The opioid peptide dynorphin A – Biophysical studies of peptide–receptorand peptide–membrane interactions

Johannes Björnerås

The opioid peptide dynorphin ABiophysical studies of peptide–receptor and peptide–membrane inter-actions

Johannes Björnerås

c©Johannes Björnerås, Stockholm 2014

Cover layout by c©Malin Augustsson: Artist's impression of a biomembrane system.

ISBN 978-91-7649-011-2

Printed in Sweden by US-AB, Stockholm 2014

Distributor: Department of Biochemistry and Biophysics, Stockholm University

To my family.

List of Papers

The following papers, referred to in the text by their Roman numerals, areincluded in this thesis.

PAPER I: Direct detection of neuropeptide dynorphin A binding to thesecond extracellular loop of the κ–opioid receptor using a solu-ble protein scaffold.Johannes Björnerås, Martin Kurnik, Mikael Oliveberg, AstridGräslund, Lena Mäler and Jens Danielsson, FEBS Journal, 281,814–824 (2013).

PAPER II: Membrane interaction of disease-related dynorphin A variants.Johannes Björnerås, Astrid Gräslund and Lena Mäler, Bio-chemistry, 52, 4157–4167 (2013).

PAPER III: Analysing the morphology of DHPC/DMPC complexes by dif-fusion NMR.Johannes Björnerås, Mathias Nilsson and Lena Mäler,Submitted.

PAPER IV: Resolving complex mixtures: trilinear diffusion data.Johannes Björnerås, Adolfo Botana, Gareth Morris and Math-ias Nilsson, Journal of Biomolecular NMR, 58, 251–257 (2014).

PAPER V: The membrane interaction of dynorphin A depends on lipid head-group charge.Johannes Björnerås, Astrid Gräslund and Lena Mäler,Manuscript.

Reprints were made with permission from the publishers.

Contents

List of Papers vii

Abbreviations xi

1 The science of life 11.1 What this thesis is about . . . . . . . . . . . . . . . . . . . . 21.2 What you will find in this thesis, and what you will not . . . . 3

2 Lipid environments 52.1 Biological membranes . . . . . . . . . . . . . . . . . . . . . 72.2 Mimetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Peptides and proteins 13

4 Methods 174.1 Nuclear magnetic resonance spectroscopy . . . . . . . . . . . 17

4.1.1 General theory . . . . . . . . . . . . . . . . . . . . . 174.1.2 Chemical shifts . . . . . . . . . . . . . . . . . . . . . 194.1.3 Dynamics . . . . . . . . . . . . . . . . . . . . . . . . 204.1.4 Diffusion . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2 Multi-way decomposition of experimental data . . . . . . . . 284.2.1 Two-way decomposition . . . . . . . . . . . . . . . . 294.2.2 Three-way decomposition . . . . . . . . . . . . . . . 29

5 The interaction between dynorphins and opioid receptors 315.1 The opioid system . . . . . . . . . . . . . . . . . . . . . . . . 31

5.1.1 Opioid receptors . . . . . . . . . . . . . . . . . . . . 325.1.2 Dynorphin A—an opioid ligand . . . . . . . . . . . . 345.1.3 Dynorphin A and the κ-opioid receptor . . . . . . . . 35

6 The interaction between dynorphins and lipids 396.1 Peptides and membranes . . . . . . . . . . . . . . . . . . . . 40

6.1.1 The use of bicelles in peptide–lipid research . . . . . . 416.2 Membrane interactions of dynorphin A . . . . . . . . . . . . . 43

7 Conclusions and outlook 457.1 What are the most important findings of this thesis work, and

how can they be built upon? . . . . . . . . . . . . . . . . . . 457.2 Critical review of methodology, results and conclusions . . . . 47

Populärvetenskaplig sammanfattning (Summary in Swedish) 49

Acknowledgements 53

References 57

Abbreviations

AMP anti-microbial peptide

CD circular dichroism

CNS central nervous system

CPP cell-penetrating peptide

CSA chemical shielding anisotropy

DHPC 1,2-dihexanoyl-sn-glycero-3-phosphocholine

DMPC 1,2-dimyristoyl-sn-glycero-3-phosphocholine

DMPG 1,2-dimyristoyl-sn-glycero-3-phospho-(1’-rac-glycerol)

DOR δ-opioid receptor

DynA dynorphin A

EL2 extracellular loop II

FCS fluorescence correlation spectroscopy

GPCR G-protein coupled receptor

GUV giant unilamellar vesicle

IDP intrinsically disordered protein

IUP intrinsically unordered protein

KOR κ-opioid receptor

LUV large unilamellar vesicle

MOR μ-opioid receptor

NMDA N-methyl-D-aspartate

NMR nuclear magnetic resonance

PC phosphatidylcholine

PCA principal component analysis

PDB Protein Data Bank (database)

PDYN proenkephalin-B

PG phosphatidylglycerol

PI phosphatidylinositol

PS phosphatidylserine

RDC residual dipolar coupling

RF radio-frequency

siRNA small interfering RNA

SOD1 super-oxide dismutase 1

SUV small unilamellar vesicle

tcf time-correlation function

I believe a leaf of grass is no less than the journey-work of the stars,And the pismire is equally perfect, and a grain of sand, and the eggof the wren.

– Walt Whitman, Leaves of Grass

1. The science of life

It is only with the wisdom of a philosopher or the courage of a fool that onedares to define human nature. And possessing nothing of the former and verylittle of the latter, it is with some reluctance that I, for the sake of this chapter,put forward two suggestions of things that makes us human: curiosity and anurge to control. First, the desire to find out, to understand, to know seems hard-wired into us. We start asking questions as kids and never really stop. Becauseeven though the playful, wild, unquenchable lust for knowledge that permeatesour younger years may eventually fade, some questions remain unsatisfyinglyunanswered in almost everyone. Questions about the stars and what lies be-yond them, about time, about the beginning and end of the world, and aboutourselves—birth, life, death and the meaning of it all. Linked to this is our willto control our lives and the world we live in. We want to exert some power overour fragile existence, we like to predict, prepare and plan. And over the courseof our brief human history we have created different systems of practices andthought to channel these two desires, systems out of which science is one.

Life science is the science of life or, to phrase it differently, the scien-tific study of living systems. As touched upon above, there are many as-tounding aspects of life and living organisms, and many unanswered—perhapsunanswerable—questions. Life is, and has probably always been, a source ofamazement and wonder. In addition to such philosophical aspects, many areaswithin life science have a huge practical impact on our lives. Consider, for ex-ample, research on diseases such as malaria or tuberculosis, on neonatal inten-sive care, on small-scale water filtration, or on bone-anchored hearing devices.The work of life scientists can and will change individual lives completely.Because of these sometimes direct and large consequences for us humans (andfor other living, sensible things), life science is symbiotically linked to theethics and norms of society. For me it is this combination of the awe-inspiringfundamental problems and the strong interplay with the world surrounding thelaboratory that makes life science so interesting and important. It is this com-bination that confers a not so small responsibility on life scientists, and givesmeaning to the daily labour.

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1.1 What this thesis is about

This thesis belongs formally to the area of biophysics, a sub-field of life sci-ence. As the name suggests, biophysical research has traditionally been basedon physics methods, particularly different forms of spectroscopy, as well as ontheoretical work and mathematical modelling, but that definition is too narrowto capture all the biophysical science of today. My area of the biophysics fieldconcerns very detailed—molecular or even atomic—aspects of living systems,whereas other scientists in the field are interested in whole molecules and up-wards, past networks of molecules to cellular organelles, complete cells andeven groups of cells. A more important question, however, than to which cat-egory or life science sub-field this thesis belongs, is what it is about.

When I am asked ‘What do you work with?’ I usually reply that I do re-search on a molecule present in the brain, a signalling compound similar toendorphin, the substance responsible for effects such as the ‘runner’s high’.This is half true, a part of this thesis is about dynorphin A, a peptide (smallprotein) that modulates neural signalling; more specifically it is about 1) howdynorphin A interacts with a part of the κ-opioid receptor, a dynorphin bind-ing partner in a brain signalling chain, and 2) how dynorphin A interacts withlipids in bilayers. The other half of the truth, and the other part of this thesis,concerns systems that try to mimic biological membranes. It involves the char-acterisation of such mimetic systems, as well as work on methods that aid thecharacterisation.

On a more general level, the results in this thesis are connected to twophenomena that are important, maybe even necessary, in living systems: com-partments and signalling. Although the definition and classification of life isnot clear-cut,1–3 some things are generally accepted as vital elements of life.A living organism 1) has some sort of information storage capability (such asDNA), 2) is able to reproduce and evolve, and 3) is able to convert and useenergy. In addition to this all known living systems have some way of keep-ing things in confined spaces. The most obvious example is an overall barrierthat creates an inside of the organism separated from the surroundings, andbeneath this surface the organism may harbour a remarkably complex archi-tecture of biological membranes. Many physiological responses depend onprocesses taking place at these boundaries, for example whether a specific par-ticle (molecule, virus, etc) is able to pass through a cellular membrane. Dynor-phin A interacting with lipids is an example of such a process; and the study ofmembrane mimetics is in extension a study of the biomembranes themselves.

A second feature that is characteristic of living organisms and, in some re-spect, connected with compartmentalisation, is a means of sending, receivingand processing signals. Signalling systems may facilitate rather simple pro-

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cesses such as microbes sensing an energy source and moving towards it, orvery complex events such as the massively parallell neural communication in ahuman brain. At its most general, a biological signalling system involves a lig-and—the physical embodiment of the signal, and a receptor—something that‘reads’ the signal through interaction with the ligand. Dynorphin A interactingwith the κ-opioid receptor is a part of such a signalling system.

1.2 What you will find in this thesis, and what you willnot

This thesis contains a number of published and unpublished articles. This ma-terial as a whole constitutes physically the second half of this thesis, but interms of underlying work and intellectual effort it is the foundation. I sharethe responsibility and the credit for this work with my co-authors. In the firsthalf of the thesis I try to provide a context for the articles; to give an idea ofwhere the work presented in this thesis fits in the great fresco of life science.First, in Chapter 2, lipids and lipid systems are introduced, with a bias towardstheir role in biological systems. The physico-chemical properties of lipids arebriefly discussed, as well as how these properties underlie the thermodynami-cal behaviour of lipid ensembles. This chapter also includes a section on lipidsystems as tools in biochemical and -physical research.

Chapter 3 treats peptides, which is the class of biomolecules to whichdynorphin A (DynA) belongs. Again the chapter begins with a general intro-duction to peptides, after which follows a section on their biological function.Two aspects of this latter subject, namely peptide–receptor and peptide–lipidinteraction, are expanded into two chapters of their own, 5 and 6 respectively,but before this, in Chapter 4 is an introduction of the main methods used inthis thesis work. Finally this first half of the thesis is concluded with Chapter 7where the main findings of the thesis work are summarised, put in context andused as the starting point for a discussion of possible extensions of the projectsas well as alternative routes ahead.

There is not room for a detailed description of any method used, or a com-prehensive historical review of a particular research field; but for the interestedreader there should be references with scopes complementary to this thesis. Aseven the number of references is limited, however, a lot of brilliant work andbrilliant researchers have been left out altogether, for this I am sorry. With thissaid, my hope is that anyone with an interest in life science will find somethingin this thesis that is both understandable and interesting—no humble hope in-deed.

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2. Lipid environments

Lipids are small biological molecules that are in general soluble in nonpolarsolvents but have a much smaller, or vanishing, solubility in polar solventssuch as water. Lipids may be defined and classified inductively, e.g. throughtheir solubility in various solvents, or based on how they are extracted frombiological membranes. Deductive approaches have also been suggested, giv-ing more stringent and systematic definitions and classifications.4 Whicheverapproach is used, most lipids share a few characteristics that will be discussedhere. Most importantly, many lipids are amphipathic, i.e. they have both hy-drophobic and hydrophilic properties, with the hydrophobicity often comingfrom fatty acid hydrocarbon chains. In a polar solvent such as water con-tact between the hydrophobic parts and the solvent molecules is energeticallyunfavourable, thus leading to the lipids spontaneously assembling into a richvariety of supramolecular structures. The architecture of these structures, theirmorphology, depends on both environmental parameters such as temperature,concentration and pH, and on lipid molecular properties, of which a few arelisted below:

Hydrophobic moiety: This part of a lipid is typically made up of fatty acids,i.e. hydrocarbon chains attached to the headgroup via a carboxyl group.A particular lipid may have up to four such chains, not necessarily ofthe same type. The chains usually contain an even number of carbons,from two to thirty-six, where in biological membranes the lengths areoften between fourteen and twenty-two.5 The carbons in the chain canbe joined by single or double bonds. The length and degree of unsat-uration of the chain in a particular lipid type strongly influence whattype of assemblies an ensemble of such lipids will form under variousconditions. The physical chemistry of the chain also influences otherthermodynamic properties such as phase transition temperatures.

Biomembrane lipids can be divided into three main classes: 1) glyc-erophospholipids (phospholipids), 2) sphingophospholipids (sphingo-lipids) and 3) sterols and linear isoprenoids,6 where the first two bothhave acyl chains as described above. The members of the third class,e.g. cholesterol, are all derived from isoprene precursors, and will notbe further discussed here. The interested reader is referred to Luckey.6

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Backbone and head group: Phospholipids and sphingolipids differ in thechemistry of the backbone, i.e. the moiety that joins the fatty acid chainsand the head group. In phospholipids the backbone is a glycerol group,where two of the carbons are bound to the fatty acid chains, while thethird is attached to a phosphate. To this molecule is then attached a headgroup moiety, where the available head group types vary between organ-isms. In the work described in this thesis only phosphatidylcholine (PC)and phosphatidylglycerol (PG) lipids have been used, these are depictedin Figure 2.1. The ionic properties of the head groups are importantfor interactions with other lipids, as well as with proteins and peptides.Phospholipids may be anionic, carrying a negative net charge at neutralpH, or zwitterionic, with no net charge. PG is an example of the former,and PC of the latter. Sphingolipids have a sphingosine backbone insteadof glycerol, but may otherwise have the same head group moieties asphospholipids.

Overall molecular geometry: The supramolecular behaviour of lipids, andtheir effect on e.g. membrane proteins, is also influenced by the overallshape of the individual lipid molecules. The effective size of the polarregion relative the size of the hydrophobic region affects the intrinsiccurvature of the surface of a lipid layer,7 and the total composition ofdifferent lipid types gives a net intrinsic curvature.8

The discussion in the list above is biased towards bilayer-forming lipids, be-cause these are one of the primary building blocks in biological membranes.But there are other classes of amphipathic molecules, or amphiphiles. Oneexample is the detergents, a subgroup of lipids that in polar solvents form mi-celles, often spherical assemblies with a surface of polar headgroups protectingthe hydrophobic core. For a thorough treatment of the physical chemistry ofmicelles see Wennerström,9 while a more practical approach to detergents inbiochemistry is given by Garavito et. al.10 Examples of lipid types togetherwith examples of lipid self-assembly are shown in Figure 2.1.

It may be unfair to say that lipids have been neglected in life science re-search, but it did take some time before the scientific interest in lipids reachedthe same level as that enjoyed by DNA/RNA and proteins. A search in thedatabase Web of Science for the topic ‘lipids’ from 1945 to 1990 gives about50000 hits, while the same search for ‘DNA or RNA’ and ‘proteins’ givesroughly 150000 and 250000, respectively. One reason for this is that thestructure–function relationship, in contrast to nucleic acids and proteins isnot so easily found for single lipid molecules, but rather lies on the assem-bly/ensemble level, making it necessary to characterise an often complex phasebehaviour. The interest in lipids has, however, grown and at the start of the

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twenty-first century the term lipidome11 was added alongside the already es-tablished genome and proteome. For an excellent review of lipids, with a focuson physical chemistry, the reader is referred to Mouritsen’s book.5

DMPC

N+

O

P O-

O C

O

OH

O

OOC

Sodium stearate

O CO-Na+a b c

d

f

e

Hydrophilic region

Hydrophobic region

O

P

OO

PG

Figure 2.1: Schematic overview of lipids and lipid assemblies. a) Sodiumstearate, the sodium salt of stearic acid. A common detergent in hand soap. b)DMPC molecule with hydrophilic region outlined, together with PG head group.c) DMPC, schematic depiction of head group and tail. d) Spherical detergentmicelle. e) Lipid bilayer. f) Unilamellar vesicle.

2.1 Biological membranes

There is now strong support for the idea that lipids or lipid structures werepresent during the dawn of life,12,13 although the details on how and whenbiological membranes entered the stage are still debated.14 The fact is, that al-ready in 1938 Oparin, one of the leading scientists in the formulation of earlyevolution in terms of physical chemistry, wrote in his seminal book Origin ofLife about the ‘outstanding role’ of fat-like substances in biomolecular com-plexes.15

Moreover, in terms of function, one needs to look no further than to asingle cell to see all the roles lipids play in a living organism. The most im-mediate function is the formation of lipid bilayers. In addition to an overall

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envelope that creates an inside and an outside, the cellular interior is com-partmentalised to various degrees of complexity, adding spatial constraint as aparameter for tuning the parameters and processes—chemical concentrations,gradients, enzymatic reaction rates, transport, storage, etc—that constitute thenon-equilibrium state of life. Much work has gone into constructing theoreticalframeworks and models of such processes; for example it was proven by Pólyain the 1920’s that for a random walk in one or two dimensions the probabilityof visiting every point is unity, while it is less than one in three dimensions.16

Adam and Delbrück used a more elaborate model to show that reducing the di-mensionality from three to two may either decrease or increase the time it takesfor a diffusing particle to encounter a fixed target (trap), depending on the ra-tio of diffusion coefficients and size of target compared to diffusion space.17

Additional work by the groups of McCloskey18 and Naqvi,19 together withmore precise values for diffusion rates in biological systems, has seriouslychallenged the idea of dimensionality reduction as a straightforward way to in-crease reaction rates. Nevertheless, a straightforward geometrical calculation,gives a rougly 500-fold increase of a concentration of nanometer-sized objectswhen going from volume to surface in a sphere with a radius of 1 µm.

In the early 1970’s Jonathan Singer and Garth Nicolson published the fluid-mosaic model of a biological membrane,20 a model that over time becamecanonical, and will be taken here as the starting point for the developmentand refinement of biomembrane models. The interested reader is referred to apersonal account by Singer of the early history of the model and the experi-mental and theoretical results on which it was built.21 The fluid-mosaic modelincorporates a number of important features:

• As the name suggests, the biological membrane is a mosaic of phospho-lipid bilayer and lipid-associated proteins.

• The bilayer is very dynamic, indeed it is ‘. . . best thought of as a two-dimensional oriented viscous solution’.20

• Based on the experimental evidence (at the time) it was impossible tosay whether the bilayer was continuous or interrupted.

• Singer concluded that there is no long-range order, but probably short-range order, in the membrane.

• The proteins interacting with the membrane may be integral, mean-ing that they are immersed in the bilayer, or peripheral, meaning thatthey are not. (In the original model this distinction was made based onwhether membrane disruption was needed to extract the proteins or not.)

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Although this four decades old model has stood the test of time remarkablywell, and the above list of features still encompasses many much more recentexperimental results, the current biomembrane model has a few correctionsand additional features. The two type-classification of membrane proteins canbe more nuanced by separating membrane-inserting proteins, where a largeportion but not the entire molecule is inserted into the bilayer, from proteinsthat are embedded in the membrane. For a detailed discussion of differentdegrees of membrane interaction, and possible protein classes, see Luckey.6 Amore fundamental adjustment of Singer’s original model, where proteins werescattered sparsely in a uniform layer of phospholipids, is that in current modelsthe membrane is much more protein-dense,22 and also that differing lipid typesappear to be more segregated than previously believed, creating a more orderedand diverse environment.23 Membranes are, with Engelman’s words, ‘moremosaic than fluid’.24 A schematic picture of a biological membrane is shownin Figure 2.2.

a

cb

Figure 2.2: Schematic overview of a biological membrane. a) Membrane-embedded proteins. b) Peptides interacting with the membrane surface and inter-facial region. c) Lipid-interacting protein. Adapted from Engelman.24

An important concept that has emerged is the lipid raft,25,26 meaning dy-namic patches on the 10 to 100 nm length scale and enriched in, or exclusivelycontaining, a limited number of lipid types. This concept of a segregated, and

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(partly) controllable lipid micro-environment was introduced to explain ob-servations of large heterogeneities in native membranes from e.g. epithelialcells.27 Add this to the idea of a richer palette of lipid–protein interactionsthan was originally understood, and the result is the model of today, where asymbiotic interplay between lipids and proteins control not only protein as-sociation,28 structure,29–31 stability32 and activity,33 but also such things asmembrane curvature and associated biological functions,34–36 transport pro-cesses37 and signal transduction.38

2.2 Mimetics

The complexity of almost any native biological membrane prohibits most atom-level studies, and even molecular information on properties of, or processesoccuring in or at the membrane are often out of reach. The spatio-temporalresolution of studies on native or near-native biological membranes is rapidlyincreasing, however, thanks to development of both experimental and compu-tational methods. A few examples from recent years of experimental methodsapplicable to large biomembrane systems are atomic force microscopy,39,40

and femtosecond X-ray laser diffraction.41 Other techniques, such as magneticresonance force microscopy42 have shown promise, but are still under devel-opment.

Another route is to create a system that shares the properties of interestwith the ‘real’ membrane environment that one wishes to study, but a systemsufficiently cut down in size and complexity for it to be manageable by themethods that are of interest and available. Such an imitation of a real mem-brane is often called a membrane mimetic. Just as for native biological mem-branes, the mimetics exploit the self-assembly properties of lipid molecules,and by tuning the aforementioned sample properties (lipid type, concentra-tions, etc) quite a few reasonably well defined models are available. In thework presented in this thesis the focus is on bicelles, a mimetic that is intro-duced below alongside a few other systems, and discussed in more detail inChapter 6.

Micelles

An aqueous suspension of only detergent molecules will above a certain con-centration form micelles, aggregates with a hydrophobic core and a polar re-gion of detergent headgroups facing the water. Micelles have been used ex-tensively to solubilise membrane-associated peptides and proteins to facili-tate biophysical and biochemical characterisation; three examples out of manyare the study of an Arg-rich voltage-sensor domain from a K+ channel,43 and

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the structure determination of the membrane protein VDAC-144 and OmpW45

by nuclear magnetic resonance spectroscopy (NMR). For surfactant/detergentsystems in the specific context of NMR spectroscopy, the reader is referred totwo excellent reviews by Stilbs46 and Furó.47

Bicelles

In the ideal model, bicelles are disk-like objects, or larger sheets of lipid bilay-ers, where the edges (holes or rims) are stabilised by detergent molecules.48–51

A schematic picture of a small bicelle is shown in Figure 6.1 in Chapter 6.Similar to many native membrane surfaces, the bilayer part has a much smallercurvature than in micellar systems, something that has been established by e.g.studies of the curvature of helical peptides in micelles and bicelles.52 Further-more, bicelles, in contrast to micelles, provide an opportunity of using manynative types of lipids. Studies have also shown that some proteins are morestable and more active in bicelles than in micelles.53

By varying the relative amounts of lipids and detergent, as well as pa-rameters such as lipid type, ionic strength, temperature, pH and overall lipidconcentration, the morphology of the bicelles changes, and there is no abso-lute consensus as to where the boundaries of different bicelle morphologies arein this parameter space. This question will be addressed more thoroughly inChapter 6.

Vesicles

Vesicles (also called liposomes) are spherical objects made up of lipid bilay-ers. I will restrict the discussion here to unilamellar vesicles, which are shellsof lipid bilayers, like solvent filled balloons as shown schematically in Figure2.1. The accessible vesicle radii range from around 10 to 200 nm, and the sizedistribution of vesicles in a particular batch can be made reasonably narrow.54

A coarse division of unilamellar vesicle sizes into small (SUV), large (LUV)and giant (GUV) is sometimes made. The controllable size, and correspond-ingly controllable membrane curvature, together with the fact that many typesof lipids may be used to form vesicles means that this membrane mimetic maybe quite similar to a native membrane, in many aspects more so than any othermimetic. Vesicles are used in many biophysical fields, such as fluorescencespectroscopy, but their large molecular mass makes reorientation so slow thatmost solution-state NMR experiments are difficult or impossible to perform,as will be explained later.

In cells vesicles are utilised, among other things, as vehicles to transportvarious signalling molecules. The unravelling of this machinery started in the1970’s,55–57 and awarded the 2013 Nobel prize in physiology and medicine

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to Rothman, Schekman and Südhof. Hence, it is not surprising that the fieldof research that concerns the use of vesicles for therapeutic interventions suchas drug delivery is a very active one,58–61 with the market for injectable drugdelivery technologies estimated to be worth more than 40 billion dollars by2017.62

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3. Peptides and proteins

In the summer of 1878 it was observed by George Francis that cattle died af-ter drinking the water from Lake Alexandrina in Australia. He concluded thatexcessive bloom of the alga Nodularia spumigena ‘render[ed] the water un-wholesome’ and published his finding in Nature.63 Neither Francis nor anyoneelse at the time knew that the poisonous agents were peptides—it would takeover a hundred years before such a toxic peptide was completely characterisedafter isolation from the alga.64,65

Peptides, just like proteins, are a sub-group of polypeptides, i.e. biologicalpolymers whose structural units are amino acids. One common and concep-tually simple feature that may be used to distinguish peptides from proteinsis size: peptides are shorter than proteins, usually not much longer than 50amino acid units, residues, and often shorter than 30 residues. In terms ofphysico-chemical properties peptides and proteins are, to a large extent, over-lapping, since the basic molecular architecture is identical. An example ofpeptide chemical strucure is given in Figure 3.1.

Peptide properties are derived from the physical chemistry of the backboneand the amino acid side-chains. The torsion angles in the polypeptide chain,the pattern of hydrogen bonding between atoms in the backbone, the amountand distribution of hydrophobic and polar side-chains and the entropical costof having more regularity in the polypeptide connect the conformation of themolecule to the free energy. If a single molecule is studied, the free energyof a particular conformational state determines the relative probability of themolecule to occupy that state. Any realistic biological system involves a largenumber of molecules, and at any given time there will be an ensemble of con-formations, the distribution of which is determined by the energetic states ofa single molecule, and by intermolecular interactions. On a local molecularscale the conformational state is called secondary structure, as exemplified bythe helical segment of the peptide in Figure 3.1 d. Although tertiary structure isless applicable to peptides than to proteins, since the former are often too shortto contain several structural elements with fixed intramolecular positions, pep-tides have been of great use as tools in fragment-based studies of the foldingand tertiary structures of larger proteins.66–69

The notion that the structure of a protein or peptide determines (much of)the biological function has been something of a paradigm, and has guided

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much research in the field. And without doubt the structure–function rela-tionship has proven extremely successful both as a guiding hypothesis in theprediction of the function of polypeptides with known structure70,71 or viceversa.72 There has also been great interest in coupling structure and functionto sequence.73 In combination with the explosive increase in sequence infor-mation generated by the developments in genomics of the recent decades,74–76

there has been an incentive to determine protein 3D structures and to developmethods that accomplish such structure–function studies.

a

c

d

O

C N

H

C C

O

CN

H

H

CH2

CH

CH3 CH3

CH2

OH

H

Phe, F Leu, L

YGGFLRRIRPKLKWDNQ

b

Figure 3.1: Different ways of representing peptides, here dynorphin A. a) Pri-mary structure (one-letter nomenclature). b) Chemical structure (two residuesshown). c) Stick model, chemical and spatial structure. d) Ribbon model, show-ing secondary structure.

The focus on structure has several causes. One is molecular stability, e.g.in the trivial sense that structured proteins are, in general, less prone to aggre-gate, less exposed to proteases, etc. Hence, these proteins have a longer lifetime, which facilitates experimental studies. Furthermore, observations arenecessarily averaged over the time-scale of the experimental method, as wellas over the molecular ensemble. Hence, an ensemble of molecules with a nar-row distribution of conformational states is more easily characterised than oneoccupying a flat energy landscape. Also, methods based on diffraction, suchas X-ray spectroscopy, require biomolecular crystals, something that strongly

14

favours order and stability.In recent years, however, the structure–function paradigm has become more

nuanced, with the emerging understanding that polypeptides that are intrinsi-cally disordered (IDPs, sometimes IUP: intrinsically unordered protein) areboth common and functionally important in organisms.77–81 Moreover, thereare now more powerful methods available, that are capable of probing the dy-namics of a biomolecular system, in addition to structural characterisation.Motional processes in a biomolecule are very complicated, they are often cou-pled, and the related observables span several orders of magnitudes. Conse-quently, studying and characterising dynamics require equally versatile meth-ods, or the combination of several different methods. NMR spectroscopy pro-vides a powerful and flexible approach in these studies, as will be discussedin Chapter 4. Another example are computational methods, where accessibletime-scales and system sizes have rapidly expanded in the last decades.

Despite their relative simplicity compared to proteins, peptides often havea rich and functionally important dynamical behaviour. For example, a bio-logical membrane may create an environment in which certain peptides existin an order-disorder dynamical equilibrium.82 An interesting observation hereis that although the peptides are often more ordered in the membrane, it is notevident whether folding precedes or follows insertion.83 We will return to phe-nomena like these later, in the context of peptide–lipid interactions. Anotherexample where the balance between order and disorder plays an important roleis in aggregation processes, such as of the Alzheimer’s Aβ-peptide.84,85

In biological systems peptides are found in a variety of contexts, just likeproteins, and an extensive overview is beyond the scope of this thesis. Pep-tides may e.g. be present as units in supramolecular structures,86–88 such asspider silk89 or the amyloid fibrils90 that are the hallmarks of a number ofdevastating human diseases including the already mentioned Alzheimer’s dis-ease.91–93 Another example is the role of peptides as toxins in e.g. the venomsof wasps and bees94 where the peptide melittin induces pores in membranebilayers.95 In many organisms peptides are also important as a defense againstmicrobes,96,97 something that has received much interest as a possible pathtowards developing novel antibiotics.98

In biological systems peptides may also function as messengers. The phe-nomenon of signal reception and transmission is at the heart of any living en-tity, and the more complex the organism, the more intricate the structure of theinformation transfer network. For a unicellular organism it may be sufficientto have a nutrient sensor coupled to a motility device, but for higher organ-isms such as humans, collections of roughly 40 trillion cells,99 the situation isvery different. Not only do individual cells throughout the organism need torespond to both intra- and extracellular chemical stimuli for them to function

15

in their local environment, information also has to flow on an organism-widelevel. To meet this requirement evolution has created transport networks suchas the cardiovascular and lymphatic systems, and the nervous system.

As implied above the carriers of information and the corresponding typesof receivers and conductors may be of rather different character. The flow ofinformation may be mediated by propagating electrical potentials, ions, chem-ical compounds, etc. This thesis will only discuss molecular ligand–receptorsystems, and more specifically neuropeptide ligands and membrane protein re-ceptors, in particular the DynA peptide and the κ-type receptor in the opioidsystem. This topic will be covered in chapter 5, while in chapter 6 the inter-action between peptides and lipid environments will be treated, again with astrong focus on DynA.

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4. Methods

Life science is a melting pot, where century-old traditions and knowledge fromchemistry, physics, biology, medicine and mathematics meet and interact. Thismeans that there is a plethora of experimental and theoretical methods, eachone rooted to various extent in one or several of the sub-fields of life science. Inthis chapter only two methods and some applications of these methods will bediscussed, the selection being based solely on the importance of the methodsfor this thesis work.

4.1 Nuclear magnetic resonance spectroscopy

The primary method I have used is nuclear magnetic resonance (NMR) spec-troscopy, more specifically as applied to biological molecules (mainly lipidsand polypeptides) in the liquid state, and this section is an overview of this.First an extremely condensed treatment of the general theory will be provided(primarily based on the standard textbooks by Levitt100 and Keeler101) afterwhich applications that have been exploited in the research treated in this the-sis will be discussed.

4.1.1 General theory

All spectroscopic techniques probe a specific set of energy levels in the stud-ied system by perturbing the system and detecting the response. The energeticregime investigated, and the equipment to perturb and detect the system, mayvary substantially. In NMR, the energy levels of interest are defined by in-trinsic angular momenta (or spin) of atomic nuclei, and the associated mag-netic properties. Perturbations are created, and the nuclear response detected,through resonance between the nuclei in the sample, exposed to a strong mag-netic field, and the instrument electronics. A nuclear spin, III, is associated witha magnetic moment µµµI which is parallel or antiparallel to the spin. The gyro-magnetic (or magnetogyric) ratio γ is the constant of proportionality, accordingto:

µµµI = γIII (4.1)

17

The magnetic energy of a nucleus is a function of the angle between the mag-netic moment and the applied field BBB0, giving a set of energy levels. The energydifference between levels correspond to the resonance frequence, the Larmorfrequency, ω0, of the nucleus:

ω0 = −γB0 (4.2)

In quantum mechanics energy is represented by the Hamiltonian operator H,while the description of the state of the system is contained in the wavefunctionψ. In the case of the interaction between a single nuclear spin and a staticmagnetic field of strength B0 applied along the z-axis, the Hamiltonian is givenby:

H = −γB0 Iz (4.3)

where Iz is the operator representation of z-angular momentum. The evolutionof the system over time is given by Schrödinger’s equation:

ddtψ(t) = −i~−1Hψ(t) (4.4)

The spin system generally contains multiple interacting spins, who may beaffected by a number of factors in addition to the strong static field. Thosefactors may be external to the sample, i.e. caused by something in the environ-ment, usually the spectrometer. One such perturbation that is a corner-stoneof modern NMR, is the application of pulses of electromagnetic radiation atthe Larmor frequency; such pulses are used to manipulate the overall state ofthe spin system. Other interactions are internal, such as the dipole-dipole cou-pling—the direct interaction of a particular nuclear magnetic moment and an-other magnetic moment (nuclear or electronic), or the J-coupling—the indirectinteraction between two nuclei connected through chemical bond(s). In addi-tion to the magnetic interactions certain nuclei are affected by electric fields,where the most important phenomenon is the interaction between the electricquadrupolar moment of the nucleus and the electric field gradient at the siteof the nucleus. All such interactions may be represented by Hamiltonian op-erators, and the dynamics of the spin system can be calculated by solving theSchrödinger’s equation, given in Equation 4.4. The formalism of such spinsystems and their time evolution under different types of interactions will notbe described here. Instead a few NMR observables, and the information theyprovide, will be discussed.

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4.1.2 Chemical shifts

NMR spectroscopy would be of limited use if every nucleus of a certain typehad the exact same resonance frequency. Luckily, this is not the case. Eachnucleus has a certain molecular surrounding, with a specific electron distribu-tion, giving a local magnetic field that slightly alters the resonance frequencyfrom the (theoretical) value of a nucleus only experiencing the applied staticfield. The frequency difference is called the chemical shift, and is generallya tensor. In solution, however, the rapid molecular reorientation averages theangular dependence to an isotropic chemical shift.

Structural information from chemical shifts The chemical shift of the res-onance frequency of a particular nuclear spin contains information on the lo-cal magnetic environment, which is primarily created by the electron distri-bution. Although in princible possible, the derivation of atomic coordinatesdirectly from the chemical shift values is not feasible, due to limitations in thetheoretical framework and computing capacity. Instead, a number of strate-gies have been developed to estimate protein structures from chemical shiftinformation, each one using some combination of a priori knowledge of themolecular system, empirical knowledge, and molecular modelling.102–104 Forexample, atoms belonging to residues in ordered structural elements have dif-ferent chemical shift values than corresponding atoms in disordered segments.Hence, a common strategy to derive structural information, or at least struc-tural propensities, is to compare observed chemical shift values with databaseaverages of values from different types of structural elements.105

The effects of ligand binding on the chemical shifts If a ligand binds to areceptor, some residues, both on the ligand and the receptor, necessarily havetheir chemical environment modified. Hence nuclei of either the receptor orligand, or both, may be studied. Depending on the details of binding (ener-getics, kinetics, etc), these perturbations will have different effects; a case ofligand-induced structure would give large shift differences for the nuclei in thereceptor backbone, while for weak binding even nuclei in the vicinity of thebinding site may have changes close to the ‘chemical shift noise’ level, as seenin Paper I.

Estimating solvent partitioning through chemical shifts In a situationwhere a molecule of interest may be in either of two chemical environmentsthe chemical shift values may give information about the equilibrium distribu-tion of molecules between the two environments. An example from the thesis

19

at hand is a system where a peptide is dissolved in a suspension of lipid com-plexes in buffer, as in Papers II and V. The time scale of the exchange betweenthe environments strongly influences the properties of the observed spectrum.If the exchange kinetics is much faster than the difference between the reso-nance frequencies of a particular spin in the two environments, the result is asingle narrow peak at a the population weighted average of the two frequen-cies. If the exchange rate goes down, the peak gradually and is eventually splitinto two. This is the intermediate exchange regime. If the exchange is muchslower, there will be two narrow peaks, one at each of the two frequencies.

4.1.3 Dynamics

Relaxation, i.e. the return from a non-equilibrium to an equilibrium state, is arich and complex process in general, and this holds true also for the specificcase of nuclei in NMR spectroscopy. For a complete treatment of relaxation inNMR, see e.g. Kowalewski et al.106 or Palmer.107

Physically, relaxation in NMR is a consequence of small, local, time-dependent magnetic field fluctuations that, if they contain components at fre-quencies corresponding to energy differences between states in the spin systemstudied, induce transitions between these states. Since the transition probabil-ities are (slightly) larger in the direction towards low energy, after some timecharacterised by a relaxation time constant the perturbations cause the systemto reach a steady-state population of the spin states affected by the perturbation.Following the discussion of e.g. Kowalewski and Mäler,106 such a perturba-tion may be represented by a stochastic functions of time Y(t), with an averagevalue at time t:

〈Y(t)〉 =

∫ ∞

−∞

yp(y, t) dy (4.5)

Here p(y, t) is the probability density, and if this density is independent oftime, the stochastic process is stationary. An auto-correlation function may bedefined as the average value of the product of Y at two different times t1 andt2, and for a stationary process, this function will look as follows:

〈Y(t1)Y(t2)〉 ≡ G(t2 − t1) = G(τ) (4.6)

In the above equation τ is the separation between two time points, and G(τ) isthe so called time-correlation function (tcf) of the stochastic process describedby Y(t). It describes the probability of two observations of Y giving the samevalue, as a function of the time τ between the observations. Based on a fewassumptions on the properties of the tcf, a reasonable form of G is an exponen-tially decaying function:

20

G(τ) = G(0)e−τ/τc (4.7)

where τc is the correlation time of the perturbation. A Fourier transformationof G(τ) gives a function that says how much effect the perturbation underlyingG(τ) has at any particular frequency. This distribution is the spectral densityfunction, evaluated here for the function G as defined above:

J(ω) = 2∫ ∞

0G(τ)e−iωτ dτ = G(0)

2τc

(1 + ω2τ2c)

(4.8)

The perturbations, and associated spectral density functions, of interest hereare time-dependent variations in the local magnetic field at specific molecularsites. Such perturbations are generated by various types of molecular motion,and hence the time constants and spectral densities contain information onthese processes. Furthermore, relaxation involves a redistribution among theenergy levels, and such redistribution is also caused by exchange processes.Hence exchange can be included in the theoretical framework of relaxation,and studied by similar experimental methods. An overview of molecular timescales and the associated NMR observables is shown in Figure 4.1.

In general, several relaxation mechanisms, some of them correlated, com-bine to give an effective relaxation from one particular state to another. Thismeans that a particular relaxation rate is a sum of the spectral density functionsof the relevant mechanisms, evaluated at the frequencies corresponding to thetransitions involved in the relaxation. Here the discussion will primarily con-cern the relaxation of a 15N spin in the backbone of a protein in solution, eventhough some of the results are valid also for other cases. In a protein back-bone the 15N nucleus has an attached 1H and relaxation is primarily caused bydipole–dipole interactions, and by chemical shielding anisotropy (CSA).

Dipole–dipole interaction As the name suggests this is the direct interactionbetween two magnetic dipoles. In the case of NMR this means two nuclei, orthe interaction between one atomic nucleus and one electron. The Hamiltonianrepresenting the dipole–dipole interaction between two spins III and SSS may befound by translating the classical dipole–dipole interaction energy into the for-malism of quantum mechanics:

HDD = −µ0γIγS ~

4πr3

(3III ·

rrrrrrr2 · SSS − IIISSS

)≡ bIS

(3III ·

rrrrrrr2 · SSS − IIISSS

)(4.9)

In an IS spin system such as the 15N–1H pair of nuclei previously introduced,there are four energy levels. Transitions between the levels are induced by

21

time [s]10-15 10-12 10-9 10-6 10-3 100 103

H transfer,H bonding Ligand binding

VibrationSide-chain rot.

Rot. diffusion

Catalysis

Folding/unfolding

R1, R2, NOE

Residual dipolar couplingChemical shifts

Relaxation disp. (R1ρ, CPMG)

Line shape analysis

frequency [hz]1015 1012 109 106 103 100 10-3

1H Larmour frequency on modern spectrometer

Transl. diff.

HD exchange

Figure 4.1: Time scales for NMR observables and physical processes inbiomolecules, together with the corresponding frequency range. Adapted fromPalmer.108

22

the dipole–dipole interaction described above with certain probabilities. Forexample W2, the probability of a transition between the αIαS and βIβS energystates, is proportional to the spectral density at the sum of the two Larmorfrequencies:

W2 ∝ b2IS J(ωI + ωS ) (4.10)

CSA interaction Earlier in this chapter, chemical shifts were introduced.The physical cause of chemical shifts are currents induced in the electronclouds surrounding the nuclear sites by the applied static field. These currentsresult in a small local magnetic field, a phenomenon that is called chemicalshielding. Since the electron distribution is not uniform in the molecule, theinduced currents have certain preferred orientations. Hence, also the inducedmagnetic field has a particular orientation in the molecular frame of reference,connected to the static field through a chemical shielding tensor σ: BBB0 − σBBB0.In the case of rapid molecular reorientation, such as in a liquid, the chemicalshift interaction is averaged to an isotropic value, as was stated earlier. Thismeans that the orientational dependence of the chemical shielding is averagedout on the experimental time scale, and the effect on the resonance frequencyis treated as if the shielding field was parallell to the external field. This doesnot mean, however, that the time-correlation function of the chemical shield-ing vanishes. Therefore, chemical shift anisotropy may contribute to relaxationand is indeed an important relaxation mechanism for nuclei in certain chemicalenvironments.

Protein and peptide backbone dynamics There are several ways of usingNMR to study molecular dynamics. In recent years it has, for example, beengreat interest in NMR methods that probe motions on the microsecond to mil-lisecond time scale,109,110 making it possible to study e.g. transition statesin protein folding111,112 or aggregation.113 Here another methodology will bediscussed, namely the study of peptide/protein backbone dynamics throughmeasurements of the R1 and R2 relaxation rates, and the NOE factors, for 15Nnuclei; see for example Kay et al.114 Mandel et al.115 and Mäler et al.116 Asdescribed previously, the rate constants are sums of spectral density functionsdescribing relaxation caused by dipole-dipole and CSA interactions, accordingto the following expressions:

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R1 =b2

NH

4[J(ωH − ωN) + 3J(ωN) + 6J(ωH + ωN)] + c2

N J(ωN) (4.11)

R2 =b2

NH

8[4J(0) + J(ωH − ωN) + 3J(ωN) + 6J(ωH) + 6J(ωH + ωN)]

+c2

N

6[4J(0) + 3J(ωN)] (4.12)

NOE = 1 +γHb2

NH

4γNR1[6J(ωH + ωN) − J(ωH − ωN)] (4.13)

where bNH = µ0~γHγN/4πr3NH is the dipole–dipole interaction strength, µ0 is

the permeability of free space, ~ is Planck’s constant, rNH is the length of theN–H bond vector, and γH and γN are the gyromagnetic ratios for 1H and 15N,respectively. cN = ∆σωN/

√3 is the CSA interaction strength, where ∆σ is

the CSA of the N spin. As can be seen in the equations, the R2 relaxation ratecontains a term proportional to J(0), meaning that this rate is affected by slowmotions of the molecule, such as the overall tumbling. The effect of this is thatthe transverse relaxation rate in general increases with molecular size, up toa point where the signals cannot be detected. This is one of the reasons whysolution-state NMR studies of membrane proteins are very challenging.

The observed relaxation rates may be analysed using Lipari-Szabo model-free formalism117 (equivalent to the two-step formalism by Halle and Wen-nerström118). Here no particular motional model is assumed, but rather a sep-aration of motional time scales. Using this assumption functional forms ofthe spectral density function can be constructed, depending on the details ofmolecular motion. For example, assuming isotropic rotational diffusion, oneform of the spectral density function is the following:117

J(ω) =S 2τm

1 + ω2τ2m

+(1 − S 2)τ1 + ω2τ2 (4.14)

where S is a generalised order parameter, τm is the overall molecular rotationalcorrelation time and τ = (1/τm + 1/τe)−1, where τe is a correlation time de-scribing intermolecular motion. Spectral density functions such as the aboveare fitted to the measured relaxation rate values and, if the assumptions arereasonable, the fitted parameters have physical relevance. For example the or-der parameter may correspond to the amplitude of N–H bond vector motions,and τe to the correlation time of the local motion of this vector. Together theseparameters give information on e.g. the flexibility of the protein backbone.Furthermore, any event that has an effect on protein backbone dynamics, suchas a conformational change or interaction with another molecule, will cause

24

the fitted order parameter(s) and correlation time(s) to change. This type ofanalysis was used in Papers I, II and V.

Paramagnetic relaxation enhancement (PRE) The magnetic moment ofan unpaired electron may enhance the relaxation of neighbouring nuclei throughthe dipole–dipole mechanism.119 As previously described, the effect of thisinteraction on the relaxation rates has a distance dependence of 1/r6. Thedipole–dipole interaction energy is proportional to the product of the gyro-magnetic ratios of the two particles, and since the electron gyromagnetic ratiois approximately 650 times larger than the nuclear equivalent, the relaxationenhancement may be dramatic.120 Examples of applications of PRE includestudies of bilayer penetration of peptides or proteins by paramagnetic probeson the lipids,121 or dissolved in the buffer.122 Such experiments were usedto estimate the positioning of DynA variants in bicelles in Paper II. Anotherway to exploit this phenomenon is to attach a chemical moiety containing anunpaired electron on a ligand. This results in a sensitive probe for ligand–receptor interaction. This was used to study the interaction between DynA andSOD1–EL2 in Paper I.

4.1.4 Diffusion

As discussed in the previous section, molecules undergo various types of mo-tion. This section will focus on how translational diffusion is investigatedby NMR, a topic that has also been treated in several excellent review arti-cles.123–127

NMR measurements of molecular translational diffusion are based on theapplication of magnetic fields whose strength vary as a function of position inthe sample. The most commonly used type is a linear variation along the z-axis, often called a z-gradient. In the conceptually simplest experiment, datingback to the early 1950’s and the work of Hahn128 and Carr and Purcell,129 thepulse sequence is a spin echo, and spatial dependence is encoded and decodedwith two gradient pulses. The pulse sequence is depicted in Figure 4.2. Inthe top panel, manipulation of the spins by RF-pulses is shown. Here a 90◦x

pulse moves the net magnetization vector to the xy-plane, where it produces adecaying signal. After this follows a time period τ, after which a 180◦y pulse isapplied. The latter pulse has the effect of reversing the spin phase distributioncreated by static field inhomogeneities, and after another interval τ the signalreemerges with an amplitude damped by a factor that depends on relaxation,which we will not go into here. With the exception of small inhomogeneities,molecules in different parts of the sample experience the same field strength,and (magnetically equivalent) spins are completely refocussed, regardless of

25

where they are in the sample. Next we include the magnetic field gradients,shown below the RF-pulses.

a

180°y90°x

1H

Gradient

b

c

δ δ

Δ

t

γg γg

xy

z

B0

B0

xy

z

xy

z

xy

z

Figure 4.2: a) Basic pulse sequence for NMR measurements of translationaldiffusion. Schematic overview of the spin dynamics during the sequence, and theresulting spectra, in the b) absence and c) presence of diffusion. Adapted fromJohnson.126

In general a position-dependent field Bz is applied, and although the gra-dient ggg(rrr, t) of this field may have components in all spatial directions, thediscussion here will be restricted to a z-gradient. We then have:

ggg(z, t) =∂Bz(t)∂z

kkk ≡ g(t)kkk (4.15)

The total magnetic field strength at time t, as a function of position z, will be asum of the static field and the applied gradient field:

B(z, t) = B0 + g(t)z (4.16)

The effect on the spins, in the absence of diffusion, is schematically shown inFigure 4.2 b. Since the precession frequency is linearly dependent on mag-netic field strength, an applied z-gradient pulse of duration δ gives every spina phase angle φ dependent on position: φ(z) = −γB(z)δ. This means that twospins separated by a distance z acquire a phase difference ∆φ(z) = −γδgz after

26

the gradient pulse (in the rotating frame). The situation after the first pulse inFigure 4.2 can be visualized as a ribbon of phases along the z-axis, as shownin the figure. The effect of the subsequent gradient pulse is to twist this ribboninto a helix. The 180◦y pulse inverts the helix, while the final gradient pulseunwinds it, and at a time τ after the 180◦y pulse, the spin vectors are once againcoherent, producing an echo. Again, if the spins do not move, the amplitudeof the echo signal is dampened only as a consequence of relaxation, which isindependent of the applied gradient strength. In such a case, the processedspectra would all have the same amplitude as a function of gradient strength,as shown in Figure 4.2 b. However, the molecules do undergo diffusion, andin a liquid this random motion through locations with different magnetic fieldstrength cannot be neglected. The effect of diffusion is that the phase accu-mulated during the first gradient pulse is in general not negated by the secondgradient pulse, giving an incomplete refocussing of phases, and a decreasedsignal amplitude. This is shown in Figure 4.2 c.

By varying the strength of the gradient pulses the signal amplitude is mod-ulated, and it can be shown that the decay of the signal amplitude as a functionof increased gradient strength ideally follows the Stejskal–Tanner equation:130

S (q) = S (0)e−Dq2∆′ = S (0)e−Dγ2δ2g2∆′ (4.17)

where ∆′ is the time ∆ between gradient pulses as shown in Figure 4.2, mi-nus a small correction due to diffusion during pulses. The attenuation factorDγ2δ2g2∆′ in Equation 4.17 is the so-called Stejskal–Tanner factor. Data froma diffusion experiment are thus (ideally) bilinear with one spectrum for eachlevel of gradient strength, and each of those spectra in general a superpositionof spectra from components with different diffusion coefficients. As describedabove the diffusion modulates the spectral amplitudes and, as Equation 4.17shows, these amplitudes theoretically have Gaussian dependence on gradientstrength. Equivalently, the data are readily arranged as a 2D matrix with fre-quency along one dimension, and exponentially decaying amplitudes in theother dimension. In standard diffusion-ordered spectroscopy (DOSY) process-ing, an exponential function, such as in Equation 4.17, is then fitted to eachpeak or each spectral point in the data, and the diffusion constant D is ex-tracted. Finally, the data are visualized in a 2D-plot with one spectral axis,and one with diffusion coefficient, with Gaussian peaks where the residualsfrom the fitting process give the peak widths. This gives a plot where signalsfrom different molecules are spread out along a diffusion dimension, makingthe technique suitable for identifying components in a mixture.

Although the data are commonly represented as a two-dimensional plot,DOSY differs from ‘real’ 2D experiments where there are two temporal di-mensions, each of which has a one-to-one mapping onto the frequency space

27

through the Fourier transform. The diffusion dimension in DOSY is a statis-tical construct, with a function fitted to data. The problem is that this func-tion may not be fully known, although the theoretical expression is available.One issue is that the decay often deviates from a simple exponential becauseof imperfections in the applied magnetic fields, etc (see for example Nilssonand Morris131). A more fundamental problem is the fact that a peak in the fre-quency dimension may be a superposition of two signals, and if the peak heightis fitted to a single exponential function, the apparent diffusion coefficient ex-tracted will have a value in between the two correct values. Theoretically, theinverse Laplace transform could be used to map such superimposed decaysonto the new dimension, in the same fashion as the Fourier transform, but theLaplace transform is mathematically different, with properties that make thisprocedure a numerically intractable problem for many realistic cases where thedata contains noise. See Istratov et al.132 for a discussion of this.

4.2 Multi-way decomposition of experimental data

Multivariate analysis is a huge sub-field of mathematical and statistical anal-ysis and can be described as the study of multiple variables and their inter-relations. The simplest multivariate case would be two variables, here calledtwo-way data, for example generated by a combined measurement of NMRfrequency and diffusion coefficient for a nucleus, as shown in Figure 4.2. Thisis discussed below. Higher order data are generated by e.g. measurements ona certain observable on several occasions and on several samples, and the gen-eral multivariate situation gives multi-way data, arranged in multi-way arrays.

One area within this field is multi-way decomposition, in which the aimis to break down a data array into a number of components of low rank. Themeasured data are assumed to be a sum of components, each of which is aproduct of factors. The factors may, but need not, directly represent physicalobservables such as spectra. The last step is the actual decomposition, wherethe model is fitted to the data and the factors are estimated.

The underlying structure of the data may vary depending on the experi-mental origin, and hence also the choice of model to which the data is fit willbe different. All applications of decomposition described in the articles of thisthesis concern multilinear data, meaning that the factors are independent. Thefollowing discussion will be restricted to analysis of data with this particularproperty, and take as its starting point data generated by diffusion NMR. Areview of multi-way methods to analyse 2D NMR data has been written byPedersen.133

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4.2.1 Two-way decomposition

As discussed in the previous section and in Paper III, an example of bilineartwo-way data is diffusion-edited NMR spectra. The data may be arranged in amatrix XXX (I × J) where each of the I rows is a spectrum of J points, recordedfor a particular gradient strength level. If the sample contains N species, eachone associated with a spectrum and a diffusion profile, the observed data XXX canbe seen as a sum of N such (I × J) matrices, and written as:

XXX = CCCTSSS + EEE (4.18)

Here CCC (N× I) contains the diffusion profiles and SSS (J×N) the spectra of the Ncomponents in the sample, and EEE contains measurement noise. A componenthere is anything with a distinct spectral profile and diffusion decay, and the aimof the analysis is to find the matrices CCC and SSS that best fit XXX. It may be notedthat this problem is a particular application of principal component analysis(PCA).134,135 Equation 4.18 can be written in an alternative element form:

xi j =

N∑r=1

cir s jr + ei j , i = 1, . . . , I, j = 1, . . . , J (4.19)

4.2.2 Three-way decomposition

To extend the two-way model, we start from the element formulation of Equa-tion 4.19, and trivially add a dimension:

xi jk =

R∑r=1

airb jrckr + ei jk , i = 1, . . . , I, j = 1, . . . , J, k = 1, . . . ,K (4.20)

This model is often called the PARAFAC model, from parallell factor analysis,as described by Harshman and others in the late 1960’s.136,137 The model canalso be written as a sum of outer vector products:

XXX =

R∑r=1

aaar M bbbr M cccr + EEE (4.21)

where XXX has the same meaning as before, but is now a three-way matrixm, andthe symbol M is the outer vector (or tensor) product. The vectors aaar (I × 1),bbbr (J × 1) and cccr (K × 1), describing the component factors, are often collectedin matrices AAA, BBB and CCC. In contrast to the two-way case, the estimated factorsof the type of three-way decomposition described here are unique (apart fromtrivial scaling and permutation), meaning that there are no two set of factors

29

with the same fit. This means that if the experiment is designed and performedin such a way that the data are indeed trilinear, the estimated factors will havedirect physical relevance.

Since trilinearity is, in principle, the only condition on the data, three-waydecomposition is extremely versatile. In the 1960’s the PARAFAC methodwas developed from factor analysis mainly by psychologists, in the particulararea of psychometry, but already from the beginning the applicability of themethod to data from such different fields as economy, electrical activity in thebrain, and weather patterns was suggested.136 Since then trilinear analysis hasbeen used on data from a variety of experimental sources, and only a few ex-amples will be given here. Apellof and Davidson used three-way decomposi-tion to analyse fluorescence data from liquid chromatography experiments,138

while Lee and Ross applied trilinear decomposition to light spectroscopy datato study ligand binding to tyrosine.139 In the NMR field multi-way decompo-sition has been used to analyse e.g. non-uniformly sampled data,140 reactionkinetics141 and toxin levels in the blood.142 In Paper IV of this thesis trilinearanalysis is applied to diffusion NMR data on a mixture of three alcohols, andthe power of the method in resolving heavily overlapping data is shown.

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5. The interaction betweendynorphins and opioid receptors

As touched upon earlier, one of the main areas of this thesis is how the peptidedynorphin A (DynA) interacts with a particular membrane receptor, an inter-action that is a key part in the chain of events that constitute DynA signalling.On a general level DynA is a neuromodulator, where neuromodulation meansthe control, either through internal or external intervention, of activity in thenervous system.143 More specifically, DynA is produced and acts in the centralnervous system (CNS),144 first and foremost as a part of the opioid system.

5.1 The opioid system

The opioid system is a collection of protein membrane receptors located inthe CNS, and a number of ligands that interact with these receptors. Here thediscussion will concern the vertebrate opioid system, which was formed earlyin vertebrate evolution.145 The ligands, the compounds that elicit their effectsthrough the opioid system, are called opioids or opiates, and can be eitherendogenous, or exogenous, the latter produced in another organism or throughchemical synthesis. The name of the system is derived from the opium poppy,from which the molecule morphine is isolated. The effects of this widely useddrug—powerful pain relief but also abuse and addiction—illustrate some ofthe physiological processes mediated by the opioid system.

While a detailed model of the parts and mechanisms of the opioid systemis only a few decades old, pure morphine was first isolated already in 1806by Sertürner146,147 and a general knowledge of opiates and their effects onhumans date back hundreds, or even thousands of years (see Brownstein148 orvan Ree149 for interesting historical reviews). Much work in the opioid areahas been motivated by the hope of replacing morphine as the prime analgesicwith a drug that has fewer adverse side effects, a hope that has been rekindledby every breakthrough in the opioid research field. For example, the succesfulcloning of the opioid receptors in the early 1990’s prompted Reisine and co-workers to write that ‘The availability of the cloned receptors will facilitate theidentification and development of more specific and selective compounds with

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greater therapeutic potential and fewer undesirable side effects.’150 Despiteconsiderable efforts, however, an opioid ligand with high potency and no abusepotential remains to be designed, and the discussions regarding when, and howopioids should be therapeutically used is as active as ever.151

A complete overview of the physiology of the opioid system is beyond thescope of this thesis, but in general this system is involved in the modulation ofpain,152–154 as well as more complex behavioural processes155 such as rewardand addiction,156–158 and different types of mental disorders.159 Opioids havealso been suggested to be a part of the immune response.160

5.1.1 Opioid receptors

The existence of the opioid receptors (originally called opiate receptors) wasdemonstrated in the early 1970’s by the work of many researchers, but primar-ily the groups of Pert and Snyder,161–163 Goldstein,164 and Terenius.165–167 Fora personal account, as well as an historical review of these years, see Snyderand Pasternak.168

The opioid receptors are divided into four subclasses: the κ-, μ- and δ-opioid receptors, (commonly abbreviated KOR, MOR and DOR) and the no-ciceptin receptor (sometimes orphanin FQ receptor). These four classes areall of the G-protein coupled receptor (GPCR) type. The GPCR membraneproteins are involved in chemical signal transduction and are currently at thecenter stage of life science; they constitute one of the most important classes ofdrug-targets,169 and in 2012 two of the pioneers in the field—Robert Lefkowitzand Brian Kobilka—were awarded the Nobel prize in chemistry for work onGPCRs. In the general agonistic situation, a ligand interacts with the receptor,increasing the probability of the receptor to interact with one or several guaninenucleotide binding proteins (G-proteins). At the other end of the spectrum arefull antagonists, i.e. compounds that block receptor activation. The activity ofGPCRs is much more nuanced than a simple on–off mechanism, however, andthere is evidence for a rather complex GPCR conformational landscape.170,171

The G-proteins function as transducers, and possibly amplifiers, propagatingthe signal by modulating one or several effector systems.172,173

As recently as 2005, there was only one GPCR crystal structure in the lit-erature, that of bacteriorhodopsin,174 but the preceding decades had seen greatprogress in both the understanding of GPCRs, and in membrane protein pro-duction technologies. The result of this has been the determination of severalnew structures in the last few years,175–184 and at the time of this thesis, thethree-dimensional strutctures of 25 GPCR proteins have been determined.185

However important these structures are to the understanding of biological sys-tems, several questions regarding GPCR function remain to be answered and

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several problems remain to be solved. One such aspect is the scarcity of NMRstructures—to date, the CXCR1 receptor is the only GPCR who has had itsstructure determined with high resolution by (solid state) NMR.186,187 In con-trast to X-ray crystallography, NMR spectroscopy may be used under near-native conditions and provides valuable information on dynamics on differenttime scales, in addition to the molecular structure. Another problem is the lackof structural information on peptide ligands bound to GPCRs where only onesuch structure exists, that of the CXCR4 chemokine receptor in complex witha cyclic peptide.177 In addition to being the endogenous receptor ligands, thecomplexity of peptides compared to small molecules allows for correspond-ingly complex modes of receptor interaction. For example, the sizes of mostpeptide ligands mean that they interact with larger regions of the GPCRs, in-volving e.g. the extracellular loops.

A compelling feature of the opioid receptors (as well as other GPCRs)is their combination of high similarity, with respect to both sequence (60 to70 %)188 and structure, and high specificity in their interaction with ligands(in particular peptides or peptide analogues). The opioid receptors all have aGPCR type A (rhodopsin-like) fold, with seven transmembrane helices linkedtogether by intra- and extracellular loops, as is shown by the schematic drawingtogether with the κ-opioid receptor (KOR) X-ray structure in Figure 5.1.

Based on structural information from all four opioid receptors,179–182 aswell as on an abundance of functional and biochemical studies, some con-clusions regarding the similarities and differences within the opioid receptorfamily have been drawn. One of the main common features of the receptorsis a binding pocket in the transmembrane region,189 a region that has a largeinter-receptor sequence similarity while the termini, and extra- and intracellu-lar loops of the receptors are quite different on a sequence level.188 Therefore,the loops have been hypothesised to provide the ligand selectivity properties ofthe opioid receptors.190–192 Surprisingly though, in the crystal structures alsothese latter regions are structurally rather similar,189 suggesting that the dif-ferences modulating ligand selectivity are very subtle. Another possibility isthat the crystal structures show the proteins in similar conformational states(all four structures have an agonist bound in the transmembrane pocket), whilethere are several other available states for the native proteins. Following thislatter line of reasoning, and taking the generally larger conformational freedomof the loop regions into account, it is reasonable to believe that the extra- andintracellular parts of the receptors may be more structurally diverse and moredynamic in the native environment than can be deduced from the crystal struc-tures alone. Indeed, recent studies have shown that the extracellular regions ofGPCRs may be conformationally regulated by ligand binding.193,194

A strong argument in support of the selectivity-providing roles of the loops

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are chimeric studies showing that the selectivity pattern of one receptor sub-type may be transferred to another subtype by exchanging extracellular frag-ments.195–197 Later in this chapter we will return to the importance of the ex-tracellular loops in the specific case of the κ-opioid receptor.

a

b

N

C

Figure 5.1: a) Schematic overview of the topology of the κ-opioid receptor(KOR). b) KOR crystal structure (PDB accession code 4DJH),180 with the extra-cellular loop II (EL2) highlighted in yellow. The DynA peptide, approximatelydrawn to scale, is included for comparison.

5.1.2 Dynorphin A—an opioid ligand

As mentioned in the opening of this section there are many types of opioidcompounds of different origin, with different pharmacological profiles, evok-ing different physiological responses. No systematic overview will be providedhere, instead the problem of selectivity between opioid receptors and ligandswill be discussed with a particular focus on peptide ligands. These molecules,like the receptors, have several features in common; one example is the socalled enkephalin sequence: Tyr–Gly–Gly–Phe–Leu/Met, that constitutes theN-terminal sequence of all typical opioid peptides.144 This story of selectivitywill be told from the viewpoint of the dynorphin A peptide, and start with abrief historical account.

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In the 1970’s, a compound with distinct morphine-like properties was iso-lated from cow brain.198 The substance was later identified as an endogenousopioid peptide, named dynorphin after its high potency in an assay using thecontractions of guinea pig ileum,199 and had its sequence determined (YG-GFLRRIRPKLKWDNQ).200 Further research established that this peptide ex-erted its opioid effects mainly through interaction with the κ-subtype of theopioid receptors (KOR),201–203 and that there were two types of dynorphins,dynorphin A (DynA) and dynorphin B (DynB), emanating from the same pre-cursor protein: proenkephalin-B (PDYN).

Slightly anecdotally, the early history of opioid peptides marks the cul-mination of a most interesting life science controversy, described from a so-ciological perspective in Collins’ and Pinch’s book The Golem.204 This con-troversy had started in the early 1960’s with results apparently showing thatmemories or capabilities could be acquired by an organism (planarian worm)through digestion of brain tissue from another, ‘trained’ organism.205 Otherresearchers questioned this,206 the debate raged on, and was eventually pro-pelled into the area of opioid peptides when George Ungar, a pharmacolo-gist at Baylor College of Medicine in Houston, published a study in Naturedescribing the isolation of ‘scotophobin’, a fifteen-residue peptide that trans-ferred a specific behaviour (dark-avoidance) when taken from trained rats andinjected in mice. Accompanying the article was a lenghty critical commentfrom one of the reviewers,207 a rather unusual thing in scientific publishing.Avram Goldstein at Stanford, one of the leading scientists in opioid research,tried to replicate Ungar’s remarkable findings208 but was unsuccessful, andwrote a critical comment on the experiments behind Ungar’s claims.209 Thedebate continued, but towards the mid-seventies a fragile consensual modelhad been established in which peptides could induce a general (such as alert-ness), rather than a specific (fear-of-the-dark), behaviour, and the neuropeptideresearch continued along this trajectory. Goldstein continued his work, and in1981 he was the first to isolate DynA from natural sources and determine theamino acid sequence.200

5.1.3 Dynorphin A and the κ-opioid receptor

In competition assays DynA has been shown to bind the κ-opioid receptor(KOR) with nanomolar affinity,203 with a five-fold or higher selectivity as com-pared to the δ and μ subtypes, depending on the specifics of the competingligands.150,210 Conversely there are peptide ligands such as Leu-enkephalin,a pentapeptide comprised of the DynA N-terminus, that have very low affin-ity for KOR.150,211 So, what are the detailed features of this interaction, andwhere does the selectivity come from? To answer this question a large number

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of research groups have used truncations, mutations, chemical modificationsand various forms of cyclisation of DynA (and other peptides) to map outthe physico-chemical and structural properties needed for potent, selective andtunable opioid receptor agonist and antagonist effects (see Paper II and refer-ences therein for a few examples). This vast body of research is not so easilycondensed into a few simple rules, but some aspects can be highlighted. First,it is now generally accepted that the N-terminal residues of the opioid pep-tides (the enkephalin sequence), in particular Tyr1 and Phe4,212 interact withresidues in the transmembrane binding pocket of the receptors to trigger re-ceptor activation. This region of the ligand is often called the ‘message’, whilethe C-terminal parts of peptide ligands are called ‘address’ regions; a conceptcoined by Goldstein et al. in 1981.201 Furthermore it has been shown thatthe second extracellular loop of KOR (EL2) is needed for high affinity dynor-phin binding.195,213,214 The position of EL2 in the KOR can be seen in Figure5.1. To account for the importance of extracellular receptor regions in opioidpeptide binding, and for the mechanisms of ligand selectivity, a few differentconcepts have been proposed:

Binding/favourable interaction: The message-address model depicts theligand as consisting of two, not necessarily completely distinct, parts.The N-terminal sequence (message) activates (or blocks) the receptorupon interaction with residues in the transmembrane binding pocket.The remaining peptide region (address) needs to have a favourable inter-action with extracellular parts of the receptor for the ligand to reach andbind in the transmembrane site.

Selection through exclusion: In this model selectivity is proposed to be im-paired through an exclusive mechanism, i.e. unfavourable energetics inthe interaction between ligands and extracellular receptor regions keep‘unwanted’ ligands out.190

Conformational mechanisms: In this hypothesis, the extracellular loopsconstrain the positions of the transmembrane helices, and, as a conse-quence, the positions of contact points in binding sites.197

In the literature there is support for all the proposed mechanisms, and areasonable conclusion is that for any ligand–receptor system, all three modelswill be valid, but to different degrees. In the case of DynA–KOR interaction,the abundance of positively charged amino acid residues in DynA (the dynor-phins are among the most basic naturally occurring peptides in the body) andnegatively charged residues in the EL2 of KOR, suggest that electrostatic in-teractions are important, or even primarily responsible, for the interaction be-tween the two. This hypothesis has been tested in several studies, with mixed

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results. Schlechtingen and co-workers215 performed binding and activity stud-ies with KOP and various analogues of DynA, showing that Arg residues, es-pecially Arg6 and Arg7 were crucial for κ-selectivity. Ferguson, on the otherhand, reported that replacing charged residues (but not all) in EL2 did not af-fect DynA affinity and function.216 Also modelling studies have downplayedthe importance of Coulomb forces in comparison with hydrophobic interac-tions,217 and experimental studies of the EL2 peptide in micelles lend somesupport to this.218

As described in Paper I, we addressed the selectivity issue by using a con-struct protein to probe the interaction of the KOR second extracellular loop(EL2) with DynA. This engineered protein consisted of a soluble β-barrel scaf-fold onto which the KOR–EL2 was grafted. The scaffold was a variant of hu-man superoxide dismutase 1 (SOD1) in which the native loops had been trun-cated,219 and the engineered protein was hence named SOD1–EL2. Our char-acterisation of the SOD–EL2 protein by various types of NMR experiments,as well as CD, showed a construct where the core part was structurally verysimilar to the ‘parent’ scaffold protein SOD1, a stable and mainly β-structuredprotein with short connecting loops. The EL2 segment taken from the KOR,on the other hand, was much more flexible. Moreover, we did not see any ev-idence of strong interaction between the EL2 and the scaffold, and concludedthat the EL2 indeed behaves as a loop in SOD1–EL2.

In the investigation of the interaction between SOD1–EL2 and DynA sev-eral NMR methods were used. The effects of DynA on NMR chemical shifts of(15N-labelled) SOD1–EL2 backbone amides were small and, for many residues,impossible to distinguish from noise. A few residues, mainly located in theEL2-region or in areas of the scaffold in close proximity to EL2, showedslightly larger shift differences, differences that also changed consistently upontitrated addition of DynA. Also the SOD1–EL2 backbone dynamics changedvery little upon addition of DynA, and these differences in relaxation parame-ters were deemed too small to be used as conclusive evidence for ligand bind-ing. A fitting of order parameters was done, suggesting an increase in back-bone rigidity in the presence of ligand (unpublished results).

Chemical shift experiments were also performed in which unlabelled pro-tein was titrated onto 15N labelled DynA. In this series of experiments SOD1–EL2, the EL2 peptide and the naked scaffold protein were used. Here the re-sults more conclusively showed an interaction between EL2 and DynA. Boththe EL2 peptide and SOD1–EL2 caused consistent changes in the chemicalshifts of DynA, while no chemical shift changes were observed when the scaf-fold without the EL2 segment (SODnoloops) was added, showing that theEL2-loop is required for DynA interaction. Assuming a one-to-one type ofbinding, dissociation constants were estimated from the chemical shift data.

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These constants were in the micromolar range, suggesting a remarkably weakinteraction compared with the nanomolar affinities previously reported forDynA binding to the full-length receptor.220,221 This indicates that the extra-cellular interaction is very different from the transmembrane binding. From abiological point of view this makes a lot of sense, however, since a too strongbinding of the ligand to the extracellular regions of the receptor could impairthe binding of the DynA N-terminus to the KOR transmembrane pocket, orsignificantly slow down the release of DynA from the receptor.

Further interaction studies were performed with a paramagnetic probe at-tached C-terminally to DynA. As described in Section 4.1.3 these experimentsgive information on the distances between an unpaired electron in the probeand surrounding nuclei, through the effects of the electron on the relaxation ofthe nuclear spins. Using this technique DynA–EL2 interaction was confirmed,with the spin-labelled DynA strongly affecting the EL2 segment in SOD1–EL2protein.

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6. The interaction betweendynorphins and lipids

In Chapter 2 it was argued that biological membranes are integral parts ofliving systems. The membranes will be encountered by other constituents ofsuch systems, for example proteins and peptides, carbohydrates, ions, and wa-ter molecules. Consequently, a very large, very diverse and very vital area oflife science deals with what happens in or at membranes. What passes through,what gets embedded, what stays at the interface, what disrupts the bilayer, etc.In addition to fundamental physico-chemical questions about e.g. lipid bilayerproperties and about biological processes such as membrane protein transla-tion, this research area also includes medical applications. An example is thedelivery of drugs (or other therapeutic entities) into cells, or into specific cel-lular organelles.

In the same chapter the various types of membrane-interacting proteins—peripheral, membrane-inserted and embedded—were introduced. The impor-tance of membrane proteins can hardly be overemphasised. This is shown bothby results from genome analysis, where membrane proteins, e.g. receptors,transporters and channels, are predicted to constitute 20 to 30 % of all pro-teins,222 and by figures from industrial and academic drug discovery research,where approximately 50 % of all drug targets are membrane proteins.223,224

Traditionally, high resolution 3D structures of membrane proteins have beenfew due to the difficulties of crystallising membrane proteins for X-ray spec-troscopy, and the inherent limitations in using solution-state NMR on particleswith large molecular mass, as discussed in the Methods chapter. Althoughtremendous progress has been made in X-ray technology as well as in theNMR area (both solution- and solid-state), structure determination of mem-brane proteins is still by no means trivial. The fact that the largest databaseof membrane protein structures225 contains approximately 1300 entries, whilethere are over 85 000 structures of soluble proteins in the Protein Data Bank(PDB) says something about the discoveries that are still waiting to be madein this area. Interesting as this subject is, in this chapter the discussion willnot concern large membrane proteins, but instead peptides. More specificallyit will treat the interaction between peptides and the lipids of biological mem-branes, again with a particular focus on DynA.

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6.1 Peptides and membranes

Although the question of biological insertion of hydrophobic peptides into themembrane during the translation process226–228 is important and fascinating,the focus here will be on peptides that encounter the membrane from a polarenvironment such as the cytosol, the synaptic cleft, or the aqueous buffer ina test tube. In many cases such peptides interact with a protein or anotherpeptide in the membrane, a situation that was discussed in the previous chapter.Here we will look at interactions directly with the lipid environment. A fewdifferent factors will determine to what extent and in what way such interactionoccurs. In many cases electrostatic interactions will be important, with chargedresidues of the peptide interacting with charged groups of the membrane lipids.Also the number and distribution of hydrophobic residues are important, inparticular for determining if the peptide will penetrate into the hydrophobiccore of the membrane or not. How well-structured the peptide is determinesto what extent hydrogen bonds can be formed between backbone atoms andbonding partners on the lipid molecules.

The different amino acid residue types have more or less specific prefer-ences for the various parts of the membrane environment. Very hydrophobicresidues such as leucines and isoleucines are comfortable in the hydrophobiccore of the bilayer, tryptophan side-chains are often located in the membraneinterface region, while residues with charged side-chains prefer the aqueousenvironment outside of the membrane, or interact with phosphate moieties inthe lipids. This characterisation is, however, slightly simplified. For exam-ple, arginine is charged at physiological pH, but the side-chain also has hy-drophobic properties, conferred by the aliphatic chain. It has been shown thatArg residues may be part of transmembrane protein regions, and minimise theenergy penalty by having the charged end of the side-chain ‘snorkeling’ tothe more polar membrane interface region.229,230 Such complicated modes ofmembrane interaction may be found also for other residues.

There are various ways of estimating the energetic cost or gain in transfer-ring the different types of amino acid residues from the solvent to the mem-brane (or the other way around), and by doing such estimates it is possibleto construct scales of hydrophobicity. Three widely used scales are 1) theKyte-Doolittle scale,231 2) the Wimley-White scale232 and 3) the ‘biologicalscale’ from von Heijne and co-workers.226 The first scale used a combina-tion of water-vapor partioning of side-chain analogues, and residue accessi-bilites derived from twelve soluble proteins, to get hydrophobicity values forall residues. Wimley and White looked at partitioning of short peptides intoPOPC vesicles to get free-energy values and compile a scale, while von Heijneet al. studied the energetics of Sec61 translocon-mediated insertion of different

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peptides into a membrane. By using such scales it is possible to estimate howprobable it is for a particular peptide to be inserted into the membrane, eitherby itself or by another mechanism.

The different modes of membrane interaction of peptides are rather diverse,both in terms of how their conformation changes when going from the polarto the hydrophobic environment, as well as if multimerisation is a part of theinteraction mechanism. A common situation is when the interaction with themembrane induces structure in a peptide that is less ordered in solution; suchpeptides are often amphitropic,233 where the transition from a water-soluble toa membrane-bound form controls peptide activity.6 An example of such a pep-tide is gramicidin, an antimicrobial peptide that multimerises in the membraneto form β-channels through the bilayer.234

A particular type of interaction is represented by amphipathic helices,235,236

where there is a tendency of one face of the helix to interact with the bilayersurface, keeping the helical axis perpendicular to the bilayer normal. As thesurface concentration of peptides increases, this interaction may be followedby a more disruptive interaction with the bilayer, as is the case for many an-timicrobial peptides (AMPs).237,238

6.1.1 The use of bicelles in peptide–lipid research

As described in Chapter 2 there are a number of available lipid systems thatprovide simplified models of a real biological membrane, for experimentalstudies where a native membrane is too complex. Here I will discuss bicelles,one type of membrane mimetic, and how they may be used to study peptide–lipid interactions, particularly with NMR spectroscopy. A general overview ofmembrane mimetics in biomolecular NMR studies is beyond the scope of thisthesis, but a good introduction is given by for example Warschawski et al.239

Bicelles were introduced in the late 1980’s as a tool for structural char-acterisation of biomolecules, based on findings that certain mixtures of phos-pholipids and detergents formed a liquid crystalline phase that was oriented bythe static magnetic field of an NMR spectrometer.240,241 This tendency of thebicelle to align in a magnetic field is, to a large extent, defined by the q-value,the ratio between the concentration of lipids, clipids, and detergent molecules,cdetergent in the bicelles:

q =clipids

cdetergent(6.1)

At certain ratios where q & 2 the field-aligning phase is formed.48,241–243 Thesebicelles will in turn weakly align other biomolecules such as proteins, creat-ing the opportunity of measuring residual dipolar couplings (RDCs) that may

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Figure 6.1: Schematic overview of an ideal bicelle and a DynA peptide, approx-imately drawn to scale.

be used to refine the protein 3D structures.240 When the q-value is below thisapproximate threshold, the tendency of the bicelles to align vanishes, and thecorrelations times of the molecules in the assemblies get sufficiently fast onan NMR timescale to give high resolution spectral information. This type ofbicelles, sometimes called isotropic bicelles, have been widely used for studiesof lipid-interacting proteins and peptides.49,51,244,245 A schematic depiction ofa bicelle together with a peptide is shown in Figure 6.1. The detailed morphol-ogy of isotropic bicelles has been a topic of debate, and concerns issues suchas to what extent lipids and detergent molecules are segregated, the phase be-haviour of the lipids, the possible coexistence of multiple types of complexesin a sample, and the overall shape of bicelles.

