Skip to main content
SpringerLink
Log in

Molecular Simulation of Protein-Surface Interactions

  • Chapter
  • First Online:
Biological Interactions on Materials Surfaces

Protein-surface interactions are fundamentally important in a broad range of applications in biomedical engineering and biotechnology. The adsorption behavior of a protein is governed by the complex set of interactions between the atoms making up the protein, surface, and surrounding solution. Molecular simulation provides a means of theoretically viewing, studying, and understanding these types of interactions at the atomic level. However, as with any application, molecular simulation methods must be properly developed and applied if protein-surface interactions are to be accurately portrayed. This chapter provides an overview of the basics of molecular simulation with a focus on the simulation of protein-surface interactions using molecular dynamics. Important issues such as force field transferability, solvation effects, and statistical sampling are addressed, as well as directions for further development. Molecular simulation methods have the potential to greatly enhance our ability to understand protein–surface interactions, leading to improvements in biomaterials design to control cellular response and to improve the sensitivity of biosensors and other bioanalytical systems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

b :

bond length

b 0 :

force field parameter: bond length at zero energy

CM:

classical mechanics

E :

potential energy

F :

force

f ij :

force vector between atoms i and j

GB:

generalized Born

K.E.:

kinetic energy

k B :

Boltzmann’s constant

k b :

force field parameter: bond stretching stiffness

k u :

stiffness constant for an umbrella-sampling simulation

k ϕ :

force field parameter: dihedral angle rotational stiffness

k θ :

force field parameter: bond angle bending stiffness

L-J:

Lennard–Jones

m :

mass

MC:

Monte Carlo

MD:

molecular dynamics

MM:

molecular mechanics

N :

number of degrees of freedom

N c :

number of constraints on the degrees of freedom

NPT:

constant number of atoms, pressure, and temperature

NVT:

constant number of atoms, volume, and temperature

P :

pressure

P-B:

Poisson–Boltzmann

p i :

probability of system to be in state i

q :

partial charge

Q :

configurational partition function of the system

QM:

quantum mechanics

REMD:

replica-exchange molecular dynamics

r ij :

distance between atoms i and j

SAM:

self-assembled monolayer

T :

temperature

TIGER:

temperature intervals with global energy reassignment

TIGER2:

temperature intervals with global exchange of replicas-2

TIP3P:

name of a type of three-site water model

U :

internal energy

V :

velocity

V :

volume

x :

coordinate position

DB ij :

biasing energy function between states i and j

Dt :

time-step increment for a molecular dynamics simulation

δ :

force field parameter: rotational dihedral shift

ε r :

relative dielectric constant

ε ij :

well-depth for Lennard–Jones interactions

ε 0 :

permittivity of free space

θ :

bond angle

θ 0 :

force field parameter: bond angle at zero energy

λ :

variable coordinate parameter for an umbrella-sampling simulation

λ h :

fixed coordinate parameter for an umbrella-sampling simulation

σ ij :

collision diameter between atoms i and j ϕdihedral angle

References

  1. Latour RA (2008) Biomaterials: Protein-surface interactions. In: The Encyclopedia of Biomaterials and Bioengineering, 2nd Ed. Informa Healthcare, New York, NY.

    Google Scholar 

  2. Castner DG, Ratner BD (2002) Biomedical surface science: Foundations to frontiers. Surf. Sci. 500:28–60.

    Article  CAS  Google Scholar 

  3. Hlady V, Buijs J (1996) Protein adsorption on solid surfaces. Curr. Opin. Biotechnol. 7:72–77.

    Article  CAS  Google Scholar 

  4. Tsai WB, Grunkemeier JM, et al (2002) Platelet adhesion to polystyrene-based surfaces preadsorbed with plasmas selectively depleted in fibrinogen, fibronectin, vitronectin, or von Willebrand’s factor. J. Biomed. Mater. Res. 60:348–359.

    Article  CAS  Google Scholar 

  5. Dee KC, Puleo DA, et al (2002) Protein-Surface Interactions, Chapter 3. Wiley, Hoboken, NJ.

    Google Scholar 

  6. Geelhood SJ, Horbett TA, et al (2007) Passivating protein coatings for implantable glucose sensors:Evaluation of protein retention. J. Biomed. Mater. Res. B 81B:251–260.

    Article  CAS  Google Scholar 

  7. Kusnezow W, Hoheisel JD (2002) Antibody microarrays: promises and problems. Biotechniques Suppl. 14–23.

    Google Scholar 

  8. Schüler C, Carusa F (2000) Preparation of enzyme multilayers on colloids for biocatalysis. Macromol. Rapid Comm. 21:750–753.

    Article  Google Scholar 

  9. Yu AM, Liang ZJ, et al (2005) Enzyme multilayer-modified porous membranes as biocatalysts. Chem. Mater. 17:171–175.

    Article  CAS  Google Scholar 

  10. Beck DAC, Daggett V (2004) Methods for molecular dynamics simulations of protein folding/unfolding in solution. Methods 34:112–120.

    Article  CAS  Google Scholar 

  11. Brooks III CL (1998) Simulations of protein folding and unfolding. Curr. Opin. Struct. Biol. 8:222–226.

    Article  CAS  Google Scholar 

  12. Gnanakaran S, Nymeyer H, et al (2003) Peptide folding simulations. Curr. Opin. Struct. Biol. 13:168–174.

    Article  CAS  Google Scholar 

  13. Wang W, Donini O, et al (2001) Biomolecular simulations: Recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu. Rev. Biophys. Biomol. Struct. 30:211–243.

    Article  CAS  Google Scholar 

  14. Ehrlich LP, Nilges M, et al (2005) The impact of protein flexibility on protein-protein docking. Proteins 58: 126–133.

    Article  CAS  Google Scholar 

  15. Chandrasekaran V, Ambati J, et al (2007) Molecular docking and analysis of interactions between vascular endothelial growth factor (VEGF) and SPARC protein. J. Mol. Graph. Model. 26:775–782.

    Article  CAS  Google Scholar 

  16. Hyvonen MT, Oorni K, et al (2001) Changes in a phospholipid bilayer induced by the hydrolysis of a phospholipase A(2) enzyme: A molecular dynamics simulation study. Biophys. J. 80:565–578.

    Article  CAS  Google Scholar 

  17. Bond PJ, Sansom MSP (2006) Insertion and assembly of membrane proteins via simulation. J. Am. Chem. Soc. 128:2697–2704.

    Article  CAS  Google Scholar 

  18. Muegge I (2003) Selection criteria for drug–like compounds. Med. Res. Rev. 23:302–321.

    Article  CAS  Google Scholar 

  19. Bernard D, Coop A, et al (2005) Conformationally sampled pharmacophore for peptidic delta opioid ligands. J. Med. Chem. 48:7773–7780.

    Article  CAS  Google Scholar 

  20. Chen HF (2008) Computational study of the binding mode of epidermal growth factor receptor kinase inhibitors. Chem. Biol. Drug Des. 71:434–446.

    Article  CAS  Google Scholar 

  21. Nick B, Suter UW (2001) Solubility of water in polymers – Atomistic simulations. Comput. Theor. Polym. Sci. 11:49–55.

    Article  CAS  Google Scholar 

  22. Pan R, Liu XK, et al (2007) Molecular simulation on structure–property relationship of polyimides with methylene spacing groups in biphenyl side chain. Comp. Mater. Sci. 39:887–895.

    Article  CAS  Google Scholar 

  23. Tarmyshov KB, Muller–Plathe F (2007) The interface between platinum(111) and poly(vinyl alcohol) melt: A molecular dynamics study. Soft Mater. 5:135–154.

    Article  CAS  Google Scholar 

  24. Zhang J, Liang Y, et al (2007) Study of the molecular weight dependence of glass transition temperature for amorphous poly(l–lactide) by molecular dynamics simulation. Polymer 28:4900–4905.

    Article  Google Scholar 

  25. Latour RA (2008) Molecular simulation of protein–surface interactions: Benefits, problems, solutions, and future directions. Biointerphases 3:FC2–FC12.

