Marine-Derived Natural Products as ATP-Competitive mTOR Kinase Inhibitors for Cancer Therapeutics

Abstract

The mammalian target of rapamycin (mTOR) is a serine/threonine kinase portraying a quintessential role in cellular proliferation and survival. Aberrations in the mTOR signaling pathway have been reported in numerous cancers including thyroid, lung, gastric and ovarian cancer, thus making it a therapeutic target. To attain this objective, an in silico investigation was designed, employing a pharmacophore modeling approach. A structure-based pharmacophore (SBP) model exploiting the key features of a selective mTOR inhibitor, Torkinib directed at the ATP-binding pocket was generated. A Marine Natural Products (MNP) library was screened using SBP model as a query. The retrieved compounds after consequent drug-likeness filtration were subjected to molecular docking with mTOR, thus revealing four MNPs with better scores than Torkinib. Successive refinement via molecular dynamics simulations demonstrated that the hits formed crucial interactions with key residues of the pocket. Furthermore, the four identified hits exhibited good binding free energy scores through MM-PBSA calculations and the subsequent in silico toxicity assessments displayed three hits deemed essentially non-carcinogenic and non-mutagenic. The hits presented in this investigation could act as potent ATP-competitive mTOR inhibitors, representing a platform for the future discovery of drugs from marine natural origin.

Potential Effects of Romanian Propolis Extracts against Pathogen Strains

“…A high percentage of all the available therapeutic drugs are derived from natural plants, microbes or animal compounds [ 7 ]. Terrestrial and aquatic species of plants and microorganisms produce unique bioactive substances that can help in the development of standardized phytomedicines with proof of quality, safety and efficacy [ 4 , 5 , 7 , 8 , 9 , 10 , 11 ].…”

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

“…This decoy set validation was initiated by the DS Ligand Pharmacophore Mapping module, which retrieved the four inhibitors used in pharmacophore generation from a given external database of 100 compounds (4 active + 96 decoys). Accordingly, the goodness of fit (GF) score was calculated as 0.72, which was found near the ideal model range value of 1 ( Table 2 ) [ 44 , 45 ]. The value of the GF score confirmed that our model is robust for further predicting active and potent Hpse compounds from an external database reasonably well.…”

Investigating natural compounds against oncogenic RET tyrosine kinase using pharmacoinformatic approaches for cancer therapeutics

“…The generation of a receptor–ligand (structure-based) pharmacophore model relies on the catalytically active site of a protein and its interaction with the bound co-crystallized ligand. 28 The knowledge acquired from the model aids in identification of essential pharmacophoric features required for a protein's inhibition. 29 The crystal structure of RET tyrosine kinase complexed with a pyrazolopyrimidine inhibitor, PP1 (1- tert -butyl-3- p -tolyl-1 H -pyrazolo[3,4- d ]pyrimidin-4-ylamine) (PDB ID: 2IVV) was downloaded from Protein Data Bank (PDB) repository for pharmacophore generation.…”

“…The GH approach thereby generates a goodness of t (GF) score which ranges between 0 (null model) and 1 (ideal model). 28,33,34 The GF score is calculated on the basis of below equation, where D stands for the dataset encompassing active as well as inactive compounds, A represents the active compounds within D, Ht denotes the hit molecules which are retrieved by the generated pharmacophore model and Ha represents the total number of active hit compounds acquired by the model. The validation of the model was also checked via the Receiver Operating Characteristic (ROC) curve and the results were assessed on the basis of the area under the curve (AUC).…”