Dynorphin A – Interactions with receptors and the membrane bilayer Licentiate thesis

Dynorphin A –
Interactions with receptors
and the membrane bilayer
Licentiate thesis
Johannes Björnerås
Under the supervision of profs. Lena Mäler and Astrid Gräslund
Department of Biochemistry and Biophysics, Stockholm University
Stockholm, Sweden
September 2013
Abstract
The work presented in this thesis concerns the dynorphin neuropeptides, and
dynorphin A (DynA) in particular. DynA belongs to the wider class of typical
opioid peptides that, together with the opioid receptors, a four-membered family
of GPCR membrane proteins, form the opioid system. This biological system is
involved or implicated in several physiological processes such as analgesia, addiction and depression, and effects caused by DynA through this system, mainly
through interaction with the κ (kappa) subtype of the opioid receptors (KOR), are
called the opioid effects. In addition to this, non-opioid routes of action for DynA
have been proposed, and earlier studies have shown that direct membrane interaction is likely to contribute to these non-opioid effects. The results discussed here
fall into either of two categories; the interaction between DynA and a fragment of
KOR, and the direct lipid interaction of DynA and two variant peptides.
For the receptor interaction case, DynA most likely causes its physiological effects
through binding its N-terminal into a transmembrane site of the receptor protein,
while the extracellular regions of the protein, in particular the extracellular loop II
(EL2), have been shown to be important for modulating the selectivity of KOR for
DynA. Here we have focussed on the EL2, and show the feasibility of transferring
this sequence into a soluble protein scaffold. Studies, predominantly by nuclear
magnetic resonance (NMR) spectroscopy, of EL2 in this new environment show
that the segment has the conformational freedom expected of a disordered loop
sequence, while the scaffold keeps its native β-barrel fold. NMR chemical shift
and paramagnetic resonance enhancement experiments show that DynA binds
with high specificity to EL2 with a dissociation constant of approximately 30 µm,
while binding to the free EL2 peptide is an order of magnitude weaker. The
strength of these interactions are reasonable for a receptor recognition event. No
binding to the naked scaffold protein is observed.
In the second project, the molecules of interest were two DynA peptide variants recently found in humans and linked to a neurological disorder. Previously
published reports from our group and collaborators pointed at very different
membrane-perturbing properties for the two variants, and here we present the
results of a follow-up study, where the variants R6W–DynA and L5S–DynA were
studied by NMR and circular dichroism (CD) spectroscopy in solutions of fasttumbling phospholipid bicelles, and compared with wild type DynA. Our results
show that R6W–DynA interacts slightly stronger with lipids compared to wild
type DynA, and much stronger compared to L5S–DynA, in terms of bicelle association, penetration and structure induction. These results are helpful for explaining
the differences in toxicity, membrane perturbation and relationship to disease,
between the studied neuropeptides.
ii
List of publications
I Johannes Björnerås, Martin Kurnik, Mikael Oliveberg, Astrid Gräslund,
Lena Mäler and Jens Danielsson; Direct detection of neuropeptide dynorphin
A binding to the second extracellular loop of the κ–opioid receptor using a
soluble protein scaffold. Submitted manuscript
II Johannes Björnerås, Astrid Gräslund and Lena Mäler; Membrane interaction
of disease-related Dynorphin A variants. Biochemistry 52, 4157–4167 (2013)
iii
Abbreviations
DHPC
DMPC
DynA
DynB
EL2
KOR
HSQC
LUV
MTSL
NOE
OR
PC
PDYN
PRE
SOD1
SUV
1,2-dihexanoyl-sn-glycero-3-phosphocholine
1,2-dimyristoyl-sn-glycero-3-phosphocholine
dynorphin A
dynorphin B
the extracellular loop II of the κ–opioid receptor
κ–opioid receptor
heteronuclear single-quantum coherence
large unilamellar vesicle
1-oxyl-2,2,5,5-tetramethyl-3-pyrroline-3-methyl) methanesulfonate
nuclear overhauser enhancement
opioid receptor(s)
phosphatidylcholine
prodynorphin
paramagnetic relaxation enhancement
human superoxide dismutase 1
small unilamellar vesicle
CONTENTS
1 Introduction
1.1 Life, what is it really? . . . . . . .
1.1.1 Compartments . . . . . .
1.1.2 Signalling . . . . . . . . .
1.2 Life, how do we deal with it? . .
1.3 Final words (of the introduction)
.
.
.
.
.
1
1
1
2
2
3
2 Biological background
2.1 The opioid system . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.2 Dynorphins . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
5
5
9
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
3 Research projects
13
3.1 DynA–KOR interactions . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Lipid interaction properties of DynA and related peptides . . . . . 13
4 Methods
4.1 Membrane mimetic systems . .
4.1.1 Bicelles . . . . . . . . . .
4.1.2 Other systems . . . . . .
4.1.3 Scales of length and time
4.2 NMR . . . . . . . . . . . . . . .
4.2.1 General theory . . . . . .
4.2.2 Chemical shifts . . . . .
4.2.3 Dynamics . . . . . . . .
4.2.4 Diffusion . . . . . . . . .
4.2.5 Labelling . . . . . . . . .
4.3 CD . . . . . . . . . . . . . . . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
15
15
15
16
16
17
17
20
21
23
23
24
5 Summary of results
25
5.1 The construct protein SOD1–EL2 has a β–barrel core, while the
inserted segment is unstructured. (Paper I) . . . . . . . . . . . . . . 25
5.2 The extracellular loop 2 of the κ opioid receptor, as expressed in the
construct protein SOD1–EL2, interacts weakly with Dynorphin A.
(Paper I) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
iv
CONTENTS
5.3
The two disease-related Dynorphin A variants R6W-DynA and L5SDynA have markedly different lipid interaction properties. (Paper
II) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
v
26
6 Conclusions and outlook
29
Acknowledgements
31
BIBLIOGRAPHY
33
APPENDIX
43
vi
CONTENTS
1
Introduction
1.1
Life, what is it really?
The questions arising from thinking about the fundamental aspects of life can
drive anyone to the brink of insanity. How did life start? Was the origin of life a
one-time event, or are there other planets, or even other universes, teeming with
weird and wonderful creatures? Is there an ultimate purpose with life in general,
and my life in particular? If not, how can I come to terms with this? How should
my life best be lived? Such things may, and will occupy the minds of humans
– from the curious five-year old to the elder watching the dusk of life deepen
around her. As for other animals, we may, sadly enough, never really know what
they think.
Even the question of how to define life is debatable (Dawkins et al. 2011, Owen
2008), and I will make no attempt to settle it. But despite the disagreements, there
are a few key concepts generally accepted as elements of life. A living organism 1)
has some sort of information storage capability – e.g. DNA; 2) is able to reproduce
and evolve; 3) is able to convert and use energy. In addition to this, two other
characteristics of life, significant to the work presented in this thesis, are listed
below.
1.1.1
Compartments
All known living systems have some way of keeping things in confined spaces.
The most obvious example is an overall envelope – like the human skin, the bark
of a tree or an outer bacterial membrane – that creates an inside of the organism
separated from the surroundings. Apart from this, the insides of organisms may
be compartmentalised to various degrees of complexity, adding spatial constraint
as a parameter for controlling the functions – enzymatic reactions, transport,
storage, etc – constituting the life of the organism.
Many physiological responses depend on things happening at these boundaries,
for example whether a specific particle (molecule, virus, etc) is able to pass through
a cellular membrane. In this thesis, one such system – a signalling peptide of the
neural system interacting with neuronal receptors and the cell membrane – is
discussed.
1
2
1.1.2
Chapter 1 Introduction
Signalling
A second feature characteristic of a living organism, and something that is connected with compartmentalisation, is a means of sending and receiving signals
(and an ability to process these signals). This can range from rather simple processes such as sensing the flow from an energy source and move towards it, to the
intricate machinery underlying a serious human discussion on, say, contemporary
Chinese art [www.youtube.com/watch?v=yA775QobZhg].
At its most general, a biological signalling system involves a ligand – the physical embodiment of the signal, and a receptor – something that ’reads’ the signal
through interaction with the ligand. But beneath this surface of simplification are
many layers of complexity, a problem that is addressed in one of the projects described in this thesis by a divide-and-conquer approach where a part of a receptor
is removed from the membrane protein and allowed to interact with the ligand in
a simplified context.
1.2
Life, how do we deal with it?
Out of the many ways of looking at life, one is from the viewpoint of science. ’Life
science’ is often used to describe the scientific study of all systems that are in some
way associated with living organisms. To state the obvious, this research area is
enormous, and encompasses a large number of seemingly very different niches,
from data mining to deep sea biology; from proton spin states to prosthetic limbs.
No one, neither Nobel prize laureate nor layman, can deny that our understanding
of living systems and our ability to predict and control them, are at a level that was
unimaginable a hundred years ago (as unimaginable as the level of the scientists
in the beginning of the 1900s to their predecessors a century earlier...). But has
this brought us nearer to an answer to the question of how to deal with life from
a personal point of view; i.e. is it easier to live a good, meaningful life today
than in 1913? Hardly. Human knowledge accumulates, but wisdom arguably
does not; and conceptions of what is good are often heterogenous, and sometimes
contradictory.
But regardless of the incapability of science to perfect human nature, the scientist
has proven to be a key figure in the modern society. Both as a contributor to
materialistic improvements, and as a producer of knowledge needed to make
informed decisions. The contributions from life scientists in these two aspects are
obvious, from modern blessings such as dental anaesthesia and antibiotics to the
influence of our increased biological understanding on discussions of racism and
sexism.
1.3 Final words (of the introduction)
1.3
3
Final words (of the introduction)
Dear reader, do not see this section as an attempt to write a moral guide to life
or life science. Or as any other guide for that matter. It is meant merely to serve
as a sketch of the world from which this thesis has sprung from a viewpoint
too seldom used in my daily life as a life scientist, and as a reminder that even
the dullest lab work on a Tuesday afternoon in November is part of the great
machinery of the world; and that there are reasons to look at our profession with
both humility and pride.
4
Chapter 1 Introduction
2
Biological background
2.1
The opioid system
Opiates are compounds eliciting their effects through the opioid system, and
the drug morphine, a molecule isolated from the opium poppy, is perhaps the
most widely known member of the family. Although knowledge of opiates and
their effects on humans date back hundreds, or even thousands of years (see
(Brownstein 1993)), a more detailed model of the cause of their effects is much
younger.
2.1.1
Receptors
The receptors of the opioid system are G protein-coupled receptors (GPCRs), the
largest class of membrane proteins (Rosenbaum et al. 2009). The research interest
in membrane proteins in general has grown steadily over the last decades [REF],
and reasons for studying this huge group of biomolecules range from pure basic
research to pharmaceutical and biotechnological applications. But despite that
membrane proteins – e.g. receptors, transporters and channels – are predicted to
constitute 20 to 30 % of all proteins (Krogh et al. 2001)), and that they by a rough
estimate make up at least half of existing (Drews 2000) and predicted (Bakheet
and Doig 2009) drug targets, they remain largely unexplored. A vast body of
biophysical and biochemical research has greatly expanded the knowledge in this
area, but for many membrane proteins, characterisation and understanding on
an atomic level has been out of reach, due to the difficulties of studying them
with high-resolution spectroscopic techniques such as NMR or X-ray crystallography. Numbers that illustrate the effects of these problems may be found in
databases of protein structures, with the database of membrane protein structures
(blanco.biomol.uci.edu/mpstruc/) containing approximately 1300 coordinate files,
compared to more than 85000 in the Protein Data Bank (PDB, at present the largest
database of protein structure information). Much simplified, the problems come
down to the following:
X-ray: A well-defined three-dimensional crystal structure is needed to get a
well-resolved diffraction pattern, and since membrane proteins by nature
reside in a 2D-like (lipid bilayer) environment, these crystals are not easily
formed.
NMR: The structures of most soluble proteins have been solved with liquid state
NMR, where anisotropy (ideally) averages out intermolecular interactions,
and scales down the informational content of the spectra. Since the averaging (partly) relies on a rapid overall motion of the protein (much faster
5
6
Chapter 2 Biological background
than the motion of an entire cell or a synthetic vesicle), this approach requires that the membrane proteins are either solubilised in some less polar
medium than water, or reconstituted in a membrane-mimicking system –
like micelles or bicelles (both of which will be discussed later). The first of
these approaches gives conditions that poorly mimicks the in-vivo environment, while the second often poses considerable experimental challenges.
A related problem is that linewidths in NMR spectra correlate with molecular tumbling rates, and since membrane proteins tend to be large, and are
likely to be complexed with lipids (in a membrane mimicking solvent), the
line broadening may simply be too severe to get data of sufficient quality to
produce a reasonable structural estimate.
Solid state NMR (ssNMR) on powder-like samples offers the advantageous
possibility of using a much more native-like lipid bilayer as an environment for the membrane protein. However, in the last two decades only
a handful of membrane protein structures have been solved by ssNMR
[http://www.drorlist.com/nmr/SPNMR.html], showing that the method is
not yet trivially applicable to protein structure determination, but the approach is in rapid development (see (Hong et al. 2012) for a review), and it is
likely that this technique will have a great impact on the field in the coming
years.
The GPCRs are involved in chemical signal transduction, and it is no exaggeration
to say that this family of proteins has lately been the superstars of biomolecules,
awarding two of the pioneers in the field – Robert Lefkowitz and Brian Kobilka –
the Nobel prize in chemistry 2012. In the general agonistic situation, a ligand interacts with the receptor, increasing the probability of the receptor to interact with
one or several guanine nucleotide binding proteins (G-proteins). The G-proteins
function as transducers, and possibly amplifiers, propagating the signal by modulating one or several effector systems (Gudermann et al. 1995, Rosenbaum et al.
2009).
As recently as 2005, only one GPCR crystal structure existed, that of bacteriorhodopsin (Palczewski et al. 2000), but several years of hard work and technical
and methodological improvement has resulted in several new structures in the
last few years, many of which are from the labs of Ray Stevens and Brian Kobilka
(see e.g. (Chien et al. 2010, Granier et al. 2012, Manglik et al. 2012, Shimamura et al.
2011, Thompson et al. 2012, Warne et al. 2008, Wu et al. 2010; 2012)), and at the time
of this thesis, 20 crystal structures have been produced (http://gpcr.scripps.edu/).
Without downplaying any of the remarkable achievements underpinning the
existing GPCR structures, several questions regarding their function remain to
be answered, and several problems remain to be solved. One such aspect is
the scarcity of NMR structures – to date, the CXCR1 receptor is the only GPCR
who has had its structure determined by NMR (Park et al. 2012). In contrast to
X-ray crystallography, NMR spectroscopy may be used under near-native conditions, and provides valuable information on dynamics on different time scales,
2.1 The opioid system
7
in addition to the molecular structure. Another problem is the lack of structural
information on peptides bound to GPCRs – to the author’s knowledge, only one
such structure exists, that of the CXCR4 chemokine receptor in complex with a
cyclic peptide (Wu et al. 2010). Being bulkier than small-molecule ligands, the
interaction of peptide agonists or antagonists necessarily involves larger regions
of the GPCRs, e.g. extracellular loops.
Opioid receptors
The existence of the GPCR subfamily opioid receptors (originally called opiate
receptors) was demonstrated by the work of Pert and Snyder (Pert and Snyder
1973), Goldstein et al. (Goldstein et al. 1971), the Swede Lars Terenius (Terenius
1973a;b, Terenius et al. 1975) and other groups, in the early seventies (for an exciting personal account, as well as an historical review of these years, see (Snyder
and Pasternak 2003)). Through interactions with various endogenous peptides,
these receptors, divided into four classes - κ, μ, δ, and the nociceptin/orphanin
FQ peptide receptor – are involved in the modulation of pain ((Przewłocki and
Przewłocka 2001, Wen et al. 1985; 1987)), as well as more complex physiological
processes (Bodnar 2012) such as reward and addiction (Bruchas et al. 2010, Bruijnzeel 2009, Drews and Zimmer 2009), and different types of mental disorders
(Tejeda et al. 2012).
A compelling feature of the opioid receptors (as well as other GPCRs) is their
combination of high similarity – both on a sequence level (60 to 70 %) (Uhl et al.
1994), and structurally – and high specificity with respect to various ligands (in
particular peptides or peptide analogues). The opioid receptors all have a GPCR
type A (rhodopsin-like) fold, with seven transmembrane helices interconnected
by intra- and extracellular loops, as is exemplified by the X-ray structure of the
KOR shown in Figure 2.1. The recent structure determination of all four opioid
receptors (Granier et al. 2012, Manglik et al. 2012, Thompson et al. 2012, Wu et al.
2012) has made it possible to draw some conclusions regarding the similarities
and differences within this receptor family. For example, in all four receptors there
is a binding pocket in the transmembrane region, a region that is well conserved,
with several conserved amino acids having the same position (Filizola and Devi
2012). The sequence dissimilarities are mainly located in the termini, and extraand intracellular loops of the receptors (Uhl et al. 1994); and hence these regions
have been in the focus of interest as conveyors of ligand selectivity (Paterlini 2005).
Surprisingly, these regions are also structurally similar (Filizola and Devi 2012),
suggesting that only subtle differences modulate the ligand selectivity, and/or
that the existing crystal structures show the proteins in similar states (all four
structures have an agonist bound), and that this is just one state out of many.
Based on the larger conformational freedom of the loop regions, it is reasonable to
believe that these parts of the receptors show the largest structural differences in
different receptor states. Chimeric studies have shown that the selectivity pattern
of one receptor subtype may be transferred to another subtype by interchanging
extracellular fragments (Dietrich et al. 1998, Meng et al. 1995, Wang et al. 1994).
8
Chapter 2 Biological background
Figure 2.1: X-ray crystal structure of the κ-opioid receptor where the extracellular
loop II has been marked with red. The figure was produced using Pymol, from a
PDB structure file, ID 4DJH, at www.pdb.org
The importance of the extracellular loops is further discussed below for the specific
case of the κ-opioid receptor.
Ligands
A wide variety of opioid receptor ligands are known, some of which are isolated
and/or synthesised from natural sources, such as morphine, heroin and salvinorin
A (Roth et al. 2002), while others are synthetic. It is beyond the scope of this thesis
to give an overview of these ligands, but the topic will be discussed in the specific
context of the κ-opioid receptor and its interaction with the endogenous peptide
ligand dynorphin A.
Making the question of receptor–ligand selectivity even more intriguing is the
fact that also among the ligands there are many common features, perhaps most
notably the so called enkephalin sequence: Tyr–Gly–Gly–Phe–Leu/Met, that constitutes the N-terminal sequence of all typical opioid peptides (Janecka et al. 2004),
including the dynorphin family which is discussed below.
2.1 The opioid system
2.1.2
9
Dynorphins
In the 1970’s, a substance with distinct morphine-like properties was isolated
from cow brain (Teschemacher et al. 1975). The substance was later identified
as an endogenous opioid peptide, named dynorphin after its high potency in an
assay using the contractions of guinea pig ileum (Goldstein et al. 1979), and had
its sequence determined (Goldstein et al. 1981). Further research established that
this powerful peptide exerted its opioid effects mainly through the κ-subtype of
the opioid receptors (KOR) (Chavkin and Goldstein 1981a;b, Chavkin et al. 1982),
and that there are two types of dynorphins, dynorphin A (DynA) and dynorphin
B (DynB), emanating from the same precursor protein: prodynorphin (PDYN).
Opioid receptor interactions
During the decades up until present day, the dynorphins, in particular DynA,
have been extensively studied and characterised – both for basic research purposes, and as a drug template. A large number of research groups have used
truncations, mutations, chemical modifications and various forms of cyclisation
of the peptides, in particular of DynA, to map out the physico-chemical and structural properties needed for potent, selective and tunable opioid receptor agonist
and antagonist effects; for examples see (Björnerås et al. 2013) and references
therein. Many times the ultimate goal with this research has been to develop
alternatives to morphine, matching its ’gold standard’ analgesic properties while
avoiding the adverse side effects, such as addiction. The results of this research
include a number of synthetic non-peptide ligands, one of which was used in the
crystallisation and structure determination of the KOR (Wu et al. 2012).
The many results of this research do not easily condense to a few simple rules,
but some aspects are worth highlighting. It is now generally accepted that the Nterminal residues of the opioid peptides (the enkephalin sequence), in particular
Tyr1 and Phe4 (Turcotte et al. 1984), interact with residues in the transmembrane
binding pocket of the receptors to trigger receptor activation. This region of the
ligand is often called the ’message’, while the C-terminal parts of peptide ligands
are called ’address’ regions; a concept coined by Goldstein et al. in 1981 (Chavkin
and Goldstein 1981b). Furthermore it has been shown that the KOR extracellular
loop II (EL2) is needed for high affinity OR–dynorphin binding (Wang et al. 1994,
Xue et al. 1994). The position of EL2 in the KOR can be seen in 2.1, where EL2 is
shown in red. As for the extracellular receptor regions, and the mechanisms for
ligand selectivity, a few different concepts have been proposed:
Binding/favourable interaction: The message-address model depicts the ligand as consisting of two (not completely distinct) parts. The N-terminal
sequence (message) activates (or blocks) the receptor upon interaction with
residues in the transmembrane binding pocket; while the remaining peptide
region (address) needs to have a favourable interaction with extracellular
parts of the receptor for the ligand to reach and bind in the transmembrane
10
Chapter 2 Biological background
site.
Selection through exclusion: Here, selectivity for the interaction is proposed
to be impaired through an exclusive mechanism – unfavourable energetics
between ligands and extracellular receptor regions keep certain ligands out.
See (Metzger and Ferguson 1995).
Conformational mechanisms: In this hypothesis, the extracellular loops constrain the positions of the transmembrane helices, and, as a consequence,
the positions of contact points in binding sites. See (Dietrich et al. 1998).
In the literature there is support for all the proposed mechanisms, and a reasonable (albeit fence-sitting) conclusion is that for any ligand–receptor system, all
three models will be valid, but to different degrees. In the case of DynA–KOR
interaction, the abundance of positively charged amino acid residues in DynA
and negatively charged residues in the EL2 of KOR, immediately gives rise to the
idea that electrostatic interactions are important, or even primarily responsible,
for the interaction between the two. This hypothesis has been tested in several
studies, with mixed results. Schlechtingen and co-workers (Schlechtingen et al.
2003), performed binding and activity studies with KOP and various analogues
of DynA, showing that Arg residues, especially Arg6 and Arg7 were crucial for κselectivity. Ferguson, on the other hand, reported that replacing charged residues
(but not all) in EL2 did not affect DynA affinity and function (Ferguson et al. 2000).
Also modelling studies have downplayed the importance of Coulomb forces in
comparison with hydrophobic interactions (Paterlini et al. 1997), and experimental studies of the EL2 peptide in micelles lend some support to this (Zhang et al.
2002).
Membrane interactions
By blocking the opioid receptors with an antagonist such as naloxone, dynorphins
have been shown to induce a variety of physiological responses, predictably called
non-opiate effects. These effects, most often studied in cellular systems, mice or
rats, include paralysis (Bakshi and Faden 1990), changes in the motor system
(Walker et al. 1982) and neural cell death (Tan-No et al. 2001). The underlying
signalling pathways do in some cases involve other receptors, see e.g. (Chen et al.
1995, Massardier and Hunt 1989), but direct membrane interactions have also
been suggested to play a role. These interactions include translocation into cells
(Marinova et al. 2005) and the formation of pores affecting calcium influx into cells
(Hugonin et al. 2006). Furthermore, DynA has been suggested to destabilise and
disrupt bilayers by lysis and bilayer fusion, and to induce formation of vesicles in
ordered bilayers (Naito et al. 2002). Interaction with the membrane may also play
a role in the receptor interaction, e.g. by inducing peptide structure, something
2.1 The opioid system
11
that has been observed in model membrane systems (Erne et al. 1985, Hugonin
et al. 2008, Lancaster et al. 1991, Lind et al. 2006, Schwyzer 1986). Moreover, the
membrane may serve as a surface for peptide adsorption and accumulation prior
to receptor binding, as has been suggested by Schwyzer and Sargent (Sargent and
Schwyzer 1986, Schwyzer 1986).
12
Chapter 2 Biological background
3
Research projects
3.1
DynA–KOR interactions
A problem of major biological and pharmacological interest is how different ligands bind to different members of the opioid receptor family. What distinguishes
agonists from antagonists? What are the binding energetics and kinetics? How
has evolution addressed the issue of receptor–ligand selectivity?
In this project, we look at the interaction between the KOR and its endogenous
ligand DynA. The main focus of our interest has so far been the interaction between
one of the extracellular receptor loops and the peptide ligand, since this interaction
has been implicated as crucial for selectivity. The ultimate goal in this project is to
study the full-length receptor, but the experimental difficulties are considerable,
and hence a fragment-based approach has been used. More specifically, the second
extracellular loop (EL2) of KOR was grafted onto a soluble protein scaffold, the
core of human superoxide dismutase 1 (SOD1), resulting in a protein, prosaically
called SOD1–EL2. Apart from being an aid in answering the underlying biological
questions related to this specific system, the feasibility of using this approach as
a general method to study the interaction between membrane protein loops and
peptide ligands was also evaluated.
3.2
Lipid interaction properties of DynA and related peptides
The properties of the peptides in the dynorphin family have been investigated
for a few decades, but many questions remain unanswered. One area of interest
is how the peptides interact with lipid bilayers, and for some years researchers in
our group have looked at these questions from a biophysical point of view. What
distinguishes the dynorphins from other neuropeptides? Are there differences in
membrane interaction properties within this peptide family? What properties of
the higher-level biological system(s) can be inferred from the biophysical results?
Questions like these are at the heart of this project, and form a starting point
for one of my research projects. More specifically, I have studied the wild-type
DynA, and a few newly discovered members of the dynorphin family, using
fast-tumbling phospholipid bicelles as the (main) membrane mimetic.
These ’new’ dynorphin peptides deserve to have further research devoted to
them, because despite the plethora of synthetically modified DynA peptides and
analogues, up until recently no naturally occurring variants were known. Then
mutations in the gene coding for PDYN were identified and shown to be the cause
of a human neurodegenerative disorder (Bakalkin et al. 2010), a discovery that was
13
14
Chapter 3 Research projects
soon followed by the identification of other mutations in this gene (Jezierska et al.
2013). Cell toxicity studies, as well as leakage assays showed that the variants had
very different properties, despite them being linked to the same disorder. These
interesting aspects prompted us to begin studying the molecular details of the
lipid-interaction properties of these peptides using NMR spectroscopy.
4
Methods
4.1
Membrane mimetic systems
For the life scientist interested in molecules and processes coupled to biological
membranes, the complexity and size of a cell envelope effectively prohibit the use
of many biophysical and biochemical techniques. This is also true for many other
native membranes, and so the scientist is forced to address the problem by creating
a system sharing the properties of interest with the ’real’ membrane environment,
but sufficiently cut down in size and complexity for it to be manageable by at
least some of the methods in the scientist’s toolbox. Such an imitation of a real
membrane is often called a membrane mimetic.
Just as for native biological membranes, the mimetics exploit that lipid molecules
have certain physicochemical properties that result in rather complicated thermodynamical behaviour, with lipids in solution self-assembling into super-molecular
complexes. A thorough treatment of this is beyond the scope of this thesis, and
the reader is referred to an excellent book by Mouritsen on the subject (Mouritsen
2005). For a review of different membrane mimetic systems in the specific context
of NMR spectroscopy, see Warschawski (Warschawski et al. 2011).
4.1.1
Bicelles
In the ideal model, bicelles are patches of bilayer-forming lipids surrounded by a
rim of short-chain detergent molecules (Andersson and Mäler 2005, Losonczi and
Prestegard 1998, Sanders and Schwonek 1992, Vold et al. 1997), as shown in Figure
4.1. The bilayer part has very little curvature, similar to a native membrane (more
on this below). By varying the relative amounts of lipids with long and short
chains, as well as parameters such as lipid type, ionic strength, temperature, pH
and overall lipid concentration, the morphology of the lipid complexes change,
and there is no absolute consensus as to where the boundaries of different bicelle
morphologies are in this parameter space (Glover et al. 2001, Lu et al. 2012, Sternin
et al. 2001, van Dam et al. 2004). In the projects described in this thesis we have
used bicelles that are so small that their reorientation is fast on an NMR timescale,
giving spectra with (reasonably) sharp lines. Such lipid complexes are sometimes
called isotropic bicelles, and are widely used for studies of lipid-interacting proteins
and peptides (Andersson and Mäler 2005; 2006, Prosser et al. 2006).
15
16
Chapter 4 Methods
Figure 4.1: Model of an ideal bicelle. From (Ye 2013), used with permission.
4.1.2
Other systems
An aqueous suspension of only detergent molecules will, above a certain concentration, form micelles, which are spherical lipid aggregates with the fatty chains
turned inwards and the polar headgroups facing the water. Micelles have been
used extensively to solubilise membrane-associated peptides and proteins to facilitate biophysical and biochemical characterisation; one arbitrary example out
of many is the study of an Arg-rich voltage-sensor domain from a K+ channel
(Unnerståle et al. 2009).
Bilayer-forming lipids in solutions without detergents will form various types
of vesicles, i.e. spherical structures with one or several bilayers. From these
complexes unilamellar vesicles may be prepared in different sizes, ranging from
small unilamellar vesicles (SUVs), with diameters from 20 to 50 nm up to LUVs
with diameters of approximately 100 nm.
4.1.3
Scales of length and time
The size differences between different types of membrane mimetics, and to cellular
membranes, are substantial, as shown in Figure 4.2, which gives an approximate
overview of the relative sizes of bicelles, LUVs and an E. coli. bacterial cell. With
notations from the figure, but in three dimensions, a surface element for a sphere
of radius r will be given as S = 2πrh. Here h, the maximum distance (projected
on the radius) between any two points on the surface segment, can be seen as
a curvature parameter. This means that for two spheres of different sizes, two
surface elements of equivalent area will have a curvature inversely related to the
radii of the spheres. As an example, a 25 nm2 patch on a sphere with r = 100 nm
17
4.2 NMR
will have h ≈ 0.04 nm – less than the length of a chemical bond.
Bicelle, top view
d ~ 10 nm
h
S
α
Bacterial cell
d ~ 1000 nm
r
Large unilamellar vesicle
d ~ 100 nm
Figure 4.2: Overview of membrane mimetic length scales; d is the diameter of the
spherical/disc shaped objects.
4.2
NMR
This is an overview of nuclear magnetic resonance (NMR) spectroscopy, as performed on biological samples (in particular polypeptides) in the liquid state. In
addition to an extremely condensed section on the general theory, which may
be found in several NMR textbooks, such as (Levitt 2008), applications that have
been exploited in the research treated in this thesis are discussed from a practical
point of view.
4.2.1
General theory
Angular momenta
The nucleons making up atomic nuclei all possess both orbital and spin angular momenta, which give a total angular momentum for each nucleon. These
momenta then add up to give atomic the nucleus a total angular momentum, or
spin, I. The nuclear angular momentum is quantised, and its magnitude takes
the following values:
18
Chapter 4 Methods
p
|I| = ~ I(I + 1)
(4.1)
where the quantum number I takes an integer or half-integer value. Stable nuclei are known with I-values between 0 and 15/2. The nuclear angular momentum along the x-, y- and z-axis may be represented by operators Iˆx , Iˆy and Iˆz ,
respectively, and an operator of total angular momentum can be defined as:
2
2
2
2
Îtot
= Iˆx + Iˆy + Iˆz
Magnetic moment
Associated with the angular momentum is a magnetic moment µI which is parallel
or antiparallel to the angular momentum, and with the so-called gyromagnetic
(or magnetogyric) ratio γ as the constant of proportionality, according to:
µI = γI
(4.2)
Polarisation
In general the spin vectors of the nuclei are isotropically distributed in the absence
of an external magnetic field, yielding no net magnetization vector. If a magnetic
field B0 is applied, the combination of magnetic moment and angular momentum
will cause the spin polarisation axis to precess around the applied magnetic field.
Since different orientations of the spin polarisation axis with respect to the applied field B0 correspond to different energies, an anisotropic distribution of spin
polarisation directions will be established after some time (if the temperature is
finite). The equilibrium situation is described by Boltzmann statistics, and results
in a bulk magnetic moment, often expressed as a magnetisation M.
Resonance
In a quantum mechanical treatment a Hamiltonian operator for the interaction
energy between the nuclear magnetic moment and the applied static magnetic
field, may be constructed as:
ˆ = −µ · B0
H
I
(4.3)
With the magnetic moment proportional to the angular momentum according to
Equation 4.2, and the applied field oriented along the z-axis, this simplifies to:
ˆ = −γB0 Îz
H
The eigenvalues, and thereby the energy levels, for this Hamiltonian are:
(4.4)
19
4.2 NMR
E = −γ~B0 mI ,
mI = −I, −I + 1, ..., I
(4.5)
For a particle with spin 1/2, such as the 1 H-nucleus, this corresponds to two energy
levels. Transitions between these two energy levels are induced by any interaction
with a frequency corresponding to the energy difference between the levels:
~|ω0 | = ∆E = |γ|~B0
(4.6)
A more detailed treatment of the dynamics of superpostition states, corresponding
to the direction of precession, gives:
ω0 = −γB0
(4.7)
This so-called Larmor frequency may also be deduced through a classical mechanical treatment of the system.
Spin ensembles and the density operator
For a macroscopic sample of nuclei, containing on the order of 1019 molecules, the
ensemble of spins could be treated by adding together all the contributions from
the single spin states to calculate the macroscopic magnetisation, but this would
be rather tedious. Instead an operator which contains the average of all members
in the ensemble is introduced as follows:
σ̂ = |ψ >< ψ| =
cα cα ∗ cα cβ ∗
cβ cα ∗ cβ cβ ∗
!
(4.8)
Here cα and cβ are coefficients of a single spin in a superposition state, while
the overbar denotes averaging over the entire ensemble. The diagonal terms
in the density operator matrix represents populations of the two states, which
gives the magnetisation along the z-axis. The off-diagonal terms are the so-called
coherences between the states, meaning somewhat loosely the degree of alignment
of spin polarisation vectors in the xy-plane.
As an example, a spin-1/2 ensemble in thermal equilibrium will have the following
density matrix:

 −Eα

 e kT
σ̂ = 


0



0 
−Eβ 

e kT
(4.9)
20
Chapter 4 Methods
1
1
ω0 , Eβ = − ω0
2
2
in units of ~), and equally distributed in the xy-plane, giving a net magnetisation
along the positive z-axis. Since a perturbation at the Larmor frequency may change
the state of a single spin, the bulk magnetisation vector may be manipulated by
applying a magnetic field oscillating at this frequency, for a given period of time τ
– e.g. as a pulse of electromagnetic radiation generated by a current through a coil.
The direction of the field is transverse to the static field, in the x-direction, say, and
its linear oscillation along the x-axis can be seen as the sum of two fields rotating
in opposite directions in the xy-plane. The field strength Br f is much smaller
than the strengh of static field, typically around four orders of magnitude, but
if the frequency of the oscillating field is in (or near) resonance with the Larmor
frequency of the spin, the field will ’join’ the precessing magnetic moment for
several cycles, letting its effect on the magnetic moment accumulate.
As discussed before, the spins are Boltzmann distributed (Eα =
As an example, the bulk magnetisation vector may be ’tipped down’ to the xyplane where the rotating magnetisation will in turn generate a current in the same
coil used to create the pulse. This response of the system will contain information
on all interactions affecting the energies of single spin states.
4.2.2
Chemical shifts
NMR spectroscopy would be of limited use if every nucleus of a certain type had
the exact same frequency. Luckily this is not the case, since each nucleus has a
certain molecular surrounding, with a specific electron distribution, giving a local
microscopic magnetic field which will slightly alter the resonance frequency from
the (theoretical) value of a nucleus only experiencing the applied static field. The
frequency difference is called the chemical shift, and is usually given as a fieldindependent offset from the Larmor frequency of the same nucleus in a reference
compound, ωrc , in units of ppm, as follows:
δ=
ω0 − ωrc
ωrc
(4.10)
Structural information
Although the chemical shift in principle contains all the details of the molecular surroundings, the theoretical framework and modelling capacity required to
deduce atomic coordinates directly from the chemical shift values are not yet sufficient. With this said, a lot of conformational information may still be deduced,
and used together with known theoretical or empirical physicochemical properties of a polypeptide chain, and complementary methods such as molecular
modelling, to estimate protein structure (Cavalli et al. 2007, Robustelli et al. 2010,
Wishart and Sykes 1994). For example, atoms belonging to residues in ordered
structural elements will have different chemical shift values than corresponding
21
4.2 NMR
atoms in disordered segments. Hence, a common strategy to estimate structure, or
at least structural propensities, is to compare observed chemical shift values with
database averages of values from different types of structural elements (Marsh
et al. 2006).
Ligand binding
If a ligand binds to a receptor, some residues – both on the ligand and the receptor –
will necessarily have their chemical environment modified, and nuclei of either the
receptor or ligand, or both, may be studied. Depending on the details of binding
(energetics, kinetics, etc), these perturbations will have different magnitudes; a
case of ligand-induced structure, e.g., would give large shift differences for the
nuclei in the receptor backbone, while for weak binding even nuclei in the vicinity
of the binding site may have changes close to the ’chemical shift noise’ level, as
seen in Paper I.
Chemical environment
In a situation where a molecule of interest may be in either of two chemical
environments the chemical shift values may give information as to the location
of the nuclei even if no ordered structure is present. An example from the thesis
at hand is a system where a peptide is dissolved in an aqueous environment and
studied in the absence and presence of a lipid bilayer, as in Paper II.
4.2.3
Dynamics
Relaxation – the return from a non-equilibrium to an equilibrium state – is a rich
and complex process in general, and this holds true also for the specific case of
atomic nuclei in NMR spectroscopy. For a more complete treatment of the subject,
see e.g. (Kowalewski and Mäler 2006) and (Palmer 2007).
Physically, relaxation in NMR is a consequence of small, local, time-varying
magnetic field disturbances that, if they contain components at frequencies corresponding to energy differences between states in the spin system studied, will
induce transitions between these states. Since the transition probabilities will
be (slightly) larger in the direction towards low energy, after some time characterised by a relaxation time constant the system will reach equilibrium. The time
variation in the local field at a specific molecular site is generated by various
types of molecular motion, and hence the time constants contain information on
these processes. An overview of molecular time scales and the associated NMR
observables is shown in Figure 4.3.
In general, several relaxation mechanisms, some of them correlated, combine to
give an effective relaxation from one particular state to another.
22
Chapter 4 Methods
Catalysis
H transfer,H bonding
Ligand binding
Libration Rot. diffusion
Folding/unfolding
Vibration
Allosteric regulation
Side-chain rot.
10-15
10-12
10-9
10-6
10-3
100
103
103
100
10-3 frequency [hz]
time [s]
1H Larmour frequency
on modern spectrometer
1015
1012
109
R1, R2, NOE
106
Relaxation disp. (R1ρ, CPMG)
Lineshape analysis
Amide exchange
Chemical shifts
Residual dipolar coupling
Figure 4.3: Top: Overview of NMR time scales and protein dynamics, and bottom:) Associated measurable NMR parameters. Adapted from Palmer et al.
(Palmer 2004).
Backbone dynamics
The relaxation rates R1 and R2 concern, respectively, the change in spin state
populations back to the equilibrium Boltzmann distribution, and the decay of
coherences between the spins. An established way to probe protein backbone
dynamics (see for example (Kay et al. 1989, Mäler et al. 2000, Mandel et al. 1995),
or previous citations in this chapter) is to measure these two parameters, together
with NOE factors describing cross-relaxation between heteronuclear spins, for
every 15 N in a (labelled) polypeptide, through 15 N-HSQC-based experiments.
The results may be analysed using a formalism where no particular motional
model is assumed, but rather a separation of motional time scales, where the
measured relaxation parameters are combined into an order parameter S2 that
describes stochastic motions of the N–H bond vectors on a ps–ns timescale, and
hence gauge the flexibility of the backbone and if it changes, e.g. as an effect of
ligand binding. This type of analysis was exploited in Paper I.
23
4.2 NMR
Paramagnetic relaxation enhancement (PRE)
The (spin) magnetism of an unpaired electron will enhance the relaxation of neighbouring nuclei through a dipole–dipole mechanism (Bloembergen and Morgan
1
1961), with an electron–nucleus distance dependence of 6 . The dipole–dipole
r
interaction energy is proportional to the product of the gyromagnetic ratios of the
two particles, and since the electron gyromagnetic ratio is approximately 650 times
larger than the nuclear equivalent, the relaxation enhancement may be dramatic
(Jahnke 2002).
One way to exploit this phenomenon is to attach a chemical moiety containing
an unpaired electron on a ligand, yielding a sensitive probe for ligand–receptor
interaction. This was used to study the interaction between DynA and SOD1–EL2
in Paper I.
4.2.4
Diffusion
NMR measurements of molecular diffusion are based on spin echoes – the reemergence of induced signal through a refocussing of spin phases – and magnetic field
gradients. The gradients introduce a position-dependence for the spins, and in
combination with spin echoes this can be used to study translational motion of
molecules in the sample. The interested reader is referred to the book by Morris
(Morris 2007).
Here, diffusion measurements were used in Paper II to study the association of
peptides to bicelles.
4.2.5
Labelling
Although not a formal requisite of NMR spectroscopy, many, if not most, of
present-day NMR experiments require isotope labelling of the studied biomolecules.
Labelling schemes may range from reasonably simple, e.g. feed bacteria with
15
N-labelled ammonium chloride during protein production, to targeting specific bacterial pathways with labelled chemicals in order to get certain (parts of)
residues labelled. In the projects described here, protein (SOD1–EL2) uniformly
labelled with 15 N and/or 13 C by growing the over-expressing bacteria (described
in the Experimentals procedure of paper I) in 15 N-labelled ammonium chloride
and/or 13 C-labelled glucose was used.
24
4.3
Chapter 4 Methods
CD
CD spectroscopy exploits the fact that some materials and molecules absorb circularly polarised light differently depending on the polarisation direction of the
light. The basis of this optical activity is an asymmetry in the molecule, and a
chemical structure that allows electrons to move in a helical fashion. For biological molecules, it is usually not the chromophore itself that has this type of optical
activity, but the electronic environment in which it resides. Hence the method is
very sensitive to structural features of e.g. a polypeptide chain. For a practical
review of the method, see the article by Kelly (Kelly et al. 2005).
In general, CD spectra provide a good estimate of overall molecular structure –
or, more correctly, an ensemble average molecular structure – while more quantitative information is difficult to extract. By fitting the experimental data to a
superposition of reference spectral profiles, either experimental (Greenfield 2007)
or theoretical (Louis-Jeune et al. 2012), one may attempt to obtain the percentage of different structural elements in the peptide or protein backbone, although
recent results suggest a risk of overinterpretation (Lin et al. 2013).
CD spectroscopy was used in both Paper I and II to estimate peptide and protein
overall secondary structure.
5
Summary of results
5.1
The construct protein SOD1–EL2 has a β–barrel
core, while the inserted segment is unstructured.
(Paper I)
In order to probe a purported selectivity mechanism for the KOR – the interaction
of the second extracellular loop (EL2) with peptide ligands – while avoiding some
of the difficulties of working with the full-length membrane receptor, a construct
protein was engineered. This protein consisted of a soluble β-barrel scaffold onto
which the EL2 from KOR was grafted. The scaffold was a variant of human superoxide dismutase 1 (SOD1) in which the native loops had been truncated (as
described in the Paper I), giving the engineered protein the name SOD1–EL2.
Overall, our characterisation of the protein by CD and various types of NMR
experiments paint the picture of a construct where the core part is structurally
very similar to the ’parent’ scaffold protein SOD1, while the segment taken from
the KOR behaves as a flexible loop.
5.2
The extracellular loop 2 of the κ opioid receptor,
as expressed in the construct protein SOD1–EL2,
interacts weakly with Dynorphin A. (Paper I)
The construct discussed in the above section was used to study the interaction
between the EL2 of KOR and DynA – the primary endogenous ligand of KOR.
The effects of (unlabelled) DynA on NMR chemical shifts of (15 N-labelled) SOD1–
EL2 backbone amides were small and, for many residues, noise-like. A few
residues, mainly located in the EL2-region or in areas of the scaffold in close
proximity to EL2, showed slightly larger shift differences, differences that also
were proportional to the DynA concentration.
Also the relaxation rates of amides in the protein backbone [umpublished results]
changed very little upon addition of DynA. Fitted order parameters suggested an
increase in backbone rigidity in the presence of ligand, but the quality of fitting
was judged insufficient to use this result as evidence for ligand binding.
Mirroring chemical shift experiments, in which unlabelled protein was titrated
onto 15 N labelled DynA (L5 and L12) were also performed. In this experimental
approach SOD1–EL2, EL2 peptide and the naked scaffold protein, SODnoloops,
were used. These experiments showed more conclusively an interaction between
25
26
Chapter 5 Summary of results
EL2 (in SOD1–EL2) and DynA. Both the EL2 peptide and SOD1–EL2 caused consistent changes in the chemical shifts of DynA, while no chemical shift changes
were observed when the scaffold without the EL2 segment was added, showing
that the EL2-loop is required for DynA interaction. Assuming a one-to-one binding, dissociation constants were estimated from the chemical shift data, and are
shown in Table 5.1. The dissociation constants describe a remarkably weak interaction, compared with the nanomolar affinities previously reported for DynA
binding to the full-length receptor (Garzón et al. 1983, Kawasaki et al. 1993). But
from a biological point of view this makes a lot of sense, since a too strong binding
of the ligand to the extracellular regions of the receptor could impair the binding
of the DynA N-terminal to the KOR transmembrane pocket, or significantly slow
down the release of DynA from the receptor.
Table 5.1: Dissociation constants for two molecules interacting with DynA.
Interacting molecule KD [µm]
EL2 peptide
310 ± 25
SOD1–EL2
32 ± 5
Further interaction studies were performed with SOD1–EL2, and DynA with a
paramagnetic probe (MTSL) attached C-terminally to DynA. As described in the
Methods section, these experiments probe the distances between an unpaired
electron (in MTSL) and any nucleus, through the effects of the electron on the relaxation of the nuclear spin. Using this technique binding was indeed confirmed,
with the EL2 loop being the region of the protein most affected by the spin-labelled
DynA. Also other areas appear to be ’close enough’ to the probe in DynA to be
affected, but since the location of these regions in the protein are reasonably near
the inserted EL2 loop, this result is consistent with a selective interaction with the
EL2 loop as also shown by the chemical shift analysis.
5.3
The two disease-related Dynorphin A variants R6WDynA and L5S-DynA have markedly different lipid
interaction properties. (Paper II)
In this project, two recently found (Bakalkin et al. 2010) DynA variants were investigated with CD and NMR spectroscopy. The main focus was on the molecular
details of peptide–lipid interaction, since a previous study had shown that two of
the variants had markedly different membrane perturbing properties in a vesicle
leakage assay (Madani et al. 2011).
As described in Paper II, a variety of proton NMR techniques were employed,
and the results show that one peptide variant – R6W–DynA – has ’aggressive’
lipid perturbing properties, with a strong association and comparatively deep
5.3 The two disease-related Dynorphin A variants R6W-DynA and L5S-DynA have
markedly different lipid interaction properties. (Paper II)
27
N-terminal positioning, while another – the L5S–DynA variant – exhibits a very
mild type of interaction. An interesting aspect of this work is the moderate degree
of ordered structure in all the peptides despite evident bilayer penetration. The
more aggressive variant, R6W–DynA, was found to bind bicelles to almost 100 %,
while remaining largely unstructured. This has also been observed for wild-type
DynA (Lind et al. 2006) and suggests that the mechanisms by which some of
these peptides e.g. induce leakage in vesicles are rather complicated, in contrast
to strongly channel- or pore-forming peptides such as gramicidin A (Allen et al.
2003, Cifu et al. 1992).
28
Chapter 5 Summary of results
6
Conclusions and outlook
All the work in this thesis seek to understand the function of the dynorphin neuropeptides, and the fate of these peptides when they come in close contact with
the outer boundary of a cell. Admittedly, the approaches here chosen to study this
biological system are, like most biophysical approaches, very reductionistic. The
interaction with the receptor is taken apart, and only a fragment (less than 10 %
of the total membrane protein sequence) is studied. Likewise, in the membrane
interaction studies, membrane mimetics involving a maximum of three different
lipid types are used. How valid are these approches, what can we say from our
results, and what are the possible routes forward?
Starting with the receptor, our approach may be criticised on the ground that
a fragment taken out of a protein context will be so far from the features of the
original system that any ligand–interaction observed in the model system has
no relevance for the interaction in the native system. In response to this there
are previous studies (Katragadda et al. 2001, Kerman and Ananthanarayanan
2005) showing that expressed membrane protein fragments keep the biophysical
properties, including structure, of the full-size system. Moreover, the segment
we study has a low structural propensity anyway, so this issue should not be of
critical importance. A question that is more difficult to address comes from the
fact that in the native receptor, the EL2 is most likely additionally constrained by
a disulfide bridge between one of its residues and a cysteine in a transmembrane
helix. Even though these cysteines are critical for antagonist binding (Ott et al.
2004), the importance for the EL2–DynA interaction is difficult to assess, since
the loss of function may also be a consequence of changes in the transmembrane
region. The conceptual distance from the native system may also be seen as an
advantage; in the native system ligands bind very strongly to a transmembrane
site, and a much weaker binding to an extracellular loop would be difficult to
disentangle from the strong one.
Our results show that the DynA ligand has a weak, positive interaction with
an extracellular loop of its native receptor, KOR. This feeble binding makes biological sense, since a stronger binding could prevent the opioid core (N-terminus)
of the peptide from reaching the transmembrane interaction site. Studies with a
scrambled DynA peptide [unpublished], suggest that this also interacts weakly
with the loop, thereby precluding strict sequence specificity. Further research on
peptides without potency in activity assays could help answer the question of
whether there is a positive ligand–receptor loop interaction that ’lets the right
ones in’, or a passive obstruction of the transmembrane binding site that keeps
the wrong peptides out. Related to this, fully labelled ligands would be of great
help, and attempts to produce such peptides are being discussed. Further, constructs with other opioid receptor loops could be a way to map out the selectivity
differences between the receptors in this family. And finally, the holy grail of this
29
30
Chapter 6 Conclusions and outlook
work would be studies of the full-length receptor under conditions accessible to
liquid-state NMR.
As for the membrane interaction of the disease-related DynA variants, the choice
of lipids in the membrane mimetics may be motivated by the abundance of PC
lipids in mammalian neural cell membranes. For example PC is the major component of rat neuronal plasma membranes, constituting almost 30 % of total lipid
amount (Calderon et al. 1995) (the second is PE, present at 15 to 20 %). PG is not
a major component in neural cells, but its overall negative charge is a substitute
for other charged lipids such as PS and PI which are present to various degrees
in biological membranes. It is of course possible that the dynorphin peptides
could interact mainly with membrane regions, in the native cellular environment,
enriched in lipids with another headgroup than PC, but the author of this thesis
is not aware of any reports of this.
Our work on the dynorphin variants show that small changes of the sequence
(single residues) may result in large differences in membrane-interaction properties. The link to a human physiological disorder makes the studied peptides both
relevant and interesting, and we think that our results may be used in future research on the disease. From a biophysical perspective, one way to go forward with
this project would be to make similiar studies of variants with more systematic
residue variations. Another is to investigate if there is any cooperative behaviour
involved, e.g. accumulation of peptides on/in the membrane, and formation of
intramolecular complexes contributing to membrane destabilisation and possibly
disruption. Here fluorescense spectroscopy techniques would be a complement
to the methods described previously.
Acknowledgements
I would like to warmly thank a number of people, whose generous contributions
of time, skill and encouragement have brought this thesis into existence.
First and foremost, main supervisors Lena Mäler and Astrid Gräslund; for your
profound knowledge, clarity of thought and common sense.
Project collaborators/supervisors, from whom I have learnt immensely: Jens
Danielsson for his inspiring unwillingness to compromise on what it means
to be a scientist, Martin Kurnik for tolerating the simultaneous presence of his
virtuosity and my inaptitude in the same confined lab spaces, and Jesper Lind
for scientifical and practical guidance during my first nervous weeks.
Present PhD colleagues: Weihua Ye, Axel Abelein, Scarlett Szpryngiel and Jobst
Liebau; and past: Anna Wahlström, Sofia Unnerståle and Fatemeh Madani.
For being around, for being funny, for helping out. Life is what happens while
you are busy making experiment plans, and you make that life interesting and
comfortable.
Other colleagues and students, past and present: Jüri Jarvet, Sebastian Wärmländer, Göran Eriksson, Luminita Moruz, Viktor Granholm, Erik Sjölund, Christian Seutter von Loetzen, Christina Bösing, Pontus Pettersson, and many others,
who are not named, but not forgotten. You deserve more recognition than these
few lines, and I am confident that you get it.
Senior Research Engineer Torbjörn Astlind, Senior Administrative Officer Haidi
Astlind, and Research Engineer Britt-Marie Olsson. Impressive titles, but your
work is infinitely more impressive. God only knows what we would do without
you.
My mentor Andreas Barth: Even if I have been fortunate enough not to need your
services, I know that you are there to help me.
Last but not least; former supervisors, teachers and evaluators: Mathias Nilsson and Gareth A. Morris of University of Manchester, and Martin Billeter of
Gothenburg University. You got me interested in biophysics and NMR, and you
have given solid support at times when I have doubted my professional choices
and competence, something for which I am immensely grateful.
31
32
Chapter 6 Conclusions and outlook
Bibliography
Allen, T. W., O. S. Andersen, and B. Roux. 2003. Structure of gramicidin A in
a lipid bilayer environment determined using molecular dynamics simulations
and solid-state NMR data. Journal of the American Chemical Society 125:9868–9877.
Andersson, A., and L. Mäler. 2005. Magnetic resonance investigations of lipid
motion in isotropic bicelles. Langmuir 21:7702–7709.
Andersson, A., and L. Mäler. 2006. Size and shape of fast-tumbling bicelles as
determined by translational diffusion. Langmuir 22:2447–2449.
Bakalkin, G., H. Watanabe, J. Jezierska, C. Depoorter, C. Verschuuren-Bemelmans,
I. Bazov, K. A. Artemenko, T. Yakovleva, D. Dooijes, B. P. C. V. de Warrenburg,
R. A. Zubarev, B. Kremer, P. E. Knapp, K. F. Hauser, C. Wijmenga, F. Nyberg,
R. J. Sinke, and D. S. Verbeek. 2010. Prodynorphin mutations cause the neurodegenerative disorder spinocerebellar ataxia type 23. American Journal of Human
Genetics 87:593–603.
Bakheet, T. M., and A. J. Doig. 2009. Properties and identification of human
protein drug targets. Bioinformatics 25:451–457.
Bakshi, R., and A. I. Faden. 1990. Competitive and non-competitive NMDA
antagonists limit dynorphin A-induced rat hindlimb paralysis. Brain Research
507:1–5.
Björnerås, J., A. Gräslund, and L. Mäler. 2013. Membrane interaction of diseaserelated dynorphin A variants. Biochemistry 52:4157–4167.
Bloembergen, N., and L. O. Morgan. 1961. Proton relaxation times in paramagnetic
solutions. effects of electron spin relaxation. The Journal of Chemical Physics 34:842.
Bodnar, R. J. 2012. Endogenous opiates and behavior: 2011. Peptides 38:463–522.
Brownstein, M. J. 1993. A brief history of opiates, opioid peptides, and opioid
receptors. Proceedings of the National Academy of Sciences of the United States of
America 90:5391–5393.
Bruchas, M. R., B. B. Land, and C. Chavkin. 2010. The dynorphin/kappa opioid
system as a modulator of stress-induced and pro-addictive behaviors. Brain
Research 1314:44–55.
Bruijnzeel, A. W. 2009. kappa-opioid receptor signaling and brain reward function. Brain Research Reviews 62:127–146.
Calderon, R. O., B. Attema, and G. H. DeVries. 1995. Lipid composition of
neuronal cell bodies and neurites from cultured dorsal root ganglia. Journal of
Neurochemistry 64:424–429.
33
34
BIBLIOGRAPHY
Cavalli, A., X. Salvatella, C. M. Dobson, and M. Vendruscolo. 2007. Protein
structure determination from NMR chemical shifts. Proceedings of the National
Academy of Sciences 104:9615–9620.
Chavkin, C., and A. Goldstein. 1981a. Demonstration of a specific dynorphin
receptor in guinea pig ileum myenteric plexus. Nature 291:591–593.
Chavkin, C., and A. Goldstein. 1981b. Specific receptor for the opioid peptide
dynorphin: structure–activity relationships. Proceedings of the National Academy
of Sciences of the United States of America 78:6543–6547.
Chavkin, C., I. F. James, and A. Goldstein. 1982. Dynorphin is a specific endogenous ligand of the kappa opioid receptor. Science 215:413–415.
Chen, L., Y. Gu, and L. Y. Huang. 1995. The mechanism of action for the block
of NMDA receptor channels by the opioid peptide dynorphin. Journal of Neuroscience 15:4602–4611.
Chien, E. Y. T., W. Liu, Q. Zhao, V. Katritch, G. Won Han, M. A. Hanson, L. Shi,
A. H. Newman, J. A. Javitch, V. Cherezov, and R. C. Stevens. 2010. Structure of
the human dopamine D3 receptor in complex with a D2/D3 selective antagonist.
Science 330:1091–1095.
Cifu, A., R. K. 2nd, and O. Andersen. 1992. On the supramolecular organization
of gramicidin channels. the elementary conducting unit is a dimer. Biophysical
Journal 61:189 – 203.
Dawkins, R., J. C. Venter, S. Altman, L. Hartwell, P. Davies, C. McKay,
L. Krauss, and R. Bingham, 2011. The great debate - what is life? Video,
thesciencenetwork.org/programs/the-great-debate-what-is-life.
Dietrich, G., G. Gaibelet, R. Capeyrou, J. L. Butour, F. Pontet, and L. J. Emorine.
1998. Implication of the first and third extracellular loops of the mu-opioid
receptor in the formation of the ligand binding site: a study using chimeric
mu-opioid/angiotensin receptors. Journal of Neurochemistry 70:2106–2111.
Drews, E., and A. Zimmer. 2009. Modulation of alcohol and nicotine responses
through the endogenous opioid system. Progress in Neurobiology .
Drews, J. 2000. Drug discovery: A historical perspective. Science 287:1960–1964.
Erne, D., D. F. Sargent, and R. Schwyzer. 1985. Preferred conformation, orientation, and accumulation of dynorphin A-(1-13)-tridecapeptide on the surface of
neutral lipid membranes. Biochemistry 24:4261–4263.
Ferguson, D. M., S. Kramer, T. G. Metzger, P. Y. Law, and P. S. Portoghese. 2000.
Isosteric replacement of acidic with neutral residues in extracellular loop-2 of the
kappa-opioid receptor does not affect dynorphin A(1–13) affinity and function.
Journal of Medicinal Chemistry 43:1251–1252.
BIBLIOGRAPHY
35
Filizola, M., and L. A. Devi. 2012. Structural biology: How opioid drugs bind to
receptors. Nature 485:314–317.
Garzón, J., P. Sánchez-Blázquez, V. Höllt, N. M. Lee, and H. H. Loh. 1983. Endogenous opioid peptides: comparative evaluation of their receptor affinities
in the mouse brain. Life Sciences 33 Suppl 1:291–294.
Glover, K. J., J. A. Whiles, G. Wu, N. jun Yu, R. Deems, J. O. Struppe, R. E.
Stark, E. A. Komives, and R. R. Vold. 2001. Structural evaluation of phospholipid bicelles for solution-state studies of membrane-associated biomolecules.
Biophysical Journal 81:2163 – 2171.
Goldstein, A., W. Fischli, L. I. Lowney, M. Hunkapiller, and L. Hood. 1981. Porcine
pituitary dynorphin: complete amino acid sequence of the biologically active
heptadecapeptide. Proceedings of the National Academy of Sciences of the United
States of America 78:7219–7223.
Goldstein, A., L. I. Lowney, and B. K. Pal. 1971. Stereospecific and nonspecific
interactions of the morphine congener levorphanol in subcellular fractions of
mouse brain. Proceedings of the National Academy of Sciences of the United States of
America 68:1742–1747.
Goldstein, A., S. Tachibana, L. I. Lowney, M. Hunkapiller, and L. Hood. 1979.
Dynorphin-(1-13), an extraordinarily potent opioid peptide. Proceedings of the
National Academy of Sciences of the United States of America 76:6666–6670.
Granier, S., A. Manglik, A. C. Kruse, T. S. Kobilka, F. S. Thian, W. I. Weis, and
B. K. Kobilka. 2012. Structure of the delta-opioid receptor bound to naltrindole.
Nature 485:400–404.
Greenfield, N. J. 2007. Using circular dichroism spectra to estimate protein
secondary structure. Nature Protocols 1:2876–2890.
Gudermann, T., B. Nürnberg, and G. Schultz. 1995. Receptors and G proteins as
primary components of transmembrane signal transduction. Journal of Molecular
Medicine 73:51–63.
Hong, M., Y. Zhang, and F. Hu. 2012. Membrane protein structure and dynamics
from NMR spectroscopy. Annual Review of Physical Chemistry 63:1–24.
Hugonin, L., A. Barth, A. Gräslund, and A. Perálvarez-Marín. 2008. Secondary
structure transitions and aggregation induced in dynorphin neuropeptides by
the detergent sodium dodecyl sulfate. Biochimica et Biophysica Acta 1778:2580–
2587.
Hugonin, L., V. Vukojević, G. Bakalkin, and A. Gräslund. 2006. Membrane leakage
induced by dynorphins. FEBS Letters 580:3201–3205.
Jahnke, W. 2002. Spin labels as a tool to identify and characterize protein-ligand
interactions by NMR spectroscopy. ChemBioChem 3:167–173.
36
BIBLIOGRAPHY
Janecka, A., J. Fichna, and T. Janecki. 2004. Opioid receptors and their ligands.
Current Topics in Medicinal Chemistry 4:1–17.
Jezierska, J., G. Stevanin, H. Watanabe, M. R. Fokkens, F. Zagnoli, J. Kok, J.-Y.
Goas, P. Bertrand, C. Robin, A. Brice, G. Bakalkin, A. Durr, and D. S. Verbeek.
2013. Identification and characterization of novel PDYN mutations in dominant
cerebellar ataxia cases. Journal of Neurology .
Katragadda, M., J. L. Alderfer, and P. L. Yeagle. 2001. Assembly of a polytopic
membrane protein structure from the solution structures of overlapping peptide
fragments of bacteriorhodopsin. Biophysical Journal 81:1029–1036.
Kawasaki, A. M., R. J. Knapp, A. Walton, W. S. Wire, T. Zalewska, H. I. Yamamura,
F. Porreca, T. F. Burks, and V. J. Hruby. 1993. Syntheses, opioid binding affinities,
and potencies of dynorphin A analogues substituted in positions, 1, 6, 7, 8 and
10. International Journal of Peptide and Protein Research 42:411–419.
Kay, L. E., D. A. Torchia, and A. Bax. 1989. Backbone dynamics of proteins as
studied by 15N inverse detected heteronuclear NMR spectroscopy: application
to staphylococcal nuclease. Biochemistry 28:8972–8979.
Kelly, S. M., T. J. Jess, and N. C. Price. 2005. How to study proteins by circular
dichroism. Biochimica et Biophysica Acta 1751:119–139.
Kerman, A., and V. S. Ananthanarayanan. 2005. Expression and spectroscopic
characterization of a large fragment of the mu-opioid receptor. Biochimica et
Biophysica Acta 1747:133–140.
Kowalewski, J., and L. Mäler. 2006. Nuclear spin relaxation in liquids: Theory,
experiments, and applications. CRC Press, Boca Raton, Florida.
Krogh, A., B. Larsson, G. von Heijne, and E. L. Sonnhammer. 2001. Predicting
transmembrane protein topology with a hidden markov model: application to
complete genomes. Journal of Molecular Biology 305:567–580.
Lancaster, C. R., P. K. Mishra, D. W. Hughes, S. A. St-Pierre, A. A. Bothner-By,
and R. M. Epand. 1991. Mimicking the membrane-mediated conformation
of dynorphin A-(1-13)-peptide: circular dichroism and nuclear magnetic resonance studies in methanolic solution. Biochemistry 30:4715–4726.
Levitt, M. H. 2008. Spin dynamics. 2nd edition. John Wiley & Sons Ltd, Chichester,
England.
Lin, K., H. Yang, Z. Gao, F. Li, and S. Yu. 2013. Overestimated accuracy of circular dichroism in determining protein secondary structure. European Biophysics
Journal 42:455–461.
Lind, J., A. Gräslund, and L. Mäler. 2006. Membrane interactions of dynorphins.
Biochemistry 45:15931–15940.
BIBLIOGRAPHY
37
Losonczi, J., and J. Prestegard. 1998. Improved dilute bicelle solutions for highresolution NMR of biological macromolecules. Journal of Biomolecular NMR
12:447–451.
Louis-Jeune, C., M. A. Andrade-Navarro, and C. Perez-Iratxeta. 2012. Prediction of protein secondary structure from circular dichroism using theoretically
derived spectra. Proteins: Structure, Function, and Bioinformatics 80:374–381.
Lu, Z., W. D. Van Horn, J. Chen, S. Mathew, R. Zent, and C. R. Sanders. 2012.
Bicelles at low concentrations. Molecular Pharmaceutics 9:752–761.
Madani, F., M. M. Taqi, S. K. T. S. Wärmländer, D. S. Verbeek, G. Bakalkin, and
A. Gräslund. 2011. Perturbations of model membranes induced by pathogenic
dynorphin A mutants causing neurodegeneration in human brain. Biochemical
and Biophysical Research Communications 411:111–114.
Mäler, L., J. Blankenship, M. Rance, and W. J. Chazin. 2000. Site-site communication in the EF-hand Ca2+-binding protein calbindin D9k. Nature Structural
Biology 7:245–250.
Mandel, A. M., M. Akke, and A. G. Palmer. 1995. Backbone dynamics of Escherichia coli ribonuclease HI:: correlations with structure and function in an
active enzyme. Journal of Molecular Biology 246:144–163.
Manglik, A., A. C. Kruse, T. S. Kobilka, F. S. Thian, J. M. Mathiesen, R. K. Sunahara,
L. Pardo, W. I. Weis, B. K. Kobilka, and S. Granier. 2012. Crystal structure of the
mu-opioid receptor bound to a morphinan antagonist. Nature .
Marinova, Z., V. Vukojevic, S. Surcheva, T. Yakovleva, G. Cebers, N. Pasikova,
I. Usynin, L. Hugonin, W. Fang, M. Hallberg, D. Hirschberg, T. Bergman, U. Langel, K. F. Hauser, A. Pramanik, J. V. Aldrich, A. Gräslund, L. Terenius, and
G. Bakalkin. 2005. Translocation of dynorphin neuropeptides across the plasma
membrane. a putative mechanism of signal transmission. Journal of Biological
Chemistry 280:26360–26370.
Marsh, J. A., V. K. Singh, Z. Jia, and J. D. Forman-Kay. 2006. Sensitivity of
secondary structure propensities to sequence differences between alpha- and
gamma-synuclein: implications for fibrillation. Protein Science 15:2795–2804.
Massardier, D., and P. F. Hunt. 1989. A direct non-opiate interaction of dynorphin(1-13) with the N-methyl-D-aspartate (NMDA) receptor. European Journal of
Pharmacology 170:125–126.
Meng, F., M. T. Hoversten, R. C. Thompson, L. Taylor, S. J. Watson, and H. Akil.
1995. A chimeric study of the molecular basis of affinity and selectivity of the
kappa and the delta opioid receptors. potential role of extracellular domains.
Journal of Biological Chemistry 270:12730–12736.
Metzger, T. G., and D. M. Ferguson. 1995. On the role of extracellular loops of
opioid receptors in conferring ligand selectivity. FEBS Letters 375:1–4.
38
BIBLIOGRAPHY
Morris, G. A. 2007. Diffusion-Ordered Spectroscopy. Wiley Blackwell (John Wiley &
Sons).
Mouritsen, O. G. 2005. Life - as a matter of fat. Springer-Verlag, Berlin Heidelberg.
Naito, A., T. Nagao, M. Obata, Y. Shindo, M. Okamoto, S. Yokoyama, S. Tuzi,
and H. Saitô. 2002. Dynorphin induced magnetic ordering in lipid bilayers as
studied by 31P NMR spectroscopy. Biochimica et Biophysica Acta - Biomembranes
1558:34–44.
Ott, D., R. Frischknecht, and A. Plückthun. 2004. Construction and characterization of a kappa opioid receptor devoid of all free cysteines. Protein Engineering,
Design & Selection 17:37–48.
Owen, J. 2008. Virus-infecting virus fuels definition of life debate. National Geographic News http://news.nationalgeographic.com/news/2008/08/080822-giantvirus.html.
Palczewski, K., T. Kumasaka, T. Hori, C. A. Behnke, H. Motoshima, B. A. Fox, I. L.
Trong, D. C. Teller, T. Okada, R. E. Stenkamp, M. Yamamoto, and M. Miyano.
2000. Crystal structure of rhodopsin: A G protein-coupled receptor. Science
289:739–745.
Palmer, A. G., 2007. Relaxation and dynamic processes. Pdf E-book.
Palmer, A. G., 3rd. 2004. NMR characterization of the dynamics of biomacromolecules. Chemical Reviews 104:3623–3640.
Park, S. H., B. B. Das, F. Casagrande, Y. Tian, H. J. Nothnagel, M. Chu, H. Kiefer,
K. Maier, A. A. De Angelis, F. M. Marassi, and S. J. Opella. 2012. Structure of
the chemokine receptor CXCR1 in phospholipid bilayers. Nature 491:779––783.
Paterlini, G., P. S. Portoghese, and D. M. Ferguson. 1997. Molecular simulation of
dynorphin A-(1-10) binding to extracellular loop 2 of the kappa-opioid receptor.
a model for receptor activation. Journal of Medicinal Chemistry 40:3254–3262.
Paterlini, M. G. 2005. The function of the extracellular regions in opioid receptor binding: insights from computational biology. Current Topics in Medicinal
Chemistry 5:357–367.
Pert, C. B., and S. H. Snyder. 1973. Opiate receptor: demonstration in nervous
tissue. Science 179:1011–1014.
Prosser, R. S., F. Evanics, J. L. Kitevski, and M. S. Al-Abdul-Wahid. 2006. Current
applications of bicelles in NMR studies of membrane-associated amphiphiles
and proteins. Biochemistry 45:8453–8465.
Przewłocki, R., and B. Przewłocka. 2001. Opioids in chronic pain. European Journal
of Pharmacology 429:79–91.
BIBLIOGRAPHY
39
Robustelli, P., K. Kohlhoff, A. Cavalli, and M. Vendruscolo. 2010. Using NMR
chemical shifts as structural restraints in molecular dynamics simulations of
proteins. Structure 18:923–933.
Rosenbaum, D. M., S. G. F. Rasmussen, and B. K. Kobilka. 2009. The structure
and function of G-protein-coupled receptors. Nature 459:356–363.
Roth, B. L., K. Baner, R. Westkaemper, D. Siebert, K. C. Rice, S. Steinberg, P. Ernsberger, and R. B. Rothman. 2002. Salvinorin A: a potent naturally occurring nonnitrogenous kappa opioid selective agonist. Proceedings of the National Academy
of Sciences of the United States of America 99:11934–11939.
Sanders, C. R., and J. P. Schwonek. 1992. Characterization of magnetically orientable bilayers in mixtures of dihexanoylphosphatidylcholine and dimyristoylphosphatidylcholine by solid-state NMR. Biochemistry 31:8898–8905.
Sargent, D. F., and R. Schwyzer. 1986. Membrane lipid phase as catalyst for
peptide-receptor interactions. Proceedings of the National Academy of Sciences of
the United States of America 83:5774–5778.
Schlechtingen, G., R. N. DeHaven, J. D. Daubert, J. A. Cassel, N. N. Chung, P. W.
Schiller, J. P. Taulane, and M. Goodman. 2003. Structure-activity relationships of
dynorphin A analogues modified in the address sequence. Journal of Medicinal
Chemistry 46:2104–2109.
Schwyzer, R. 1986. Estimated conformation, orientation, and accumulation of
dynorphin A-(1-13)-tridecapeptide on the surface of neutral lipid membranes.
Biochemistry 25:4281–4286.
Shimamura, T., M. Shiroishi, S. Weyand, H. Tsujimoto, G. Winter, V. Katritch,
R. Abagyan, V. Cherezov, W. Liu, G. W. Han, T. Kobayashi, R. C. Stevens, and
S. Iwata. 2011. Structure of the human histamine H1 receptor complex with
doxepin. Nature 475:65–70.
Snyder, S. H., and G. W. Pasternak. 2003. Historical review: Opioid receptors.
Trends in Pharmacological Sciences 24:198–205.
Sternin, E., D. Nizza, and K. Gawrisch. 2001. Temperature dependence of
DMPC/DHPC mixing in a bicellar solution and its structural implications. Langmuir 17:2610–2616.
Tan-No, K., G. Cebers, T. Yakovleva, B. H. Goh, I. Gileva, K. Reznikov, M. AguilarSantelises, K. F. Hauser, L. Terenius, and G. Bakalkin. 2001. Cytotoxic effects
of dynorphins through nonopioid intracellular mechanisms. Experimental Cell
Research 269:54–63.
Tejeda, H. A., T. S. Shippenberg, and R. Henriksson. 2012. The dynorphin/kappaopioid receptor system and its role in psychiatric disorders. Cellular and Molecular Life Sciences 69:857–896.
40
BIBLIOGRAPHY
Terenius, L. 1973a. Characteristics of the "receptor" for narcotic analgesics in
synaptic plasma membrane fraction from rat brain. Acta Pharmacol Toxicol
(Copenh) 33:377–384.
Terenius, L. 1973b. Stereospecific interaction between narcotic analgesics and a
synaptic plasm a membrane fraction of rat cerebral cortex. Acta Pharmacol Toxicol
(Copenh) 32:317–320.
Terenius, L., W. H. Gispen, and D. De Wied. 1975. ACTH-like peptides and
opiate receptors in the rat brain: Structure-activity studies. European Journal of
Pharmacology 33:395–399.
Teschemacher, H., K. E. Opheim, B. M. Cox, and A. Goldstein. 1975. A peptidelike substance from pituitary that acts like morphine. i. isolation. Life Sciences
16:1771–1775.
Thompson, A. A., W. Liu, E. Chun, V. Katritch, H. Wu, E. Vardy, X.-P. Huang,
C. Trapella, R. Guerrini, G. Calo, B. L. Roth, V. Cherezov, and R. C. Stevens. 2012.
Structure of the nociceptin/orphanin FQ receptor in complex with a peptide
mimetic. Nature 485:395–399.
Turcotte, A., J.-M. Lalonde, S. St-Pierre, and S. Lemaire. 1984. Dynorphin-(1–13). i.
structure-function relationships of Ala-containing analogs. International Journal
of Peptide and Protein Research 23:361–367.
Uhl, G. R., S. Childers, and G. Pasternak. 1994. An opiate-receptor gene family
reunion. Trends in Neurosciences 17:89 – 93.
Unnerståle, S., J. Lind, E. Papadopoulos, and L. Mäler. 2009. Solution structure of
the HsapBK K+ channel voltage-sensor paddle sequence. Biochemistry 48:5813–
5821.
van Dam, L., G. Karlsson, and K. Edwards. 2004. Direct observation and characterization of DMPC/DHPC aggregates under conditions relevant for biological
solution {NMR}. Biochimica et Biophysica Acta (BBA) - Biomembranes 1664:241 –
256.
Vold, R., R. Prosser, and A. Deese. 1997. Isotropic solutions of phospholipid bicelles: A new membrane mimetic for high-resolution NMR studies of polypeptides. Journal of Biomolecular NMR 9:329–335.
Walker, J. M., H. C. Moises, D. H. Coy, G. Baldrighi, and H. Akil. 1982. Nonopiate
effects of dynorphin and des-Tyr-dynorphin. Science 218:1136–1138.
Wang, J. B., P. S. Johnson, J. M. Wu, W. F. Wang, and G. R. Uhl. 1994. Human
kappa opiate receptor second extracellular loop elevates dynorphin’s affinity for
human mu/kappa chimeras. The Journal Of Biological Chemistry 269:25966–25969.
Warne, T., M. J. Serrano-Vega, J. G. Baker, R. Moukhametzianov, P. C. Edwards,
R. Henderson, A. G. W. Leslie, C. G. Tate, and G. F. X. Schertler. 2008. Structure
of a [bgr]1-adrenergic G-protein-coupled receptor. Nature 454:486–491.
BIBLIOGRAPHY
41
Warschawski, D. E., A. A. Arnold, M. Beaugrand, A. Gravel, Étienne Chartrand,
and I. Marcotte. 2011. Choosing membrane mimetics for NMR structural studies of transmembrane proteins. Biochimica et Biophysica Acta - Biomembranes
1808:1957 – 1974.
Wen, H., Z. Mehal, B. Ong, W. Ho, and D. Wen. 1985. Intrathecal administration
of beta-endorphin and dynorphin-(1-13) for the treatment of intractable pain.
Life Sciences 37:1213 – 1220.
Wen, H. L., Z. D. Mehal, B. H. Ong, and W. K. Ho. 1987. Treatment of pain in
cancer patients by intrathecal administration of dynorphin. Peptides 8:191–193.
Wishart, D. S., and B. D. Sykes. 1994. Chemical shifts as a tool for structure
determination. Methods in Enzymology 239:363–392.
Wu, B., E. Y. T. Chien, C. D. Mol, G. Fenalti, W. Liu, V. Katritch, R. Abagyan,
A. Brooun, P. Wells, F. C. Bi, D. J. Hamel, P. Kuhn, T. M. Handel, V. Cherezov,
and R. C. Stevens. 2010. Structures of the CXCR4 chemokine GPCR with smallmolecule and cyclic peptide antagonists. Science 330:1066–1071.
Wu, H., D. Wacker, M. Mileni, V. Katritch, G. W. Han, E. Vardy, W. Liu, A. A.
Thompson, X.-P. Huang, F. I. Carroll, S. W. Mascarella, R. B. Westkaemper, P. D.
Mosier, B. L. Roth, V. Cherezov, and R. C. Stevens. 2012. Structure of the human
kappa-opioid receptor in complex with JDTic. Nature 485:327–332.
Xue, J. C., C. Chen, J. Zhu, S. Kunapuli, J. K. DeRiel, L. Yu, and L. Y. Liu-Chen.
1994. Differential binding domains of peptide and non-peptide ligands in the
cloned rat kappa opioid receptor. Journal of Biological Chemistry 269:30195–30199.
Ye, W., 2013. Biophysical studies of membrane associated biomolecular Interactions. Licentiate thesis, Department of biochemistry and biophysics, Stockholm
University.
Zhang, L., R. DeHaven, and M. Goodman. 2002. NMR and modeling studies of
a synthetic extracellular loop ii of the kappa opioid receptor in a DPC micelle.
Biochemistry 41:61–68.
42
BIBLIOGRAPHY
Paper I
Paper II