    Article  CAS  Google Scholar 

  26. Brandon C, Tooze J (1999) Introduction to Protein Structure, 2nd Ed. Garland, New York, NY.

    Google Scholar 

  27. Voet D, Voet JG, et al (2002) Fundamentals of Biochemistry. Wiley, New York, NY..

    Google Scholar 

  28. Bryngelson JD, Onuchic JN, et al (1995) Funnels, pathways, and the energy landscape of protein–folding – A synthesis. Proteins 21:167–195.

    Article  CAS  Google Scholar 

  29. Onuchic JN, Wolynes PG, et al (1995) Toward an outline of the topography of a realistic protein–folding funnel. Proc. Natl. Acad. Sci. USA. 92:3626–3630.

    Article  CAS  Google Scholar 

  30. Wolynes PG, Luthey–Schulten Z, et al (1996) Fast folding experiments and the topography of protein folding energy landscapes. Chem. Biol. 3:425–432.

    Article  CAS  Google Scholar 

  31. Agashe M, Raut V, et al (2005) Molecular simulation to characterize the adsorption behavior of a fibrinogen gamma–chain fragment. Langmuir 21:1103–1117.

    Article  CAS  Google Scholar 

  32. Lee C–S, Belfort G (1989) Changing activity of ribonuclease A during adsorption: A molecular explanation. Biophysics 86:8392–8396.

    CAS  Google Scholar 

  33. Lenk T, Horbett T, et al (1991) Infrared spectroscopic studies of time–dependent changes in fibrinogen adsorbed to polyurethanes. Langmuir 7:1755–1764.

    Article  CAS  Google Scholar 

  34. Chinn JA, Posso SE, et al (1992) Postadsorptive transitions in fibrinogen adsorbed to polyurethanes – Changes in antibody–binding and sodium dodecyl–sulfate elutability. J. Biomed. Mater. Res. 26:757–778.

    Article  CAS  Google Scholar 

  35. Agnihortri A, Siedlecki CA (2004) Time–dependent conformational changes in fibrinogen measured by atomic force micrscopy. Langmuir 20:8846–8852.

    Article  Google Scholar 

  36. Leach AR (1996) Molecular Modelling. Principles and Applications. Pearson Education, Harlow, UK.

    Google Scholar 

  37. Zhou J, Zheng J, et al (2004) Molecular simulation studies of the orientation and conformation of cytochrome c adsorbed on self–assembled monolayers. J. Phys. Chem. B 108:17418–17424.

    Article  CAS  Google Scholar 

  38. Oostenbrink C, Villa A, et al (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force–field parameter sets 53A5 and 53A6. J. Comput. Chem. 25:1656–1676.

    Article  CAS  Google Scholar 

  39. Rick SW, Stuart SJ (2002) Potentials and algorithms for incorporating polarizability in computer simulations. In: Reviews in Computational Chemistry, Wiley, New York, NY.

    Google Scholar 

  40. Kaminski GA, Stern HA et al (2002) Development of a polarizable force field for proteins via ab initio quantum chemistry: First generation model and gas phase tests. J. Comput. Chem. 23:1515–1531.

    Article  CAS  Google Scholar 

  41. Warshel A, Kato M et al (2007) Polarizable force fields: History, test cases, and prospects. J. Chem. Theory Comput. 3:2034–2045.

    Article  CAS  Google Scholar 

  42. Wei Y, Latour RA (2008) Determination of the adsorption free energy for peptide–surface interactions by SPR spectroscopy. Langmuir 24:6721–6729.

    Article  CAS  Google Scholar 

  43. Wang F, Stuart SJ, et al (2008) Calculation of adsorption free energy for solute–surface interactions using biased replica–exchange molecular dynamics. Biointerphases 3:9–18.

    Article  Google Scholar 

  44. Raut VP, Agashe M, et al (2005) Molecular dynamics simulations of peptide–surface interactions. Langmuir 21:1629–1639.

