• In Silico Investigation and Discovery of Novel Small Molecule Inhibitors Targeting MET Receptor Tyrosine Kinase in Gastric Cancer
  • MohammadPouya Ashrafzadeh,1,* Aida Ashrafzadeh,2
    1. Islamic Azad University of Shiraz, Department of Chemistry
    2. Cambridge University, Pembroke College, Departmant of Pharmacology


  • Introduction: MET is a receptor tyrosine kinase that transduces signals from the extracellular matrix into the cytoplasm by binding to hepatocyte growth factor/HGF ligand and regulates many physiological processes including proliferation, scattering, morphogenesis and survival. Ligand binding at the cell surface induces autophosphorylation of MET on its intracellular domain that provides docking sites for downstream signaling molecules. Recruitment of downstream effectors by MET leads to the activation of several signaling cascades including the RAS-ERK, PI3 kinase-AKT. While MET-overexpressing tumors are demonstrated to have poorer prognosis, MET pathway seems to be a culprit of cancer invasiveness. Therefore, in this study, we worked on ligand-based design (docking studies) and structure-based design (QSAR and pharmacophore modeling studies) to discover MET receptor tyrosine kinase inhibitors as a therapeutic target in gastric cancer.
  • Methods: After predicting the first structure of a protein using Expasy and TMHMM portals and predicting the secondary structure of the protein by PRABI PHD, it was defined that the active site locates inside and the protein is unstable considering the instability index>40. Predicting and validating the tertiary structure by PDB/ Uniprot, 4R1V model with the resolution of 1.20 Å was selected for the next steps. Before performing high-throughput screening, tertiary structure of the protein was refined by 3Drefine web server on the basis of evaluating the Ramachandran Plot and its quality factor before and after refinement. Furthermore, we developed a local library of 632 lead-like and purchasable compounds from the Zinc12 Library and prepared them for virtual screening. After performing docking study of ligands by PyRx – Vina Wizard, in the next step, pharmacokinetic (ADME) parameters and drug-likeness of ligand molecules according to Lipinski’s rules were predicted by SwissADME. To perform virtual screening of ligands by QSAR analysis, 100 known MET inhibitors from BindingDB with 0.6<IC50<1 were used and prepared for multivariant calibration by Chemoface through PaDEL and then SMLR. After predicting -LogIC50 of selected ligands, PHASE pharmacophore modeling study was done by Schrodinger software.
  • Results: While 632 compounds were docked, 40 compounds with -10<binding affinity<-8 and RMSD=0 were selected for computational ADME studies through which compounds that inhibited hERG 1/2 were removed and 14 ligands remained for the QSAR study. After building a QSAR model with R2:0.82 and selecting 20% of known inhibitors as the test and 80% as the sample group randomly by the analog mode, -LogIC50 of those 14 ligands were predicted. While -LogIC50 of leads with zinc ID 19796894 and 02739483 were lesser than 0, they were removed and the 00373982 compound had the highest –LogIC50= 1.1. After selecting the OPLS force field, LigPrep was done by Schrodinger to build the model using those 100 MET inhibitors from the BindingDB server. Among developed pharmacophore models, ARR_1 and ARR_2 with Survival Score: 5.014 were selected for screening those 12 filtered Zinc ligands from previous steps. While 10 models were built, the one related to lead with Zinc ID: 13126733 had the highest fitness of 2.143 and then lead with Zinc ID: 19796848 stayed in the second stage with fitness: 1.7.
  • Conclusion: Throughout this computational drug discovery study, leads with Zinc ID 13126733 and 19796848 were indicated to inhibit MET Receptor Tyrosine Kinase effectively among 632 compounds from Zinc12 Library. Afterward, more evaluations like molecular dynamics simulation studies should be done in the future to further results.
  • Keywords: MET Receptor Tyrosine Kinase, In Silico Drug Discovery, Gastric Cancer