Skip to main content
Springer Nature Link
Log in

Proteins Flexibility as a Criterion for Elucidation of Activating Mutants in Personalized Cancer Medicine

  • Conference paper
  • First Online:
Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 676))

  • 609 Accesses

Abstract

We developed a new strategy for elucidation of functional impact of mutations in proteins. Using molecular dynamics simulations, we explore flexibility of proteins in the sites of their binding to the other proteins. Binding of two or more proteins go through the stage of intermediate binding complexes. On this stage the number of possible conformations of the proteins’ binding sites are interacting with each other. Increasing flexibility in the binding sites increase a probability of the best-energy docking of proteins. Our computational simulations demonstrated that a missense alteration of MET (p.Tyr501Cys), which lead to an increase of flexibility of the protein, may improve the binding of the receptor with its ligand HGF (hepatocyte growth factor) and thus be considered as activating. Accordingly to this conclusion, a patient presenting a hepatocellular carcinoma MET Y501C-mutated showed a good response when treated by a potent MET inhibitor (cabozantinib), with a decrease of −65% of the alpha-foeto-protein (AFP).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bamford, S., Dawson, E., Forbes, S., Clements, J., Pettett, R., Dogan, A., Flanagan, A., Teague, J., Futreal, P.A., Stratton, M.R., Wooster, R.: The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br. J. Cancer 91(2), 355–358 (2004)

    Article  Google Scholar 

  2. Berendsen, H.J.C., Postma, J.P.M., van Gunsteren, W.F., DiNola, A., Haak, J.R.: Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984)

    Article  Google Scholar 

  3. Bozic, I., Antal, T., Ohtsuki, H., Carter, H., Kim, D., Chen, S., Karchin, R., Kinzler, K.W., Vogelstein, B., Nowak, M.A.: Accumulation of driver and passenger mutations during tumor progression. Proc. Natl. Acad. Sci. U.S.A. 107(43), 18545–18550 (2010)

    Article  Google Scholar 

  4. Case, D.A., Babin, V., Berriman, J.T., et al.: AMBER 14. University of California, San Francisco (2014)

    Google Scholar 

  5. Darden, T., York, D., Pedersen, L.: Particle mesh Ewald: an Nlog(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993)

    Article  Google Scholar 

  6. Di Renzo, M.F., Narsimhan, R.P., Olivero, M., Bretti, S., Giordano, S., Medico, E., Gaglia, P., Zara, P., Comoglio, P.M.: Expression of the Met/HGF receptor in normal and neoplastic human tissues. Oncogene 6(11), 1997–2003 (1991)

    Google Scholar 

  7. Dykeman, E.C., Twarock, R.: All-atom normal-mode analysis reveals an RNA-induced allostery in a bacteriophage coat protein. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 81(3 Pt. 1), 031908 (2010)

    Google Scholar 

  8. Forbes, S.A., Beare, D., Bindal, N., Bamford, S., Ward, S., Cole, C.G., Jia, M., Kok, C., Boutselakis, H., De, T., Sondka, Z., Ponting, L., Stefancsik, R., Harsha, B., Tate, J., Dawson, E., Thompson, S., Jubb, H., Campbell, P.J.: COSMIC: high-resolution cancer genetics using the catalogue of somatic mutations in cancer. Curr. Protoc. Hum. Genet. 91, 10.11.1–10.11.37 (2016)

    Google Scholar 

  9. Gherardi, E., Youles, M.E., Miguel, R.N., Blundell, T.L., Iamele, L., Gough, J., Bandyopadhyay, A., Hartmann, G., Butler, P.J.G.: Functional map and domain structure of MET, the product of the c-Met protooncogene and receptor for hepatocyte growth factor/scatter factor. Proc. Natl. Acad. Sci. U.S.A. 100(21), 12039–12044 (2003)

    Article  Google Scholar 

  10. Götz, A.W., Williamson, M.J., Xu, D., Poole, D., Le Grand, S., Walker, R.C.: Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born. J. Chem. Theory Comput. 8, 1542–1555 (2012)

    Article  Google Scholar 

  11. Hartmann, G., Naldini, L., Weidner, K.M., Sachs, M., Vigna, E., Comoglio, P.M., Birchmeier, W.: A functional domain in the heavy chain of scatter factor/hepatocyte growth factor binds the c-Met receptor and induces cell dissociation but not mitogenesis. Proc. Natl. Acad. Sci. U.S.A. 89, 11574–11578 (1992)

