Models for membrane curvature sensing of curvature generating proteins

Abstract

The curvature-sensitive localisation of proteins on membranes is vital for many cell biological processes. Coarse-grained models are routinely employed to study the curvature-sensing phenomena and membrane morphology at the length scale of a few micrometres. Two prevalent phenomenological models exist for modelling the experimental observations of curvature sensing: (1) the spontaneous curvature (SC) model and (2) the curvature mismatch (CM) model, which differ in their treatment of the change in elastic energy due to the binding of proteins on the membrane. In this work, the prediction of sensing and generation behaviour by these two models are investigated using analytical calculations as well as dynamic triangulation Monte Carlo simulations of quasispherical vesicles. While the SC model yields a monotonically decreasing sensing curve as a function of the vesicle radius, the CM model results in a non-monotonic sensing curve. We highlight the main differences in the interpretation of the protein-related parameters in the two models. We further propose that the SC model is appropriate for modelling peripheral proteins employing the hydrophobic insertion mechanism, with minimal modification of membrane rigidity, while the CM model is appropriate for modelling curvature generation using scaffolding mechanism where there is significant stiffening of the membrane due to protein binding.

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References

  1. 1.

    H T McMahon and J L Gallop, Nature 438, 590 (2005)

    ADS  Article  Google Scholar 

  2. 2.

    J Zimmerberg and M M Kozlov, Nat. Rev. Mol. Cell Biol. 7, 9 (2006)

    Article  Google Scholar 

  3. 3.

    I K Jarsch, F Daste and J L Gallop, J. Cell Biol.214, 375 (2016)

    Article  Google Scholar 

  4. 4.

    P Bassereau, R Jin, T Baumgart, M Deserno, R Dimova, V A Frolov, P V Bashkirov, H Grubmüller, R Jahn and H J Risselada, J. Phys. D 51, 343001 (2018)

    Article  Google Scholar 

  5. 5.

    K Farsad and P De Camilli, Curr. Opin. Cell Biol.15, 372 (2003)

  6. 6.

    G K Voeltz and W A Prinz, Nat. Rev. Mol. Cell Biol. 8, 258 (2007)

    Article  Google Scholar 

  7. 7.

    Y Shibata, J Hu, M M Kozlov and T A Rapoport, Annu. Rev. Cell Dev. Biol. 25, 329 (2009)

    Article  Google Scholar 

  8. 8.

    B J Peter, H M Kent, I G Mills, Y Vallis, P J G Butler, P R Evans and H T McMahon, Science 303, 495 (2004)

    ADS  Article  Google Scholar 

  9. 9.

    V K Bhatia, K L Madsen, P Y Bolinger, A Kunding, P Hedegård, U Gether and D Stamou, EMBO J.28, 3303 (2009)

    Article  Google Scholar 

  10. 10.

    N S Hatzakis, V K Bhatia, J Larsen, K L Madsen, P Y Bolinger, A H Kunding, J Castillo, U Gether, P Hedegård and D Stamou, Nat. Chem. Biol.5, 835 (2009)

    Article  Google Scholar 

  11. 11.

    B Antonny, Annu. Rev. Biochem.80, 101 (2011)

    Article  Google Scholar 

  12. 12.

    V Wasnik, N S Wingreen and R Mukhopadhyay, PLoS ONE10, 1 (2015)

    Article  Google Scholar 

  13. 13.

    R L Gill, J P Castaing, J Hsin, I S Tan, X Wang, K C Huang, F Tian and K S Ramamurthi, Proc. Natl. Acad. Sci. USA112, E1908 (2015)

    Article  Google Scholar 

  14. 14.

    A Martyna, J Gómez-Llobregat, M Lindén and J S Rossman, Biochem. J. 55, 3493 (2016)

    Article  Google Scholar 

  15. 15.

    W Draper and J Liphardt, Nat. Commun.8, 14838 (2017)

    ADS  Article  Google Scholar 

  16. 16.

    T Baumgart, B R Capraro, C Zhu and S L Das, Annu. Rev. Phys. Chem.62, 483 (2011)

    ADS  Article  Google Scholar 

  17. 17.

    S Aimon, A Callan-Jones, A Berthaud, M Pinot, G E S Toombes and P Bassereau, Dev. Cell28, 212 (2014)

    Article  Google Scholar 

  18. 18.

    K R Rosholm, N Leijnse, A Mantsiou, V Tkach, S L Pedersen, V F Wirth, L B Oddershede, K J Jenses, K L Martinez and N S Hatzakis, Nat. Chem. Biol.13, 724 (2017)

    Article  Google Scholar 

  19. 19.

    C Zhu, S L Das and T Baumgart, Biophys. J.102, 1837 (2012)

    ADS  Article  Google Scholar 

  20. 20.

    B Božič, S L Das and S Svetina, Soft Matter11, 2479 (2015)

    ADS  Article  Google Scholar 

  21. 21.

    S Svetina, Eur. Biophys. J.44, 513 (2015)

    Article  Google Scholar 

  22. 22.

    B Sorre, A Callan-Jones, J Manzi, B Goud, J Prost, P Bassereau and A Roux, Proc. Natl. Acad. Sci. USA 109, 173 (2012)

    ADS  Article  Google Scholar 

  23. 23.

    M Mally, B Božič, S V Hartman, U Klančnik, M Mur, S Svetina and J Derganc, RSC Adv.7, 36506 (2017)

    ADS  Article  Google Scholar 

  24. 24.

    W Helfrich, Z. Naturforsch. C28, 693 (1973)

    Article  Google Scholar 

  25. 25.

    V S Markin, Biophys. J.36, 1 (1981)

    Article  Google Scholar 

  26. 26.

    S Leibler, J. Phys.47, 507 (1986)

    Article  Google Scholar 

  27. 27.

    T V Sachin Krishnan, S L Das and P B Sunil Kumar, Soft Matter15, 201 (2019)

    ADS  Article  Google Scholar 

  28. 28.

    C Prévost, H Zhao, J Manzi, E Lemichez, P Lappalainen, A Callan-Jones and P Bassereau, Nat. Commun.6, 8529 (2015)

    ADS  Article  Google Scholar 

  29. 29.

    P Mahata and S L Das, FEBS Lett.591, 1333 (2017)

    Article  Google Scholar 

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Acknowledgements

Sachin Krishnan thanks IIT Palakkad for their hospitality and computational resources. The authors thank Department of Biotechnology, Ministry of Science and Technology, Government of India for the financial support through Grant No. BT/PR8025/BRB/10/1023/2013.

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Correspondence to T V Sachin Krishnan.

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Krishnan, T.V.S., Das, S.L. & Kumar, P.B.S. Models for membrane curvature sensing of curvature generating proteins. Pramana - J Phys 94, 47 (2020). https://doi.org/10.1007/s12043-020-1915-z

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Keywords

  • Biological membranes
  • curvature sensing
  • curvature generation

PACS Nos

  • 87.16.D–
  • 87.14.E–