Structural Chemistry

, Volume 28, Issue 3, pp 625–633 | Cite as

Structure and dynamics of a free aquaporin (AQP1) by a coarse-grained Monte Carlo simulation

  • R. B. Pandey
  • B. L. Farmer
Original Research


Structure and dynamics of a free aquaporin (AQP1) are studied by a coarse-grained Monte Carlo simulation as a function of temperature using a phenomenological potential with the input of a knowledge-based residue–residue interaction. Response of the radius of gyration (R g) of the protein to the temperature (T) is found to be nonlinear: Decay of R g at T ≤ T c is followed by a continuous increase at T ≥ T c before reaching its saturation. In thermo-responsive regime, the protein exhibits segmental globularization with the persistence of three regions along its sequence involving residues 1M–25V and 250V–269K toward the beginning and end segments with a narrow intermediate region around 155A–163D. A detail analysis of the structure factor S(q) shows a global random coil conformation at high temperatures with an effective dimension D e ~ 1.74 and a globular structure (D e ~ 3) at low temperatures. In thermo-responsive regime, the variation of S(q) with the wave vector q reveals a systematic redistribution of self-organizing residues (in globular and fibrous sections) that depends on the length scale and the temperature.


Protein folding Aquaporin Coarse-grained model Monte Carlo simulation 



We thank Minttu Virkki for suggesting us to look at AQP1.