    Article  Google Scholar 

  12. Huff, J.L., Jelinek, M.A., Borgman, C.A., Lansing, T.J., Parsons, J.T.: The protooncogene c-sea encodes a transmembrane proteintyrosine kinase related to the Met/hepatocyte growth factor/scatter factor receptor. Proc. Natl. Acad. Sci. U.S.A. 90, 6140–6144 (1993)

    Article  Google Scholar 

  13. Joung, I.S., Cheatham III, T.E.: Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J. Phys. Chem. B 112, 9020–9041 (2008)

    Article  Google Scholar 

  14. Jücker, M., Günther, A., Gradl, G., Fonatsch, C., Krueger, G., Diehl, V., Tesch, H.: The Met/hepatocyte growth factor receptor (HGFR) gene is overexpressed in some cases of human leukemia and lymphoma. Leuk. Res. 18(1), 7–16 (1994)

    Article  Google Scholar 

  15. Kawakami, H., Okamoto, I., Okamoto, W., Tanizaki, J., Nakagawa, K., Nishio, K.: Targeting MET amplification as a new oncogenic driver. Cancers 6(3), 1540–1552 (2014)

    Article  Google Scholar 

  16. Kurzrock, R., Giles, F.J.: Precision oncology for patients with advanced cancer: the challenges of malignant snowflakes. Cell Cycle (Georgetown, Tex.) 14(14), 2219–2221 (2015)

    Google Scholar 

  17. Levy, Y., Cho, S.S., Onuchic, J.N., Wolynes, P.G.: A survey of flexible protein binding mechanisms and their transition states using native topology based energy landscapes. J. Mol. Biol. 346(4), 1121–1145 (2005)

    Article  Google Scholar 

  18. Liu, Q., Li, Z., Li, J.: Use B-factor related features for accurate classification between protein binding interfaces and crystal packing contacts. BMC Bioinform. 15(Suppl. 16), S3 (2014)

    Google Scholar 

  19. Loncharich, R.J., Brooks, B.R., Pastor, R.W.: Langevin dynamics of peptides: the frictional dependence of isomerization rates of N-acetylalanyl-N’-methylamide. Biopolymers 32, 523–535 (1992)

    Article  Google Scholar 

  20. Montesano, R., Matsumoto, K., Nakamura, T., Orci, L.: Identification of a fibroblast-derived epithelial morphogen as hepatocyte growth factor. Cell 67, 901–908 (1991)

    Article  Google Scholar 

  21. Organ, S.L., Tsao, M.-S.: An overview of the c-MET signaling pathway. Ther. Adv. Med. Oncol. 3(Suppl. 1), S7–S19 (2011)

    Article  Google Scholar 

  22. Panjkovich, A., Daura, X.: Exploiting protein flexibility to predict the location of allosteric sites. BMC Bioinform. 2012(13), 273 (2012)

    Article  Google Scholar 

  23. Porollo, A., Meller, J.: Prediction-based fingerprints of protein-protein interactions. Proteins 66, 630–645 (2007)

    Article  Google Scholar 

  24. Prat, M., Narsimhan, R.P., Crepaldi, T., Nicotra, M.R., Natali, P.G., Comoglio, P.M.: The receptor encoded by the human c-MET oncogene is expressed in hepatocytes, epithelial cells and solid tumors. Int. J. Cancer 49(3), 323–328 (1991)

    Article  Google Scholar 

  25. Reva, B., Antipin, Y., Sander, C.: Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39(17), e118 (2011)

    Article  Google Scholar 

  26. Roe, D.R., Cheatham III, T.E.: PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013)

    Article  Google Scholar 

  27. Ronsin, C., Muscatelli, F., Mattei, M.G., Breathnach, R.: A novel putative receptor protein tyrosine kinase of the met family. Oncogene 8, 1195–1202 (1993)

    Google Scholar 

  28. Ryckaert, J.-P., Ciccotti, G., Berendsen, H.J.C.: Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977)

    Article  Google Scholar 

  29. Salomon-Ferrer, R., Case, D.A., Walker, R.C.: An overview of the Amber biomolecular simulation package. WIREs Comput. Mol. Sci. 3, 198–210 (2013)

    Article  Google Scholar 

  30. Salomon-Ferrer, R., Götz, A.W., Poole, D., Le Grand, S., Walker, R.C.: Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput. 9, 3878–3888 (2013)

    Google Scholar 

  31. Santarpia, L., Bottai, G., Kelly, C.M., Győrffy, B., Székely, B., Pusztai, L.: Deciphering and targeting oncogenic mutations and pathways in breast cancer. Oncologist 21(9), 1063–1078 (2016)

