Download Sequence-based predictions of membrane-protein topology, homology and insertion

Sequence-based predictions of
membrane-protein topology,
homology and insertion
Andreas Bernsel
Stockholm University
© Andreas Bernsel, Stockholm 2008, pages 1-57
ISBN 978-91-628-7565-7
Printed in Sweden by US-AB, Stockholm 2008
Distributor: Stockholm University Library
"Trying is the first step
towards failure"
-- Homer
List of publications
Publications included in this thesis
I
Bernsel A, von Heijne G. (2005)
Improved membrane protein topology prediction by
domain assignments.
Protein Sci. 14(7):1723-28.
II
Bernsel A, Viklund H, Elofsson A. (2008)
Remote homology detection of integral membrane
proteins using conserved sequence features.
Proteins. 71(3):1387-99.
III
Hessa T*, Meindl-Beinker NM*, Bernsel A*, Kim H,
Sato Y, Lerch-Bader M, Nilsson I, White SH,
von Heijne G. (2007)
Molecular code for transmembrane-helix recognition by the
Sec61 translocon.
Nature. 450(7172):1026-30.
IV
Bernsel A*, Viklund H*, Falk J, Lindahl E,
von Heijne G, Elofsson A. (2008)
Prediction of membrane-protein topology from first
principles.
Proc Natl Acad Sci U S A. 105(20):7177-81.
* These authors contributed equally
Reprints were made with permission from the publishers.
Additional publications
Médigue C, Krin E, Pascal G, Barbe V, Bernsel A, Bertin PN,
Cheung F, Cruveiller S, D'Amico S, Duilio A, Fang G, Feller G,
Ho C, Mangenot S, Marino G, Nilsson J, Parrilli E, Rocha EP,
Rouy Z, Sekowska A, Tutino ML, Vallenet D, von Heijne G,
Danchin A. (2005)
Coping with cold: the genome of the versatile marine Antarctica bacterium
Pseudoalteromonas haloplanktis TAC125.
Genome Res. 15(10):1325-35.
Prediction servers
Two web servers have been developed that implement methods described in
the papers:
∆G-pred
Prediction of ∆G for TM-helix insertion
http://www.cbr.su.se/DGpred/
TOPCONS Consensus prediction of membrane protein topology
http://topcons.cbr.su.se/
Abstract
Membrane proteins comprise around 20-30% of a typical proteome and play crucial
roles in a wide variety of biochemical pathways. Apart from their general biological
significance, membrane proteins are of particular interest to the pharmaceutical
industry, being targets for more than half of all available drugs. This thesis focuses
on prediction methods for membrane proteins that ultimately rely on their amino
acid sequence only.
By identifying soluble protein domains in membrane protein sequences, we
were able to constrain and improve prediction of membrane protein topology, i.e.
what parts of the sequence span the membrane and what parts are located on the
cytoplasmic and extra-cytoplasmic sides. Using predicted topology as input to a
profile-profile based alignment protocol, we managed to increase sensitivity to detect distant membrane protein homologs.
Finally, experimental measurements of the level of membrane integration of
systematically designed transmembrane helices in vitro were used to derive a scale
of position-specific contributions to helix insertion efficiency for all 20 naturally
occurring amino acids. Notably, position within the helix was found to be an important factor for the contribution to helix insertion efficiency for polar and charged
amino acids, reflecting the highly anisotropic environment of the membrane. Using
the scale to predict natural transmembrane helices in protein sequences revealed
that, whereas helices in single-spanning proteins are typically hydrophobic enough
to insert by themselves, a large part of the helices in multi-spanning proteins seem to
require stabilizing helix-helix interactions for proper membrane integration. Implementing the scale to predict full transmembrane topologies yielded results comparable to the best statistics-based topology prediction methods.
Contents
1.
2.
Introduction ..........................................................................................12
Biological membranes..........................................................................15
2.1 Membrane lipids ...................................................................................................... 15
2.2 Lipid bilayers............................................................................................................ 16
3.
Membrane Proteins..............................................................................18
3.1 Types of membrane proteins .................................................................................. 18
3.1.1 α-helical transmembrane proteins .................................................................. 19
3.1.2 β-barrel transmembrane proteins ................................................................... 19
3.2 Membrane protein targeting and translocation in the ER ....................................... 20
3.3 Amino acid statistics of TM-helices ......................................................................... 21
3.3.1 Helix-breaking residues .................................................................................. 21
3.3.2 The aromatic belt ............................................................................................ 22
3.3.3 The positive-inside rule ................................................................................... 23
3.4 Membrane protein topology..................................................................................... 23
3.4.1 Topology mapping techniques........................................................................ 24
3.4.2 Dual topology proteins .................................................................................... 24
3.5 Membrane protein structure .................................................................................... 25
3.5.1 Structure determination techniques ................................................................ 25
3.5.2 Structural features of membrane proteins ...................................................... 26
3.5.3 Helix-helix interactions .................................................................................... 27
4.
Computational approaches..................................................................28
4.1 Substitution matrices, profiles and HMMs............................................................... 28
4.1.1 Sequence alignment and substitution matrices .............................................. 28
4.1.2 Sequence profiles ........................................................................................... 29
4.1.3 Hidden Markov Models ................................................................................... 30
4.2 Aritifical neural networks ......................................................................................... 31
4.3 Hydrophobicity scales ............................................................................................. 32
4.4 Transmembrane topology predictors ...................................................................... 33
4.4.1 TopPred .......................................................................................................... 33
4.4.2 TMHMM / PRO-TMHMM / PRODIV-TMHMM ................................................ 33
4.4.3 HMMTOP ........................................................................................................ 34
4.4.4 MEMSAT......................................................................................................... 34
4.4.5 PHDhtm........................................................................................................... 34
4.4.6 Phobius / PolyPhobius .................................................................................... 34
5.
Present investigation............................................................................36
5.1 Improving transmembrane topology prediction by domain assignments (Paper I). 36
5.2 Using topology to predict homology (Paper II)........................................................ 38
5.3 Deciphering the molecular code for transmembrane helix recognition (Paper III).. 39
5.4 Prediction of membrane-protein topology from first principles (Paper IV) .............. 44
Acknowledgements .......................................................................................46
References....................................................................................................48
Abbreviations
ANN
BLAST
DOPC
ER
GFP
GPCR
GPI
HMM
MD
NMR
ORF
PDB
PhoA
PSI-BLAST
PSSM
SP
SRP
TM
Artificial neural network
Basic local alignment search tool
Dioleoyl-phosphatidylcholine
Endoplasmic reticulum
Green fluorescent protein
G-protein coupled receptor
Glycosyl-phosphatidylinositol
Hidden Markov model
Molecular dynamics
Nuclear magnetic resonance
Open reading frame
Protein data bank
Alkaline phosphatase
Position specific iterative BLAST
Position specific scoring matrix
Signal peptide
Signal recognition particle
Transmembrane
Amino acids
Ala
Cys
Asp
Glu
Phe
Gly
His
Ile
Lys
Leu
(A)
(C)
(D)
(E)
(F)
(G)
(H)
(I)
(K)
(L)
Alanine
Cysteine
Aspartic acid
Glutamic acid
Phenylalanine
Glycine
Histidine
Isoleucine
Lysine
Leucine
Met
Asn
Pro
Gln
Arg
Ser
Thr
Val
Trp
Tyr
(M)
(N)
(P)
(Q)
(R)
(S)
(T)
(V)
(W)
(Y)
Methionine
Asparagine
Proline
Glutamine
Arginine
Serine
Threonine
Valine
Tryptophan
Tyrosine
1. Introduction
Life is a complex thing (Keyes 1966). During the short time a biological cell
spends on earth before dividing itself into two new cells, or worse, committing suicide (Hengartner 2000), a myriad of biochemical reactions will have
taken place, each of which is required for the development, survival and
propagation of the cell and the genetic material it carries. Key players in this
chemical factory of life are the proteins, catalyzing the specific conversion
of substrates to products, regulating each others' concentrations, transporting
molecules across barriers, and converting light into sugar, among many other
things. The specific function of a protein molecule is determined from its
three-dimensional shape, which in turn is a function only of its amino acid
sequence (Anfinsen 1973), and ultimately of its corresponding DNA sequence. In a more general sense, not focusing on a specific molecule, however, the function of a protein also depends on its concentration and localization in the cell.
In order to separate itself from the rest of the world, all living cells are
equipped with a plasma membrane, the advantage being that the cell can
modify its own internal conditions independently of, or sometimes in response to, what is going on in the external environment. Within eukaryotic
cells, organelles are also delimited by at least one membrane, resulting in
different compartments with disparate biochemical conditions. However, the
isolation between a cell and its surroundings is not complete. The interior of
the cell is only partially shielded from the exterior, and this balance is actively regulated by membrane spanning channels, transporters and receptors.
