Skip to main content
Log in

Thermodynamic free energy methods to investigate shape transitions in bilayer membranes

  • Published:
International Journal of Advances in Engineering Sciences and Applied Mathematics Aims and scope Submit manuscript

Abstract

The conformational free energy landscape of a system is a fundamental thermodynamic quantity of importance particularly in the study of soft matter and biological systems, in which the entropic contributions play a dominant role. While computational methods to delineate the free energy landscape are routinely used to analyze the relative stability of conformational states, to determine phase boundaries, and to compute ligand-receptor binding energies its use in problems involving the cell membrane is limited. Here, we present an overview of four different free energy methods to study morphological transitions in bilayer membranes, induced either by the action of curvature remodeling proteins or due to the application of external forces. Using a triangulated surface as a model for the cell membrane and using the framework of dynamical triangulation Monte Carlo, we have focused on the methods of Widom insertion, thermodynamic integration, Bennett acceptance scheme, and umbrella sampling and weighted histogram analysis. We have demonstrated how these methods can be employed in a variety of problems involving the cell membrane. Specifically, we have shown that the chemical potential, computed using Widom insertion, and the relative free energies, computed using thermodynamic integration and Bennett acceptance method, are excellent measures to study the transition from curvature sensing to curvature inducing behavior of membrane associated proteins. The umbrella sampling and WHAM analysis has been used to study the thermodynamics of tether formation in cell membranes and the quantitative predictions of the computational model are in excellent agreement with experimental measurements. Furthermore, we also present a method based on WHAM and thermodynamic integration to handle problems related to end-point-catastrophe that are common in most free energy methods.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Calculated as (nanocarrier radius + length of the antibody + length of the receptor + \(d_{0}\)).

References

  1. Israelachvili, J.N.: Intermolecular and Surface Forces, 3rd edn. Academic Press, Boston (2011)

    Google Scholar 

  2. Escribá, P.V., González-Ros, J.M., Goñi, F.M., Kinnunen, P.K.J., Vigh, L., Sánchez-Magraner, L., Fernández, A.M., Busquets, X., Horváth, I., Barceló-Coblijn, G.: Membranes: a meeting point for lipids, proteins and therapies. J. Cell. Mol. Med. 12(3), 829 (2008). doi:10.1111/j.1582-4934.2008.00281.x

    Article  Google Scholar 

  3. Singer, S.J., Nicolson, G.L.: The fluid mosaic model of the structure of cell membranes. Science 175(4023), 720 (1972). http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1972Sci...175.720S&link_type=EJOURNAL

  4. Edidin, M.: Lipids on the frontier: a century of cell-membrane bilayers. Nat. Rev. Mol. Cell Biol. 4(5), 414 (2003). doi:10.1038/nrm1102. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=12728275&retmode=ref&cmd=prlinks

  5. Engelman, D.M.: Membranes are more mosaic than fluid. Nat. Cell Biol. 438(7068), 578 (2005). doi:10.1038/nature04394. http://www.nature.com/doifinder/10.1038/nature04394

  6. Conner, S.D., Schmid, S.L.: Regulated portals of entry into the cell. Nature 422(6927), 37 (2003). doi:10.1038/nature01451. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003Natur.422...37C&link_type=ABSTRACT

  7. Doherty, G.J., McMahon, H.T.: Mechanisms of endocytosis. Annu. Rev. Biochem. 78(1), 857 (2009). doi:10.1146/annurev.biochem.78.081307.110540

    Article  Google Scholar 

  8. Ewers, H., Helenius, A.: Lipid-mediated endocytosis. Cold Spring Harb. Perspect. Biol. 3(8), a004721 (2011). doi:10.1101/cshperspect.a004721

    Article  Google Scholar 

  9. Canton, I., Battaglia, G.: Endocytosis at the nanoscale. Chem. Soc. Rev. 41(7), 2718 (2012). doi:10.1039/c2cs15309b

    Article  Google Scholar 

  10. Kholodenko, B.N.: Cell-signalling dynamics in time and space. Nature 7(3), 165 (2006). doi:10.1038/nrm1838

    Google Scholar 

  11. Sorkin, A., von Zastrow, M.: Endocytosis and signalling: intertwining molecular networks. Nat. Rev. Mol. Cell Biol. 10(9), 609 (2009). doi:10.1038/nrm2748

