Identification of Motions in Membrane Proteins by Elastic Network Models and Their Experimental Validation

  • Basak Isin
  • Kalyan C. Tirupula
  • Zoltán N. Oltvai
  • Judith Klein-Seetharaman
  • Ivet Bahar
Part of the Methods in Molecular Biology book series (MIMB, volume 914)


Identifying the functional motions of membrane proteins is difficult because they range from large-scale collective dynamics to local small atomic fluctuations at different timescales that are difficult to measure experimentally due to the hydrophobic nature of these proteins. Elastic Network Models, and in particular their most widely used implementation, the Anisotropic Network Model (ANM), have proven to be useful computational methods in many recent applications to predict membrane protein dynamics. These models are based on the premise that biomolecules possess intrinsic mechanical characteristics uniquely defined by their particular architectures. In the ANM, interactions between residues in close proximity are represented by harmonic potentials with a uniform spring constant. The slow mode shapes generated by the ANM provide valuable information on the global dynamics of biomolecules that are relevant to their function. In its recent extension in the form of ANM-guided molecular dynamics (MD), this coarse-grained approach is augmented with atomic detail. The results from ANM and its extensions can be used to guide experiments and thus speedup the process of quantifying motions in membrane proteins. Testing the predictions can be accomplished through (a) direct observation of motions through studies of structure and biophysical probes, (b) perturbation of the motions by, e.g., cross-linking or site-directed mutagenesis, and (c) by studying the effects of such perturbations on protein function, typically through ligand binding and activity assays. To illustrate the applicability of the combined computational ANM—experimental testing framework to membrane proteins, we describe—alongside the general protocols—here the application of ANM to rhodopsin, a prototypical member of the pharmacologically relevant G-protein coupled receptor family.

Key words

Anisotropic network model (ANM) Normal mode analysis (NMA) Structural dynamics Molecular dynamics (MD) simulations Structure prediction Conformational changes Ensembles of structures G-protein coupled receptors Multiscale models and methods 



This work was in part supported by the National Science Foundation grant CCF-1144281, a CAREER grant CC044917 and by the National Institutes of Health Grant 5R01LM007994-06 (IB) and NIAID U01 AI070499 (ZNO).


  1. 1.
    Becker OM, MacKerell ADJ, Roux B, Wanatabe M (2001) Computaional biochemistry and biophysics. Marcel Dekker, New YorkGoogle Scholar
  2. 2.
    Cui Q, Bahar I (2006) Normal mode analysis. Theory and applications to biological and chemical systems. CRC Press, Taylor & Francis Group, Boca Raton, FLGoogle Scholar
  3. 3.
    Leach AR (2001) Molecular modelling: principles and applications. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  4. 4.
    Schlick T (2002) Molecular modeling and simulation: an interdisciplinary guide. Springer, New YorkGoogle Scholar
  5. 5.
    Dror RO, Jensen MO, Borhani DW, Shaw DE (2010) Exploring atomic resolution physiology on a femtosecond to millisecond timescale using molecular dynamics simulations. J Gen Physiol 135:555–562PubMedGoogle Scholar
  6. 6.
    Grossfield A, Zuckerman DM (2009) Quantifying uncertainty and sampling quality in biomolecular simulations. Annu Rep Comput Chem 5:23–48PubMedGoogle Scholar
  7. 7.
    Bahar I (2010) On the functional significance of soft modes predicted by coarse-grained models for membrane proteins. J Gen Physiol 135:563–573PubMedGoogle Scholar
  8. 8.
    Bahar I, Lezon TR, Bakan A, Shrivastava IH (2010) Normal mode analysis of biomolecular structures: functional mechanisms of membrane proteins. Chem Rev 110:1463–1497PubMedGoogle Scholar
  9. 9.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242PubMedGoogle Scholar
  10. 10.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802PubMedGoogle Scholar
  11. 11.
    Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330:341–346PubMedGoogle Scholar
  12. 12.
    Grossfield A, Feller SE, Pitman MC (2007) Convergence of molecular dynamics simulations of membrane proteins. Proteins 67:31–40PubMedGoogle Scholar
  13. 13.
    Brooks B, Karplus M (1985) Normal modes for specific motions of macromolecules: application to the hinge-bending mode of lysozyme. Proc Natl Acad Sci U S A 82:4995–4999PubMedGoogle Scholar
  14. 14.
    Berendsen HJ, Hayward S (2000) Collective protein dynamics in relation to function. Curr Opin Struct Biol 10:165–169PubMedGoogle Scholar
  15. 15.
    Bahar I, Rader AJ (2005) Coarse-grained normal mode analysis in structural biology. Curr Opin Struct Biol 15:586–592PubMedGoogle Scholar
  16. 16.
    Bahar I, Lezon TR, Yang LW, Eyal E (2010) Global dynamics of proteins: bridging between structure and function. Annu Rev Biophys 39:23–42PubMedGoogle Scholar
  17. 17.
    Tirion MM (1996) Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys Rev Lett 77:1905–1908PubMedGoogle Scholar
  18. 18.
    Bahar I, Atilgan AR, Erman B (1997) Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Fold Des 2:173–181PubMedGoogle Scholar
  19. 19.
    Haliloglu T, Bahar I, Erman B (1997) Gaussian dynamics of folded proteins. Phys Rev Lett 79:3090–3093Google Scholar
  20. 20.
    Flory PJ (1976) Statistical thermodynamics of random networks. Proc R Soc London A 351:351–380Google Scholar
  21. 21.
    Atilgan AR, Durell SR, Jernigan RL, Demirel MC, Keskin O, Bahar I (2001) Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys J 80:505–515PubMedGoogle Scholar
  22. 22.
    Doruker P, Atilgan AR, Bahar I (2000) Dynamics of proteins predicted by molecular dynamics simulations and analytical approaches: application to alpha-amylase inhibitor. Proteins 40:512–524PubMedGoogle Scholar
  23. 23.
    Isin B, Doruker P, Bahar I (2002) Functional motions of influenza virus hemaglutinin: a structure-based analytical approach. Biophys J 82:569–581PubMedGoogle Scholar
  24. 24.
    Keskin O, Durrell SR, Bahar I, Jernigan RL, Covell DG (2002) Relating molecular flexibility to function. A case study of tubulin. Biophys J 83:663–680PubMedGoogle Scholar
  25. 25.
    Tama F, Sanejouand YH (2001) Conformational change of proteins arising from normal mode calculations. Protein Eng 14:1–6PubMedGoogle Scholar
  26. 26.
    Tama F, Brooks CL (2006) Symmetry, form, and shape: guiding principles for robustness in macromolecular machines. Annu Rev Biophys Biomol Struct 35:115–133PubMedGoogle Scholar
  27. 27.
    Eyal E, Yang LW, Bahar I (2006) Anisotropic network model: systematic evaluation and a new web interface. Bioinformatics 22:2619–2627PubMedGoogle Scholar
  28. 28.
    Ma J (2005) Usefulness and limitations of normal mode analysis in modeling dynamics of biomolecular complexes. Structure 13:373–380PubMedGoogle Scholar
  29. 29.
    Isin B, Schulten K, Tajkhorshid E, Bahar I (2008) Mechanism of signal propagation upon retinal isomerization: insights from molecular dynamics simulations of rhodopsin restrained by normal modes. Biophys J 95:789–803PubMedGoogle Scholar
  30. 30.
    Isralewitz B, Baudry J, Gullingsrud J, Kosztin D, Schulten K (2001) Steered molecular dynamics investigations of protein function. J Mol Graph Model 19:13–25PubMedGoogle Scholar
  31. 31.
    Amadei A, Linssen AB, Berendsen HJ (1993) Essential dynamics of proteins. Proteins 17:412–425PubMedGoogle Scholar
  32. 32.
    Amadei A, Linssen AB, de Groot BL, van Aalten DM, Berendsen HJ (1996) An efficient method for sampling the essential subspace of proteins. J Biomol Struct Dyn 13:615–625PubMedGoogle Scholar
  33. 33.
    de Groot BL, Amadei A, Scheek RM, van Nuland NA, Berendsen HJ (1996) An extended sampling of the configurational space of HPr from E. coli. Proteins 26:314–322PubMedGoogle Scholar
  34. 34.
    Abseher R, Nilges M (2000) Efficient sampling in collective coordinate space. Proteins 39:82–88PubMedGoogle Scholar
  35. 35.
    Zhang Z, Shi Y, Liu H (2003) Molecular dynamics simulations of peptides and proteins with amplified collective motions. Biophys J 84:3583–3593PubMedGoogle Scholar
  36. 36.
    Yan Q, Murphy-Ullrich JE, Song YH (2010) Structural insight into the role of thrombospondin-1 binding to calreticulin in calreticulin-induced focal adhesion disassembly. Biochemistry 49:3685–3694PubMedGoogle Scholar
  37. 37.
    Levitt M, Warshel A (1975) Computer simulation of protein folding. Nature 253:694–698PubMedGoogle Scholar
  38. 38.
    Levitt M (1976) A simplified representation of protein conformations for rapid simulation of protein folding. J Mol Biol 104:59–107PubMedGoogle Scholar
  39. 39.
    Sansom MS, Scott KA, Bond PJ (2008) Coarse-grained simulation: a high-throughput computational approach to membrane proteins. Biochem Soc Trans 36:27–32PubMedGoogle Scholar
  40. 40.
    Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, de Vries AH (2007) The MARTINI force field: coarse grained model for biomolecular simulations. J Phys Chem B 111:7812–7824PubMedGoogle Scholar
  41. 41.
    Bond PJ, Holyoake J, Ivetac A, Khalid S, Sansom MS (2007) Coarse-grained molecular dynamics simulations of membrane proteins and peptides. J Struct Biol 157:593–605PubMedGoogle Scholar
  42. 42.
    Psachoulia E, Fowler PW, Bond PJ, Sansom MS (2008) Helix-helix interactions in membrane proteins: coarse-grained simulations of glycophorin a helix dimerization. Biochemistry 47:10503–10512PubMedGoogle Scholar
  43. 43.
    Shih AY, Freddolino PL, Arkhipov A, Schulten K (2007) Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations. J Struct Biol 157:579–592PubMedGoogle Scholar
  44. 44.
    Shrivastava IH, Bahar I (2006) Common mechanism of pore opening shared by five different potassium channels. Biophys J 90:3929–3940PubMedGoogle Scholar
  45. 45.
    Valadie H, Lacapcre JJ, Sanejouand YH, Etchebest C (2003) Dynamical properties of the MscL of Escherichia coli: a normal mode analysis. J Mol Biol 332:657–674PubMedGoogle Scholar
  46. 46.
    Taly A, Delarue M, Grutter T, Nilges M, Le NN, Corringer PJ, Changeux JP (2005) Normal mode analysis suggests a quaternary twist model for the nicotinic receptor gating mechanism. Biophys J 88:3954–3965PubMedGoogle Scholar
  47. 47.
    Weng J, Ma J, Fan K, Wang W (2008) The conformational coupling and translocation mechanism of vitamin B12 ATP-binding cassette transporter BtuCD. Biophys J 94:612–621PubMedGoogle Scholar
  48. 48.
    Rader AJ, Anderson G, Isin B, Khorana HG, Bahar I, Klein-Seetharaman J (2004) Identification of core amino acids stabilizing rhodopsin. Proc Natl Acad Sci U S A 101:7246–7251PubMedGoogle Scholar
  49. 49.
    Isin B, Rader AJ, Dhiman HK, Klein-Seetharaman J, Bahar I (2006) Predisposition of the dark state of rhodopsin to functional changes in structure. Proteins 65:970–983PubMedGoogle Scholar
  50. 50.
    Bakan A, Bahar I (2009) The intrinsic dynamics of enzymes plays a dominant role in determining the structural changes induced upon inhibitor binding. Proc Natl Acad Sci U S A 106:14349–14354PubMedGoogle Scholar
  51. 51.
    Levitt M (2009) Nature of the protein universe. Proc Natl Acad Sci U S A 106:11079–11084PubMedGoogle Scholar
  52. 52.
    Ganapathiraju M, Jursa CJ, Karimi HA, Klein-Seetharaman J (2007) TMpro web server and web service: transmembrane helix prediction through amino acid property analysis. Bioinformatics 23:2795–2796PubMedGoogle Scholar
  53. 53.
    Yanamala N, Tirupula KC, Klein-Seetharaman J (2008) Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors. BMC Bioinformatics 9(Suppl 1):S16PubMedGoogle Scholar
  54. 54.
    Doruker P, Jernigan RL, Bahar I (2002) Dynamics of large proteins through hierarchical levels of coarse-grained structures. J Comput Chem 23:119–127PubMedGoogle Scholar
  55. 55.
    Chennubhotla C, Rader AJ, Yang LW, Bahar I (2005) Elastic network models for understanding biomolecular machinery: from enzymes to supramolecular assemblies. Phys Biol 2:S173–S180PubMedGoogle Scholar
  56. 56.
    Ming D, Kong YF, Lambert MA, Huang Z, Ma JP (2002) How to describe protein motion without amino acid sequence and atomic coordinates. Proc Natl Acad Sci U S A 99:8620–8625PubMedGoogle Scholar
  57. 57.
    Bahar I, Atilgan AR, Demirel MC, Erman B (1998) Vibrational dynamics of folded proteins: significance of slow and fast motions in relation to function and stability. Phys Rev Lett 80:2733–2736Google Scholar
  58. 58.
    Demirel MC, Atilgan AR, Jernigan RL, Erman B, Bahar I (1998) Identification of kinetically hot residues in proteins. Protein Sci 7:2522–2532PubMedGoogle Scholar
  59. 59.
    Sakmar TP, Menon ST, Marin EP, Awad ES (2002) Rhodopsin: insights from recent structural studies. Annu Rev Biophys Biomol Struct 31:443–484PubMedGoogle Scholar
  60. 60.
    Gether U (2000) Uncovering molecular mechanisms involved in activation of G protein-coupled receptors. Endocr Rev 21:90–113PubMedGoogle Scholar
  61. 61.
    Klein-Seetharaman J (2002) Dynamics in rhodopsin. Chembiochem 3:981–986PubMedGoogle Scholar
  62. 62.
    Meng EC, Bourne HR (2001) Receptor activation: what does the rhodopsin structure tell us? Trends Pharmacol Sci 22:587–593PubMedGoogle Scholar
  63. 63.
    Lambright DG, Sondek J, Bohm A, Skiba NP, Hamm HE, Sigler PB (1996) The 2.0 angstrom crystal structure of a heterotrimeric G protein46. Nature 379:311–319PubMedGoogle Scholar
  64. 64.
    Changeux JP, Edelstein SJ (2005) Allosteric mechanisms of signal transduction. Science 308:1424–1428PubMedGoogle Scholar
  65. 65.
    Eisenmesser EZ, Millet O, Labeikovsky W, Korzhnev DM, Wolf-Watz M, Bosco DA, Skalicky JJ, Kay LE, Kern D (2005) Intrinsic dynamics of an enzyme underlies catalysis. Nature 438:117–121PubMedGoogle Scholar
  66. 66.
    Lange OF, Lakomek NA, Fares C, Schroder GF, Walter KF, Becker S, Meiler J, Grubmuller H, Griesinger C, de Groot BL (2008) Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution. Science 320:1471–1475PubMedGoogle Scholar
  67. 67.
    Yang LW, Eyal E, Bahar I, Kitao A (2009) Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics. Bioinformatics 25:606–614PubMedGoogle Scholar
  68. 68.
    Grassetti DR, Murray JF Jr (1967) Determination of sulfhydryl groups with 2,2′- or 4,4′-dithiodipyridine. Arch Biochem Biophys 119:41–49PubMedGoogle Scholar
  69. 69.
    Dutta A, Tirupula KC, Alexiev U, Klein-Seetharaman J (2010) Characterization of membrane protein non-native states. 1. Extent of unfolding and aggregation of rhodopsin in the presence of chemical denaturants. Biochemistry 49:6317–6328PubMedGoogle Scholar
  70. 70.
    Hubbell WL, Gross A, Langen R, Lietzow MA (1998) Recent advances in site-directed spin labeling of proteins. Curr Opin Struct Biol. 8:649–656PubMedGoogle Scholar
  71. 71.
    Resek JF, Farahbakhsh ZT, Hubbell WL, Khorana HG (1993) Formation of the meta II photointermediate is accompanied by conformational changes in the cytoplasmic surface of rhodopsin. Biochemistry 32:12025–12032PubMedGoogle Scholar
  72. 72.
    Altenbach C, Yang K, Farrens DL, Farahbakhsh ZT, Khorana HG, Hubbell WL (1996) Structural features and light-dependent changes in the cytoplasmic interhelical E-F loop region of rhodopsin: a site-directed spin-labeling study. Biochemistry 35:12470–12478PubMedGoogle Scholar
  73. 73.
    Altenbach C, Klein-Seetharaman J, Hwa J, Khorana HG, Hubbell WL (1999) Structural features and light-dependent changes in the sequence 59–75 connecting helices I and II in rhodopsin: a site-directed spin-labeling study. Biochemistry 38:7945–7949PubMedGoogle Scholar
  74. 74.
    Altenbach C, Cai K, Khorana HG, Hubbell WL (1999) Structural features and light-dependent changes in the sequence 306–322 extending from helix VII to the palmitoylation sites in rhodopsin: a site-directed spin-labeling study. Biochemistry 38:7931–7937PubMedGoogle Scholar
  75. 75.
    Cai K, Langen R, Hubbell WL, Khorana HG (1997) Structure and function in rhodopsin: topology of the C-terminal polypeptide chain in relation to the cytoplasmic loops. Proc Natl Acad Sci U S A 94:14267–14272PubMedGoogle Scholar
  76. 76.
    Farahbakhsh ZT, Ridge KD, Khorana HG, Hubbell WL (1995) Mapping light-dependent structural changes in the cytoplasmic loop connecting helices C and D in rhodopsin: a site-directed spin labeling study. Biochemistry 34:8812–8819PubMedGoogle Scholar
  77. 77.
    Farrens DL, Altenbach C, Yang K, Hubbell WL, Khorana HG (1996) Requirement of rigid-body motion of transmembrane helices for light activation of rhodopsin. Science 274:768–770PubMedGoogle Scholar
  78. 78.
    Hubbell WL, Altenbach C, Hubbell CM, Khorana HG (2003) Rhodopsin structure, dynamics, and activation: a perspective from crystallography, site-directed spin labeling, sulfhydryl reactivity, and disulfide cross-linking. Adv Protein Chem 63:243–290PubMedGoogle Scholar
  79. 79.
    Klein-Seetharaman J, Hwa J, Cai K, Altenbach C, Hubbell WL, Khorana HG (2001) Probing the dark state tertiary structure in the cytoplasmic domain of rhodopsin: proximities between amino acids deduced from spontaneous disulfide bond formation between Cys316 and engineered cysteines in cytoplasmic loop 1. Biochemistry 40:12472–12478PubMedGoogle Scholar
  80. 80.
    Rasmussen SG, Choi HJ, Rosenbaum DM, Kobilka TS, Thian FS, Edwards PC, Burghammer M, Ratnala VR, Sanishvili R, Fischetti RF, Schertler GF, Weis WI, Kobilka BK (2007) Crystal structure of the human beta2 adrenergic G-protein-coupled receptor. Nature 450:383–387PubMedGoogle Scholar
  81. 81.
    Rosenbaum DM, Zhang C, Lyons JA, Holl R, Aragao D, Arlow DH, Rasmussen SG, Choi HJ, Devree BT, Sunahara RK, Chae PS, Gellman SH, Dror RO, Shaw DE, Weis WI, Caffrey M, Gmeiner P, Kobilka BK (2011) Structure and function of an irreversible agonist-beta(2) adrenoceptor complex. Nature 469:236–240PubMedGoogle Scholar
  82. 82.
    Warne T, Moukhametzianov R, Baker JG, Nehme R, Edwards PC, Leslie AG, Schertler GF, Tate CG (2011) The structural basis for agonist and partial agonist action on a beta(1)-adrenergic receptor. Nature 469:241–244PubMedGoogle Scholar
  83. 83.
    Altenbach C, Cai K, Klein-Seetharaman J, Khorana HG, Hubbell WL (2001) Structure and function in rhodopsin: mapping light-dependent changes in distance between residue 65 in helix TM1 and residues in the sequence 306–319 at the cytoplasmic end of helix TM7 and in helix H8. Biochemistry 40:15483–15492PubMedGoogle Scholar
  84. 84.
    Cai K, Klein-Seetharaman J, Farrens D, Zhang C, Altenbach C, Hubbell WL, Khorana HG (1999) Single-cysteine substitution mutants at amino acid positions 306–321 in rhodopsin, the sequence between the cytoplasmic end of helix VII and the palmitoylation sites: sulfhydryl reactivity and transducin activation reveal a tertiary structure. Biochemistry 38:7925–7930PubMedGoogle Scholar
  85. 85.
    Cai K, Klein-Seetharaman J, Altenbach C, Hubbell WL, Khorana HG (2001) Probing the dark state tertiary structure in the cytoplasmic domain of rhodopsin: proximities between amino acids deduced from spontaneous disulfide bond formation between cysteine pairs engineered in cytoplasmic loops 1, 3, and 4. Biochemistry 40:12479–12485PubMedGoogle Scholar
  86. 86.
    Klein-Seetharaman J, Hwa J, Cai K, Altenbach C, Hubbell WL, Khorana HG (1999) Single-cysteine substitution mutants at amino acid positions 55–75, the sequence connecting the cytoplasmic ends of helices I and II in rhodopsin: reactivity of the sulfhydryl groups and their derivatives identifies a tertiary structure that changes upon light-activation. Biochemistry 38:7938–7944PubMedGoogle Scholar
  87. 87.
    Okada T, Fujiyoshi Y, Silow M, Navarro J, Landau EM, Shichida Y (2002) Functional role of internal water molecules in rhodopsin revealed by X- ray crystallography. Proc Natl Acad Sci U S A 99:5982–5987PubMedGoogle Scholar
  88. 88.
    Dror RO, Arlow DH, Borhani DW, Jensen MO, Piana S, Shaw DE (2009) Identification of two distinct inactive conformations of the beta2-adrenergic receptor reconciles structural and biochemical observations. Proc Natl Acad Sci U S A 106:4689–4694PubMedGoogle Scholar
  89. 89.
    Han DS, Wang SX, Weinstein H (2008) Active state-like conformational elements in the beta2-AR and a photoactivated intermediate of rhodopsin identified by dynamic properties of GPCRs. Biochemistry 47:7317–7321PubMedGoogle Scholar
  90. 90.
    Romo TD, Grossfield A, Pitman MC (2010) Concerted interconversion between ionic lock substrates of the beta(2) adrenergic receptor revealed by microsecond timescale molecular dynamics. Biophys J 98:76–84PubMedGoogle Scholar
  91. 91.
    Borhan B, Souto ML, Imai H, Shichida Y, Nakanishi K (2000) Movement of retinal along the visual transduction path. Science 288:2209–2212PubMedGoogle Scholar
  92. 92.
    Nakayama TA, Khorana HG (1990) Orientation of retinal in bovine rhodopsin determined by cross-linking using a photoactivatable analog of 11-cis-retinal. J Biol Chem 265:15762–15769PubMedGoogle Scholar
  93. 93.
    Patel AB, Crocker E, Eilers M, Hirshfeld A, Sheves M, Smith SO (2004) Coupling of retinal isomerization to the activation of rhodopsin. Proc Natl Acad Sci U S A 101:10048–10053PubMedGoogle Scholar
  94. 94.
    Strader CD, Candelore MR, Hill WS, Sigal IS, Dixon RA (1989) Identification of two serine residues involved in agonist activation of the beta-adrenergic receptor. J Biol Chem 264:13572–13578PubMedGoogle Scholar
  95. 95.
    Cai K, Klein-Seetharaman J, Hwa J, Hubbell WL, Khorana HG (1999) Structure and function in rhodopsin: effects of disulfide cross-links in the cytoplasmic face of rhodopsin on transducin activation and phosphorylation by rhodopsin kinase. Biochemistry 38:12893–12898PubMedGoogle Scholar
  96. 96.
    Bourne HR (1997) How receptors talk to trimeric G proteins. Curr Opin Cell Biol 9:134–142PubMedGoogle Scholar
  97. 97.
    Hwa J, Reeves PJ, Klein-Seetharaman J, Davidson F, Khorana HG (1999) Structure and function in rhodopsin: further elucidation of the role of the intradiscal cysteines, Cys-110, -185, and -187, in rhodopsin folding and function. Proc Natl Acad Sci U S A 96:1932–1935PubMedGoogle Scholar
  98. 98.
    Richards JE, Scott KM, Sieving PA (1995) Disruption of conserved rhodopsin disulfide bond by Cys187Tyr mutation causes early and severe autosomal dominant retinitis pigmentosa. Ophthalmology 102:669–677PubMedGoogle Scholar
  99. 99.
    Vaithinathan R, Berson EL, Dryja TP (1994) Further screening of the rhodopsin gene in patients with autosomal dominant retinitis pigmentosa. Genomics 21:461–463PubMedGoogle Scholar
  100. 100.
    Hwa J, Klein-Seetharaman J, Khorana HG (2001) Structure and function in rhodopsin: mass spectrometric identification of the abnormal intradiscal disulfide bond in misfolded retinitis pigmentosa mutants. Proc Natl Acad Sci U S A 98:4872–4876PubMedGoogle Scholar
  101. 101.
    Berson EL (1993) Retinitis pigmentosa. The Friedenwald lecture. Invest Ophthalmol Vis Sci 34:1659–1676PubMedGoogle Scholar
  102. 102.
    Olsson JE, Gordon JW, Pawlyk BS, Roof D, Hayes A, Molday RS, Mukai S, Cowley GS, Berson EL, Dryja TP (1992) Transgenic mice with a rhodopsin mutation (Pro23His): a mouse model of autosomal dominant retinitis pigmentosa. Neuron 9:815–830PubMedGoogle Scholar
  103. 103.
    Wang M, Lam TT, Tso MO, Naash MI (1997) Expression of a mutant opsin gene increases the susceptibility of the retina to light damage. Vis Neurosci 14:55–62PubMedGoogle Scholar
  104. 104.
    Farrens DL, Khorana HG (1995) Structure and function in rhodopsin. Measurement of the rate of metarhodopsin II decay by fluorescence spectroscopy. J Biol Chem 270:5073–5076PubMedGoogle Scholar
  105. 105.
    Yang LW, Liu X, Jursa CJ, Holliman M, Rader AJ, Karimi HA, Bahar I (2005) iGNM: a database of protein functional motions based on Gaussian Network Model. Bioinformatics 21:2978–2987PubMedGoogle Scholar
  106. 106.
    Yang LW, Rader AJ, Liu X, Jursa CJ, Chen SC, Karimi HA, Bahar I (2006) oGNM: online computation of structural dynamics using the Gaussian Network Model. Nucleic Acids Res 34:W24–W31PubMedGoogle Scholar
  107. 107.
    Bakan A, Meireles LM, Bahar I (2011) ProDy: protein dynamics inferred from theory and experiments. Bioinformatics 27(11):1575–1577PubMedGoogle Scholar
  108. 108.
    Bruschweiler R (1995) Collective protein dynamics and nuclear-spin relaxation. J Chem Phys 102:3396–3403Google Scholar
  109. 109.
    Tellez-Sanz R, Cesareo E, Nuccetelli M, Aguilera AM, Baron C, Parker LJ, Adams JJ, Morton CJ, Lo BM, Parker MW, Garcia-Fuentes L (2006) Calorimetric and structural studies of the nitric oxide carrier S-nitrosoglutathione bound to human glutathione transferase P1-1. Protein Sci 15:1093–1105PubMedGoogle Scholar
  110. 110.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Biol 14:33–38Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Basak Isin
    • 1
  • Kalyan C. Tirupula
    • 2
  • Zoltán N. Oltvai
    • 1
  • Judith Klein-Seetharaman
    • 2
  • Ivet Bahar
    • 3
  1. 1.Department of PathologyUniversity of Pittsburgh School of MedicinePittsburghUSA
  2. 2.Department of Structural BiologyUniversity of Pittsburgh School of MedicinePittsburghUSA
  3. 3.Department of Computational and Systems Biology, School of MedicineUniversity of PittsburghPittsburghUSA

Personalised recommendations