1. V1 SS 2009 Membrane Bioinformatics 1 Membrane Bioinformatics SoSe 2009 Böckmann & Helms

2. V1 SS 2009 Membrane Bioinformatics 2 What is “Membrane Bioinformatics” ? Increasing interest in structure & function of membrane proteins (ion channels, G-protein coupled receptors), but only few structures are known  structure prediction of membrane proteins, prediction of function from sequence Function of Membrane Proteins: depends on membrane composition, lipid-protein interactions, lipid mediated protein-protein interactions... Drug Transport through Membranes: depends on physico-chemical membrane properties Membranes may also play a direct role in signal transduction Diseases associated with changes in lipid composition (diabetes, schizophrenia, Tay-Sachs syndrome)

3. V1 SS 2009 Membrane Bioinformatics 3 Pharmaceutical relevance Membrane proteins are crucial for survival: - they are key components for cell-cell signaling - they mediate the transport of ions and solutes across the membrane - they are crucial for recognition of self. The pharmaceutical industry preferably targets membrane-bound receptors. Particularly important: large superfamily of G protein-coupled receptors (GPCRs) - receptors for hormones, neurotransmitters, growth factors, light and odor-related ligands. More than 50% of the prescription drugs act on GPCRs.

4. V1 SS 2009 Membrane Bioinformatics 4 Lecture Content Properties of Lipid Membranes (Rainer Böckmann) Properties of Membrane Proteins (Volkhard Helms) - Insertion of TM proteins into membrane: Translocon, MINS (today, V1) - Prediction of TM segments from sequence (V2) - Composition of Lipid membrane, Phase transitions (V3) - Elasticity of membranes (V4) - Predicting lipid-facing helix faces from sequence: TMX (V5) - Predicting helix interactions from sequence (V6) - Electrostatics of membranes (V7) - Electroporation of membranes (V8) - Classification of membrane protein function from sequence (V9) - Predicting the topology of beta-barrel proteins (V10) - Membrane-protein interactions (V11) - partitioning of alcohols in membranes (V12)

5. V1 SS 2009 Membrane Bioinformatics 55 Physico-Chemical Properties of Membranes (Composition, Chemical Structure, Self-Organisation, Phase Transitions) S.J. Marrink and A.E. Mark JACS 125 (2003) 15233-15242

6. V1 SS 2009 Membrane Bioinformatics 6 Molecular Theory of Membranes (Chain Packing, Elasticity)

7. V1 SS 2009 Membrane Bioinformatics 7 Electrostatic Properties of Membranes and Ion Channels R.A. Böckmann, A. Hac, T. Heimburg, H. Grubmüller Biophys.J. 85 (2003) 1647-1655

8. V1 SS 2009 Membrane Bioinformatics 8 Electroporation of Membranes

9. V1 SS 2009 Membrane Bioinformatics 9 Membrane-Protein Interactions, Role of Lipids S.W.I.Siu and R.A. Böckmann (2009)

10. V1 SS 2009 Membrane Bioinformatics 10 Certification Grade of certification (Schein) is based on an individual final oral exam. Condition for the participation in the final oral exam: (a) more than 50% of points from 4 assignments (b) every student needs to present once in tutorial. Assignments are given out after lectures V2 (Helms), V4 (Böckmann), V6 (Helms), V8 (Böckmann). Each assignment is to be completed within two weeks. Up to two students can submit a solution. Tutorial (every 2 weeks) will take place after submission of each assignment; date to be decided. Credit points: 5 (2V + 1Ü) for a special lecture in bioinformatics

11. V1 SS 2009 Membrane Bioinformatics 11 Role of the Membrane Membranes enable formation of compartments! Intracellular space is sub-divided (organelles, cytosol) Distribution of different molecules among the subspaces Membranes allow gradient of composition between nucleus and plasma membrane: directed flow of newly synthesized material from ER to plasma membrane, trafficking of nutrition molecules in opposite direction Membranes allow ionic/pH gradients in organelles: electrochemical gradient, activity control of specialized proteins (lysosomes), accumulation of specific proteins O.G. Mouritsen Life – as a Matter of Fat Springer (2005)

12. V1 SS 2009 Membrane Bioinformatics 12 Architecture of the Plasma-Membrane Plasma membrane has 3 layers: 1.glycocalix: film formed by oligosaccharides of glycolipid head groups 2. center: lipid/protein layer 3. Intracellular side: cytoskeleton In this lecture, we will focus on region 2. Addison-Wesley 1999

13. V1 SS 2009 Membrane Bioinformatics 13 Inside the hydrophobic core of the lipid bilayer, the protein backbone may not form hydrogen bonds with the aliphatic chains of the phospholipid molecules  the backbone atoms need to form H-bonds among eachother.  they must adopt either  -helical or  -sheet conformations. Topology of Membrane Proteins

14. V1 SS 2009 Membrane Bioinformatics 14 Lipid bilayer simplifies the prediction problem TM proteins are forced into two classes:  -helical, or  -sheet.  -helices are typically tilted with respect to the membrane normal between 10 – 45°. The hydrophobic lipid bilayer reduces the three-dimensional structure formation almost to a 2D problem. http://www.biologie.uni-konstanz.de/folding/Structure%20gallery%201.html

15. V1 SS 2009 Membrane Bioinformatics 15 History of membrane protein structure determination 1984 bacterial reaction center (Martinsried) noble price to Michel, Deisenhöfer, Huber 1987 1990EM map of bacteriorhodopsinHenderson 1997 high-resolution structure by Lücke now many intermediates of the photocycle 1992porin (complete  -barrel) Schulz (Freiburg) 1995 Cytochrome c Oxidase Michel (Frankfurt) 1998F 1 ATPasenoble price to John Walker 1997 1998KCSA ion channelnoble price to Roderick McKinnon 2003 2000aquaporin 2000rhodopsin (Palczewski) 2002SERCA Ca 2+ ATPase (Toyoshima) 2003voltage-gated ion channel (McKinnon) 2008First GPCR: adrenergic receptor 2009 P-glycoprotein (Chang)

16. V1 SS 2009 Membrane Bioinformatics 16 Partitioning across membranes Partitioning from neutron diffraction data or from MD simulations. White, von Heijne, Annu Rev Biophys (2008)

17. V1 SS 2009 Membrane Bioinformatics 17 Kyte-Doolittle hydrophobicity scale (1982) Assign hydropathy value to each amino acid. Use sliding-window to identify membrane regions. Sum the hydrophobicity scale over all w residues in the window of length w. Use threshold T to assign segment as predicted membrane helix. w = 19 residues could best discriminate between membrane and globular proteins. Threshold T > 1.6 was suggested for the average over 19 residues.

18. V1 SS 2009 Membrane Bioinformatics 18 More refined indices One drawback of pure hydropathy-based methods is that they fail to discriminate accurately between membrane regions and highly hydrophobic globular segments. -Wimley & White scale : based on partition experiments of peptides between water/lipid bilayer and water/octanol http://blanco.biomol.uci.edu/hydrophobicity_scales.html

19. V1 SS 2009 Membrane Bioinformatics 19 Translocon-assisted insertion of TM proteins from Ribosome into ER membrane White, von Heijne, Annu Rev Biophys (2008)

20. V1 SS 2009 Membrane Bioinformatics 20 Crystal structure of translocon Sec YEG White, von Heijne, Annu Rev Biophys (2008) Tom Rapoport (Harvard University)

21. V1 SS 2009 Membrane Bioinformatics 21 Integration of H-segments into the microsomal membrane Hessa et al., Nature 433, 377 (2005) b, Membrane integration of H-segments with the Leu/Ala composition 2L/17A, 3L/16A and 4L/15A. Bands of unglycosylated protein are indicated by a white dot; singly and doubly glycosylated proteins are indicated by one and two black dots, respectively. Ingenious experiment! Introduce marker that shows whether helix segment H is inserted into membrane or not. a, Wild-type Lep has two N-terminal TM segments (TM1 and TM2) and a large luminal domain (P2). H-segments were inserted between residues 226 and 253 in the P2-domain. Glycosylation acceptor sites (G1 and G2) were placed in positions 96–98 and 258–260, flanking the H-segment. For H- segments that integrate into the membrane, only the G1 site is glycosylated (left), whereas both the G1 and G2 sites are glycosylated for H-segments that do not integrate in the membrane (right). Gunnar von Heijne (Stockholm University)

22. V1 SS 2009 Membrane Bioinformatics 22 Insertion determined by simple physical chemistry Hessa et al., Nature 433, 377 (2005) c,  G app values for H-segments with 2–4 Leu residues. Individual points for a given n show  G app values obtained when the position of Leu is changed. d, Mean probability of insertion (p) for H-segments with n = 0–7 Leu residues. measure fraction of singly glycosylated (f 1g ) vs. doubly glycosylated (f 2g ) Lep molecules

23. V1 SS 2009 Membrane Bioinformatics 23 Biological and biophysical  G aa scales Hessa et al., Nature 433, 377 (2005) a,  G app aa scale derived from H-segments with the indicated amino acid placed in the middle of the 19-residue hydrophobic stretch. Only Ile, Leu, Phe, Val really favor membrane insertion. All polar and charged ones are very unfavored. b, Correlation between  G app aa values measured in vivo and in vitro. c, Correlation between the  G app aa and the Wimley–White water/octanol free energy scale for partitioning of peptides.

24. V1 SS 2009 Membrane Bioinformatics 24 Positional dependencies in  G app Hessa et al., Nature 433, 377 (2005) a, Symmetrical H-segment scans with pairs of Leu (red), Phe (green), Trp (pink) or Tyr (light blue) residues. The Leu scan is based on symmetrical 3L/16A H-segments with a Leu-Leu separation of one residue (sequence shown at the top; the two red Leu residues are moved symmetrically outwards) up to a separation of 17 residues. For the Phe scan, the composition of the central 19-residues of the H- segments is 2F/1L/16A, for the Trp scan it is 2W/2L/15A, and for the Tyr scan it is 2Y/3L/14A. The  G app value for the 4L/15A H-segment GGPGAAALAALAAAAALAALAAAGPGG is also shown (dark blue). b, Red lines show  G app values for symmetrical scans of 2L/17A (triangles), 3L/16A (circles), and 4L/15A (squares) H-segments. c, Same as b but for a symmetrical scan with pairs of Ser residues in H-segments with the composition 2S/4L/13A. Tyr and Trp are favorable in interface region.

