Skip to main content
SpringerLink
Log in

Effect of Force Field Resolution on Membrane Mechanical Response and Mechanoporation Damage under Deformation Simulations

  • Original Paper
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

Damage induced by transient disruption and mechanoporation in an intact cell membrane is a vital nanoscale biomechanical mechanism that critically affects cell viability. To complement experimental studies of mechanical membrane damage and disruption, molecular dynamics (MD) simulations have been performed at different force field resolutions, each of which follows different parameterization strategies and thus may influence the properties and dynamics of membrane systems. Therefore, the current study performed tensile deformation MD simulations of bilayer membranes using all-atom (AA), united-atom (UA), and coarse-grained Martini (CG-M) models to investigate how the damage biomechanics differs across atomistic and coarse-grained (CG) simulations. The mechanical response and mechanoporation damage were qualitatively similar but quantitatively different in the three models, including some progressive changes based on the coarse-graining level. The membranes yielded and reached ultimate strength at similar strains; however, the coarser systems exhibited lower average yield stresses and failure strains. The average failure strain in the UA model was approximately 7% lower than the AA, and the CG-M was 20% lower than UA and 27% lower than AA. The CG systems also nucleated a higher number of pores and larger pores, which resulted in higher damage during the deformation process. Overall, the study provides insight on the impact of force field—a critical factor in modeling biomolecular systems and their interactions—in inspecting membrane mechanosensitive responses and serves as a reference for justifying the appropriate force field for future studies of more complex membranes and more diverse biomolecular assemblies.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kirsch, S. A., & Böckmann, R. A. (2016). Membrane pore formation in atomistic and coarse-grained simulations. Biochim. Biophys. Acta - Biomembr., 1858(10), 2266–2277. https://doi.org/10.1016/j.bbamem.2015.12.031

    Article  CAS  Google Scholar 

  2. Ingólfsson, H. I., Carpenter, T. S., Bhatia, H., Bremer, P. T., Marrink, S. J., & Lightstone, F. C. (2017). Computational lipidomics of the neuronal plasma membrane. Biophysical Journal, 113(10), 2271–2280. https://doi.org/10.1016/j.bpj.2017.10.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Marrink, S. J., Corradi, V., Souza, P. C. T., Ingólfsson, H. I., Tieleman, D. P., & Sansom, M. S. P. (2019). Computational modeling of realistic cell membranes. Chemical Reviews, 119(9), 6184–6226. https://doi.org/10.1021/acs.chemrev.8b00460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ingólfsson, H. I., et al. (2014). Lipid organization of the plasma membrane. Journal of the American Chemical Society, 136(41), 14554–14559. https://doi.org/10.1021/ja507832e

    Article  CAS  PubMed  Google Scholar 

  5. Pluhackova, K., & Böckmann, R. A. (2015). Biomembranes in atomistic and coarse-grained simulations. Journal of Physics. Condensed Matter : an Institute of Physics Journal. https://doi.org/10.1088/0953-8984/27/32/323103

    Article  PubMed  Google Scholar 

  6. Sliozberg, Y., & Chantawansri, T. (2014). Damage in spherical cellular membrane generated by the shock waves: Coarse-grained molecular dynamics simulation of lipid vesicle. The Journal of Chemical Physics. https://doi.org/10.1063/1.4901130

    Article  PubMed  Google Scholar 

  7. Huang, C. H., Hsiao, P. Y., Tseng, F. G., Fan, S. K., Fu, C. C., & Pan, R. L. (2011). Pore-spanning lipid membrane under indentation by a probe tip: A molecular dynamics simulation study. Langmuir, 27(19), 11930–11942. https://doi.org/10.1021/la201977d

    Article  CAS  PubMed  Google Scholar 

  8. Bennett, W. F. D., & Tieleman, D. P. (2011). Water defect and pore formation in atomistic and coarse-grained lipid membranes: Pushing the limits of coarse graining. Journal of Chemical Theory and Computation, 7(9), 2981–2988. https://doi.org/10.1021/ct200291v

    Article  CAS  PubMed  Google Scholar 

  9. Shigematsu, T., Koshiyama, K., & Wada, S. (2015). Effects of stretching speed on mechanical rupture of phospholipid/cholesterol bilayers: Molecular dynamics simulation. Science and Reports, 5, 1–10. https://doi.org/10.1038/srep15369

    Article  CAS  Google Scholar 

  10. Murphy, M. A., et al. (2018). Molecular dynamics simulations showing 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC) membrane mechanoporation damage under different strain paths. Journal of Biomolecular Structure & Dynamics, 37(5), 1346–1359. https://doi.org/10.1080/07391102.2018.1453376

    Article  CAS  Google Scholar 

  11. Hu, Y., Sinha, S. K., & Patel, S. (2015). Investigating hydrophilic pores in model lipid bilayers using molecular simulations: Correlating bilayer properties with pore-formation thermodynamics. Langmuir, 31(24), 6615–6631. https://doi.org/10.1021/la504049qs

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Grafmüller, A., Shillcock, J., & Lipowsky, R. (2007). Pathway of membrane fusion with two tension-dependent energy barriers. Physical Review Letters. https://doi.org/10.1103/PhysRevLett.98.218101

    Article  PubMed  Google Scholar 

  13. Neder, J., West, B., Nielaba, P., & Schmid, F. (2010). Coarse-grained simulations of membranes under tension. The Journal of Chemical Physics. https://doi.org/10.1063/1.3352583

    Article  PubMed  Google Scholar 

  14. Shigematsu, T., Koshiyama, K., & Wada, S. (2014). Molecular dynamics simulations of pore formation in stretched phospholipid/cholesterol bilayers. Chemistry and Physics of Lipids, 183, 43–49. https://doi.org/10.1016/j.chemphyslip.2014.05.005

    Article  CAS  PubMed  Google Scholar 

  15. Murphy, M. A., & Vo, A. (2022). The multiscale nature of the brain and traumatic brain injury. Multiscale Biomech. Model. Brain. https://doi.org/10.1016/B978-0-12-818144-7.00004-9

    Article  Google Scholar 

  16. Needham, D., & Nunn, R. S. (1990). Elastic deformation and failure of lipid bilayer membranes containing cholesterol. Biophysical Journal, 58(4), 997–1009. https://doi.org/10.1016/S0006-3495(90)82444-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Ovalle-García, E., Torres-Heredia, J. J., Antillón, A., & Ortega-Blake, I. (2011). Simultaneous determination of the elastic properties of the lipid bilayer by atomic force microscopy: Bending, tension, and adhesion. The Journal of Physical Chemistry B, 115(16), 4826–4833. https://doi.org/10.1021/jp111985z

    Article  CAS  PubMed  Google Scholar 

  18. Evans, E., Heinrich, V., Ludwig, F., & Rawicz, W. (2003). Dynamic tension spectroscopy and strength of biomembranes. Biophysical Journal, 85(4), 2342–2350. https://doi.org/10.1016/S0006-3495(03)74658-X

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. LaPlaca, M. C., Cullen, D. K., McLoughlin, J. J., & Cargill, R. S. (2005). High rate shear strain of three-dimensional neural cell cultures: A new in vitro traumatic brain injury model. Journal of Biomechanics, 38(5), 1093–1105. https://doi.org/10.1016/j.jbiomech.2004.05.032

    Article  PubMed  Google Scholar 

  20. LaPlaca, M. C., & Prado, G. R. (2010). Neural mechanobiology and neuronal vulnerability to traumatic loading. Journal of Biomechanics, 43(1), 71–78. https://doi.org/10.1016/j.jbiomech.2009.09.011

    Article  PubMed  Google Scholar 

  21. Li, F., Chan, C. U., & Ohl, C. D. (2013). Yield strength of human erythrocyte membranes to impulsive stretching. Biophysical Journal, 105(4), 872–879. https://doi.org/10.1016/j.bpj.2013.06.045

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Murphy, M. A., et al. (2016). Nanomechanics of phospholipid bilayer failure under strip biaxial stretching using molecular dynamics. Modelling and Simulation in Materials Science and Engineering, 24(5), 55008. https://doi.org/10.1088/0965-0393/24/5/055008

    Article  CAS  Google Scholar 

  23. Tomasini, M. D., Rinaldi, C., & Tomassone, M. S. (2010). Molecular dynamics simulations of rupture in lipid bilayers. Experimental Biology and Medicine, 235(2), 181–188. https://doi.org/10.1258/ebm.2009.009187

    Article  CAS  PubMed  Google Scholar 

  24. Tieleman, D. P., Leontiadou, H., Mark, A. E., & Marrink, S. J. (2003). Simulation of pore formation in lipid bilayers by mechanical stress and electric fields. Journal of the American Chemical Society. https://doi.org/10.1021/ja029504i

    Article  PubMed  Google Scholar 

  25. Vo, A. T. N., Murphy, M. A., Phan, P. K., Stone, T. W., & Prabhu, R. K. (2023). Molecular dynamics simulation of membrane systems in the context of traumatic brain injury. Curr. Opin. Biomed. Eng. https://doi.org/10.1016/j.cobme.2023.100453

    Article  Google Scholar 

  26. M. S. P. Sansom and P. C. Biggin, Molecular Simulations and Biomembranes: From Biophysics to Function. 2010.

  27. Unke, O. T., et al. (2021). Machine learning force fields. Chemical Reviews, 121(16), 10142–10186. https://doi.org/10.1021/acs.chemrev.0c01111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bradley, R., & Radhakrishnan, R. (2013). Coarse-grained models for protein-cell membrane interactions. Polymers (Basel), 5(3), 890–936. https://doi.org/10.3390/polym5030890

    Article  CAS  PubMed  Google Scholar 

  29. Marx, D. C., & Fleming, K. G. (2021). Local bilayer hydrophobicity modulates membrane protein stability. Journal of the American Chemical Society, 143(2), 764–772.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Di Meo, F., et al. (2016). In silico pharmacology: Drug membrane partitioning and crossing. Pharmacological Research, 111, 471–486. https://doi.org/10.1016/J.PHRS.2016.06.030

    Article  PubMed  Google Scholar 

  31. Bennion, B. J., et al. (2017). Predicting a drug’s membrane permeability: a computational model validated with in vitro permeability assay data. The Journal of Physical Chemistry B, 121(20), 5228–5237.

    Article  CAS  PubMed  Google Scholar 

  32. Klauda, J. B., et al. (2010). Update of the CHARMM all-atom additive force field for lipids. The Journal of Physical Chemistry B, 114(23), 7830–7843. https://doi.org/10.1021/jp101759q.Update

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ingólfsson, H. I., Arnarez, C., Periole, X., & Marrink, S. J. (2016). Computational ‘microscopy’ of cellular membranes. Journal of Cell Science, 129(2), 257–268. https://doi.org/10.1242/jcs.176040

    Article  CAS  PubMed  Google Scholar 

  34. Lee, S., Tran, A., Allsopp, M., Lim, J. B., Hénin, J., & Klauda, J. B. (2014). CHARMM36 united atom chain model for lipids and surfactants. The Journal of Physical Chemistry B, 118(2), 547–556. https://doi.org/10.1021/jp410344g

    Article  CAS  PubMed  Google Scholar 

  35. Yu, Y., & Klauda, J. B. (2020). Update of the CHARMM36 united atom chain model for hydrocarbons and phospholipids. The Journal of Physical Chemistry B, 124(31), 6797–6812. https://doi.org/10.1021/acs.jpcb.0c04795

    Article  CAS  PubMed  Google Scholar 

  36. Marrink, S. J., Risselada, H. J., Yefimov, S., Tieleman, D. P., & De Vries, A. H. (2007). The MARTINI force field: Coarse grained model for biomolecular simulations. The Journal of Physical Chemistry B, 111(27), 7812–7824. https://doi.org/10.1021/jp071097f

    Article  CAS  PubMed  Google Scholar 

  37. Marrink, S. J., & Tieleman, D. P. (2013). Perspective on the martini model. Chemical Society Reviews, 42(16), 6801–6822. https://doi.org/10.1039/c3cs60093a

    Article  CAS  PubMed  Google Scholar 

  38. Yesylevskyy, S. O., Schäfer, L. V., Sengupta, D., & Marrink, S. J. (2010). Polarizable water model for the coarse-grained MARTINI force field. PLoS Computational Biology, 6(6), 1–17. https://doi.org/10.1371/journal.pcbi.1000810

    Article  CAS  Google Scholar 

  39. Saeedimasine, M., Montanino, A., Kleiven, S., & Villa, A. (2019). Role of lipid composition on the structural and mechanical features of axonal membranes: A molecular simulation study. Science and Reports, 9(1), 1–12. https://doi.org/10.1038/s41598-019-44318-9

