Design Strategies for New Drugs Targeting Multicomponent Systems

Focusing on Class II MHC Proteins and Acetylcholinesterase

Cecilia Lindgren

Doctoral Thesis, 2017 Department of Chemistry Umeå University, Sweden

Responsible publisher under swedish law: the Dean of the Faculty of Science and Technology This work is protected by the Swedish Copyright Legislation (Act 1960:729) ISBN: 978-91-7601-737-1 Electronic version available at http://umu.diva-portal.org/ Tryck/Printed by: VMC-KBC Umeå Umeå, Sweden, 2017

“Good judgement comes from experience, and experience_ —well, that comes from poor judgement.”_

—Alan Alexander Milne_

“Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.” —Marie Curie Table of Contents

Abstract ii Strategier för utveckling av nya läkemedel mot multikomponentsystem iii List of Publications iv List of Abbreviations and Notations vi 1. Introduction 1 1.1. Drug Discovery, Design, and Development 1 1.2. Multicomponent System I: Focus on Class II MHC Proteins and Rheumatoid Arthritis 4 1.3. Multicomponent System II: Focus on Acetylcholinesterase and Nerve Agent Antidotes 10 2. Scope of the Thesis 17 2.1. Towards a Vaccine against Rheumatoid Arthritis 17 2.2. Towards a Broad-Spectrum Nerve Agent Antidote 17 3. Methods and Techniques 18 3.1. Organic Synthesis in Drug Design 18 3.2. Computational Chemistry in Drug Design 20 3.3. Structure Determination in Drug Design 22 3.4. Biochemical and Biological Evaluation in Drug Design 23 4. Towards a Vaccine against Rheumatoid Arthritis: Focus on Class II MHC Proteins 27 4.1. A Dynamic Study of Class II MHC•Glycopeptide Complexes 27 4.2. Hydroxyethylene and Ketomethylene Isosteres Introduced in CII259–273 Affect the Dynamics of Aq 32 4.3. Modifications of Anchor Residues and the Backbone of CII259–273 to Probe Interactions in DR4 38 5. Towards a Broad-Spectrum Nerve Agent Antidote: Focus on Acetylcholinesterase 51 5.1. Development of Inhibitors of Acetylcholinesterase 51 5.2. The Transformation of an Inhibitor into a Reactivator of Nerve Agent Inhibited AChE 57 6. Summary and Future Directions 66 6.1. Towards a Vaccine against Rheumatoid Arthritis 66 6.2. Towards a Broad-Spectrum Nerve Agent Antidote 68 6.3. Concluding Remarks 69 7. Acknowledgements 70 8. References 72 9. Appendix 87

i “The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.” —Albert Einstein Abstract

The field of medicinal chemistry is constantly evolving. Aided by advances within techniques as well as knowledge of biological systems, increasingly complex targets and drugs can be considered. This thesis includes two projects focusing on the design of drugs targeting multicomponent systems, referring to systems for which multiple components must be considered during the drug design process. In the first project, the long-term goal is to develop a vaccine against the autoimmune disease rheumatoid arthritis (RA). The cause of RA is unknown, but it is genetically linked to expression of class II MHC proteins that present antigens to T- cell receptors (TCRs), responsible for initiating an immune response. A glycopeptide fragment, CII259–273, from type II collagen has shown promising results as a vaccine against arthritis resembling RA in mice. CII259–273 binds to the class II MHC protein followed by presentation to the TCR, forming a multicomponent system. We have used molecular dynamics (MD) simulations to study the effect that modifications of CII259–273 have on the multicomponent system. Non-native amino acids and amide bond isosteres have been introduced. This has demonstrated the importance of retaining the backbone conformation of CII259–273, as well as the hydrogen bonds formed to the backbone. The ability to introduce such modifications would be of value to affect the potency towards the MHC protein, and prevent degradation of the glycopeptide. The studies have revealed a multicomponent system that is highly sensitive to even small modifications that can affect the dynamics of the entire complex. In the second project, the long-term goal is to develop a broad-spectrum antidote against nerve agents. Nerve agents are extremely toxic compounds that act by covalently inhibiting the enzyme acetylcholinesterase (AChE), which is essential for termination of nerve signalling. A major limitation of current antidotes is that their efficiency is dependent on the type of nerve agent. A broad-spectrum antidote must be able to bind to the multicomponent system consisting of AChE covalently inhibited by different nerve agents. It will then act by performing a nucleophilic attack on the nerve agent adduct, thus breaking the covalent bond to AChE. We have used statistical molecular design (SMD) and quantitative structure- activity relationship (QSAR) modelling to identify a fragment with a potency for AChE inhibited by different nerve agents. A nucleophilic component able to restore the enzyme to the active form was thereafter introduced. This resulted in a functional reactivator, efficient for multiple nerve agents. Furthermore, the mechanism of reactivation has been investigated through structural studies, enabled by a combination of X-ray crystallography and molecular modelling. A high flexibility of the reactivator, as well as the ability to bind to AChE in multiple conformations, are defined as important properties for a broad-spectrum antidote.

ii “[…] it isn't reasonable to ask that we achieve perfection. What is reasonable is to ask that we never cease to aim for it.” —Atul Gawande Strategier för utveckling av nya läkemedel mot multikomponentsystem

Ett läkemedel kan definieras som en molekyl som interagerar med, och påverkar funktionen hos, ett biologiskt system. Forskningen inom läkemedelskemi utvecklas ständigt, och idag kan man angripa allt mer komplexa biologiska system. Den här avhandlingen inkluderar två projekt som båda fokuserar på design av läkemedel mot multikomponentsystem, vilket syftar på biologiska system där man vid läkemedelsdesign måste ta hänsyn till flera olika komponenter. I det första projektet är det långsiktiga målet att utveckla ett vaccin mot ledgångsreumatism. Detta är en autoimmun sjukdom där immunförsvaret inte längre kan skilja kroppens egna beståndsdelar från främmande material, vilket leder till att brosk i de egna lederna börjar brytas ner. Man vet inte vad som orsakar sjukdomen, men man vet att den är genetiskt kopplad till klass II MHC-proteiner. Dessa proteiner presenterar icke kroppseget material för T-cellreceptorer, vilket triggar immunceller och därmed immunförsvaret. Ett läkemedel med detta biologiska system som mål måste därför samtidigt kunna interagera med ett klass II MHC-protein och en T- cellreceptor, vilket leder till ett komplext multikomponentsystem. Ett fragment från kollagen II (CII259–273), som förekommer i brosk, interagerar med dessa proteiner och kan lindra symptom och häva motsvarande sjukdom i möss. Vi har undersökt hur modifieringar av detta fragment påverkar proteinerna, för att undersöka mekanismen bakom initiering av ett immunförsvar. Detta har visat på ett mycket känsligt multikomponentsystem. Även till synes minimala förändringar påverkar hur CII259–273 interagerar med klass II MHC-proteinet, vilket i sin tur påverkar presentationen till T-cellreceptorer. I det andra projektet är det långsiktiga målet att utveckla ett universellt motmedel mot nervgaser, som används vid kemisk krigsföring. Nervgaser fungerar genom att blockera det livsnödvändiga proteinet acetylcholinesteras (AChE) som ansvarar för att avsluta nervsignalering. Exponering för nervgaser leder till överstimulering av nervsystemet och även en mycket liten dos kan vara dödlig. Det finns medicinska motmedel som kan häva nervgasernas effekt, men deras effektivitet är beroende av vilken nervgas som blockerar AChE. Därmed finns det ett behov av nya motmedel som kan interagera med det multikomponentsystem som består av AChE blockerat av olika nervgaser. Vi presenterar en strategi för design av motmedel som går ut på att man först identifierar en molekyl som interagerar med AChE oberoende av vilken nervgas som blockerar proteinet. I nästa steg bygger man vidare på denna molekyl för att introducera en komponent som kan utföra den kemiska reaktion som är nödvändig för att avlägsna nervgasen från AChE. På så sätt har vi utvecklat en molekyl som kan interagera med AChE på olika sätt beroende på vilken nervgas som blockerar proteinet, och kan häva effekten av samtliga nervgaser som vi har undersökt.

iii “I find the great thing in this world is not so much where we stand, as in what direction we are moving.” —Oliver Wendell Holmes, Sr. List of Publications

This thesis is based on the following papers, which are referred to in the text by the corresponding Roman numerals.

I Lindgren, C.; Andersson, I.E.; Berg, L.; Dobritzsch, D.; Ge, C.; Haag, S.; Uciechowska, U.; Holmdahl, R.; Kihlberg, J. Linusson, A. Hydroxyethylene Isosteres Introduced in Type II Collagen Fragments Substantially Alter the Structure and Dynamics of Class II MHC Aq/Glycopeptide Complexes. Org. Biomol. Chem. 2015, 13(22):6203–6216. II Lindgren, C.*; Tyagi, M.*; Viljanen, J.; Toms, J.; Ge, C.; Zhang, N.; Holmdahl, R.; Kihlberg, J.; Linusson, A. Modification of a Fragment from Type II Collagen Affect the Dynamics of Human Class II MHC Proteins and the Subsequent T- Cell Response, Associated with Rheumatoid Arthritis. Manuscript. III Andersson, C.D.; Hillgren, J.M.; Lindgren, C.; Qian, W.; Akfur, C.; Ekström, F.; Linusson, A. Benefits of Statistical Molecular Design, Covariance Analysis, and Reference Models in QSAR: a Case Study on Acetylcholinesterase. J. Comp. Aided Mol. Des. 2015, 29(3):199–215. IV Lindgren, C.; Forsgren, N.; Akfur, C.; Berg, L.; Andersson, C.D.; Hillgren, M; Qian, W.; Svensson, R.; Worek, F.; Ekström, F.; Linusson, A. Design of Reactive Drugs: Structure and Mechanism of Novel Nerve Agent Antidotes. Manuscript.

*These authors contributed equally to the work.

All papers have been reprinted with the kind permission from the publishers.

iv Author’s Contributions Paper I: All of the molecular modelling including analysis of the results, and major writing of manuscript. Paper II: Major planning and design, all of the molecular modelling including analysis of the results, and major writing of manuscript. Paper III: Parts of the QSAR modelling and evaluation, half of the synthesis, and minor writing of manuscript. Paper IV: Major planning, all of the synthesis, minor contributions to X-ray crystallography, minor contributions to the molecular modelling, analysis of results, and major writing of manuscript.

v “Chemists do not usually stutter. It would be very awkward if they did, seeing that they have at times to get out such words as methylethylamylophenylium.” —William Crookes List of Abbreviations and Notations

Ac acetate AcCoA acetyl coenzyme A ACh acetylcholine AChE acetylcholinesterase APC antigen presenting cell Aq H-2Aq aq aqueous Boc tert-Butoxycarbonyl BPh m-bromo phenylalanine Bta benzothiophenealanine CA choline acetyltransferase CAS catalytic site Cbz carboxybenzyl CDR complementary-determining region Ch choline CIA collagen induced arthritis CII type II collagen CLIP class II associated invariant chain peptide CNS central nervous system Cyh cyclohexylalanine Cyp cyclopropylalanine DCM dichloromethane DELFIA dissociation-enhanced lanthanide fluoroimmunoassay DIBAL-H diisobutylaluminium hydride DIC N,N′-diisopropylcarbodiimide DMARD disease modifying antirheumatic drug DMF N,N-dimethylformamide DR1 human leukocyte antigen-D related 1, HLA-DR1 DR4 human leukocyte antigen-D related 4, HLA-DR4 DTNB 5,5’-dithiobis-(2-nitrobenzoic acid) e.g. exempli gratia (for example) ELISA enzyme linked immunosorbent assay ER endoplasmic reticulum EtOH ethanol Fmoc fluorenylmethyloxycarbonyl Fmoc-OSu Fmoc N-hydroxysuccinimide ester FPh m-fluoro phenylalanine G Gibbs free energy GalHyl β-D-galactopyronasyl hydroxylysine

vi H enthalpy hAChE Homo sapiens acetylcholinesterase HATU 1-[bis(dimethylamino)methylene]-1H-1,2,3- triazolo[4,5-b]pyridinium 3-oxid hexafluorophosphate HOAt 1-hydroxy-7-azabenzotriazole HOBt 1-hydroxybenzotriazole HOMO highest occupied molecular orbital HPLC high-performance liquid chromatography HTS high throughput screening

IC50 half-maximal inhibitory concentration i.e. id est (that is) Ii invariant chain IL-2 interleukin-2 IR infrared

Ka association constant KD dissociation constant KHMDS potassium bis(trimethylsilyl)amide kobs observed first-order rate constant kr first-order rate constant kr2 second-order rate constant mAChE Mus musculus acetylcholinesterase logP logarithm of the partition coefficient LUMO lowest occupied molecular orbital MD molecular dynamics MeCN acetonitrile MeOH methanol MHC major histocompatibility complex min minute MWI microwave irradiation NaOMe sodium methoxide NMR nuclear magnetic resonance NSAIDs nonsteroidal anti-inflammatory drugs PC principal component PCA principal component analysis PDB protein databank Pd/C palladium on charcoal PEG polyethylene glycol PI propidium iodide pIC50 the negative logarithm of the half-maximal inhibitory concentration pKa logarithm of the acid dissociation constant PLS partial least square projections to latent structures

vii PNS peripheral nervous system PS peripheral site Pyr 3-pyridylalanine Q2 cross-validated R2 (amount of predicted variation) QSAR quantitative structure-activity relationship R2 amount of explained variation RA rheumatoid arthritis RMSD root-mean-square deviation RMSEcv root-mean-square error of cross validation RMSEE root-mean-square error of estimation RMSEP root-mean-square error of the prediction r.t. room temperature S entropy SAR structure-activity relationship SMD statistical molecular design SPPS solid phase peptide synthesis T temperature TBDMSCl tert-butyldimethylsilyl chloride tBu tert-butyl TcAChE Torpedo californica acetylcholinesterase TCR T-cell receptor TCSPC time-correlated single photon counting TEA triethylamine TFA trifluoroacetic acid THF tetrahydrofurane TLC thin-layer chromatography TNB 3-thi-6-nitrobenzoate TNF-α tumor necrosis factor-alpha TPSA total polar surface area triflate trifluoromethanesulfonate Trt trityl V variable domain vdW van der Waals

viii “The glory of medicine is that it is constantly moving forward, that there is always more to learn. The ills of today do not cloud the horizon of tomorrow, but act as a spur to greater effort.” 1. Introduction —William James Mayo

A drug is a compound with the purpose of treating or preventing a disease, commonly by interacting with biological systems in our bodies and affecting their function. The tremendous cost for developing a new drug and bringing it to the market has been estimated to $2.6 billion1 and the process can take up to 15 years. Thus, design strategies for novel drugs are of importance to rationalize the process.

1.1. Drug Discovery, Design, and Development Drug discovery refers to the process where a chemical starting point for the design of a new potential drug is identified. A therapeutic area (i.e. a disease) is selected, usually followed by determining the drug target (e.g. an enzyme, receptor, or nucleic acid).2 Thereafter a suitable lead compound that has a desired pharmacological or biological activity is identified.3-12 Next, the drug design process will begin where the structure- activity relationship (SAR) will be investigated, identifying the structural elements of the lead that are of importance for biological activity.13 This enables optimization of the lead, important to improve potency, solubility, pharmacodynamic, and pharmacokinetic properties.10, 14, 15 When a promising drug candidate has been identified the process of getting it to the market (drug development) is initiated, where clinical trials are performed to investigate safety and efficacy.10

