Probing How Defects in Self-assembled Monolayers Affect Peptide Adsorption with Molecular Simulation

Chapter

Abstract

Due to their flexible chemical functionality and simple formulation, self-assembled monolayer (SAM) surfaces have become an ideal choice for a multitude of wide-ranging applications. However, a major issue in the preparation of SAM surfaces is naturally occurring defects that manifest in a number of different ways, including depressions in the underlying gold substrate that cause surface roughness or through incorrect self-assembly of the chains that causes domain boundary effects. Molecular simulations can provide valuable insight into the origins of these defects and the effect they have on biological and other processes. Molecular dynamics (MD) simulations have been performed on a SAM surface with a carboxylic acid/carboxylate terminal functionality and induced with two types of experimentally observed defects. The enhanced sampling method PTMetaD-WTE has been used to model the adsorption of LKα14 onto the two types of defective SAM surfaces and onto a control SAM surface with no defective chains. An advanced clustering algorithm has been used to elucidate the effect of the surface defects on the conformations of the adsorbed peptide. Results show significant structural differences arise as a result of the defects. Specifically, both types of defects lead to a near-complete loss of secondary structure of the adsorbed peptide as compared to the control simulation, in which LKα14 adopts a perfect helical structure at the SAM/water interface. On the surface with domain boundary effects, extended conformations of the peptide are stabilized, whereas on the SAM with surface roughness (i.e., chains of various lengths), random coil conformations dominate the ensemble of surface-bound structures.

Keywords

Self-assembled monolayers Surface defects Peptide adsorption Molecular dynamics Enhanced sampling 