Thus, in response to external changes, channels open and close, energydriven pumps are activated and de-activated, and ligand-binding receptors
give rise to signaling cascades that propagate through the cell. Indeed, the
concept of regulated partial isolation is general, and applies not only to cells
with respect to their environments, but also to organelles, organs, individuals
and even species (Weiss 2005).
The amount of biological sequence data is currently increasing at an exponential rate (Overbeek 2000). To date, the sequences of more than 700
complete
genomes
are
publicly
available
(http://www.ebi.ac.uk/GenomeReviews/stats/; August 2008), together comprising the result of one of the largest scientific endeavors in history. This
rapid increase in the amount of biological information available has played a
12
major role in the origin of the field of bioinformatics, which deals with annotation, storage and analysis of such biological data.
Accordingly, one of the first disciplines within bioinformatics, and still
one of the largest, has been that of sequence analysis. Methods for automatic
annotation of new DNA and protein sequences are often based on the assumption that only the sequence is known, and try to predict various features
such as DNA exons and introns, protein domains or subcellular location
using only sequence as input. Thus, there is a certain rationale for developing prediction methods that are fully sequence based, not just because they
do not require data from expensive biochemical experiments, but also for the
prospect that the wealth of training data can only be expected to increase.
Development of such sequence based prediction methods is only becoming
increasingly important as the sequence collection continues to grow.
In particular, sequence based predictions on membrane spanning proteins
are of great importance. Membrane proteins comprise about 25% of all proteins encoded by most genomes (Wallin and von Heijne 1998; Krogh et al.
2001) and are responsible for numerous vital processes in the cell, but apart
from their general biological significance, there are also specific reasons for
why additional effort is being put into membrane protein predictions. First,
the medical importance of membrane bound receptors, channels and pumps
as targets for drugs is well established. It has been estimated that more than
50% of all available drugs target membrane proteins (Drews 1996; Hopkins
and Groom 2002). Second, structural information is hard to obtain experimentally, both because membrane proteins are particularly hard to overexpress and purify, but also since the large hydrophobic surfaces complicate
the growth of crystals.
This thesis focuses on prediction methods for membrane proteins that ultimately rely on their amino acid sequence only. As a consequence of the
peculiar surroundings in which integral membrane proteins find themselves,
spanning the apolar hydrocarbon core and projecting into the polar waterphase on either side of the membrane, they are somewhat constrained in
terms of what amino acid sequence is compatible with the different environments. This makes it possible to find sequence patterns and predict the
topology of the protein, i.e. what parts of the sequence that actually span the
membrane, and what parts are located on the cytoplasmic and extracytoplasmic side of the membrane. In paper I, we attempt to improve sequence-based topology prediction of membrane proteins by introducing additional information about the presence of extra-membranous protein domains in the sequence, which are believed to always reside on just one side
of the membrane. In paper II, we instead use the sequence constraints placed
by the hydrophobic interior of the membrane to improve detection of distant
membrane protein homologs. Paper III and IV relate to the actual insertion
of membrane proteins into the membrane, and how transmembrane helices
are recognized in the cell. In paper III, a scale describing the energetics of
13
this process is developed based on experimental measurements, and in paper
IV, this scale is implemented to predict transmembrane topologies.
14
2. Biological membranes
All living cells are surrounded by membranes, separating the interior of the
cell from the surrounding environment and protecting it from foreign substances. In the case of eukaryotic cells, membranes also enclose subcellular
compartments. The fundamental structural component of biological membranes is lipids, although a large fraction of the membrane mass comes from
membrane proteins embedded within the lipids (Guidotti 1972).
2.1 Membrane lipids
Membrane lipids are amphiphilic, consisting of one or more hydrophobic
hydrocarbon tails, and a hydrophilic sometimes charged headgroup. Depending on the relative sizes of the headgroup and the hydrophobic tails,
lipids are more or less prone to form flat bilayer structures rather than mi-
Figure 2.1. Different shapes of membrane lipids. Cylindrically shaped
lipids (left) stabilize a flat lipid bilayer structure. Conical lipids (middle)
have large headgroups or skinny hydrocarbon tails. Inverted conical
lipids (right) have small headgroups or bulky unsaturated hydrocarbon
tails.
celles or other structures when placed in water, Fig. 2.1. When the
headgroup is of approximately the same size as the hydrocarbon tails, the
lipid becomes roughly cylindrical, which promotes bilayer formation,
whereas if the headgroup is either smaller or larger than the hydrocarbon
tails, the conical shape induces curvature in the lipid monolayer (Epand
1998).
15
In addition to the variations in lipid shape, there are also differences in
charge of the lipid headgroup, being neutral, zwitterionic or negatively
charged. The lipids predominantly found in biological membranes are phospholipids and glycolipids, with the overall shape as in Fig. 2.1, and cholesterol, a neutral lipid containing a ring structure that breaks the tight packing
of fatty acid chains, making the membrane more fluidic. In general, the
headgroup region of a natural mix of lipids in a biological membrane tends
to be slightly negative on average.
2.2 Lipid bilayers
As a consequence of their amphiphilic nature, lipids aggregate and spontaneously self-organize in water, generally into either micelles or bilayers. The
biological membrane is a bilayer structure, where the hydrophobic fatty acid
chains avoid contact with water molecules by arranging themselves into two
oppositely oriented fluidic leaflets, Fig. 2.2. An early description of the system was the fluid mosaic model (Singer and Nicolson 1972), in which lipids
and proteins diffuse laterally in the plane of the bilayer. The width of the
membrane is determined by the lengths of the hydrocarbon tails, and is gen-
Figure 2.2 Structure of a DOPC bilayer, after a 1.5 ns molecular dynamics simulation (Feller et al. 1997). Due to thermal motions, the interfaces
are of roughly the same width as the hydrocarbon core. Colors are as in
Fig. 2.3.
erally around 60Å, of which about half constitutes the hydrophobic core.
However, the actual thickness, as well as fluidity and curvature, depends on
16
the lipid composition which varies between different membranes, and local
fluctuations may occur due to the presence of lipid rafts (Simons and Ikonen
1997).
The actual distributions of the functional groups in Fig. 2.2 across a
DOPC lipid bilayer have been measured experimentally (Wiener and White
1992), and are shown in Fig. 2.3. From these distributions, which are quite
Figure 2.3 Structure of a DOPC bilayer, as determined by X-ray and neutron diffraction measurements. The distributions reflect the probability of
finding a particular structural group at a specific location in the bilayer.
Colors are as in Fig 2.2. Figure adapted from (Wiener and White 1992).
consistent with the molecular dynamics simulation in Fig 2.2, the emerging
picture is that thermal fluctuations lead to a highly heterogeneous environment, where the interface regions together occupy roughly the same width as
the hydrophobic core of the membrane. Thus, the chemical environment
across the membrane varies markedly over short distances, which is reflected
in the amino acid distributions of TM helices at different depths into the
bilayer (see section 3.3).
Lipids with conical shapes (Fig. 2.1; middle and right) might induce curvature when present in different concentrations in the two leaflets of a lipid
bilayer. A membrane consisting primarily of phosphatidylcholine (cylindrical) and phosphatidylethanolamine (inverted conical) in both leaflets will
experience curvature stress, meaning there will be higher lateral pressure in
the lipid tail region than in the headgroup region. Such forces could be important for correct folding and functioning of integral membrane proteins
(Dan and Safran 1998; Epand 1998).
17
3. Membrane Proteins
3.1 Types of membrane proteins
For biological membranes, lipids are only half the story. The other half or so
of the membrane mass comes from proteins (Guidotti 1972), interspersed
among the lipids and contributing to the overall stability of the membrane,
Figure 3.1 Structural classes of transmembrane proteins. (Left) The αhelical transmembrane protein bacteriorhodopsin, found in the plasma
membrane of archaea. (Right) The β-barrel transmembrane protein
OmpA, found in the outer membrane of gram-negative bacteria. Meshes
indicate the approximate borders of the hydrocarbon core of the respective membranes.
although the exact protein-lipid ratio varies between different membranes.
Broadly, membrane proteins may be classified as peripheral or integral, and
the integral membrane proteins can be further subdivided into monotopic,
bitopic and polytopic ones (Blobel 1980). Peripheral membrane proteins are
only loosely attached to the membrane through electrostatic or hydrophobic
interactions with the lipid headgroups or other membrane proteins, or
through a covalently attached GPI anchor (Chatterjee and Mayor 2001).
Monotopic integral membrane proteins are largely water soluble, but also
contain a hydrophobic part that partly penetrates into the membrane from
18
one side only. Bitopic integral membrane proteins span the membrane once
and polytopic integral membrane proteins, finally, span the lipid bilayer multiple times. Polytopic proteins come in two basic architectures, either as
bundles of tightly packed α-helices, or as closed barrels of amphipathic βstrands, Fig. 3.1.
3.1.1 α-helical transmembrane proteins
Because of the low-polarity environment in the hydrophobic core of the
membrane, there is a particular need for the protein backbone to form internal hydrogen bonds, and one way of satisfying all backbone hydrogen bonds
is to form α-helices in the membrane-spanning parts of the protein. The αhelical bundle type membrane proteins are the most abundant, and are predicted to constitute around 25% of a typical genome (Krogh et al. 2001).
Mainly, the TM helices are composed of hydrophobic amino acids that are
able to form van der Waals-interactions with fatty acids of the surrounding
lipids. However, polar and even charged amino acids are occasionally also
found deep into the bilayer, although generally not in direct contact with
lipids, but rather buried against a protein surface. The TM-helices are typically roughly perpendicular to the membrane plane, and around 26 residues
long (Ulmschneider et al. 2005), although local deformations of the bilayer
and tilting of TM-helices, respectively, can accommodate lengths as short as
15 and as long as 43 residues (Granseth et al. 2005). α-helical transmembrane proteins are the most well-studied class of membrane proteins, and the
only class investigated in this thesis, and will be referred to simply as 'membrane proteins' throughout the text.
3.1.2 β-barrel transmembrane proteins
Another way of solving the problem to satisfy internally all backbone hydrogen bonds is seen in the β-barrel type of transmembrane proteins, in which
the membrane spanning parts are composed of an even number of antiparallel β-strands, which are tilted around 45° relative to the membrane plane
(Galdiero et al. 2007). Each strand is hydrogen bonding to the neighbouring
strands, and the first and last strands hydrogen bond with each other to close
the barrel (Fig. 3.1). Residues in the β-strands alternately point outwards,
facing the lipids, and inwards, facing the inside of the barrel, resulting in a
sequence pattern in which the residues are typically alternately polar and
hydrophobic. β-barrel type membrane proteins have so far only been found
in the outer membrane of gram-negative bacteria (Fischer et al. 1994), although predictions suggest they are also present in the outer membranes of
chloroplasts and mitochondria (Schleiff et al. 2003). Although difficult to
predict, they have been estimated to account for less than 3% of the ORFs in
the genomes in which they are present (Garrow et al. 2005).
19
3.2 Membrane protein targeting and translocation in
the ER
Most α-helical transmembrane proteins, and water-soluble proteins destined
for the secretory pathway, are targeted by an N-terminal signal sequence to
the ER membrane, where protein translation and translocation or membrane
insertion occur simultaneously (Simon and Blobel 1991; White and von
Heijne 2005; White 2007). Briefly, as the nascent polypeptide chain emerges
from the ribosomal exit tunnel, an N-terminal hydrophobic segment of ~20
amino acids is recognized by a ribonucleoprotein complex known as the
Signal Recognition Particle (SRP). The binding of SRP to the signal sequence temporarily halts elongation, and the SRP-ribosome-polypeptide
complex, with mRNA also bound to it, is targeted to the SRP receptor protein at the ER membrane. SRP is released as the ribosome binds to a heterotrimeric channel complex known as the Sec translocon, whereupon translation is resumed and the nascent polypeptide chain is threaded directly
through the channel as it is being synthesized, Fig. 3.2. The Sec translocon is
Figure 3.2 Co-translational translocation and membrane insertion in the
ER membrane. As the newly synthesized polypeptide chain emerges from
the ribosome, the translocon channel must decide whether to insert it into
the membrane or translocate it. Figure adapted from (White and von Heijne 2004).
responsible both for the membrane integration of membrane proteins and for
the export of secreted proteins, first into the ER lumen and then, by means of
vesicular transport, through the Golgi and out of the cell. Membrane spanning α-helices, as well as the hydrophobic signal peptide, are recognized by
the translocon and transferred laterally into the lipid phase, whereas the hydrophilic lumenal loops are translocated through the channel and the cytoplasmic loops are retained on the cytoplasmic side. For secreted proteins, the
20
signal peptide is cleaved off after translocation, releasing the folded protein
on the lumenal side of the membrane. Membrane proteins either contain a
cleavable signal peptide or an uncleaved signal sequence which then effectively becomes the first TM-helix of the mature protein.
How is the recognition of TM segments accomplished by the translocon
channel? It has been suggested that the channel opens and closes rapidly as
translocation occurs, thereby allowing the TM segments to partition into the
lipid environment (Rapoport et al. 2004). Favourable interactions between
lipids and amino acid side chains should then promote membrane integration
rather than translocation. With the view of the membrane as a highly heterogeneous environment (Fig. 2.2) where chemical conditions change markedly
over short distances, it follows that amino acid side chains will have different preferences for different slabs of the lipid bilayer. On the sequence level,
the efficiency with which a TM segment is integrated by the translocon
should then be sensitive to the relative positions of amino acids within the
segment. This trend is evident when looking at amino acid distributions in
known membrane protein structures.
3.3 Amino acid statistics of TM-helices
Although the most prominent feature of TM helices is their overall hydrophobicity, there is also a considerable variation in the preferences of amino
acids along the membrane normal, as revealed in the known membrane protein structures. A recent statistical analysis of the frequencies of amino acids
in TM helices shows a notable positional dependence, where hydrophobic
residues (Ala, Ile, Val, Leu) mostly populate the hydrophobic core of the
membrane, while polar (Asn, Gln) and polar aromatic (Trp, Tyr, His) residues avoid the core but are more abundant in the interface regions
(Ulmschneider et al. 2005). The small polar residues Ser and Thr were found
to be equally well represented in core as interface, which could possibly be
explained by their ability to sometimes form hydrogen bonds with the backbone (Gray and Matthews 1984). Charged residues (Arg, Asp, Glu, Lys)
show a sharp peak indicating that they are almost absent from the very center
of the hydrophobic core, but seem to be well tolerated in the interface regions.
3.3.1 Helix-breaking residues
Pro plays a special role in membrane helices, in that it breaks the helix and
induces a backbone kink that can be important for function or stabilization of
the structure (von Heijne 1991; Sansom and Weinstein 2000; Cordes et al.
2002). It even seems that the kink-inducing Pro residue can subsequently be
substituted, as long as the kink is stabilized by specific interactions with
21
surrounding residues (Yohannan et al. 2004). Pro is also overrepresented at
TM-helix ends in the interface regions (Granseth et al. 2005).
In addition to Pro, Gly is also known to induce breaks in helices and promote turns, and also occurs with high frequency near TM-helix ends in the
interface regions (Granseth et al. 2005). Despite its helix breaking nature, it
is also quite abundant within the membrane. One reason for this is that its
small side chain allows for two TM-helices to come close together, and it
thus plays an important role in helix-helix packing (see section 3.5.3).
3.3.2 The aromatic belt
The polar aromatic residues Tyr and Trp are often found near the ends of
TM-helices, sometimes referred to as the "aromatic belt", where they have
been suggested to anchor and position the helix in the membrane (de Planque et al. 2003). These residues have a hydrophilic part as well as an apolar
Figure 3.3 Three-dimensional structure of the AQPM aquaporin water
channel from Methanothermobacter marburgensis. The structure is colored according to hydrophobicity, where red is hydrophobic and white
hydrophilic. Tyr and Trp residues are colored green and Lys and Arg
residues are colored blue. Cytoplasm is downwards in the picture. The
aromatic belts are evident, as is the overrepresentation of Lys and Arg
residues on the cytoplasmic side of the membrane. Two re-entrant loops
(section 3.5.2) are visible in the middle of the structure.
aromatic ring, and being positioned near the borders of the hydrocarbon core
region, they can both interact with the lipid headgroups and at the same time
bury the aromatic ring among the apolar lipid tails (Yau et al. 1998).
22
3.3.3 The positive-inside rule
A strong determinant for the topology and overall orientation of membrane
proteins is the asymmetric distribution of positively charged amino acids
(Lys and Arg) between loops on opposite sides of the membrane, being overrepresented in short cytoplasmic loops (von Heijne 1992), known as the
positive-inside rule. This charge imbalance was first discovered for signal
peptides (von Heijne 1984), and was subsequently found to apply also to
membrane helices (von Heijne and Gavel 1988). Site-directed mutagenesis
studies have shown that the positive charges are sufficient to determine
overall topology (von Heijne 1989; Nilsson and von Heijne 1990), and the
rule seems to be universal across all types of membranes in a large number
of species (Nilsson et al. 2005).
3.4 Membrane protein topology
The topology of a membrane protein describes what segments of the amino
acid sequence span the membrane, and what segments protrude into the respective compartments on opposite sides of the membrane. Although this
simple specification is clearly not as informative and accurate as the full
three-dimensional structure of the protein, there are certain reasons why
topology information can still be useful. First, even though full structure
prediction is currently almost impossible to do with any reasonable reliability, if such methods are to arise in the future they will most probably have to
start by making correct topology assumptions (von Heijne 2006). Thus, development of topology prediction algorithms has the prospect of paying off
in the future, since the predicted information is so fundamental. Second,
certain characteristics of membrane proteins can be deduced from topology
information alone. Frequently, membrane protein superfamilies, e.g. the
important 7TM receptors, have highly conserved topologies which can provide some information for detection of new members (Wistrand et al. 2006).
Moreover, function can to a certain extent be predicted simply from the
number of transmembrane helices in the protein (Sugiyama et al. 2003).
Transmembrane topology can either be mapped experimentally (section
3.4.1), or predicted from amino acid sequence (section 4.4). Attempts have
also been made at combining limited experimental information with prediction techniques to provide higher quality topology models for a relatively
large number of proteins (Daley et al. 2005; Kim et al. 2006). According to
these studies, in both the Escherichia coli and Saccharomyces cerevisiae
entire membrane proteomes, proteins with the C-terminus on the cytoplasmic side of the membrane were roughly four times as frequent as those with
the C-terminus on the exoplasmic side. In addition, proteins with an even
number of TM-helices were clearly over-represented, and consequently, the
23
N-terminus is also more commonly located on the cytoplasmic side of the
membrane. This might indicate that helical hairpins are the basic blocks of
membrane insertion by the Sec translocon. This is a debated subject, however, since the X-ray structure of the translocon reveals that the pore seems
too small to accommodate more than one helix at a time (Van den Berg et al.
2004).
3.4.1 Topology mapping techniques
A number of techniques exist to experimentally map the locations of a few
strategically selected parts of the sequence, in order to produce a topology
model of the protein. One approach exploits recognition of sequence segments by agents that have access only to one side of the membrane, like
glycosylation mapping (Chang et al. 1994), cysteine labeling (Kimura et al.
1997) or epitope mapping (Canfield and Levenson 1993). Another technique
involves fusion of a reporter protein that is active only on one side of the
membrane (Manoil and Beckwith 1986). By fusing the reporter protein to a
number of differently truncated versions of the membrane protein, the in/out
location of several points in the sequence can be derived and used to produce
a full topology model. To avoid having to interpret the absence of activity of
the reporter protein as a positive result, two reporter proteins with activity on
opposite sides of the membrane are often used in combination. Reporter
proteins frequently employed include PhoA (active in periplasm only) and
GFP (active in cytoplasm only) (Drew et al. 2002; Daley et al. 2005).
3.4.2 Dual topology proteins
Although the large majority of membrane proteins have a well defined topology, recent studies have demonstrated the existence of a few membrane
proteins that seem to insert with some probability in oppositely oriented
directions, termed dual-topology membrane proteins (Rapp et al. 2006; Rapp
et al. 2007). These proteins have in common a small or non-existent bias of
Arg and Lys (KR-bias) to either side of the membrane (section 3.3.3). Interestingly, they sometimes have homologues with large KR-biases, and in
those cases, two copies of the gene with opposite KR-biases are always present within the same genome, presumably inserting in opposite directions
into the membrane. In one family, the two oppositely oriented copies were
sometimes fused, such that both protein length and KR-bias were doubled. If
both directional variants are required for function, this points towards a possible route of evolution for membrane proteins in which a dual topology
protein is duplicated, whereupon the two copies evolve a KR-bias to stably
insert in opposite directions, and may subsequently be fused into a single
protein. Indeed, internal symmetry is a common phenomenon in known
membrane protein structures (Choi et al. 2008).
24
3.5 Membrane protein structure
Membrane proteins are notoriously difficult to crystallize, which has led to
the situation that, although about 25% of a typical genome are predicted to
encode membrane proteins (Krogh et al. 2001), they only account for less
Figure 3.4 The number of known membrane protein structures grows exponentially, although at a lower rate than soluble proteins during the
equivalent time period. Figure reproduced from (White 2004) with permission.
than 1% of the known protein structures, with about 160 unique structures
known (http://blanco.biomol.uci.edu/Membrane_Proteins_xtal.html; August
2008). Encouragingly though, the number of known membrane protein structures is growing exponentially, although at a rate that lags behind the corresponding rate for soluble proteins during the equivalent time period, Fig. 3.4.
3.5.1 Structure determination techniques
Structure determination starts with protein overexpression, which is often
difficult with membrane proteins, both because large amounts of the protein
may be toxic to the cell, and because the proteins sometimes aggregate to
form inclusion bodies (Drew et al. 2003). After solubilization with a detergent and purification, there are a number of alternative methods to determine
the three-dimensional structure. The majority of currently known membrane
protein structures have been solved by X-ray crystallography. The main
obstacle using this method is that growth of well-ordered diffracting crystals
is difficult due to the amphiphilicity of the protein. To overcome this, membrane proteins are sometimes fused to soluble domains, increasing the polar
25
surface area available for crystal lattice contacts (Cherezov et al. 2007;
Rosenbaum et al. 2007).
Apart from X-ray crystallography, the other main method in use for obtaining structural information of membrane proteins is NMR spectroscopy.
Here, no crystals are required, but on the other hand, the method is limited to
quite small proteins (Torres et al. 2003). This can be particularly problematic
for membrane proteins, since they are typically quite large by themselves
(Mitaku and Hirokawa 1999), and need to be solubilized in detergent micelles, adding to the size of the complex (Torres et al. 2003).
Although membrane proteins are reluctant to arrange themselves into
three-dimensional crystals, they more easily pack into two-dimensional planar structures, which can be analysed by electron microscopy (EM). A threedimensional model of the protein can be produced by combining twodimensional projections at different angles (Henderson et al. 1990). Although the method gives relatively low resolution compared to the 3D methods, higher resolution can be achieved with cryo-EM, where the sample is
studied at temperatures approaching -260°C (Torres et al. 2003). Cryo-EM
has been used to solve a number of near-atomic membrane protein structures, including aquaporin (Murata et al. 2000) and the acetylcholine receptor (Unwin 2005).
3.5.2 Structural features of membrane proteins
Incidentally, the first membrane protein structures to be solved were relatively simple in their structures (Deisenhofer et al. 1985; Henderson et al.
1990), with straight hydrophobic helices penetrating the membrane at angles
roughly perpendicular to the membrane plane and interconnected by short
loops. With increasing insight into membrane protein structures, the picture
is now somewhat more complex, and certain recurring structural features not
evident from just a membrane topology representation have emerged.
One obvious observation is that not all helices are straight rods, but a few
contain Pro-induced kinks (section 3.3.1), which might be important for
protein function. Whereas Pro is quite rare in α-helices of soluble proteins,
they occur with a relatively high frequency in transmembrane α-helices,
where they seem to play an important role in the biological activity of the
peptide (Cordes et al. 2002).
Another recurring structural element in membrane proteins is the reentrant loop, which penetrates only halfway through the membrane, and
enters and exits on the same side (Lasso et al. 2006; Viklund et al. 2006).
Commonly, such loops contain residues important for function of the protein
(Lasso et al. 2006), and they are found in, e.g., the translocon channel (Van
den Berg et al. 2004), the aquaporin water conducting channel (Fig. 3.3 and
(Harries et al. 2004)), and the chloride ion channel (Dutzler et al. 2003).
26
In addition to the transmembrane α-helices perpendicular to the membrane plane, some structures also contain shorter interfacial helices, lying
roughly parallel to the membrane. The function of these helices is not entirely clear, but they might be involved in positioning of the transmembrane
helices (Granseth et al. 2005). They are on average 9 residues long and rich
in Trp and Tyr residues (Granseth et al. 2005).
Finally, preferences of amino acid side chains can be studied from the
structures. Lys and Arg have long hydrophobic side chains with a positive
charge at the very end, and when these residues are situated within the hydrocarbon core of the membrane, they often point their side chain away from
the membrane core and out towards the hydrophilic interface region, a phenomenon described as 'snorkelling' (Chamberlain et al. 2004). Similarly,
Phe residues tend to direct its hydrophobic aromatic ring towards the membrane core, termed 'anti-snorkelling'.
3.5.3 Helix-helix interactions
Whereas for globular proteins, the hydrophobic effect is the main driving
force of folding (Honig and Yang 1995; Lins and Brasseur 1995), the circumstances are somewhat different for membrane proteins, being embedded
in the hydrophobic environment of the membrane. For a long time, hydrogen
bonding has been considered the major stabilizing force of membrane protein structure through inter-helical interactions (Engelman 1982; Choma et
al. 2000; Zhou et al. 2000), but recently, this view has been questioned (Joh
et al. 2008). A dominant force of helix packing in the hydrocarbon core
might be van der Waals interactions between helices (White and Wimley
1999). Two small residues (Gly, Ala, Ser) separated by three residues will
end up on the same side of the α-helix, and thus allow two helices to come
close together and pack tightly. The GX3G-motif has been implicated as the
main driving force of homodimerization of the single-spanning Glycophorin
A (Lemmon et al. 1992; Treutlein et al. 1992), and both this motif and the
GX3GX3G- and GX7G-motifs are overrepresented in TM-helices in general
(Liu et al. 2002; Kim et al. 2005).
The assembly of membrane protein chains into complexes can be better
understood by considering the thermodynamics of the surrounding membrane lipids. Even though the dimerization of two TM chains into a rigid
arrangement seems to reduce the entropy of the proteins, this is counterbalanced and outweighed by the entropy increase resulting from the lipids being
released back into the lipid pool of the membrane as the total protein-lipid
surface area decreases (Helms 2002).
27
4. Computational approaches
4.1 Substitution matrices, profiles and HMMs
In principle, all characteristics of a newly synthesized protein, including
targeting to different subcellular components, folding of secondary and tertiary structure, complex assembly, function and ultimately degradation, are
determined by its amino acid sequence (Anfinsen 1973). One of the most
important and challenging areas of bioinformatics is thus the decoding of the
biological signals intrinsic in the sequence, and prediction of biochemical
properties from this source of information. Among the many different types
of signals that can be recognized, evolutionary relationships and classification of proteins into superfamilies and families are among the most important
4.1.1 Sequence alignment and substitution matrices
Given two protein sequences, the alignment problem can be described as
finding the most probable set of point mutations, insertions and deletions
defining the evolutionary divergence of the two proteins. In the simplest
case, each amino acid substitution is associated with a certain cost, and a
dynamic programming algorithm, such as the Smith-Waterman (Smith and
Waterman 1981) or Needleman-Wunsch algorithm (Needleman and Wunsch
1970), is employed to search for the set of substitutions that together sum to
the lowest total cost for the alignment. Insertions and deletions can be included in the model by also associating appropriate costs with gap opening
and gap insertion respectively. Some different versions of the 20x20 amino
acid substitution matrix, describing the costs for aligning all amino acids to
each other, are regularly used in bioinformatics applications, of which the
most popular are the PAM (Dayhoff 1978) and BLOSUM (Henikoff and
Henikoff 1992) set of matrices respectively. Incidentally, the BLOSUM
matrices were recently shown to contain errors due to a software source code
bug, although the 'erroneous' matrices actually perform better than the 'intended' ones (Styczynski et al. 2008).
Dynamic programming algorithms are generally too slow for searching
large sequence databases, but heuristic algorithms exist that are significantly
28
faster at the cost of being slightly less sensitive. BLAST (Altschul et al.
1990) first searches sequences for high-scoring 'words', and the hits can subsequently be extended using dynamic programming, and FASTA (Pearson
1990) uses a similar approach.
4.1.2 Sequence profiles
By aligning two sequences using a single substitution matrix, it is impossible
to model the very common situation where substitution rates are not constant
over the whole sequence, but rather vary according to the relative importance between different parts of the protein. Structurally and functionally
important regions in a protein structure should be expected to exhibit higher
level of conservation than e.g. flexible loops far from the active site. In order
to capture this effect, sequence profiles or Position Specific Scoring Matrices
(PSSMs) (Altschul et al. 1997) are commonly used to represent common
motifs in protein families. In a sequence profile, each position of the sequence motif or sequence family contains a 20x1-vector of probabilities for
observing each of the 20 amino acids in that particular position, as given by
a multiple sequence alignment (Fig. 4.1).
Figure 4.1 (Above) Multiple sequence alignment of a Zinc finger domain. (Below) A sequence profile for the first ten residues of the same
alignment. The profile is a compact representation of the multiple sequence alignment. Zeros in the profile are omitted for clarity. A number
of conserved residues can be easily detected.
29
When evaluating the alignment of a sequence to a sequence profile, in principle the probability for the aligned amino acid in the single sequence is
given by the corresponding frequency vector in the profile, and these probabilities are multiplied to give the total probability of the full alignment. To
avoid the very small accumulated probabilities that can result from long
protein sequences, in practice these calculations are always performed in
log-space.
It is well established that including evolutionary information in the form
of profiles improves homology detection between protein sequences
(Lindahl and Elofsson 2000). By aligning two sequence profiles against each
other, evolutionary information is included for both query and database sequences, which has been shown to improve remote homology detection even
further (Park et al. 1998; Rychlewski et al. 2000; Mittelman et al. 2003;
Ohlson et al. 2004).
4.1.3 Hidden Markov Models
Hidden Markov Models (HMMs) are probabilistic models that were early
applied to the problem of speech recognition (Jelinek et al. 1975; Rabiner
1989), and have later on been used in a number of biological sequence
analysis problems (Churchill 1989; Krogh et al. 1994). The model consists
of a set of interconnected states, each of which contains a probability distribution, called emission probabilities, over a set of symbols, referred to as the
alphabet of the model. In addition, each state also contains a probability
distribution over possible transitions to all states in the model (including
itself), known as transition probabilities. According to the parameters of the
model, the emission and transition probabilities, the model generates a sequence of symbols from the alphabet by first emitting a symbol according to
the emission probability distribution of the current state, then transits to a
new state according to the transition probability distribution and repeats this
until a certain stop state is reached. By tuning the parameters of the model, it
can be designed to generate a set of sequences with high probability.
There are basically three different problems associated with HMMs. First,
the probability for the model to generate a certain sequence of symbols can
be calculated, known as the evaluation problem. The forward and backward
algorithms (Rabiner 1989) are used to solve this problem. Second, the most
likely state path through the model can be calculated for a certain symbol
sequence, known as the decoding problem. This problem is solved by the
Viterbi algorithm (Viterbi 1967). Finally, the estimation problem relates to
finding the most likely model parameters given a set of symbol sequences,
and the Baum-Welch algorithm (Baum 1972) is used for this task.
Profile HMMs (Eddy et al. 1995; Eddy 1998) are a certain kind of HMM,
that has achieved widespread use in biological sequence analysis applications. These models resemble sequence profiles (section 4.1.2) in that they
30
are used to represent common characteristics of a whole protein family.
States in profile HMMs are sequentially arranged and connected so that positions in a multiple sequence alignment are represented by one Match state
(M), one Insert state (I) and one Delete state (D) (Fig. 4.2). Each Match and
Insert state in the model contains both amino acid emission probabilities, in
Figure 4.2 Graphical representation of a profile HMM. Match state (M),
Insert state (I), Delete state (D). The model starts in the Begin state (B)
and ends in the End state (E). All nonzero state transitions are indicated
with arrows.
analogy with the probability vector in ordinary sequence profiles, and transition probabilities, whereas the Delete states are 'silent states' representing
gaps, and do not emit symbols. A profile HMM can easily be constructed
from a multiple sequence alignment by setting emission and transition probabilities according to the observed amino acids and gaps at different positions in the alignment.
4.2 Aritifical neural networks
Artificial neural networks (ANNs) (Brian and Hjort 1995) is a standard machine learning technique for pattern recognition and classification, that has
been applied to many problems in bioinformatics, including TM topology
prediction. The model consists of an interconnected set of nodes (neurons),
each taking one or several signals as input. The output from a node is frequently a nonlinear function of a weighted sum of its input values, where
hyperbolic tangent functions are particularly common. In the simplest case,
the nodes are interconnected in a directed fashion, being arranged into layers, where each layer of nodes is only connected to the neighboring layers
and signals through the network are propagated from left to right, called a
feed-forward network. The output from the network effectively becomes a
'function of functions' corresponding to a non-linear mapping of a set of
input variables to a set of output variables.
31
ANNs have been successfully applied to e.g. prediction of secondary
structure (Qian and Sejnowski 1988), prediction of subcellular location
(Bendtsen et al. 2004) and protein homology detection (Frishman and Argos
1992). In TM topology prediction, ANNs are sometimes used to predict a
residue preference score, that is subsequently used as input to a dynamic
programming algorithm (Rost et al. 1996; Viklund and Elofsson 2008)
4.3 Hydrophobicity scales
In order to search for hydrophobicity signals in membrane protein sequences, a prerequisite is a scale assigning hydrophobicity values for all
amino acids. Numerous such hydrophobicity scales are available, based on
e.g. the partitioning of amino acids between two immiscible liquid phases,
chromatographic techniques or accessible surface area calculations. A few of
the available scales are outlined below, but many more exist.
One of the most frequently cited hydrophobicity scales, the KyteDoolittle scale (Kyte and Doolittle 1982), combines accessible surface area
measurements in globular proteins with water-vapor partitioning preferences. By implementing this scale in a sliding-window approach, it was possible both to distinguish exterior from interior in globular proteins, and to
identify TM regions in membrane proteins.
Another hydrophobicity scale specifically tailored to TM helices is the
Goldman-Engelman-Steitz (GES) scale (Engelman et al. 1986). Here, a semi
theoretical approach is taken, accounting for the attachment of side chains to
an α-helical backbone structure. This scale is used to predict TM helices in
the TopPred topology prediction algorithm (section 4.4.1).
The Wimley-White scale (White and Wimley 1999) takes into account
the (unfavorable) contribution of partitioning the backbone peptide bonds
into the bilayer. Partitioning of pentapeptides into POPC bilayer interfaces
and n-octanol, respectively, were used to construct this scale.
Finally, the Zhao-London scale (Zhao and London 2006) is based on propensities of amino acids in known membrane protein structures. The scale
was refined to separate well between transmembrane and soluble sequences
from protein sequence databases, and should in that sense be quite suited to
the problem of predicting TM helices.
32
4.4 Transmembrane topology predictors
Several methods exist that, given an amino acid sequence, predict the full
transmembrane topology. These methods are either based on physical properties of the amino acids, such as the hydrophobicity scales described above,
or rely on statistical over- or underrepresentation of amino acids in transmembrane helices, as observed from known topologies of membrane proteins. TopPred (section 4.4.1) is the only method of the ones outlined below
that is based directly on physical principles (i.e. a hydrophobicity scale) for
predicting TM-helices.
4.4.1 TopPred
One of the first topology predictors, TopPred (von Heijne 1992), takes advantage of the GES hydrophobicity scale (section 4.3), and creates a hydropathy plot of the sequence, using a sliding window approach. First, all hydrophobicity peaks above a certain cutoff value are identified and marked as
'certain' TM segments, whereas all peaks below this cutoff but above a second lower cutoff value are marked 'putative' TM helices. Second, all possible
topologies, including all certain TM segments and either including or excluding each of the putative TM segments are generated, and the topology
that best complies with the positive-inside rule (section 3.3.3) is chosen as
the final prediction.
4.4.2 TMHMM / PRO-TMHMM / PRODIV-TMHMM
Among the first methods to implement HMMs to predict TM topology, and
one of the most frequently used, is TMHMM (Krogh et al. 2001). Common
to all HMM based topology predictors is that all states carry a label, and
paths through the model correspond to a labeling defining the topology of
the protein. The architecture of the model is cyclic, such that two loops with
a membrane region in between will always reside on opposite sides of the
membrane. The N-best algorithm (Krogh 1997) is used to find the most
probable labeling (the most probable topology) of the input sequence, which
may correspond to more than one state path through the model.
In PRO-TMHMM (Viklund and Elofsson 2004), a development of
TMHMM, sequence profiles rather than single sequences can be scored
against the model. This was found to improve prediction performance by a
few percentage points. In the same study, another method, PRODIVTMHMM, was found to improve performance even further. This method
uses a re-optimization procedure which was first introduced with HMMTOP
(see below).
33
4.4.3 HMMTOP
Another early method to use HMMs for TM topology prediction is
HMMTOP (Tusnady and Simon 2001). The architecture of the model is
cyclic, just like TMHMM, but differs in some respects such as the upper and
lower boundaries for helix lengths. HMMTOP can take either a single sequence or a sequence profile as input. One notable feature of the method is
that the model is re-optimized on every query sequence or sequence profile
after the first scoring, and then scored a second time with the new parameters. This has the effect that hydrophobicity relative to the rest of the sequence determines the locations of TM regions, rather than absolute hydrophobicity.
4.4.4 MEMSAT
The very first method to combine all sequence signals and calculate a globally best topology according to the model was MEMSAT (Jones et al. 1994).
Five different states (inside loop, outside loop, helix inside, helix outside and
helix middle) each contain an amino acid distribution, and dynamic programming is used to align the query sequence to the model. One important
difference between this approach and the later HMM-based methods is that
the model is not cyclic. Instead, all possible topologies are explored, assuming a linear model with an increasing number of TM-helices, starting from
just one. Later versions of MEMSAT (Jones 2007) integrates ANNs with
HMMs, and can take sequence profiles as input.
4.4.5 PHDhtm
Another ANN-based approach for TM topology prediction is the PHDhtm
method, which is part of a larger package, PHD (Rost et al. 1996), for predicting secondary structure of proteins. The method takes a sequence profile
as input and predicts a preference score for each profile column based on a
sliding window of neighboring columns. A topology is then generated using
a dynamic programming algorithm, and the overall orientation is determined
by the 'positive-inside rule'.
4.4.6 Phobius / PolyPhobius
A common problem in TM topology prediction is the confusion of cleaved
signal peptides (SPs) and TM regions, since both have an overall hydrophobic character. The first TM topology prediction method to address this problem was the HMM-based method Phobius (Käll et al. 2004), in which TMregions and SPs are modeled separately. The method is particularly efficient
in datasets containing a mixture of proteins with and without TM-regions,
34
and with and without SPs. A later version of the method, PolyPhobius (Käll
et al. 2005), can take a sequence profile as input.
35
5. Present investigation
5.1 Improving transmembrane topology prediction by
domain assignments (Paper I)
Topology prediction of membrane proteins is typically based on sequence
statistics, reflecting amino acid preferences for different compartments. In
particular, three sequence signals are especially important (Fig. 3.3): the
hydrophobicity of membrane spanning segments, the disposition of Trp and
Tyr residues to the membrane-interface border (section 3.3.2), and the overrepresentation of Arg and Lys residues in short cytoplasmic loops (section
3.3.3). In this paper, we explored the possibility that the presence of globular
protein domains in membrane protein sequences can be used to constrain
topology prediction methods and thereby improve prediction performance.
Protein domains are often compartment specific, and information about
domain occurrence has previously been used to predict subcellular location
of soluble proteins (Mott et al. 2002). Moreover, although covalent combinations between transmembrane domains (i.e. protein domains with membrane
spanning regions) are rare, covalent combinations between one transmembrane domain and one soluble domain are observed frequently (Liu et al.
2004). Taken together, these two observations suggest that soluble domains
with compartment specific localization, when found in membrane protein
sequences, can be used to constrain that part of the sequence to one side of
the membrane, before a sequence-based method is used to predict the topology of the rest of the sequence. This is the basic idea explored in the paper.
Related to this idea are earlier studies, where experimentally determined
'anchor points' were used to constrain topology predictions and provide improved topology models (section 3.4) (Daley et al. 2005; Kim et al. 2006).
An important difference to these studies is that, here, no experiments are
necessary, but rather one type of prediction (topology prediction) is improved by another type of prediction (domain assignments).
From the SMART domain database (Letunic et al. 2004), 367 domains
carrying an annotation about subcellular location (146 cytoplasmic, 221 extracellular) were extracted. Searching for these domains in two test sets, one
small set of membrane proteins with known topologies, and one large set of
36
membrane proteins with predicted topologies, gave the results shown in Fig.
5.1. Briefly, the fraction of sequences containing at least one of the
40%
297 membrane proteins
with known topologies
30%
~78.000 membrane
proteins with predicted
topologies
20%
10%
0%
Fraction sequences
with domain hits
Fraction domain hits
in conflict with
topology
Figure 5.1 Results of domain assignments to the two test sets. The compartment specific domains are found at similar rates in both sets, but
agreement with topology differs.
compartment-specific domains was around 10% in both sets, but the fraction
domain hits in conflict with the topology was much higher in the large set
with predicted topologies, than in the small set with known topologies.
These conflicts are the cases where our approach can be used to constrain
predictions and thereby improve topology models.
We found that the domains were overrepresented in single-spanning proteins compared to the whole data set. Single-spanning proteins are often
mispredicted by topology predictors, mostly due to an inversion such that the
TM-segment is correctly located but the overall orientation is wrong. Large
extra-membranous domains carry little or no information in other predictors,
and our approach thus solves a major weakness in these methods.
After this paper was published, two similar studies have also employed
the strategy of using the occurrence of compartment-specific soluble protein
domains in membrane protein sequences to improve topology prediction. In
LocaloDom (Lee et al. 2006), experimentally verified Swiss-Prot annotations (Boutet et al. 2007) were used to classify Pfam domains (Bateman et
al. 2004) as being cytoplasmic or extracellular. In addition, conserved topologies of transmembrane Pfam domains were identified using the Phobius
topology prediction algorithm (Käll et al. 2004), and subsequently used to
infer TM topologies of sequences in which the Pfam domain was found.
TOPDOM (Tusnady et al. 2008) is also based on Swiss-Prot annotations, but
also includes predicted information from Swiss-Prot, which makes the domain collection much larger. For the most part, these three studies agree on
37
the subcellular location of domains, although there are also a few exceptions
(Tusnady et al. 2008).
5.2 Using topology to predict homology (Paper II)
In this study, we introduced a new profile-profile based alignment method
for remote homology detection of membrane proteins. Compared with
globular proteins, membrane proteins are surrounded by a more intricate
environment and, consequently, amino acid substitution rates in the membrane spanning regions differ from those of globular proteins and hydrophilic loops in TM proteins. Since existing algorithms for homology detection are often developed with globular proteins in mind, they may not be
optimal to detect remote membrane protein homologs. In this study, we take
advantage of the sequence constraints placed by the hydrophobic core of the
membrane to detect distant homology relationships between membrane proteins. The approach is to include information about predicted topology in the
alignment, and thereby promote alignments where membrane spanning regions are aligned against each other.
Although for globular proteins, it is well established that homology detection can be improved by including information from secondary structure
predictions (Fischer and Eisenberg 1996; Rice and Eisenberg 1997; Hargbo
and Elofsson 1999), prior to this study, only a few attempts had been made
to utilize the sequence constraints specific to membrane proteins. In one
study (Ng et al. 2000), TM regions are clustered to calculate an amino acid
substitution matrix specifically designed to TM proteins. In another study
(Hedman et al. 2002), topology predictions were used to promote alignments
where TM regions are aligned against each other in a profile-sequence based
approach.
By including evolutionary information for both query and target sequences, homology detection of globular proteins can be substantially improved (Park et al. 1998; Rychlewski et al. 2000; Mittelman et al. 2003;
Ohlson et al. 2004), and this has recently been shown to apply also to membrane proteins (Forrest et al. 2006).
In this paper, we presented the (to our knowledge) first method to combine the membrane protein specific sequence constraints with the strength of
profile-profile based methods aiming to detect remote members of membrane protein families. Starting from a query sequence, a two-track profile
HMM (section 4.1.3) is constructed, combining sequence information with
predicted topology. This HMM is then searched against a database of sequence profiles, also containing predicted topologies, and the HMM emission probabilities are tuned such that alignment between TM regions in the
two proteins is favored.
38
We first applied the method to remote homology detection within the
GPCRDB (Horn et al. 2003) database of G-protein coupled receptors. Here,
it was apparent both that the profile-profile based methods performed clearly
better than the profile-sequence based ones, and that the additional topology
information improved prediction results. The advantage of adding topology
information was less clear in two other test sets, the OPM database (Lomize
et al. 2006) and the HOMEP database (Forrest et al. 2006), although the
profile-profile based methods performed consistently better also here. An
example of an improved alignment between two GPCRs is shown in Fig.
5.2.
Figure 5.2 Alignment between distant homologs BAR1_SCHCO and
Q752Q1_ASHGO (both GPCRs), using HMMs with one (only sequence) and two (sequence and topology) alphabets, respectively. Adding topology information shifts the alignment such that the correct TMregions are aligned against each other. At the same time, the score for
the alignment becomes positive and the overlap of TM residues between the two proteins (TM-overlap) is increased.
5.3 Deciphering the molecular code for
transmembrane helix recognition (Paper III)
Because of the heterogeneous environment in lipid bilayers (section 2.2),
amino acid propensities in TM proteins vary with the distance to the center
of the hydrocarbon core of the membrane (section 3.3). As secreted or TM
39
proteins are being synthesized by the ribosome, they are directly threaded
through the translocon channel (section 3.2), where membrane spanning
segments are recognized and inserted into the bilayer, possibly by the rapid
opening and closing of a lateral gate (Rapoport et al. 2004), allowing the
segment to partition into the lipid environment if this is energetically favorable. In this paper, we have investigated the 'molecular code' for the recognition of TM helices by the Sec translocon, describing the requirements in
terms of amino acid composition for a sequence segment to be recognized as
a TM helix and inserted into the membrane, as opposed to being translocated
across. An earlier study from our lab (Hessa et al. 2005) laid the basis for the
experiments (described below), and derived a 'biological hydrophobicity
scale' for the contributions to membrane insertion of each amino acid when
placed in the middle of a 19 residue segment.
The experimental data underlying the derivation of the 'code' comes from
insertion efficiency measurements of designed putative TM segments in a
model membrane protein, Fig. 5.3. Briefly, systematically designed test
Figure 5.3 Insertion efficiency measurements of systematically designed
test segments (red), engineered into a model membrane protein with two
TM helices (black). The G2 site is only glycosylated in the translocated
form (right) and thus the two forms (left/right) can be size separated on a
gel. The relative amounts of the two forms quantify the level of insertion.
segments were introduced into the model protein, which was expressed and
inserted into rough microsomal membranes in an in vitro translation system.
The test segment is flanked by two glycosylation sites and, since glycosylation only occurs on the lumenal side, the inserted and translocated forms of
the protein can be separated by size on a polyacrylamide gel. From the relative amounts of inserted and translocated forms of the protein, an apparent
free energy of membrane insertion of the segment, ∆G app , can be calculated.
Almost 500 designed and natural sequences were expressed and tested for
aa (i )
membrane integration in this way. To quantify the contributions, ∆Gapp
,
from individual amino acids to ∆G app , an additive model was assumed in
40
which the position, i, of the amino acid within the segment was taken into
account:
l
pred
aa ( i )
∆Gapp
= ∑ ∆Gapp
+ c 0 µ + c1 + c 2 l + c3 l 2
(1)
i =1
where l is the length of the segment, µ is the hydrophobic moment of the
helix, c0 is a weight parameter for the hydrophobic moment, and c1, c2 and c3
are parameters describing the contribution from the helix length. The posiaa (i )
tion-specific amino acid contributions, ∆Gapp
, were optimized by iterapred
values
tively minimizing the squared differences between predicted ∆Gapp
and measured ∆G app values, using a standard non-linear optimization strategy. To reduce the number of parameters of the model, the contribution from
aa (i )
a particular amino acid ∆Gapp
as a function of position, i, was first expressed as a simple gaussian function with two parameters, except for Trp
and Tyr, which were both modeled with double gaussian functions containing five parameters each. Using this representation, the model contained in
∆Gaa(i)
(kcal/mol)
app
A
C
D
E
F
2
2
2
2
2
1
1
1
1
1
0
0
0
0
0
−1
−1
−1
−1
−1
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
G
H
I
K
−9 −6 −3 0 3 6 9
L
2
2
2
2
2
1
1
1
1
1
0
0
0
0
0
−1
−1
−1
−1
−1
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
M
N
P
Q
−9 −6 −3 0 3 6 9
R
2
2
2
2
2
1
1
1
1
1
0
0
0
0
0
−1
−1
−1
−1
−1
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
S
T
V
W
−9 −6 −3 0 3 6 9
Y
2
2
2
2
2
1
1
1
1
1
0
0
0
0
−1
−1
−9 −6 −3 0 3 6 9
−1
−9 −6 −3 0 3 6 9
0
−1
−9 −6 −3 0 3 6 9
Position
−1
−9 −6 −3 0 3 6 9
−9 −6 −3 0 3 6 9
Experimental
Statistical
Figure 5.4 Position specific amino acid contributions to membrane insertion efficiency of TM helices. Position 0 corresponds to the center of the
membrane. (Blue) Curves derived from experimental data as described in
Fig. 5.3. (Red) Statistical curves from high-resolution membrane protein
structures.
41
total 50 parameters, optimized using more than 400 constructs. The results
from the optimization are shown in Fig. 5.4. Overall, the curves correspond
well to statistical curves, obtained from the relative over- or underrepresentation of amino acids at different distances to the membrane center in known
membrane protein structures. Charged residues (D, E, K, R) give highly
position-specific contributions to membrane insertion, and are much better
tolerated towards the interface regions than inside the hydrophobic core.
Contributions from polar residues (H, N, Q, S, T) have the same general
trend as the charged ones, but are less pronounced. Hydrophobic residues
(A, F, I, L, M, V) give negative (favorable) contributions and are less position-specific. Finally, the polar aromatic residues (W, Y) are favorable for
membrane insertion when located at a certain distance from the membrane
center, but unfavorable in the middle.
0.3
Single−span TM
Multi−span TM
Cytoplasmic
Secreted
Frequency
0.2
0.1
0
−10
−8
−6
−4
−2
0
2
4
6
8
10
pred
∆Gapp (kcal/mol)
Figure 5.5 ∆Gapp distributions in different sets of proteins. A large
fraction of the multi-spanning helices seem to be only inefficiently recognized by themselves, and might rely on stabilizing interactions for membrane integration.
pred
To see how well the model would identify TM helices in natural proteins,
we collected four test sets of secreted proteins, cytoplasmic proteins, singlespanning membrane proteins, and TM helices from multi-spanning mempred
brane proteins and identified, in each set, the segment with lowest ∆Gapp
,
Fig. 5.5. The overlap between the secreted and single-spanning distribution
is small, and they cross close to the zero-point defined by the experimental
setup. A relatively large fraction of helices in multi-spanning proteins have
42
pred
>0, indicating that such segments would not be efficiently integrated
∆Gapp
by themselves, and might rely on stabilizing interactions from neighboring
helices.
Molecular dynamics simulations of lipid-embedded TM-helices containing charged residues (Dorairaj and Allen 2007) have resulted in free energies
considerably more unfavorable than suggested by the 'biological hydrophobicity scale' first presented in (Hessa et al. 2005) and further developed in
this paper. Concerns raised about the experimental setup involve the possibility of interactions between the H-segment and TM1 or TM2 (Fig. 5.3)
(Shental-Bechor et al. 2006) and the potential for the H-segment to slide
away from its intended position such that the charged residue ends up being
closer to the interfacial region (Dorairaj and Allen 2007). However, control
experiments have shown that the identity of TM1 and TM2 have little influence on the efficiency with which the H-segment is incorporated (MeindlBeinker et al. 2006). In addition, series of constructs where two polar or
charged residues are scanned symmetrically from the middle towards the
ends of the segment agree well with the additive model, arguing against sliding of the H-segment as being a crucial factor (Hessa et al. 2007). When
interpreting the experimental results, a few things should be kept in mind.
First, the position of an amino acid within the H-segment (Fig. 5.4) refers to
the Cα-carbon, meaning that polar and charged residues, especially Arg and
Lys, might be expected to point their side chains away from the immediate
center of the hydrocarbon core. Second, a discrepancy to the MDsimulations is that biological membranes consist to a large part of proteins
(Guidotti 1972), possibly influencing the polarity of the membrane environment compared to pure lipid bilayers. Third, charged residues in the membrane may induce local deformations of the bilayer and pull water into the
membrane (Johansson and Lindahl 2006). Potentially, a combination of several explanations will eventually make it possible to reconcile the MD data
with the experimental results.
A recent study (Park and Helms 2008) derived a statistical positionspecific free-energy scale for the contributions of all amino acids, analogous
to the statistical curves in Fig. 5.4. Using this scale to predict TM segments
yielded results comparable to the best available TM topology prediction
methods. However, scanning globular proteins, this scale did not show any
difference between cytoplasmic and secreted proteins, as the one suggested
in Fig. 5.5.
43
5.4 Prediction of membrane-protein topology from first
principles (Paper IV)
In an attempt to predict full transmembrane topologies using the model deaa (i )
-scale in
veloped in paper III, we implemented the position-specific ∆Gapp
two prediction algorithms. Topology prediction algorithms (section 4.4) are
typically based on machine learning methods that extract statistical signals
from known membrane protein structures, thereby optimizing a large number of parameters. An important difference to those machine learning approaches is that our methods are only based on data from experiments as in
Fig. 5.3, and should be more directly linked to the physico-chemical principles underlying TM helix recognition in vivo.
One method, TopPred∆G, basically employs the original TopPred algorithm (section 4.4.1), but the position independent GES hydrophobicity scale
aa (i )
is replaced by the position dependent ∆Gapp
-scale. Two parameters, the
pred
upper and lower ∆Gapp -cutoffs, corresponding to putative and certain TMhelices respectively, needed to be optimized on known structures. A variant
of the algorithm that takes a sequence profile rather than a single sequence as
input was also developed, which improved performance by a few percentage
points.
aa (i )
The ∆Gapp
-scale was also implemented in an HMM-based method,
aa (i )
SCAMPI, designed to be as simple as possible, such that only the ∆Gapp
scale and no other parameters would influence the predicted topology. As
such, all transition probabilities throughout the model are set equal to 1, and
emission probabilities in the loop regions of the model are flat distributions
and emit all amino acids with equal probability. In the membrane region of
aa (i )
the model, the ∆Gapp
-scale is used to calculate the free energy of insertion,
and this energy is converted to the corresponding expected fraction of insertion in an experiment as in Fig. 5.3, which is used as emission probability in
the model. Only two parameters in SCAMPI needed to be optimized on
known structures. First, since many TM-helices are predicted not to be hydrophobic enough to insert by themselves (cf. Fig. 5.5), a constant correction
pred
factor is subtracted from the predicted ∆Gapp
before this value is converted
aa (i )
-scale does not
to a corresponding probability. Second, since the ∆Gapp
contain any orientational information, the emission probabilities for Lys and
Arg are increased by a constant value in the part of inside loops closest to the
membrane, in order to comply with the positive-inside rule (section 3.3.3).
For both TopPred∆G and SCAMPI, the two parameters were optimized on
one dataset, and used for prediction on another independent dataset. An example prediction is shown in Fig. 5.6. Somewhat unexpectedly, our simple
methods based on experimental measurements of artificial TM helices were
found to match the performance of the best available statistics based methods, predicting 80-85% correct topologies in our benchmark on all known
membrane protein structures from PDB. We further analyzed the TM helices
44
pred
in the dataset with respect to their ∆Gapp
values, and found that helices that
pred
values than
were highly buried in the structure typically had higher ∆Gapp
helices that were exposed towards the lipid phase, and that TM-helices in
pred
proteins with many TM-helices tend to have higher ∆Gapp
values on average. These observations support the hypothesis that helix-helix interactions
might be an important factor for incorporation of marginally hydrophobic
helices into the membrane.
Figure 5.6 Topology predictions of Cytochrome c oxidase (PDB code
1ehkA) by TopPred∆G and SCAMPI. (Top) Crystal structure of the protein
pred
chain. (Middle) the ∆Gapp profile, with the TopPred∆G parameters
∆Ghigh and ∆Glow shown as dashed lines and the topology predicted by
TopPred∆G indicated beneath the curve. (Bottom) The path through the
SCAMPI model, with the vertical position in the model (see y axis) plotted as a function of sequence position. The structure and predicted topred
pologies are colored according to ∆Gapp values. Locations of the TM
helices as annotated in the OPM database (Lomize et al. 2006) are shown
as gray boxes. Lys and Arg residues are marked with plus signs.
45
Acknowledgements
Lots of people have contributed, helped and encouraged me during the work
of this thesis. Thanks to all of you. I would like to especially thank the following people:
My supervisor, Gunnar, for giving me the opportunity to work in your lab.
Thanks for always knowing everything, and for explaining it in a way that
the rest of us can understand. Thanks for giving me the freedom to try my
own ideas and, when they fail, for having better suggestions. And thanks for
always answering emails within 5 minutes, irrespective of time or the timezone you're in.
My co-supervisor, Arne, for creating a great, relaxed atmosphere at work.
Thanks for having more ideas than the rest of us taken together, and for enthusiastic explanations of them. And thanks for all the espresso.
Past and present room-mates: Kristoffer, for julmys and for watering the
flowers, Håkan, for all the science we learnt in Aspen, Tomas, for the
amazing chip-tricks, Diana, for everything I know about the importance of
conquering Kas.
Erik Sj., the system guru, for your unique dart-throwing technique and for
gathering people for the Thursday pub.
SBC/CBR-people: Erik L., for your positive and encouraging attitude and
for the PS3, Åsa, for baking amazing cake-club cakes, Per, for having the
only working version of pymol, Erik G., for voting for me in the karaokecontest, Jenny, for teaching me how to spin the ball in wii-bowling, Björn,
for being so reasonable with Zlatans morsa, Joppe, for choosing the sunexposed part of the table in the "hand-on-hot-table" contest in Brazil, Anna,
for never occupying more than necessary of the cluster, Pär, for nervewrecking dart rounds, Aron, for taking care of the left-overs, Ali, for never
bluffing, Örjan, for all the sparris, Linnea, Rauan, Marcin, Anni, Yana,
Christine, Katarina for being great collegues.
Mia for all the asian lunches.
46
My Biosapiens work package collegues: Rita, for sightseeing and shopping
in Barcelona, Gert, for teaching me about GPCRs and for buying me lunch.
My dear family: Mamma and Pappa, for life, for being so kind, generous
and supportive, and for everything else, Morfar, for all your encouragement
and interest, Johanna and Thomas, for long phone calls and for the sun
deck at Lusten, Alma and Adam, for never saying no to play with me,
Fredrik and Laura, for fine wines and private cinema shows, Bianca, for
being cute and for forcing your parents to buy a bigger car, Flicka, for always being happy.
My Sweetheart, Kirsi, for all your love and support, and for all the good
times we've had together. You are my sunshine.
47
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