  1. 1.
    Papadopoulos MC, Saadoun S (2015) Key roles of aquaporins in tumor biology. Biochim Biophys Acta. doi: 10.1016/j.bbamem.2014.09.001 Google Scholar
  2. 2.
    Kumar M, Grzelakowski M, Zilles J, Clark M, Meier W (2007) Highly permeable polymeric membranes based on the incorporation of the functional water channel protein Aquaporin Z. Proc Natl Acad Sci USA 104:20719–20724CrossRefGoogle Scholar
  3. 3.
    Wang J, Feng L, Zhu Z, Zheng M, Wang D, Chen Z, Sun H (2015) Aquaporins as diagnostic and therapeutic targets in cancer: how far we are? J Transl Med 13:96CrossRefGoogle Scholar
  4. 4.
    To J, Torres J (2015) Can stabilization and inhibition of aquaporins contribute to future development of biomimetic membranes? Membranes 5:352–368CrossRefGoogle Scholar
  5. 5.
    Boassa D, Stamer WD, Yool AJ (2006) Ion channel function of aquaporin-1 natively expressed in choroid plexus. J Neurosci 26:7811–7819CrossRefGoogle Scholar
  6. 6.
    Ash WL, Zlomislic MR, Oloo EO, Tieleman DP (2004) Computer simulations of membrane proteins. Biochim Biophys Acta 1666:158–189CrossRefGoogle Scholar
  7. 7.
    Kong Y, Ma J (2001) Dynamic mechanisms of the membrane water channel aquaporin-1 (AQP1). PNAS 98:14345–14349CrossRefGoogle Scholar
  8. 8.
    Wang Y, Tajkhorshid E (2007) Molecular mechanisms of conduction and selectivity in aquaporin water channels. J Nutr 137:1509S–1515SGoogle Scholar
  9. 9.
    Jensen MO, Mouritsen OG (2006) Single-channel water permeabilities of Escherichia coli aquaporins AqpZ and GlpF. Biophys J 90:2270–2284CrossRefGoogle Scholar
  10. 10.
    Hashido M, Kidera A, Ikeguchi M (2007) Water transport in aquaporins: osmotic permeability matrix analysis of molecular dynamics simulations. Biophys J 93:373–385CrossRefGoogle Scholar
  11. 11.
    Hashido M, Ikeguchi M, Kidera A (2005) Comparative simulations of aquaporin family: AQP1, AQPZ, AQP0 and GlpF. FEBS Lett 579:5549–5552CrossRefGoogle Scholar
  12. 12.
    Gumbart J, Wang Y, Aksimentiev A, Tajkhorshid E, Schulten K (2005) Molecular dynamics simulations of proteins in lipid bilayers. Curr Opin Struct Biol 15:423–431CrossRefGoogle Scholar
  13. 13.
    Yu J, Yool AJ, Schulten K, Tajkhorshid E (2006) Mechanism of gating and ion conductivity of a possible tetrameric pore in aquaporin-1. Structure 14:1411–1423CrossRefGoogle Scholar
  14. 14.
    Bond PJ, Holyoake J, Ivetac A, Khalid S, Sansom MSP (2007) Coarse-grained molecular dynamics simulations of membrane proteins and peptides. J Struct Biol 157:593–605CrossRefGoogle Scholar
  15. 15.
    Lindahl E, Sansom MSP (2008) Membrane proteins: molecular dynamics simulations. Curr Opin Struct Biol 18:425–431CrossRefGoogle Scholar
  16. 16.
    Lyubartsev AP, Laaksonen A (1995) Calculation of effective interaction potential from radial distribution functions: a reverse Monte Carlo approach. Phys Rev E 52:3730–3737CrossRefGoogle Scholar
  17. 17.
    Zhou J, Chen S, Jiang S (2003) Orientation of adsorbed antibodies on charged surfaces by computer simulation based on a united-residue model. Langmuir 19:3472–3478CrossRefGoogle Scholar
  18. 18.
    van Giessen AE, Straub JE (2005) Mote Carlo simulations of polyalanine using a reduced model and statistics-based interaction potential. J Chem Phys 122:0249041–0249049Google Scholar
  19. 19.
    Reith D, Putz M, Muller-Plathe F (2003) Deriving effective mesoscale potentials from atomistic simulations. J Comput Chem 24:1624CrossRefGoogle Scholar
  20. 20.
    Pandey RB, Farmer BL (2010) Global structure of a human immunodeficiency virus-1 protease (1DIFA dimer) in an effective solvent medium by a Monte Carlo simulation. J Chem Phys 132:125101–125106CrossRefGoogle Scholar
  21. 21.
    Liwo A, Czaplewski C, Oldziej S, Scheraga HA (2008) Computational techniques for efficient conformational sampling of protein. Curr Opin Struct Biol 18:134–139CrossRefGoogle Scholar
  22. 22.
    Ercolessi F, Adams J (1994) Interatomic potentials from first-principle calculations: the force-matching method. Europhys Lett 26:583–588CrossRefGoogle Scholar
  23. 23.
    Zhou J, Thorpe IF, Izvekov S, Voth GA (2007) Coarse-grained peptide modeling using a systematic multiscale approach. Biophys J 92:4289–4303CrossRefGoogle Scholar
  24. 24.
    de Jong DH, Singh G, Drew Bennett WF, Arnarez C, Wassenar TA et al (2013) Improved parameters for the martini coarse-grained protein force field. J Chem Theory Comput 9:687–697CrossRefGoogle Scholar
  25. 25.
    Sorensen J, Xavier P, Skeby KK, Marrink SJ, Schiott B (2011) Protofibrillar assembly towards the formation of amyloid fibrils. J Phys Chem Lett 2:2385–2390CrossRefGoogle Scholar
  26. 26.
    Haliloglu T, Bahar I (1998) Coarse-grained simulations of conformational dynamics of proteins: application to apomyoglobin. Proteins 31:27–281CrossRefGoogle Scholar
  27. 27.
    Pandey RB, Farmer BL, Gerstman BS (2015) Self-assembly dynamics for the transition of a globular aggregate to a fibril network of lysozyme proteins via a coarse-grained Monte Carlo simulation. AIP Adv 5:092502-1–092502-12CrossRefGoogle Scholar
  28. 28.
    Mirau P, Farmer BL, Pandey RB (2015) Structural variation of alpha-synuclein with temperature by a coarse-grained approach with knowledge-based interactions. AIP Adv 5:092504-1–092504-10CrossRefGoogle Scholar
  29. 29.
    Tanaka S, Scheraga HA (1976) Medium and long range interaction parameters between amino acids for predicting three dimensional structures of proteins. Macromolecules 9:945–950CrossRefGoogle Scholar
  30. 30.
    Miyazawa S, Jernigan RL (1985) Estimation of effective inter residue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18:534–552CrossRefGoogle Scholar
  31. 31.
    Miyazawa S, Jernigan RL (1996) Residue–residue potentials with a favorable contact pair term for simulation and treading. J Mol Biol 256:623–644CrossRefGoogle Scholar
  32. 32.
    Betancourt MR, Thirumalai D (1999) Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci 2:361–369Google Scholar
  33. 33.
    Godzik A, Kolinski A, Skolnick J (1996) Knowledge-based potentials for protein folding: what can we learn from protein structures? Proteins 4:363–366Google Scholar
  34. 34.
    Huang S-Y, Xiaoqin Z (2011) Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures. Proteins 79:2648–2661CrossRefGoogle Scholar
  35. 35.
    Fritsche M, Pandey RB, Farmer BL, Heermann D (2012) Conformational temperature-dependent behavior of a histone h2ax: a coarse-grained Monte Carlo approach via knowledge-based interaction potentials. PLoS ONE 7:e32075-1–e32075-8Google Scholar
  36. 36.
    Pandey RB, Farmer BL (2012) Random coil to globular thermal response of a protein (H3.1) with three knowledge-based coarse-grained potentials. PLoS ONE 7:e49352-1–e49352-9Google Scholar
  37. 37.
    Pandey RB, Farmer BL (2013) Conformational response to solvent interaction and temperature of a protein (histone h3.1) by a multi-grained Monte Carlo simulation. PLoS ONE 8:e76069-1–e76069-9Google Scholar
  38. 38.
    Binder K (ed) (1995) Monte Carlo and molecular dynamics simulations in polymer science. Oxford University Press, OxfordGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Physics and AstronomyUniversity of Southern MississippiHattiesburgUSA
  2. 2.Materials and Manufacturing Directorate, Air Force Research LaboratoryWright Patterson Air Force BaseDaytonUSA

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