References

  1. 1.
    McDermott, C.A., McDermott, M.T., Green, J.-B., Porter, M.D.: Structural origins of the surface depressions at alkanethiolate monolayers on Au(111): a scanning tunneling and atomic force microscopic investigation. J. Phys. Chem. 99, 13257–13267 (1995)CrossRefGoogle Scholar
  2. 2.
    Noh, J., Hara, M.: Molecular-scale desorption processes and the alternating missing-row phase of alkanethiol self-assembled monolayers on Au(111). Langmuir 17, 7280–7285 (2001)CrossRefGoogle Scholar
  3. 3.
    Godin, M., Williams, P.J., Tabard-Cossa, V., Laroche, O., Beaulieu, L.Y., Lennox, R.B., Grütter, P.: Surface stress, kinetics, and Structure of alkanethiol self-assembled monolayers. Langmuir 20, 7090–7096 (2004)CrossRefGoogle Scholar
  4. 4.
    Gannon, G., Greer, J.C., Larsson, J.A., Thompson, D.: Molecular dynamics study of naturally occurring defects in self-assembled monolayer formation. ACS Nano 4, 921–932 (2010)CrossRefGoogle Scholar
  5. 5.
    Vemparala, S., Karki, B.B., Kalia, R.K., Nakano, A., Vashishta, P.: Large-scale molecular dynamics simulations of alkanethiol self-assembled monolayers. J. Chem. Phys. 121, 4323–4330 (2004)CrossRefGoogle Scholar
  6. 6.
    Prathima, N., Harini, M., Rai, N., Chandrashekara, R.H., Ayappa, K.G., Sampath, S., Biswas, S.K.: thermal study of accumulation of conformational disorders in the self-assembled monolayers of C8 and C18 alkanethiols on the Au(111) surface. Langmuir 21, 2364–2374 (2005)CrossRefGoogle Scholar
  7. 7.
    Jiang, L., Sangeeth, C.S.S., Yuan, L., Thompson, D., Nijhuis, C.A.: One-nanometer thin monolayers remove the deleterious effect of substrate defects in molecular tunnel junctions. Nano Lett. (2015)Google Scholar
  8. 8.
    O’Mahony, S., O’Dwyer, C., Nijhuis, C.A., Greer, J.C., Quinn, A.J., Thompson, D.: Nanoscale dynamics and protein adhesivity of alkylamine self-assembled monolayers on graphene. Langmuir 29, 7271–7282 (2013)CrossRefGoogle Scholar
  9. 9.
    Ahn, Y., Saha, J.K., Schatz, G.C., Jang, J.: Molecular dynamics study of the formation of a self-assembled monolayer on gold. J. Phys. Chem. C 115, 10668–10674 (2011)CrossRefGoogle Scholar
  10. 10.
    Deighan, M., Bonomi, M., Pfaendtner, J.: Efficient simulation of explicitly solvated proteins in the well-tempered ensemble. JCTC 8, 2189–2192 (2012)Google Scholar
  11. 11.
    Deighan, M., Pfaendtner, J.: Exhaustively sampling peptide adsorption with metadynamics. Langmuir 29, 7999–8009 (2013)CrossRefGoogle Scholar
  12. 12.
    Levine, Z.A., Fischer, S.A., Shea, J.-E., Pfaendtner, J.: Trp-Cage folding on organic surfaces. J. Phys. Chem. B. 119, 10417–10425 (2015)CrossRefGoogle Scholar
  13. 13.
    DeGrado, W.F., Lear, J.D.: Induction of peptide conformation at apolar water interfaces. 1. a study with model peptides of defined hydrophobic periodicity. J. Am. Chem. Soc. 107, 7684–7689 (1985)CrossRefGoogle Scholar
  14. 14.
    Humphrey, W., Dalke, A., Schulten, K.: VMD: visual molecular dynamics. J. Mol. Graphics. 14, 33–38 (1996)CrossRefGoogle Scholar
  15. 15.
    Weidner, T., Samuel, N.T., McCrea, K., Gamble, L.J., Ward, R.S., Castner, D.G.: Assembly and structure of α-helical peptide films on hydrophobic fluorocarbon surfaces. Biointerphases 5, 9–16 (2010)CrossRefGoogle Scholar
  16. 16.
    Weidner, T., Apte, J.S., Gamble, L.J., Castner, D.G.: Probing the orientation and conformation of α-helix and β-strand model peptides on self-assembled monolayers using sum frequency generation and nexafs spectroscopy. Langmuir 26, 3433–3440 (2010)CrossRefGoogle Scholar
  17. 17.
    Mermut, O., Phillips, D.C., York, R.L., McCrea, K.R., Ward, R.S., Somorjai, Ga: In situ adsorption studies of a 14-amino acid leucine-lysine peptide onto hydrophobic polystyrene and hydrophilic silica surfaces using quartz crystal microbalance, atomic force microscopy, and sum frequency generation vibrational spectroscopy. J. Am. Chem. Soc. 128, 3598–3607 (2006)CrossRefGoogle Scholar
  18. 18.
    York, R.L., Browne, W.K., Geissler, P.L., Somorjai, G.A.: Peptides adsorbed on hydrophobic surfaces—a sum frequency generation vibrational spectroscopy and modeling study. Isr. J. Chem. 47, 51–58 (2007)CrossRefGoogle Scholar
  19. 19.
    York, R.L., Mermut, O., Phillips, D.C., McCrea, K.R., Ward, R.S., Somorjai, G.A.: Influence of ionic strength on the adsorption of a model peptide on hydrophilic silica and hydrophobic polystyrene surfaces: insight from SFG vibrational spectroscopy. J. Phys. Chem. C 111, 8866–8871 (2007)CrossRefGoogle Scholar
  20. 20.
    Apte, J.S., Gamble, L.J., Castner, D.G., Campbell, C.T.: Kinetics of leucine-lysine peptide adsorption and desorption at -CH3 and -COOH terminated alkylthiolate monolayers. Biointerphases 5, 97–104 (2010)CrossRefGoogle Scholar
  21. 21.
    Long, J.R., Oyler, N., Drobny, G.P., Stayton, P.S.: Assembly of α-helical peptide coatings on hydrophobic surfaces. J. Am. Chem. Soc. 124, 6297–6303 (2002)CrossRefGoogle Scholar
  22. 22.
    Phillips, D.C., York, R.L., Mermut, O., McCrea, K.R., Ward, R.S., Somorjai, G.A.: Side chain, chain length, and sequence effects on amphiphilic peptide adsorption at hydrophobic and hydrophilic surfaces studied by sum-frequency generation vibrational spectroscopy and quartz crystal microbalance. J. Phys. Chem. C 111, 255–261 (2007)CrossRefGoogle Scholar
  23. 23.
    Apte, J.S., Collier, G., Latour, R.A., Gamble, L.J., Castner, D.G.: XPS and ToF-SIMS investigation of α-helical and β-strand peptide adsorption onto SAMs. Langmuir 26, 3423–3432 (2010)CrossRefGoogle Scholar
  24. 24.
    Fears, K.P., Creager, S.E., Latour, R.A.: Determination of the surface pK of carboxylic- and amine-terminated alkanethiols using surface plasmon resonance spectroscopy. Langmuir 24, 837–843 (2008)CrossRefGoogle Scholar
  25. 25.
    Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J.L., Dror, R.O., Shaw, D.E.: improved side-chain torsion potentials for the amber ff99SB protein force field. Proteins 78, 1950–1958 (2010)Google Scholar
  26. 26.
    Ulman, A., Eilers, J.E., Tillman, N.: Packing and molecular orientation of alkanethiol monolayers on gold surfaces. Langmuir 5, 1147–1152 (1989)CrossRefGoogle Scholar
  27. 27.
    Essmann, U., Perera, L., Berkowitz, M.L., Darden, T., Lee, H., Pedersen, L.G.: A smooth particle mesh ewald method. J. Chem. Phys. 103, 8577–8593 (1995)CrossRefGoogle Scholar
  28. 28.
    Hess, B., Kutzner, C., van der Spoel, D., Lindahl, E.: GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. JCTC. 4, 435–447 (2008)Google Scholar
  29. 29.
    Tribello, G.A., Bonomi, M., Branduardi, D., Camilloni, C., Bussi, G.: PLUMED 2: new feathers for an old bird. Comput. Phys. Commun. 185, 604–613 (2014)CrossRefGoogle Scholar
  30. 30.
    Laio, A., Parrinello, M.: Escaping free-energy minima. PNAS 99, 12562–12566 (2002)CrossRefGoogle Scholar
  31. 31.
    Barducci, A., Pfaendtner, J., Bonomi, M.: Tackling sampling challenges in biomolecular simulations. Methods Mol. Bio. 1215, 151–171 (2015)CrossRefGoogle Scholar
  32. 32.
    Barducci, A., Bussi, G., Parrinello, M.: Well-tempered metadynamics: a smoothly converging and tunable free-energy method. Phys. Rev. Lett. 100, 020603 (2008)CrossRefGoogle Scholar
  33. 33.
    Dama, J.F., Parrinello, M., Voth, G.A.: Well-tempered metadynamics converges asymptotically. Phys. Rev. Lett. 112, 240602 (2014)CrossRefGoogle Scholar
  34. 34.
    Hansmann, U.H.E.: Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 281, 140–150 (1997)CrossRefGoogle Scholar
  35. 35.
    Sugita, Y., Okamoto, Y.: Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999)CrossRefGoogle Scholar
  36. 36.
    Bussi, G., Gervasio, F., Laio, A., Parrinello, M.: Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics. J. Am. Chem. Soc. 128, 13435 (2006)CrossRefGoogle Scholar
  37. 37.
    Bonomi, M., Parrinello, M.: Enhanced sampling in the well-tempered ensemble. Phys. Rev. Lett. 104, 190601 (2010)CrossRefGoogle Scholar
  38. 38.
    Daura, X., Gademann, K., Jaun, B., Seebach, D., van Gunsteren, W.F., Mark, A.E.: peptide folding: when simulation meets experiment. Angew. Chem. Int. Ed. 38, 236–240 (1999)CrossRefGoogle Scholar
  39. 39.
    Branduardi, D., Bussi, G., Parrinello, M.: Metadynamics with adaptive Gaussians. JCTC 8, 2247–2254 (2012)Google Scholar
  40. 40.
    Torrie, G.M., Valleau, J.P.: Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 23, 187–199 (1977)CrossRefGoogle Scholar
  41. 41.
    Thyparambil, A.A., Wei, Y., Latour, R.A.: Determination of peptide-surface adsorption free energy for material surfaces not conducive to SPR or QCM using AFM. Langmuir 28, 5687–5694 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Department of Chemical EngineeringUniversity of WashingtonSeattleUSA
  2. 2.College of Chemical and Biological EngineeringZhejiang UniversityHangzhouChina

Personalised recommendations