmacology investigation platform

MotivationDrug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals.ResultsThe SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction.Availabilityhttps://smartgraph.ncats.io/.

Network Pharmacology-Based Study on the Mechanism of Pinellia ternata in Asthma Treatment.

Background Pinellia ternata (PT), a medicinal plant, has had an extensive application in the treatment of asthma in China, whereas its underlying pharmacological mechanisms remain unclear. Methods Firstly, a network pharmacology method was adopted to collect activated components of PT from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Targets of PT were assessed by exploiting the PharmMapper website; asthma-related targets were collected from the OMIM website, and target-target interaction networks were built. Secondly, critical nodes exhibiting high possibility were identified as the hub nodes in the network, which were employed to conduct Gene Ontology (GO) comment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis. Finally, the tissue expression profiles of key candidate genes were identified by the Gene Expression Omnibus (GEO) database, and the therapeutic effect of PT was verified by an animal experiment. Results 57 achievable targets of PT on asthma were confirmed as hub nodes through using the network pharmacology method. As revealed from the KEGG enrichment analysis, the signaling pathways were notably enriched in pathways of the T-cell receptor signaling pathway, JAK-STAT signaling pathway, and cytokine-cytokine receptor interaction. The expression profiles of candidate genes including Mmp2, Nr3c1, il-10, il-4, il-13, il-17a, il-2, tlr4, tlr9, ccl2, csf2, and vefgα were identified. Moreover, according to transcriptome RNA sequencing data from lung tissues of allergic mice compared to normal mice, the mRNA level of Mmp2 and il-4 was upregulated (P < 0.001). In animal experiments, PT could alleviate the allergic response of mice by inhibiting the activation of T-helper type 2 (TH2) cells and the expression of Mmp2 and il-4. Conclusions Our study provides candidate genes that may be either used for future studies related to diagnosis/prognosis or as targets for asthma management. Besides, animal experiments showed that PT could treat asthma by regulating the expression of Mmp2 and il-4.

Network pharmacology study of traditional Chinese medicines for stroke treatment and effective constituents screening

Ethnopharmacological relevanceStroke is one of the most frequent causes of death and disability. So far there are no effective preventives or treatments. The therapeutic system of traditional Chinese medicines (TCMs) has been in use for several thousand years and still affords a valuable resource for today's clinicians in preserving health. Materials and methodsWe had collected the Chinese medicinal formulae and then commonly used single herbs or drug combinations were analyzed through data mining. The ingredients from the top 30 frequently used herbs which have good druggability and blood-brain barrier permeability were collected as a natural product library. Targets of the related ingredients were predicted using various databases and analyzed by GO and KEGG pathway mapping. The potential stroke targets were validated in the market or from clinical trials, and used to establish molecular docking, HipHop and SBP models to screen new compounds for multi-target activity. Lastly, in vitro experiments with models for oxygen and glucose deprivation and reperfusion (OGDR) were conducted to test the activities of compounds identified by screening. ResultsA total of 1679 Chinese medicinal formulas were selected and their prescription rules were analyzed. 4277 compounds were from the top 30 herbs and 3560 molecules were filtered to build the natural product library. The ingredient-target network, target-disease network and target-target interaction network were established to explain the characteristics and mechanisms of the TCMs. Thirty-one molecules were selected to have multi-target activity on targets of stroke via virtual screening. Five of these had already been reported to have therapeutic effects on stroke. Three of the eight compounds which have been examined showed protective effects on OGDR model. ConclusionsThis paper details a novel strategy for exploring the characteristics and mechanisms of herbal medicines from a systematic standpoint in an attempt to identify those affecting specific target pathways related to stroke. Using this methodology on our natural products library, we found a number of lead candidates with multi-target activity.

Dose-dependent target diversion of Danhong injection on the Glu-GLT-1/Gly-GlyRα dynamic balance module of cerebral ischemia.

Function-oriented modular structure analysis is a great challenge in module-based pharmacological studies. A strategy to uncover target-target interaction (TTI) and dynamic balance regularity (DBR) was established to discover the structural factors influencing modular functions and explore the mechanism of Danhong injection (DHI) in treating cerebral ischemia. The dose-related metabolic features of DHI intervention were investigated using metabolomics and modular pharmacology. The findings indicated that Glu/Gly was a biomarker and Glu-GLT-1/Gly-GlyRα was the core unit regulated by DHI. Gly and Glu displayed opposite patterns and functional roles, representing intra-modular balance. GlyRα was identified as the upstream target and GLT-1 as the downstream target by inhibiting or activating GlyRα, indicating that DHI has two dose-dependent regulatory modes. GlyRα was the major target at low doses, while GLT-1 was activated as the dominant target as doses accumulated. Our study reveals that target-target interaction and dynamic balance regularity are the key factors influencing modular functions, which is a promising breakthrough for module-based pharmacological studies.

PceRBase: a database of plant competing endogenous RNA.

Competition for microRNA (miRNA) binding between RNA molecules has emerged as a novel mechanism for the regulation of eukaryotic gene expression. Competing endogenous RNA (ceRNA) can act as decoys for miRNA binding, thereby forming a ceRNA network by regulating the abundance of other RNA transcripts which share the same or similar microRNA response elements. Although this type of RNA cross talk was first described in Arabidopsis, and was subsequently shown to be active in animal models, there is no database collecting potential ceRNA data for plants. We have developed a Plant ceRNA database (PceRBase, http://bis.zju.edu.cn/pcernadb/index.jsp) which contains potential ceRNA target-target, and ceRNA target-mimic pairs from 26 plant species. For example, in Arabidopsis lyrata, 311 candidate ceRNAs are identified which could affect 2646 target–miRNA–target interactions. Predicted pairing structure between miRNAs and their target mRNA transcripts, expression levels of ceRNA pairs and associated GO annotations are also stored in the database. A web interface provides convenient browsing and searching for specific genes of interest. Tools are available for the visualization and enrichment analysis of genes in the ceRNA networks. Moreover, users can use PceRBase to predict novel competing mimic-target and target–target interactions from their own data.

PhIN: A Protein Pharmacology Interaction Network Database

Network pharmacology is a new and hot concept in drug discovery for its ability to investigate the complexity of polypharmacology, and becomes more and more important in drug development. Here we report a protein pharmacology interaction network database (PhIN), aiming to assist multitarget drug discovery by providing comprehensive and flexible network pharmacology analysis. Overall, PhIN contains 1,126,060 target–target interaction pairs in terms of shared compounds and 3,428,020 pairs in terms of shared scaffolds, which involve 12,419,700 activity data, 9,414 targets, 314 viral targets, 652 pathways, 1,359,400 compounds, and 309,556 scaffolds. Using PhIN, users can obtain interacting target networks within or across human pathways, between human and virus, by defining the number of shared compounds or scaffolds under an activity cutoff. We expect PhIN to be a useful tool for multitarget drug development. PhIN is freely available at http://cadd.pharmacy.nankai.edu.cn/phin/.

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data

In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

Comparison of BC and MD simulations of low-energy ion implantation

The range of applicability of the binary collision (BC) code Crystal-TRIM is studied by comparing with full molecular dynamics (MD) simulations. Range profiles and intersections of projectile trajectories with lattice planes are calculated for 250, 500 and 1000 eV silicon implantation into silicon, into axial channeling and “random” directions. Similar physical conditions are used for BC and MD simulations. It is shown that half of the differences between results of BC and MD calculations can be attributed to target-target interactions whereas the remainder is due to the BC approximation.

Comparison of BC and MD simulations of low-energy ion implantation

The range of applicability of the binary collision (BC) code Crystal-TRIM is studied by comparing with full molecular dynamics (MD) simulations. Range profiles and intersections of projectile trajectories with lattice planes are calculated for 250, 500 and 1000 eV silicon implantation into silicon, into axial channeling and “random” directions. Similar physical conditions are used for BC and MD simulations. It is shown that half of the differences between results of BC and MD calculations can be attributed to target-target interactions whereas the remainder is due to the BC approximation.

Preferential recoil implantation in polyatomic targets

Five distinct effects have been identified which contribute to preferential recoil implantation in a polyatomic material. Their directions and relative magnitudes each depend on some combination of the following parameters: Masses and atomic numbers of the target atoms; mass and atomic number of the bombarding ion; ion-target and target-target interatomic potential; ion energy (indirectly). High ion energy favors a negative mass effect, i.e., light species are implanted preferentially. A positive mass effect is found at low ion energies, caused by differences in the angular scattering of light and heavy recoil atoms, respectively. The magnitude of this effect as well as the point of transition between positive and negative mass dependence are found to be governed primarily by the shape of the target-target interaction potential. The origin of conflicting conclusions in the theoretical literature on the sign of preferential recoil implantation is located. Estimates are given in the limit of small differences in atomic masses and charge numbers of the constituents of the target.