Research article Topical Sections

RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices

  • Received: 18 October 2015 Accepted: 17 December 2015 Published: 21 December 2015
  • Membrane proteins make up approximately one third of all proteins, and they play key roles in a plethora of physiological processes. However, membrane proteins make up less than 2% of experimentally determined structures, despite significant advances in structure determination methods, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy. One potential alternative means of structure elucidation is to combine computational methods with experimental EPR data. In 2011, Hirst and others introduced RosettaEPR and demonstrated that this approach could be successfully applied to fold soluble proteins. Furthermore, few computational methods for de novo folding of integral membrane proteins have been presented. In this work, we present RosettaTMH, a novel algorithm for structure prediction of helical membrane proteins. A benchmark set of 34 proteins, in which the proteins ranged in size from 91 to 565 residues, was used to compare RosettaTMH to Rosetta’s two existing membrane protein folding protocols: the published RosettaMembrane folding protocol (“MembraneAbinitio”) and folding from an extended chain (“ExtendedChain”). When EPR distance restraints are used, RosettaTMH+EPR outperforms ExtendedChain+EPR for 11 proteins, including the largest six proteins tested. RosettaTMH+EPR is capable of achieving native-like folds for 30 of 34 proteins tested, including receptors and transporters. For example, the average RMSD100SSE relative to the crystal structure for rhodopsin was 6.1 ± 0.4 Å and 6.5 ± 0.6 Å for the 449-residue nitric oxide reductase subunit B, where the standard deviation reflects variance in RMSD100SSE values across ten different EPR distance restraint sets. The addition of RosettaTMH and RosettaTMH+EPR to the Rosetta family of de novo folding methods broadens the scope of helical membrane proteins that can be accurately modeled with this software suite.

    Citation: Stephanie H. DeLuca, Samuel L. DeLuca, Andrew Leaver-Fay, Jens Meiler. RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices[J]. AIMS Biophysics, 2016, 3(1): 1-26. doi: 10.3934/biophy.2016.1.1

    Related Papers:

  • Membrane proteins make up approximately one third of all proteins, and they play key roles in a plethora of physiological processes. However, membrane proteins make up less than 2% of experimentally determined structures, despite significant advances in structure determination methods, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy. One potential alternative means of structure elucidation is to combine computational methods with experimental EPR data. In 2011, Hirst and others introduced RosettaEPR and demonstrated that this approach could be successfully applied to fold soluble proteins. Furthermore, few computational methods for de novo folding of integral membrane proteins have been presented. In this work, we present RosettaTMH, a novel algorithm for structure prediction of helical membrane proteins. A benchmark set of 34 proteins, in which the proteins ranged in size from 91 to 565 residues, was used to compare RosettaTMH to Rosetta’s two existing membrane protein folding protocols: the published RosettaMembrane folding protocol (“MembraneAbinitio”) and folding from an extended chain (“ExtendedChain”). When EPR distance restraints are used, RosettaTMH+EPR outperforms ExtendedChain+EPR for 11 proteins, including the largest six proteins tested. RosettaTMH+EPR is capable of achieving native-like folds for 30 of 34 proteins tested, including receptors and transporters. For example, the average RMSD100SSE relative to the crystal structure for rhodopsin was 6.1 ± 0.4 Å and 6.5 ± 0.6 Å for the 449-residue nitric oxide reductase subunit B, where the standard deviation reflects variance in RMSD100SSE values across ten different EPR distance restraint sets. The addition of RosettaTMH and RosettaTMH+EPR to the Rosetta family of de novo folding methods broadens the scope of helical membrane proteins that can be accurately modeled with this software suite.


    加载中
    [1] Krishnamurthy H, Gouaux E (2012) X-ray structures of LeuT in substrate-free outward-open and apo inward-open states. Nature 481: 469–474. doi: 10.1038/nature10737
    [2] Sanders CR, Sonnichsen F (2006) Solution NMR of membrane proteins: practice and challenges. Magn Reson Chem 44 Spec No: S24–40.
    [3] Horst R, Stanczak P, Stevens RC, et al. (2013) beta2-Adrenergic receptor solutions for structural biology analyzed with microscale NMR diffusion measurements. Angew Chem Int Ed Engl 52: 331–335. doi: 10.1002/anie.201205474
    [4] Chun E, Thompson AA, Liu W, et al. (2012) Fusion partner toolchest for the stabilization and crystallization of G protein-coupled receptors. Structure 20: 967–976. doi: 10.1016/j.str.2012.04.010
    [5] Baker LA, Baldus M (2014) Characterization of membrane protein function by solid-state NMR spectroscopy. Curr Opin Struct Biol 27: 48–55. doi: 10.1016/j.sbi.2014.03.009
    [6] Hopkins AL, Groom CR (2002) The druggable genome. Nat Rev Drug Discov 1: 727–730. doi: 10.1038/nrd892
    [7] Overington JP, Al-Lazikani B, Hopkins AL (2006) How many drug targets are there? Nat Rev Drug Discov 5: 993–996. doi: 10.1038/nrd2199
    [8] Bakheet TM, Doig AJ (2009) Properties and identification of human protein drug targets. Bioinformatics 25: 451–457. doi: 10.1093/bioinformatics/btp002
    [9] Maslennikov I, Choe S (2013) Advances in NMR structures of integral membrane proteins. Curr Opin Struct Biol 23: 555–562. doi: 10.1016/j.sbi.2013.05.002
    [10] Klammt C, Maslennikov I, Bayrhuber M, et al. (2012) Facile backbone structure determination of human membrane proteins by NMR spectroscopy. Nat Methods 9: 834–839. doi: 10.1038/nmeth.2033
    [11] Berman HM, Battistuz T, Bhat TN, et al. (2002) The Protein Data Bank. Acta Crystallogr D Biol Crystallogr 58: 899–907. doi: 10.1107/S0907444902003451
    [12] Berman HM, Westbrook J, Feng Z, et al. (2000) The Protein Data Bank. Nucleic Acids Res 28: 235–242. doi: 10.1093/nar/28.1.235
    [13] Tang M, Comellas G, Rienstra CM (2013) Advanced solid-state NMR approaches for structure determination of membrane proteins and amyloid fibrils. Acc Chem Res 46: 2080–2088. doi: 10.1021/ar4000168
    [14] Ni QZ, Daviso E, Can TV, et al. (2013) High frequency dynamic nuclear polarization. Acc Chem Res 46: 1933–1941. doi: 10.1021/ar300348n
    [15] Zou P, McHaourab HS (2010) Increased sensitivity and extended range of distance measurements in spin-labeled membrane proteins: Q-band double electron-electron resonance and nanoscale bilayers. Biophys J 98: L18–20. doi: 10.1016/j.bpj.2009.12.4193
    [16] Mchaourab HS, Steed PR, Kazmier K (2011) Toward the fourth dimension of membrane protein structure: insight into dynamics from spin-labeling EPR spectroscopy. Structure 19: 1549–1561. doi: 10.1016/j.str.2011.10.009
    [17] Tusnady GE, Dosztanyi Z, Simon I (2004) Transmembrane proteins in the Protein Data Bank: identification and classification. Bioinformatics 20: 2964–2972. doi: 10.1093/bioinformatics/bth340
    [18] Mchaourab HS, Lietzow MA, Hideg K, et al. (1996) Motion of spin-labeled side chains in T4 lysozyme. Correlation with protein structure and dynamics. Biochemistry 35: 7692–7704.
    [19] Hubbell WL, McHaourab HS, Altenbach C, et al. (1996) Watching proteins move using site-directed spin labeling. Structure 4: 779–783. doi: 10.1016/S0969-2126(96)00085-8
    [20] Fanucci GE, Cafiso DS (2006) Recent advances and applications of site-directed spin labeling. Curr Opin Struct Biol 16: 644–653. doi: 10.1016/j.sbi.2006.08.008
    [21] Weierstall U, James D, Wang C, et al. (2014) Lipidic cubic phase injector facilitates membrane protein serial femtosecond crystallography. Nat Commun 5: 3309.
    [22] Liu W, Wacker D, Gati C, et al. (2013) Serial femtosecond crystallography of G protein-coupled receptors. Science 342: 1521–1524. doi: 10.1126/science.1244142
    [23] Li D, Boland C, Walsh K, et al. (2012) Use of a robot for high-throughput crystallization of membrane proteins in lipidic mesophases. J Vis Exp: e4000.
    [24] Li D, Boland C, Aragao D, et al. (2012) Harvesting and cryo-cooling crystals of membrane proteins grown in lipidic mesophases for structure determination by macromolecular crystallography. J Vis Exp: e4001.
    [25] Alexander N, Al-Mestarihi A, Bortolus M, et al. (2008) De novo high-resolution protein structure determination from sparse spin-labeling EPR data. Structure 16: 181–195. doi: 10.1016/j.str.2007.11.015
    [26] Hirst SJ, Alexander N, Mchaourab HS, et al. (2011) RosettaEPR: an integrated tool for protein structure determination from sparse EPR data. J Struct Biol 173: 506–514. doi: 10.1016/j.jsb.2010.10.013
    [27] Islam SM, Stein RA, McHaourab HS, et al. (2013) Structural refinement from restrained-ensemble simulations based on EPR/DEER data: application to T4 lysozyme. J Phys Chem B 117: 4740–4754. doi: 10.1021/jp311723a
    [28] Fischer AW, Alexander NS, Woetzel N, et al. (2015) BCL::MP-Fold: Membrane protein structure prediction guided by EPR restraints. Proteins 83: 1947–1962. doi: 10.1002/prot.24801
    [29] Jeschke G, Chechik V, Ionita P, et al. (2006) DeerAnalysis2006 - a comprehensive software package for analyzing pulsed ELDOR data. Appl Magn Reson 30: 473–498. doi: 10.1007/BF03166213
    [30] Hagelueken G, Ward R, Naismith JH, et al. (2012) MtsslWizard: In Silico Spin-Labeling and Generation of Distance Distributions in PyMOL. Appl Magn Reson 42: 377–391. doi: 10.1007/s00723-012-0314-0
    [31] Beasley KN, Sutch BT, Hatmal MM, et al. (2015) Computer Modeling of Spin Labels: NASNOX, PRONOX, and ALLNOX. Methods Enzymol 563: 569–593. doi: 10.1016/bs.mie.2015.07.021
    [32] Hatmal MM, Li Y, Hegde BG, et al. (2012) Computer modeling of nitroxide spin labels on proteins. Biopolymers 97: 35–44. doi: 10.1002/bip.21699
    [33] Alexander NS, Stein RA, Koteiche HA, et al. (2013) RosettaEPR: Rotamer Library for Spin Label Structure and Dynamics. PLoS One 8: e72851. doi: 10.1371/journal.pone.0072851
    [34] Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234: 779–815. doi: 10.1006/jmbi.1993.1626
    [35] Fiser A, Sali A (2003) Modeller: generation and refinement of homology-based protein structure models. Methods Enzymol 374: 461–491. doi: 10.1016/S0076-6879(03)74020-8
    [36] Webb B, Sali A (2014) Protein structure modeling with MODELLER. Methods Mol Biol 1137: 1–15. doi: 10.1007/978-1-4939-0366-5_1
    [37] Webb B, Sali A (2014) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 47: 5.6.1–5.6.32.
    [38] Rohl CA, Strauss CEM, Chivian D, et al. (2004) Modeling structurally variable regions in homologous proteins with rosetta. Proteins 55: 656–677. doi: 10.1002/prot.10629
    [39] Misura KM, Chivian D, Rohl CA, et al. (2006) Physically realistic homology models built with ROSETTA can be more accurate than their templates. Proc Natl Acad Sci U S A 103: 5361–5366. doi: 10.1073/pnas.0509355103
    [40] Schwede T, Kopp J, Guex N, et al. (2003) SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Res 31: 3381–3385. doi: 10.1093/nar/gkg520
    [41] Zhang Y (2009) I-TASSER: fully automated protein structure prediction in CASP8. Proteins 77 Suppl 9: 100–113.
    [42] Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9: 40. doi: 10.1186/1471-2105-9-40
    [43] Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5: 725–738. doi: 10.1038/nprot.2010.5
    [44] Combs SA, Deluca SL, Deluca SH, et al. (2013) Small-molecule ligand docking into comparative models with Rosetta. Nat Protoc 8: 1277–1298. doi: 10.1038/nprot.2013.074
    [45] Stevens RC, Cherezov V, Katritch V, et al. (2013) The GPCR Network: a large-scale collaboration to determine human GPCR structure and function. Nat Rev Drug Discov 12: 25–34.
    [46] Kroeze WK, Sheffler DJ, Roth BL (2003) G-protein-coupled receptors at a glance. J Cell Sci 116: 4867–4869. doi: 10.1242/jcs.00902
    [47] Yamashita A, Singh SK, Kawate T, et al. (2005) Crystal structure of a bacterial homologue of Na+/Cl--dependent neurotransmitter transporters. Nature 437: 215–223. doi: 10.1038/nature03978
    [48] Faham S, Watanabe A, Besserer GM, et al. (2008) The crystal structure of a sodium galactose transporter reveals mechanistic insights into Na+/sugar symport. Science 321: 810–814. doi: 10.1126/science.1160406
    [49] Perez C, Koshy C, Yildiz O, et al. (2012) Alternating-access mechanism in conformationally asymmetric trimers of the betaine transporter BetP. Nature 490: 126–130. doi: 10.1038/nature11403
    [50] Ma D, Lu P, Yan C, et al. (2012) Structure and mechanism of a glutamate-GABA antiporter. Nature 483: 632–636. doi: 10.1038/nature10917
    [51] Kazmier K, Sharma S, Quick M, et al. (2014) Conformational dynamics of ligand-dependent alternating access in LeuT. Nat Struct Mol Biol 21: 472–479. doi: 10.1038/nsmb.2816
    [52] Gregory KJ, Nguyen ED, Reiff SD, et al. (2013) Probing the metabotropic glutamate receptor 5 (mGlu(5)) positive allosteric modulator (PAM) binding pocket: discovery of point mutations that engender a "molecular switch" in PAM pharmacology. Mol Pharmacol 83: 991–1006. doi: 10.1124/mol.112.083949
    [53] Yarov-Yarovoy V, Schonbrun J, Baker D (2006) Multipass membrane protein structure prediction using Rosetta. Proteins 62: 1010–1025.
    [54] Barth P, Schonbrun J, Baker D (2007) Toward high-resolution prediction and design of transmembrane helical protein structures. Proc Natl Acad Sci U S A 104: 15682–15687. doi: 10.1073/pnas.0702515104
    [55] Barth P, Wallner B, Baker D (2009) Prediction of membrane protein structures with complex topologies using limited constraints. Proc Natl Acad Sci U S A 106: 1409–1414. doi: 10.1073/pnas.0808323106
    [56] 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 U S A 109: E1540–1547. doi: 10.1073/pnas.1120036109
    [57] Nugent T, Jones DT (2013) Membrane protein orientation and refinement using a knowledge-based statistical potential. BMC Bioinformatics 14: 276. doi: 10.1186/1471-2105-14-276
    [58] Hopf TA, Colwell LJ, Sheridan R, et al. (2012) Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149: 1607–1621. doi: 10.1016/j.cell.2012.04.012
    [59] Weiner BE, Woetzel N, Karakas M, et al. (2013) BCL::MP-fold: folding membrane proteins through assembly of transmembrane helices. Structure 21: 1107–1117. doi: 10.1016/j.str.2013.04.022
