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Spring 2022 challenge: phase 2 contenstant

Screening Potential mTOR Inhibitors Targeting the Breast Cancer

Hezha Rasul, Charmo University, Sulaimani, Iraq

With the help of SeeSAR, we were able to locate the druggable pocket in the mTOR (PDB ID:4DRH) protein. In addition, the druggable pocket was determined, and the empty spaces were examined in detail. About 121 components of Hibiscus were retrieved and further used for docking via SeeSAR and Autodock Vina. For molecular docking of the identified ligands, we are taking two different approaches. First, the compounds were docked against mTOR using Autodock Vina. Second, the selected molecules were then docked with mTOR to check affinity in SeeSAR. As a result, 16 molecules with the best affinity were selected for further analysis. Following that, several poses of selected ligands were evaluated using HYDE calculations, and 10 ligands were chosen. Later, the top ten most active compounds and two FDA-approved medications were tested for drug-likeness and in-silico ADMET. Subsequently, molecular dynamics simulations with MM/ GBSA calculation were performed on the selected ligands.
After 3 months, Hezha has achieved the following goals:
  1. In the first month, we have reviewed the literature and searched numerous databases for all the reported compounds of the targeted plant. Approximately 121 components of Hibiscus and ten FDA-approved anti-cancer medicines, including everolimus, an mTOR inhibitor, were retrieved and further used for docking via SeeSAR and Autodock Vina. A Spatial Data File of 121 ligands was made after the data was cleaned up. The ligands were found in the PubChem database ( and the DrugBank database ( ChemDraw Ultra v19.1 using the MMFF94 force field and a 0.1 RMS gradient reduced the ligands' energy to the minimum possible value. Then standard docking was performed with these molecules on mTOR (PDB ID:4DRH). Out of 121, 16 molecules with the best affinity were selected for further analysis. These docking complexes were checked for lipophilic ligand efficiency, torsion quality, intra and inter molecular clashes.
  2. In the second month, the analysis continued with SeeSAR which proved to be a user-friendly software and gave easily interpretable results. SeeSAR was used to continue the analysis, and it proved to be an easy-to-use program with clear results. All mTOR protein binding sites were examined in the beginning. Then, a reference ligand everolimus and its binding pocket were then chosen and visualized to indicate the interaction between the ligand-protein complexes, and the results were promising. PyMOL (, PLIP (, BIOVIA DS (, LIGPLOT ( were used to investigate the interactions of targeted protein active pocket and rapamycin (co-crystalline ligand). Later, an evaluation of multiple poses of selected ligands based upon HYDE estimations was performed (10 ligands selected).
  3. During the third month, the 10 most active compounds and two FDA-approved drugs were subjected to drug-likeness and in-silico ADMET “Absorption, Distribution, Metabolism, Excretion, and Toxicity” utilizing integrated online platforms ADMETlab [] and Molinspiration []. Subsequently, the Schrodinger software, Desmond 2019-4 package, was used to run molecular dynamics (MD) simulations on the selected docked complexes at 100 nanosecond time intervals to examine receptor stability and ligand interaction. In addition, the relative binding free energy for each mTOR-ligand combination and the reference complexes was computed using the molecular mechanics generalized Born surface area (MM/ GBSA) calculation. However, we are currently working on an analytical evaluation of the respective result of MD and MM/ GBSA.