Skip to main content
SpringerLink
Log in

Folding a viral peptide in different membrane environments: pathway and sampling analyses

  • Original Paper
  • Published:
Journal of Biological Physics Aims and scope Submit manuscript

Abstract

Flock House virus (FHV) is a well-characterized model system to study infection mechanisms in non-enveloped viruses. A key stage of the infection cycle is the disruption of the endosomal membrane by a component of the FHV capsid, the membrane active γ peptide. In this study, we perform all-atom molecular dynamics simulations of the 21 N-terminal residues of the γ peptide interacting with membranes of differing compositions. We carry out umbrella sampling calculations to study the folding of the peptide to a helical state in homogenous and heterogeneous membranes consisting of neutral and anionic lipids. From the trajectory data, we evaluate folding energetics and dissect the mechanism of folding in the different membrane environments. We conclude the study by analyzing the extent of configurational sampling by performing time-lagged independent component analysis.

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

Similar content being viewed by others

References

  1. Seelig, J.: Thermodynamics of lipid–peptide interactions. Biochim. Biophys. Acta Biomembr. 1666(1–2), 40–50 (2004). https://doi.org/10.1016/j.bbamem.2004.08.004

  2. Leontiadou, H., Mark, A.E., Marrink, S.J.: Antimicrobial peptides in action. J. Am. Chem. Soc. 128(37), 12156–12161 (2006). https://doi.org/10.1021/ja062927q

  3. Marčelja, S.: Lipid-mediated protein interaction in membranes. Biochim. Biophys. Acta Biomembr. 455(1), 1–7 (1976). https://doi.org/10.1016/0005-2736(76)90149-8

  4. Owicki, J.C., McConnell, H.M.: Theory of protein–lipid and protein–protein interactions in bilayer membranes. Proc. Natl. Acad. Sci. U. S. A 76(10), 4750–4754 (1979). https://doi.org/10.1073/pnas.76.10.4750

  5. Nymeyer, H., Woolf, T.B., Garcia, A.E.: Folding is not required for bilayer insertion: Replica exchange simulations of an α-helical peptide with an explicit lipid bilayer. Proteins. Struct. Funct. Genet. 59(4), 783–790 (2005). https://doi.org/10.1002/prot.20460

  6. Cymer, F., Von, H., White, S.H.: Mechanisms of integral membrane protein insertion and folding. J. Mol. Biol. 427(5), 999–1022 (2015). https://doi.org/10.1016/j.jmb.2014.09.014

  7. Sato, H., Feix, J.B.: Peptide–membrane interactions and mechanisms of membrane destruction by amphipathic α-helical antimicrobial peptides. Biochim. Biophys. Acta Biomembr. 1758(9), 1245–1256 (2006). https://doi.org/10.1016/j.bbamem.2006.02.021

  8. Li, C., Salditt, T.: Structure of magainin and alamethicin in model membranes studied by X-ray reflectivity. Biophys. J 91(9), 3285–3300 (2006). https://doi.org/10.1529/biophysj.106.090118

  9. Schümann, M., Dathe, M., Wieprecht, T., Beyermann, M., Bienert, M.: The tendency of magainin to associate upon binding to phospholipid bilayers. Biochemistry 36(14), 4345–4351 (1997). https://doi.org/10.1021/bi962304x

    Article  Google Scholar 

  10. Leavitt, S., Freire, E.: Direct measurement of protein binding energetics by isothermal titration calorimetry. Curr. Opin. Struct. Biol. 11(5), 560–566 (2001). https://doi.org/10.1016/S0959-440X(00)00248-7

    Article  Google Scholar 

  11. Perrin, B.S., Tian, Y., Fu, R., et al.: High-resolution structures and orientations of antimicrobial peptides piscidin 1 and piscidin 3 in fluid bilayers reveal tilting, kinking, and bilayer immersion. J. Am. Chem. Soc. 136(9), 3491–3504 (2014). https://doi.org/10.1021/ja411119m

