1. V6 SS 2009 Membrane Bioinformatics 1 V6 Membrane Beta Barrels – Membrane Positioning Beta-barrels are the second important type of transmembrane proteins. They are mostly found in the outer membranes of bacteria, chloroplasts, and mitochondria. They function as: (1) Simple passive pores for transport across bacterial membranes (2) active ion transporters for nutrient uptake, membrane anchors, defense against pathogenic proteins. Schulz, Curr Opin Struct Biol 10, 443 (2000) Georg Schulz (Uni Freiburg): First X-ray structure of porin (1992)

2. V6 SS 2009 Membrane Bioinformatics 2 (1) Schulz: 10 roles for Membrane Beta Barrels 1. The number of β-strands is even. The N and C termini are at the periplasmic barrel end. 2. The β-strand tilt is always around 45° and corresponds to the common β-sheet twist. Only one of the two possible tilt directions is assumed, the other one is an energetically disfavored mirror image. (Today: tilt between 20 and 45°) 3. The shear number of an n-stranded barrel is positive and around n+2, in agreement with the observed tilt. 4. All β-strands are antiparallel and connected locally to their next neighbors along the chain, resulting in a maximum neighborhood correlation. 5. The strand connections at the periplasmic barrel end are short turns of a couple of residues named T1, T2 and so on. Schulz, Curr Opin Struct Biol 10, 443 (2000)

3. V6 SS 2009 Membrane Bioinformatics 3 Schulz: 10 roles for Membrane Beta Barrels 6. At the external barrel end, the strand connections are usually long loops named L1, L2 and so on. 7. The β-barrel surface contacting the nonpolar membrane interior consists of aliphatic sidechains forming a nonpolar ribbon with a width of about 22 Å. 8. The aliphatic ribbon is lined by two girdles of aromatic sidechains, which have intermediate polarity and contact the two nonpolar–polar interface layers of the membrane. 9. The sequence variability of all parts of the β-barrel during evolution is high when compared with soluble proteins. 10. The external loops show exceptionally high sequence variability and they are usually mobile. Schulz, Curr Opin Struct Biol 10, 443 (2000)

4. V6 SS 2009 Membrane Bioinformatics 4 shear Ideal topology, see Fig. on the right. However, TM  -strands do not span the membrane at 90° (perpendicular to the membrane). They are usually inclined at an angle to the vertical TM axis. This results in a shift in the H-bonded residues, termed the shear number. A shear number of +1 means that the H-bonded partner of the residue at position i is at position j + 1 rather than j. Waldispühl et al. Proteins 65, 61 (2006)

5. V6 SS 2009 Membrane Bioinformatics 5 Structures of Membrane Beta Barrels Nowadays: β-barrels size from small 8-stranded to large 22-stranded proteins. Oligomerization state: TMBs can against exist as monomers or oligomers. Their topology is defined by the strand number and shear number (measure of inclination angle of beta-strand against the axis).

6. V6 SS 2009 Membrane Bioinformatics 6 partiFold Model is motivated by an abstract physical description of omps. It uses  -strand contact energy parameters for globular proteins taken from the program BETAWRAP [statistical potentials: W(r) = -kT ln p(r)] Jerome Waldispühl (MIT)

7. V6 SS 2009 Membrane Bioinformatics 7 Structural features Fundamental features of beta-barrel structures: (i)The overall shape of the barrel (# of strands, their relative arrangement) (ii)A list of antiparallel  -strand pairs; residue contacts and side chain orientation (iii)Inclination of TM  -strands through the membrane plane. Waldispühl et al. Proteins 71, 1097 (2008)

8. V6 SS 2009 Membrane Bioinformatics 8 2-tape representation Decomposition of TMB into individual blocks of antiparallel  -strands. Each strand is involved in two „pairings“. Figure shows 2-tape representation. Pairings are made from one tape to the other. Waldispühl et al. Proteins 71, 1097 (2008)

9. V6 SS 2009 Membrane Bioinformatics 9 New notation Each block is represented as 4-tuple where i 1 and j 1 are the indices of the strand on the first tape and i 2 and j 2 are those on the second tape. M :  -strand residues with side-chains oriented toward the membrane. C : residues with side-chain oriented toward the channel. E : unpaired  -strand residues

10. V6 SS 2009 Membrane Bioinformatics 10 partiFold Model is based on an abstract physical description of omps. It uses  -strand contact energy parameters for globular proteins taken from the program BETAWRAP. Waldispühl et al. Proteins 71, 1097 (2008)

11. V6 SS 2009 Membrane Bioinformatics 11 partiFold: computation of structures Compute energies of all conformations using statistical potential for amino acid stacking pairs. Use dynamic programming approach to sample all possible TMB structures, compute their energies, and thus the partition function. partiFold algorithms then predicts an ensemble of structural conformations for a TMB. Energy function apparently needs to be refined further... Waldispühl et al. Proteins 71, 1097 (2008)

