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      New Mammalian Target of Rapamycin (mTOR) Modulators Derived from Natural Product Databases and Marine Extracts by Using Molecular Docking Techniques

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          Abstract

          Mammalian target of rapamycin (mTOR) is a PI3K-related serine/threonine protein kinase that functions as a master regulator of cellular growth and metabolism, in response to nutrient and hormonal stimuli. mTOR functions in two distinct complexes—mTORC1 is sensitive to rapamycin, while, mTORC2 is insensitive to this drug. Deregulation of mTOR’s enzymatic activity has roles in cancer, obesity, and aging. Rapamycin and its chemical derivatives are the only drugs that inhibit the hyperactivity of mTOR, but numerous side effects have been described due to its therapeutic use. The purpose of this study was to identify new compounds of natural origin that can lead to drugs with fewer side effects. We have used computational techniques (molecular docking and calculated ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) parameters) that have enabled the selection of candidate compounds, derived from marine natural products, SuperNatural II, and ZINC natural products, for inhibitors targeting, both, the ATP and the rapamycin binding sites of mTOR. We have shown experimental evidence of the inhibitory activity of eleven selected compounds against mTOR. We have also discovered the inhibitory activity of a new marine extract against this enzyme. The results have been discussed concerning the necessity to identify new molecules for therapeutic use, especially against aging, and with fewer side effects.

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          Most cited references39

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          PLIP: fully automated protein–ligand interaction profiler

          The characterization of interactions in protein–ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein–ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein–ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web . The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein–ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.
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            Rapamycin: one drug, many effects.

            The mammalian target of rapamycin (mTOR) signaling pathway is a master regulator of cell growth and metabolism. Deregulation of the mTOR pathway has been implicated in a number of human diseases such as cancer, diabetes, obesity, neurological diseases, and genetic disorders. Rapamycin, a specific inhibitor of mTOR, has been shown to be useful in the treatment of certain diseases. Here we discuss its mechanism of action and highlight recent findings regarding the effects and limitations of rapamycin monotherapy and the potential utility of combination therapy with rapamycin. Copyright © 2014 Elsevier Inc. All rights reserved.
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              BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology

              BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole.
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                Author and article information

                Journal
                Mar Drugs
                Mar Drugs
                marinedrugs
                Marine Drugs
                MDPI
                1660-3397
                15 October 2018
                October 2018
                : 16
                : 10
                : 385
                Affiliations
                [1 ]Institute of Research, Development and Innovation in Biotechnology of Elche (IDiBE) and Molecular and Cell Biology Institute (IBMC), Miguel Hernández University (UMH), Elche, 03202 Alicante, Spain; vruiz@ 123456umh.es (V.R.-T.); mlosada@ 123456umh.es (M.L.-E.); mherranz@ 123456umh.es (M.H.-L.); e.barrajon@ 123456umh.es (E.B.-C.); vmicol@ 123456umh.es (V.M.)
                [2 ]Department of Physics and Computer Architecture, Miguel Hernández University (UMH), Elche, 03202 Alicante, Spain; vgaliano@ 123456umh.es
                [3 ]Centro de Investigación Biomédica en Red (CIBER) (CB12/03/30038), Fisiopatología de la Obesidad y la Nutrición, CIBERobn, Instituto de Salud Carlos III., 07122 Palma de Mallorca, Spain
                Author notes
                [* ]Correspondence: jant.encinar@ 123456umh.es ; Tel.: +34-96-65-84-53; Fax: +34-96-665-87-58
                Author information
                https://orcid.org/0000-0002-9083-3692
                https://orcid.org/0000-0002-1819-7978
                https://orcid.org/0000-0001-8113-0795
                https://orcid.org/0000-0002-6997-9953
                https://orcid.org/0000-0002-7219-3863
                Article
                marinedrugs-16-00385
                10.3390/md16100385
                6213183
                30326670
                a0592b71-1918-4923-811a-5200e01c7990
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 24 September 2018
                : 12 October 2018
                Categories
                Article

                Pharmacology & Pharmaceutical medicine
                mtor kinase,marine natural products,natural products,inhibitors,aging,obesity,cancer,virtual screening,molecular docking,calculated admet

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