Skip to main content

Molecular Modeling of Transporters: From Low Resolution Cryo-Electron Microscopy Map to Conformational Exploration. The Example of TSPO

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1635))

Abstract

This chapter describes a protocol to establish a three-dimensional (3D) model of a protein and to explore its conformational landscape. It combines predictions from up-to-date bioinformatics methods with low-resolution experimental data. It also proposes to examine rapidly the dynamics of the protein using molecular dynamics simulations with a coarse-grained force field. Tools for analyzing these trajectories are suggested as well as those for constructing all-atoms models. Thus, starting from a protein sequence and using free software, the user can get important conformational information, which might improve the knowledge about the protein function.

This is a preview of subscription content, log in via an institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Hinsen K, Vaitinadapoule A, Ostuni MA, Etchebest C, Lacapere J-J (2015) Construction and validation of an atomic model for bacterial TSPO from electron microscopy density, evolutionary constraints, and biochemical and biophysical data. Biochim Biophys Acta 1848:568–580

    Article  CAS  PubMed  Google Scholar 

  2. Weigt M, White RA, Szurmant H, Hoch JA, Hwa T (2009) Identification of direct residue contacts in protein–protein interaction by message passing. Proc Natl Acad Sci 106:67–72

    Article  CAS  PubMed  Google Scholar 

  3. Hopf TA, Colwell LJ, Sheridan R, Rost B, Sander C, Marks DS (2012) Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149:1607–1621

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS One 6:e28766

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. De Brevern AG (2010) 3D structural models of transmembrane proteins. Methods Mol Biol (Clifton NJ) 654:387–401

    Article  Google Scholar 

  6. Etchebest C, Debret G (2010) Critical review of general guidelines for membrane proteins model building and analysis. Methods Mol Biol (Clifton NJ) 654:363–385

    Article  CAS  Google Scholar 

  7. van Drunen R, berendsen HJ (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comp Phys Commun 91:43–56

    Article  Google Scholar 

  8. Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447

    Article  CAS  PubMed  Google Scholar 

  9. Kaján L, Hopf TA, Kalaš M, Marks DS, Rost B (2014) FreeContact: fast and free software for protein contact prediction from residue co-evolution. BMC Bioinformatics 15:85

    Article  PubMed  PubMed Central  Google Scholar 

  10. Morcos F, Pagnani A, Lunt B, Bertolino A, Marks DS, Sander C, Zecchina R, Onuchic JN, Hwa T, Weigt M (2011) Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc Natl Acad Sci 108:E1293–E1301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Barneaud-Rocca D, Etchebest C, Guizouarn H (2013) Structural model of the anion exchanger 1 (SLC4A1) and identification of transmembrane segments forming the transport site. J Biol Chem 288:26372–26384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kaufmann KW, Lemmon GH, DeLuca SL, Sheehan JH, Meiler J (2010) Practically useful: what the rosetta protein modeling suite can do for you. Biochemistry (Mosc) 49:2987–2998

    Article  CAS  Google Scholar 

  13. Jeong C-S, Kim D (2012) Reliable and robust detection of coevolving protein residues. Protein Eng Des Sel 25:705–713

    Article  CAS  PubMed  Google Scholar 

  14. Jones DT, Buchan DWA, Cozzetto D, Pontil M (2012) PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28:184–190

    Article  CAS  PubMed  Google Scholar 

  15. Wriggers W (2010) Using Situs for the integration of multi-resolution structures. Biophys Rev 2:21–27

    Article  PubMed  PubMed Central  Google Scholar 

  16. Olson MA, Feig M, Brooks CL (2008) Prediction of protein loop conformations using multiscale modeling methods with physical energy scoring functions. J Comput Chem 29:820–831

    Article  CAS  PubMed  Google Scholar 

  17. Hildebrand PW, Goede A, Bauer RA, Gruening B, Ismer J, Michalsky E, Preissner R (2009) SuperLooper—a prediction server for the modeling of loops in globular and membrane proteins. Nucleic Acids Res 37:W571–W574

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Tang K, Zhang J, Liang J (2014) Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method. PLoS Comput Biol 10:e1003539

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tang K, Wong SWK, Liu JS, Zhang J, Liang J (2015) Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method. Bioinformatics 31:2646–2652

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Reeb J, Kloppmann E, Bernhofer M, Rost B (2015) Evaluation of transmembrane helix predictions in 2014. Proteins 83:473–484

    Article  CAS  PubMed  Google Scholar 

  21. Tsirigos KD, Peters C, Shu N, Käll L, Elofsson A (2015) The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res 43:W401–W407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Käll L, Krogh A, Sonnhammer ELL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036

    Article  PubMed  Google Scholar 

  23. Sonnhammer EL, von Heijne G, Krogh A (1998) A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol 6:175–182

    CAS  PubMed  Google Scholar 

  24. Käll L, Krogh A, Sonnhammer ELL (2007) Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res 35:W429–W432

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nugent T, Jones DT (2012) Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis. Proc Natl Acad Sci 109:E1540–E1547

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Murail S, Robert J-C, Coïc Y-M, Neumann J-M, Ostuni MA, Yao Z-X, Papadopoulos V, Jamin N, Lacapère J-J (2008) Secondary and tertiary structures of the transmembrane domains of the translocator protein TSPO determined by NMR. Stabilization of the TSPO tertiary fold upon ligand binding. Biochim Biophys Acta 1778:1375–1381

    Article  CAS  PubMed  Google Scholar 

  27. Adamian L, Liang J (2006) Prediction of transmembrane helix orientation in polytopic membrane proteins. BMC Struct Biol 6:13

    Article  PubMed  PubMed Central  Google Scholar 

  28. Illergård K, Callegari S, Elofsson A (2010) MPRAP: an accessibility predictor for a-helical transmembrane proteins that performs well inside and outside the membrane. BMC Bioinformatics 11:333

    Article  PubMed  PubMed Central  Google Scholar 

  29. Raghava GPS, Searle SMJ, Audley PC, Barber JD, Barton GJ (2003) OXBench: a benchmark for evaluation of protein multiple sequence alignment accuracy. BMC Bioinformatics. 4:47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Penn O, Privman E, Landan G, Graur D, Pupko T (2010) An alignment confidence score capturing robustness to guide tree uncertainty. Mol Biol Evol 27:1759–1767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Penn O, Privman E, Ashkenazy H, Landan G, Graur D, Pupko T (2010) GUIDANCE: a web server for assessing alignment confidence scores. Nucleic Acids Res 38:W23–W28

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chang J-M, Tommaso PD, Notredame C (2014) TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction. Mol Biol Evol 31:1625–1637

    Article  CAS  PubMed  Google Scholar 

  33. Chang J-M, Di Tommaso P, Lefort V, Gascuel O, Notredame C (2015) TCS: a web server for multiple sequence alignment evaluation and phylogenetic reconstruction. Nucleic Acids Res 43:W3–W6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ma J, Peng J, Wang S, Xu J (2012) A conditional neural fields model for protein threading. Bioinformatics 28:i59–i66

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Casbon JA, Saqi MA (2004) Analysis of superfamily specific profile-profile recognition accuracy. BMC Bioinformatics 5:200

    Article  PubMed  PubMed Central  Google Scholar 

  36. Savojardo C, Fariselli P, Martelli PL, Casadio R (2013) Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations. BMC Bioinformatics. 14:S10

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Dunn SD, Wahl LM, Gloor GB (2008) Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 24:333–340

    Article  CAS  PubMed  Google Scholar 

  38. Lee B-C, Kim D (2009) A new method for revealing correlated mutations under the structural and functional constraints in proteins. Bioinformatics 25:2506–2513

    Article  CAS  PubMed  Google Scholar 

  39. Barnoud J, Monticelli L (2015) Coarse-grained force fields for molecular simulations. Methods Mol Biol 1215:125–149

    Article  CAS  PubMed  Google Scholar 

  40. Rawi R, Whitmore L, Topf M (2010) CHOYCE: a web server for constrained homology modelling with cryoEM maps. Bioinformatics 26:1673–1674

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Seeber M, Felline A, Raimondi F, Muff S, Friedman R, Rao F, Caflisch A, Fanelli F (2011) Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J Comput Chem 32:1183–1194

    Article  CAS  PubMed  Google Scholar 

  42. Wassenaar TA, Pluhackova K, Böckmann RA, Marrink SJ, Tieleman DP (2014) Going backward: a flexible geometric approach to reverse transformation from coarse grained to atomistic models. J Chem Theory Comput 10:676–690

    Article  CAS  PubMed  Google Scholar 

  43. 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 111:7812–7824

    Article  CAS  PubMed  Google Scholar 

  44. Lombardi LE, Martí MA, Capece L (2016) CG2AA: backmapping protein coarse-grained structures. Bioinformatics 32:1235–1237

    Article  CAS  PubMed  Google Scholar 

  45. Studer G, Biasini M, Schwede T (2014) Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane). Bioinformatics 30:i505–i511

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Postic G, Ghouzam Y, Gelly J-C (2016) OREMPRO web server: orientation and assessment of atomistic and coarse-grained structures of membrane proteins. Bioinformatics 32:2548–2550

    Article  CAS  PubMed  Google Scholar 

  47. Daura X, Gademann K, Jaun B, Seebach D, van Gunsteren WF, Mark AE (1999) Peptide folding: when simulation meets experiment. Angew Chem Int Ed 38:236–240

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Catherine Etchebest .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Vaitinadapoule, A., Etchebest, C. (2017). Molecular Modeling of Transporters: From Low Resolution Cryo-Electron Microscopy Map to Conformational Exploration. The Example of TSPO. In: Lacapere, JJ. (eds) Membrane Protein Structure and Function Characterization. Methods in Molecular Biology, vol 1635. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7151-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7151-0_21

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7149-7

  • Online ISBN: 978-1-4939-7151-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics