Dataset Information

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

ABSTRACT: Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.7 M commercially available compounds. Out of seven candidate compounds biologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical assay, two compounds have shown a decent level of inhibitory activity with the IC50 values of less than 10 nM and 10 ?M. Relatively simple scores based on molecular docking or MM-PBSA (molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting the effect of structural modification or for accurate ranking of the compounds based on their binding energies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP) calculations allowed us to further decipher the structure-activity relationships and retrospectively analyze the docking-based virtual screening performance. This approach can be applied at the subsequent lead optimization stages.

SUBMITTER: Berishvili VP

PROVIDER: S-EPMC7397142 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

Berishvili Vladimir P VP Kuimov Alexander N AN Voronkov Andrew E AE Radchenko Eugene V EV Kumar Pradeep P Choonara Yahya E YE Pillay Viness V Kamal Ahmed A Palyulin Vladimir A VA

Molecules (Basel, Switzerland) 20200711 14

Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction a ...[more]

PMID: 32664504

Similar Datasets

Project description:Background:In experimental therapy of cancer, survivin is considered to be one of the well-established targets. Studies have found that it is overexpression in most of the human tumors, but it is rarely found in normal tissues. It is having varied structural and functional role. It controls cell division and cellular stress response and also regulates metastasis and migration of cancerous cells. It has also been recognized as a biomarker which makes it unconventional drug target. In spite of being one of the centrally active components in metastasis and invasion, their clinical use is minimal. To increase the therapeutic efficiency of cancer and its various stages, it is important to survey novel reagents targeting the pathways and mechanism involving survivin. Objective:The aim of this study was to identify novel survivin inhibitor candidates using in silico screening. Materials and Methods:In this course of work, virtual screening on a dataset of natural compounds retrieved from ZINC and other libraries were performed. Comparative analysis of the protein was done by studying the binding affinity of inhibitors that are already available. The best interacting complex was set for molecular dynamics simulation for 25 ns to validate the stability of system. These molecules were checked for their toxicity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties using OSIRIS and pre-ADMET tools. Results:We discovered ten such candidates with better binding efficiency with survivin in comparison to marketed chemical against the same. Furthermore, these inhibitor candidates did not induce cell toxicity. Binding affinity of reference molecules was varied from -6.8 to -8.5 kcal/mol while that of top scoring compound ZINC00689728 is -9.3 kcal/mol binding energy. Good placement and strong bond formation of selected molecule was observed during course of work. It is also having permissible ADMET property. Conclusion:Considering all the parameters, the screened molecule can be considered as a potential lead compound for designing new drug against survivin. Further investigation and testing will be required to make it to the final stage. SUMMARY:Survivin is one of the important protein of metastasis. Inhibiting survivin might led to the increased therapeutic efficiency of cancer. In this work we are screening library of natural compounds in view of finding some potent inhibitor against survivin. Abbreviations used: MD: Molecular dynamics, LogS: Aqueous solubility, Acceptor HB: Hydrogen bond acceptor, Donor HB: Donor hydrogen bond donor, ADMET: Absorption, distribution, metabolism, excretion, and toxicity, RCSB: Research Collaboratory for Structural Bioinformatics, OPLS: Optimized potentials for liquid simulations, RMSD: Root-mean-square deviation.

Dataset Information

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

Publications

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets