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Autophagy pp 17-56 | Cite as

Structural Studies of Autophagy-Related Proteins

  • Melanie Schwarten
  • Oliver H. Weiergräber
  • Dušan Petrović
  • Birgit Strodel
  • Dieter WillboldEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1880)

Abstract

Information about the structure and dynamics of proteins is crucial for understanding their physiological functions as well as for the development of strategies to modulate these activities. In this chapter we will describe the work packages required to determine the three-dimensional structures of proteins involved in autophagy by using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. Further we will provide instructions how to perform a molecular dynamics (MD) simulation using GABARAP as example protein.

Key words

X-ray crystallography NMR spectroscopy MD simulation 

Notes

Acknowledgments

The authors acknowledge funding by the Deutsche Forschungsgemeinschaft (SFB1208 and SFB974) and the Jürgen Manchot Stiftung.

References

  1. 1.
    Weiergräber OH, Stangler T, Thielmann Y, Mohrlüder J, Wiesehan K, Willbold D (2008) Ligand binding mode of GABAA receptor-associated protein. J Mol Biol 381:1320–1331CrossRefGoogle Scholar
  2. 2.
    Thielmann Y, Weiergräber OH, Mohrlüder J, Willbold D (2009) Structural framework of the GABARAP-calreticulin interface—implications for substrate binding to endoplasmic reticulum chaperones. FEBS J 276:1140–1152CrossRefGoogle Scholar
  3. 3.
    Ma P, Schillinger O, Schwarten M, Lecher J, Hartmann R, Stoldt M, Mohrlüder J, Olubiyi O, Strodel B, Willbold D, Weiergräber OH (2015) Conformational polymorphism in autophagy-related protein GATE-16. Biochemistry 54:5469–5479CrossRefGoogle Scholar
  4. 4.
    Michel M, Schwarten M, Decker C, Nagel-Steger L, Willbold D, Weiergräber OH (2015) The mammalian autophagy initiator complex contains 2 HORMA domain proteins. Autophagy 11:2300–2308CrossRefGoogle Scholar
  5. 5.
    Stangler T, Mayr LM, Dingley AJ, Luge C, Willbold D (2001) Sequence-specific 1H, 13C and 15N resonance assignments of human GABA receptor associated protein. J Biomol NMR 21:183–184CrossRefGoogle Scholar
  6. 6.
    Schwarten M, Stoldt M, Mohrlüder J, Willbold D (2009) Sequence-specific 1H, 13C, and 15N resonance assignment of the autophagy-related protein Atg8. Biomol NMR Assign 3:137–139CrossRefGoogle Scholar
  7. 7.
    Pervushin K, Riek R, Wider G, Wüthrich K (1997) Attenuated T2 relaxation by mutual cancellation of dipole-dipole coupling and chemical shift anisotropy indicates an avenue to NMR structures of very large biological macromolecules in solution. Proc Natl Acad Sci U S A 94:12366–12371CrossRefGoogle Scholar
  8. 8.
    Schanda P, Van Melckebeke H, Brutscher B (2006) Speeding up three-dimensional protein NMR experiments to a few minutes. J Am Chem Soc 128:9042–9043CrossRefGoogle Scholar
  9. 9.
    Solyom Z, Schwarten M, Geist L, Konrat R, Willbold D, Brutscher B (2013) BEST-TROSY experiments for time-efficient sequential resonance assignment of large disordered proteins. J Biomol NMR 55:311–321CrossRefGoogle Scholar
  10. 10.
    Stangler T, Mayr LM, Willbold D (2002) Solution structure of human GABA(A) receptor-associated protein GABARAP: implications for biological function and its regulation. J Biol Chem 277:13363–13366CrossRefGoogle Scholar
  11. 11.
    Sattler M, Schleucher J, Griesinger C (1999) Heteronuclear multidimensional NMR experiments for the structure determination of proteins in solution employing pulsed field gradients. Prog Nucl Magn Reson Spectrosc 34:93–158CrossRefGoogle Scholar
  12. 12.
    Vranken WF, Boucher W, Stevens TJ, Fogh RH, Pajon A, Llinas M, Ulrich EL, Markley JL, Ionides J, Laue ED (2005) The CCPN data model for NMR spectroscopy: development of a software pipeline. Proteins 59:687–696CrossRefGoogle Scholar
  13. 13.
    Yamazaki T, Formankay JD, Kay LE (1993) 2-dimensional Nmr experiments for correlating C-13-Beta and H-1-Delta/epsilon chemical-shifts of aromatic residues in C-13-Labeled proteins via scalar couplings. J Am Chem Soc 115:11054–11055CrossRefGoogle Scholar
  14. 14.
    Rieping W, Habeck M, Bardiaux B, Bernard A, Malliavin TE, Nilges M (2007) ARIA2: automated NOE assignment and data integration in NMR structure calculation. Bioinformatics 23:381–382CrossRefGoogle Scholar
  15. 15.
    Shen Y, Bax A (2013) Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. J Biomol NMR 56:227–241CrossRefGoogle Scholar
  16. 16.
    Brünger AT, Adams PD, Clore GM, DeLano WL, Gros P, Grosse-Kunstleve RW, Jiang JS, Kuszewski J, Nilges M, Pannu NS, Read RJ, Rice LM, Simonson T, Warren GL (1998) Crystallography & NMR system: a new software suite for macromolecular structure determination. Acta Crystallogr D Biol Crystallogr 54:905–921CrossRefGoogle Scholar
  17. 17.
    Güntert P (2004) Automated NMR structure calculation with CYANA. Methods Mol Biol 278:353–378PubMedGoogle Scholar
  18. 18.
    Schwarten M, Stoldt M, Mohrlüder J, Willbold D (2010) Solution structure of Atg8 reveals conformational polymorphism of the N-terminal domain. Biochem Biophys Res Commun 395:426–431CrossRefGoogle Scholar
  19. 19.
    Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8:477–486CrossRefGoogle Scholar
  20. 20.
    Chen VB, Arendall WB 3rd, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66:12–21CrossRefGoogle Scholar
  21. 21.
    Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25CrossRefGoogle Scholar
  22. 22.
    Pronk S, Pall S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E (2013) GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29:845–854CrossRefGoogle Scholar
  23. 23.
    Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78:1950–1958PubMedPubMedCentralGoogle Scholar
  24. 24.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935CrossRefGoogle Scholar
  25. 25.
    Olsson MH, Søndergaard CR, Rostkowski M, Jensen JH (2011) PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J Chem Theory Comput 7:525–537CrossRefGoogle Scholar
  26. 26.
    Anandakrishnan R, Aguilar B, Onufriev AV (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res 40:W537–W541CrossRefGoogle Scholar
  27. 27.
    Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:014101CrossRefGoogle Scholar
  28. 28.
    Hess B (2008) P-LINCS: a parallel linear constraint solver for molecular simulation. J Chem Theory Comput 4:116–122CrossRefGoogle Scholar
  29. 29.
    Berendsen HJC, Postma JPM, Vangunsteren WF, Dinola A, Haak JR (1984) Molecular-dynamics with coupling to an external bath. J Chem Phys 81:3684–3690CrossRefGoogle Scholar
  30. 30.
    Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182–7190CrossRefGoogle Scholar
  31. 31.
    Miao YL, McCammon JA (2016) Unconstrained enhanced sampling for free energy calculations of biomolecules: a review. Mol Simul 42:1046–1055CrossRefGoogle Scholar
  32. 32.
    Bernardi RC, Melo MCR, Schulten K (2015) Enhanced sampling techniques in molecular dynamics simulations of biological systems. BBA-Gen Subjects 1850:872–877CrossRefGoogle Scholar
  33. 33.
    Abrams C, Bussi G (2014) Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration. Entropy 16:163–199CrossRefGoogle Scholar
  34. 34.
    Klenin K, Strodel B, Wales DJ, Wenzel W (2011) Modelling proteins: conformational sampling and reconstruction of folding kinetics. Biochim Biophys Acta 1814:977–1000CrossRefGoogle Scholar
  35. 35.
    Bussi G (2013) Hamiltonian replica exchange in GROMACS: a flexible implementation. Mol Phys 112:379–384CrossRefGoogle Scholar
  36. 36.
    Wang LL, Friesner RA, Berne BJ (2011) Replica exchange with solute scaling: a more efficient version of replica exchange with solute tempering (REST2). J Phys Chem B 115:9431–9438CrossRefGoogle Scholar
  37. 37.