12. V6 SS 2009 Membrane Bioinformatics 12 Another interesting approach: statistics of NP-patterns Shown here: Pattern frequencies in Soluble proteins. Need to perform analogous statistics for TM barrels. (ongoing work by Sikander) Mandel-Gutfreund, Gregoret, JMB 323, 453 (2002)

13. Membrane Bioinformatics – Part II 13 TMHMM: 1000 of 4288 predicted E.coli genes are inner membrane proteins. 737 genes encode proteins with > 100 residues and  2 TM helices. 714 were suitable for cloning into phoA and gfp fusion vectors. Both fusions could be obtained for 573 genes, one fusion for an additional 92 genes. (2) Global Topology Analysis Daley et al. Science 308, 1321 (2005) Knowing the topology of a TM protein is essential to understanding its function. Idea: generate reference point, e.g. the location of a protein‘s C terminus. E.coli attach alkaline phosphatase (PhoA) to C-terminus that is active only in the periplasm of E.coli, or green fluorescent protein (GFP) that fluoresces only in the cytoplasm.

14. Membrane Bioinformatics – Part II 14 Using homology, 601 proteins could be assigned a topology. For 71 of these, the location of the C terminus was already established. The results agreed except for 2 cases. The error rate is therefore ~ 1%. TMHMM alone predicts the correct C-terminal location for 78% of the 601 proteins. By providing unambiguous C-terminal locations, the TMHMM reliability score increases for 526 proteins and decreases for 75 proteins. Global Topology Analysis Daley et al. Science 308, 1321 (2005)

15. Membrane Bioinformatics – Part II 15 Functional categorization of E.coli inner membrane proteome Daley et al. Science 308, 1321 (2005)  clear trend for N in – C in topologies (even number of TMH) - largest functional category is transport proteins, many with 6 or 12 TM helices. Most proteins with unknown function have  6 TM helices.

16. Membrane Bioinformatics – Part II 16 Idea: transfer experimental data set from PhoA and GFP-fusions of 608 proteins to homologous proteins. In March 2005 were available, 204 annotated eubacterial and 21 archeal genomes, with 658,210 annotated protein sequences. Perform BLAST searches (E-value < 10 -5 )  30,744 sequence hits where TMHMM predicts  1 TM helix Second BLAST query with these 30,744 sequences  17,111 „secondary homologs“. Extend predictions by sequence homology Granseth et al., J.Mol.Biol. 352, 489 (2005)

17. Membrane Bioinformatics – Part II 17 Unconstrained vs. constrained prediction Granseth et al., J.Mol.Biol. 352, 489 (2005) (a) Unconstrained TMHMM predictions for the full set of 158,182 sequences with  1 predicted TM helix (grey bars) and constrained predictions for the 51,208 sequences for which the C- terminal location or the location of an internal residue could be annotated (black bars). The number of proteins with different topologies are shown; C in topologies are plotted upwards, C out downwards. The number of C out proteins with a single TM helix (39,322) is off-scale. The unconstrained algorithm predicts too many proteins as C out. (b) TMHMM predictions for the 51,208 annotated sequences before (grey bars) and after (black bars) constraining the predictions with the location of the annotated residue.

18. Membrane Bioinformatics – Part II 18 Most TM proteins are expected to adopt only one topology in the membrane. Global topology analysis of E.coli inner membrane proteome identified 5 dual- topology candidates: EmrE, SugE, CrcB, YdgC, YnfY. All are quite small (~ 100 aa), contain 4 strongly predicted TM segments, contain only few K and R residues and have very small (K + R) bias. (3) Dual-topology proteins? Rapp et al., Nat.Struct.Biol. 13, 112 (2006) (a) A dual-topology protein inserts into the membrane in two opposite directions. As nearly all helix-bundle membrane proteins have a higher number of lysine (K) and arginine (R) residues in cytoplasmic (in) than in periplasmic (out) loops (the ‚positive-inside‘ rule), dual-topology proteins are expected to have very small (K + R) biases. Rectangles: TM segments black dots: K and R residues

19. Membrane Bioinformatics – Part II 19 Without solving their 3D structures, how can one prove that a protein has dual topology? Such a protein would be particularly sensitive to the addition or removal of a single positively charged residue in a loop or tail.  measure activities of two different, C-terminally fused reporter proteins: PhoA (only enzymatically active when in the periplasm) GFP (fluorescent only when in the cytoplasm). Concentrate on N-terminus and first loop. Dual-topology proteins? Rapp et al., Nat.Struct.Biol. 13, 112 (2006)

20. Membrane Bioinformatics – Part II 20 (a) wt YdgE-PhoA fusion is active, wt YdgE-GFP fusion is inactive  C-terminus in periplasm (C out ) wt YdgF behaves oppositely (C in ) These 2 proteins are topologically stable. (b – d) C-terminal orientation of EmrE, SugE, CrcB, YnfA and YdgC is highly sensitve to charge mutations. For 14 or 19 charge mutations, both PhoA and GFP activities change in the direction expected from the change in (K + R) bias. Charge mutations shift the orientations of dual-topology TM proteins Rapp et al., Nat.Struct.Biol. 13, 112 (2006)

21. Membrane Bioinformatics – Part II 21 Experimental techniques to study orientation of proteins in membranes are: - chemical modification - spin-labeling - fluorescence quenching - X-ray scattering - neutron diffraction - electron cryomicroscopy - NMR - polarized infrared spectroscopy. It is very desirable to complement them by computational methods. - e.g. explicit-solvent molecular dynamics simulations - here: simplified approach that minimize the protein transfer energy from water to a hydrophobic slab used as a membrane model. (4) Positioning of proteins in membranes – OPM database Adamian & Liang, Proteins 63, 1 (2006)

22. Membrane Bioinformatics – Part II 22 important parameters Lomize et al. Prot.Sci. 15, 1318 (2006)

23. Membrane Bioinformatics – Part II 23 Model protein as a rigid body that freely floats in the planar hydrocarbon core of a lipid bilayer. Calculation of transfer energy Adamian & Liang, Proteins 63, 1 (2006) ASA i : accessible surface area of atom i  i W-M : solvation parameter of atom i (transfer energy of the atom from water to membrane interior in kcal/(mol.Å 2 ) ) f(z i ): interfacial water concentration profile with = 0.9 Å

24. Membrane Bioinformatics – Part II 24 ionization of charged residues Residues that are typically charged in soluble proteins may become neutral in the hydrophobic inside of the bilayer! The ionization/protonation energies of charged residues are described by the Henderson-Hasselbalch equation: Lomize et al. Prot.Sci. 15, 1318 (2006) at pH = 7 average pK a value  G ioniz in proteins[kcal/mol] Arg12.06.9 Lys10.44.7 Asp3.44.9 Glu4.14.0 His6.60.6

25. Membrane Bioinformatics – Part II 25 use deterministic 2-step search strategy: (1) grid scan to determine a set of low-energy combinations of variables z 0, d, ,  grid steps: 0.5 Å for z 0 and d, 5° for , 2° for  (2) local energy minimization (Davidon-Fletcher-Powell method) starting from low- energy points Also consider energetically best rotation of solvent-exposed charged side chains (e.g. Lys and Arg) that are situated close to the calculated boundaries and could be rotated away from the hydrophobic core  snorkeling. Global energy optimization Adamian & Liang, Proteins 63, 1 (2006)

26. Membrane Bioinformatics – Part II 26 Which solvation parameters to use?  chx and dcd results agree well with experiment, oct agrees poorly. Lomize et al. Prot.Sci. 15, 1318 (2006)

27. Membrane Bioinformatics – Part II 27 Pay attention to … slightly different parameter sets should be applied for proteins in detergents and bilayers  G transfer should not include contributions of atoms that face internal polar cavities of TM proteins and that do not directly interact with surrounding bulk lipid Otherwise, the orientation of many  -barrels and pore-forming transporters would be computed incorrectly Lomize et al. Prot.Sci. 15, 1318 (2006)

28. Membrane Bioinformatics – Part II 28 Main features of model necessary and sufficient approximations for reproducing the exp. Data (1)lipid bilayer is represented as planar hydrophobic slab with adjustable thickness and a narrow interfacial area with a sigmoidal polarity profile (2)proteins are considered as rigid bodies with flexible side chains; their transfer energies are minimized with respect to 4 variables (3)transfer free energy is calculated at an all-atom level using atomic solvation parameters determined for the water-decadiene system (4)neglect explicit electrostatic interactions, account for neutralization of charged residues (5) eliminate contributions of pore-facing atoms Lomize et al. Prot.Sci. 15, 1318 (2006)

29. Membrane Bioinformatics – Part II 29 parameters of model The model only depends on 5 atomic solvation parameters (N, O, S, sp2 C, sp3 C), one constant, and the ionization energies of charged groups. All can be obtained independently from experimental sources. Verify method for 24 TM proteins of known 3D structure whose spatial position in bilayers have been exp studied. Lomize et al. Prot.Sci. 15, 1318 (2006)

30. Membrane Bioinformatics – Part II 30 Average tilt angles (a) hydrophobic thickness matches well (table 2) Lomize et al. Prot.Sci. 15, 1318 (2006) (b) the calculated tilt values are in excellent agreement with NMR data, they also correlate well with ATR-FTIR data (table 3), although the exp. values are systematically larger  orientational disorder in the experiments?

31. Membrane Bioinformatics – Part II 31 Membrane penetration depths Lomize et al. Prot.Sci. 15, 1318 (2006)

32. Membrane Bioinformatics – Part II 32 Biological membranes differ Lomize et al. Prot.Sci. 15, 1318 (2006)

33. Membrane Bioinformatics – Part II 33 Membrane pentration depths Lomize et al. Prot.Sci. 15, 1318 (2006)

34. Membrane Bioinformatics – Part II 34 Membrane core boundaries Lomize et al. Prot.Sci. 15, 1318 (2006)

35. Additional slides

36. Membrane Bioinformatics – Part II 36 application to all other 109 TM protein complexes 80  -helical 28  -barrels gramicidin dimer control set: 20 water-soluble proteins 32 monotopic and peripheral proteins Application to all TM proteins from the PDB Lomize et al. Prot.Sci. 15, 1318 (2006)

37. Membrane Bioinformatics – Part II 37 Peripheral and monotopic proteins have low penetration depths. Calculated tilt angles vary from 0° - 6°.  TM proteins tend to be nearly perpendicular to the membrane, although the individual helices are on average tilted by 21°. Application to membrane proteins Lomize et al. Prot.Sci. 15, 1318 (2006)

38. Membrane Bioinformatics – Part II 38 Global topology analysis of E.coli inner membrane proteome showed that ca. 20 – 25% of the TM proteins have  10 TM helices. These are often involved in transport of small molecules across the membrane. Many of these proteins will have buried helices. Can we identify those? Develop an empirical helix burial function f based on a few assumptions. (i) residues in buried helices are more conserved because of structural and functional contraints. (ii) the residue composition of the buried helices is different from the composition of helices facing the lipid environment. (iii) the difference between the minimal and maximal values of conservation entropy for every position in MSAs of TM helices should be smaller in buried helices than in lipid-exposed helices because of the homogenous environment. (4) Prediction of buried TM helices Adamian & Liang, Proteins 63, 1 (2006)

39. Membrane Bioinformatics – Part II 39 f: burial function s: average entropy of all residue positions of the TM helix l : average lipophilicity k: sorted entropy values of all residue positions in a helix of length d for helices 1... n of the TM protein Problems: the average entropy depends on the number of sequences in the MSA.  needs MSAs with exactly the same set of sequences from the same set of species. Also, the stability of different membrane proteins in the lipid environment may be different. Account for ambiguity in the definition of TM helix ends. Burial Function Adamian & Liang, Proteins 63, 1 (2006)

40. Membrane Bioinformatics – Part II 40 Ranking of TM helices by burial function and robustness Adamian & Liang, Proteins 63, 1 (2006)

41. Membrane Bioinformatics – Part II 41 (a) TM helices TM4, TM5, TM6, TM8 form core, consistent with prediction. (b) TM4, TM10 are most buried. (c) one can explain prediction of TM8 as buried by considering a tightly bound cardiolipin molecule identified in the X-ray structure. Examples of buried TM helices that are correctly predicted Adamian & Liang, Proteins 63, 1 (2006)

42. Membrane Bioinformatics – Part II 42 Is the method applicable to TM proteins where only sequence data is available? Test on structure of Leu transporter. TMHMM predicts 12 TM helices. Good overlap with X-ray helices. Problem that no additional sequences exist that are annotated as Na + -dependent Leu transporters. LeuT Aa has 3 significantly buried helices: 1, 6 and 8. 1 and 6 are true positives, 2 is a false positive, 8 is a false negative. Test ranking results Adamian & Liang, Proteins 63, 1 (2006)

43. Membrane Bioinformatics – Part II 43 Pfam searches in 174 fully sequenced bacterial genomes for homologs (E < 10 -10 ) to SugE, EmrE, YdgE, CrcB, YnfA, YdgC and YdgO/YdgL. Create multiple sequence alignment with ClustalW. Use TMHMM to predict the positions of TM helices. Obtain consensus TM helix prediction, compute (K + R) biases for individual proteins. 10 residues from each of the flanking TM helices were included to allow for possible misprediction of the exact positions of the loop ends. Dual-topology homologs occur as gene pairs or singletons Rapp et al., Nat.Struct.Biol. 13, 112 (2006)

44. Membrane Bioinformatics – Part II 44 Interpretation: SMR and CrcB occur as closely spaced pairs or as singletons. Paired genes encode homologous proteins with opposite (K + R) bias. Dual-topology homologs occur as gene pairs or singletons Rapp et al., Nat.Struct.Biol. 13, 112 (2006)

45. Membrane Bioinformatics – Part II 45 Most likely evolutionary scenario: a single dual-topology protein undergoes gene duplication, the two resulting proteins become fixed in opposite orientations and finally fuse into a single polypeptide. An internally duplicated protein with opposite topology Rapp et al., Nat.Struct.Biol. 13, 112 (2006)