    Article  Google Scholar 

  12. Sheetz, M.P.: Cell control by membrane-cytoskeleton adhesion. Nat. Rev. Mol. Cell Biol. 2(5), 392 (2001). doi:10.1038/35073095

    Article  Google Scholar 

  13. Ananthakrishnan, R., Ehrlicher, A.: The forces behind cell movement. Int. J. Biol. Sci. 3(5), 303 (2007). http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=17589565&retmode=ref&cmd=prlinks

  14. Keren, K.: Cell motility: the integrating role of the plasma membrane. Eur. Biophys. J. 40(9), 1013 (2011). doi:10.1007/s00249-011-0741-0

    Article  Google Scholar 

  15. Chaikin, P.M., Lubensky, T.C.: Principles of Condensed Matter Physics. Cambridge University Press, Cambridge (2000). http://books.google.co.in/books?id=P9YjNjzr9OIC

  16. Frenkel, D., Smit, B.: Understanding Molecular Simulation : From Algorithms to Applications, 2nd edn. Academic Press, New York (2001). http://www.worldcat.org/isbn/0122673514

  17. Seifert, U.: Configurations of fluid membranes and vesicles. Adv. Phys. 46(1), 13 (1997). http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=mekentosj&SrcApp=Papers&DestLinkType=FullRecord&DestApp=WOS&KeyUT=A1997WE91800002

  18. Tieleman, D.P., Marrink, S.J., Berendsen, H.J.: A computer perspective of membranes: molecular dynamics studies of lipid bilayer systems. Biochim. Biophys. Acta (BBA) Rev. Biomembr. 1331(3), 235 (1997). http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=9512654&retmode=ref&cmd=prlinks

  19. Venturoli, M., Maddalena Sperotto, M., Kranenburg, M., Smit, B.: Mesoscopic models of biological membranes. Phys. Rep. 437(1), 1 (2006). doi:10.1016/j.physrep.2006.07.006. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006PhR...437....1V&link_type=ABSTRACT

  20. Ayton, G.S., Voth, G.A.: Multiscale simulation of protein mediated membrane remodeling. Semin. Cell Dev. Biol. 21(4), 357 (2010). doi:10.1016/j.semcdb.2009.11.011. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=19922811&retmode=ref&cmd=prlinks

  21. Shinoda, W., DeVane, R., Klein, M.L.: Computer simulation studies of self-assembling macromolecules. Curr. Opin. Struct. Biol. 22(2), 175 (2012). doi:10.1016/j.sbi.2012.01.011

    Article  Google Scholar 

  22. Bradley, R.P., Radhakrishnan R.: Coarse-grained models for protein-cell membrane interactions. Polymers 5(3), 890 (2013). doi:10.3390/polym5030890. http://www.mdpi.com/2073-4360/5/3/890/

  23. Ramakrishnan, N., Sunil Kumar, P.B., Radhakrishnan, R.: Mesoscale computational studies of membrane bilayer remodeling by curvature-inducing proteins. Phys. Rep. 543(1), 1 (2014). doi:10.1016/j.physrep.2014.05.001

    Article  MathSciNet  Google Scholar 

  24. Deserno, M.: Fluid lipid membranes: from differential geometry to curvature stresses. Chem. Phys. Lipids (2014). doi:10.1016/j.chemphyslip.2014.05.001. http://linkinghub.elsevier.com/retrieve/pii/S000930841400053X

  25. Canham, P.B.: The minimum energy of bending as a possible explanation of the biconcave shape of the human red blood cell. J. Theor. Biol. 26(1), 61 (1970)

    Article  Google Scholar 

  26. Helfrich, W.: Elastic properties of lipid bilayers: theory and possible experiments. Z. Naturforsch. C 28, 693 (1973). http://www.ncbi.nlm.nih.gov/pubmed/4273690

