Elucidating the Gating Mechanism of Cys-Loop Receptors

ÖZGE YOLUK

Doctoral Thesis Stockholm, Sweden 2016 TRITA FYS 2016-26 ISSN 0280-316X KTH School of Engineering Sciences ISRN KTH/FYS/–16:26–SE SE-100 44 Stockholm ISBN 978-91-7729-009-4 SWEDEN

Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till oﬀentlig granskning för avläggande av teknologie doktorsexamen i biologisk fysik måndagen den 13 juni 2016 klockan 14.00 i F3, Lindstedtsvägen 26, KTH Campus, Kungl Tekniska högskolan, Stockholm.

Tryck: Universitetsservice US-AB iii

Abstract

Cys-loop receptors are membrane proteins that are key players for the fast synaptic neurotransmission. Their ion transport initiates new nerve signals after activation by small agonist molecules, but this function is also highly sensitive to allosteric modulation by a number of compounds such as anesthetics, alcohol or anti-parasitic agents. For a long time, these modulators were believed to act primarily on the membrane, but the availability of high- resolution structures has made it possible to identify several binding sites in the transmembrane domains of the ion channels. It is known that lig- and binding in the extracellular domain causes a conformational earthquake that interacts with the transmembrane domain (and the allosteric modulator sites), which leads to channel opening. The investigations carried out in this thesis aim at understanding the connection between ligand binding and channel opening with molecular modeling and computer simulations.

I present new models of the mammalian GABAA receptor based on the eukaryotic structure GluCl co-crystallized with an anti-parasitic agent, and show how these models can be used to study receptor-modulator interactions. I also show how removal of the bound modulator leads to gradual closing of the channel in molecular dynamics simulations. In contrast, simulations of the receptor with both the agonist and the modulator remain stable in an open-like conformation. This makes it possible to extract several key interactions, and I propose mechanisms for how the extracellular domain motion is initiated.

The rapid increase in the number of cys-loop receptor structures the last few years has further made it possible to use principal component analysis (PCA) to create low-dimensional descriptions of the conformational land- scape. By performing PCA on the crystal structure ensemble, I have been able to divide the structures into functional clusters. Sampling of these clusters with molecular dynamics simulations reveals transitions that occupy the intermediate positions in the principal component space.

The studies presented in this thesis contribute to our understanding of the gating mechanism and the functional clustering of the cys-loop receptor structures, which both are important to design new allosteric modulator drugs that inﬂuence the channel function, in particular to treat neurological disorders.

Keywords: cys-loop receptor, ion channel, neuroscience, molecular dynamics, activation, modulation, modeling, gating, GABAA iv

Sammanfattning

Cys-loop-receptorer är membranproteiner som spelar en nyckelroll för snabb synaptisk neurotransmission. Deras jontransport initierar nya nerv- signaler efter aktivering av små agonistmolekyler, men denna funktion är också mycket känslig för allosterisk modulering av ett antal föreningar såsom bedövningsmedel, alkohol eller antiparasitmedel. Under en lång tid troddes dessa modulatorer främst verka på membranet, men tillgången till högupp- lösta strukturer har gjort det möjligt att identifiera flera bindningsställen i transmembrandomänerna hos jonkanalerna. Det är känt att ligandbindning i den extracellulära domänen orsakar en jordbävningsliknande stor konfor- mationsförändring som interagerar med transmembrandomänen och regioner specifika för de allosteriska modulatorerna, vilket leder till att kanalen öpp- nas. De studier som presenteras i denna avhandling syftar till att förstå sam- bandet mellan ligandbindning och kanalöppning med molekylmodellering och datorsimuleringar.

Jag presenterar nya modeller för GABAA-receptor för däggdjur som är baserade på en eukaryot struktur av GluCl vars struktur bestämts bunden till ett antiparasitmedel, och visar hur dessa modeller kan användas för att studera interaktioner mellan receptor och modulator. Jag visar också hur kanalen gradvis stänger i molekyldynamiksimuleringar om den bundna mo- dulatorn tas bort. I simuleringar med både agonist och modulator bundna förblir kanalen i en relativt öppen konformation. Detta gör det möjligt att identifera ﬂera viktiga interaktioner, och jag föreslår mekanismer för hur den extracellulära domänens rörelse initieras.

Den snabba ökningen av antalet cys-loop-receptorstrukturer de senas- te åren har vidare gjort det möjligt att använda principalkomponentanalys (PCA) för att skapa lågdimensionella beskrivningar av hela det tillgängli- ga rummet av strukturer. Genom att utföra PCA på en stor grupp av kri- stallstrukturer har jag kunnat dela in strukturerna i funktionella grupper. Molekyldynamiksimuleringar av dessa grupper gör det möjligt att identiﬁera övergångar som motsvarar mellantillstånd i rummet av principalkomponenter.

De studier som presenteras i denna avhandling bidrar till vår förståelse av kanalöppningsmekanismen och den funktionella grupperingen av cys-loop receptorstrukturer. Denna kunskap är viktig för att kunna utforma nya läke- medel som genom allosterisk modulering påverkar funktionen hos proteinet, i synnerhet för att behandla neurologiska störningar.

Nyckelord: cys-loop-receptor, jonkanal, neurovetenskap, molekyldynamik, aktivering, modulering, modellering, grind, GABAA “Feet, what do I need you for when I have wings to ﬂy?”

Frida Kahlo

List of Publications

This thesis is based on following publications, which are referred to in the text by their roman numerals:

Paper I Edward J Bertaccini, Özge Yoluk, Erik R Lindahl, and James R Trudell. Assessment of Homology Templates and an Anesthetic Binding Site within the γ-Aminobutyric Acid Receptor. Anesthesiology, 2013 vol. 119 (5) pp. 1087–1095. Paper II Özge Yoluk, Torben Broemstrup, Edward J Bertaccini, James R Trudell, and Erik Lindahl. Stabilization of the GluCl Ligand-Gated Ion Channel in the Presence and Absence of Ivermectin. Biophys. J., 2013 vol. 105 (3) pp. 640–647. Paper III Özge Yoluk, Erik Lindahl, and Magnus Andersson. Conformational Gating Dynamics in the GluCl Anion-Selective Chloride Channel. ACS Chem Neurosci. 2015 vol. 6 (8) pp. 1459–1467. Paper IV Stephanie A. Heusser, Özge Yoluk, Göran Klement, Erika A. Riederer, Erik Lindahl, Rebecca J. Howard. Functional Characterization of Neuro- transmitter Activation and Modulation in a Nematode Model Ligand-gated Ion Channel. J. Neurochem., in press. doi: 10.1111/jnc.13644 Paper V Laura Orellana, Özge Yoluk, Oliver Carrillo, Modesto Orozco and Erik Lindahl. Prediction and Validation of Protein Intermediate States from Struc- turally Rich Ensembles and Coarse-Grained Simulations. Revision submitted to Nat. Commun.

Additional publications:

Mieke Nys, Ana Farinha, Özge Yoluk, Magnus Andersson, Eveline Wijck- mans, Marijke Brams, Radovan Spurny, Erik Lindahl, Chris Ulens. A Novel Al- losteric Binding Site in a Cys-Loop Receptor Ligand-Binding Domain Unveiled in the Crystal Structure of ELIC in Complex with Chlorpromazine. Revision submitted to Proc. Natl. Acad. Sci. U.S.A.

vii viii

Author Contributions to the Papers

For paper I E.B. came up with the idea, did most of the calculations, and most of the manuscript writing together with J.R.T and E.L.. Ö.Y. created and analyzed the Rosetta model.

For paper II, E.L. developed the idea. Ö.Y. did all the calculations and the analysis. T.B. parametrised the ivermectin molecule. Ö.Y. and E.L. did most of the manuscript writing in collaboration with the other co-authors.

Paper III was initiated by M.A. All the calculations and the analysis were performed by Ö.Y. in discussion with M.A. and E.L. Manuscript writing was done by all co-authors.

The mutational analysis in Paper IV was designed by Ö.Y. based on the results in paper III. Ö.Y. generated the mutated constructs. S.A.H. did the functional studies in oocytes, and the data analysis in collaboration with G.K. and R.J.H., except for the modulation data which was obtained by E.A.R and R.J.H. Manuscript writing was done mostly by S.A.H. and R.J.H. in collaboration with the author co-authors.

The code for eBDIMS in Paper V was written by L.O. and O.C. L.O. came up with the idea, did most of the calculations and the manuscript writing. Ö.Y. helped with the ensemble preparations and analysis, ran some of the transition methods, and helped with ﬁgure preparations. All authors contributed to the manuscript writing and discussions. Contents

Contents ix

Abbreviations xi

1 Introduction 1

2 Molecular Neurobiology 5 2.1 Neurons ...... 5 2.1.1 Sensory Neurons ...... 7 2.1.2 Interneurons ...... 7 2.1.3 Motor Neurons ...... 7 2.2 Neuronal Activity ...... 8 2.2.1 Electrical synapses ...... 9 2.2.2 Chemical synapses ...... 10 2.3 The Ion Channel Superfamily ...... 11

3 Cys-Loop Receptors 13 3.1 The Discovery of Cys-Loop Receptors ...... 13 3.2 Channelopathies ...... 14 3.2.1 Hyperekplexia ...... 14 3.2.2 Epilepsy ...... 15 3.3 Anesthesia and Substance Abuse ...... 15 3.4 The First Structure ...... 16 3.5 Origins of Cys-Loop Receptors ...... 17 3.5.1 ELIC ...... 18

ix x CONTENTS

3.5.2 GLIC ...... 18 3.5.3 Are Prokaryotic Models Relevant? ...... 19

4 Modeling and Channel Dynamics in GluCl 21 4.1 Glutamate-Gated Chloride Channel ...... 21 4.1.1 Topology ...... 22 4.1.2 GluCl-based Models Capture Modulator Properties ...... 24 4.2 Channel Dynamics of GluCl ...... 26 4.2.1 Simulations of GluCl can Capture Closing Dynamics . . . . . 27 4.2.2 From Agonist Binding to Channel Opening ...... 30 4.3 From in silico to in vitro ...... 33

5 A Gating Mechanism 35 5.1 Principal Component Analysis ...... 35 5.2 Gating Intermediates in the GLIC ensemble ...... 36 5.3 Functional Clusters of Eukaryotic Structures ...... 37 5.4 Cys-loop Receptor Dynamics Derived From Long-Scale Simulations . 39

6 Methods 45 6.1 Homology Modeling ...... 45 6.2 Molecular Dynamics Simulations ...... 46 6.3 Free Energy Calculations of Ion Permeation ...... 48

7 Summary of Papers 51

8 Concluding Remarks 55

A Sequence Alignment 57

Acknowledgments 59

Bibliography 63 Abbreviations

Cl Chloride EC Extracellular LGIC Ligand-gated ion channel MD Molecular dynamics Na Sodium PCA Principal component analysis PDB Protein data bank PMF Potential of mean force RMSD Root mean square deviation TM Transmembrane WT Wild-type

Amino acids, three and one letter codes:

Xaa (X) Any amino acid Glu (E) Glutamic acid Phe (F) Phenylalanine Ala (A) Alanine Gly (G) Glycine Pro (P) Proline Arg (R) Arginine His (H) Histidine Ser (S) Serine Asn (N) Asparagine Ile (I) Isoleucine Thr (T) Threonine Asp (D) Aspartic acid Leu (L) Leucine Trp (W) Tryptophan Cys (C) Cysteine Leu (L) Lysine Tyr (Y) Tyrosine Gln (Q) Glutamine Met (M) Methionine Val (V) Valine

Chapter 1

Introduction

The doctor rose to mark the the session’s end. “We have done very well this time, seeing where some of the ghosts of the past still clutch at you in the present.”

Joanne Greenberg, I Never Promised You a Rose Garden

Neurological disorders are complex phenomena that are often associated with demonic possessions. The records of such associations date all the way back to Babylonian times. Babylonian tablets excavated in 1952, in Urfa, Turkey [1] contain detailed descriptions of a disease called Sakkiku and the treatment methods for Sakkiku at that time. Sakkiku, which we today call epilepsy, was believed to be the work of evil spirits. The Babylonian doctor, asipu, determined which type of spirit the patient was possessed by based on the symptoms. For example, “if a patient was possessed two three times a day, ﬂushed in the morning and pale in the evening” the spirit that possed is miqtu but “if his eyes are red and yellow-han of ardath Lili” then the spirit is a diﬀerent one.

It is possible to see similar beliefs for various diseases; even today, it happens that people explain the cause of some diseases with demons. Personally, I do not need to look 3000 years back in time to see examples of such claims. My mother and grandmother have also retold stories that people who were sick with seizures were accused of being possessed by demons. The idiom “Desto serd nishto tuya” was used to refer to these individuals in my village (Hawiq Pax, Dersim, Turkey) within the last century. This epigram translates into being touched by a cold hand (an invisible hand). Cold hand, here, refers to the hand of a milaket, a demon.

1 2 CHAPTER 1. INTRODUCTION

Claiming that epilepsy, or any other disease for that matter, is the result of demonic possession may seem like a harmless thought. However, the handling of the sick depends highly on how you explain the underlying cause. If an epileptic person was believed to be possessed, it is possible that he or she was in the centre of spiritual rituals that could very well be another trigger of the epileptic seizures.

Throughout history, there are accounts of people finding alternative explanations other than demonic possession. One of these alternative explanations, the so-called natural cause can be dated back to Hippocrates (Figure 1.1). It was not until the 19th century that Hippocrates was shown to be right about the natural cause of epilepsy. In the 19th century Robert Bentely Todd and John Huglings Jack- son [2] described epilepsy as a disease caused by electrical currents or discharges. However, without a device to measure the electrical activity of the brain, Todd’s and Jackson’s theories could not be confirmed. In 1924, Hans Berger managed to record the electrical activity of the brain. This recording led to the invention of the modern day electroencephalo- gram (EEG) machines. This measure- ment was a major milestone for research on epilepsy and many other neurological disorders. The existence of the natural cause could now be objectively quantified. Within a decade of this discovery, animal models were introduced and tests for drug candidates had be- gun (summarised by Grone and Bara- ban [3]). However, despite these ad- vancements on measuring nerve system activity, there was still no clear model how this activity was generated.

Explaining the mechanism of electrical activity had its own developmen- tal stages that trace back to the 18th century. In 1786, Luigi Galvani had demonstrated that he could make the leg of a dead frog twitch. After excluding all the forces that could move the leg, Galvani concluded that the leg Figure 1.1: Hippocrates’ natural cause is must generate and transmit electricity. one of the first explanations in history The twitching frog leg was intriguing that separates the occurrence of a disease for many researchers, but not all agreed from demonic possessions. Illustration by with the conclusion, in particular not the author. Alessandro Volta. Volta rather argued twitching had nothing to do with the 3 muscle cell. He designed a set of experiments to show that the sparks seen in Galvani’s experiments could be explained by moisture trapped between two metal plates. The experimental design also led to Volta’s invention of an early battery. This well-received invention brought Volta fame and support. Although the animal electricity theory of Galvani was shadowed by Volta’s fame (For the full story see Geddes and Hoff [4]), Galvani’s work did not vanish completely and was continued by his nephew, Giovanni Aldini. Aldani mesmerized the public by animating the limbs of dead humans. These experiments even made it into popular culture, in particular the 19th century novel by Mary Shelley: Frankestein. The slow progress around Galvani’s animal electricity theory eventually led to the discovery of dif- ferentially charged compounds, which we now call ions. In subsequent studies, scientists tried different compounds and solutions to attempt to tweak the activity of excitable cells, e.g. muscles. Eventually, in 1952, Hodgkin and Huxley showed that the electrical properties of the cells arise from an influx of ions [5]. They also hypothesized that there were specialized proteins responsible for these ion fluxes.

In the 20th century, epilepsy was no longer explained as a form of demonic possession but the result of abnormal electrical activity in the brain and the most likely cause was the hypothetical ion channels. Moreover, scientists were able to isolate and demonstrate that ion channels existed and they were indeed able to generate electrical activity. In 2005, structure of an ion channel involved in neuronal communication was published in medium-resolution by Unwin [6]. Somewhat surprisingly, subsequent studies identiﬁed similar channels in bacteria, although these organisms do not have any component similar to the nerve system. Bacteria are important model systems in structural biology since it is typically much easier to produce (overexpress) their channels compared to mammalian ones. This mass production was then used for structure determination that yielded atomistic detailed crystal structures of ion channels in multiple states. Since 2007, the number of available crystal structures has been growing rapidly, which has allowed both us and many other groups to utilise in silico methods to study single channel behaviors in computers.

In silico methods can model the structural changes leading to function and map the effect of disease-related mutations on these models. The more information that is available about these channels, the easier it is to explain the pathology of neurological diseases and design drugs that are more specific, less costly, and with fewer side effects. The studies presented in this thesis will focus on this point: the functional and structural changes of ion channels. I will demonstrate the insights gained from in silico methods of atomistic structures and how it might be utilized to help targeted treatment of neurological diseases.

I start with a brief overview of the nervous system, followed by a history of 4 CHAPTER 1. INTRODUCTION an important ion channel subfamily, cys-loop receptors.1 Chapters 3-5 discuss the current state of the field and introduce some previously unpublished results as well as papers I through V. Chapter 6 summarizes the different methods employed in Papers I-V. The studies in this thesis cover many subfamilies of cys-loop and related receptors. To help the reader keep track of different channels and residues, a sequence alignment with markings is presented in Appendix.

1The term cys-loop receptor speciﬁcally refers to the eukaryotic channels with a cysteine bridge. Bacterial homologs of these receptors lack this bridge, and are thus technically not cys- loop receptors. However, they are highly similar to each other, both functionally and structurally. The superfamily of these channels is called pentameric ligand-gated ion channels. Recently, Jaiteh et al. [7] suggested the term “pro-loop” as more ﬁtting than “cys-loop”. However, since the term cys-loop receptor is very common, I will use it to refer both to eukaryotic and prokaryotic channels in this thesis. Chapter 2

Molecular Neurobiology

In a hole in the ground there lived a hobbit.

J.R.R. Tolkien, The Hobbit

Neuroscience is a fascinating ﬁeld that explores the development and function of the nervous system, which controls the body and responds to the environment. There are billions of neurons in the brain and spread throughout the body. Molec- ular neuroscience works at this cellular level and explores structural and functional changes in neuronal proteins in order to explain the physiology of neurological diseases.

I will brieﬂy describe the anatomy and physiology of neurons and explain the neuronal activity in the following sections, but the interested reader can ﬁnd more detailed descriptions in a number of good textbooks, for instance “Introduction to Neurobiology” by Reichert [8] or “Neuroscience” by Purves [9].

2.1 Neurons

Neurons, unlike other cells in the body, can sense and respond to the environment. They are highly specialized information-processing units. These units contain several projections of dendrites and an axon enabling complex networks with multiple contact points to other excitable cells. To allow complex tasks and to be able to sense different signals, neurons adopt different morphology during development; the number of dendritic arms, the length of the axon and the shape of the core vary a lot. The functional and morphological diversity makes classification of neurons a complex problem.

5 6 CHAPTER 2. MOLECULAR NEUROBIOLOGY

For this thesis, the most relevant classiﬁcation of neurons is based on their function. There are three major groups (Figure 2.1): (a) Sensory neurons that are connected to organs such as the ear, eye or the skin, (b) motor neurons that activate muscles, and (c) interneurons that establish neuronal communication.

(a) Sensory Neurons (b) Motor Neurons

Figure 2.1: Neurons are divided into three major groups based on their function: (a) Sensory neurons that are connected to organs, (b) motor neurons that regu- late muscle activity, and (c) interneurons that establish neuronal communication. Illustration by the author. 2.1. NEURONS 7

2.1.1 Sensory Neurons

Sensory neurons are part of the peripheral nervous system. They are responsible for collecting information from the internal or sensory organs like ears or eyes. These neurons are highly specialized for processing a particular type of signal; for instance sound or pain. The component receiving the signal, which is usually an ion channel, diﬀers between sensory neurons. For example, neurons responsible for the sensation of smell contain cyclic nucleotide-gated ion channels, while neurons that respond to touch are populated with mechanosensitive ion channels. The signal collected by sensory neurons has two potential paths: Either it can be transmitted to the interneurons in direct contact with motor neurons, or it can be transmitted to the complex interneuron network in the brain. While the former is important for providing a fast response, a reﬂex, to painful stimulus, the latter is important for learning and adapting to the environment.

2.1.2 Interneurons

Interneurons are small neurons that enable internal communication and interplay between sensory and motor neurons. They are involved in reﬂexes, learning and memory. Interneurons especially in the brain form complex networks connecting not only local regions but also distant parts to each other. This complex network is where the information is integrated and transmitted to motor neurons. Interneurons can be inhibitory (preventing the neuronal signaling by depolarizing the neighbor cell) or excitatory (initiating polarization in the neighbor cells). While excitatory interneurons are important in motor reﬂexes, inhibitory neurons play a major role in learning.

2.1.3 Motor Neurons

Motor neurons originate from the spinal cord and extend to organs, muscles, or glands where they control the function. As described above, a motor neuron can be connected to a sensory neuron directly with an interneuron, but it also receives input from many other interneurons. Motor neurons constitute the last point of the signal transduction pathway. The connection between motor neurons and muscles is where the neuronal signal is converted into behavioral responses like walking or talking. In vertebrates, motor neurons always create an excitatory response. Thus, the inhibition of muscle movements is achieved by the inhibition of the motor neuron itself. The experiments conducted by Galvani in the 18th century were conveniently repeatable in diﬀerent animals due to the always-excitatory nature of motor neurons. Motor neurons are also the type of neurons studied most extensively. The most important component of this thesis is the family of cys-loop receptors that establish neuronal communication, and it should not come as a surprise that the ﬁrst cys-loop receptor to be isolated was of neuromuscular origin. 8 CHAPTER 2. MOLECULAR NEUROBIOLOGY

2.2 Neuronal Activity

The neuronal activity can be described as repeated changes in membrane potential initiated by a signal. This activity consists of two steps: (i) signal transmission within the nerve cell, and (ii) the signal generation and transfer between cells. While the ﬁrst part of the activity involves action potentials, the latter is achieved via synaptic potentials.

The resting potential of a neuronal membrane is around -70mV [8]. For an excitable cell to transfer or make use of information, the membrane potential needs to be increased (depolarized) to the threshold value -55mV [8]. This initial depolarization is achieved via synaptic potentials. Following depolarization, voltage-gated ion channels are activated whose activity in turn activates more of the same channels like a domino eﬀect. The result of this is a spike in membrane potential, which is called an action potential. The action potential serves as a trigger to another synaptic potential event in the neighboring cell. The main topic of this thesis is synaptic potentials; therefore, I will not go into further detail on action potentials, but leave them as the acting messengers between diﬀerent synaptic potentials.

Synaptic potentials are the collectors of information from external sources such as another neuron or the outside world. A signal from the outside world can for example be the sound of your advisor’s footsteps (Figure 2.2). The basic workflow in the nervous system upon receiving this signal is as follows: The sound activates the sensory neurons via synaptic potentials and marks the starting point for the synaptic potentials. When the synaptic potentials reach the threshold value -55mV, an action potential is fired. The action potential is then transmitted to your brain. The brain processes this information and based on the reflex of a seasoned student, a new signal is sent to the body to start working.

The simpliﬁed example above depicts synaptic potentials as if they were always excitatory. In reality, this is a much more complex process because synaptic potentials can also be inhibitory. Moreover, a single excitable cell is capable of receiving several synaptic potentials simultaneously. In fact, the threshold value is deﬁned as the sum of all the individual synaptic potentials.

Synapses are specialized junctions formed between two excitable cells where the synaptic potentials are initiated. There are two components in a synapse: The signal-initiating cell, also called pre-synaptic, and the receiving cell, also called post-synaptic. There are also two types of synaptic transmission: electrical or chemical. 2.2. NEURONAL ACTIVITY 9

Figure 2.2: The neuronal activity when receiving an outside signal involves transmission of the signal to the brain by sensory neurons, after which the brain re- turns a new signal that eventually leads to a motor neuron activating muscles. Strip from “Piled higher and deeper” reproduced with permission from Jorge Cham www.phdcomics.com (top) and illustration by the author (bottom).

2.2.1 Electrical synapses

Electrical synapses (Figure 2.3) are less abundant than chemical synapses and they establish direct contact between neurons. In these type of synapses, both pre- synaptic and post-synaptic cells contain gap junction channels. These channels consist of a wide physical passage allowing the direct exchange of ions and other molecules. Thus, neurons connected via electrical synapses are able share their internal signaling molecules. The distribution of signaling molecules ensures the harmony in a neuronal population, meaning the transmitted response by the neuronal population is guaranteed to be the same. Such synchronized input from neurons to other organs is vital especially for breathing and hormone secretion [9]. Although electrical synapses create a harmony and enable direct signal transfer between neurons, it does not leave too much room for changes in the signal amplitude or the signal type. 10 CHAPTER 2. MOLECULAR NEUROBIOLOGY

Figure 2.3: Electrical synapses enable synchronization of neuronal populations. Illustration by the author (left). An example of a gap junction channel that connects the inside of two diﬀerent nerve cells [10] (right).

2.2.2 Chemical synapses Chemical synapses (Figure 2.4) establish communication between two neurons by using molecules called neurotransmitters. When an action potential reaches the end of the axon in a pre-synaptic cell, it triggers the fusion of neurotransmitter-ﬁlled vesicles with the neuronal membrane. A fused vesicle releases the neurotransmitter molecules to the synaptic cleft. These neurotransmitters then alter the membrane potential in the post-synaptic cell by binding to cys-loop receptors.

There are two advantages of a chemical synapse over an electrical one. First, the signal received can be transmitted to the next neuron with a different sign. For example, the inhibition of a motor neuron can be achieved through an excitation of an interneuron. Second, neurotransmitter release is needed only once for multiple cells to be activated. Moreover, by releasing different neurotransmitters from different synapses simultaneously, the transmission can be fine-tuned at the post-synaptic cell.

The surface of a post-synaptic cell is crowded with ion channels that are activated by neurotransmitters. These ion channels are named ligand-gated ion channels. One of the most common types of such channels consists of ﬁve subunits around a central ion pore. This constitutes to the family of cys-loop receptors that is the central focus of this thesis. 2.3. THE ION CHANNEL SUPERFAMILY 11

Figure 2.4: Chemical synapses enable fast neuronal communication. Illustration by the author (left). An example of a ligand-gated ion channel that converts chemical signals into electrical ones at the chemical synapse [11] (right).

2.3 The Ion Channel Superfamily

As the reader will likely have realized by now, ion channels are the key elements of the neuronal activity. In previous sections, I have already named a few ion channels specialized for diﬀerent tasks. The shared structural feature of ion channels is a transmembrane domain that creates a safe passage for water molecules and hydrophilic compounds such as ions through the hydrophobic environment (membrane). Ion channels are grouped into superfamilies based on the activation mechanism (Figure 2.5). Each superfamily is then grouped into families and subfamilies with closely related proteins. The two largest superfamilies are called voltage-gated ion channels and ligand-gated ion channels, and as the names indicate they are activated by membrane potential and ligands, respectively. Although the topology and the gating mechanisms depend on the family, they share many functional behaviours and similar methods can be used to study all of them.

The ligand-gated ion channel superfamily contains many subfamilies grouped into larger families based on the number of subunits. The subfamilies of the pentameric ligand-gated ion channel family, also called cys-loop receptors, is grouped by the receptors’ neurotransmitter sensitivity. 12 CHAPTER 2. MOLECULAR NEUROBIOLOGY

Figure 2.5: Ion channels are grouped into superfamilies based on the activation mechanism, such as binding of small molecules (ligand-gated), change in membrane potential (voltage-gated), or other events. Later these superfamilies are divided into smaller groups. For instance ligand-gated ion channel superfamily is separated into diﬀerent groups based on their subunit stoichiometry. Illustration by the author. Chapter 3

Cys-Loop Receptors

Dwell as near as possible to the channel in which your life ﬂows.

Henry David Thoreau

3.1 The Discovery of Cys-Loop Receptors

The term cys-loop receptor emerged more than a decade ago following the discovery of channels responding to different ligands and neurotransmitters in eukaryotes [12]. In 1905, John Newport Langley presented the first evidence for nicotine and curare stimuli on muscles. He proposed that the muscle activity was due to receptive substances present in red muscles [13]. Moreover, he concluded that the nature of this receptive substance differed between species. Based on this observation, these receptive substances were named nicotinic receptors. Today we know that they are sensitive to a much wider range of compounds. In 1921, Otto Lowei confirmed that the chemical process causing a heart to beat is based on the same principle. In his experiments, Lowei stimulated the nerve in contact with a heart. He then applied the solution extracted from this first experiment on a second heart, which caused it to beat without any neuronal stimulation [14]. The active substance in Lowei’s experiments was later confirmed to be acetylcholine, which was shown to be the natural ligand of the nicotinic receptors [12].

By 1990, several homologs of the nicotinic receptors had been discovered together with their corresponding neurotransmitters. The receptors were named based on the neurotransmitter to which they respond, for instance γ-Aminobutyric acid receptors (GABAAR), glycine receptors (GlyR), or serotonin receptors (5HT3). Each of these receptors contains several diﬀerent subunit types (Table 3.1). In vivo,

13 14 CHAPTER 3. CYS-LOOP RECEPTORS

Table 3.1: The cys-loop receptor family includes several different receptors that can be either anion- or cation-selective. Each receptor subfamily has several different subunit types that are assembled into different combinations to form channels with slightly different functional properties.

Subfamily Subunits Common Form Neurotransmitter Selectivity

GABAAR α (1-6), β (1-3), γ (1-3), ρ (1-3), π, , δ, θ (α 1)2(β 2)2γ GABA Anion GlyR α (1-4), β (α 1)3(β) 2 Glycine

nAChR α (1-10), β (1-5), γ, , δ (α 4)2(β 2)3 or (α 7)5 Acetylcholine Cation 5-HT3 A, B, C, D, E (5-HT3A)5 Serotonin functional receptors are formed by combination of different subunits, and this combination will vary depending on the tissue or organ. There are for instance at least 30 possible rearrangements of GABAAR subunits [15], but the major functional form identified consists of α1, β2, and γ1 subunits [16]. Regardless of their differences in subunit composition and neurotransmitter sensitivity, subsequent studies identified conserved features within the family, in particular a disulfide bridge [17]. This conserved disulfide bridge is the origin of the “cys-loop receptor” nomenclature for the pentameric ligand-gated ion channel subfamily.

3.2 Channelopathies

Several diseases have been linked to abnormalities in cys-loop receptor function, for instance depression, epilepsy and hyperekplexia. The term channelopathy has been introduced to define this type of diseases that are caused by malfunctioning ion channels [18]. In most cases the connection between the disease and a specific ion channel has been identified only recently and is still under investigation.

3.2.1 Hyperekplexia Hyperekplexia is a neurological disease characterized by excessive startle response and muscle stiffness, and it was identified in 1960s. A number of studies later revisited hyperekplexia to identify the genetic component, and several mutations on the GlyR-α1 subunit were identified as the probable cause of hyperekplexia [19, 20]. Since then, several other mutations on GlyR have been identified in patients diagnosed with hyperekplexia, and anti-epileptic drugs are now frequently used in the treatment. In vitro studies of mutated GlyR constructs have revealed the functional aspects of the mutations as well, but there is still some debate about how the sequence mutations alter the channel function. As argued by Langosch et al. [21], reduced ligand affinity for a particular mutated channel could either be a direct effect from less efficient ligand binding, or an indirect effect due to changed coupling between ligand binding and gating. Functional studies have not yet been able to distinguish between these two effects, and this is one example of an 3.3. ANESTHESIA AND SUBSTANCE ABUSE 15 area where it might be possible to use computational methods to study the gating process in molecular detail.