    Article  Google Scholar 

  12. Afonin, S., Grage, S.L., Ieronimo, M., Wadhwani, P., Ulrich, A.S.: Temperature-dependent transmembrane insertion of the amphiphilic peptide PGLa in lipid bilayers observed by solid state 19F NMR spectroscopy. J. Am. Chem. Soc. 130(49), 16512–16514 (2008). https://doi.org/10.1021/ja803156d

    Article  Google Scholar 

  13. Ladokhin, A.S., White, S.H.: Folding of amphipathic α-helices on membranes: Energetics of helix formation by melittin. J. Mol. Biol. 285(4), 1363–1369 (1999). https://doi.org/10.1006/jmbi.1998.2346

    Article  Google Scholar 

  14. Wieprecht, T., Seelig, J.: Isothermal titration calorimetry for studying interactions between peptides and lipid membranes. Curr. Top. Membr. 52, 31–56 (2002)

    Article  Google Scholar 

  15. Bechinger, B., Kim, Y., Chirlian, L.E., et al.: Orientations of amphipathic helical peptides in membrane bilayers determined by solid-state NMR spectroscopy. J. Biomol. Nmr. 1(2), 167–173 (1991). https://doi.org/10.1007/BF01877228

    Article  Google Scholar 

  16. Bechinger, B., Gierasch, L.M., Montal, M., Zasloff, M., Opella, S.J.: Orientations of helical peptides in membrane bilayers by solid state NMR spectroscopy. Solid State Nucl. Magn. Reson. 7(3), 185–191 (1996). https://doi.org/10.1016/0926-2040(95)01224-9

    Article  Google Scholar 

  17. Beschiaschvili, G., Seelig, J.: Melittin Binding to Mixed Phosphatidylglycerol/Phosphatidylcholine Membranes. Biochemistry 29(1), 52–58 (1990). https://doi.org/10.1021/bi00453a007

    Article  Google Scholar 

  18. Chen, C.H., Wiedman, G., Khan, A., Ulmschneider, M.B.: Absorption and folding of melittin onto lipid bilayer membranes via unbiased atomic detail microsecond molecular dynamics simulation. Biochim. Biophys. Acta Biomembr. 1838(9), 2243–2249 (2014). https://doi.org/10.1016/j.bbamem.2014.04.012

  19. Von, D., Knecht, V.: Antimicrobial selectivity based on zwitterionic lipids and underlying balance of interactions. Biochim. Biophys. Acta Biomembr. 1818(9), 2192–2201 (2012). https://doi.org/10.1016/j.bbamem.2012.05.012

    Article  Google Scholar 

  20. Irudayam, S.J., Berkowitz, M.L.: Binding and reorientation of melittin in a POPC bilayer: Computer simulations. Biochim. Biophys. Acta Biomembr. 1818(12), 2975–2981 (2012). https://doi.org/10.1016/j.bbamem.2012.07.026

    Article  Google Scholar 

  21. Koehler, L., Ulmschneider, M.B., Gray, J.J.: Computational modeling of membrane proteins. Proteins Struct. Funct. Bioinforma. 83(1), 1–24 (2015). https://doi.org/10.1002/prot.24703

    Article  Google Scholar 

  22. Ash, W.L., Zlomislic, M.R., Oloo, E.O., Tieleman, D.P.: Computer simulations of membrane proteins. Biochim. Biophys. Acta Biomembr. 1666(1-2), 158–189 (2004). https://doi.org/10.1016/j.bbamem.2004.04.012

    Article  Google Scholar 

  23. Andersson, M., Ulmschneider, J.P., Ulmschneider, M.B., White, S.H.: Conformational states of melittin at a bilayer interface. Biophys. J 104(6), L12–L14 (2013). https://doi.org/10.1016/j.bpj.2013.02.006

    Article  Google Scholar 

  24. Bereau, T., Bennett, W.F.D., Pfaendtner, J., Deserno, M., Karttunen, M.: Folding and insertion thermodynamics of the transmembrane WALP peptide. J. Chem. Phys. 143, 24 (2015). https://doi.org/10.1063/1.4935487

    Article  Google Scholar 

  25. Im, W., Brooks III, C.L.: Interfacial folding and membrane insertion of designed peptides studied by molecular dynamics simulations. Proc. Natl. Acad. Sci. 102(19), 6771–6776 (2005). https://doi.org/10.1073/pnas.0408135102

    Article  ADS  Google Scholar 

  26. Tieleman, D.P., Berendsen, H.J.C., Sansom, M.S.P.: Surface binding of alamethicin stabilizes its helical structure: Molecular dynamics simulations. Biophys. J 76(6), 3186–3191 (1999). https://doi.org/10.1016/S0006-3495(99)77470-9

    Article  Google Scholar 

  27. Lin, D., Grossfield, A.: Thermodynamics of antimicrobial lipopeptide binding to membranes: Origins of affinity and selectivity. Biophys. J 107(8), 1862–1872 (2014). https://doi.org/10.1016/j.bpj.2014.08.026

    Article  Google Scholar 

  28. Lindahl, E., Sansom, M.S.: Membrane proteins: molecular dynamics simulations. Curr. Opin. Struct. Biol. 18(4), 425–431 (2008). https://doi.org/10.1016/j.sbi.2008.02.003

    Article  Google Scholar 

  29. Ward, M.D., Nangia, S., May, E.R.: Evaluation of the hybrid resolution PACE model for the study of folding, insertion, and pore formation of membrane associated peptides. J. Comput. Chem. 38(16), 1462–1471 (2017). https://doi.org/10.1002/jcc.24694

    Article  Google Scholar 

  30. Shai, Y.: Mode of action of membrane active antimicrobial peptides. Biopolym. - Pept. Sci. Sect. 66(4), 236–248 (2002). https://doi.org/10.1002/bip.10260

    Article  Google Scholar 

  31. Shai, Y.: Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by α-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim. Biophys. Acta Biomembr. 1462(1–2), 55–70 (1999). https://doi.org/10.1016/S0005-2736(99)00200-X

    Article  Google Scholar 

  32. Yuan, T., Zhang, X., Hu, Z., Wang, F., Lei, M.: Molecular dynamics studies of the antimicrobial peptides piscidin 1 and its mutants with a DOPC lipid bilayer. Biopolymers 97(12), 998–1009 (2012). https://doi.org/10.1002/bip.22116

    Article  Google Scholar 

  33. Rahmanpour, A., Ghahremanpour, M.M., Mehrnejad, F., Moghaddam, M.E.: Interaction of Piscidin-1 with zwitterionic versus anionic membranes: A comparative molecular dynamics study. J. Biomol. Struct. Dyn. 31(12), 1393–1403 (2013). https://doi.org/10.1080/07391102.2012.737295

    Article  Google Scholar 

  34. Tieleman, D.P., Sansom, M.S.P., Berendsen, H.J.C.: Alamethicin helices in a bilayer and in solution: Molecular dynamics simulations. Biophys. J 76(1 I), 40–49 (1999)

    Article  Google Scholar 

  35. Perrin, B.S., Pastor, R.W.: Simulations of membrane-disrupting peptides I: alamethicin pore stability and spontaneous insertion. Biophys. J 111(6), 1248–1257 (2016). https://doi.org/10.1016/j.bpj.2016.08.014

    Article  Google Scholar 

  36. Perrin, B.S., Fu, R., Cotten, M.L., Pastor, R.W.: Simulations of membrane-disrupting peptides II: AMP piscidin 1 favors surface defects over pores. Biophys. J 111(6), 1258–1266 (2016). https://doi.org/10.1016/j.bpj.2016.08.015

    Article  Google Scholar 

  37. Nangia, S., May, E.R.: Influence of membrane composition on the binding and folding of a membrane lytic peptide from the non-enveloped flock house virus. Biochim. Biochim. Biophys. Acta Biomembr. 1859(7), 1190–1199 (2017). https://doi.org/10.1016/j.bbamem.2017.04.002

  38. Bong, D.T., Steinem, C., Janshoff, A., Johnson, J.E., Ghadiri, M.R.: A highly membrane-active peptide in flock house virus: Implications for the mechanism of nodavirus infection. Chem. Biol. 6(7), 473–481 (1999). https://doi.org/10.1016/S1074-5521(99)80065-9

    Article  Google Scholar 

  39. Sugita, Y., Okamoto, Y.: Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314(1–2), 141–151 (1999)

    Article  ADS  Google Scholar 

  40. Gallicchio, E., Levy, R.M., Parashar, M.: Asynchronous replica exchange for molecular simulations. J. Comput. Chem. 29(5), 788–794 (2008). https://doi.org/10.1002/jcc.20839

    Article  Google Scholar 

  41. Periole, X., Mark, A.E.: Convergence and sampling efficiency in replica exchange simulations of peptide folding in explicit solvent. J. Chem. Phys. 126, 1 (2007). https://doi.org/10.1063/1.2404954

    Article  Google Scholar 

  42. Lee, K.H., Chen, J.: Multiscale enhanced sampling of intrinsically disordered protein conformations. J. Comput. Chem. 37(6), 550–557 (2016). https://doi.org/10.1002/jcc.23957

    Article  Google Scholar 

  43. Faradjian, A.K., Elber, R.: Computing time scales from reaction coordinates by milestoning. J. Chem. Phys. 120(23), 10880–10889 (2004). https://doi.org/10.1063/1.1738640

    Article  ADS  Google Scholar 

  44. Allen, R.J., Warren, P.B., Ten, W.: Sampling rare switching events in biochemical networks. Phys. Rev. Lett. 94, 1 (2005). https://doi.org/10.1103/PhysRevLett.94.018104

    Article  Google Scholar 

  45. Dellago, C., Bolhuis, P.G., Csajka, F.S., Chandler, D.: Transition path sampling and the calculation of rate constants. J. Chem. Phys. 108(5), 1964 (1998). https://doi.org/10.1063/1.475562

    Article  ADS  Google Scholar 

  46. Grubmller, H.: Predicting slow structural transitions in macromolecular systems: conformational flooding. Phys. Rev. E 52(3), 2893–2906 (1995). https://doi.org/10.1103/PhysRevE.52.2893

    Article  ADS  Google Scholar 

  47. Laio A, Gervasio FL. Metadynamics: A method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys. 2008;71(12). https://doi.org/10.1088/0034-4885/71/12/126601.

  48. Torrie, G.M., Valleau, J.P.: Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 23(2), 187–199 (1977). https://doi.org/10.1016/0021-9991(77)90121-8

    Article  ADS  Google Scholar 

  49. Young, W.S., Brooks III, C.L.: A microscopic view of helix propagation: N and C-terminal helix growth in alanine helices. J. Mol. Biol. 259(3), 560–572 (1996). https://doi.org/10.1006/jmbi.1996.0339

    Article  Google Scholar 

  50. Bursulaya, B.D., Brooks III, C.L.: Folding free energy surface of a three-stranded β-sheet protein. J. Am. Chem. Soc. 121(43), 9947–9951 (1999). https://doi.org/10.1021/ja991764l

    Article  Google Scholar 

  51. Mahdavi, S., Kuyucak, S.: Why the Drosophila shaker K+ channel is not a good model for ligand binding to voltage-gated Kv1 channels. Biochemistry 52(9), 1631–1640 (2013). https://doi.org/10.1021/bi301257p

    Article  Google Scholar 

  52. Vijayaraj, R., Van, D., Bultinck, P., Subramanian, V.: Molecular dynamics and umbrella sampling study of stabilizing factors in cyclic peptide-based nanotubes. J. Phys. Chem. B 116(33), 9922–9933 (2012). https://doi.org/10.1021/jp303418a

    Article  Google Scholar 

  53. Yesudhas, D., Anwar, M.A., Panneerselvam, S., Kim, H.-K., Choi, S.: Evaluation of Sox2 binding affinities for distinct DNA patterns using steered molecular dynamics simulation. FEBS Open Bio 7(11), 1750–1767 (2017). https://doi.org/10.1002/2211-5463.12316

    Article  Google Scholar 

  54. Vermaas, J.V., Tajkhorshid, E.: Differential membrane binding mechanics of synaptotagmin isoforms observed in atomic detail. Biochemistry 56(1), 281–293 (2017). https://doi.org/10.1021/acs.biochem.6b00468

    Article  Google Scholar 

  55. Mascarenhas, N.M., Kästner, J.: How maltose influences structural changes to bind to maltose-binding protein: results from umbrella sampling simulation. Proteins Struct. Funct. Bioinforma. 81(2), 185–198 (2013). https://doi.org/10.1002/prot.24174

    Article  Google Scholar 

  56. Patrascu, M.B., Malek-Adamian, E., Damha, M.J., Moitessier, N.: Accurately modeling the conformational preferences of nucleosides. J. Am. Chem. Soc. 139(39), 13620–13623 (2017). https://doi.org/10.1021/jacs.7b07436

    Article  Google Scholar 

  57. Schaefer, M., Bartels, C., Karplus, M.: Solution conformations and thermodynamics of structured peptides: molecular dynamics simulation with an implicit solvation model. J. Mol. Biol. 284(3), 835–848 (1998). https://doi.org/10.1006/jmbi.1998.2172

    Article  Google Scholar 

  58. Banerjee, M., Johnson, J.E.: Activation, exposure and penetration of virally encoded, membrane-active polypeptides during non-enveloped virus entry. Curr. Protein Pept. Sci. 9(1), 16–27 (2008). https://doi.org/10.2174/138920308783565732

  59. Kumar, C., Dey, D., Ghosh, S., Banerjee, M.: Breach: Host membrane penetration and entry by nonenveloped viruses. Cell Press Rev. (Trends In Microbiology). https://doi.org/10.1016/j.tim.2017.09.010

  60. Lewis, J.R., Cafiso, D.S.: Correlation between the free energy of a channel-forming voltage-gated peptide and the spontaneous curvature of bilayer lipids. Biochemistry 38(18), 5932–5938 (1999). https://doi.org/10.1021/bi9828167

    Article  Google Scholar 

  61. Bulet, P., Hetru, C., Dimarcq, J.-L., Hoffmann, D.: Antimicrobial peptides in insects; structure and function. Dev. Comp. Immunol. 23(4-5), 329–344 (1999). https://doi.org/10.1016/S0145-305X(99)00015-4

    Article  Google Scholar 

  62. Banerjee, M., Khayat, R., Walukiewicz, H.E., Odegard, A.L., Schneemann, A., Johnson, J.E.: Dissecting the functional domains of a nonenveloped virus membrane penetration peptide. J. Virol. 83(13), 6929–6933 (2009). https://doi.org/10.1128/JVI.02299-08

    Article  Google Scholar 

  63. Bajaj, S., Dey, D., Bhukar, R., Kumar, M., Banerjee, M.: Non-enveloped virus entry: structural determinants and mechanism of functioning of a viral lytic peptide. J. Mol. Biol. 428(17), 3540–3556 (2016). https://doi.org/10.1016/j.jmb.2016.06.006

    Article  Google Scholar 

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

    Article  ADS  Google Scholar 

  65. Best, R.B., Zhu, X., Shim, J., et al.: Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ 1 and χ 2 Dihedral Angles. J. Chem. Theory Comput. 8(9), 3257–3273 (2012). https://doi.org/10.1021/ct300400x

  66. Klauda, J.B., Venable, R.M., Freites, J.A., et al.: Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114(23), 7830–7843 (2010). https://doi.org/10.1021/jp101759q

    Article  Google Scholar 

  67. Essmann, U., Perera, L., Berkowitz, M.L., Darden, T., Lee, H., Pedersen, L.G.: A smooth particle mesh Ewald method. J. Chem. Phys. 103(19), 8577–8593 (1995)

    Article  ADS  Google Scholar 

  68. Humphrey, W., Dalke, A., Schulten, K.: VMD: Visual molecular dynamics. J. Mol. Graph. 14(1), 33–38 (1996). https://doi.org/10.1016/0263-7855(96)00018-5

    Article  Google Scholar 

  69. Tribello, G.A., Bonomi, M., Branduardi, D., Camilloni, C., Bussi, G.: PLUMED 2: New feathers for an old bird. Comput. Phys. Commun. 185(2), 604–613 (2014). https://doi.org/10.1016/j.cpc.2013.09.018

