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Transcript
Membrane Bioinformatics
SoSe 2009
Böckmann & Helms
Membrane Bioinformatics
V1 SS 2009
1
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)
Membrane Bioinformatics
V1 SS 2009
2
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.
Membrane Bioinformatics
V1 SS 2009
3
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)
Membrane Bioinformatics
V1 SS 2009
4
Physico-Chemical Properties of Membranes
(Composition, Chemical Structure, Self-Organisation,
Phase Transitions)
S.J. Marrink and A.E. Mark JACS 125 (2003) 15233-15242
Membrane Bioinformatics
V1 SS 2009
5
Molecular Theory of Membranes
(Chain Packing, Elasticity)
Membrane Bioinformatics
V1 SS 2009
6
Electrostatic Properties of Membranes and Ion Channels
R.A. Böckmann, A. Hac, T. Heimburg, H. Grubmüller Biophys.J. 85 (2003) 1647-1655
Membrane Bioinformatics
V1 SS 2009
7
Electroporation of Membranes
Membrane Bioinformatics
V1 SS 2009
8
Membrane-Protein Interactions, Role of Lipids
S.W.I.Siu and R.A. Böckmann (2009)
Membrane Bioinformatics
V1 SS 2009
9
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
Membrane Bioinformatics
V1 SS 2009
10
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)
Membrane Bioinformatics
V1 SS 2009
11
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
Membrane Bioinformatics
V1 SS 2009
12
Topology of Membrane Proteins
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.
Membrane Bioinformatics
V1 SS 2009
13
Lipid bilayer simplifies the prediction problem
TM proteins are forced into two classes: -helical, or -sheet.
http://www.biologie.uni-konstanz.de/folding/Structure%20gallery%201.html
-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.
Membrane Bioinformatics
V1 SS 2009
14
History of membrane protein structure determination
1984
1990
1992
1995
1998
1998
2000
2000
2002
2003
2008
2009
bacterial reaction center (Martinsried) noble price to Michel, Deisenhöfer, Huber 1987
EM map of bacteriorhodopsin Henderson
1997 high-resolution structure by Lücke
now many intermediates of the photocycle
porin (complete -barrel) Schulz (Freiburg)
Cytochrome c Oxidase Michel (Frankfurt)
F1ATPase
noble price to John Walker 1997
KCSA ion channel
noble price to Roderick McKinnon 2003
aquaporin
rhodopsin (Palczewski)
SERCA Ca2+ ATPase (Toyoshima)
voltage-gated ion channel (McKinnon)
First GPCR: adrenergic receptor
P-glycoprotein (Chang)
Membrane Bioinformatics
V1 SS 2009
15
Partitioning across membranes
Partitioning from neutron diffraction data or from MD simulations.
White, von Heijne, Annu Rev Biophys (2008)
Membrane Bioinformatics
V1 SS 2009
16
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.
Membrane Bioinformatics
V1 SS 2009
17
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
Membrane Bioinformatics
V1 SS 2009
18
http://blanco.biomol.uci.edu/hydrophobicity_scales.html
Translocon-assisted insertion of TM proteins from
Ribosome into ER membrane
White, von Heijne, Annu Rev Biophys (2008)
Membrane Bioinformatics
V1 SS 2009
19
Crystal structure of translocon Sec YEG
Tom Rapoport
(Harvard University)
White, von Heijne, Annu Rev Biophys (2008)
Membrane Bioinformatics
V1 SS 2009
20
Integration of H-segments into the microsomal membrane
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 Hsegments 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).
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.
Gunnar von Heijne
(Stockholm University)
Hessa et al., Nature 433, 377 (2005)
Membrane Bioinformatics
V1 SS 2009
21
Insertion determined by simple physical chemistry
measure fraction of singly glycosylated (f1g) vs. doubly glycosylated (f2g) Lep molecules
p
f1g
f1g  f 2 g
K app 
f1g
f2g
Gapp   RT ln K app
c, Gapp values for H-segments with 2–4 Leu residues.
Individual points for a given n show Gapp values obtained when the position of Leu is changed.
d, Mean probability of insertion (p) for H-segments with n = 0–7 Leu residues.
Hessa et al., Nature 433, 377 (2005)
Membrane Bioinformatics
V1 SS 2009
22
Biological and biophysical Gaa scales
a, Gappaa 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 Gappaa values measured in vivo and in vitro.
c, Correlation between the Gappaa and the Wimley–White water/octanol free
energy scale for partitioning of peptides.
Hessa et al., Nature 433, 377 (2005)
Membrane Bioinformatics
V1 SS 2009
23
Positional dependencies in Gapp
Tyr and Trp are favorable in
interface region.
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 Hsegments 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.
Hessa et al., Nature 433, 377 (2005)
Membrane Bioinformatics
V1 SS 2009
24
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)
Membrane Bioinformatics
V1 SS 2009
25
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 multispanning MPs needs to be established.
White, von Heijne, Annu Rev Biophys (2008)
Membrane Bioinformatics
V1 SS 2009
26
Structure modelling for helical membrane proteins
>P52202 RHO -- Rhodopsin.
1D
MNGTEGPDFYIPFSNKTGVVRSPFEYPQYYLAEPWKYSALAAYMFMLIILGFPINFLTLYVTVQHK
KLRSPLNYILLNLAVADLFMVLGGFTTTLYTSMNGYFVFGVTGCYFEGFFATLGGEVALWCLVVL
AIERYIVVCKPMSNFRFGENHAIMGVVFTWIMALTCAAPPLVGWSRYIPEGMQCSCGVDYYTLK
PEVNNESFVIYMFVVHFAIPLAVIFFCYGRLVCTVKEAAAQQQESATTQKAEKEVTRMVIIMVVSF
LICWVPYASVAFYIFSNQGSDFGPVFMTIPAFFAKSSAIYNPVIYIVMNKQFRNCMITT
LCCGKNPLGDDETATGSKTETSSVSTSQVSPA
2D
www.gpcr.org
3D
EMBO Reports (2002)
Membrane Bioinformatics
V1 SS 2009
27
MINS: predict membrane insertion G from sequence
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
Park & Helms, Bioinformatics 24, 1271 (2008)
Membrane Bioinformatics
V1 SS 2009
28
MINS: membrane insertion G
To convert frequencies into free energies,
calibrate against exp. G for Hessa et al.
peptides.
r: frequency of amino acid i at depth z
ai(z) and bi: fit parameters for linear fit.
Plot of MINS-predicted and experimentally
measured membrane insertion free energies
for 357 known cases
Park & Helms, Bioinformatics 24, 1271 (2008)
Membrane Bioinformatics
V1 SS 2009
29
MINS result for TM helices
TM helices and helices of secreted or cytoplasmic proteins are well separated!
Park & Helms, Bioinformatics 24, 1271 (2008)
Membrane Bioinformatics
V1 SS 2009
30
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
Membrane Bioinformatics
V1 SS 2009
31
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
Membrane Bioinformatics
V1 SS 2009
32
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.
Membrane Bioinformatics
V1 SS 2009
33