3.2.2 Epilepsy Epilepsy is a complex neurological disease characterized mainly by seizures. There are several different types of epilepsy, each with a different underlying cause such as physical trauma, substance abuse, or genetic anomalies. Although it is not possible to explain the cause with a single variable, treatment of epilepsy started to evolve already in the 19thcentury. In 1857, Sir Locock was successful in treating his patients by administering potassium bromide [22], which remained in use until the introduction of phenobarbital in 1912 by Bayer. The introduction of animal models in epilepsy research led to the emergence of several other anti-epileptic drugs [3], but they were all based on physical observations and phenomenology rather than specific knowledge of their action.

With the introduction of genome sequencing in diagnostics [23], several mutations have been identified in the cys-loop receptor family members nAChR and GABAAR that are likely to contribute to epilepsy. Mutations in nAChR α4 subunit were some of the first mutations shown to cause seizures (for a good review, see Bertrand [24]). An antiepileptic drug, carbamazepine, was especially effective on autosomal dominant nocturnal frontal lobe epilepsy to which the mutations on the nAChR-α4 subunit were linked [25]. This correlation was of course not a coinci- dence, and subsequent studies identified carbamazepine as an open channel blocker. Moreover, epilepsy mutations in nAChR-α4 have been shown to alter the receptor sensitivity to carbamazepine and acetylcholine. This is a wonderful example of how modern research made it possible to link complex diseases with specific channels and mutations. We still do not fully understand the exact mechanism of action, but we have come a long way from being possessed by spirits.

3.3 Anesthesia and Substance Abuse

In addition to their importance in channelopathies, cys-loop receptors are also the targets for anesthetics and many addictive compounds such as alcohol or anti- depressants. Even tough in small doses these molecules have therapeutic eﬀects, the transition rates from medicinal use to dependence is rather high for them [26], and this can have deadly consequences.

The term anesthesia, literally “lack of sensation”, was first used by Oliver Wen- dell Holmes in 1846 to describe the state of a patient who after inhaling ether went into surgery without feeling any pain [27]. Anesthetics were, in fact, used long before the term emerged: The oldest example known is ethanol. The anesthetic properties of ethanol are highly dependent on the age of the subject, in contrast to modern inhaled anesthetics like desflurane. The age dependence implies that the 16 CHAPTER 3. CYS-LOOP RECEPTORS density or the properties of ion channels, on which anesthetics act, could be different between elderly patients and young adults. Indeed, the expression of GABAAR subunits has been shown to be age-dependent in rats [28], and a similar age-dependent expression exists in other mammals. Although the age dependence can be a com- plication for practical anesthesia, the differences between various channel subunits proved helpful in identifying molecular mechanisms. Long before high-resolution structures were available, researchers were able to postulate a separate anesthetic binding site located within the transmembrane domain than the neurotransmitter binding site [29]. Today, several groups are targeting this site using computational drug screening techniques combined with high-throughput experiments to try and identify new anesthetic agents with better properties [30].

The World Health Organization (WHO) defines substance abuse as “the harm- ful use of psychoactive substances including alcohol and illicit drugs”. Substance abuse is a very complex phenomenon with a social aspect to it. Most of the psychoactive substances interfere with the regular rhythm of synaptic transmission, and frequently by interacting with ligand-gated ion channels. Just as anesthesia, there is subunit dependence for these compounds and early studies identified similar residues to be responsible for the effects of psychoactive substances as in anesthethics. Although functional studies point to a conserved binding site for psychoactive substances, this has not yet been accepted as direct proof. To design substances for treatment of drug abuse it is necessary that studies be combined with specific high-resolution structural information about the binding sites. One example of this is how researchers the last few years have started to use the newly available structures of cys-loop receptors to repurpose an anti-parasitic agent, ivermectin, for substance abuse treatment [31].

3.4 The First Structure

Membrane proteins have been the most challenging targets in crystallography. This is largely because of their hydrophobic nature and instability, which makes it difficult to obtain crystals, let alone getting them to diffract beyond 5Å [32]. An alternative is to use flash-freezing in combination with electron microscopy (cryo-EM), which bypasses the requirement for crystallization. This technique is easier to use for membrane proteins, but historically it has been much lower resolution (although this is now changing with better detectors). Already in the 1980s, Nigel Unwin was succesful in obtaining images of nAChR with electron microscopy [33, 34]. It was evident from these images that the receptor contained three domains: extracellular, transmembrane and intracellular ones. In the 1990s, Unwin published more detailed studies with higher resolution structures comparing the open and the closed state of the channels with electron microscopy [35, 36]. These new maps revealed motions in proximity of the predicted acetylcholine-binding site at the extracellular domain. The motions of the gating helices in the transmembrane domain were 3.5. ORIGINS OF CYS-LOOP RECEPTORS 17 revealed as rigid-body motions, largely independent of the other transmembrane segments. However, apart from the gating helices, neither the detailed structure nor the overall topology of the transmembrane domain could be determined from these maps.

A number of subsequent studies tried to establish the topology of all three domains using cysteine crosslinking analysis [37], mass spectrometry [38] and computational topology prediction methods [39]. However, results were inconsistent: The largest controversy concerned the secondary structure composition of the transmembrane domain. While computational studies predicted alpha helices [39], diﬀrac- tion data [36] and mass spectrometry [38] results indicated existence of beta sheets also within the transmembrane domain.

In the meantime, Unwin continued his work on nAChR and was able to obtain medium-resolution data. A 4Å resolution structure was published in 2005 (Protein data bank identifier 2BG9), 20 years after the first image was obtained [6]. This was the first structure to show the cys-loop receptors in close to atomic detail. The resolution of this structure was limited, and the sidechain placement has later been shown to be incompatible with high-resolution structures. However, Unwin’s work provided a new way of looking at these receptors and enabled the first comparisons between functional data and amino acid locations in the structure [40]. The nAChR structure made it clear that the transmembrane domain of each cys-loop receptor monomer is formed by four membrane-spanning alpha helices, while the extracellular domain mainly consists of beta sheets. The intracellular domain was only partially resolved in this structure, but predicted to be in alpha helical form.

3.5 Origins of Cys-Loop Receptors

Until 2005, cys-loop receptors were believed to be limited to metezoa (also called animals). However, phylogenetic studies of another large ion channel family, voltage- gated ion channels, have surprisingly found homologous sequences in non-metezoan eukaryotes and prokaryotes [41]. Driven by this, Tasneem et al. [42] identiﬁed several putative cys-loop receptor homologs in diﬀerent bacteria. Although the role of these putative channels is not clear, it appears cys-loop receptors might have direct prokaryotic ancestry.

Prokaryotic channels are important model systems for a number of reasons. Bacterial cultures are easier to handle than mammalian cell lines, and bacterial membrane proteins appear to be more stable under crystallographic conditions, which makes them very important for structural biology. Bacterial proteins are also easier to work with in many other experimental setups, for instance two-electrode voltage clamp electrophysiology in Xenopus laevis oocytes. As one would expect, several diﬀerent groups picked up the putative channels identiﬁed by Tasneem et al. 18 CHAPTER 3. CYS-LOOP RECEPTORS

[42] for further characterization. Two receptors from Gloeobacter violaceus (GLIC) and from Erwinia chrysantemi (ELIC) became the focus in the cys-loop receptor ﬁeld. Both ELIC and GLIC share about 20% sequence identity with the cys-loop receptors, but lack the intracellular domain. While this made it likely that their structure was highly similar, little or nothing was known about the proteins:

• Did the putative bacterial proteins express and form functional channels in cells commonly used for in vitro electrophysiology?

• What was the activation proﬁle for these putative channels? Did they respond to the same ligands as cys-loop receptors?

• Did mutations alter the activation proﬁle as for cys-loop receptors?

3.5.1 ELIC

ELIC was characterized as a cationic channel, similar to nAChR and 5-HT3A [43], and its structure was determined at 3.3Å resolution (PDB:2VL0). In contast to the Unwin nAChR structure, ELIC was in a presumed-closed state with a very narrow pore radius. The central pore region in this structure is entirely dehydrated, and there has been some controversy whether the channel is collapsed. The limited number of crystal structures at the time made it difficult to assign a specific state, but computational studies of ELIC have confirmed that it appears to be in a non- conducting state [44].

In 2011, Zimmermann and Dutzler [45] identified several different compounds including GABA as agonists of ELIC. The response of ELIC to GABA, and the potential binding site, provided evidence that structurally and functionally ELIC is highly similar to cys-loop receptors. Later co-crystals of ELIC with acetylcholine and GABA confirmed that the binding site for neurotransmitters is the same between ELIC and cys-loop receptors [46, 47].

Today, there are as much as 17 structures of ELIC, excluding a few special chimeric constructs. However, the catch is that all those structures are in the same closed state, despite some of the structures having an agonist bound in the extracellular domain (which should open a ligand-gated channel). These structures still provide information about the ligand-binding sites, but with only a single state available it has been diﬃcult to elucidate the gating mechanisms.

3.5.2 GLIC G. violaceusis is an aquatic cyanobacterium, which lacks the fundamental components of modern day photosynthesis machinery. With the uncommon structure of 3.5. ORIGINS OF CYS-LOOP RECEPTORS 19 its photosynthetic machinery, G. violaceusis is commonly found in wet-rock habi- tats. Studies focusing on its evolution are very limited and the actual role of the GLIC channel is not yet known. However, several groups have been able to deter- mine X-ray structures of GLIC [48, 49], and showed that that this structure too is very similar to the nAChR and ELIC ones.

There are still no ligands known to activate GLIC, and it lacks the intracellular domain and the disulfide bridge that both are important components of cys-loop receptors. However, a number of studies have shown that GLIC is still remarkably similar to human receptors from a functional perspective. GLIC was found to be inhibited by anesthethics by Weng et al. [50], and Nury et al. [51] managed to co-crystallize the channel with them and determined the binding site. This binding site, located within each subunit, was proposed as a model for nAChR primarily for its similar anesthetics inhibition profile. In contrast, the hypothetical anesthetic binding site for GABAAR and GlyR corresponds to another cavity in close proximity of the identified site in GLIC [52]. Computational studies of GlyR models using GLIC as a template [53] indicated that this hypothetical cavity could accommodate ethanol, and showed this allosteric ligand helped stabilize the open state in agreement with the pharmacological profile of GlyR [54].

The conflicting results were partly settled when Howard et al. [55] demonstrated that in contrast to GlyR, GLIC is inhibited by alcohols. Moreover, the modulation profile by alcohols could be altered and made more similar to GlyR or GABAAR with a single point mutation. Computational studies predicted that this mutation would expand a second cavity located at the intersubunit interface similar to the hypothetical cavity in GlyR [56, 57]. The existence of the intersubunit binding site and its ability to bind anesthetics was later confirmed with X-ray structures of the mutant [58].

3.5.3 Are Prokaryotic Models Relevant? The prokaryotic ELIC and GLIC structures have been extremely important models for cys-loop receptors despite their relatively low sequence identity to eukaryotic receptors. In particular the GLIC structure in the open state has made it possible to compare with the Unwin structure and to run a large number of computer simulations of mammalian cys-loop receptors [53, 59].

The first suggested gating motions were identified already from the structural studies of nAChR [6], and the chimeric model that is assembled using the structures of acetylcholine-binding protein (AChBP) and the transmembrane domain of nAChR [60]. Both crystal structures and the molecular dynamics simulation of GLIC [61] supported the gating hypothesis proposed by Unwin in 1995. While these studies overall point to a conserved gating mechanism, it has not been possible to probe agonist binding and its connection to gating with GLIC - since there is no 20 CHAPTER 3. CYS-LOOP RECEPTORS known ligand. Studies have also pointed out several key differences in gating between anionic (GABAAR, GlyR) and cationic (nAChR, 5-HT3) receptors [62] such as the radius of the pore at the cytoplasmic side. Moreover the dominant modulator binding site appears to be different between anionic and cationic channels [51, 55, 56], which naturally calls for a new model system for anionic channels as both GLIC and ELIC are cationic channels. Chapter 4

Modeling and Channel Dynamics in GluCl

Would it not be better if one could really “see” whether molecules...were just as experiments suggested?

Dorothy Hodgkin

4.1 Glutamate-Gated Chloride Channel

Caenorhabditis elegans is a non-parasitic worm living in soil. Its feeding/digestion movements involve the contraction of pharynx muscles. The control over this muscle activity is achieved primarily through the post-synaptic glutamate-gated chloride channel, GluCl. The subfamily of this channel contains two members as α and β subunit types. GluCl was ﬁrst isolated in 1994 by Cully et al. [63]. They also characterized the subunits in their homopentameric forms and compared with the heteromeric mixture of subunits. Results showed that the α subunit alone is not gated by glutamate, but activated by ivermectin. However, unlike the α homomers, the homomeric β channels respond to glutamate in a dose dependent manner, but they are not activated by ivermectin. This distinctive behavior of GluCl subunits resembles the mammalian channels of which homopentameric and heteropentameric forms often respond to the same ligand in a diﬀerent manner [64, 65].

Early studies of GluCl were focused on its use as a neuronal silencing tool [66, 67], likely due to the small response magnitudes and diﬃculties expressing the channel in oocytes. Using antibody fragments and other modiﬁcations (discussed in section 4.3) to increase the stability of the receptor, Hibbs and Gouaux [11] managed

21 22 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL to obtain a crystal structure of GluCl-α homopentamers at 3.4Å resolution. The crystal structure has accelerated the use of GluCl as a model system, both for modeling human receptors such as GABAAR and to study gating dynamics in general for eukaryotic channels.

4.1.1 Topology

Like its mammalian counterparts, GluCl subunits contain extracellular, transmembrane and intracellular domains (Figure 4.1). The extracellular domain of GluCl starts with a short α-helix, followed by ten β-strands and loops. The last β strand forms part of a loop that caps the ligand-binding site (loop 10), which also continues into the covalent connection to the transmembrane domain. The transmembrane domain consists of four α-helices (M1-M4) and three loop regions. The M2 helices line the pore, and the M2-M3 loop interfaces to the extracellular domain where it interacts with loops 2 and 7. The M3-M4 loop extends into the cell and forms an intracellular domain. The structure of this domain is still not known; despite many attempts, crystallization of this domain has still not been successful, and for this reason it is usually excluded or cut away from the sequence constructs in experiments [68].

GluCl is activated by the neurotransmitter L-glutamate and its binding site overlaps with those of mammalian receptors. This binding site, also called the orthosteric site, is located in the extracellular domain between two subunits. The subunit that provides the capping loop, loop 10 (loop C), is referred to as the principal subunit, while the neighbouring subunit is referred to as the complementary subunit. In addition to loop 10, the orthosteric site is formed by β1 (loop G), β2 (loop D) and loop 9 (loop F) on the complementary subunit, and loop 4 (loop A) and loop 8 (loop B) located in the principal subunit.1 To distinguish between intersubunit interactions, we usually use a scheme where residues located in the complementary (next) subunit are marked with a prime sign.