25. V1 SS 2009 Membrane Bioinformatics 25 Summary – TM helix insertion 1. MPs are in thermodynamic equilibrium with the cell membrane’s lipid bilayer, which means that the stability and three-dimensional structure of MPs are ultimately determined by lipid-protein physical chemistry. 2. α-Helical MPs are identified during translation on the ribosome by the signal recognition particle that initiates docking of the ribosome to the membrane-embedded multi-protein translocon complex. 3. Elongating polypeptides from the ribosome pass through a translocon TM channel within the translocon complex. 4. The translocon’s U-shaped structure allows diversion of TM helices sideways into the lipid bilayer. 5. The diversion of the helices into the bilayer appears fundamentally to be a physicochemical partitioning process between translocon and bilayer. 6. The partitioning process can be described quantitatively by apparent free energies that serve as a code for the selection of TM helices by the translocon working in concert with the lipid bilayer. White, von Heijne, Annu Rev Biophys (2008)

26. V1 SS 2009 Membrane Bioinformatics 26 Summary – TM helix insertion FUTURE ISSUES 1. Much more structural information about translocons and translocon complexes is needed, especially an atomic-resolution structure of a translocon engaged in polypeptide secretion. 2. Although there is a clear connection between the physical chemistry of lipid-protein interactions and selection of TM helices by the translocon, a quantitative molecular description of the empirical apparent free energies of the translocon’s selection code is needed. 3. The molecular basis for the translocon-assisted assembly of multi- spanning MPs needs to be established. White, von Heijne, Annu Rev Biophys (2008)

27. V1 SS 2009 Membrane Bioinformatics 27 Structure modelling for helical membrane proteins >P52202 RHO -- Rhodopsin. MNGTEGPDFYIPFSNKTGVVRSPFEYPQYYLAEPWKYSALAAYMFMLIILGFPINFLTLYVTVQHK KLRSPLNYILLNLAVADLFMVLGGFTTTLYTSMNGYFVFGVTGCYFEGFFATLGGEVALWCLVVL AIERYIVVCKPMSNFRFGENHAIMGVVFTWIMALTCAAPPLVGWSRYIPEGMQCSCGVDYYTLK PEVNNESFVIYMFVVHFAIPLAVIFFCYGRLVCTVKEAAAQQQESATTQKAEKEVTRMVIIMVVSF LICWVPYASVAFYIFSNQGSDFGPVFMTIPAFFAKSSAIYNPVIYIVMNKQFRNCMITT LCCGKNPLGDDETATGSKTETSSVSTSQVSPA www.gpcr.org EMBO Reports (2002) 1D 2D 3D

28. V1 SS 2009 Membrane Bioinformatics 28 MINS: predict membrane insertion  G from sequence Park & Helms, Bioinformatics 24, 1271 (2008) Idea: amino acids in TM proteins accumulate at the most favorable regions (1) Analyze distribution of amino acids at various membrane depth in all known X-ray structures of TM proteins. (2) Compute frequencies as a function of membrane depth

29. V1 SS 2009 Membrane Bioinformatics 29 MINS: membrane insertion  G Park & Helms, Bioinformatics 24, 1271 (2008) To convert frequencies into free energies, calibrate against exp.  G for Hessa et al. peptides. r: frequency of amino acid i at depth z a i (z) and b i : fit parameters for linear fit. Plot of MINS-predicted and experimentally measured membrane insertion free energies for 357 known cases

30. V1 SS 2009 Membrane Bioinformatics 30 MINS result for TM helices Park & Helms, Bioinformatics 24, 1271 (2008) TM helices and helices of secreted or cytoplasmic proteins are well separated!

31. V1 SS 2009 Membrane Bioinformatics 31 MINS1 Similar prediction as with MINS can be made with standard hydrophobicity scales: WW: Wimely-White KD: Kyte-Doolittle GES: EIS But they give larger error than with MINS

32. V1 SS 2009 Membrane Bioinformatics 32 Performance of MINS1 to predict TM helices: accuracy check TM helices of polytopic TM proteins are not well predicted. This indicates cooperative insertion of TM helices

33. V1 SS 2009 Membrane Bioinformatics 33 Summary TM proteins are a separate world; very different from soluble proteins. Properties of TM proteins are intimately related to properties of the surrounding lipid bilayer! Structural Bioinformatics of Membrane Proteins is entering into a very exciting phase right now. Large interest of pharmaceutical companies due to recent availability of new X-ray structures of adrenergic receptor, membrane transporters, ion channels, and P-glycoprotein. Structural data is now sufficient for developping data-driven bioinformatics methods.