    Article  CAS  Google Scholar 

  45. Frenkel D, Smit B (1996) Understanding Molecular Simulation. Academic, New York, NY.

    Google Scholar 

  46. Ryckaert JP, Ciccotti G, et al (1977) Numerical integration of the cartesian equation of motion of a system with constraints: molecular dynamics of n–alkanes. J. Comput. Phys. 23:327–341.

    Article  CAS  Google Scholar 

  47. Andersen HC (1983) Rattle: A “velocity” version of the Shake algorithm for molecular dynamics calculations. J. Comput. Phys. 52:24–34.

    Article  CAS  Google Scholar 

  48. MacKerell AD, Bashford D, et al (1998) All–atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102:3586–3616.

    Article  CAS  Google Scholar 

  49. Darden T, York D, et al (1993) Particle mesh Ewald: An N–log(N) method for Ewald sums in large systems. J. Chem. Phys. 98:10089–10092.

    Article  CAS  Google Scholar 

  50. Essmann U, Perera L, et al (1995) A smooth particle mesh Ewald method. J. Chem. Phys. 103:8577–8593.

    Article  CAS  Google Scholar 

  51. West JB (1985) Physiology of the Body Fluids, Chapter 26. Williams, Baltimore, MD.

    Google Scholar 

  52. Glattli A, Daura X, et al (2002) Derivation of an improved simple point charge model for liquid water: SPC/A and SPC/L. J. Chem. Phys. 116:9811–9828.

    Article  CAS  Google Scholar 

  53. Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A 105:9954–9960.

    Article  CAS  Google Scholar 

  54. Jorgensen WL, Chandrasekhar J, et al (1983) Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79:926–935.

    Article  CAS  Google Scholar 

  55. Jorgensen WL, Madura JD (1985) Temperature and size dependence for Monte–Carlo simulations of TIP4P water. Mol. Phys. 56:1381–1392.

    Article  CAS  Google Scholar 

  56. Jorgensen WL, Jenson C (1998) Temperature dependence of TIP3P, SPC, and TIP4P water from NPT Monte Carlo simulations: Seeking temperatures of maximum density. J. Comput. Chem. 19:1179–1186.

    Article  CAS  Google Scholar 

  57. Horn HW, Swope WC, et al (2004) Development of an improved four–site water model for biomolecular simulations: TIP4P–Ew. J. Chem. Phys. 120:9665–9678.

    Article  CAS  Google Scholar 

  58. Horn HW, Swope WC, et al (2005) Characterization of the TIP4P–Ew water model: Vapor pressure and boiling point. J. Chem. Phys. 123:1–12.

    Article  Google Scholar 

  59. Mahoney MW, Jorgensen WL (2001) Diffusion constant of the TIP5P model of liquid water. J. Chem. Phys. 114:363–366.

    Article  CAS  Google Scholar 

  60. Schaefer M, Bartels C, et al (1999) Solution conformations of structured peptides: Continuum electrostatics versus distance–dependent dielectric functions. Theor. Chem. Acc. 101:194–204.

    Article  CAS  Google Scholar 

  61. Sun Y, Latour RA (2006) Comparison of implicit solvent models for the simulation of protein–surface interactions. J. Comp. Chem. 27:1908–1922.

    Article  CAS  Google Scholar 

  62. Sharp KA, Honig B (1990) Calculating total electrostatic energies with the nonlinear Poisson–Boltzmann equation. J. Phys. Chem. 94:7684–7692.

    Article  CAS  Google Scholar 

  63. Bertonati C, Honig B, et al (2007) Poisson–Boltzmann calculations of nonspecific salt effects on protein–protein binding free energies. Biophys. J. 92:1891–1899.

    Article  CAS  Google Scholar 

  64. Still WC, Tempczyk A, et al (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J. Am. Chem. Soc. 112:6127–6129.

    Article  CAS  Google Scholar 

  65. Dominy BN, Brooks III CL (1999) Development of a generalized Born model parameterization for proteins and nucleic acids. J. Phys. Chem. B 103:3765–3773.

    Article  CAS  Google Scholar 

  66. Bashford D, Case DA (2000) Generalized Born models of macromolecular solvation effects. Annu. Rev. Phys. Chem. 51:129–152.

    Article  CAS  Google Scholar 

  67. Feig M, Brooks III CL (2004) Recent advances in the development and application of implicit solvent models in biomolecule simulations. Curr. Opin. Struct. Biol. 14:217–224.

    Article  CAS  Google Scholar 

  68. Feig M, Onufriev A, et al (2004) Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures. J. Comput. Chem. 25:265–284.

    Article  CAS  Google Scholar 

  69. Formaneck MS, Cui Q (2006) The use of a generalized born model for the analysis of protein conformational transitions: A comparative study with explicit solvent simulations for chemotaxis Y protein (CheY). J. Comput. Chem. 27:1923–1943.

    Article  CAS  Google Scholar 

  70. Sun Y, Dominy BN, et al (2007) Comparison of solvation–effect methods for the simulation of peptide interactions with a hydrophobic surface. J. Comput. Chem. 28:1883–1892.

    Article  CAS  Google Scholar 

  71. McQuarrie DA (1976) The Canonical Ensemble, Chapter 2. In: Statistical Thermodynamics, Harper, New York, NY.

    Google Scholar 

  72. Beutler TC, van Gunsteren WF (1994) The computation of a potential of mean force – Choice of the biasing potential in the umbrella sampling technique. J. Chem. Phys. 100:1492–1497.

    Article  CAS  Google Scholar 

  73. Friedman RA, Mezei M (1995) The potentials of mean force of sodium–chloride and sodium dimethylphosphate in water – An application of adaptive umbrella sampling. J. Chem. Phys. 102:419–426.

    Article  CAS  Google Scholar 

  74. Bartels C, Karplus M (1998) Probability distributions for complex systems: Adaptive umbrella sampling of the potential energy. J. Phys. Chem. B 102:865–880.

    Article  CAS  Google Scholar 

  75. Bartels C, Schaefer M, et al (1999) Adaptive umbrella sampling of the potential energy: Modified updating procedure of the umbrella potential and application to peptide folding. Theor. Chem. Acc. 101:62–66.

    Article  CAS  Google Scholar 

  76. Depaepe JM, Ryckaert JP, et al (1993) Sampling of molecular–conformations by molecular–dynamics techniques. Mol. Phys. 79:515–522.

    Article  CAS  Google Scholar 

  77. Souaille M, Roux B (2001) Extension to the weighted histogram analysis method: Combining umbrella sampling with free energy calculations. Comput. Phys. Commun. 135:40–57.

    Article  CAS  Google Scholar 

  78. Harvey SC, Prabhakaran M (1987) Umbrella sampling – Avoiding possible artifacts and statistical biases. J. Phys. Chem. 91:4799–4801.

    Article  CAS  Google Scholar 

  79. Kumar S, Bouzida D, et al (1992) The weighted histogram analysis method for free–energy calculations of biomolecules. 1. The Method. J. Comput. Chem. 13:1011–1021.

    Article  CAS  Google Scholar 

  80. Kumar S, Rosenberg JM, et al (1995) Multidimensional free–energy calculations using the weighted histogram analysis method. J. Comput. Chem. 16:1339–1350.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  82. Garcia AE, Sanbonmatsu KY (2001) Exploring the energy landscape of a beta hairpin in explicit solvent. Proteins 42:345–354.

    Article  CAS  Google Scholar 

  83. Gallicchio E, Andrec M, et al (2005) Temperature weighted histogram analysis method, replica exchange, and transition paths. J. Phys. Chem. B. 109:6722–6731.

    Article  CAS  Google Scholar 

  84. Hansmann UHE (1997) Parallel tempering algorithm for conformational studies of biological molecules. Chem. Phys. Lett. 281:140–150.

    Article  CAS  Google Scholar 

  85. Mitsutake A, Sugita Y, et al (2001) Generalized–ensemble algorithms for molecular simulations of biopolymers. Biopolymers 60:96–123.

    Article  CAS  Google Scholar 

  86. Okamoto Y (2004) Generalized–ensemble algorithms: Enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J. Mol. Graph. Model.22: 425–439.