There are multiple different allosteric binding sites, located in different parts of cys-loop receptors. One such site is located at the extracellular domain between subunits, similar to the neurotransmitter-binding site. The transmembrane domain contains two different sites that have been extensively studied in the context of anesthetic and alcohol binding: One located inside each subunit, the intrasubunit site, and one located between subunits, the intersubunit site. The latter is also the binding pocket for the antiparasitic agent, ivermectin, which was co-crystallized with GluCl [11]. The allosteric modulators used in this work mostly bind to the

1The loops were originally named before any structure was available, which corresponds to the names in the parentheses as well as papers II-V. However, when looking at the structure it is much easier to enumerate the loops, so to facilitate for the reader I use this simpler scheme in this thesis. 4.1. GLUTAMATE-GATED CHLORIDE CHANNEL 23

Figure 4.1: (a) Each cys-loop receptor subunit contains ten β strands in the extracellular domain, and 4 α-helices in the transmembrane domain. (b) There are several diﬀerent binding sites in cys-loop receptors. Two of the most important ones for this thesis are the orthosteric site in the extracellular domain and the intersubunit allosteric site in the transmembrane domain. 24 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL intersubunit site in mammalian channels [69], while the intrasubunit site appears to be more pronounced in prokaryotic receptors, such as GLIC [51, 58].

A sequence alignment of cys-loop receptors reveals several conserved residues, in particular in the M2 helices. Since diﬀerent channels have completely diﬀerent sequence enumeration, it is convenient to introduce a standard labeling of the M2 residues that does not vary between sequences. For historical reasons, this too uses a prime notation (which should not be confused with the complementary subunit). This has been widely accepted, and starts from the cytoplasmic side with 00 and increases as it it moves higher up in the pore. There is a set of hydrophobic residues in the middle of the pore, which are conserved in all cys-loop receptors. This position is referred to as 90, or the hydrophobic gate.

Functionally, GluCl in its heteromeric form is more similar to mammalian channels than GLIC. The biggest advantage is the known agonists that activate the channel, in contrast to GLIC where the gating is achieved by titration eﬀects that cannot be assigned to a single site [70, 71]. In addition, crystal structures of GluCl in its holo [11] and apo [72] forms provide possibilities to study the eﬀects of ligand binding. Although GLIC has been shown to capture the channel dynamics [61], GluCl is likely to provide a better model of the actual activation and allosteric modulation of mammalian channels, and for this reason I have used it both for homology modeling and molecular simulations when studying these properties.

4.1.2 GluCl-based Models Capture Modulator Properties The availability of the GluCl eukaryotic structure makes it possible to assess the quality of Unwin’s cryo-EM nAChR structure and how similar the prokaryotic structures are to a high-resolution eukaryotic structure. As shown by Ernst et al. [73], there is a register shift in the residue alignment of the nAChR structure that is inconsistent. This inconsistency could of course be a diﬀerence between species, but the crystal structure of GluCl and chimeric studies of GLIC and GlyR all support the GLIC model [11, 70]. The key quality determinant in homology modeling is to obtain a correct sequence alignment. Such discrepencies, as we showed in paper I, would lead to misalignment at the ligand-binding sites (Figure 4.2). In GluCl-based models, regardless of the alignment or the modeling program used, these residues converged to a single site in agreement with the literature, which supports that there really is a residue register shift (error) in the nAChR structure.

In paper I, we also explored how the binding of ivermectin perturbs the receptor backbone. The ivermectin binding site in the GluCl structure overlaps with the anesthethic binding site, and it is a large enough molecule to increase the subunit spacing signiﬁcantly. In some cases, this might be an advantage as anesthetics might have similar eﬀects on the structure. However, most known anesthethics are much smaller than ivermectin and models based on GluCl may not be a particularly 4.1. GLUTAMATE-GATED CHLORIDE CHANNEL 25

Figure 4.2: Superimposed structures of GLIC (pink), GluCl (gold) and nAChR (blue) indicate alignment inconsistencies at the anesthethic binding site. Residues N265 [74] and M286 [75] have been shown to alter anesthetic sensitivity, but they are not positioned correctly in the nAChR structure. good representation of their binding to cys-loop receptors. In paper I, we explored this issue by building two different models. One model was built using Modeller [76], relying only on the GluCl structure as a template. For the second model, we took a slightly different approach to explore the effect of ivermectin binding. In this model the individual subunits were modeled based on the GluCl structure using Rosetta [77], and then superimposed on the GLIC structure to construct a pentameric channel. The resulting model subunit packing should be less perturbed by the location of ivermectin in the GluCl template. While a valuable control, this model was subject to significantly larger larger changes in the modeling, which could cancel the potential advantage of using a co-crystal structure as a template.

The resulting homology models turned out to be remarkably similar. The root- mean-square deviation (rmsd) between the two models around the anesthetic binding cavity was 1Å, which indicates that the conformational changes induced by ivermectin do not distort the backbone signiﬁcantly. For the later docking studies, this caused us to choose the Modeller-based structure. While the Rosetta approach can model a state in which ivermectin is not present, the inherent sidechain re- modeling might deteriorate the docking results compared to the straightforward homology model created with Modeller. In this study, we analyzed the site that overlaps with the ivermectin-binding site, which is also the site we predicted to be of importance for anesthetic action in this receptor. 26 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL

The docking scores for propofol and its derivatives showed a linear correlation with the known drug concentrations that gives half maximal response, EC50. Residues N265 and M286 have been identified as a contact site for propofol in paper I. Although labelling studies originally did not show any effect for N265 in propofol binding [78], a more recent study has now confirmed the role of N265 in modulation by propofol [74] in agreement with in vivo studies in mice [79].

4.2 Channel Dynamics of GluCl

Early studies that relied on chimeric models [60], as well as structures of nAChR [36] and GLIC [61], identiﬁed plausible gating motions such as quaternary twist and iris- like gating motions of the M2 helices. The quaternary twist motion was described as anti-correlated rotational movements of the extracellular and transmembrane domains around the channel axis (i.e., they rotate relative to each other). The iris-like gating motion can be broken down into two components in the form of tilt and twist motions of the M2 helices that alter the pore radius.

These geometrical motions can be calculated in structures and simulations, and compared to the results in the literature (Figure 4.3). This is done both in papers II-III and V, and I also present some previously unpublished results here. The quaternary twist can be defined as the average rotation angle of each subunit. The relative rotation of the two domains in each subunit is calculated from the difference in the angle between the vectors from the protein center-of-mass to the center-of-mass of each domain. For the calculation of the M2 helix twist and tilt, a right-handed coordinate system was defined such that:

The x-axis is the vector from the protein center-of-mass to the M2 helix center- of-mass,

The z-axis is the principal component of inertia of the whole protein, which is parallel to the symmetry axis in the pore,

The y-axis is the vector normal to the ﬁrst two vectors.

With these axes, the tilt angle can be calculated between the projected helical axis onto the xz plane and the z-axis, and the twist is similarly deﬁned from the angle between the projected helical axis in the yz-plane and the z-axis.

The closing dynamics of GluCl, in addition to the quaternary twist and tilt/twist motions of the M2 helices, is likely to include a reduced volume for the allosteric site, simply because subunits move closer. This can be observed with cavity volume calculations in simulations, as we did in paper I, or with distance measurements between the M3 and the M10 helices [11]. 4.2. CHANNEL DYNAMICS OF GLUCL 27

(a) (b)

Figure 4.3: Collective variables. (a) Quaternary twist for a single subunit is defined as the angle between the vectors from the receptor center to their respective centers of mass, and this is then averaged over all five subunits. (b) Tilt and twist angles are defined by projections of the helix axis on orthogonal planes defined from the receptor symmetry axis and the vector from the receptor center to the helix. Illustrations by the author.

To study receptor dynamics and in particular to enable comparisons between different systems, papers II and III use the co-crystal structures of GluCl in presence and absence of agonists and modulators. While paper II reports data from single microsecond-long simulations, paper III relies on a series of five microsecond-long simulations. All the data presented here are averages over these five simulations unless otherwise stated (four more simulations of the system in paper II were run to improve sampling). The set of simulations run with the ligands removed are referred to as holoNL (-IVM in paper II), while simulations with ivermectin bound are holoI (+IVM in paper II, -GLU in paper III). Finally, the fully liganded simulations are referred to as holoGI (+GLU in paper III). Apo refers to the actual apo state crystal structure and its corresponding single microsecond simulation.

4.2.1 Simulations of GluCl can Capture Closing Dynamics

The dynamics of GluCl in absence of ligands was similar to GLIC, in the sense that removal of ligands led each subunit to be twisted by 2 degrees on average (Figure 4.4). Although there is a change towards the apo state, simulations are yet 28 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL to reach the apo state level (Figure 4.4). In contrast to a global motion like the quaternary twist, the tilt motions of the M2 helices and the intersubunit distances at the allosteric site relaxed much faster (Figure 4.5 and paper II). The narrowing of the allosteric site was not only caused by sidechains moving into the site, but also a gradual motion of M3 and M10 helices into the binding site (Figure 4.6). This shrinking was limited to the allosteric site and it did not aﬀect the distances in the lower half of the transmembrane domain (paper I).

(a) (b)

Figure 4.4: Quaternary twist motion. (a) On average, simulations of unliganded GluCl (holoNL) displayed a 2 degree twist. (b) However, these simulations did not reach the apo state values (Box plots show the last 500ns of holoNL simulations in comparison to the apo simulations).

Similar to GLIC, the local closing of the pore at 90 occurs within the ﬁrst few nanoseconds of the simulation. However, in the upper part of the pore, the closing is a gradual motion that occurs slowly over the entire simulation. Unlike the closing in the upper part of the pore, the narrowing at 90 has been observed even in the presence of ivermectin both by other researches [80] and us (papers II-III). This observation is likely not related to the natural gating transition, and I discuss it in more detail in the following section and the next chapter.

A key residue pair (V45-P268) was introduced as the gate keeper of pore closing by Calimet et al. [81], and this was conﬁrmed by the crystal structure of GluCl in the apo state. A similar increase in distance can be seen in structures of the other family members; GlyR cryo-EM structures [82] in presumed open/desensitized forms exhibit distances of 4-4.5Å, while it increased to 7.8Å in the antagonist-bound (closed) state. Interestingly, while we observe an almost full change in M2 helix tilt angles and a gradual decrease in pore radius towards the closed state, we do not see a large increase in distance between these two residues in the simulations (Figure 4.7). 4.2. CHANNEL DYNAMICS OF GLUCL 29

(a) (b)

Figure 4.5: Tilt motion of M2 helices. (a) On average, simulations of unliganded GluCl (holoNL) displayed a 2 degree tilt, which led to pore closure. (b) The tilting of M2 helices displayed a faster relaxation than the domain rotation and were able to reach values similar to the apo state (Box plots show the last 500ns of holoNL simulations in comparison to the apo simulations).

Figure 4.6: The presence of ivermectin increases the size of the cavity between subunits (measured between G281 on M3 and L218 on M10) in a reversible manner, such that it again decreases when the ligand is removed. 30 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL

The closing dynamics of GluCl appears to occur in two stages: First, a rather fast closing of the allosteric cavity takes place, followed by the change in tilt of the M2 helices. Second, a global twist motion happens gradually, which fully closes the channel. A similar closing mechanism has previously been reported in GLIC [61], in this study too, the transition was not complete although the gating helices closed within a microsecond. The time scale of the study in Calimet et al. [81] suggests that it corresponds to an early stage of the closing, where gating helices are closed but the quaternary twist motion is not complete. Interestingly, the reported average quaternary twist angles in their work are closer to the apo state than the result reported here, which raises the question whether the transition observed is an artefact or channel collapse.

(a) (b)

Figure 4.7: (a) The distance between the proposed gatekeeper residues V45 and P268 does not show a large shift upon removal of ivermectin. (b) However the same distance does display a more prominent change between apo (blue) and holo (red) state crystal structures.

4.2.2 From Agonist Binding to Channel Opening GluCl is expected to achieve a fully conducting state when the receptor is populated with both ivermectin and L-glutamate, but the crystal structures of GluCl in presence (holoGI) and absence (holoI) of L-glutamate exhibit no large differences. In fact, simulations of both structures lead to a fast closure of the hydrophobic gate (paper III) within the first few nanoseconds. An apparent increase in pore 0 radius at 9 was induced by L-glumate binding in most of the holoGI simulations (Figure 4.8). The opening event in one of the simulation was considerably larger and more stable, which kept the channel open for at least 400ns. Clearly, the receptor appears to sample more open states when populated with both ligands. The increase in pore radius also influences the Cl- passage. In paper III, we estimate the free energy of Cl- passage using umbrella sampling (see Chapter 6) and detected a 4.2. CHANNEL DYNAMICS OF GLUCL 31 barrier at 90 similar to the barrier detected in ENM studies of GluCl [80]. In fact, this hydrophobic gate has been proposed to be a major contributor to gating both in GLIC [83, 61] and in nAChR [84]. Furthermore, recent crystal structures of the apo state of GluCl [72], as well as the locally closed/closed states of GLIC [85, 86], confirmed the conformational changes observed at the 90 gate. More importantly, GluCl structures might not be in fully open states, since with sufficient sampling they appear to move towards more open states.

Figure 4.8: The conserved hydrophobic gate at position 90 initially appears to close, but when using a set of ﬁve diﬀerent simulations the fully ligated form of GluCl (holoGI) actually displays a slightly increased open probability.

The ligand binding at the orthosteric site led to several other conformational changes in the receptor, in particular in loops 7 and 10 (paper III). Both loop regions have functionally been associated with conformational changes upon ligand binding. Loop 7 has been shown to undergo helix to loop transitions upon ligand binding in mammalian receptors [87, 88]. Upon ligand binding, the helical content of this loop was reduced in simulations in agreement with previous ﬁndings (In paper IV, we explored this conformational change with conventional mutagenesis). On the other hand, ivermectin binding even in the absence of L-glutamate appeared to induce conformational changes in loop 10. The same loop has also been identiﬁed to undergo a capping motion in the AChBP, which is a globular homolog to the acetylcholine receptor [89, 90]. The motions we observe in simulations were not just limited to capping: The loop lost its ordered structure, which increased the solvent exposure of the orthosteric site.

The conformational changes in these loops and the rest of the receptor induced by the agonist binding are likely due to rearrangements in the hydrogen-bonding network. In an attempt to identify a gating path from the changes in hydrogen bonding network upon ligand binding, we performed a statistical comparison between holoGI and holoI systems. In total, L-glutamate binding altered interactions 32 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL of 10 intersubunit and 53 intrasubunit residue pairs. Of these interactions, the intersubunit connections formed by Y151 were identiﬁed as the most likely contributors to the gating. The interactions of Y151 with the complementary subunit appear to be important not only in GluCl, but also several other eukaryotic receptors [91, 92].

Y151 is involved in hydrogen bonding with two other residues in the complementary subunit (Figure 4.9). One interaction is formed with the residue T54, which shows correlated changes with the quaternary twist and the pore opening at 90. The other interaction is formed with the S121 residue, which had a strong correlation with the Y151-T54 interaction. Mutations of both Y151 and S121 in GluCl-β homopentamers have been shown to reduce receptor agonist sensitivity [93]. Although we did not detect any direct correlations between the changes in Y151-S121 interaction and the pore opening, there appears to be an indirect role where S121 assist in altering the position of Y151, which in turn leads to T54 relaying the eﬀect to the rest of the channel.

Figure 4.9: Interactions of Y151 with residues T540 and S1210 are likely contributors to gating (marked as I and II in the ﬁgure, respectively). Loop 10 is not shown for clarity. Snapshot from the end state of a liganded GluCl simulation (holoGI-sim4).

There is, however, one drawback with this analysis. The statistical model I used in paper III might miss some key interactions or underestimate their correlations, thus improvements and post-hoc analysis is required to validate the results. One such example could be the interactions between β1 and β2 strands. In GluCl, R37 of β1 interacts with the ligand, and mutations of this residue in the GluCl-β subunit reduces the response to agonist binding [93]. Our data indicates that in 4.3. FROM IN SILICO TO IN VITRO 33 addition to ligand binding, the interactions of the region in which R37 is located with the β2 strand are affected by the agonist binding, but the correlations do not reach the significance threshold. Indeed, a hyperekplexia phenotype mutation in the β1 subunit of GlyR [94] has been predicted to interact with the β2 strand and mutations in this region have been shown to influence the gating [95].

4.3 From in silico to in vitro

The sequence of GluCl residues where we detected the conformational change (residue index 170-178) is close to the orthosteric site. Of these residues, L174 is located right in the middle of the region which is likely to favor a helical conformation due to two other leucine residues located three register higher and lower; LXXXLXXXL. The helical conformation of this loop could be the reason for the lack of agonist response in GluCl-α subunits. In this scenario, then the mutations of L174 that destabilize the helical conformation should, in theory, rescue the agonist response. To test the possible role of this loop in agonist-induced gating of GluCl-α as indicated by the simulation data in paper III, we have explored the channel activity in several L174 mutants in paper IV.

Four diﬀerent mutations were tested: L174A and L174E that favor helical conformation, as well as L174G and L174P that destabilize the helix. We did not detect any increase in L-glutamate response, but two mutants were less sensitive to L-glutamate: L174A and L174P. The experimental response had no correlation with the expected change in helicity. However, potentiation of the L-glutamate response by ivermectin was not aﬀected in these mutations, which indicates a role for this loop in ligand binding rather than gating.

To better understand the nature of the reduced L-glutamate response of these mutations, I performed docking and molecular dynamics simulations using apo state structure of the receptor. Full length GluCl-α homomers do not respond to L-glutamate [63], but in paper IV we detected shallow sigmoidal response to L-glutamate in the crystal structure construct of GluCl. This is likely to be the effect of modiﬁcations that improve crystallization, such as removal the intracellular domain and several residues from the terminal ends. This behaviour of the GluCl crystal structure construct in vitro enabled a possibility of studying direct agonist response in simulations using the apo state crystal structure as a starting point [72]. However, in spite of the experimental results, neither wild type nor mutated constructs were responsive to L-glutamate in simulations within a microsecond. There could be several explanations for this. For instance, as we showed in paper III, detecting pore opening events upon agonist binding only at 90 required long simulation times. Considering the fact that the apo state structure has even nar- rower pore structure (Figure 4.10), very much like ELIC, detecting agonist-induced conformational changes may require biasing (e.g. distance restraints) to increase 34 CHAPTER 4. MODELING AND CHANNEL DYNAMICS IN GLUCL sampling of the agonist-bound conformations, or much longer simulations than a microsecond. The structural eﬀects of these mutations have not been resolved, but the GluCl crystal structure construct appears to be a better model system for studying receptor dynamics than the full-length receptor.

Figure 4.10: The L-glutamate docked apo state structure remains closed in the microsecond simulation, with a narrow pore on the extracellular side - somewhat similar to ELIC. The plot shows the pore radius (color scale on the right) as a function of the pore position (z) and simulation time. The zero value on the z axis corresponds to the hydrophobic gate 90. Chapter 5

A Gating Mechanism

The Road goes ever on and on Down from the door where it began. Now far ahead the Road has gone, And I must follow, if I can, Pursuing it with eager feet, Until it joins some larger way Where many paths and errands meet. And whither then? I cannot say

J.R.R. Tolkien, The Lord of the Rings

5.1 Principal Component Analysis

Molecular dynamics simulations of crystal structures have been useful in explaining the fast dynamics of complex systems such as ion channels, as seen in previous chapters and papers II-III. However, simulations are often noisy, and it is not easy to separate large-scale correlated motions, which may represent transitions between open-closed states, from local ﬂuctuations. A number of techniques have been developed to help identify the most important motions, for instance essential dynamics that extracts the most correlated particle motions from simulations (for a good review on essential dynamics and how to apply it to trajectories see David and Jacobs [96]).

The foundation of essential dynamics is principal component analysis (PCA),

35 36 CHAPTER 5. A GATING MECHANISM which is a widely used statistical method that reduces the number of dimensions required to explain the data. To do that a covariance matrix of the data is generated, and eigenvectors of this matrix will form a new basis set of orthogonal vectors called the principal components. Since the eigenvalues corresponding to each of these components describe the amount of covariation, the lowest-eigenvalue principal components correspond to the largest and most correlated variations. This often makes it possible to describe high-dimensional data by using only 1-2 degrees of freedom, for instance when analyzing trajectories of proteins.

The growth in the number of available protein structures enables another way to extract transitions from structures. As demonstrated by Van Aalten et al. [97], just as PCA can be applied to the variation of positions in a simulation, it is possible to use it directly on experimental data. In the last five years the number of cys-loop receptor structures, in particular, has grown substantially, and there is now a wide ensemble of structures covering all different states of these channels. In paper V, we constructed an ensemble of GLIC structures to extract the largest variation and elucidated the plausible gating mechanisms. In a follow-up study, we are also working on building an ensemble using all the eukaryotic cys-loop receptor structures. Using the principal components of these ensembles as a basis, we then analyzed the transitions between different states. In case of GLIC the transitions were analyzed with different transition methods, and for eukaryotic ensemble we used MD simulations (both unpublished data and data from papers II-III).

5.2 Gating Intermediates in the GLIC ensemble

There are in total of 46 structures of GLIC, which are believed to represent three diﬀerent states of the channel: the open state [48, 49, 98], the closed state [86], and the locally closed [85, 99], which should be an intermediate along the transition path. In paper V, we have computed the principal components describing the variation in this entire GLIC ensemble with the closed state as the reference.

The first two principal components of the ensemble explain 42% and 30% of the total variance, respectively. Both components display high correlations with the collective variables such as quaternary twist, blooming and the motions of the M2 helices (Figure 5.1). We identified two open-state clusters that differed in the degree of blooming motion, which could be an effect of systematic variations in crystallization temperature.

Locally closed state structures of GLIC [85] are believed to capture the intermediate states along the transition path as they were mutated in M2-M3 loop to trap the M2 helix motion halfway between its open and closed conformations. This is conﬁrmed by the PCA analysis, where they are distributed along the path between the bloomed-open state and the fully closed state. The second, more compact, 5.3. FUNCTIONAL CLUSTERS OF EUKARYOTIC STRUCTURES 37

Figure 5.1: Correlations of gating motions such as quaternary twist or blooming with a speciﬁc component motion reveals the varition explained by the ﬁrst two principal components. Data from paper V. open state cluster also appeared to be related to two intermediate states that carry similar mutations to previous locally closed structures [99].

The principal component space can be used to evaluate sampling in various transition methods. In paper V, we explored such methods for transitions within the GLIC ensemble. As end points, we chose the closed state [86] and the compact- open state structures [98]. Both open state clusters are likely to be equivalent, but the number of intermediates were higher for the compact-open state cluster, which is why we selected it. The rmsd between open and closed states of GLIC in this ensemble is 2.6Å, which is quite small compared to other ensembles in paper V. However, the diﬃculty with the GLIC ensemble is that it requires correlated motions of all ﬁve subunits. The projections of GLIC trajectories onto principal component space show smooth transitions for all the methods that successfully reach the end points, and they pass close to the intermediates, in good correlation with the collective variables.

5.3 Functional Clusters of Eukaryotic Structures

The rapid increase in available structural data is wonderful for the ﬁeld, but it also poses challenges due to potential bias and variations both in structure and gating properties between channels. In particular, only a few months ago a cryo- EM structure of the GlyR channel was published [82], which appears to shift the deﬁnition of the open state away from the previously available GluCl structures. The comparison of GluCl with these new structures indicated that the former might actually represent a more desensitized state, which would not be surprising. 38 CHAPTER 5. A GATING MECHANISM

Crystal structures of GluCl display a rather tight pore at the cytoplasmic site. In addition, the reader might recall that the simulations of GluCl displayed a fast closure at the hydrophobic gate (see 4.2.2). However, for the channel to be considered to be in a desensitized state, the GluCl structures is expected to display a much more open pore at the extracellular side, similar to the GABAA-β3 crystal structure [100]. Comparisons using collective variables such as pore radius reveal a great deal about the functional state, but it is still not suﬃcient to relate different channels or structures to each other. The principal component analysis of the GLIC ensemble eﬀectively separated its’ structures into functional clusters, and with the increasing amount of eukaryotic experimental structures available it might be possible to take a similar approach and reveal the functional clusters for these channels too.

There are 14 near-intact eukaryotic structures (Table 5.1). Of these, only the GluCl apo structure is in the true unliganded state and therefore it was chosen as the reference structure. The core regions of each structures, deﬁned from a multiple sequence alignment (appendix A) were aligned to the reference structure for PCA. The ﬁrst two principal components (PC1 and PC2) explain 65% and 13% of the total variation and divide the ensemble into four functional clusters (Figure 5.2); closed, desensitized (liganded, but not open), presumed open (likely to be ion permeable), and antagonist-bound closed (liganded, but closed) states. While it is important to initially separate eukaryotic and prokaryotic structures (since GLIC would otherwise completely dominate the analysis, due to the number of structures), the prokaryotic structures can be projected onto the eukaryotic principal component space afterwards. The projection of prokaryotic structures appears to strongly support the eukaryotic clustering. The locally closed state structures of GLIC, which are the only intermediates available for cys-loop receptors, were projected between presumed-open and closed states.

The presumed open state structures of GLIC, as opposed to locally closed structures, project onto the desensitized state cluster. The state of GLIC represented in the X-ray structures has been questioned before, because of the narrow pore radius at the cytoplasmic site [101]. Cationic channels are expected to have a wider pore radius than anionic channels in order to allow permeation of fully solvated positively charged ions [62]. Correlation analysis of gating variables with principal components reveals that the second principal component, which separates the desensitized state from open state structures, is correlated with the increase in pore radius at the cytoplasmic site. A recent study, in agreement with this analysis, as- signs an important role to the cytoplasmic site in desensitization [102]. In contrast to the second principal component, the ﬁrst component shows correlation with all the other gating variables. This component also describes the coupling between the two domains via the M2-M3 loop. As shown by the successful locally-closed structure mutations [85]. this loop appears to be responsible for opening the channel. 5.4. CYS-LOOP RECEPTOR DYNAMICS DERIVED FROM LONG-SCALE SIMULATIONS 39

Figure 5.2: Principal component analysis reveals functional clusters of cys-loop receptors. Eukaryotic structures are plotted with colored symbols and labeled with PDB IDs (see Table 5.1). The prokaryotic structures are plotted as gray plus (GLIC) or black star (ELIC) symbols.

5.4 Cys-loop Receptor Dynamics Derived From Long-Scale Simulations

In order to study transitions between clusters in principal component space, we have performed unliganded simulations of each crystal structure (paper II-III and unpublished data), which is also illustrated in Figure 5.3.

The desensitized state cluster identified by PCA appears to split into two slightly different clusters in the simulations. The region between -100Å and -80Å in PC1 remains unsampled even by the five microsecond simulation of GABAAR. This indicates there is a relatively large transition barrier separating two smaller clusters: One cluster shows a closing transition in the unliganded simulations, while the other displays a slightly wider opening at the extracellular side of the pore (e.g. GABAAR), but it does not exhibit any large-scale motions. Based on this data, the GluCl structures appear to resemble a partially desensitized state that is not fully 40 CHAPTER 5. A GATING MECHANISM

Table 5.1: Crystal structures of eukaryotic cys-loop receptors. The assignment of each structure to a cluster in the PCA is marked with ’D’ for the desensitized cluster, ’C’ for the closed, ’AC’ for antagonist-bound closed, and ’O’ for the presumed-open state cluster.

Protein Species PDB ID Ligands PC Cluster GluCl C.elegans 3RHW IVM D 3RIF IVM, L-Glu D 3RIU IVM, IOD D 3RI5 IVM, RI5 D 4TNV - C 4TNW POPC AC GlyR D.rerio 3JAE L-Gly (?) O 3JAF IVM D 3JAD SY9 AC H.sapiens 5CFB SY9 AC 5-HT3 M.musculus 4PIR (?) O* GABAAR H.sapiens 4COF BEN D desensitized as proposed by Du et al. [82]. In paper III, we observed pore openings in fully liganded simulations of GluCl. In the light of the new clustering, the simulations appear to be sampling the transition between these two subclusters (Figure 5.4). This transition is also in the direction of the open state, but simulations have not yet reached the fully open state within the timescales studied here.

In fact, the rmsd between the presumed open state of GlyR and the desensitized state structures is only in the range of 1-1.7Å. This naturally raises the question of how open the open state structure is. The stability and conductance of this presumed-open state in its liganded form has not yet been conﬁrmed, but the clustering approach suggests that this structure at least has a more open pore at the cytoplasmic site than the previous structures, which were considered to be open. The conditions under which the GlyR open state structure was obtained suggest that the channel was exposed to high concentrations of L-glycine over a long period of time. In comparison with kinetic models derived from 5-HT3A receptors, it is likely that this structure corresponds to an open-like state that has the highest probability of transition towards the desensitized state [103]. Indeed, when performing molecular dynamics simulations of GlyR, the structure quickly moves towards the desensitized state even in the absence of the glycine agonist (Figure 5.3), which leaves the area between open and closed states unsampled in the principal component space.

It is evident that obtaining a complete transition even with the closing trajectories does not seem likely in a single simulation with the timescales accessible here, 5.4. CYS-LOOP RECEPTOR DYNAMICS DERIVED FROM LONG-SCALE SIMULATIONS 41

Figure 5.3: Projections of unliganded simulations on the ﬁrst two principal components that separate the desensitized state clusters into two (marked with I and II). Black dots represents the projections of the crystal structure ensemble. The areas sampled in the simulations are marked with circles and labeled. but by combining information from all simulations we can obtain the conformational changes moving an open like state to the closed state. For this, the collective variables mentioned in chapter 4 were calculated in each simulation, and these data were plotted in the principal component space as the third dimension. Linear interpolation was used for positions not sampled in the simulations, therefore Figure 5.5 represents a rough estimate.

The gating motions involve roughly 4 degree change in quaternary twist, as observed both in earlier studies [81, 60] and paper III. The blooming motion is highly correlated with the quaternary twist, and in addition it appears to separate the antagonist-bound states from the closed states. Unlike blooming and quaternary twist, the enlargement of the allosteric cavity (measured as a distance between M3- M10 helices) follows mainly the ﬁrst principal component, meaning that both open and desensitized states have larger allosteric-binding sites compared to the closed state. This indicates that the increased distances in GluCl structures are not only due to the presence of ivermectin. The ﬁrst component also contains variations 42 CHAPTER 5. A GATING MECHANISM

Figure 5.4: Projections of the fully liganded simulations of GluCl (holoGI) onto principal component space show transitions between two desensitized state clusters marked as I and II (black dots represent the crystal structures). Only three selected simulations simulations (out of five) are presented. The time evolution of the trajectory is represented by the colored of the dots. in the M2-M3 loop, and the motions of this loop were monitored as defined by the distance between V45-P268 residues. In contrast to the M3-M10 distance, this distance show much smaller changes, yet the fully closed state is predicted to be significantly different than the open state from this analysis. 5.4. CYS-LOOP RECEPTOR DYNAMICS DERIVED FROM LONG-SCALE SIMULATIONS 43

(a) (b)

Figure 5.5: Correlations between collective variables and the principal component space reveal the gating mechanism: (a) quaternary twist, (b) blooming, (c) M3-M10 distance, and (d) the distance V45-P268. Linear interpolation is used in plotting the collective variable data onto principal component space, data from the simulations in Figure 5.3 onto the principal component space. Projections of crystal structures are represented as black dots.

Chapter 6

Methods

6.1 Homology Modeling

Protein structure determination methods such as X-ray crystallography or cryo- electron microscopy (cryo-EM) provide atomic detail of biological macromolecules. Although there are now more than 200,000 structures of proteins chains available in the Protein Data Bank, this number is not comparable with the amount of protein sequences registered in the UniProt database; 42 million [104]. This gap is particularly severe for membrane proteins, which are typically very diﬃcult to overexpress and crystallize. In these cases where a structure is not available, a model can sometimes be generated based on available sequences. The most common method to accomplish this is homology modeling, which relies on evolutionary relationship between sequences. When the sequence identity to a previously know structure exceeds 25%, it is usually possible to build a high-quality model.

There are several programs available to create homology models. In paper I, we used two such programs: Rosetta [77] and Modeller [76]. A simple flowchart for both programs is shown in Figure 6.1. They have similar workflows, although they rely on different algorithms. Modeller makes use of spatial restrains, which are generated from the dihedral angles of the template. In Rosetta, the aligned region between two sequences is copied into an incomplete model from the template structure, and the missing regions are then completed with fragment-based loop modelling by performing insertions of fragments using Monte Carlo sampling. In practice, with Modeller only a handful of structures are generated, while the Rosetta approach suggests generating around 10,000 structures, using clustering to select the final model.

Homology modeling is the ﬁrst step in most of the in silico studies where an experimental structures is not available. The quality of the homology model will

45 46 CHAPTER 6. METHODS

Figure 6.1: A simplified workflow for homology modelling using (a) Modeller and (b) Rosetta. Modeller uses spatial restraints based on the template structure and requires only a sequence alignment to be provided in addition to the structure. Rosetta, on the other hand, uses a fragment-based modeling in which missing regions are modelled by fragment-based loop modelling. greatly affect the outcome of drug screening.

6.2 Molecular Dynamics Simulations

Experimental structures or homology models contribute to our knowledge by allowing comparisons of diﬀerent states, but they provide little or no information about intermediate states or dynamics. To understand concepts such as transition states, kinetics and binding, it is frequently useful to run computer simulations of the available structures or models. A computer simulation can explain the microscopic interactions in a structure, and makes it possible to sample an entire ensemble of states, including ones not seen in equilibrium experimental structures. Ultimately, the goal of a simulation is to compare the results with experimental observations to validate the model. When successful, conclusions can be drawn about the atomic-scale motions, interactions and kinetics of biological macromolecules that are not available with any other method. Molecular dynamics (MD) simulation is a computer simulation technique that bridges this gap and allows calculations of macroscopic variables from molecular interactions.

In an MD simulation of a system with coordinates r, the molecular interactions are used to derive the potential energy U, which is then used to calculate the forces F in the system:

Fri = −∇Uri ri for i=1, 2, ..., N 6.2. MOLECULAR DYNAMICS SIMULATIONS 47

The forces can then be used to calculate new positions by integrating Newton’s second law over small time steps:

2 d ri Fi = miai = mi i=1, 2, 3, ..., N dt2 By repeating the calculation cycle (calculate forces, integrate, generate new coordinates) several times, a trajectory is generated with positions and velocities as a function of time.

The potential energy function U in these expressions can be determined from molecular interactions and the speciﬁc positions of atoms in the system. These interactions can be calculated from relatively simple approximations that depend on the positions of all atoms in the system, and include terms such as bond distances, angles between bonds and dihedrals that explain rotations around bonds. There are also nonbonded interactions due to electrostatics and van der Waals interactions that explain steric repulsion and weak dispersion attractions (Figure 6.2).

Figure 6.2: Molecular interactions of a glycine tripeptide and a water molecule: (I) bonds, (II) angles, (III) dihedrals, (IV) electrostatic interactions, and (V) van der Waals interactions that are weak repulsions/attractions between atoms.

The link between macroscopic terms and the molecular interactions comes from quantities such as temperature, pressure, or energy that can be calculated during the simulation. Molecular dynamics is a computationally intensive technique even with modern supercomputers, which means that it is diﬃcult to model processes beyond the microsecond scale. However, simulations provide a powerful tool to understand conformational dynamics and transitions on intermediate scales, while we still need to rely on experiments for slow processes.

To study conformational changes in a membrane protein, a simulation box has to be prepared with the protein structure embedded into a bilayer patch. This 48 CHAPTER 6. METHODS system must then be solvated with waters and ions to create a realistic description of the natural environment of the protein. (Figure 6.3). The trajectory generated from a simulation of this system can then be used to study quantities as a function of time, for instance pore radius, hydrogen bond interactions, or binding properties of small molecules.

Figure 6.3: A membrane protein (the glumate-gated chloride channel) embedded in a membrane; waters have been removed for clarity. The protein is shown with a cartoon representation (green), lipids with licorice (yellow), and ions as spheres (purple, pink).

6.3 Free Energy Calculations of Ion Permeation

The amount of work required to move a system inﬁnitely slowly between two states is deﬁned as the change in free energy:

∆G = ∆H − T ∆S = ∆U + p∆V − T ∆S where H refers to enthalpy, T temperature and S entropy. Free energy is a property of the state and in general it is not possible to directly evaluate the absolute free energy in a simulation. For a state consisting of many microstates, the enthalpy is the average of the potential energy introduced earlier, and the entropy is related to the number of microscopic states that correspond to the observable state. While the enthalpy component converges quickly, the entropy of a state requires extensive sampling to estimate.

Calculating the change in free energy between two states is easier than trying to evaluate the absolute free energy. For instance, the work done when a particle is pulled slowly from one position to another (such that the system is always under equilibrium) is the integral of the average force in each position along this path during the simulation. Formulating this in a more general fashion: the ensemble average of the derivative of the potential energy U with respect to an arbitrary coordinate (λ) along a reaction corresponds to the derivative of the free energy. 6.3. FREE ENERGY CALCULATIONS OF ION PERMEATION 49

Since these averages can be calculated in a simulation, we get the change in free energy as

Z dU ∆G = dλ. dλ

For an ion channel system, we are often interested in estimating the free energy of ion permeation as a function of the position in the pore. In this system our reaction coordinate λ is the ion position in the pore. The derivative of the potential (with respect to ion position) corresponds to the negative force. Due to the aver- aging in the simulation, this type of free energy calculation is frequently called a potential of mean force (PMF). Although we know the states an ion will go through during permeation, some of these will correspond to high free energies (barriers), and the system will hardly sample these regions in an unbiased MD simulation, which in practice makes it diﬃcult to calculate the free energy.

There are several methods that address this sampling issue and allow the free energy to be calculated. The most common approach is to use a large number of simulations for diﬀerent ion positions in the channel, and introduce a biasing potential in each of them to force the ion to sample the correct region. This makes it possible to evaluate the local free energy change accurately in a short position interval. Since the biasing potential is also known, It is possible to reconstruct the original unbiased free energy, because the biasing potential is known. The complete PMF is obtained by combining all simulations.

The strength and shape of the biasing potential depends on the system, but it is usually most eﬃcient to employ stiﬀ harmonic potentials. Because of the (inverted) shape of these curves, the technique is typically called umbrella sampling.

One drawback of umbrella sampling is that it requires prior knowledge of the intermediate states. This drawback is typically overcome by pulling an ion through the system, or simply placing ions at a number of positions prior to relaxation. These systems are then simulated independently and the force on the ion (or its deviation from equilibrium position) is recorded to calculate the PMF, which will describe the change in free energy.

Chapter 7

Summary of Papers

Paper I: Assessment of Homology Templates and an Anesthetic Binding Site within the γ-Aminobutyric Acid Receptor

Mammalian cys-loop receptors are targeted by anesthetics and several other molecules. Although the exact location of the anesthetic-binding site is not known, functional studies on GABAAR point to a cavity located in the transmembrane domain between subunits. In this paper, we use a recent co-crystal structure of the glutamate-gated chloride channel (GluCl), in which the anesthetic-binding site is already occupied with a modulator, for homology modeling of the GABAAR.

Two models are constructed: the first is built entirely based on GluCl, while the second model has individual subunits modeled after GluCl, but relies on GLIC for subunit packing. The two models end up remarkably similar, with the same residues lining the anesthetic-binding site, and there is a very low rmsd (1Å) between the two models around this region. Although the model based on the GLIC subunit spacing is important to assess the influence of removing the allosteric modulator, it is also subject to side-chain repacking which could affect the docking calculations. For this reason we used the first model for the evaluation of the anesthetic binding. The calculated affinities of propofol and its derivatives to the predicted binding site appear to correlate well with the known EC50 values. These results suggest that GluCl-based models are efficient tools to study receptor-anesthetic interactions and to screen for allosteric modulators likely to interact with this cavity.

51 52 CHAPTER 7. SUMMARY OF PAPERS

Paper II: Stabilization of the GluCl Ligand-Gated Ion Channel in the Presence and Absence of Ivermectin

GluCl has been co-crystallized with an allosteric modulator called ivermectin, but it is unclear how much ivermectin binding inﬂuenced the structure of the protein, and how the structure and dynamics would change without it. In this paper, we address this issue and study how the removal of ivermectin inﬂuences the allosteric site and channel dynamics in GluCl. We performed two simulations: one where ivermectin was retained in the binding site, and one where it was removed prior to relaxation.

We find that upon removal of ivermectin, the binding cavity cavity volume is reduced due to rapid side chain reorientation. In addition, the extracellular side of the transmembrane helices surrounding the binding site, M3 and M10, gradually approach each other, while the cytoplasmic side remains unaffected. Similarly, the extracellular side of the gating helices appears to be more flexible. This flexibility gradually decreases the radius in this upper part of the pore over the simulation trajectory. In contrast, both the subunit spacing and the pore radius remain stable in the simulations with ivermectin, but a fast closure at the 90 hydrophobic gate is observed. While there are transient fluctuations and increases in this pore radius, the channel does not reach a stable open state in the microsecond-scale simulations used.

Paper III: Conformational Gating Dynamics in the GluCl Anion-Selective Chloride Channel

GluCl is in a stable fully conducting state only when both the agonist (L-glutamate) and the allosteric modulator (ivermectin) are bound. In this paper, we study the inﬂuence of agonist binding on channel dynamics. We used a set of ﬁve simulations with both L-glutamate and ivermectin bound to the receptor.

The fast initial closure observed in paper II at the 90 gate appears to be a common feature of the channel, as it also occurs in the fully liganded simulations, which are expected to represent the open state. However, in this case the bound agonist appears to be a stronger effect, and after roughly 200ns these simulations exhibit stable opening events. The calculated free energy of a Cl- ion passage reflects a clear barrier in this region, which is reduced by 10-15 kcal/mol when the agonist is bound. The pore opening events also appear to be correlated with quaternary twist motions, which are defined by the the rotation of extracellular and transmembrane domains.

The channel undergoes local conformational transitions upon ligand binding, both in the pore and the extracellular domain. It is likely that agonist binding 53 induces these conformational changes by altering the hydrogen-bonding network. To identify the key interactions that create a conformational earthquake leading to pore opening, we compare the hydrogen-bonding network of the complete receptor between the simulations with and without L-glutamate. The results of this comparison point to Y151 and its interactions with T54 as key contributors to gating, while S121 appears to facilitate the interactions of Y151.

Paper IV: Functional characterization of Activation and Modulation in a Nematode Model Ligand-Gated Ion Channel

The full-length GluCl receptors have been well characterized pharmacologically, but the homopentameric crystal structure (GluClcryst) construct is relatively new and we know little or nothing about its pharmacological proﬁle. In particular, its response to alcohols and anesthetics was not yet known. In this paper, we assess its pharmacological proﬁle as well as its relevance as a model channel.

We compare the pharmacological profile of GluClcryst to the full-length GluCl- α homopentamers and show that GluClcryst responds to L-glutamate even in the absence of ivermectin, unlike the full-length receptor. Although this is a key difference, it also makes it possible to study the direct agonist activation. We first study the modulation of the agonist-induced response by alcohols and anesthetics, and demonstrate that GluClcryst is inhibited by these allosteric modulators. Second, we study the effect of a series of mutations in loop 7 on agonist binding (since we detected conformational changes to this loop in the simulations in a previous study).

Our results support a role for loop 7 in binding, and we show that the pharmacological profile of GluClcryst is similar to the mammalian GABAA-ρ homopentameric channels. This pharmacological profile not only confirms that GluClcryst is a useful model for the mammalian receptors, but in fact the response levels to L-glutamate suggests it might be a better model than the full-length receptors - with the caveat that it can be difficult to over-express.

Paper V: Prediction and Validation of Protein Intermediate States from Structurally Rich Ensembles and Coarse-Grained Simulations

Conformational changes of proteins drive several important functions such as repair, signaling or transport. However, capturing intermediate states via experimental methods often requires mutations that alter the channel function to trap these transient states. As an alternative, the rapidly growing number of experimental 54 CHAPTER 7. SUMMARY OF PAPERS structures makes it possible to use computational methods - in particular principal component analysis (PCA) with entire collections of structures as a data set to understand their motions.

In this paper we perform PCA on several diﬀerent structural ensembles, including an ensemble of the cys-loop receptor homolog GLIC. Using the ﬁrst two principal components, we are able to describe transitions between conformations and identify functional intermediates. In addition to PCA, we use a hybrid elastic-network brownian dynamics simulation (eBDIMS) to generate transition paths between selected conformations.

The ﬁrst two principal components successfully separate structures into open and closed stated, with functional intermediates located between clusters in all the ensembles. Analysis of the GLIC ensemble suggests that, suprisingly, the open state cluster has two subpopulations, which could be due to the diﬀerences in crystallization procedures. The brownian dynamics interpolation method generates transition paths that successfully reach their target and pass very close to the functional intermediate conformations on the way. Other methods tested in this paper either progress too fast, or fail to reach their target, except for Climber. The transition trajectories generated by Climber and eBDIMS agree well, and likely sample the functional intermediates. Chapter 8

Concluding Remarks

Computational structural biology will be increasingly important in improving human well-being, for which we can all say: "Thank you!"

Michael Levitt

The discovery of ion channels was a major milestone that changed the way neurological phenomena were perceived. This discovery also marks the collaboration of several different fields, such as structural biology or biochemistry, for studying these phenomena. With the emergence of atomistic structures, computational structural biology joined this field, and brought methods from molecular dynamics simulations to small molecule docking. However, not all ion channel superfamilies are sufficiently well characterized, and most of the time there are no structures available, which limits the use of computational methods.

The cys-loop receptor family, in particular, had too few structures due to dif- ficulties in obtaining well-diffracting crystals. However, researches succeeded in overcoming these obstacles mostly by using engineered channels and antibodies for crystallization, or by looking for distant homologues that are easier to work with. The structures of cys-loop receptors enabled the use of computational methods, where we have seen an explosion of activities the last decade. Even tough it is a relatively new field, homology modelling or molecular dynamics simulations have become powerful allies to electrophysiology experiments. In addition, these methods were able to demonstrate plausible closing mechanisms [81], identify energy barriers in ion transfer [80], and assess the functional clustering of structures and what we can learn from transitions between them, as I did in the last chapter of

55 56 CHAPTER 8. CONCLUDING REMARKS this thesis.

The studies I have presented in this work demonstrate that we can capture the eﬀects of ligand binding with full atomistic simulations, but we are yet to show conductance upon ligand binding. The methods to perform electrophysiology in simulations [105] will surely enable a more accurate characterization of structural transitions. Even then, the sampling issue is an obstacle to overcome. I believe we can paint the impression of a full gating transition with a pointillist approach by combining coarse-grained transitions with full atomistic simulations. Appendix A

Sequence Alignment

Figure A.1: Sequence alignment of eukaryotic cys-loop receptors with known structures. Gray bars represent the core region used in PCA.

Acknowledgements

The burden of the One Ring lies on an individual’s shoulders; however, the journey to Mount Doom would not be possible without the help and support of the Fellow- ship. I had quite a few diﬃculties along the way, happy and sad days, but I cherish every memory the same, for without them, I would not be me and I would have never made this journey. I would like to thank you all for your being part of it.

Erik, not sure if you remember our first skype meeting but I do. People who saw me that day have heard me repeating: “Oh, I’m the happiest person in the world! I will do simulations and experiments! I will work with proteins! I will do a PhD!” I did jump on the PhD offer because of the possibility of combining experiments with simulations, I admit. Not long after I started, I realized how much I did not know. Naturally, I questioned whether I was a good match for this position but your contagious optimism has essentially become one of the pillars supporting my work. During my PhD, I have not just learned about science and research from you, but also other aspects of a scientist’s life: management, networking, and writing. The freedom and opportunities you have provided these past five years allowed me to see the world, meet with wonderful people, and sail for the first time. Ok, the sailing trip ended quite scary for me, but except the ending it was fun. I have enjoyed doing a PhD and I have come to love research even more. I enjoyed working with you. Thank you, Captain! I am truly grateful for everything.

Berk, whenever I think I should not be allowed to write any scripts for a better world, I recall your words from the PhD interview “... I don’t know Python, but you wrote this beautiful code...” There was, and is, hope for me in the computational world after all. Thank you for your encouraging words and support.

David, and Erik M., thanks for introducing me to the computational world. With your support and help, I have taken my ﬁrst steps. I would not have continued to do computational work, if it wasn’t for the great introduction. Lynn, you are an inspirational scientist. Your energy and determination amazes me. It gives me hope to see young successful women such as you in academia. Thank you for sharing your experience and wisdom with me. I could not make the trip to Durham, but I

59 60 APPENDIX A. SEQUENCE ALIGNMENT hope I will get a chance to share the same ﬂight with Aviator Duck. I am an avid fan!

Reba, it has been a pleasure to work with you. Your passion for research and teaching inspires me. Watching you work in the lab, showed me the true meaning of eﬃciency and planning. Whichever direction we take, I hope we will have many more chances to discuss science. Wish you all the best in your new home. Göran, I am grateful for the time you have invested teaching me electrophysiology. I will miss the days we have spent unpacking Christmas deliveries, our discussions while we were poking oocytes and our brainstorming sessions on exciting methods.

Magnus A., and Laura, thanks for the discussions and help with the projects. I would not be as productive as I have been without our discussions. It was your determination, experience and patience that helped me turn the results into articles. Christian W., the member of the lab I have known the longest, thanks for all the help with the formalities and being an awesome board game companion. Christian B, it was great to have another perspective, thanks for all the feedback you have provided. Mark, the neighbor, thanks for the tips on how to survive in Australia and the technical help in GROMACS. Magnus L., thanks for sharing your expertise in chemistry, and docking. Rossen, thanks for being the bridge between the group and the PDC. Special thanks for making me aware of the lack of privacy on mobile numbers in Sweden. Lucie, I wish we had the chance to spend more time, welcome to SciLifeLab and good luck with your new group. Ali, thanks for bringing in a new research area to the group, meetings become more colorful.

Szilard, I would not have all this data without your great work on GROMACS. I admire your enthusiasm for your work, and your courage to speak up. Bjorn and Dari; neither group meetings nor lunch breaks would be the same without your sense of humor. Thanks for being great conference buddies, I will miss hearing your ideas on science and everything else. Iman and Anders, thanks for joining the ion channel team. You were great travel buddies at Lipari. Next time stop me if I declare that we have to eat mussels! Stephanie, it was a pleasure to work with you. Thanks for answering my questions on electrophysiology patiently. Keep up the good work! I wish the days were twice as long, so I could spend more time in the lab. Stefan, the administrator, thanks for bringing in the order to our digital life. Petter, thanks for bringing in more color to the group with your research. Harsha, hope your experiments will yield great results as your are an amazing researcher, good luck with your studies.

Viveca, and Sammy, it is a privilege to have you as friends and colleagues. You will ﬁnish your journey soon. Let’s be the Golden Girls of Lindahl lab and meet many many years from now for more beers, dinners, movies, and discussions on science, feminisim, and culture. Mariama, Fighting! My dramabuddy! I hope we will visit Japan and Korea together one day. Do not let Uppsala turn you to the 61 dark side! Narges, I miss you already! Stay strong and hope to see you in Australia. My amazing swimming instructor, Oxana, if it was not for your encouragement I would have never ever tried. I still may not have the courage to jump into a pool, but I will certainly survive as a log in the water. Special thanks for starting PyLadies group! It was a great initiative.

Anirudh, Axel, Walter, Marco, Par, Karolis, Kostas and the honorary members of alfa6 Luisa, and Enrichetta... Thank you for the joy and laughter. Without you, there would certainly be less beers, board game nights, Friday dinners, and cakes during this journey. I will miss all the great discussions about science and life.

Kate and Oleg, my beloved friends. I remember the ﬁrst day we have met; yet, it feels like I have known you my entire life. You do not need to share some long chain organic molecules to call someone a family, you are part of my family and you will always be. Thank you for standing by my side, landing me a shoulder to cry, and providing me a safe hold when the world was just too much. Thank you for making me feel at home every time I visited. I am looking forward to meet the new family member.

Canan, Funda, Burcu, Burcin ... M’ladies... There are some friends you do not call everyday. You don’t keep in touch as often as you should, but you know that they know you think about them, they think about you. I am holding onto every memory we have built up for the last 12 years. I would be lost without you my dear dear friends. Life can only try to scatter us like leaves in the wind, but we will always ﬁnd each other. You have been deeply missed. I cannot wait to see you all again.

The real Avengers team; Filip the Hawkeye, Can the Iron Man, Anna the Cat Woman, Tanya the Black Widow, Bora the Batman and the Civilians, Burcu, Cihan and Kagan. The Mightiest Heroes. Stockholm turned into a place to be remembered with you all. The movie nights we had brought light into the nights of Stockholm that are dark and full of terrors.

Annem-babam, sizin desteginiz, paha bicilemez emekleriniz olmadan bulun- dugum yere asla gelemezdim. Kardeslerim (Ismail, Sakine, Zulfu, Medine, Yil- maz, Pinar, Arzu, Ufuk, Ezgi, Caner) sizinle actim gozlerimi hayata. Hayatimin en degerli parcalarisiniz, tabi ki dunyaya getirdiginiz yegenlerimden sonra ;) Laser, en sevgili yegenim, senin gulusun ve enerjin bana umut veriyor. Seninle ne kadar gurur duysam azdir. Hayat sensiz cok renksiz olurdu. Bilinda ve Miraz, bir elmanin iki yarilari... Buyudugunuzu gorebilmek ne kadar guzel.

Özge Yoluk, Stockholm, June 2016

Bibliography

[1] J V Kinnier Wilson and E H Reynolds. Translation and Analysis of a cuneiform text forming part of a Babylonian treatise on epilepsy. Med. Hist., 34(02):185–198, 2012.

[2] E H Reynolds. Todd, Hughlings Jackson, and the electrical basis of epilepsy. Lancet, 358(9281):575–577, 2001.

[3] B P Grone and S C Baraban. Animal models in epilepsy research: legacies and new directions. Nat. Neurosci., 18(3):339–343, 2015.

[4] L A Geddes and H E Hoﬀ. The Discovery of Bioelectricity and Current Electricity The Galvani-Volta Controversy. Spectrum, 8(12):38–46, 1971.

[5] A L Hodgkin and A F Huxley. A Quantitative Description of Membrane Cur- rent and Its Application to Conduction and Excitation in Nerve. J. Physiol., 117(4):500–544, 1952.

[6] N Unwin. Reﬁned Structure of the Nicotinic Acetylcholine Receptor at 4Å Resolution. J. Mol. Biol., 346(4):967–989, 2005.

[7] M Jaiteh, A Taly, and J Hénin. Evolution of Pentameric Ligand-Gated Ion Channels: Pro-Loop Receptors. PLoS ONE, 11(3):e0151934, 2016.

[8] H Reichert. Introduction to Neurobiology. Oxford University Press, USA, 1992. ISBN 9780195210101.

[9] D Purves. Neuroscience. Sinauer Associates Incorporated, 2012. ISBN 9780878936953.

[10] S Maeda, S Nakagawa, M Suga, E Yamashita, A Oshima, Y Fujiyoshi, and T Tsukihara. Structure of the Connexin 26 Gap-junction Channel at 3.5Å Resolution. Nature, 458(7238):597–602, 2009.

[11] R E Hibbs and E Gouaux. Principles of Activation and Permeation in an Anion-Selective Cys-Loop Receptor. Nature, 474(7349):54–60, 2011.

63 64 BIBLIOGRAPHY

[12] F A Stephenson. Introduction to Thematic Minireview Series on Celebrating the Discovery of the Cysteine Loop Ligand-Gated Ion Channel Superfamily. J. Biol. Chem., 287(48):40205–40206, 2012. [13] J N Langley. On the Reaction of Cells and of Nerve-endings to Certain Poisons, Chieﬂy as Regards the Reaction of Striated Muscle to Nicotine and to Curari. J. Physiol., 33(4-5):374–413, 1905. [14] J W Hurst, W B Fye, and H G Zimmer. Otto Loewi and the Chemical Transmission of Vagus Stimulation in the Heart. Clin. Cardiol., 29(3):135– 136, 2006.

[15] E Sigel and M E Steinmann. Structure, Function, and Modulation of GABAA Receptors. J. Biol. Chem., 287(48):40224–40231, 2012. [16] S Pirker, C Schwarzer, A Wieselthaler, W Sieghart, and G Sperk. GABAA Receptors: Immunocytochemical Distribution of 13 Subunits in the Adult Rat Brain. Neuroscience, 101(4):815–850, 2000.

[17] H Betz. Ligand-Gated Ion Channels in the Brain: The Amino Acid Receptor Superfamily. Neuron, 5(4):383–392, 1990. [18] R S Kass. The Channelopathies: Novel Insights into Molecular and Genetic Mechanisms of Human Disease. J. Clin. Invest., 115(8):1986–1989, 2005.

[19] R Shiang, S G Ryan, Y Zhu, A F Hahn, P O’Connell, and J J Wasmuth. Mutations in the α1 Subunit of the Inhibitory Glycine Receptor Cause the Dominant Neurologic Disorder, Hyperekplexia. Nat. Genet., 5(4):351–358, 1993. [20] M A J Tijssen, R Shiang, J van Deutekom, R H Boerman, J J Wasmuth, L A Sandkuijl, R R Frants, and G W Padberg. Molecular Genetic Reevaluation of the Dutch Hyperekplexia Family. Arch. Neurol., 52(6):578–582, 1995. [21] D Langosch, B Laube, N Rundström, V Schmieden, J Bormann, and H Betz. Decreased agonist aﬃnity and chloride conductance of mutant glycine Re- ceptors associated with human hereditary hyperekplexia. EMBO J., 13(18): 4223–4228, 1994.

[22] M J Eadie. Sir Charles Locock and Potassium Bromide. J. R. Coll. Physicians Edinb., 2012. [23] M J Ackerman, C A Mu, and D J Tester. Personalized Medicine: Genetic Di- agnosis for Inherited Cardiomyopathies/Channelopathies. Rev. Esp. Cardiol., 66(04):298–307, 2013. [24] D Bertrand. Neuronal Nicotinic Acetylcholine Receptors and Epilepsy. Epilepsy Curr., 2(6):191–193, 2002. BIBLIOGRAPHY 65

[25] F Picard, S Bertrand, O K Steinlein, and D Bertrand. Mutated Nico- tinic Receptors Responsible for Autosomal Dominant Nocturnal Frontal Lobe Epilepsy are More Sensitive to Carbamazepine. Epilepsia, 40(9):1198–1209, 1999.

[26] C Lopez-Quintero, J P de los Cobos, D S Hasin, M Okuda, S Wang, B F Grant, and C Blanco. Probability and Predictors of Transition from First Use to Dependence on Nicotine, Alcohol, Cannabis, and Cocaine: Results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Drug Alcohol Depend., 115(1-2):120–130, 2011.

[27] C Prys-Roberts. Anasthesia: A Practical or Impractical Construct? Br. J. Anaesth., 59(11):1341–1345, 1987.

[28] Z Yu, W Wang, J Fritschy, O W Witte, and C Redecker. Changes in Neocor- tical and Hippocampal GABAA Receptor Subunit Distribution During Brain Maturation and Aging. Brain Res., 1099(1):73–81, 2006.

[29] A Jenkins, E P Greenblatt, H J Faulkner, E Bertaccini, A Light, A Lin, A Andreasen, A Viner, J R Trudell, and N L Harrison. Evidence for a Common Binding Cavity for Three General Anesthetics within the GABAA Receptor. J. Neurosci., 21(6):RC136–RC136, 2001.

[30] J J Irwin and B K Shoichet. Docking Screens for Novel Ligands Conferring New Biology. J. Med. Chem., 59(9):4103–4120, 2016.

[31] M M Yardley, N Huynh, Kathleen E Rodgers, Ronald L Alkana, and D L Davies. Oral Delivery of Ivermectin Using a Fast dissolving Oral Film: Im- plications for Repurposing Ivermectin as a Pharmacotherapy for Alcohol Use Disorder. Alcohol, 49(6):553–559, 2015.

[32] E P Carpenter, K Beis, A D Cameron, and S Iwata. Overcoming the Chal- lenges of Membrane Protein Crystallography. Curr. Opin. Struct. Biol., 18 (5):581–586, 2008.

[33] A Brisson and N Unwin. Tubular Crystals of Acetylcholine Receptor. J. Cell Biol., 99(4):1202–1211, 1984.

[34] C Toyoshima and N Unwin. Ion Channel of Acetylcholine Receptor Recon- structed from Images of Postsynaptic Membranes. Nature, 336(6196):247– 250, 1988.

[35] N Unwin. Nicotinic Acetylcholine Receptor an 9 Å Resolution. J. Mol. Biol., 229(4):1101–1124, 1993.

[36] N Unwin. Acetylcholine Receptor Channel Imaged in the Open State. Nature, 373(6509):37–43, 1995. 66 BIBLIOGRAPHY

[37] P Perret, B Laube, R Schemm, H Betz, M Goeldner, and B Foucaud. Aﬃnity Labeling of Cysteine-mutants Evidences Contact Residues in Modeled Recep- tor Binding Sites. J. Recept. and Sig. Transd., 22(1-4):345–356, 2002. [38] J F Leite. Coupled Proteolytic and Mass Spectrometry Studies Indicate a Novel Topology for the Glycine Receptor. J. Biol. Chem., 275(18):13683– 13689, 2000. [39] E Bertaccini and J R Trudell. Predicting the Transmembrane Secondary Structure of Ligand-Gated Ion Channels. Protein Eng. Des. Sel., 15(6):443– 453, 2002. [40] D A Dougherty. Cys-Loop NeuroReceptors: Structure to the Rescue? Chem. Rev., 108(5):1642–1653, 2008. [41] G Kuan, M H Saier Jr, and M Montal. Modular Assembly of Voltage-gated Channel Proteins: a Sequence Analysis and Phylogenetic Study. J. Mol. Microbiol. Biotechnol., 1999. [42] A Tasneem, L M Iyer, E Jakobsson, and L Aravind. Identiﬁcation of the Prokaryotic Ligand-Gated Ion Channels and Their Implications for the Mech- anisms and Origins of Animal Cys-loop Ion Channels. Genome Biol., 6(1): R4, 2005. [43] R J C Hilf and R Dutzler. X-ray Structure of a Prokaryotic Pentameric Ligand-Gated Ion Channel. Nature, 452(7185):375–379, 2008. [44] X Cheng, I Ivanov, H Wang, S M Sine, and J A McCammon. Molecular- Dynamics Simulations of ELIC—a Prokaryotic Homologue of the Nicotinic Acetylcholine Receptor. Biophys. J., 96(11):4502–4513, 2009. [45] I Zimmermann and R Dutzler. Ligand Activation of the Prokaryotic Pen- tameric Ligand-Gated Ion Channel ELIC. PLoS Biol., 9(6):e1001101, 2011. [46] J Pan, Q Chen, D Willenbring, K Yoshida, T Tillman, O B Kashlan, A Cohen, X Kong, Y Xu, and P Tang. Structure of the Pentameric Ligand-Gated Ion Channel ELIC Cocrystallized with its Competitive Antagonist Acetylcholine. Nat. Commun., 3:714, 2012. [47] R Spurny, J Ramerstorfer, K Price, M Brams, M Ernst, H Nury, M Verheij, P Legrand, D Bertrand, S Bertrand, D A Dougherty, I J P de Esch, P J Cor- ringer, W Sieghart, S C R Lummis, and C Ulens. Pentameric Ligand-Gated Ion Channel ELIC is Activated by GABA and Modulated by Benzodiazepines. Proc. Natl. Acad. Sci. U.S.A., 109(44):E3028–E3034, 2012. [48] R J C Hilf and R Dutzler. Structure of a Potentially Open State of a Proton- activated Pentameric Ligand-Gated Ion Channel. Nature, 457(7225):115–118, 2008. BIBLIOGRAPHY 67

[49] N Bocquet, H Nury, M Baaden, C Le Poupon, J Changeux, M Delarue, and P Corringer. X-ray Structure of a Pentameric Ligand-Gated Ion Channel in an Apparently Open Conformation. Nature, 457(7225):111–114, 2008. [50] Y Weng, L Yang, P Corringer, and J M Sonner. Anesthetic Sensitivity of the Gloeobacter violaceus Proton-gated Ion Channel. Anesthesia & Analgesia, 110(1):59–63, 2010. [51] H Nury, C Van Renterghem, Y Weng, A Tran, M Baaden, V Dufresne, J Changeux, J M Sonner, M Delarue, and P Corringer. X-ray Structures of General Anaesthetics Bound to a Pentameric Ligand-Gated Ion Channel. Nature, 469(7330):428–431, 2011. [52] S J Mihic, Q Ye, M J Wick, V V Koltchine, M D Krasowski, S E Finn, M P Mascia, C F Valenzuela, K K Hanson, E P Greenblatt, R A Harris, and N L Harrison. Sites of Alcohol and Volatile Anaesthetic Action on GABA(A) and Glycine Receptors. Nature, 389(6649):385–389, 1997. [53] S Murail, B Wallner, J R Trudell, E Bertaccini, and E Lindahl. Microsec- ond Simulations Indicate that Ethanol Binds between Subunits and Could Stabilize an Open-State Model of a Glycine Receptor. Biophys. J., 100(7): 1642–1650, 2011. [54] M P Mascia, T K Machu, and R A Harris. Enhancement of homomeric glycine Receptor function by longchain alcohols and anaesthetics. Br. J. Pharmacol., 119(7):1331–1336, 1996. [55] R J Howard, S Murail, K E Ondricek, P J Corringer, E Lindahl, J R Trudell, and R A Harris. Structural Basis for Alcohol Modulation of a Pentameric Ligand-Gated Ion Channel. Proc. Natl. Acad. Sci. U.S.A., 108(29):12149– 12154, 2011. [56] S Murail, R J Howard, T Broemstrup, E J Bertaccini, R A Harris, J R Trudell, and E Lindahl. Molecular Mechanism for the Dual Alcohol Modulation of Cys-loop Receptors. PLoS Comput. Biol., 8(10):e1002710, 2012. [57] T Broemstrup, R J Howard, J R Trudell, R A Harris, and E Lindahl. Inhibi- tion Versus Potentiation of Ligand-Gated Ion Channels Can Be Altered by a Single Mutation That Moves Ligands Between Intra- and InterSubunit Sites. Structure, 21(8):1307–1316, 2013. [58] L Sauguet, R J Howard, L Malherbe, U S Lee, P Corringer, R A Harris, and M Delarue. Structural Basis for Potentiation by Alcohols and Anaesthetics in a Ligand-Gated Ion Channel. Nat. Commun., 4:1697, 2013. [59] D I Perkins, J R Trudell, L Asatryan, D L Davies, and R L Alkana. Charge and Geometry of Residues in the Loop 2 β Hairpin Diﬀerentially Aﬀect Ag- onist and Ethanol Sensitivity in Glycine Receptors. J. Pharmacol. and Exp. Ther., 341(2):543–551, 2012. 68 BIBLIOGRAPHY

[60] A Taly, M Delarue, T Grutter, Michael Nilges, N Le Novère, P Corringer, and J Changeux. Normal Mode Analysis Suggests a Quaternary Twist Model for the Nicotinic Receptor Gating Mechanism. Biophys. J., 88(6):3954–3965, 2005.

[61] H Nury, F Poitevin, C Van Renterghem, J P Changeux, P J Corringer, M De- larue, and M Baaden. One-microsecond Molecular Dynamics Simulation of Channel Gating in a Nicotinic Receptor Homologue. Proc. Natl. Acad. Sci. U.S.A., 107(14):6275–6280, 2010.

[62] A Keramidas, A J Moorhouse, P R Schoﬁeld, and P H Barry. Ligand-Gated Ion Channels: Mechanisms Underlying Ion selectivity. Prog. Biophys. Mol. Biol., 86(2):161–204, 2004.

[63] D F Cully, D K Vassilatis, K K Liu, P S Paress, L H T Van der Ploeg, J M Schaeﬀer, and J P Arena. Cloning of an Avermectin-Sensitive Glutamate- Gated Chloride Channel from Caenorhabditis elegans. Nature, 371(6499): 707–711, 1994.

[64] G W Boyd, P Low, J I Dunlop, L A Rson, A Vardy, J J Lambert, J A Peters, and C N Connolly. Assembly and Cell Surface Expression of Homomeric and Heteromeric 5-HT3 Receptors: the Role of Oligomerization and Chaperone Proteins. Mol. Cell. Neurosci., 21(1):38–50, 2002.

[65] B J Krishek, S J Moss, and T G Smart. Homomeric β1 γ-aminobutyric Acid A Receptor-Ion Channels: Evaluation of Pharmacological and Physiological Properties. Mol. Pharmacol., 49(3):494–504, 1996.

[66] S J Frazier, B N Cohen, and H A Lester. An Engineered Glutamate-gated Chloride (GluCl) Channel for Sensitive, Consistent Neuronal Silencing by Ivermectin. J. Biol. Chem., 288(29):21029–21042, 2013.

[67] P Li, E M Slimko, and H A Lester. Selective Elimination of Glutamate Activation and Introduction of Fluorescent Proteins into a Caenorhabditis elegans Chloride Channel. FEBS Lett., 528(1-3):77–82, 2002.

[68] G Hassaine, C Deluz, L Grasso, R Wyss, M B Tol, R Hovius, A Graﬀ, H Stahlberg, T Tomizaki, A Desmyter, C Moreau, X Li, F Poitevin, H Vo- gel, and H Nury. X-ray Structure of the Mouse Serotonin 5-HT3 Receptor. Nature, 512(7514):276–281, 2014.

[69] T Lynagh and B Laube. Opposing Eﬀects of the Anesthetic Propofol at Pentameric Ligand-Gated Ion Channels Mediated by a Common Site. J. Neurosci., 34(6):2155–2159, 2014.

[70] G Duret, C Van Renterghem, Y Weng, M Prevost, G Moraga-Cid, C Huon, J M Sonner, and P J Corringer. Functional Prokaryotic-Eukaryotic Chimera BIBLIOGRAPHY 69

from the Pentameric Ligand-Gated Ion Channel Family. Proc. Natl. Acad. Sci. U.S.A., 108(29):12143–12148, 2011. [71] M Rienzo, S C R Lummis, and D A Dougherty. Structural Requirements in the Transmembrane Domain of GLIC Revealed by Incorporation of Non- canonical Histidine Analogs. Chem. Biol., 21(12):1700–1706, 2014. [72] T Althoff, R E Hibbs, S Banerjee, and E Gouaux. X-ray Structures of GluCl in apo States Reveal a Gating Mechanism of Cys-Loop Receptors. Nature, 512(7514):333–337, 2014. [73] M Ernst, S Bruckner, S Boresch, and W Sieghart. Comparative Models of GABAA Receptor Extracellular and Transmembrane Domains: Important Insights in Pharmacology and Function. Mol. Pharmacol., 68(5):1291–1300, 2005. [74] D S Stewart, D W Pierce, M Hotta, A T Stern, and S A Forman. Mutations at Beta N265 in γ-Aminobutyric Acid Type A Receptors Alter Both Binding Affinity and Efficacy of Potent Anesthetics. PLoS ONE, 9(10):e111470, 2014. [75] M D Krasowski, K Nishikawa, N Nikolaeva, A Lin, and N L Harrison. Methio- nine 286 in Transmembrane Domain 3 of the GABAA Receptor Beta Subunit Controls a Binding Cavity for Propofol and Other Alkylphenol General Anes- thetics. Neuropharmacology, 41(8):952–964, 2001. [76] B Webb and A Sali. Comparative Protein Structure Modeling Using MOD- ELLER. Curr. Protoc. Bioinformatics, 47:5.6.1–32, 2014. [77] S Raman, R Vernon, J Thompson, M Tyka, R Sadreyev, J Pei, D Kim, E Kellogg, F DiMaio, O Lange, L Kinch, W Sheffler, B H Kim, R Das, N V Grishin, and D Baker. Structure Prediction for CASP8 with All-Atom Refinement Using Rosetta. Proteins, 77(S9):89–99, 2009. [78] M Bali and Myles H Akabas. Defining the Propofol Binding Site Location on the GABAA Receptor. Mol. Pharmacol., 65(1):68–76, 2004. [79] R Jurd, M Arras, S Lambert, B Drexler, R Siegwart, F Crestani, M Zaugg, K E Vogt, B Ledermann, B Antkowiak, and U Rudolph. General Anesthetic Actions In Vivo Strongly Attenuated by a Point Mutation in the GABA(A) Receptor Beta3 Subunit. FASEB J., 17(2):250–252, 2003. [80] M H Cheng and R D Coalson. Energetics and Ion Permeation Characteristics in a Glutamate-Gated Chloride (GluCl) Receptor Channel. J Phys Chem. B, 116(46):13637–13643, 2012. [81] N Calimet, M Simoes, J P Changeux, M Karplus, A Taly, and M Cecchini. A Gating Mechanism of Pentameric Ligand-Gated Ion Channels. Proc. Natl. Acad. Sci. U.S.A., 110(42):3987–3996, 2013. 70 BIBLIOGRAPHY

[82] J Du, W Lü, S Wu, Y Cheng, and E Gouaux. Glycine Receptor Mechanism Elucidated by Electron Cryo-Microscopy. Nature, 526(7572):224–229, 2015.

[83] F Zhu and G Hummer. Drying Transition in the Hydrophobic Gate of the GLIC Channel Blocks Ion Conduction. Biophys. J., 103(2):219–227, 2012.

[84] A Jha, P Purohit, and A Auerbach. Energy and Structure of the M2 Helix in Acetylcholine Receptor-Channel Gating. Biophys. J., 96(10):4075–4084, 2009.

[85] M S Prevost, L Sauguet, H Nury, C Van Renterghem, C Huon, F Poitevin, M Baaden, M Delarue, and P Corringer. A Locally Closed Conformation of a Bacterial Pentameric Proton-Gated Ion Channel. Nat. Struct. Mol. Biol., 19(6):642–649, 2012.

[86] L Sauguet, A Shahsavar, F Poitevin, C Huon, A Menny, A Nemecz, A Haouz, J P Changeux, P J Corringer, and M Delarue. Crystal Structures of a Pen- tameric Ligand-Gated Ion Channel provide a Mechanism for Activation. Proc. Natl. Acad. Sci. U.S.A., 111(3):966–971, 2014.

[87] J Zhang, F Xue, and Y Chang. Agonist- and Antagonist-Induced Conforma- tional Changes of Loop F and Their Contributions to the ρ1 GABA Receptor Function. J. Physiol., 587(1):139–153, 2009.

[88] A Khatri, A Sedelnikova, and D S Weiss. Structural Rearrangements in Loop F of the GABA Receptor Signal Ligand Binding, Not Channel Activation. Biophys. J., 96(1):45–55, 2009.

[89] S B Hansen, G Sulzenbacher, T Huxford, P Mhot, P Taylor, and Y Bourne. Structures of Aplysia AChBP Complexes with Nicotinic Agonists and Antag- onists Reveal Distinctive Binding Interfaces and Conformations. EMBO J., 24(20):3635–3646, 2005.

[90] P H N Celie, S E van Rossum-Fikkert, W J van Dijk, K Brejc, A B Smit, and T K Sixma. Nicotine and Carbamylcholine Binding to Nicotinic Acetylcholine Receptors as Studied in AChBP Crystal Structures. Neuron, 41(6):907–914, 2004.

[91] E A Gay, R Giniatullin, A Skorinkin, and J L Yakel. Aromatic Residues at Position 55 of Rat α7 Nicotinic Acetylcholine Receptors are Critical for Maintaining Rapid Desensitization. J. Physiol., 586(4):1105–1115, 2008.

[92] E Sigel, R Baur, S Kellenberger, and P Malherbe. Point Mutations Aﬀect- ing Antagonist Aﬃnity and Agonist Dependent Gating of GABAA Receptor Channels. EMBO J., 11(6):2017–2023, 1992. BIBLIOGRAPHY 71

[93] K N M Daeﬄer, H A Lester, and D A Dougherty. Functional Evaluation of Key Interactions Evident in the Structure of the Eukaryotic Cys-Loop Receptor GluCl. ACS Chem. Biol., 9(10):2283–2290, 2014.

[94] M Traka, K L Seburn, and B Popko. Nmf11 is a novel ENU-induced Mutation in the Mouse Glycine Receptor alpha 1 Subunit. Mamm. Genome, 17(9):950– 955, 2006.

[95] M E Wilkins, A Caley, M C Gielen, R J Harvey, and T G Smart. Murine Startle Mutant Nmf11 Aﬀects the Structural Stability of the Glycine Receptor and Increases Deactivation. J. Physiol., 2016, Advance Online Publication. doi: 10.1113/JP272122.

[96] C C David and D J Jacobs. Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins. In Homology Modeling, pages 193–226. Humana Press, Totowa, NJ, 2013.

[97] D M Van Aalten, D A Conn, B L De Groot, H J Berendsen, J B Findlay, and A Amadei. Protein Dynamics Derived From Clusters of Crystal Structures. Biophys. J., 73(6):2891–2896, 1997.

[98] Z Fourati, L Sauguet, and M Delarue. Genuine Open Form of the Pentameric Ligand-Gated Ion Channel GLIC. Acta Crystallogr. Sect. D, 71(3):454–460, 2015.

[99] G Gonzalez-Gutierrez, L G Cuello, S K Nair, and C Grosman. Gating of the Proton-Gated Ion Channel from Gloeobacter violaceus at pH 4 as Revealed by X-ray Crystallography. Proc. Natl. Acad. Sci. U.S.A., 110(46):18716–18721, 2013.

[100] P S Miller and A R Aricescu. Crystal Structure of a Human GABAA Recep- tor. Nature, 512(7514):270–275, 2014.

[101] M H Akabas. Using Molecular Dynamics to Elucidate the Structural Basis for Function in pLGICs. Proc. Natl. Acad. Sci. U.S.A., 110(42):16700–16701, 2013.

[102] M Gielen, P T, and T G Smart. The Desensitization Gate of Inhibitory Cys-loop Receptors. Nat. Commun., 6:6829, 2015.

[103] J Corradi, F Gumilar, and C Bouzat. Single-Channel Kinetic Analysis for Activation and Desensitization of Homomeric 5-HT3A Receptors. Biophys. J., 97(5):1335–1345, 2009.

[104] J Mistry, E Kloppmann, B Rost, and M Punta. An Estimated 5% of New Protein Structures Solved Today Represent a New Pfam Family. Acta Crys- tallogr. Sect. D, 69:2186–2193, 2013. 72 BIBLIOGRAPHY

[105] C Kutzner, D A Köpfer, J Machtens, B L de Groot, C Song, and U Zachariae. Insights into the Function of Ion Channels by Computational Electrophysi- ology Simulations. BBA-Biomembranes, 2016, Advance Online Publication. doi: 10.1016/j.bbamem.2016.02.006.