    Article  CAS  Google Scholar 

  40. Sharma, S., Kim, B. N., Stansfeld, P. J., Sansom, M. S. P. P., & Lindau, M. (2015). A coarse grained model for a lipid membrane with physiological composition and leaflet asymmetry. PLoS ONE. https://doi.org/10.1371/journal.pone.0144814

    Article  PubMed  PubMed Central  Google Scholar 

  41. Vo, A. T. N., Murphy, M. A., Stone, T. W., Phan, P. K., Baskes, M. I., & Prabhu, R. K. (2021). Molecular dynamics simulations of phospholipid bilayer mechanoporation under different strain states—a comparison between GROMACS and LAMMPS. Modelling and Simulation in Materials Science and Engineering. https://doi.org/10.1088/1361-651x/abfeaf

    Article  Google Scholar 

  42. Leontiadou, H., Mark, A. E., & Marrink, S. J. (2004). Molecular dynamics simulations of hydrophilic pores in lipid bilayers. Biophysical Journal, 86(4), 2156–2164. https://doi.org/10.1016/S0006-3495(04)74275-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tieleman, D. P., Leontiadou, H., Mark, A. E., & Marrink, S. J. (2003). Simulation of pore formation in lipid bilayers by mechanical stress and electric fields. Journal of the American Chemical Society, 125(21), 6382–6383. https://doi.org/10.1021/ja029504i

    Article  CAS  PubMed  Google Scholar 

  44. Marrink, S. J., De Vries, A. H., & Mark, A. E. (2004). Coarse grained model for semiquantitative lipid simulations. The Journal of Physical Chemistry B, 108(2), 750–760. https://doi.org/10.1021/jp036508g

    Article  CAS  Google Scholar 

  45. Arnarez, C., et al. (2015). Dry martini, a coarse-grained force field for lipid membrane simulations with implicit solvent. Journal of Chemical Theory and Computation, 11(1), 260–275. https://doi.org/10.1021/ct500477k

    Article  CAS  PubMed  Google Scholar 

  46. Hossain, D., Tschopp, M. A., Ward, D. K., Bouvard, J. L., Wang, P., & Horstemeyer, M. F. (2010). Molecular dynamics simulations of deformation mechanisms of amorphous polyethylene. Polymer (Guildf), 51(25), 6071–6083. https://doi.org/10.1016/j.polymer.2010.10.009

    Article  CAS  Google Scholar 

  47. Li, C., Choi, P., & Sundararajan, P. R. (2010). Simulation of chain folding in polyethylene: A comparison of united atom and explicit hydrogen atom models. Polymer (Guildf), 51(13), 2803–2808. https://doi.org/10.1016/j.polymer.2010.04.049

    Article  CAS  Google Scholar 

  48. Henry, A., & Chen, G. (2009). Explicit treatment of hydrogen atoms in thermal simulations of polyethylene. Nanoscale and Microscale Thermophysical Engineering, 13(2), 99–108. https://doi.org/10.1080/15567260902834707

    Article  CAS  Google Scholar 

  49. Alessandri, R., Souza, P. C. T., Thallmair, S., Melo, M. N., De Vries, A. H., & Marrink, S. J. (2019). Pitfalls of the martini model. Journal of Chemical Theory and Computation, 15(10), 5448–5460. https://doi.org/10.1021/acs.jctc.9b00473

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/10.1002/jcc.20291

    Article  CAS  PubMed  Google Scholar 

  51. Abraham, M. J., et al. (2015). Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  52. MacKerell, A. D., et al. (1998). All-atom empirical potential for molecular modeling and dynamics studies of proteins. The Journal of Physical Chemistry B, 102(18), 3586–3616. https://doi.org/10.1021/jp973084f

    Article  CAS  PubMed  Google Scholar 

  53. Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008). CHARMM-GUI: A web-based graphical user interface for CHARMM. Journal of Computational Chemistry, 29(11), 1859–1865. https://doi.org/10.1002/jcc.20945

    Article  CAS  PubMed  Google Scholar 

  54. Lee, J., et al. (2016). CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. Journal of Chemical Theory and Computation, 12(1), 405–413. https://doi.org/10.1021/acs.jctc.5b00935

