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Setting Up All-Atom Molecular Dynamics Simulations to Study the Interactions of Peripheral Membrane Proteins with Model Lipid Bilayers

  • Viviana Monje-GalvanEmail author
  • Linnea Warburton
  • Jeffery B. Klauda
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1949)

Abstract

All-atom molecular dynamics (MD) simulations enable the study of biological systems at atomic detail, complement the understanding gained from experiment, and can also motivate experimental techniques to further examine a given biological process. This method is based on statistical mechanics; it predicts the trajectory of atoms over time by solving Newton’s Laws of motion taking into account all forces. Here, we describe the use of this methodology to study the interaction between peripheral membrane proteins and a lipid bilayer. Specifically, we provide step-by-step instructions to set up MD simulations to study the binding and interaction of the amphipathic helix of Osh4, a lipid transport protein, and Thanatin, an antimicrobial peptide (AMP), with model lipid bilayers using both fully detailed lipid tails and the highly mobile membrane-mimetic (HMMM) method to enhance conformational sampling.

Key words

Peripheral membrane proteins Lipid bilayers Amphipathic helices (AHs) Antimicrobial peptides (AMPs) All-atom molecular dynamics 

Notes

Acknowledgments

The AAMD and HMMM method discussed here were used to study the binding mechanism of ALPS to model membranes, results are presented in [11, 40], respectively; the Thanatin system is currently under study. These simulation trajectories were partially supported by NSF grant DBI-1145652 and MCB-1149187 and the High Performance Deepthought & Deepthought 2 Computing Clusters at the University of Maryland, College Park administered by the Division of Information Technology. The AAMD runs were possible thanks to time on the Anton Computer provided by the Pittsburgh Supercomputing Center (PSC) through Grant R01GM116961 from the National Institutes of Health and our specific time associated with the grant PSCA14030P. The Anton machine at PSC was generously made available by D.E. Shaw Research.

References

  1. 1.
    Leach AR (ed) (2001) Molecular modeling principles and applications, 2nd edn. Great Britain, Pearson EducationGoogle Scholar
  2. 2.
    Stryer L (ed) (1989) Molecular design of life. W.H. Freeman and Company, New York, USAGoogle Scholar
  3. 3.
    Kalli AC, Sansom MSP (2014) Interactions of peripheral proteins with model membranes as viewed by molecular dynamics simulations. Biochem Soc Trans 42:1418–1424CrossRefGoogle Scholar
  4. 4.
    Monje-Galvan V, Klauda JB (2016) Peripheral membrane proteins: Tying the knot between experiment and computation. Biochim Biophys Acta 1858:1584–1593CrossRefGoogle Scholar
  5. 5.
    Mori T, Miyashita N, Im W et al (2016) Molecular dynamics simulations of biological membranes and membrane proteins using enhanced conformational sampling algorithms. Biochim Biophys Acta 1858:1635–1651CrossRefGoogle Scholar
  6. 6.
    Barducci A, Bonomi M, Parrinello M (2016) Metadynamics. Wiley Interdiscip Rev Comput Mol Sci 1:826–843CrossRefGoogle Scholar
  7. 7.
    Doucet CM, Esmery N, de Saint-Jean M et al (2015) Membrane curvature sensing by amphipathic helices is modulated by the surrounding protein backbone. PLoS One 10:e0137965CrossRefGoogle Scholar
  8. 8.
    Cui H, Mim C, Vazquez FX et al (2013) Understanding the role of amphipathic helices in N-BAR domain driven membrane remodeling. Biophys J 104:404–411CrossRefGoogle Scholar
  9. 9.
    Sinha S, Zheng L, Mu Y et al (2017) Structure and interactions of a host defense antimicrobial peptide thanatin in lipopolysaccharide micelles reveal mechanism of bacterial cell agglutination. Nat Sci Rep 7:17795CrossRefGoogle Scholar
  10. 10.
    Drin G, Casella J-F, Gautier R et al (2007) A general amphipathic alpha-helical motif for sensing membrane curvature. Nat Struct Mol Biol 14:138–146CrossRefGoogle Scholar
  11. 11.
    Monje-Galvan V, Klauda JB (2018) Preferred binding mechanism of Osh4's amphipathic lipid-packing sensor motif, insights from molecular dynamics. J Phys Chem B 122:9713–9723CrossRefGoogle Scholar
  12. 12.
    Ohkubo YZ, Pogorelov TV, Arcario MJ et al (2012) Accelerating membrane insertion of peripheral proteins with a novel membrane mimetic model. Biophys J 102:2130–2139CrossRefGoogle Scholar
  13. 13.
    Mandard N, Sodano P, Labbe H et al (1998) Solution structure of thanatin, a potent bactericidal and fungicidal insect peptide, determined from protein two-dimensional nuclear magnetic resonance data. Eur J Biochem 256:404–410CrossRefGoogle Scholar
  14. 14.
    Jo S, Kim T, Iyer VG et al (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem 29:1859–1865CrossRefGoogle Scholar
  15. 15.
    Lee J, Cheng X, Swails JM et al (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput 12:405–413CrossRefGoogle Scholar
  16. 16.
    Qi YF, Cheng X, Lee J et al (2015) CHARMM-GUI HMMM builder for membrane simulations with the highly mobile membrane-mimetic model. Biophys J 109:2012–2022CrossRefGoogle Scholar
  17. 17.
    Wu EL, Cheng X, Jo S et al (2014) CHARMM-GUI membrane builder toward realistic biological membrane simulations. J Comput Chem 35:1997–2004CrossRefGoogle Scholar
  18. 18.
    Klauda JB, Venable RM, Freites JA et al (2010) Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J Phys Chem B 114:7830–7843CrossRefGoogle Scholar
  19. 19.
    Lim JB, Rogaski B, Klauda JB (2012) Update of the cholesterol force field parameters in CHARMM. J Phys Chem B 116:203–210CrossRefGoogle Scholar
  20. 20.
    Venable RM, Sodt AJ, Rogaski B et al (2014) CHARMM all-atom additive force field for sphingomyelin: elucidation of hydrogen bonding and of positive curvature. Biophys J 107:134–145CrossRefGoogle Scholar
  21. 21.
    Li Z, Venable RM, Rogers LA et al (2009) Molecular dynamics simulations of PIP2 and PIP3 in lipid bilayers: determination of ring orientation, and the effects of surface roughness on a Poisson-Boltzmann description. Biophys J 97:155–163CrossRefGoogle Scholar
  22. 22.
    Huang J, Rauscher S, Nawrocki G et al (2016) CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods 14:71–73CrossRefGoogle Scholar
  23. 23.
    Reif MM, Winger M, Oostenbrink C (2013) Testing of the GROMOS force-field parameter set 54A8: structural properties of electrolyte solutions, lipid bilayers, and proteins. J Chem Theory Comput 9:1247–1264CrossRefGoogle Scholar
  24. 24.
    Harder E, Damm W, Maple J et al (2016) OPLS3: a force field providing broad coverage of drug-like small molecules and proteins. J Chem Theory Comput 12:281–296CrossRefGoogle Scholar
  25. 25.
    Dickson CJ, Madej BD, Skjevik ÅA et al (2014) Lipid14: the amber lipid force field. J Chem Theory Comput 10:865–879CrossRefGoogle Scholar
  26. 26.
    Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  27. 27.
    Brooks BR, Brooks CL, Mackerell JAD et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614CrossRefGoogle Scholar
  28. 28.
    Phillips JC, Braun R, Wang W et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802CrossRefGoogle Scholar
  29. 29.