    Article  Google Scholar 

  32. Stamos, J., Lazarus, R.A., Yao, X., Kirchhofer, D., Wiesmann, C.: Crystal structure of the HGF beta-chain in complex with the Sema domain of the Met receptor. EMBO 23, 2325–2335 (2004)

    Article  Google Scholar 

  33. Stoler, D.L., Chen, N., Basik, M., Kahlenberg, M.S., Rodriguez-Bigas, A., Petrelli, N.J., Anderson, G.R.: The onset and extent of genomic instability in sporadic colorectal tumor progression. Proc. Natl. Acad. Sci. U.S.A. 96(26), 15121–15126 (1999)

    Article  Google Scholar 

  34. Studer, R.A., Dessailly, B.H., Orengo, C.A.: Residue mutations and their impact on protein structure and function: detecting beneficial and pathogenic changes. Biochem. J. 449(3), 581–594 (2013)

    Article  Google Scholar 

  35. Tomlinson, I., Sasieni, P., Bodmer, W.: How many mutations in a cancer? Am. J. Pathol. 160(3), 755–758 (2002)

    Article  Google Scholar 

  36. Trusolino, L., Comoglio, P.M.: Scatter-factor and semaphorin receptors: cell signalling for invasive growth. Nat. Rev. Cancer 2(4), 289–300 (2002)

    Article  Google Scholar 

  37. Tsigelny, I.F., Wheler, J.J., Greenberg, J.P., Kouznetsova, V.L., Stewart, D.J., Bazhenova, L., Kurzrock, R.: Molecular determinants of drug-specific sensitivity for Epidermal Growth Factor Receptor (EGFR) exon 19 and 20 mutants in non-small cell lung cancer. Oncotarget 6, 6029–6039 (2015)

    Article  Google Scholar 

  38. Tsigelny, I.F., Kurzrock, R., Skjevik, Å.A., Kouznetsova, V.L., Ikeda, S.: Molecular dynamics use in personalized cancer medicine: example of MET Y501C mutation. In: Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Lisbon, Portugal, 29–31 July 2016, pp. 71–74 (2016)

    Google Scholar 

  39. Vogelstein, B., Papadopoulos, N., Velculescu, V.E., Zhou, S., Diaz, L.A., Kinzler, K.W.: Cancer genome landscapes. Science 339(6127), 1546–1558 (2013)

    Article  Google Scholar 

  40. Zenali, M., deKay, J., Liu, Z., Hamilton, S., Zuo, Z., Lu, X., Bakkar, R., Mills, G., Broaddus, R.: Retrospective review of MET gene mutations. Oncoscience 2(5), 533–541 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Moores Cancer Center, University of California, San Diego, La Jolla, San Diego, CA, USA

    Igor F. Tsigelny, Razelle Kurzrock, Valentina L. Kouznetsova, Amélie Boichard & Sadakatsu Ikeda

  2. San Diego Supercomputer Center, University of California, San Diego, La Jolla, San Diego, CA, USA

    Igor F. Tsigelny, Åge Aleksander Skjevik & Valentina L. Kouznetsova

  3. Department of Neurosciences, University of California, San Diego, La Jolla, San Diego, CA, USA

    Igor F. Tsigelny

  4. Department of Biomedicine, University of Bergen, Bergen, Norway

    Åge Aleksander Skjevik

  5. CureMatch Inc., San Diego, CA, USA

    Igor F. Tsigelny

Authors
  1. Igor F. Tsigelny
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Razelle Kurzrock
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Åge Aleksander Skjevik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Valentina L. Kouznetsova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amélie Boichard
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sadakatsu Ikeda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Igor F. Tsigelny .

Editor information

Editors and Affiliations

  1. King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan

    Mohammad S. Obaidat

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  3. Department of Modelling and Simulation, Riga Technical University, Riga, Latvia

    Yuri Merkuryev

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Tsigelny, I.F., Kurzrock, R., Skjevik, Å.A., Kouznetsova, V.L., Boichard, A., Ikeda, S. (2018). Proteins Flexibility as a Criterion for Elucidation of Activating Mutants in Personalized Cancer Medicine. In: Obaidat, M., Ören, T., Merkuryev, Y. (eds) Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2016. Advances in Intelligent Systems and Computing, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-319-69832-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-69832-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69831-1

  • Online ISBN: 978-3-319-69832-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics