Identification of mangiferin as a potential Glucokinase activator by structure-based virtual ligand screening.
Ontology highlight
ABSTRACT:
SUBMITTER: Min Q
PROVIDER: S-EPMC5357792 | biostudies-literature | 2017 Mar
REPOSITORIES: biostudies-literature
Publications
Identification of mangiferin as a potential Glucokinase activator by structure-based virtual ligand screening.
Min Qiuxia Q Cai Xinpei X Sun Weiguang W Gao Fei F Li Zhimei Z Zhang Qian Q Wan Luo-Sheng LS Li Hua H Chen Jiachun J
Scientific reports 20170320
The natural product mangiferin (compound 7) has been identified as a potential glucokinase activator by structure-based virtual ligand screening. It was proved by enzyme activation experiment and cell-based assays in vitro, with potency in micromolar range. Meanwhile, this compound showed good antihyperglycemic activity in db/db mice without obvious side effects such as excessive hypoglycaemia. ...[more]
Similar Datasets
Project description:Background: The new coronavirus pandemic has had a significant impact worldwide, and therapeutic treatment for this viral infection is being strongly pursued. Efforts have been undertaken by medicinal chemists to discover molecules or known drugs that may be effective in COVID-19 treatment - in particular, targeting the main protease (Mpro) of the virus. Materials & methods: We have employed an innovative strategy - application of ligand- and structure-based virtual screening - using a special compilation of an approved and diverse set of SARS-CoV-2 crystallographic complexes that was recently published. Results and conclusion: We identified seven drugs with different original indications that might act as potential Mpro inhibitors and may be preferable to other drugs that have been repurposed. These drugs will be experimentally tested to confirm their potential Mpro inhibition and thus their effectiveness against COVID-19.
| S-EPMC8240648 | biostudies-literature
Project description:Overexpression of Forkhead box protein C2 (FOXC2) has been associated with different types of carcinomas. FOXC2 plays an important role in the initiation and maintenance of the epithelial-mesenchymal transition (EMT) process, which is essential for the development of higher-grade tumors with an enhanced ability for metastasis. Thus, FOXC2 has become a therapeutic target for the development of anticancer drugs. MC-1-F2, the only identified experimental inhibitor of FOXC2, interacts with the full length of FOXC2. However, only the DNA-binding domain (DBD) of FOXC2 has resolved crystal structure. In this work, a three-dimensional (3D) structure of the full-length FOXC2 using homology modeling was developed and used for structure-based drug design (SBDD). The quality of this 3D model of the full-length FOXC2 was evaluated using MolProbity, ERRAT, and ProSA modules. Molecular dynamics (MD) simulation was also carried out to verify its stability. Ligand-based drug design (LBDD) was carried out to identify similar analogues for MC-1-F2 against 15 million compounds from ChEMBL and ZINC databases. 792 molecules were retrieved from this similarity search. De novo SBDD was performed against the full-length 3D structure of FOXC2 through homology modeling to identify novel inhibitors. The combination of LBDD and SBDD helped in gaining a better insight into the binding of MC-1-F2 and its analogues against the full length of the FOXC2. The binding free energy of the top hits was further investigated using MD simulations and MM/GBSA calculations to result in eight promising hits as lead compounds targeting FOXC2.
| S-EPMC9975119 | biostudies-literature
Project description:The excessive activity of acetylcholinesterase enzyme (AChE) causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration (FDA) approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer's disease (AD) these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute (NCI) containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword "indol" were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship (QSAR) models. We then performed docking, molecular dynamics (MD) simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.
| S-EPMC7671205 | biostudies-literature
Project description:Poly (ADP-ribose) polymerase 1 (PARP1) has high therapeutic value as biomolecular target for research and development of small molecules with antineoplastic activity, since it is upregulated in many cancers, especially in ovarian and BRCA 1/2 mutated breast cancers. Decades of investigation of PARP inhibitors (PARPi) have led to the approval of several drug compounds, however clinical application of PARPi in cancer therapy is limited due to a number of factors, including low selectivity, weak affinity and undesired side effects. Thus, identification of novel drug-like chemical compounds with alternatives to the known PARPi chemical scaffolds, binding modes and interaction patterns with amino acid residues in the active site is of high therapeutic importance. In this study we applied a combination of ligand- and structure-based virtual screening approaches with the goal of identification of novel potential PARPi.
| S-EPMC9467328 | biostudies-literature
Project description:The efforts to limit the spread of the tuberculosis epidemic have been challenged by the rise of drug-resistant strains of Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. It is critical to discover new chemical scaffolds acting on novel or unexploited targets to beat this drug-resistant pathogen. MraY (phospho-MurNAc-pentapeptide translocase or translocase I) is an in vivo validated target for antibacterials-discovery. MraY is inhibited by nucleoside-based natural products that suffer from poor in vivo efficacy. The current study is focused on discovering novel chemical entities, particularly, non-nucleoside small molecules, as MraYMtb inhibitors possessing antituberculosis activity. In the absence of any reported X-ray crystal structures of MraYMtb, we used a homology model-based virtual screening approach combined with the ligand-based e-pharmacophore screening. We screened ∼12 million commercially available compounds from the ZINC15 database using GOLD software. The resulting hits were filtered using a 2-pronged screening method comprising e-pharmacophore hypotheses and docking against the MraYMtb homology model using Glide. Further clustering based on Glide scores and optimal binding interactions resulted in 15 in silico hits. We performed molecular dynamics (MD) simulations for the three best-ranking compounds and one other poorer-ranking compound, out of the 15 in silico hits, to analyze the interaction modes in detail. The MD simulations indicated stable interactions between the compounds and key residues in the MraY active site that are crucial for maintaining the enzymatic activity. These in silico hits could advance the antibacterial drug discovery campaign to find new MraY inhibitors for tuberculosis treatment.Communicated by Ramaswamy H. Sarma.
| S-EPMC9948644 | biostudies-literature
Project description:The association of DNA Ligase IV (Lig4) with XRCC4 is essential for repair of DNA double-strand breaks (DSBs) by Non-homologous end-joining (NHEJ) in humans. DSBs cytotoxicity is largely exploited in anticancer therapy. Thus, NHEJ is an attractive target for strategies aimed at increasing the sensitivity of tumors to clastogenic anticancer treatments. However the high affinity of the XRCC4/Lig4 interaction and the extended protein-protein interface make drug screening on this target particularly challenging. Here, we conducted a pioneering study aimed at interfering with XRCC4/Lig4 assembly. By Molecular Dynamics simulation using the crystal structure of the complex, we first delineated the Lig4 clamp domain as a limited suitable target. Then, we performed in silico screening of ~95,000 filtered molecules on this Lig4 subdomain. Hits were evaluated by Differential Scanning Fluorimetry, Saturation Transfer Difference-NMR spectroscopy and interaction assays with purified recombinant proteins. In this way we identified the first molecule able to prevent Lig4 binding to XRCC4 in vitro. This compound has a unique tripartite interaction with the Lig4 clamp domain that suggests a starting chemotype for rational design of analogous molecules with improved affinity.
| S-EPMC4786802 | biostudies-literature
Project description:Virtual ligand screening is an integral part of the modern drug discovery process. Traditional ligand-based, virtual screening approaches are fast but require a set of structurally diverse ligands known to bind to the target. Traditional structure-based approaches require high-resolution target protein structures and are computationally demanding. In contrast, the recently developed threading/structure-based FINDSITE-based approaches have the advantage that they are as fast as traditional ligand-based approaches and yet overcome the limitations of traditional ligand- or structure-based approaches. These new methods can use predicted low-resolution structures and infer the likelihood of a ligand binding to a target by utilizing ligand information excised from the target's remote or close homologous proteins and/or libraries of ligand binding databases. Here, we develop an improved version of FINDSITE, FINDSITE(filt), that filters out false positive ligands in threading identified templates by a better binding site detection procedure that includes information about the binding site amino acid similarity. We then combine FINDSITE(filt) with FINDSITE(X) that uses publicly available binding databases ChEMBL and DrugBank for virtual ligand screening. The combined approach, FINDSITE(comb), is compared to two traditional docking methods, AUTODOCK Vina and DOCK 6, on the DUD benchmark set. It is shown to be significantly better in terms of enrichment factor, dependence on target structure quality, and speed. FINDSITE(comb) is then tested for virtual ligand screening on a large set of 3576 generic targets from the DrugBank database as well as a set of 168 Human GPCRs. Excluding close homologues, FINDSITE(comb) gives an average enrichment factor of 52.1 for generic targets and 22.3 for GPCRs within the top 1% of the screened compound library. Around 65% of the targets have better than random enrichment factors. The performance is insensitive to target structure quality, as long as it has a TM-score ≥ 0.4 to native. Thus, FINDSITE(comb) makes the screening of millions of compounds across entire proteomes feasible. The FINDSITE(comb) web service is freely available for academic users at http://cssb.biology.gatech.edu/skolnick/webservice/FINDSITE-COMB/index.html.
| S-EPMC3557555 | biostudies-literature
Project description:Neuroblastoma is the most common extracranial solid tumor found in children and survival rate is extremely meager. HDAC8, a class I zinc-dependent enzyme, is a potential drug target for treatment of neuroblastoma and T cell lymphoma. Most of the HDAC8 inhibitors discovered till date contains a hydroxamic acid group which acts as a zinc binding group. The high binding affinity to the zinc and other ions results in adverse effects. Also, the non-selective inhibition of HDACs cause a variety of side effects. The objective of this is to identify structurally diverse, non-hydroxamate, novel, potential and selective HDAC8 inhibitors. A number of five featured pharmacophore hypotheses were generated using 32 known selective HDAC8 inhibitors. The hypotheses ADDRR.4 were selected for building 3D QSAR model. This model has an excellent correlation coefficient and good predictive ability, which was employed for virtual screening of Phase database containing 4.3?×?106 molecules. The resultant hits with fitness score >1.0 were optimized using in-silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) and XP glide docking studies. On the basis of pharmacophore matching, interacting amino acid residues, XP glide score, more affinity towards HDAC8 and less affinity towards other HDACs, and ADME results five hits- SD-01, SD-02, SD-03, SD-04 and SD-05 with new structural scaffolds, non-hydroxamate were selected for in vitro activity study. SD-01 and SD-02 were found to be active in the nanomolar (nM) range. SD-01 had considerably good selectivity for HDAC8 over HDAC6 and SD-02 had marginal selectivity for HDAC6 over HDAC8. The compounds SD-01 and SD-02 were found to inhibit HDAC8 at concentrations (IC50) 9.0?nM and 2.7?nM, respectively.
| S-EPMC6868012 | biostudies-literature
Project description:A combined ligand and structure-based drug design approach provides a synergistic advantage over either methods performed individually. Present work bestows a good assembly of ligand and structure-based pharmacophore generation concept. Ligand-oriented study was accomplished by employing the HypoGen module of Catalyst in which we have translated the experimental findings into 3-D pharmacophore models by identifying key features (four point pharmacophore) necessary for interaction of the inhibitors with the active site of HIV-1 protease enzyme using a training set of 33 compounds belonging to the cyclic cyanoguanidines and cyclic urea derivatives. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond acceptors and two hydrophobic, showed a correlation (r) of 0.90 and a root mean square of 0.71 and cost difference of 56.59 bits between null cost and fixed cost. The model was validated using CatScramble technique, internal and external test set prediction. In the second phase of our study, a structure-based five feature pharmacophore hypothesis was generated which signifies the importance of hydrogen bond donor, hydrogen bond acceptors and hydrophobic interaction between the HIV-1 protease enzyme and its inhibitors. This work has taken a significant step towards the full integration of ligand and structure-based drug design methodologies as pharmacophoric features retrieved from structure-based strategy complemented the features from ligand-based study hence proving the accuracy of the developed models. The ligand-based pharmacophore model was used in virtual screening of Maybridge and NCI compound database resulting in the identification of four structurally diverse druggable compounds with nM activities.
| S-EPMC3493599 | biostudies-literature
Project description:Adenosine monophosphate-activated protein kinase (AMPK) plays a critical role in the regulation of energy metabolism and has been targeted for drug development of therapeutic intervention in Type II diabetes and related diseases. Recently, there has been renewed interest in the development of direct ?1-selective AMPK activators to treat patients with diabetic nephropathy. To investigate the details of AMPK domain structure, sequence alignment and structural comparison were used to identify the key amino acids involved in the interaction with activators and the structure difference between ?1 and ?2 subunits. Additionally, a series of potential ?1-selective AMPK activators were identified by virtual screening using molecular docking. The retrieved hits were filtered on the basis of Lipinski's rule of five and drug-likeness. Finally, 12 novel compounds with diverse scaffolds were obtained as potential starting points for the design of direct ?1-selective AMPK activators.
| S-EPMC5535900 | biostudies-literature