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Hepatocyte growth factor receptor (HGFR) as a potential lung cancer target
Author(s) -
Ahmed Haidar,
Skouta Rachid
Publication year - 2018
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2018.32.1_supplement.531.20
Subject(s) - hepatocyte growth factor receptor , epidermal growth factor receptor , kras , receptor tyrosine kinase , virtual screening , cancer research , lung cancer , signal transduction , protein data bank (rcsb pdb) , c met , tyrosine kinase , hepatocyte growth factor , docking (animal) , biology , pharmacophore , drug discovery , growth factor receptor , cancer , receptor , bioinformatics , microbiology and biotechnology , medicine , biochemistry , colorectal cancer , genetics , nursing
Lung cancer is the leading cause of cancer‐related death in the world affecting both sexes, with more than 222,500 new cases each year in the U.S, and poor survival rate. Due to low efficacy of current therapies, recent studies are focusing on discovering new targets that generates better and more effective therapies. While most of the current drugs target the epidermal growth factor receptor (EGFR) and KRAS, other targets have not been very well studied. c‐Met, also known as tyrosine‐protein kinase Met or hepatocyte growth factor receptor (HGFR), belonging to the tyrosine kinase family play a major role in signaling transduction pathways and has been a potential target of lung cancer. For this reason, kinase receptors have been the target of many modern drugs. In solid tumors, such as lung cancer, upregulation of c‐Met occurs as mechanism to promote proliferation, survival, metastasis and angiogenesis. Here, we present a low cost drug discovery approach to identify a novel effective inhibitor of c‐Met using structure based virtual screening (SBVS). We obtained c‐Met top crystal structures from the Protein Data Bank (PDB ID: 3BUX, 3F66, 3ZCL, 3DKC, 4R1V). Potential binding site were screened using Schrodinger software package to generate receptor grids. Using the obtained receptor grids a drug library was docked using Glide. After this docking results were compared by the free energy of binding. Using Phase e‐pharmacophore and “create consensus” top ligands were further screened to understand ligand‐protein interaction to improve efficacy of drug. Future directions is to adapt top compound to in vitro . Support or Funding Information This lab is supported in part by the USDA‐NIFA‐HSI grant 2012‐38422‐19910 and NIH Grant #5G12MD007592 from the National Center for Research Resources to the UTEP Border Biomedical Research Center. And by the Lung Cancer Research Foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .