Identification of Phytochemicals Targeting c-Met Kinase Domain using Consensus Docking and Molecular Dynamics Simulation Studies

  • Shima Aliebrahimi
  • Shideh Montasser Kouhsari
  • Seyed Nasser Ostad
  • Seyed Shahriar Arab
  • Leila Karami
Original Paper

Abstract

c-Met receptor tyrosine kinase is a proto-oncogene whose aberrant activation is attributed to a lower rate of survival in most cancers. Natural product-derived inhibitors known as “fourth generation inhibitors” constitute more than 60% of anticancer drugs. Furthermore, consensus docking approach has recently been introduced to augment docking accuracy and reduce false positives during a virtual screening. In order to obtain novel small-molecule Met inhibitors, consensus docking approach was performed using Autodock Vina and Autodock 4.2 to virtual screen Naturally Occurring Plant-based Anti-cancer Compound–Activity–Target database against active and inactive conformation of c-Met kinase domain structure. Two hit molecules that were in line with drug-likeness criteria, desired docking score, and binding pose were subjected to molecular dynamics simulations to elucidate intermolecular contacts in protein–ligand complexes. Analysis of molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area studies showed that ZINC08234189 is a plausible inhibitor for the active state of c-Met, whereas ZINC03871891 may be more effective toward active c-Met kinase domain compared to the inactive form due to higher binding energy. Our analysis showed that both the hit molecules formed hydrogen bonds with key residues of the hinge region (P1158, M1160) in the active form, which is a hallmark of kinase domain inhibitors. Considering the pivotal role of HGF/c-Met signaling in carcinogenesis, our results propose ZINC08234189 and ZINC03871891 as the therapeutic options to surmount Met-dependent cancers.

Keywords

Consensus docking c-Met inhibitor Molecular dynamics simulation Binding free energy Natural products 

References

  1. 1.
    Maroun, C. R., & Rowlands, T. (2014). The Met receptor tyrosine kinase: A key player in oncogenesis and drug resistance. Pharmacology & Therapeutics, 142, 316–338.CrossRefGoogle Scholar
  2. 2.
    Tai, W., Lu, T., Yuan, H., Wang, F., Liu, H., Lu, S., Leng, Y., Zhang, W., Jiang, Y., & Chen, Y. (2012). Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors. Journal of Molecular Modeling, 18, 3087–3100.CrossRefPubMedGoogle Scholar
  3. 3.
    Elnagar, A. Y., Sylvester, P. W., & El Sayed, K. A. (2011). (−)-Oleocanthal as a c-Met inhibitor for the control of metastatic breast and prostate cancers. Planta Medica, 77, 1013–1019.CrossRefPubMedGoogle Scholar
  4. 4.
    Ye, L., Ou, X., Tian, Y., Yu, B., Luo, Y., Feng, B., Lin, H., Zhang, J., & Wu, S. (2013). Indazoles as potential c-met inhibitors: Design, synthesis and molecular docking studies. European Journal of Medicinal Chemistry, 65, 112–118.CrossRefPubMedGoogle Scholar
  5. 5.
    Li, C., Wu, J. J., Hynes, M., Dosch, J., Sarkar, B., Welling, T. H., Pasca di Magliano, M., & Simeone, D. M. (2011). c-Met is a marker of pancreatic cancer stem cells and therapeutic target. Gastroenterology, 141, 2218–2227.CrossRefPubMedGoogle Scholar
  6. 6.
    Akl, M. R., Ayoub, N. M., Mohyeldin, M. M., Busnena, B. A., Foudah, A. I., Liu, Y.-Y., & El Sayed, K. A. (2014). Olive phenolics as c-Met inhibitors: (−)-Oleocanthal attenuates cell proliferation, invasiveness, and tumor growth in breast cancer models. PLoS One, 9, e97622.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Gherardi, E., Birchmeier, W., Birchmeier, C., & Woude, G. V. (2012). Targeting MET in cancer: rationale and progress. Nature Reviews Cancer, 12, 89–103.CrossRefPubMedGoogle Scholar
  8. 8.
    Yan, X.-J., Gong, L.-H., Zheng, F.-Y., Cheng, K.-J., Chen, Z.-S., & Shi, Z. (2014). Triterpenoids as reversal agents for anticancer drug resistance treatment. Drug Discovery Today, 19, 482–488.CrossRefPubMedGoogle Scholar
  9. 9.
    Carocho, M., & Ferreira, I. C. (2013). The role of phenolic compounds in the fight against cancer-a review. Anti-cancer Agents in Medicinal Chemistry, 13, 1236–1258.CrossRefPubMedGoogle Scholar
  10. 10.
    Vahedi, F., Najafi, M. F., & Bozari, K. (2008). Evaluation of inhibitory effect and apoptosis induction of Zyzyphus Jujube on tumor cell lines, an in vitro preliminary study. Cytotechnology, 56, 105–111.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Shi, M.-D., Liao, Y.-C., Shih, Y.-W., & Tsai, L.-Y. (2013). Nobiletin attenuates metastasis via both ERK and PI3K/Akt pathways in HGF-treated liver cancer HepG2 cells. Phytomedicine : International Journal of Phytotherapy and Phytopharmacology, 20, 743–752.CrossRefGoogle Scholar
  12. 12.
    Lee, W.-J., Chen, W.-K., Wang, C.-J., Lin, W.-L., & Tseng, T.-H. (2008). Apigenin inhibits HGF-promoted invasive growth and metastasis involving blocking PI3K/Akt pathway and β4 integrin function in MDA-MB-231 breast cancer cells. Toxicology and Applied Pharmacology, 226, 178–191.CrossRefPubMedGoogle Scholar
  13. 13.
    Ashtawy, H. M., & Mahapatra, N. R. (2015). Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins. BMC Bioinformatics, 16, S3.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Kumar, V., Krishna, S., & Siddiqi, M. I. (2015). Virtual screening strategies: Recent advances in the identification and design of anti-cancer agents. Methods (San Diego, Calif.), 71, 64–70.CrossRefGoogle Scholar
  15. 15.
    Ferreira, L. G., dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015). Molecular docking and structure-based drug design strategies. Molecules (Basel, Switzerland), 20, 13384–13421.CrossRefGoogle Scholar
  16. 16.
    Houston, D. R., & Walkinshaw, M. D. (2013). Consensus docking: Improving the reliability of docking in a virtual screening context. Journal of Chemical Information and Modeling, 53, 384–390.CrossRefPubMedGoogle Scholar
  17. 17.
    Ren, W., Truong, T. M., & Ai, H.-w (2015). Study of the binding energies between unnatural amino acids and engineered orthogonal tyrosyl-tRNA synthetases. Science Reports, 5, 12632.CrossRefGoogle Scholar
  18. 18.
    Berry, M., Fielding, B. C., & Gamieldien, J. (2015). Potential broad spectrum inhibitors of the coronavirus 3CLpro: A virtual screening and structure-based drug design study. Viruses, 7, 6642–6660.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Tuccinardi, T., Poli, G., Romboli, V., Giordano, A., & Martinelli, A. (2014). Extensive consensus docking evaluation for ligand pose prediction and virtual screening studies. Journal of Chemical Information and Modeling, 54, 2980–2986.CrossRefPubMedGoogle Scholar
  20. 20.
    Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: A free tool to discover chemistry for biology. J Chem Inf Model 52, 1757–1768.Google Scholar
  21. 21.
    Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23, 3–25.CrossRefGoogle Scholar
  22. 22.
    Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45, 2615–2623.CrossRefPubMedGoogle Scholar
  23. 23.
    Liang, Z., Zhang, D., Ai, J., Chen, L., Wang, H., Kong, X., Zheng, M., Liu, H., Luo, C., Geng, M., Jiang, H., & Chen, K. (2011). Identification and synthesis of N′-(2-oxoindolin-3-ylidene)hydrazide derivatives against c-Met kinase. Bioorganic & Medicinal Chemistry Letters, 21, 3749–3754.CrossRefGoogle Scholar
  24. 24.
    Schiering, N., Knapp, S., Marconi, M., Flocco, M. M., Cui, J., Perego, R., Rusconi, L., & Cristiani, C. (2003). Crystal structure of the tyrosine kinase domain of the hepatocyte growth factor receptor c-Met and its complex with the microbial alkaloid K-252a. Proceedings of the National Academy of Sciences of the United States of America, 100, 12654–12659.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Peach, M. L., Tan, N., Choyke, S. J., Giubellino, A., Athauda, G., Burke, Jr, T. R., Nicklaus, M. C., & Bottaro, D. P. (2009). Directed discovery of agents targeting the Met tyrosine kinase domain by virtual screening. Journal of Medicinal Chemistry, 52, 943–951.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Šali, A., & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. Journal of Molecular Biology, 234, 779–815.CrossRefPubMedGoogle Scholar
  27. 27.
    Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31, 455–461.PubMedPubMedCentralGoogle Scholar
  28. 28.
    Goodsell, D. S Morris, G. M,. & Olson, A. J. (1996). Automated docking of flexible ligands: applications of AutoDock. Journal of Molecular Recognition, 9, 1–5CrossRefPubMedGoogle Scholar
  29. 29.
    Koes, D. R., & Camacho, C. J. (2012). ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Research, 40, W409–W414.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., & van der Spoel, D. (2013). GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics (Oxford, England), 29, 845–854.CrossRefGoogle Scholar
  31. 31.
    Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: visual molecular dynamics. Journal of Molecular Graphics, 14, 33–38.CrossRefPubMedGoogle Scholar
  32. 32.
    Kumari, R., Kumar, R., & Lynn, A. (2014). g_mmpbsa—A GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54, 1951–1962.CrossRefPubMedGoogle Scholar
  33. 33.
    Gu, M., Yu, Y., Gunaherath, G. M., Gunatilaka, A. A., Li, D., & Sun, D. (2014). Structure-activity relationship (SAR) of withanolides to inhibit Hsp90 for its activity in pancreatic cancer cells. Investigational New Drugs, 32, 68–74.CrossRefPubMedGoogle Scholar
  34. 34.
    Johnson, J. J. (2011). Carnosol: A promising anti-cancer and anti-inflammatory agent. Cancer letters, 305, 1–7.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Mohyeldin, M. M., Busnena, B. A., Akl, M. R., Dragoi, A. M., Cardelli, J. A., & El Sayed, K. A. (2016). Novel c-Met inhibitory olive secoiridoid semisynthetic analogs for the control of invasive breast cancer. European Journal of Medicinal Chemistry, 118, 299–315.CrossRefPubMedGoogle Scholar
  36. 36.
    Anwar, M. A., Panneerselvam, S., Shah, M., & Choi, S. (2015). Insights into the species-specific TLR4 signaling mechanism in response to Rhodobacter sphaeroides lipid A detection. Science Reports, 5, 7657.CrossRefGoogle Scholar
  37. 37.
    Chang, M. W., Ayeni, C., Breuer, S., & Torbett, B. E. (2010). Virtual screening for HIV protease inhibitors: a comparison of AutoDock 4 and Vina. PLoS One, 5, e11955.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    He, J.-Y Li, C., & Wu, G. (2014). Discovery of potential drugs for human-infecting H7N9 virus containing R294K mutation. Drug Design, Development and Therapy, 8, 2377CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Shima Aliebrahimi
    • 1
  • Shideh Montasser Kouhsari
    • 1
  • Seyed Nasser Ostad
    • 2
  • Seyed Shahriar Arab
    • 3
  • Leila Karami
    • 4
  1. 1.Department of Cellular and Molecular BiologySchool of Biology, College of Science, University of TehranTehranIran
  2. 2.Department of Toxicology and PharmacologyFaculty of Pharmacy and Poisoning Research Center, Tehran University of Medical SciencesTehranIran
  3. 3.Department of BiophysicsSchool of Biological Sciences, Tarbiat Modares UniversityTehranIran
  4. 4.Department of Cell and Molecular BiologyFaculty of Biological Sciences, Kharazmi UniversityTehranIran

Personalised recommendations