    Article  CAS  Google Scholar 

  87. Okur A, Wickstrom L, et al (2006) Improved efficiency of replica exchange simulations through use of a hybrid explicit/implicit solvation model. J. Chem. Theory Comput. 2:420–433.

    Article  CAS  Google Scholar 

  88. Okur A, Roe DR, et al (2007) Improving convergence of replica–exchange simulations through coupling to a high–temperature structure reservoir. J. Chem. Theory Comput. 3:557–568.

    Article  CAS  Google Scholar 

  89. Li XF, O’Brien CP, et al (2007) An improved replica–exchange sampling method: Temperature intervals with global energy reassignment. J. Chem. Phys. 127:1–10.

    Google Scholar 

  90. Li XF, Stuart SJ, Latour RA (2009) TIGER2: An improved algorithm for temperature intervals with global exchange of replicas, J. Chem. Phys., in press.

    Google Scholar 

  91. Fukunishi H, Watanabe O, et al (2002) On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction. J. Chem. Phys. 116:9058–9067.

    Article  CAS  Google Scholar 

  92. Affentranger R, Tavernelli I, et al (2006) A novel Hamiltonian replica exchange MD protocol to enhance protein conformational space sampling. J. Chem. Theory Comput. 2:217–228.

    Article  CAS  Google Scholar 

  93. O’Brien CP, Stuart SJ, et al. (2008) Modeling of peptide adsorption interactions with a poly(lactic acid) surface. Langmuir 24:14115–14124.

    Article  Google Scholar 

  94. Brenner DW, Shenderova OA, et al (2002) A second–generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons. J. Phys. Condens. Matter 14:783–802.

    Article  CAS  Google Scholar 

  95. Ni B, Lee KH, et al (2004) A reactive empirical bond order (REBO) potential for hydrocarbon–oxygen interactions. J. Phys. Condens. Matter 16:7261–7275.

    Article  CAS  Google Scholar 

  96. Liu A, Stuart SJ (2008) Empirical bond–order potential for hydrocarbons: Adaptive treatment of van der Waals interactions. J. Comp. Chem. 29:601–611.

    Article  CAS  Google Scholar 

  97. Chu JW, Izveko S, et al (2006) The multiscale challenge for biomolecular systems: Coarse–grained modeling. Mol. Simul. 32:211–218.

    Article  CAS  Google Scholar 

  98. de Pablo JJ, Curtin WA (2007) Multiscale modeling in advanced materials research: Challenges, novel methods, and emerging applications. MRS Bull. 32:905–911.

    Article  Google Scholar 

  99. Zhou J, Thorpe IF, et al (2007) Coarse–grained peptide modeling using a systematic multiscale approach. Biophys. J. 92:4289–4303.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

I thank my colleague Dr. Steven Stuart, Department of Chemistry, Clemson University, for numerous helpful discussions over the years regarding molecular simulation and statistical thermodynamics, Mr. Galen Collier, a current doctoral student in my group for assistance with Fig. 4.5, and Dr. Feng Wang, a former doctoral student in my group, for helpful discussions regarding the protein-folding funnel and protein adsorption. I also thank the NIH and NSF for providing funding support for my research program on the development of molecular simulation methods to simulate protein-surface interactions: NIH R01 EB006163, R01 GM074511, the NJ Center for Biomaterials RESBIO (NIH, P41 EB001046), and the Center for Advanced Engineering Fibers and Films (CAEFF, NSF-ERC, EPS-0296165).

Author information

Authors and Affiliations

  1. Department of Bioengineering, Clemson University, Clemson, SC, 29634, USA

    Robert A. Latour

Authors
  1. Robert A. Latour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert A. Latour .

Editor information

Editors and Affiliations

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2009 Springer Science+Business Media, LLC

    About this chapter

    Cite this chapter

    Latour, R.A. (2009). Molecular Simulation of Protein-Surface Interactions. In: Puleo, D., Bizios, R. (eds) Biological Interactions on Materials Surfaces. Springer, New York, NY. https://doi.org/10.1007/978-0-387-98161-1_4

    Download citation

    Publish with us

    Policies and ethics

    Search

    Navigation