    [60] Simons KT, Kooperberg C, Huang E, et al. (1997) Assembly of Protein Tertiary Structures from Fragments with Similar Local Sequences using Simulated Annealing and Bayesian Scoring Functions. J Mol Biol 268: 209–225. doi: 10.1006/jmbi.1997.0959
    [61] Rohl CA, Strauss CE, Misura KM, et al. (2004) Protein structure prediction using Rosetta. Methods Enzymol 383: 66–93. doi: 10.1016/S0076-6879(04)83004-0
    [62] Kazmier K, Alexander NS, Meiler J, et al. (2011) Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination. J Struct Biol 173: 549–557. doi: 10.1016/j.jsb.2010.11.003
    [63] Dimaio F, Leaver-Fay A, Bradley P, et al. (2011) Modeling symmetric macromolecular structures in rosetta3. PLoS One 6: e20450. doi: 10.1371/journal.pone.0020450
    [64] Leaver-Fay A, Tyka M, Lewis SM, et al. (2011) ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol 487: 545–574. doi: 10.1016/B978-0-12-381270-4.00019-6
    [65] Metropolis N, Rosenbluth A, Rosenbluth M, et al. (1953) Equations of state calculations by fast computing machines. J Chem Phys 21: 1087–1091. doi: 10.1063/1.1699114
    [66] Metropolis NU, Ulam S (1949) The Monte Carlo Method. J Am Stat Assoc 44: 335–341. doi: 10.1080/01621459.1949.10483310
    [67] Perozo E, Cortes DM, Cuello LG (1999) Structural rearrangements underlying K+-channel activation gating. Science 285: 73–78. doi: 10.1126/science.285.5424.73
    [68] Liu YS, Sompornpisut P, Perozo E (2001) Structure of the KcsA channel intracellular gate in the open state. Nat Struct Biol 8: 883–887. doi: 10.1038/nsb1001-883
    [69] Zou P, Mchaourab HS (2009) Alternating Access of the Putative Substrate-Binding Chamber in the ABC Transporter MsbA. J Mol Biol 393: 574–585. doi: 10.1016/j.jmb.2009.08.051
    [70] Altenbach C, Cai K, Klein-Seetharaman J, et al. (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: 15483–15492. doi: 10.1021/bi011546g
    [71] Viklund H, Elofsson A (2008) OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24: 1662–1668. doi: 10.1093/bioinformatics/btn221
    [72] Adamian L, Liang J (2006) Prediction of transmembrane helix orientation in polytopic membrane proteins. BMC Struct Biol 6: 13. doi: 10.1186/1472-6807-6-13
    [73] Okada T, Sugihara M, Bondar AN, et al. (2004) The retinal conformation and its environment in rhodopsin in light of a new 2.2 A crystal structure. J Mol Biol 342: 571–583.
    [74] Misura KM, Baker D (2005) Progress and challenges in high-resolution refinement of protein structure models. Proteins 59: 15–29. doi: 10.1002/prot.20376
    [75] Tyka MD, Jung K, Baker D (2012) Efficient sampling of protein conformational space using fast loop building and batch minimization on highly parallel computers. J Comput Chem 33: 2483–2491. doi: 10.1002/jcc.23069
    [76] Woetzel N, Karakas M, Staritzbichler R, et al. (2012) BCL::Score--knowledge based energy potentials for ranking protein models represented by idealized secondary structure elements. PLoS One 7: e49242. doi: 10.1371/journal.pone.0049242
    [77] Karakas M, Woetzel N, Staritzbichler R, et al. (2012) BCL::Fold--de novo prediction of complex and large protein topologies by assembly of secondary structure elements. PLoS One 7: e49240. doi: 10.1371/journal.pone.0049240
  • Reader Comments
  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6680) PDF downloads(1314) Cited by(1)

Article outline

Figures and Tables

Figures(6)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog