Abstract
Intrinsically disordered proteins (IDPs) are involved in a wide range of essential biological processes, including in particular signalling and regulation. We are only beginning, however, to develop a detailed knowledge of the structure and dynamics of these proteins. It is becoming increasingly clear that, as IDPs populate highly heterogeneous states, they should be described in terms of conformational ensembles rather than as individual structures, as is instead most often the case for the native states of globular proteins. Within this context, in this chapter we describe the conceptual tools and methodological aspects associated with the description of the structure and dynamics of IDPs in terms of conformational ensembles. A major emphasis is given to methods in which molecular simulations are used in combination with experimental nuclear magnetic resonance (NMR) measurements, as they are emerging as a powerful route to achieve an accurate determination of the conformational properties of IDPs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
IDPbyNMR (High resolution tools to understand the functional role of protein intrinsic disorder) is a Marie Curie activity funded under the FP7 people programme, project number 264257; http://www.idpbynmr.eu/home/.
- 3.
References
Baker CM, Best RB (2013) Matching of additive and polarizable force fields for multiscale condensed phase simulations. J Chem Theor Comp 9(6):2826–2837
Bernadό P, Bertoncini CW, Griesinger C et al (2005) Defining long-range order and local disorder in native αsynuclein using residual dipolar couplings. J Am Chem Soc 127(51):17968–17969
Best RB (2012) Atomistic molecular simulations of protein folding. Curr Op Struct Biol 22(1):52–61
Blundell TL, Johnson LN (1976) Protein crystallography. Academic Press, New York
Board JA Jr, Causey JW, Leathrum JF Jr et al (1992) Accelerated molecular dynamics simulation with the parallel fast multipole algorithm. Chem Phys Lett 198(1):89–94
Boehr DD, Nussinov R, Wright PE (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 5(11):789–796
Bonvin A, Brunger AT (1995) Conformational variability of solution nuclear-magnetic-resonance structures. J Mol Biol 250(1):80–93. doi:10.1006/jmbi.1995.0360
Bonvin A, Boelens R, Kaptein R (1994) Time-averaged and ensemble-averaged direct NOE restraints. J Biomol NMR 4(1):143–149
Boomsma W, Ferkinghoff-Borg J, Lindorff-Larsen K (2014) Combining experiments and simulations using the maximum entropy principle. PLoS Comp Biol 10(2):e1003406
Bottaro S, Lindorff-Larsen K, Best RB (2013) Variational optimization of an all-atom implicit solvent force field to match explicit solvent simulation data. J Chem Theor Comp 9(12):5641–5652
Brooks BR, Bruccoleri RE, Olafson BD et al (1983) CHARMM—a program for macromolecular energy, minimization, and dynamics calculations. J Comp Chem 4(2):187–217
Brunger AT, Adams PD, Clore GM et al (1998) Crystallography & NMR system: a new software suite for macromolecular structure determination. Acta Crystallogr D 54:905–921
Burgi R, Pitera J, van Gunsteren WF (2001) Assessing the effect of conformational averaging on the measured values of observables. J Biomol NMR 19(4):305–320. doi:10.1023/a:1011295422203
Camilloni C, Vendruscolo M (2014) Statistical mechanics of the denatured state of a protein using replica-averaged metadynamics. J Am Chem Soc 136:8982–8991
Camilloni C, Cavalli A, Vendruscolo M (2013) Replica-averaged metadynamics. J Chem Theor Comp 9(12):5610–5617
Cavalli A, Camilloni C, Vendruscolo M (2013) Molecular dynamics simulations with replica-averaged structural restraints generate structural ensembles according to the maximum entropy principle. J Chem Phys 138(9):094112
Chandler D (1987) Introduction to modern statistical mechanics. Oxford University Press, New York
Choy WY, Forman-Kay JD (2001) Calculation of ensembles of structures representing the unfolded state of an SH3 domain. J Mol Biol 308(5):1011–1032
Clore GM, Schwieters CD (2004) How much backbone motion in ubiquitin is required to account for dipolar coupling data measured in multiple alignment media as assessed by independent cross-validation? J Am Chem Soc 126(9):2923–2938
Constantine KL, Mueller L, Andersen NH et al (1995) Structural and dynamic properties of a βhairpin-forming linear peptide.1. Modeling using ensemble-averaged constraints. J Am Chem Soc 117(44):10841–10854. doi:10.1021/ja00149a007
Das R, Baker D (2008) Macromolecular modeling with Rosetta. Annu Rev Biochem 77:363–382
Dedmon MM, Lindorff-Larsen K, Christodoulou J et al (2005) Mapping long-range interactions in αsynuclein using spin-label NMR and ensemble molecular dynamics simulations. J Am Chem Soc 127(2):476–477
Dyson HJ, Wright PE (2005) Intrinsically unstructured proteins and their functions. Nat Rev Mol Cell Biol 6(3):197–208
Fennen J, Torda AE, van Gunsteren WF (1995) Structure refinement with molecular-dynamics and a Boltzmann-weighted ensemble. J Biomol NMR 6(2):163–170
Fersht AR (1999) Structure and mechanism in protein science: a guide to enzyme catalysis and protein folding. W. H. Freeman, New York
Francis CJ, Lindorff-Larsen K, Best RB et al (2006) Characterization of the residual structure in the unfolded state of the ∆131∆ fragment of staphylococcal nuclease. Proteins 65(1):145–152
Frauenfelder H, Sligar SG, Wolynes PG (1991) The energy landscapes and motions of proteins. Science 254(5038):1598–1603
Gillespie JR, Shortle D (1997) Characterization of long-range structure in the denatured state of staphylococcal nuclease. 2. Distance restraints from paramagnetic relaxation and calculation of an ensemble of structures. J Mol Biol 268(1):170–184
Grishaev A, Bax A (2004) An empirical backbone-backbone hydrogen-bonding potential in proteins and its applications to NMR structure refinement and validation. J Am Chem Soc 126(23):7281–7292
Gsponer J, Hopearuoho H, Whittaker SBM et al (2006) Determination of an ensemble of structures representing the intermediate state of the bacterial immunity protein Im7. Proc Natl Acad Sci U S A 103(1):99–104
Haas E (2005) The study of protein folding and dynamics by determination of intramolecular distance distributions and their fluctuations using ensemble and single-molecule FRET measurements. ChemPhysChem 6(5):858–870
Heise H, Luca S, de Groot BL et al (2005) Probing conformational disorder in neurotensin by two-dimensional solid-state NMR and comparison to molecular dynamics simulations. Bioph J 89(3):2113–2120
Hornak V, Abel R, Okur A et al (2006) Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725
Hub JS, De Groot BL, Van Der Spoel D (2010) g_wham—a free weighted histogram analysis implementation including robust error and autocorrelation estimates. J Chem Theor Comp 6(12):3713–3720
Karplus M, Kuriyan J (2005) Molecular dynamics and protein function. Proc Natl Acad Sci U S A 102(19):6679–6685
Kemmink J, Scheek RM (1995) Dynamic modeling of a helical peptide in solution using NMR data—multiple conformations and multi-spin effects. J Biomol NMR 6(1):33–40. doi:10.1007/bf00417489
Kessler H, Griesinger C, Lautz J et al (1988) Conformational dynamics detected by nuclear magnetic-resonance NOE values and J-coupling constants. J Am Chem Soc 110(11):3393–3396. doi:10.1021/ja00219a008
Klein-Seetharaman J, Oikawa M, Grimshaw SB et al (2002) Long-range interactions within a nonnative protein. Science 295(5560):1719–1722
Knott M, Best RB (2012) A preformed binding interface in the unbound ensemble of an intrinsically disordered protein: evidence from molecular simulations. PLoS Comp Biol 8(7):e1002605
Knowles TP, Vendruscolo M, Dobson CM (2014) The amyloid state and its association with protein misfolding diseases. Nat Rev Mol Cell Biol 15(6):384–396
Korzhnev DM, Salvatella X, Vendruscolo M et al (2004) Low-populated folding intermediates of Fyn SH3 characterized by relaxation dispersion NMR. Nature 430(6999):586–590
Krzeminski M, Marsh JA, Neale C et al (2013) Characterization of disordered proteins with ensemble. Bioinformatics 29(3):398–399
Kumar S, Rosenberg JM, Bouzida D et al (1992) The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comp Chem 13(8):1011–1021
Laio A, Gervasio FL (2008) Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep Prog Phys 71(12):126601
Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci U S A 99(20):12562–12566
Lange OF, Lakomek N-A, Farès C et al (2008) Recognition dynamics up to microseconds revealed from an RDC-derived ubiquitin ensemble in solution. Science 320(5882):1471–1475
Lindorff-Larsen K, Kristjansdottir S, Teilum K et al (2004) Determination of an ensemble of structures representing the denatured state of the bovine acyl-coenzyme A binding protein. J Am Chem Soc 126(10):3291–3299
Lindorff-Larsen K, Best RB, DePristo MA et al (2005) Simultaneous determination of protein structure and dynamics. Nature 433(7022):128–132
Lindorff-Larsen K, Maragakis P, Piana S et al (2012a) Systematic validation of protein force fields against experimental data. PLoS ONE 7(2):e32131
Lindorff-Larsen K, Trbovic N, Maragakis P et al (2012b) Structure and dynamics of an unfolded protein examined by molecular dynamics simulation. J Am Chem Soc 134(8):3787–3791
Markwick PR, Bouvignies G, Blackledge M (2007) Exploring multiple timescale motions in protein GB3 using accelerated molecular dynamics and NMR spectroscopy. J Am Chem Soc 129(15):4724–4730
Mittermaier A, Kay LE (2006) New tools provide new insights in NMR studies of protein dynamics. Science 312(5771):224–228
Moglich A, Joder K, Kiefhaber T (2006) End-to-end distance distributions and intrachain diffusion constants in unfolded polypeptide chains indicate intramolecular hydrogen bond formation. Proc Natl Acad Sci U S A 103(33):12394–12399
Monticelli L, Kandasamy SK, Periole X et al (2008) The Martini coarse-grained force field: extension to proteins. J Chem Theor Comp 4(5):819–834
Moult J, Fidelis K, Kryshtafovych A et al (2014) Critical assessment of methods of protein structure prediction (CASP)—round X. Proteins 82(S2):1–6
Piana S, Klepeis JL, Shaw DE (2014) Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Curr Op Struct Biol 24:98–105
Pitera JW, Chodera JD (2012) On the use of experimental observations to bias simulated ensembles. J Chem Theor Comp 8(10):3445–3451
Rosato A, Bagaria A, Baker D et al (2009) CASD-NMR: critical assessment of automated structure determination by NMR. Nat Methods 6(9):625–626
Rosato A, Aramini JM, Arrowsmith C et al (2012) Blind testing of routine, fully automated determination of protein structures from NMR data. Structure 20(2):227–236
Roux B, Weare J (2013) On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method. J Chem Phys 138(8):084107
Schuler B, Lipman EA, Eaton WA (2002) Probing the free-energy surface for protein folding with single-molecule fluorescence spectroscopy. Nature 419(6908):743–747
Schwieters CD, Kuszewski JJ, Clore GM (2006) Using Xplor-NIH for NMR molecular structure determination. Prog Nucl Mag Res Spectrosc 48(1):47–62
Shaw DE, Maragakis P, Lindorff-Larsen K et al (2010) Atomic-level characterization of the structural dynamics of proteins. Science 330(6002):341–346
Sherman E, Haran G (2006) Coil-globule transition in the denatured state of a small protein. Proc Natl Acad Sci U S A 103(31):11539–11543
Smith LJ, Bolin KA, Schwalbe H et al (1996) Analysis of main chain torsion angles in proteins: Prediction of NMR coupling constants for native and random coil conformations. J Mol Biol 255(3):494–506
Spronk C, Nabuurs SB, Krieger E et al (2004) Validation of protein structures derived by NMR spectroscopy. Prog Nucl Mag Res Spectrosc 45(3–4):315–337
Torda AE, Scheek RM, van Gunsteren WF (1989) Time-dependent distance restraints in molecular-dynamics simulations. Chem Phys Lett 157(4):289–294. doi:10.1016/0009-2614(89)87249-5
Tozzini V (2005) Coarse-grained models for proteins. Curr Op Struct Biol 15(2):144–150
Uversky VN (2013) A decade and a half of protein intrinsic disorder: Biology still waits for Physics. Protein Sci 22(6):693–724
van Kampen NG (1992) Stochastic processes in physics and chemistry. North-Holland, Amsterdam, New York
Varadi M, Kosol S, Lebrun P et al (2014) pE-DB: A database of structural ensembles of intrinsically disordered and of unfolded proteins. Nucl Acids Res 42(D1):D326–D335
Vendruscolo M, Dobson CM (2006) Dynamic visions of enzymatic reactions. Science 313(5793):1586–1587
Vendruscolo M (2007) Structure determination of highly heterogenous states of proteins. Curr Op Struct Biol 17:15–20
Vendruscolo M, Dobson CM (2011) Protein dynamics: Moore’s law in molecular biology. Curr Biol 21(2):R68–R70
Wüthrich K (1986) NMR of proteins and nucleic acids. Wiley, New York
Zhu F, Hummer G (2012) Convergence and error estimation in free energy calculations using the weighted histogram analysis method. J Comp Chem 33(4):453–465
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Fu, B., Vendruscolo, M. (2015). Structure and Dynamics of Intrinsically Disordered Proteins. In: Felli, I., Pierattelli, R. (eds) Intrinsically Disordered Proteins Studied by NMR Spectroscopy. Advances in Experimental Medicine and Biology, vol 870. Springer, Cham. https://doi.org/10.1007/978-3-319-20164-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-20164-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20163-4
Online ISBN: 978-3-319-20164-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)