    Article  ADS  Google Scholar 

  70. Bonomi, M., Branduardi, D., Bussi, G., et al.: PLUMED: A portable plugin for free-energy calculations with molecular dynamics. Comput. Phys. Commun. 180(10), 1961–1972 (2009). https://doi.org/10.1016/j.cpc.2009.05.011

    Article  ADS  Google Scholar 

  71. Pietrucci, F., Laio, A.: A collective variable for the efficient exploration of protein beta-sheet structures: application to SH3 and GB1. J. Chem. Theory Comput. 5(9), 2197–2201 (2009). https://doi.org/10.1021/ct900202f

    Article  Google Scholar 

  72. Roux, B.: The calculation of the potential of mean force using computer simulations. Comput. Phys. Commun. 91(1-3), 275–282 (1995). https://doi.org/10.1016/0010-4655(95)00053-I

    Article  ADS  Google Scholar 

  73. Grossfield A. Grossfield, Alan, “WHAM: the weighted histogram analysis method”, version.

  74. McGibbon, R.T., Beauchamp, K.A., Harrigan, M.P., et al.: MDTraj: A modern open library for the analysis of molecular dynamics trajectories. Biophys. J 109(8), 1528–1532 (2015). https://doi.org/10.1016/j.bpj.2015.08.015

    Article  Google Scholar 

  75. Husic BE, McGibbon RT, Sultan MM, Pande VS. Optimized parameter selection reveals trends in Markov state models for protein folding. J. Chem. Phys. 2016;145(19). https://doi.org/10.1063/1.4967809.

  76. Pérez-Hernández G, Paul F, Giorgino T, De Fabritiis G, Noé F. Identification of slow molecular order parameters for Markov model construction. J. Chem. Phys. 2013;139(1). https://doi.org/10.1063/1.4811489.

  77. Schwantes, C.R., Pande, V.S.: Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J. Chem. Theory Comput. 9(4), 2000–2009 (2013). https://doi.org/10.1021/ct300878a

    Article  Google Scholar 

  78. Wu H, Mey ASJS, Rosta E, Noé F. Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states. J. Chem. Phys. 2014;141(21). https://doi.org/10.1063/1.4902240.

  79. Prinz JH, Wu H, Sarich M, et al. Markov models of molecular kinetics: generation and validation. J. Chem. Phys. 2011;134(17). https://doi.org/10.1063/1.3565032.

  80. Hills, R.D., Brooks III, C.L.: Subdomain competition, cooperativity, and topological frustration in the folding of CheY. J. Mol. Biol. (2008). https://doi.org/10.1016/j.jmb.2008.07.007

  81. Wu H, Paul F, Wehmeyer C, Noé F. Multiensemble Markov models of molecular thermodynamics and kinetics. 2016. https://doi.org/10.1073/pnas.1525092113.

  82. Jo, S., Suh, D., He, Z., Chipot, C., Roux, B.: Leveraging the information from Markov state models to improve the convergence of umbrella sampling simulations. J. Phys. Chem. B 120(33), 8733–8742 (2016). https://doi.org/10.1021/acs.jpcb.6b05125

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by the National Institutes of Health through grant R35GM119762 to E.R.M. Computational resources have been provided through the University of Connecticut Hornet HPC cluster and NSF XSEDE program (grant number TG-MCB140016). We thank Kevin Boyd for his critical reading and providing constructive suggestions on this manuscript.

Funding

This study was funded by NIH (grant number R35GM119762).

Author information

Authors and Affiliations

  1. Department of Molecular and Cell Biology, University of Connecticut, Storrs, CT, 06269, USA

    Shivangi Nangia, Jason G. Pattis & Eric R. May

Authors
  1. Shivangi Nangia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason G. Pattis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eric R. May
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eric R. May.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

ESM 1

(DOCX 1.92 mb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nangia, S., Pattis, J.G. & May, E.R. Folding a viral peptide in different membrane environments: pathway and sampling analyses. J Biol Phys 44, 195–209 (2018). https://doi.org/10.1007/s10867-018-9490-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10867-018-9490-y

Keywords

Search

Navigation