Molecular Modeling of Transporters: From Low Resolution Cryo-Electron Microscopy Map to Conformational Exploration. The Example of TSPO
Protocol
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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.
Key words
3D molecular modeling Coevolution methods Dynamics Coarse-grained models Electron microscopy dataReferences
- 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 1848:568–580CrossRefPubMedGoogle 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 106:67–72CrossRefPubMedGoogle 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 149:1607–1621CrossRefPubMedPubMedCentralGoogle 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:e28766CrossRefPubMedPubMedCentralGoogle Scholar
- 5.De Brevern AG (2010) 3D structural models of transmembrane proteins. Methods Mol Biol (Clifton NJ) 654:387–401CrossRefGoogle Scholar
- 6.Etchebest C, Debret G (2010) Critical review of general guidelines for membrane proteins model building and analysis. Methods Mol Biol (Clifton NJ) 654:363–385CrossRefGoogle Scholar
- 7.van Drunen R, berendsen HJ (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comp Phys Commun 91:43–56CrossRefGoogle 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 4:435–447CrossRefPubMedGoogle 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 15:85CrossRefPubMedPubMedCentralGoogle 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–E1301CrossRefPubMedPubMedCentralGoogle 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 288:26372–26384CrossRefPubMedPubMedCentralGoogle 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) 49:2987–2998CrossRefGoogle Scholar
- 13.Jeong C-S, Kim D (2012) Reliable and robust detection of coevolving protein residues. Protein Eng Des Sel 25:705–713CrossRefPubMedGoogle 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 28:184–190CrossRefPubMedGoogle Scholar
- 15.Wriggers W (2010) Using Situs for the integration of multi-resolution structures. Biophys Rev 2:21–27CrossRefPubMedPubMedCentralGoogle 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 29:820–831CrossRefPubMedGoogle 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–W574CrossRefPubMedPubMedCentralGoogle 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:e1003539CrossRefPubMedPubMedCentralGoogle 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 31:2646–2652CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Reeb J, Kloppmann E, Bernhofer M, Rost B (2015) Evaluation of transmembrane helix predictions in 2014. Proteins 83:473–484CrossRefPubMedGoogle 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–W407CrossRefPubMedPubMedCentralGoogle Scholar
- 22.Käll L, Krogh A, Sonnhammer ELL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036CrossRefPubMedGoogle 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 6:175–182PubMedGoogle 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–W432CrossRefPubMedPubMedCentralGoogle 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–E1547CrossRefPubMedPubMedCentralGoogle 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 1778:1375–1381CrossRefPubMedGoogle Scholar
- 27.Adamian L, Liang J (2006) Prediction of transmembrane helix orientation in polytopic membrane proteins. BMC Struct Biol 6:13CrossRefPubMedPubMedCentralGoogle 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 11:333CrossRefPubMedPubMedCentralGoogle 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. 4:47CrossRefPubMedPubMedCentralGoogle 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 27:1759–1767CrossRefPubMedPubMedCentralGoogle 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–W28CrossRefPubMedPubMedCentralGoogle 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 31:1625–1637CrossRefPubMedGoogle 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–W6CrossRefPubMedPubMedCentralGoogle Scholar
- 34.Ma J, Peng J, Wang S, Xu J (2012) A conditional neural fields model for protein threading. Bioinformatics 28:i59–i66CrossRefPubMedPubMedCentralGoogle Scholar
- 35.Casbon JA, Saqi MA (2004) Analysis of superfamily specific profile-profile recognition accuracy. BMC Bioinformatics 5:200CrossRefPubMedPubMedCentralGoogle 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:S10PubMedPubMedCentralGoogle Scholar
- 37.Dunn SD, Wahl LM, Gloor GB (2008) Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 24:333–340CrossRefPubMedGoogle Scholar
- 38.Lee B-C, Kim D (2009) A new method for revealing correlated mutations under the structural and functional constraints in proteins. Bioinformatics 25:2506–2513CrossRefPubMedGoogle Scholar
- 39.Barnoud J, Monticelli L (2015) Coarse-grained force fields for molecular simulations. Methods Mol Biol 1215:125–149CrossRefPubMedGoogle Scholar
- 40.Rawi R, Whitmore L, Topf M (2010) CHOYCE: a web server for constrained homology modelling with cryoEM maps. Bioinformatics 26:1673–1674CrossRefPubMedPubMedCentralGoogle 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 32:1183–1194CrossRefPubMedGoogle 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 10:676–690CrossRefPubMedGoogle 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 111:7812–7824CrossRefPubMedGoogle Scholar
- 44.Lombardi LE, Martí MA, Capece L (2016) CG2AA: backmapping protein coarse-grained structures. Bioinformatics 32:1235–1237CrossRefPubMedGoogle 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–i511CrossRefPubMedPubMedCentralGoogle 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 32:2548–2550CrossRefPubMedGoogle 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 38:236–240CrossRefGoogle Scholar
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