    Bonomi M, Branduardi D, Bussi G, Camilloni C, Provasi D, Raiteri P, Donadio D, Marinelli F, Pietrucci F, Broglia RA, Parrinello M (2009) PLUMED: a portable plugin for free-energy calculations with molecular dynamics. Comput Phys Commun 180:1961–1972CrossRefGoogle Scholar
  38. 38.
    Moreno A (2017) Advanced methods of protein crystallization. Methods Mol Biol 1607:51–76CrossRefGoogle Scholar
  39. 39.
    Garman EF, Owen RL (2006) Cryocooling and radiation damage in macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 62:32–47CrossRefGoogle Scholar
  40. 40.
    Scapin G (2013) Molecular replacement then and now. Acta Crystallogr D Biol Crystallogr 69:2266–2275CrossRefGoogle Scholar
  41. 41.
    Dodson E (2008) The befores and afters of molecular replacement. Acta Crystallogr D Biol Crystallogr 64:17–24CrossRefGoogle Scholar
  42. 42.
    Vagin A, Teplyakov A (1997) MOLREP: an automated program for molecular replacement. J Appl Crystallogr 30:1022–1025CrossRefGoogle Scholar
  43. 43.
    McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ (2007) Phaser crystallographic software. J Appl Crystallogr 40:658–674CrossRefGoogle Scholar
  44. 44.
    Kissinger CR, Gehlhaar DK, Fogel DB (1999) Rapid automated molecular replacement by evolutionary search. Acta Crystallogr D Biol Crystallogr 55:484–491CrossRefGoogle Scholar
  45. 45.
    Taylor GL (2010) Introduction to phasing. Acta Crystallogr D Biol Crystallogr 66:325–338CrossRefGoogle Scholar
  46. 46.
    Grosse-Kunstleve RW, Schneider TR (2007) Substructure determination in isomorphous replacement and anomalous diffraction experiments. Methods Mol Biol 364:197–214PubMedGoogle Scholar
  47. 47.
    Cowtan K (2010) Recent developments in classical density modification. Acta Crystallogr D Biol Crystallogr 66:470–478CrossRefGoogle Scholar
  48. 48.
    Sheldrick GM (2008) A short history of SHELX. Acta Crystallogr A 64:112–122CrossRefGoogle Scholar
  49. 49.
    Terwilliger TC, Adams PD, Read RJ, Mccoy AJ, Moriarty NW, Grosse-Kunstleve RW, Afonine PV, Zwart PH, Hung LW (2009) Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizard. Acta Crystallogr D Biol Crystallogr 65:582–601CrossRefGoogle Scholar
  50. 50.
    Tronrud DE (2004) Introduction to macromolecular refinement. Acta Crystallogr D Biol Crystallogr 60:2156–2168CrossRefGoogle Scholar
  51. 51.
    Murshudov GN, Vagin AA, Dodson EJ (1997) Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr D Biol Crystallogr 53:240–255CrossRefGoogle Scholar
  52. 52.
    Afonine PV, Grosse-Kunstleve RW, Echols N, Headd JJ, Moriarty NW, Mustyakimov M, Terwilliger TC, Urzhumtsev A, Zwart PH, Adams PD (2012) Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr D Biol Crystallogr 68:352–367CrossRefGoogle Scholar
  53. 53.
    Vajpai N, Gentner M, Huang JR, Blackledge M, Grzesiek S (2010) Side-chain χ1 conformations in urea-denatured ubiquitin and protein G from (3)J coupling constants and residual dipolar couplings. J Am Chem Soc 132:3196–3203CrossRefGoogle Scholar
  54. 54.
    Dux P, Whitehead B, Boelens R, Kaptein R, Vuister GW (1997) Measurement of (15)N- (1)H coupling constants in uniformly (15)N-labeled proteins: application to the photoactive yellow protein. J Biomol NMR 10:301–306CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Melanie Schwarten
    • 1
  • Oliver H. Weiergräber
    • 1
  • Dušan Petrović
    • 4
    • 1
  • Birgit Strodel
    • 1
    • 2
  • Dieter Willbold
    • 1
    • 3
    Email author
  1. 1.Institute of Complex Systems ICS-6 (Structural Biochemistry)Forschungszentrum JülichJülichGermany
  2. 2.Institute of Theoretical and Computational ChemistryHeinrich Heine University DüsseldorfDüsseldorfGermany
  3. 3.Institut für Physikalische BiologieHeinrich Heine University DüsseldorfDüsseldorfGermany
  4. 4.Department of ChemistryBMC, Uppsala UniversityUppsalaSweden

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