    Pronk S, Pall S, Schulz R et al (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854CrossRefGoogle Scholar
  30. 30.
    Case DA, Betz RM, Botello-Smith W et al (2016) AMBER16. University of California, San FranciscoGoogle Scholar
  31. 31.
    Humphrey W, Dalke A, Schulten K (1996) VMD: Visual molecular dynamics. J Mol Graph 14:33–38CrossRefGoogle Scholar
  32. 32.
    Pettersen EF, Goddard TD, Huang CC et al (2004) Chimera - a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612CrossRefGoogle Scholar
  33. 33.
    Martinez L, Andrade R, Birgin EG et al (2009) PACKMOL: a package for building initial configurations for molecular dynamics simulations. J Comput Chem 30:2157–2164CrossRefGoogle Scholar
  34. 34.
    Jo S, Lim JB, Klauda JB et al (2009) CHARMM-GUI Membrane Builder for mixed bilayers and its application to yeast membranes. Biophys J 97:50–58CrossRefGoogle Scholar
  35. 35.
    Monje-Galvan V, Klauda JB (2015) Modelling yeast organelle membranes and how lipid diversity influences bilayer properties. Biochemistry 54:6852–6861CrossRefGoogle Scholar
  36. 36.
    Boughter CT, Monje-Galvan V, Im W et al (2016) Influence of cholesterol on phospholipid bilayer structure and dynamics. J Phys Chem B 120:11761–11772CrossRefGoogle Scholar
  37. 37.
    Khakbaz P, Monje-Galvan V, Zhuang X et al (2017) Modeling lipid membranes. In: Geiger O (ed) Biogenesis of fatty acids, lipids and membranes. Springer International Publishing, Cham, pp 1–19Google Scholar
  38. 38.
    Marquardt D, Geier B, Pabst G (2015) Asymmetric lipid membranes: towards more realistic model systems. Membranes 5:180–196CrossRefGoogle Scholar
  39. 39.
    Ingólfsson HI, Melo MN, van Eerden FJ et al (2014) Lipid organization of the plasma membrane. J Am Chem Soc 136:14554–14559CrossRefGoogle Scholar
  40. 40.
    Wildermuth KD, Monje-Galvan V, Warburton LM et al (2018) Effect of membrane lipid packing on stable binding of the ALPS peptide. J Chem Theory Comput. AcceptedGoogle Scholar
  41. 41.
    Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  42. 42.
    Hoover WG (1985) Canonical dynamics: equilibrium phase-space distributions. Phys Rev A 31:1695–1697CrossRefGoogle Scholar
  43. 43.
    Frenkel D, Smit B (2001) Understanding molecular simulation from algorithms to applications, 2nd edn. Academic Press, CambridgeGoogle Scholar
  44. 44.
    Feller SE, Zhang Y, Pastor RW et al (1995) Constant pressure molecular dynamics simulation: the Langevin piston method. J Chem Phys 103:4613–4621CrossRefGoogle Scholar
  45. 45.
    Martyna GJ, Tobias DJ, Klein ML (1994) Constant pressure molecular dynamics algorithms. J Chem Phys 101:4177–4189CrossRefGoogle Scholar
  46. 46.
    Steinbach PJ, Brooks BR (1994) New spherical-cutoff methods for long-range forces in macromolecular simulation. J Comput Chem 15:667–683CrossRefGoogle Scholar
  47. 47.
    Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N·log(N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092CrossRefGoogle Scholar
  48. 48.
    Jo S, Kim T, Im W (2007) Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS One 2:e880CrossRefGoogle Scholar
  49. 49.
    Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM Trans Math Softw 22:469–483CrossRefGoogle Scholar
  50. 50.
    Allen WJ, Lemkul JA, Bevan DR (2009) GridMAT-MD: a grid-based membrane analysis tool for use with molecular dynamics. J Comput Chem 30:1952–1958CrossRefGoogle Scholar
  51. 51.
    Shaw DE, Deneroff MM, Dror RO et al (2008) Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM 51:91–97CrossRefGoogle Scholar
  52. 52.
    Castro-Román F, Benz RW, White SH et al (2006) Investigation of finite system-size effects in molecular dynamics simulations of lipid bilayers. J Phys Chem B 110:24157–24161CrossRefGoogle Scholar
  53. 53.
    Vanni S, Hirose H, Barelli H et al (2014) A sub-nanometre view of how membrane curvature and composition modulate lipid packing and protein recruitment. Nat Commun 5:4916CrossRefGoogle Scholar
  54. 54.
    Robert E, Lefèvre T, Fillion M et al (2015) Mimicking and understanding the agglutination effect ofthe antimicrobial peptide thanatin using model phospholipid vesicles. Biochemistry 54:3932–3941CrossRefGoogle Scholar
  55. 55.
    Ikeguchi M (2004) Partial rigid-body dynamics in NPT, NPAT, and NPγT ensembles for proteins and membranes. J Comput Chem 25:529–541CrossRefGoogle Scholar
  56. 56.
    Vanni S, Vamparys L, Gautier R et al (2013) Amphipathic lipid packing sensor motifs: probing bilayer defects with hydrophobic residues. Biophys J 104:575–584CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Viviana Monje-Galvan
    • 1
    • 2
    Email author
  • Linnea Warburton
    • 2
  • Jeffery B. Klauda
    • 2
    • 3
  1. 1.Department of ChemistryThe University of ChicagoChicagoUSA
  2. 2.Department of Chemical and Biomolecular EngineeringUniversity of MarylandCollege ParkUSA
  3. 3.Biophysics ProgramUniversity of MarylandCollege ParkUSA

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