  27. Diz-Muñoz, A., Fletcher, D.A., Weiner, O.D.: Use the force: membrane tension as an organizer of cell shape and motility. Trends Cell Biol. 23(2), 47 (2013). doi:10.1016/j.tcb.2012.09.006

    Article  Google Scholar 

  28. Shi, Z., Baumgart, T.: Membrane tension and peripheral protein density mediate membrane shape transitions. Nat. Commun. 6, 5974 (2015). doi:10.1038/ncomms6974. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25569184&retmode=ref&cmd=prlinks

  29. Deserno, M.: Fluid lipid membranes: from differential geometry to curvature stresses. Chem. Phys. Lipids 185, 11 (2015). doi:10.1016/j.chemphyslip.2014.05.001. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24835737&retmode=ref&cmd=prlinks

  30. do Carmo, M.P.: Differential Geometry of Curves and Surfaces. Prentice Hall, New Jersey (1976)

    MATH  Google Scholar 

  31. Lipowsky, R.: Spontaneous tubulation of membranes and vesicles reveals membrane tension generated by spontaneous curvature. Faraday Discuss. 161, 305 (2013). doi:10.1039/c2fd20105d. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013FaDi.161.305L&link_type=EJOURNAL

  32. Schnur, J.M.: Lipid tubules: a paradigm for molecularly engineered structures. Science 262(5140), 1669 (1993). doi:10.1126/science.262.5140.1669. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993Sci...262.1669S&link_type=ABSTRACT

  33. Kohyama, T., Kroll, D.M., Gompper, G.: Budding of crystalline domains in fluid membranes. Phys. Rev. E 68(6), 061905 (2003). doi:10.1103/PhysRevE.68.061905

    Article  Google Scholar 

  34. Sunil Kumar, P.B., Gompper, G., Lipowsky, R.: Modulated phases in multicomponent fluid membranes. Phys. Rev. E 60(4 Pt B), 4610 (1999). doi:10.1103/PhysRevE.60.4610. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999PhRvE.60.4610K&link_type=ABSTRACT

  35. Nelson, D.R., Piran, T.: Statistical mechanics of membranes and surfaces. World Sci. (2004). http://books.google.com/books?id=FbcMqgNrVjcC&pg=PA323&dq=intitle:Statistical+mechanics+of+membranes+and+surfaces&hl=&cd=1&source=gbs_api

  36. Ramakrishnan, N., Sunil Kumar, P.B., Ipsen, J.H.: Monte Carlo simulations of fluid vesicles with in-plane orientational ordering. Phys. Rev. E 81(4), 041922 (2010). doi:10.1103/PhysRevE.81.041922

    Article  Google Scholar 

  37. Agrawal, N.J., Nukpezah, J., Radhakrishnan, R.: Minimal mesoscale model for protein-mediated vesiculation in Clathrin-dependent endocytosis. PLoS Comput. Biol. 6(9), e1000926 (2010). doi:10.1371/journal.pcbi.1000926.s008

    Article  Google Scholar 

  38. Ramanan, V., Agrawal, N.J., Liu, J., Engles, S., Toy, R., Radhakrishnan, R.: Systems biology and physical biology of clathrin-mediated endocytosis. Integr. Biol. 3(8), 803 (2011). doi:10.1039/c1ib00036e. http://www.ncbi.nlm.nih.gov/pubmed/21792431

  39. Liu, J., Tourdot, R.W., Ramanan, V., Agrawal, N.J., Radhakrishanan, R.: Mesoscale simulations of curvature-inducing protein partitioning on lipid bilayer membranes in the presence of mean curvature fields. Mol. Phys. 110(11–12), 1127 (2012). doi:10.1080/00268976.2012.664661

    Article  Google Scholar 

  40. Tourdot, R.W., Ramakrishnan, N., Radhakrishnan R.: Defining the free-energy landscape of curvature-inducing proteins on membrane bilayers. Phys. Rev. E 90, 022717 (2014). http://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.022717

  41. Zhao, Y., Liu, J., Yang, C., Capraro, B.R., Baumgart, T., Bradley, R.P., Ramakrishnan, N., Xu, X., Radhakrishnan, R., Svitkina, T., Guo, W.: Exo70 generates membrane curvature for morphogenesis and cell migration. Dev. Cell 26(3), 266 (2013). doi:10.1016/j.devcel.2013.07.007. http://linkinghub.elsevier.com/retrieve/pii/S1534580713004152

  42. Tourdot, R.W., Bradley, R.P., Ramakrishnan, N., Radhakrishnan, R.: Multiscale computational models in physical systems biology of intracellular trafficking. IET Syst. Biol. 8(5), 198 (2014). doi:10.1049/iet-syb.2013.0057. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25257021&retmode=ref&cmd=prlinks

  43. Ramakrishnan, N., Radhakrishnan, R.: Phenomenology based multiscale models as tools to understand cell mand organelle morphologies. In: Iglič, Aleš, Kulkarni, Chandrashekhar V, Rappolt, Michael (eds.), Academic Press, pp. 129–175 (2015) doi:10.1016/bs.adplan.2015.06.004. http://www.sciencedirect.com/science/article/pii/S1554451615000320

  44. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087 (1953). doi:10.1063/1.1699114. http://link.aip.org/link/?JCP/21/1087/1

  45. Widom, B.: Some topics in the theory of fluids. J. Chem. Phys. 39(11), 2808 (1963). doi:10.1063/1.1734110. http://link.aip.org/link/JCPSA6/v39/i11/p2808/s1&Agg=doi

  46. Bennett, C.H.: Efficient estimation of free-energy differences from Monte-Carlo data. J. Comput. Phys. 22(2), 245 (1976). doi:10.1016/0021-9991(76)90078-4. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1976JCoPh.22.245B&link_type=ABSTRACT

  47. Roux, B.: The calculation of the potential of mean force using computer simulations. Comput. Phys. Commun. 91(1), 275 (1995). http://www.sciencedirect.com/science/article/pii/001046559500053I

  48. Ramakrishnan, N., Eckmann, D.M., Ayyaswamy, P.S., Weaver,Valerie M., Radhakrishnan, R.: Subcellular membrane mechanotyping using local estimates of cell membrane excess area (Unpublished data)

  49. Souaille, M., Roux, B.: Extension to the weighted histogram analysis method: combining umbrella sampling with free energy calculations. Comput. Phys. Commun. 135(1), 40 (2001). doi:10.1016/S0010-4655(00)00215-0. http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001CoPhC.135...40S&link_type=ABSTRACT

  50. Liu, J., Weller, G.E., Zern, B., Ayyaswamy, P.S., Eckmann, D.M., Muzykantov, V.R., Radhakrishnan, R.: Computational model for nanocarrier binding to endothelium validated using in vivo, in vitro, and atomic force microscopy experiments. Proc. Natl. Acad. Sci. USA 107(38), 16530 (2010). doi:10.1073/pnas.1006611107/-/DCSupplemental. http://www.pnas.org/content/107/38/16530.short

  51. Liu, J., Agrawal, N.J., Calderon, A., Ayyaswamy, P.S., Eckmann, D.M., Radhakrishnan, R.: Multivalent binding of nanocarrier to endothelial cells under shear flow. Biophys. J. 101(2), 319 (2011). doi:10.1016/j.bpj.2011.05.063. http://linkinghub.elsevier.com/retrieve/pii/S0006349511006680

Download references

Acknowledgments

This work was supported in part by the US National Science Foundation Grants DMR-1120901, and CBET-1244507. The research leading to these results has received funding from the European Commission Grant FP7-ICT-2011-9-600841, US NIH U01-EB016027, and NIH 1U54CA193417. Computational resources were provided in part by the National Partnership for Advanced Computational Infrastructure under Grant No. MCB060006 from XSEDE.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ravi Radhakrishnan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramakrishnan, N., Tourdot, R.W. & Radhakrishnan, R. Thermodynamic free energy methods to investigate shape transitions in bilayer membranes. Int J Adv Eng Sci Appl Math 8, 88–100 (2016). https://doi.org/10.1007/s12572-015-0159-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12572-015-0159-5

Keywords

Navigation