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V9 – orientation of TM helices

V9 – orientation of TM helices. Modelling 3D structures of helical TM bundles Park, Staritzbichler, Elsner & Helms, Proteins (2004), Park & Helms, Proteins (2006) Beuming & Weinstein (2004) T. Beming & H. Weinstein (2004) Bioinformatics 20, 1822 Adamian & Liang (2006)

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V9 – orientation of TM helices

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  1. V9 – orientation of TM helices • Modelling 3D structures of helical TM bundles • Park, Staritzbichler, Elsner & Helms, Proteins (2004), Park & Helms, Proteins (2006) • Beuming & Weinstein (2004) • T. Beming & H. Weinstein (2004) Bioinformatics 20, 1822 • Adamian & Liang (2006) • L. Adamian & J. Liang (2006) BMC Struct. Biol. 6, 13 • TMX: predict lipid-accessible sides of TM helices from sequence • Park & Helms, Bioinformatics (2007), Park, Hayat & Helms, BMC Bioinformatics (2007), Membrane Bioinformatics SS09

  2. Structure modelling for helical membrane proteins >P52202 RHO -- Rhodopsin.MNGTEGPDFYIPFSNKTGVVRSPFEYPQYYLAEPWKYSALAAYMFMLIILGFPINFLTLYVTVQHKKLRSPLNYILLNLAVADLFMVLGGFTTTLYTSMNGYFVFGVTGCYFEGFFATLGGEVALWCLVVLAIERYIVVCKPMSNFRFGENHAIMGVVFTWIMALTCAAPPLVGWSRYIPEGMQCSCGVDYYTLKPEVNNESFVIYMFVVHFAIPLAVIFFCYGRLVCTVKEAAAQQQESATTQKAEKEVTRMVIIMVVSFLICWVPYASVAFYIFSNQGSDFGPVFMTIPAFFAKSSAIYNPVIYIVMNKQFRNCMITT LCCGKNPLGDDETATGSKTETSSVSTSQVSPA 1D 2D www.gpcr.org 3D EMBO Reports (2002) Membrane Bioinformatics SS09

  3. Design helical bundles using effective energy functions Aim: assemble TM bundles Glycophorin A dimer, Erb/Neu dimer, phospholamban pentamer Method: scan 6-D conformational space of dimers of ideal helices Membrane Bioinformatics SS09

  4. docking of helix-dimers: energy scoring Example for parametrised energy function between 2 residues search 5 degrees of freedom systematically. score conformations by residue-residue energy function. Park et al. Proteins (2004) Membrane Bioinformatics SS09

  5. docking of helix-dimers Test for Glycophorin A, dimer of two identical helices, NMR structure available • Energy landscape • around the minimum • Minimum is truly global minimum. RMSD between best model and NMR structure only 0.8 Å. Park et al. Proteins (2004) However, this is not the case for dimers in larger TMH proteins. Membrane Bioinformatics SS09

  6. Need more/other information to orient helices • Early suggestion: TM proteins are „inside-out“ proteins. • That means that are hydrophobic outside and hydrophilic inside. • compute hydrophobic moment = the direction of largest hydrophobicity here, rproj(i) is the projection of the side-chain onto the helical axis, i.e. the vector difference describes the shortest distance between residue i and the helix axis. H(i) is the hydrophobicity of residue i. This method was introduced by David Eisenberg (1982, Nature) Membrane Bioinformatics SS09 6

  7. role of hydrophobic moment According to the concept of Eisenberg, all helices would orient their most hydrophobic side towards the bilayer. However, this measure is quite unprecise (Park & Helms, Biopolymers 2006). Hydrophobicity scales ww: Wimley-White scale eis: Eisenberg scale ges: Goldman/Engelman/Steitz scale kd: Kyte-Doolittle scale Specialized scales kP: kProt bw: Beuming & Weinstein scale tmlip1/2: Adamian & Liang Membrane Bioinformatics SS09 7

  8. Beuming & Weinstein (2004): amino acid propensities • Hydrophobic residues (A, I, L, V) make up 48.7 % of all residues in TM proteins • Charged residues (D, E, H, K, R) constitute only 5.5% • Glycine (G) is relatively abundant • Small residues (A, C, S, T) form 30.6% • Aromatic residues (F,W,Y) represent 15.8% • -branched residues (T, I, V) form 24.9%. • Proline is a helix-breaker and is underrepresented • Also, Cys, Gln, and Asn are rarely found. Membrane Bioinformatics SS09

  9. amino acid propensities: conclusions The overall amino acid composition deviates significantly from that of the whole genome. Hydrophobic residues (A, F, G, I, L, M, V, W) occur more frequently in MPs than in the whole genomes. Conversely, residues C, D, E, K, N, P, Q, R are underrepresented in MPs. H, S, T, and Y have equal distributions in MPs and whole genomes. Membrane Bioinformatics SS09 9

  10. Beuming & Weinstein (2004): inside vs. outside • Most of the exposed (lipid facing) charged residues (D, E, K, H, R) that are found in TMs are located in the terminal regions (4.4%) rather than in the central region (2.7%). • The exposed terminal parts are very rich in aromatic residues (21.3%) compard to the central part (16.1%). Membrane Bioinformatics SS09

  11. Beuming & Weinstein (2004): surface propensity scale Table shows fraction SF of exposed residue i. Trp has highest value of SF, His has smallest value. Normalize SP values with respect to His (SP=0) and Trp (SP=1). Membrane Bioinformatics SS09

  12. correlation of SP scale with other scales Compute correlation coefficient. SP propensity scale has high correlation with hydrophobicity or volume scales. Combine SP scale with conservation index: pa : a priori distribution of residues Membrane Bioinformatics SS09

  13. Beuming & Weinstein (2004) Add propensity score and conservation score: total score(i) = SPi + CIi Accuracy to detect the buried resides is ca. 70%. Membrane Bioinformatics SS09

  14. Beuming & Weinstein (2004) (top) correct SASA in X-ray structure (middle): prediction based on amino-acid propensity + conservation BEST! (bottom): prediction based only on conservation Membrane Bioinformatics SS09

  15. Adamian & Liang (2006): interacting helices Example for two interacting TM helices in succinate dehydrogenase. Interacting residues follow heptad motiv. Note the periodicity of 3.6 residues per turn in an ideal -helix. Membrane Bioinformatics SS09

  16. Adamian & Liang (2006) Heptad motifs are generally preferred for interacting helix pairs. For left-handed helices, about 94.7% and 92.4% of interacting residues can be mapped to heptad repeats for parallel and anti-parallel helices. For right-handed pairs the number are slightly less. Assume that the residues of lipid-accessible helices follows a similar pattern. Membrane Bioinformatics SS09

  17. Adamian & Liang (2006) Each TM helix has „7 faces“. A: the anchoring residues are 0, 7, 14, and 21 contacts are also formed by residues 3, 4, 10, 11, 17, 18 Membrane Bioinformatics SS09

  18. Adamian & Liang (2006) Combine lipophilicity score Lf and positional entropy Ef of a helical face by simply multiplying them. Membrane Bioinformatics SS09

  19. Adamian & Liang (2006): Test fo TRP channel Membrane Bioinformatics SS09

  20. Adamian & Liang (2006): discuss failures Sometimes, binding sites for individual lipids (e.g. cardiolipin) are formed on the surfaces of TM proteins. Those residues will also be highly conserved, and the method will therefore fail. Membrane Bioinformatics SS09

  21. What is needed for true de novo design of helical bundles? Aim: explore new TM protein topologies. distance-dependent residue-residue force field Generate energetically favorable geometries of helix dimers. Overlap helix dimers  full protein structure. Membrane Bioinformatics SS09

  22. Derivation of position scores • For each test protein,  1000 similar sequences • from non-redundant database using BLAST URLAPI. • (2) generate initial multiple sequence alignment (MSA) with ClustalW. • Delete fragments < 80% of length of query sequence. From these refined MSA, apply 6 different % identity criteria, • 6 final MSAs for each test protein. • Pei & Grishin: need to align ≥ 20 sequences to accurately estimate conservation indices from MSAs. Membrane Bioinformatics SS09

  23. predicting the TM-helix-orientation from sequences Assumption: lipid-exposed positions are less conserved. CI: conservation index in MSA SASA: Solvent accessible surface area, relative to a single, free helix fj(i): frequency of amino acid j in position i. fj : frequency of amino acid j in full alignment. C : average conservation index (CI) : Standard deviation Positive values: conserved positions Negative values: variable positions Test: correct orientation (0,0) has lowest score. Membrane Bioinformatics SS09

  24. Ab initio structure prediction of TM bundles • Aim: construct structural model for a bundle of ideal transmembrane helices. • Construct 12 good geometries for every helix pair AB, BC, CD, DE, EF, FG • overlay ABCDEFG • „thin out“ solution space containing ca. 126 models • (a) remove „solutions“ where helices collide with eachother • (b) delete non-compact „solutions“ • score remaining 106 solutions by sequence conservation • cluster 500 best solutions in 8 models • rigid-body refinement, select 5 models with best sequence conservation. Membrane Bioinformatics SS09

  25. Rigid-body refinement Membrane Bioinformatics SS09

  26. Compare best models with X-ray structures dark: Model light: X-ray structure Additional input: known connectivity of the helices A-B-C-D-E-F-G. Otherwise, the search space would have been too large. Bacteriorhodopsin Halorhodopsin Sensory Rhodopsin Rhodopsin NtpK Membrane Bioinformatics SS09

  27. Comparing the best models with X-ray structures Membrane Bioinformatics SS09

  28. Can one select the best model? • These are our 4 best • non-native models of bR. • Because contact between A and E was not imposed, very different topologies were obtained. • In 2006, our methods could not distinguish between these models. • but they could serve as input for further experiments. Membrane Bioinformatics SS09

  29. “Success case”: True de novo model of 4-helix bundle Membrane Bioinformatics SS09

  30. Predicting lipid-exposure Membrane Bioinformatics SS09

  31. Predicting lipid-exposure Aim: derive optimal scale to predict exposure of residues to hydrophobic part of lipid bilayer. Scale should optimally correlate with SASA  minimize quadratical error. Solution for minimization task  Y: SASA values of the training set (N = 2901 residue positions) X: profile of residue frequencies from multiple sequence alignment ( N  21 matrix) : wanted propensity scale for 20 amino acids + 1 intercept value (21) Membrane Bioinformatics SS09

  32. What does MO scale capture? Membrane Bioinformatics SS09

  33. Improved prediction of exposure by statistical learning Beuming & Weinstein (2004) method Membrane Bioinformatics SS09

  34. Improved method by statistical learning The theory of Support Vector Classifiers evolves from a simpler case of optimal separating hyperplanes that, while separating two separable classes, maximize the distance between a separating hyperplane and the closest point from either class. A: The two classes can be fully separable by a hyperplane, and the optimal separating hyperplane can be obtained by solving Eq. 9. B: It is not possible to separate the two classes with a hyperplane, and the optimal hyperplane can be obtained by solving Eq. 17. Membrane Bioinformatics SS09

  35. Improved method by statistical learning Stockholm Univ. Sept. 2008 Membrane Bioinformatics SS09

  36. Improved method by statistical learning Membrane Bioinformatics SS09

  37. Improved method by statistical learning Membrane Bioinformatics SS09

  38. Improved method by statistical learning Membrane Bioinformatics SS09

  39. http://service.bioinformatik.uni-saarland.de/tmx/ input: Putative TM helices TopoView draws Snake plot Master thesis Nadine Schneider Membrane Bioinformatics SS09

  40. http://service.bioinformatik.uni-saarland.de/tmx/ Top: TMD11, Bottom: TMD 12 Membrane Bioinformatics SS09

  41. http://service.bioinformatik.uni-saarland.de/tmx/ Top: TMD5, Bottom: TMD 12 Membrane Bioinformatics SS09

  42. Summary TMX and related methods • Sequences of TM proteins reveal many powerful features to allow prediction of 2D- and 3D structural features, function, and oligomerization status. • TMX server can predict lipid exposure with ca. 78% accuracy.: • http://service.bioinformatik.uni-saarland.de/tmx/ • Possible applications: • predict transporter pores • (2) predict lipid-exposed surface of TM proteins: • correlate with different membrane composition • collaborate with us  do you have lots of solubility data? • (3) Conserved surface residues may indicate interaction sites Membrane Bioinformatics SS09

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