Several groups have studied DHPC/DMPC bicelles and observed that cer-tain nuclei in the two molecules have different chemical shifts in bicelle sam-ples but identical chemical shift values when solubilised in non-polar sol-vents.246–248 This is a strong indication that there is segregation between thetwo molecules in the bicelles. Furthermore, attempts at determining the sizeand shape of bicelles under various conditions have been made through mea-surements of e.g. NMR relaxation51,249 and diffusion,244 scattering meth-ods246,247 and electron microscopy.247,250 For DHPC/DMPC mixtures at low

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q-values and near room temperature, both Luchette et al.246 and Glover etal.247 proposed disk-like objects, clearly distinguished from mixed micelles,although the latter group noted a change in morphology below a total PC con-centration of 130 mM. A recent report by Beaugrand et al.251 suggests thatthe DMPC and DHPC molecules are only segregated for q & 1 and that con-centrations lower than 250 mM may lead to larger aggregates. In Paper IIIof this thesis, we studied DHPC/DMPC, q = 0.5, bicelles with 1H and 31PNMR spectroscopy, together with the decomposition methodology discussedin Paper IV and showed that the lipids are largely segregated. Furthermore,we concluded that the only species present are bicelles and free DHPC, andthat the morphology does not change qualitatively for concentrations down toapproximately 75 mM. This work also demonstrated the applicability and ro-bustness of two-way decomposition of diffusion NMR data as an approach inanalysing data on lipid mixtures such as bicelles.

6.2 Membrane interactions of dynorphin A

In the decade after their discovery, dynorphins were found to cause a vari-ety of physiological responses even when the opioid receptors were blockedby antagonists such as naloxone. These non-opiate effects, including paraly-sis,252 changes in the motor system,253 and neural cell death,254 have mainlybeen studied in cellular systems, mice or rats. In some cases the underly-ing signalling pathways have been shown to involve other receptors, such asthe NMDA receptor252,255–259 but dynorphins also interact directly with mem-branes. This is likely to have biological functions, and will be discussed below.Moreover, it appears plausible that membrane and receptor interactions workin concert; a particular lipid-induced conformation could for example be pre-ferred by the receptor. Such structure induction has been observed in modelmembrane systems.260–264 Moreover, the membrane may serve as a surfacefor peptide adsorption and accumulation prior to receptor binding, as has beensuggested by Schwyzer and Sargent.264,265

Reciprocal to being affected by lipids, DynA has been shown to cause ef-fects on membranes. The peptide does for example translocate into cells,266

and induce calcium influx into vesicles267 as well as leakage in liposomes.268

Furthermore, DynA has been suggested to induce formation of vesicles in or-dered bilayers.269 It may be noted that some of these properties overlap withcell-penetrating peptides (CPP), an interesting class of peptides that has beenmuch studied,270–275 and also used as a tool to deliver cargo such as siRNA276

and proteins277 into cells.In a fairly recent study specific mutations in the DynA precursor gene were

linked to a neural disorder.278 Some of these DynA variants were studied bio-

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physically by Madani et al. who showed that the membrane interacting prop-erties differed substantially between variants.279 In Paper II we describe in-vestigations by CD and NMR spectroscopy of the bicelle interactions of twoof these variants. One peptide variant, R6W–DynA, was found to have clearlipid perturbing properties, with a strong association and comparatively deepN-terminal positioning in bicelles, while another, with an L5S mutation, in-teracted only weakly with the bicelles. The more aggressive variant, R6W–DynA, associated strongly to bicelles, and in certain regions of the peptide amoderate helicity was induced, but no fully formed secondary structure wasdetected. Such behaviour has been seen also in wild-type DynA.260 In con-trast to this, strongly channel- or pore-forming peptides such as gramicidin Aexhibit a much more evident structure induction in the bilayer.234,280,281

In Paper II, as well as in previous studies260 it was shown that wild-typeDynA associates strongly with bicelles, and to better understand this inter-action we examined the detailed molecular dynamics of wild-type DynA inbicelles, as described in Paper V. Here three residues, G2, L5 and L12, werelabelled with 15N nuclei, giving the opportunity to use a battery of NMR 15N-relaxation experiments, as described in Chapter 4. Overall, the observed dy-namics were quite different in the labelled sites, indicating that the DynA pep-tide has a complex motional behaviour in the bicelle medium. Analysis of therelaxation data within the model-free framework indicated that the bicelles re-strict the dynamics of the peptide, slowing down local correlation times andgiving rather large order parameters. The restriction was slightly strongerwith anionic DMPG lipids present in the bicelles, and most evident for theL5 residue. Such charge-dependent dynamics has also been observed for theCPP transportan,282 although this peptide is more structured in bicelles. Over-all, the results suggest that DynA has a residue-specific interaction with lipids,despite the fact that only limited peptide structure is induced by the lipids.

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7. Conclusions and outlook

The last chapters contained summaries of the main findings of this thesis work,in the respective area. Here I will draw a few conclusions, and put these con-clusions in context.

7.1 What are the most important findings of this thesiswork, and how can they be built upon?

Below is a list of findings, followed by a discussion on each item in the list.

1. An opioid receptor extracellular loop may be studied in a soluble proteinenvironment.

2. DynA binds weakly to the KOR–EL2 loop.

3. The molecular dynamics of the membrane interaction of DynA is deli-cately balanced, and is affected by single-site mutations, as well as smallchanges in lipid environment.

4. Multi-way decomposition methods may be applied to NMR diffusiondata on bicelle samples, and provide information that is complementaryto DOSY analysis.

5. Mixtures of DHPC/DMPC, q = 0.5, behave like classical isotropic bi-celles at concentrations from 300 to 50 mM.

The first item on the list is a conclusion from Paper I. The concept of movinga segment of a protein to a new protein has been attempted with some suc-cess,283,284 but constructs with membrane protein segments in soluble proteinshave been more difficult to design.285 Moreover, the idea of transplanting asite of weak binding rather than the main binding pocket is to my knowledgemuch less investigated. Our succesful attempt suggests a more general oppor-tunity of studying the weak interactions involved in ‘filtering’ of ligands insystems similar to DynA–KOR, such as the other opioid receptors, or otherGPCRs in general. In such systems the much stronger interaction of the ligand

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with the primary binding pocket of the receptor may obscure the more delicatedynamics and kinetics of the extracellular interactions.

The results on the interaction between DynA and SOD1–EL2 have a fewinteresting aspects. One is the small magnitude of the interaction; judgingfrom the amount of positive and negative charge in DynA and EL2, respec-tively, one would be forgiven for expecting a stronger interaction. But such afeeble binding actually makes biological sense, a stronger binding could pos-sibly have prevented the opioid core (N-terminus) of the DynA peptide fromreaching the transmembrane interaction site.

Studies on a scrambled DynA peptide (unpublished), suggest that this alsointeracts weakly with the loop, hence apparently downplaying sequence speci-ficity. An obvious continuation of this research is the design and production ofconstructs similar to SOD1–EL2 but with other (opioid) receptor loops could,and interaction studies of these with an expanded library of ligands, startingwith other opioid peptides. A more complete experimental system based onthe SOD1–EL2 methodology may be one way to map out some of the selec-tivity differences between the receptors in this family.

The third point highlights the fascinating ambiguity of DynA lipid inter-action. In many ways the peptide is evidently membrane disrupting, but itdoes not form stable structures in the membrane. These properties are likelyto have biological functions for e.g. receptor binding or peptide trafficking. Itwas recently shown that β-endorphins and other peptide hormones are storedin particular types of vesicles in the cell, tightly packed as amyloids.286 Suchresults add to a more detailed understanding of the properties, mechanisms andfunctions of similar peptides. Moreover, an increased knowledge on the role ofelectrostatics and hydrophobicity for e.g. transient pore formation is importantfor applications such as novel antibiotics inspired by antimicrobial peptides.

Possible routes forward with the research on the DynA–lipid interactionsincludes mutation studies with systematic residue variations. Another interest-ing issue to address is the possibility of cooperative behaviour such as accu-mulation of peptides on or in the membrane, and formation of intramolecularcomplexes. So far we have no evidence of such multimerisation, but it mayoccur under certain circumstances, contributing to membrane destabilisationand disruption.

Overall, the combined results from the studies of DynA interacting withKOR and membranes may be used to interpret the link between the DynAvariants described in Paper II and SCA, a disease that they cause.278 Such aninterpretation, it has to be remarked, needs to be made with some caution. Thedisease is caused by the death of certain neural cells in the cerbellum, and thiscell death must in some way be coupled, at least partly, to DynA. The twoDynA variants that we investigated had single-site mutations on residue five

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and six, respectively. For the latter variant, R6W, a charged amino acid is re-placed with a tryptophan. This means substituting a very polar residue for atype that interacts strongly with lipid bilayers. Not surprisingly this varianthad clear membrane-perturbing properties, and both these properties per seand their influence on DynA-mediated opioid receptor activity may contributeto cell malfunction. For the L5S variant, the relatively low membrane activitymost likely excludes the possibility of induction of cell death through bilayerdisruption. On the other hand, membrane-induced priming of the peptide forreceptor interaction may also be abolished. Perhaps more importantly, the sub-stituted residue belongs to the N-terminal enkephalin pentapeptide, commonto all typical opioid peptides, and highly important for peptide-mediated re-ceptor activation. Hence, a substitution in this sequence very likely diminishesthe ability of the peptide to activate the receptor, and in extension decreasesneural signalling.

Item 4) and 5) on the list concern bicelles, and methods to study them.Multi-way decomposition of NMR data is a powerful tool to analyse mixtures,something that is thoroughly discussed in Paper IV. In Paper III one type ofmulti-way analysis was employed to study fast-tumbling DHPC/DMPC bi-celles, and one of the things we could show was that this method providesvaluable information on samples where there are no resolved signals from thetwo species. As for the morphology of the samples, we detected only bicellesof one type, together with free DHPC, but no other complexes such as micelles.Both these findings are valuable when considering bicelles against other mem-brane mimetics for a particular experimental design.

An interesting continuation of these studies would be to increase the di-mensionality of the data by additional measurements, and use three-way (orhigher) analysis methods. Another possibility is fluorescence correlation spec-troscopy (FCS) to get single-particle information on bicelle morphology.

7.2 Critical review of methodology, results and conclu-sions

Like in much of biophysical research, the approaches used here to study bio-logical systems are very reductionistic. In the case of KOR-DynA interaction,only a fragment (less than 10 % of the total membrane protein sequence) wasstudied, in an environment that lacks all membrane components. Likewise,in the membrane interaction studies, membrane mimetics involving a maxi-mum of three different lipid types were used, and instead of the continous andhighly irregular surface of a biological membrane there was a suspension ofsmall lipid complexes. How valid are these approches?

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Starting with the DynA–KOR-EL2 interaction studies, a criticism of ourapproach may be that a fragment taken out of a protein context is not represen-tative of the native system and, hence, any ligand–interaction observed in themodel system has no relevance for the interaction in the native system. In re-sponse to this there are previous studies287,288 showing that isolated membraneprotein fragments keep many of the biophysical properties of the full-size sys-tem. A valid question is, however, the importance of a disulfide bridge in thenative receptor, anchoring EL2 to a transmembrane helix. There are other suchdisulfide bridges, and even though these are, in general, critical for antagonistinactivation of the receptor,289 the importance for the EL2–DynA interactionis difficult to assess since the loss of function may be a consequence of othereffects caused by the removal of the cysteines.

Moving to the membrane interaction of DynA, the choice of bicelles asmembrane mimetic system can be discussed. The arguments in favour of us-ing bicelles were formulated in Chapter 6, and may be summarised in twosentences. First, for high-resolution solution state NMR experiments, systemsmuch larger than bicelles are, in practice, impossible to use. Second, the pri-mary alternative in the bicelle size range is a micellar system, but although mi-celles are often used to solubilise membrane-interacting proteins and peptides,in contrast to bicelles they do not contain a lipid bilayer environment, and theyhave a very curved surface. Also the types of lipids used in our studies maybe questioned. The use of phophatidylcholine (PC) lipids is motivated by theabundance of PC lipids in mammalian neural cell membranes. For examplePC is the major component of rat neuronal plasma membranes, constitutingalmost 30 % of total lipid amount.290 PG is, on the other hand, not a majorcomponent in neural cells, but its overall negative charge is a surrogate forother charged lipids such as PS and PI which are present to various degrees inbiological membranes. It is of course possible that the dynorphin peptides inthe native cellular environment could interact mainly with membrane regionsenriched in lipids with another headgroup than PC, but I am not aware of anysuch findings.

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Jag ser att det existerar större makterAtt den största delen av mig kontrollerar sig självAtt flertalet celler virvlar runt med mitt livSom om jag inte var närvarande

– Inger Christensen, Det

Populärvetenskaplig sammanfattning(Summary in Swedish)

I dikten Sonnet to Science beklagar sig Edgar Allan Poe över hur vetenskapen,den “gam vars vingar är den trista verkligheten”, hindrar det poetiska manö-verutrymmet. Hur saker ändras när de väl träffats av vetenskapens granskandeblick. “Det är ju du som [...] skingrat flodens trolska skymningsvakter, och äl-vors dans som genom kvällen drog” utbrister Edgar, “och det är du som drivitbort i vinden, min sommars drömmar under tamarinden.”

Och vetenskapen har verkligen satt tänderna i mycket av det fantastiskaoch mystiska. Utomjordingar, telepati, att göra guld av svavel och järn, till ex-empel. För att inte tala om oss själva. Skapelseberättelser om hur vi föddes urjordens lera, och själens olika skepnader genom den mänskliga historien, harpå många sätt fått ge vika för en annan beskrivning av vilka vi är. Kolonier avceller och bakterier, behållare fyllda med kemiska ämnen och reaktioner långtfrån jämvikt. Den del av naturvetenskapen som handlar om att förstå det somlever kallas livsvetenskap, eller på engelska life science. Denna avhandling hörtill detta område, ett område som är enormt stort. Forskning inom livsveten-skap handlar om cellers energiförbränning, om hur flyttfåglar navigerar, omlivets uppkomst och om att utveckla nya läkemedel. Och mycket annat. Tillexempel hur molekyler i kroppen kan skicka signaler genom att samverka medandra molekyler, och om hur cellernas membran uppför sig. Forskningen sombeskrivs i denna avhandling handlar i grunden om de här två sista punkterna,signalering och membran.

Vissa ser det magiska och fantastiska också i det vetenskapliga, andra tyc-ker, som Poe, att något går förlorat. Oavsett vilken åsikt man har är det svårt attförneka att de vetenskapliga modellerna av komplicerade mänskliga fenomensom minnen, personlighet, medvetande, känslor, och så vidare blir allt bättre.Sådana komplexa saker hänger ihop med våra hjärnor, och vad som händeri dem. Och här är signalering ett oerhört viktigt begrepp. Cellerna i hjärnantar emot information i form av elektriska signaler från resten av nervsystemet,och denna information måste sedan processas, vilket innebär att hjärncellerna,neuronerna, måste kommunicera med varandra. Kommunikationen är till stordel kemisk, det är molekyler som skickas som signaler mellan cellerna. Såda-na molekyler kallas signalsubstanser, eller neurotransmittorer, och en signal-

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koppling mellan celler är en synaps. I varje millimeterstor kub i hjärnan finnsdet ungefär 80 000 neuroner och 4.5 miljoner synapser.

Den signalsubstans som står i centrum i denna avhandling är dynorfin A(DynA). DynA är en peptid uppbyggd av 17 aminosyrarester, vilket gör denungefär hälften så stor som sin kanske mer kända släkting endorfin. Ändelsen-orfin i dessa båda signalsubstansers namn är ingen slump, både dynorfin ochendorfin har smärtlindrande effekter som liknar morfin (endorfin betyder just“kroppens eget morfin”) och båda ämnena tillhör en större grupp molekylersom kallas opioida peptider. I Figure 3.1 visas olika avbildningar av en peptid.Produktion av DynA i det centrala nervsystemet hänger ihop med belönings-och beroendesystem, och forskare tror också att det är inblandat i vissa formerav psykisk sjukdom, även om kopplingen inte är helt klar. Stora mängder avDynA kan verka bedövande och till och med paralyserande.

För att en signalsubstans skall kunna förmedla en signal till en cell mås-te det finnas en matchande bindingspartner, en receptor, på cellens yta. Fördynorfin A heter bindningspartnern κ-opioidreceptorn (KOR), och är ett stortprotein som sitter inbäddat i cellmembranet. En modell av KOR kan ses i Figur5.1. Det finns dessutom ytterligare tre receptorer i samma familj, men DynAinteragerar mindre med dessa tre receptorer. Sådana preferenser för de olikareceptorerna finns också för endorfin och andra signalsubstanser. Denna se-lektivitet har intresserat forskare sedan de opioida peptiderna upptäcktes på1970-talet, och har visat sig bland annat bero på särskilda receptordelar somsticker ut från cellytan. De här delarna är det första signalsubstansen stöter på,och en hypotes är därför att den interaktion som sker här avgör om signal-substansen får tillgång till resten av receptorn, och därmed kan förmedla sinsignal, eller inte. En viktig del av den här avhandlingen handlar om en sådandel av κ-opioidreceptorn och hur den växelverkar med DynA. För att kunnastudera just den här interaktionen har vi tagit ut receptordelen och satt in den iett system som är mycket mindre, och mycket mindre komplext, än ett riktigtcellmembran. Med hjälp av olika biofysikaliska mätmetoder, framför allt kärn-magnetisk resonans, på engelska nuclear magnetic resonance (NMR), kan vivisa att DynA binder till receptordelen, men att interaktionen är väldigt svag.Denna svaga bindning tror vi är avgörande för att DynA skall kunna signaleragenom receptorn. Vi tror också att interaktionens svaghet är biologiskt nöd-vändig, en starkare bindning hade kunnat sakta ner signaleringen, alternativtgjort den svårare att stänga av.

Ytterligare en intressant fråga är hur DynA växelverkar direkt med de lipi-der, fettliknande ämnen, som är en av de viktigaste byggstenarna i biologiskamembran. Hur forskare tänker sig att biologiska membran ser ut visas i Figur2.2. Många peptider påverkar lipider, vissa kraftigt, som till exemel peptidernai giftet hos bin och getingar, som gör hål i membranet. Andra peptider har mer

50

nyanserade effekter, och till denna grupp hör dynorfin A. DynA kan passeragenom membran, och vid höga koncentrationer orsakar läckage i membranet,men hur dessa effekter uppkommer är inte känt. En del av den här avhandlingenhandlar om hur DynA uppför sig tillsammans med lipider. Vi har gjort studierpå atomnivå av DynA i biceller, små diskusliknande klumpar av lipider ochdetergenter. En schematisk bild av en bicell visas i Figur 6.1. Resultaten visaratt största delen av DynA befinner sig i lipidernas ytlager, och att peptiden,utan att bli helt stel, blir rörelsemässigt begränsad i denna miljö. Vi visar ocksåatt enstaka ändringar i DynA:s aminosyrasekvens har stora konsekvenser förhur denna peptid växelverkar med lipider. Dessutom har vi karakteriserat enviss typ av biceller, hur stora de är och hur lipid- och detergentmolekylernabygger upp bicellerna.

Sammantaget bidrar resultaten i denna avhandling till en bättre förståelseav hur DynA interagerar med receptorn KOR. Dessutom kan de metoder viutvecklat användas för att studera liknande system där en peptid, eller annanmolekyl, binder till en receptor. Även om vi inte arbetat med sådana tillämp-ningar, är peptid–receptor-system mycket viktiga inom läkemedelsutveckling,eftersom receptorer liknande KOR är betydelsefulla för en mängd biologiskaprocesser, och därmed mycket viktiga mål för olika läkemedel.

Även resultaten av bicellstudierna är viktiga både för den grundläggandeförståelsen av dessa membranliknande, eller membranmimetiska, system, ochrent praktiskt för dem som använder biceller som ett verktyg i sin forskning.

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Acknowledgements

I would like to warmly thank a number of people, whose generous contribu-tions of time, skill and encouragement have brought this thesis into existence.This section has not room for everyone who deserves to be acknowledged, andI can only hope that you who are not named accept this general expression ofgratitude. I could not have done it without you.

Lena Mäler, min huvudhandledare: Du är förnuftig i ordets bästa och mestallomfattande bemärkelse, och har en känsla för sakers relativa vikt och be-tydelse—vetenskapligt och vidare—som återställer ordning och lugn där denyss kändes hopplöst förlorade. Och bihandledare Astrid Gräslund, för dinerfarenhet och intellekt som skär genom tvivel och tvekan, för din osvikligakänsla för riktning, och för ditt sätt att navigera genom de stora strömningarna.Tusen tack till er båda, för ert förtroende och vägledning.

There are also a number of people with whom I have had the honour andpleasure to collaborate more closely: Jens Danielsson: Ibland uppfordrande,ofta upplysande, alltid förtroendeingivande. Innanför den barska ytan finns enförstklassig hjärna och ett hjärta av guld. Martin Kurnik: En skoningslöskritiker av flagellatteorin, men en vänlig själ som lärt mig det mesta av vad jagkan av laboratoriearbete, och den som fått mig att inse hur mycket som finnsatt lära. Thanks also to Lisa and the rest of the MO lab, for much needed andhighly valued help and company.Min introduktion till dynorfinerna var en tecknad peptid med bister uppsyn ochAIK-keps under rubriken “the evil cousins of Endorphins”. Tack Jesper Lindför det, och för en ohejdbar entusiasm inför nya projekt och naturvetenskap iallmänhet.Mathias Nilsson: En genuint sympatisk kosmopolit som briljerar på ovän-tade områden, och på väntade. Tack för att du alltid fått mig att känna migvälkommen. Thank you, Gareth A. Morris, for your kindness, patience andwry sense of humour. A seemingly bottomless source of knowledge and an-swers, and with an understanding of NMR the depth of which is truly remark-able. Thanks also to Adolfo, Maryam, Andy, Penny, Adam and all the otherpeople at the University of Manchester whom I met during my Master thesisproject and my brief stint as a graduate student.

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Och hur fattig vore inte tillvaron utan doktorandkollegor, nya och gamla! TackWeihua Ye för nya perspektiv, jag hoppas att jag någon gång får chansen att tadel av kinesisk kultur på samma engagerade sätt som du studerat den svenska;Axel Abelein för djärva projektplaner och instrumentidéer, och för lektioneni fussball jag fick av dig och din bror; Scarlett Szpryngiel för de politiskadiskussionerna—det är tur att vi inte har mer olika åsikter; tack Jobst Liebauför din klara och toleranta syn på världen, jag önskar att fler tänkte som du;Pontus Pettersson för föredömlig arbetsdisciplin, och mer eller mindre strin-genta resonemang om kost och träning. Tack också Anna Wahlström för attdu återvände till korridoren, och för dans och Brunnsviksbad; Sofia Unner-ståle—sluta aldrig att möta världen med lekfullhet och öppenhet; och Fate-meh Madani för filmtips, och för din generositet och vänlighet.Ett stort tack också till Haidi och Torbjörn Astlind: Jag tackar min lyckligastjärna för att ni funnits i huset under mina doktorandår, och för att jag slipperuppleva ett DBB utan er. Det finns säkert någon annan som kan sköta tekniskutrustning och administration, men ni är oersättliga på alla andra sätt.Tack, naturligtvis, Viktor Granholm, för att du aldrig respekterade gränsenmellan skoj och allvar, mellan science och sci-fi, mellan levd, fantiserad ochiscensatt verklighet. Ett särskilt tack till mina första kontorskamrater på DBB:Lumi, Karin och Michael. Det kommer aldrig en andra chans att göra ettförsta intryck, och jag tror inte att någon skulle gjort det bättre än ni.Thanks to corridor colleagues, past and present: Jüri, Sebastian, Göran, Gus-tav, Margareta, Britt-Marie Sjögren, Britt-Marie Olsson, Erik, and others,for coffee breaks and discussions.Thanks to the people I have spent time with at the magnets, and, as a bonus,learnt immensely from: In particular Robert, Jerk, Olle, Christoffer andZoltan. Tack till mina undervisningskollegor för trevliga timmar i student-labben, och för att ni svarade på de svåra frågorna: Karin, Charlotta, Patrik,Nadja, Patricia, Candan, och Gabriella. Till Andreas Barth: Tack för gottmentorskap, men också för många intressanta, kluriga och lärorika frågor ochkommentarer. And, last but not least, a big and sweeping thank you to every-one at DBB. Some I have had more contact with than others, you know whoyou are, but to all of you: Thanks for a good time!

Going a few years back, I would like to thank former university teachers, es-pecially Martin Billeter of Gothenburg University who got me interested inNMR, who pointed out the direction, and who has offered help and guidancewhenever I have needed it.Jag skulle också vilja rikta ett sent tack till tidigare lärare från grundskolan ochgymnasiet. Jag gav er säkert inte det erkännande eller den respekt ni förtjä-

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nade, men ni var fantastiska.Ett stort och varmt tack till vänner och familj för trygghet, umgänge och samtalom allt utom biofysik. Ni betyder enormt mycket.Och tack älskade Johanna, för alla stunder som varit, och för allt som liggerframför oss.

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