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Xiangqun Xie - One of the best experts on this subject based on the ideXlab platform.

  • DAKB-GPCRs: An Integrated Computational Platform for Drug Abuse Related GPCRs
    2019
    Co-Authors: Maozi Chen, Zhiwei Feng, Lirong Wang, Yankang Jing, Xiangqun Xie
    Abstract:

    Drug abuse (DA) or drug addiction is a complicated brain disorder which is commonly considered as neurobiological impairments caused by both genetic factors and environmental effects. Among DA-related targets, G protein-coupled receptors (GPCRs) play an important role in DA therapy. However, only 52 GPCRs have been published with crystal structures in the recent two decades. In the effort to overcome the limitations of crystal structure and conformational diversity of GPCRs, we built homology models and performed conformational searches by molecular dynamics (MD) simulation. To accelerate and facilitate the drug abuse research, we construct a DA-related GPCR-specific Chemogenomics knowledgebase (KB) (DAKB-GPCRs) for its research that can be implemented with our established and novel Chemogenomics tools as well as algorithms for data analysis and visualization. Our established TargetHunter and HTDocking tools, as well as our novel tools that include target classification and Spider Plot, are compiled into the platform. Our DAKB-GPCRs provides the following results for a query compound: (1) blood–brain barrier (BBB) plot via our BBB predictor, (2) docking scores via HTDocking, (3) similarity score via TargetHunter, (4) target classification via machine learning methods that utilize both docking scores and similarity scores, and (5) a drug–target interaction network via Spider Plot

  • Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification salvinorin a as a case study
    Journal of Molecular Graphics & Modelling, 2016
    Co-Authors: Zhiwei Feng, Lirong Wang, Xiangqun Xie
    Abstract:

    Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific Chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational Chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.

  • alzplatform an alzheimer s disease domain specific Chemogenomics knowledgebase for polypharmacology and target identification research
    Journal of Chemical Information and Modeling, 2014
    Co-Authors: Haibin Liu, Lirong Wang, Rongrong Pei, Zhong Pei, Yonggang Wang, Xiangqun Xie
    Abstract:

    Alzheimer’s disease (AD) is one of the most complicated progressive neurodegeneration diseases that involve many genes, proteins, and their complex interactions. No effective medicines or treatments are available yet to stop or reverse the progression of the disease due to its polygenic nature. To facilitate discovery of new AD drugs and better understand the AD neurosignaling pathways involved, we have constructed an Alzheimer’s disease domain-specific Chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing functions. AlzPlatform is implemented with powerful computational algorithms, including our established TargetHunter, HTDocking, and BBB Predictor for target identification and polypharmacology analysis for AD research. The platform has assembled various AD-related Chemogenomics data records, including 928 genes and 320 proteins related to AD, 194 AD drugs approved or in clinical trials, and 405 188 chemicals associated with 1 023 137 records of reported bioac...

David J Wild - One of the best experts on this subject based on the ideXlab platform.

  • systems chemical biology and the semantic web what they mean for the future of drug discovery research
    Drug Discovery Today, 2012
    Co-Authors: David J Wild, Ying Ding, Amit P Sheth, Lee Harland, Eric M Gifford, Michael S Lajiness
    Abstract:

    Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as Chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine. Recent Semantic Web technologies such as RDF and SPARQL are technical enablers of systems chemical biology, facilitating the deployment of advanced algorithms for searching and mining large integrated datasets. In this paper, we aim to demonstrate how these technologies together can change the way that drug discovery is accomplished.

  • Improving integrative searching of systems chemical biology data using semantic annotation
    Journal of Cheminformatics, 2012
    Co-Authors: Bin Chen, Ying Ding, David J Wild
    Abstract:

    Background Systems chemical biology and Chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. Results We developed a generalized Chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in Chemogenomics and systems chemical biology. Availability Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl . The document is available at http://chem2bio2owl.wikispaces.com .

Chen Bin - One of the best experts on this subject based on the ideXlab platform.

  • Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data
    BMC Bioinformatics, 2010
    Co-Authors: Ding Ying, Wild, David J, Zhu Qian, Wang Huijun, Jiao Dazhi, Dong Xiao, Chen Bin
    Abstract:

    Background: Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited. Results: We have created a single repository called Chem2Bio2RDF by aggregating data from multiple Chemogenomics repositories that is cross-linked into Bio2RDF and LODD. We have also created a linked-path generation tool to facilitate SPARQL query generation, and have created extended SPARQL functions to address specific chemical/biological search needs. We demonstrate the utility of Chem2Bio2RDF in investigating polypharmacology, identification of potential multiple pathway inhibitors, and the association of pathways with adverse drug reactions. Conclusions: We have created a new semantic systems chemical biology resource, and have demonstrated its potential usefulness in specific examples of polypharmacology, multiple pathway inhibition and adverse drug reaction - pathway mapping. We have also demonstrated the usefulness of extending SPARQL with cheminformatics and bioinformatics functionality

  • Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis.
    'American Chemical Society (ACS)', 2009
    Co-Authors: Scheiber Josef, Chen Bin, Milik Mariusz, Sukuru, Sai Chetan, Bender Andreas, Mikhailov Dmitri, Whitebread Steven, Hamon Jacques, Azzaoui Kamal, Urban Laszlo
    Abstract:

    We present a workflow that leverages data from Chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs

Cetin Atalay R. - One of the best experts on this subject based on the ideXlab platform.

  • A novel thiazolidine compound induces caspase-9 dependent apoptosis in cancer cells
    'Elsevier BV', 2012
    Co-Authors: Onen Bayram F. E., Scherman D., Herscovici J., Cetin Atalay R.
    Abstract:

    Cataloged from PDF version of article.The forward Chemogenomics strategy allowed us to identify a potent cytotoxic thiazolidine compound as an apoptosis-inducing agent. Chemical structures were designed around a thiazolidine ring, a structure already noted for its anticancer properties. Initially, we evaluated these novel compounds on liver, breast, colon and endometrial cancer cell lines. The compound 3 (ALC67) showed the strongest cytotoxic activity (IC50 ∼5 μM). Cell cycle analysis with ALC67 on liver cells revealed SubG1/G1 arrest bearing apoptosis. Furthermore we demonstrated that cytotoxicity of this compound was due to the activation of caspase-9 involved apoptotic pathway, which is death receptor independent. © 2012 Elsevier Ltd. All rights reserve

  • A novel thiazolidine compound induces caspase-9 dependent apoptosis in cancer cells
    'Elsevier BV', 2012
    Co-Authors: Onen-bayram F.e., Scherman D., Herscovici J., Cetin Atalay R.
    Abstract:

    The forward Chemogenomics strategy allowed us to identify a potent cytotoxic thiazolidine compound as an apoptosis-inducing agent. Chemical structures were designed around a thiazolidine ring, a structure already noted for its anticancer properties. Initially, we evaluated these novel compounds on liver, breast, colon and endometrial cancer cell lines. The compound 3 (ALC67) showed the strongest cytotoxic activity (IC50 ∼5 μM). Cell cycle analysis with ALC67 on liver cells revealed SubG1/G1 arrest bearing apoptosis. Furthermore we demonstrated that cytotoxicity of this compound was due to the activation of caspase-9 involved apoptotic pathway, which is death receptor independent. © 2012 Elsevier Ltd. All rights reserved

Véronique Stoven - One of the best experts on this subject based on the ideXlab platform.

  • Efficient multi-task Chemogenomics for drug specificity prediction
    PLoS ONE, 2018
    Co-Authors: Benoit Playe, Chloé-agathe Azencott, Véronique Stoven
    Abstract:

    Adverse drug reactions, also called side effects, range from mild to fatal clinical events and significantly affect the quality of care. Among other causes, side effects occur when drugs bind to proteins other than their intended target. As experimentally testing drug specificity against the entire proteome is out of reach, we investigate the application of Chemogenomics approaches. We formulate the study of drug specificity as a problem of predicting interactions between drugs and proteins at the proteome scale. We build several benchmark datasets, and propose NN-MT, a multi-task Support Vector Machine (SVM) algorithm that is trained on a limited number of data points, in order to solve the computational issues or proteome-wide SVM for Chemogenomics. We compare NN-MT to different state-of-the-art methods, and show that its prediction performances are similar or better, at an efficient calculation cost. Compared to its competitors, the proposed method is particularly efficient to predict (protein, ligand) interactions in the difficult double-orphan case, i.e. when no interactions are previously known for the protein nor for the ligand. The NN-MT algorithm appears to be a good default method providing state-of-the-art or better performances, in a wide range of prediction scenario that are considered in the present study: proteome-wide prediction, protein family prediction, test (protein, ligand) pairs dissimilar to pairs in the train set, and orphan cases.