Membrane Protein Structure: Prediction versus RealityArne Elofsson and Gunnar von Heijne Center for Biomembrane Research, Stockholm Bioinformatics Center, Department ofBiochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden; email: [email protected], [email protected]Annu. Rev. Biochem. 2007. 76:125–40 First published online as a Review in Advance on January 11, 2007 TheAnnual Review of Biochemistryis online atbiochem.annualreviews.org This article’ s doi: 10.1146/annurev.biochem.76.052705.163539 Copyrightc 2007 by Annual Reviews. All rights reserved 0066-4154/07/0707-0125$20.00 Key Words bioinformatics, membrane protein structure prediction, topologyAbstractSince high-resolution structural data are still scarce, different kinds of theoretical structure prediction algorithms are of major impor- tance in membrane protein biochemistry. But how well do the cur- rent prediction methods perform? Which structural features can be predicted and which cannot? An d what can we expect in the next fewyears? 125 A n n u . R e v . B i o c h e m . 2 0 0 7 . 7 6 : 1 2 5 1 4 0 . D o w n l o a d e d f r o m w w w . a n n u a l r e v i e w s . o r g A c c e s s p r o v i d e d b y U n i v e r s i d a d N a c i o n a l A u t o n o m a d e M e x i c o o n 0 9 / 0 1 / 1 5 . F o r p e r s o n a l u s e o n l y .
un pequeño articulo cientifico donde se escalrecen las conformaciones estructurales de las proteinas de membrana
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Membrane ProteinStructure: Prediction versus Reality
Arne Elofsson and Gunnar von Heijne
Center for Biomembrane Research, Stockholm Bioinformatics Center, Department oBiochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden;email: [email protected], [email protected]
Annu. Rev. Biochem. 2007. 76:125–40
First published online as a Review in Advance on January 11, 2007
The Annual Review of Biochemistry is online at
biochem.annualreviews.org This article’s doi:10.1146/annurev.biochem.76.052705.163539
Copyright c 2007 by Annual Reviews. All rights reserved
0066-4154/07/0707-0125$20.00
Key Words
bioinformatics, membrane protein structure prediction, topology
Abstract
Since high-resolution structural data are still scarce, different kindsof theoretical structure prediction algorithms are of major importance in membrane protein biochemistry. But how well do the cur-
rent prediction methods perform? Which structural features can bepredicted and which cannot? And what can we expect in the next few
MEMBRANE PROTEIN BIOINFORMATICS: WHAT THESEQUENCES TELL
For the helix-bundle membrane proteins,amino acid sequences told their story long be-fore the first high-resolution structures were
determined: the typical transmembrane seg-ment is formed by a stretch of predominantly
hydrophobic residues long enough to spanthe lipid bilayer as an α -helix (25–29). The
early topology prediction methods were con-sequently little more than plots of the seg-
mental hydrophobicity (averaged over 10–20 residues) along the sequence (30–32). With
more sequences came the realizations that aromatic Trp and Tyr residues tend to clus-ter near the ends of the transmembrane seg-
ments (10, 33) and that the loops connectingthe helices differ in amino acid composition,
depending on whether they face the inside oroutside of the cell (the “positive-inside” rule)
(34–36). More recent analyses have focusedon the higher-than-random appearance of se-
quence motifs, such as the GxxxG-motif intransmembrane segments (37, 38) as well as
other periodic patterns within the membranehelices (39), with the aim of providing infor-
mation that may help in predicting helix-helixpacking and 3D structure.
MEMBRANE PROTEIN BIOINFORMATICS: WHAT THESTRUCTURES TELL
For a long time, the general view has beenthat membrane proteins form simple he-
lix bundles, with their transmembrane he-lices crisscrossing the membrane in more or
less perpendicular orientations. Indeed, many membrane proteins abide by this principle.
However, some more recently solved mem-
brane protein structures show that reality isnot always this simple. This is illustrated by
the structure of the glutamate transporter ho-
Figure 2
(a) The glutamate transporter homolog (1XFH) contains both disruptedtransmembrane helices and reentrant loops. Disrupted helices are shown(cyan and green), and reentrant loops are also shown. The mesh indicates theapproximate extent of the lipid tail region ( ±15 ˚ A). (b) Topology (upper
part ) and z-coordinate plot. The z-coordinate plot shows the distance fromthe center of the membrane for each residue. The coloring is the same as inpanel a. Modified with permission of Oxford University Press (79).
Reentrant loop: astructural motif in which thepolypeptide dipsonly partway acrossthe membrane
mologfrom Pyrococcus horikoshii (40), shown in
Figure 2. This protein has six typical trans-membrane helices and two irregular helices
with breaks inside the lipid bilayer. The struc-
ture also contains two reentrant loops that go only halfway through the membrane and
then turn back to the side from which they
www.annualreviews.org • Membrane Protein Structure Prediction 129
how they are received by the scientific com-munity at large, it is clear that membrane pro-tein structure prediction algorithmsholdtheir
ground; to give but one example, TMHMM(5,47)hasbeencitedwellover1200times.But
precisely what sort of information can one ex-pect to get from the various prediction meth-
ods? And what sort of advances can we see onthe horizon?
First and foremost, do not expect the com-puter to tell you the truth! Topology predic-
tions are just predictions. True, high-scoringpredictions are nearly always right (51, 63,109), but this only means that the really clear-
cut cases (i.e., those that can equally wellbe done by hand) are easy to predict. Still,
if taken with a grain of salt, topology mod-els, predicted lipid-exposed residues, poten-
tial reentrant loops, and lists of possibly in-teracting partner proteins can be invaluable
guides for planning experiments and inter-preting results. Andlarge-scalecomputational
studies of entire genomes can provide tanta-
lizing clues to everything from the basic pat-terns of membrane protein evolution (110
111) to differences in lifestyle between differ-ent organisms—show me your transporters
and I will tell you where you live.Still, much remains to be done, both in
perfecting the current arsenal of predictionmethods and devising entirely newalgorithmsto do new things. Our current representation
of membrane protein topology as a simplestring of membrane-spanning α -helices or β-
strands does not fully capture the structuraldiversity seen in membrane proteins; defin-
ing a fuzzy area between the 2D and 3Dstructure is in need of more exploration. The
rapid growth in known membrane protein 3Dstructures improves the prospects for effec-
tive fold-recognition and homology model-ing approaches, although the day when most
of membrane protein fold space has been
mapped experimentally seems desperately faroff (4). Computational means to map out the
membrane interactome will become an im-portant complement to high-throughput (but
error-prone) experimental studies, and hereas in so many other areas, tight integration
between the “wet” and “dry” approaches iscertainly the best way forward.
SUMMARY POINTS
1. Integral membrane proteins come in two basic architectures: α -helix bundles and
β-barrels.
2. The lipid-facing surface of integral membrane proteins is composed of a central
“hydrophobic belt” flanked by two “aromatic girdles.”
3. In the helix-bundle proteins, nontranslocated loops are enriched in Lys and Arg com-
pared to translocated loops (the positive-inside rule).
4. Helix-bundle membrane proteins are built from transmembrane α -helices, interfacial
helices lying flat on the membrane, reentrant loops, and extramembraneous globulardomains.
5. For the β-barrel protein, the number of β-strands is even, the N and C termini are at the periplasmic barrel end, the β-strand tilt is ∼45◦, and all β-strands are antiparallel
and connected locally to their next neighbors along the chain.
6. The best topology prediction algorithms forecast the correct topology for ≤70%of all proteins but cannot accurately predict the start and end of a transmembrane
segment.
7. Only a few recent prediction algorithms attempt to identify surface helices and reen-
trant loops.
8. Ab initio high-resolution 3D structure prediction is still not feasible for membraneproteins. Homology-based structure modeling of membrane proteins performs on apar with homology modeling of globular proteins.
ACKNOWLEDGMENTS
The authors’ laboratories are supported by grants from the Swedish Foundation for Strategic
Research, the Marianne and Marcus Wallenberg Foundation, the Swedish Cancer Founda-tion, the Swedish Research Council, and the European Commission (BioSapiens, Genefun,
EMBRACE).
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Indexes
Cumulative Index of Contributing Authors, Volumes 72–76 849
Cumulative Index of Chapter Titles, Volumes 72–76 853
Errata
An online log of corrections to Annual Review of Biochemistry chapters (if any, 1997to the present) may be found at http://biochem.annualreviews.org/errata.shtml