Obtaining Soft Matter Models of Proteins and their Phase Behavior

  • Irem AltanEmail author
  • Patrick Charbonneau
Part of the Methods in Molecular Biology book series (MIMB, volume 2039)


Globular proteins are roughly spherical biomolecules with attractive and highly directional interactions. This microscopic observation motivates describing these proteins as patchy particles: hard spheres with attractive surface patches. Mapping a biomolecule to a patchy model requires simplifying effective protein–protein interactions, which in turn provides a microscopic understanding of the protein solution behavior. The patchy model can indeed be fully analyzed, including its phase diagram. In this chapter, we detail the methodology of mapping a given protein to a patchy model and of determining the phase diagram of the latter. We also briefly describe the theory upon which the methodology is based, provide practical information, and discuss potential pitfalls. Data and scripts relevant to this work have been archived and can be accessed at

Key words

Soft matter Phase behavior Protein crystallization Coarse-grained simulation 


  1. 1.
    Chen V, Davis I, Richardson D (2009) KiNG (Kinemage, next generation): a versatile interactive molecular and scientific visualization program. Protein Sci 18(11):2403–2409CrossRefGoogle Scholar
  2. 2.
    Berendsen H, van der Spoel D, van Drunen R (1995) Gromacs: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1):43–56CrossRefGoogle Scholar
  3. 3.
    Case D, Cerutti D, Cheatham T III, Darden T, Duke R, Giese T, Gohlke H, Goetz A, Greene D, Homeyer N, Izadi S, Kovalenko A, Lee T, LeGrand S, Li P, Lin C, Liu J, Luchko T, Luo R, Mermelstein D, Merz K, Monard G, Nguyen H, Omelyan I, Onufriev A, Pan F, Qi R, Roe D, Roitberg A, Sagui C, Simmerling C, Botello-Smith W, Swails J, Walker R, Wang J, Wolf R, Wu X, Xiao L, York D, PA K (2017) Amber 2017. Tech. rep., University of California, San FranciscoGoogle Scholar
  4. 4.
    Frenkel D, Smit B (2001) Understanding molecular simulation: from algorithms to applications. Academic, OrlandoGoogle Scholar
  5. 5.
    Rovigatti L, Russo J, Romano F (2018) How to simulate patchy particles. Eur Phys J E 41(5):59CrossRefGoogle Scholar
  6. 6.
    Rovigatti L, Romano F, Russo J (2018) lorenzo-rovigatti/patchyparticles v1.0.1.
  7. 7.
    Fusco D, Headd J, De Simone A, Wang J, Charbonneau P (2014) Characterizing protein crystal contacts and their role in crystallization: rubredoxin as a case study. Soft Matter 10(2):290–302CrossRefGoogle Scholar
  8. 8.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefGoogle Scholar
  9. 9.
    Bönisch H, Schmidt C, Bianco P, Ladenstein R (2005) Ultrahigh-resolution study on Pyrococcus abyssi rubredoxin. I. 0.69 Å X-ray structure of mutant W4L/R5S. Acta Crystallogr D 61(7):990–1004CrossRefGoogle Scholar
  10. 10.
    Kästner J (2011) Umbrella sampling. Wiley Interdiscip Rev Comput Mol Sci 1(6):932–942CrossRefGoogle Scholar
  11. 11.
    Krissinel E, Henrick K (2007) Inference of macromolecular assemblies from crystalline state. J Mol Biol 372(3):774–797CrossRefGoogle Scholar
  12. 12.
    Rostkowski M, Olsson M, Søndergaard C, Jensen J (2011) Graphical analysis of pH-dependent properties of proteins predicted using PROPKA. BMC Struct Biol 11(1):1CrossRefGoogle Scholar
  13. 13.
    Hub J, De Groot B, Van Der Spoel D (2010) g_wham – a free weighted histogram analysis implementation including robust error and autocorrelation estimates. J Chem Theory Comput 6(12):3713–3720CrossRefGoogle Scholar
  14. 14.
    Kern N, Frenkel D (2003) Fluid–fluid coexistence in colloidal systems with short-ranged strongly directional attraction. J Chem Phys 118(21):9882–9889CrossRefGoogle Scholar
  15. 15.
    Fusco D, Charbonneau P (2016) Soft matter perspective on protein crystal assembly. Colloids Surf B 137:22–31CrossRefGoogle Scholar
  16. 16.
    Sear R (1999) Phase behavior of a simple model of globular proteins. J Chem Phys 111(10):4800–4806CrossRefGoogle Scholar
  17. 17.
    Wentzel N, Gunton J (2008) Effect of solvent on the phase diagram of a simple anisotropic model of globular proteins. J Phys Chem B 112(26):7803–7809CrossRefGoogle Scholar
  18. 18.
    Dixit N, Zukoski C (2002) Crystal nucleation rates for particles experiencing anisotropic interactions. J Chem Phys 117(18):8540–8550CrossRefGoogle Scholar
  19. 19.
    Vega C, Sanz E, Abascal J, Noya E (2008) Determination of phase diagrams via computer simulation: methodology and applications to water, electrolytes and proteins. J Phys Condens Matter 20(15):153101CrossRefGoogle Scholar
  20. 20.
    Romano F, Sanz E, Sciortino F (2010) Phase diagram of a tetrahedral patchy particle model for different interaction ranges. J Chem Phys 132(18):184501CrossRefGoogle Scholar
  21. 21.
    Frenkel D, Ladd A (1984) New Monte Carlo method to compute the free energy of arbitrary solids. Application to the FCC and HCP phases of hard spheres. J Chem Phys 81(7):3188–3193CrossRefGoogle Scholar
  22. 22.
    Riley KF, Hobson MP, Bence SJ (2006) Mathematical methods for physics and engineering: a comprehensive guide. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  23. 23.
    Kofke D (1993) Gibbs-Duhem integration: a new method for direct evaluation of phase coexistence by molecular simulation. Mol Phys 78(6):1331–1336CrossRefGoogle Scholar
  24. 24.
    Kofke D (1993) Direct evaluation of phase coexistence by molecular simulation via integration along the saturation line. J Chem Phys 98(5):4149–4162CrossRefGoogle Scholar
  25. 25.
    Panagiotopoulos A (1987) Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble. Mol Phys 61(4):813–826CrossRefGoogle Scholar
  26. 26.
    Noro MG, Frenkel D (2000) Extended corresponding-states behavior for particles with variable range attractions. J Chem Phys 113(8):2941–2944CrossRefGoogle Scholar
  27. 27.
    Blote H, Luijten E, Heringa J (1995) Ising universality in three dimensions: a Monte Carlo study. J Phys A 28(22):6289CrossRefGoogle Scholar
  28. 28.
    Fusco D, Charbonneau P (2013) Crystallization of asymmetric patchy models for globular proteins in solution. Phys Rev E 88(1):012721CrossRefGoogle Scholar
  29. 29.
    Tang Z, Zhang Z, Wang Y, Glotzer S, Kotov N (2006) Self-assembly of CdTe nanocrystals into free-floating sheets. Science 314(5797):274–278CrossRefGoogle Scholar
  30. 30.
    Ye X, Chen J, Engel M, Millan J, Li W, Qi L, Xing G, Collins J, Kagan C, Li J et al. (2013) Competition of shape and interaction patchiness for self-assembling nanoplates. Nat Chem 5(6):466CrossRefGoogle Scholar
  31. 31.
    Glotzer S, Solomon M (2007) Anisotropy of building blocks and their assembly into complex structures. Nat Mater 6(8):557CrossRefGoogle Scholar
  32. 32.
    Bianchi E, Capone B, Kahl G, Likos C (2015) Soft-patchy nanoparticles: modeling and self-organization. Faraday Discuss 181:123–138CrossRefGoogle Scholar
  33. 33.
    de las Heras D, da Gama M (2016) Temperature (de)activated patchy colloidal particles. J Phys Condens Matter 28(24):244008Google Scholar
  34. 34.
    Wilber AW, Doye JP, Louis AA, Noya EG, Miller MA, Wong P (2007) Reversible self-assembly of patchy particles into monodisperse icosahedral clusters. J Chem Phys 127(8):08B618CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of ChemistryDuke UniversityDurhamUSA
  2. 2.Department of PhysicsDuke UniversityDurhamUSA

Personalised recommendations