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Zhenting Gao

Bio: Zhenting Gao is an academic researcher from Novartis. The author has contributed to research in topics: EZH2 & Medicine. The author has an hindex of 7, co-authored 9 publications receiving 770 citations. Previous affiliations of Zhenting Gao include East China University of Science and Technology & Chinese Academy of Sciences.

Papers
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Journal ArticleDOI
TL;DR: TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products, and a reverse ligand–protein docking program for seeking potential protein targets by screening an appropriate protein database.
Abstract: TarFisDock is a web-based tool for automating the procedure of searching for small molecule-protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand-protein docking program. In contrast to conventional ligand-protein docking, reverse ligand-protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand-protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at http://www.dddc.ac.cn/tarfisdock/.

364 citations

Journal ArticleDOI
TL;DR: PDTD serves as a comprehensive and unique repository of drug targets and in conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules and may be a valuable platform for the pharmaceutical researchers.
Abstract: Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Tar get Fis hing Dock ing) http://www.dddc.ac.cn/tarfisdock , which has been used widely by others. Recently, we have constructed a protein target database, P otential D rug T arget D atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/ .

259 citations

Journal ArticleDOI
TL;DR: It is demonstrated that specific and direct inhibition of EED can be effective as an anticancer strategy for the first time.
Abstract: Overexpression and somatic heterozygous mutations of EZH2, the catalytic subunit of polycomb repressive complex 2 (PRC2), are associated with several tumor types. EZH2 inhibitor, EPZ-6438 (tazemetostat), demonstrated clinical efficacy in patients with acceptable safety profile as monotherapy. EED, another subunit of PRC2 complex, is essential for its histone methyltransferase activity through direct binding to trimethylated lysine 27 on histone 3 (H3K27Me3). Herein we disclose the discovery of a first-in-class potent, selective, and orally bioavailable EED inhibitor compound 43 (EED226). Guided by X-ray crystallography, compound 43 was discovered by fragmentation and regrowth of compound 7, a PRC2 HTS hit that directly binds EED. The ensuing scaffold hopping followed by multiparameter optimization led to the discovery of 43. Compound 43 induces robust and sustained tumor regression in EZH2MUT preclinical DLBCL model. For the first time we demonstrate that specific and direct inhibition of EED can be effective as an anticancer strategy.

91 citations

Journal ArticleDOI
TL;DR: It is discovered that ESD could significantly inhibit the proliferation of different cancer cells and induce apoptosis and cell cycle arrest at G(2)/M phase in HeLa cell and biochemical and biophysical assays revealed that E SD acted as a specific partial agonist against PPARgamma.

42 citations

Journal ArticleDOI
10 Jan 2017-PLOS ONE
TL;DR: The identification and validation of four other EED binders along with EED162, the parental compound of EED226, revealed a common deep pocket induced by the binding of this diverse set of compounds, and provides structural insights for rational design of novel EED binder for the inhibition of PRC2 complex activity.
Abstract: Polycomb repressive complex 2 (PRC2), a histone H3 lysine 27 methyltransferase, plays a key role in gene regulation and is a known epigenetics drug target for cancer therapy. The WD40 domain-containing protein EED is the regulatory subunit of PRC2. It binds to the tri-methylated lysine 27 of the histone H3 (H3K27me3), and through which stimulates the activity of PRC2 allosterically. Recently, we disclosed a novel PRC2 inhibitor EED226 which binds to the K27me3-pocket on EED and showed strong antitumor activity in xenograft mice model. Here, we further report the identification and validation of four other EED binders along with EED162, the parental compound of EED226. The crystal structures for all these five compounds in complex with EED revealed a common deep pocket induced by the binding of this diverse set of compounds. This pocket was created after significant conformational rearrangement of the aromatic cage residues (Y365, Y148 and F97) in the H3K27me3 binding pocket of EED, the width of which was delineated by the side chains of these rearranged residues. In addition, all five compounds interact with the Arg367 at the bottom of the pocket. Each compound also displays unique features in its interaction with EED, suggesting the dynamics of the H3K27me3 pocket in accommodating the binding of different compounds. Our results provide structural insights for rational design of novel EED binder for the inhibition of PRC2 complex activity.

41 citations


Cited by
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Journal ArticleDOI
TL;DR: The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release and contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs.
Abstract: DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With approximately 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more 'experimental' drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food-drug interactions, drug-drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca.

2,380 citations

Journal ArticleDOI
TL;DR: DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ applications, particularly with regard to drug target, drug description and drug action data.
Abstract: DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data ‘depth’). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug–drug and food–drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.

1,732 citations

Journal ArticleDOI
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.

806 citations

Journal ArticleDOI
TL;DR: Three supervised inference methods were developed here to predict DTI and used for drug repositioning and indicated that these methods could be powerful tools in prediction of DTIs and drugRepositioning.
Abstract: Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.

709 citations