    Article  CAS  PubMed  Google Scholar 

  55. Berendsen, H. J. C., Postma, J. P. M., Van Gunsteren, W. F., Dinola, A., & Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. The Journal of Chemical Physics, 81(8), 3684–3690. https://doi.org/10.1063/1.448118

    Article  CAS  Google Scholar 

  56. Hoover, W. G. (1985). Canonical dynamics: Equilibrium phase-space distributions. Physical Review A, 31(3), 1695–1697. https://doi.org/10.1103/PhysRevA.31.1695

    Article  CAS  Google Scholar 

  57. Parrinello, M., & Rahman, A. (1981). Polymorphic transitions in single crystals: A new molecular dynamics method. Journal of Applied Physics, 52(12), 7182–7190. https://doi.org/10.1063/1.328693

    Article  CAS  Google Scholar 

  58. Hockney, R. W., Goel, S. P., & Eastwood, J. W. (1974). Quiet high-resolution computer models of a plasma. Journal of Computational Physics, 14(2), 148–158. https://doi.org/10.1016/0021-9991(74)90010-2

    Article  Google Scholar 

  59. Essmann, U., Perera, L., Berkowitz, M. L., Darden, T., Lee, H., & Pedersen, L. G. (1995). A smooth particle mesh Ewald method. The Journal of Chemical Physics, 103(19), 8577–8593. https://doi.org/10.1063/1.470117

    Article  CAS  Google Scholar 

  60. Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/0263-7855(96)00018-5

    Article  CAS  PubMed  Google Scholar 

  61. Stukowski, A. (2010). Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. Modelling and Simulation in Materials Science and Engineering. https://doi.org/10.1088/0965-0393/18/1/015012

    Article  Google Scholar 

  62. M. MATLAB and S. Release, “The MathWorks, Inc., Natick, Massachusetts, United States,” 2016.

  63. Horstemeyer, M. F. (2000). A numerical parametric investigation of localization and forming limits. International Journal of Damage Mechanics, 9(3), 255–285. https://doi.org/10.1177/105678950000900304

    Article  Google Scholar 

  64. Pinisetty, D., Moldovan, D., & Devireddy, R. (2006). The effect of methanol on lipid bilayers: An atomistic investigation. Annals of Biomedical Engineering, 34(9), 1442–1451. https://doi.org/10.1007/s10439-006-9148-y

    Article  CAS  PubMed  Google Scholar 

  65. Benedetto, A., Bingham, R. J., & Ballone, P. (2015). Structure and dynamics of POPC bilayers in water solutions of room temperature ionic liquids. The Journal of Chemical Physics. https://doi.org/10.1063/1.4915918

    Article  PubMed  Google Scholar 

  66. Ong, E. E. S., & Liow, J. L. (2019). The temperature-dependent structure, hydrogen bonding and other related dynamic properties of the standard TIP3P and CHARMM-modified TIP3P water models. Fluid Phase Equilibria, 481, 55–65. https://doi.org/10.1016/J.FLUID.2018.10.016

    Article  CAS  Google Scholar 

Download references

Funding

This material is based upon work supported by the Department of Agricultural and Biological Engineering and the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University.

Author information

Authors and Affiliations

  1. Center for Advanced Vehicular Systems (CAVS), Mississippi State University, 200 Research Blvd, Starkville, MS, 39759, USA

    Anh T. N. Vo, Michael A. Murphy, Phong K. Phan & Tonya W. Stone

  2. Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, Starkville, MS, 39762, USA

    Anh T. N. Vo & Phong K. Phan

  3. NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX, 77058, USA

    Raj K. Prabhu

  4. Department of Mechanical Engineering, Mississippi State University, Mississippi State, Starkville, MS, 39762, USA

    Tonya W. Stone

Authors
  1. Anh T. N. Vo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael A. Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phong K. Phan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raj K. Prabhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tonya W. Stone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anh T. N. Vo.

Ethics declarations

Conflict of interest

The authors declare that they have no financial or non-financial interests that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vo, A.T.N., Murphy, M.A., Phan, P.K. et al. Effect of Force Field Resolution on Membrane Mechanical Response and Mechanoporation Damage under Deformation Simulations. Mol Biotechnol 66, 865–875 (2024). https://doi.org/10.1007/s12033-023-00726-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12033-023-00726-x

Keywords

Search

Navigation