1.1.1. Drug Design Today Traditionally, the main focus in medicinal chemistry has been the design of small organic molecules that inhibit a protein via non-covalent interactions. However, the field is constantly evolving, and nowadays increasingly complex systems are considered, including unconventional drug targets and ligands. This is enabled by advances within techniques applied in medicinal chemistry, as well as an increased knowledge of biological systems.16 The importance of the dynamics of proteins is acknowledged, and commonly studied to connect biological responses and mechanisms to structural changes.17-23 Covalent drugs have been previously avoided in drug discovery due to safety issues. However, they form a group of compounds that have proven successful in the treatment of various diseases (Figure 1), and rational design for developing selective covalent drugs are beginning to evolve.24, 25 An increased interest in protein targets that interact with nucleic acids or other proteins, rather than small molecules, is also evident.26 The binding sites of such targets are in general large and difficult for traditional drugs that follow the Lipinski’s rule of five27 to bind to. This has led to the development of drugs that span a larger chemical space that consciously violate these rules, such as peptides and macrocycles.28 Another example of non-traditional drug design is when the pathological function of the protein is not clear. In case of neurodegenerative diseases, for instance, accumulation and aggregation of misfolded proteins are common. The function of the misfolded proteins and the reason why they

Figure 1. The chemical structures of four well established covalent drugs. Aspirin can be used to reduce fever and pain, penicillin is an antibiotic, clopidogrel reduces the risk of heart disease, and lansoprazole is used in treatment for stomach and intestinal ulcers. cause the disease is not always known. Thus, rather than inhibiting or enhancing the function of the proteins, research aim at lowering the level of the protein, targeting RNA or DNA.29 Furthermore, design of multitarget drugs is performed today, where focus is shifted from designing drugs that act solely on one target and mechanism, to drugs that interact with a set of targets associated with a disease. The aim of multitarget drugs is to deal with the fact that biological systems are highly complex, often involving multiple pathways and components.30 This approach is in line with the design of compounds targeting multicomponent systems, which is the focus of this thesis. We refer to multicomponent systems as biological systems where multiple components must be considered during the drug design process. In the first system that we have studied (Chapter 1.2) the effect of a potential drug is dependent on the drug simultaneously interacting with multiple proteins. In the second system (Chapter 1.3) the drug will interact with a complex of a protein covalently inhibited by varying molecules. (Figure 2).

Figure 2. Schematic representation of the two multicomponent systems investigated in this thesis. A) Multicomponent system I: a potential drug interacts simultaneously with two separate proteins. B) Multicomponent system II: a potential drug interacts with a protein in complex with different covalent inhibitors.

1.1.2. Protein-Ligand Interactions When a drug (i.e. a ligand) binds to a protein (or another macromolecule), thus forming a complex, attractive forces (generally non-covalent interactions) such as hydrogen bonds, ionic bonds, Van der Waals forces, and arene interactions are involved. Drug design generally focuses on optimizing these interactions to improve the binding strength between the ligand and protein. The Gibbs free energy of binding

(Eq. 1) describes the energy difference that occurs upon the formation of a protein- ligand complex. The enthalpic term (H) describes the energy difference that occurs upon association and dissociation of non-covalent interactions. The entropic term (S) is dependent on the freedom of movement of a molecule and describes the disorder in the complete system, including ligand and protein as well as the solvent. The temperature in Kelvin is denoted by T.

ΔG = ΔH – TΔS (1)

In order for binding of a ligand to a protein to occur, the Gibbs free energy (G) of binding must be negative, meaning that the energy of the complex is lower than the sum of energy of the ligand and protein alone. By chemically modifying a ligand to optimize the interactions with the protein, it will become more enthalpically favorable. However, this will also affect the entropic term, since a tighter and more directed interaction is generally less entropically favorable. Thus, large changes in ΔH can lead to the opposite change in ΔS, which can ultimately result in a very small change in ΔG. Furthermore, the Gibbs free energy will also be affected by desolvation. Upon binding of a ligand to a protein, water will be displaced from the ordered state in the binding site and around the ligand into the water in solution, which is entropically favorable. However, it is not entropically favorable for the free ligand to bind to the protein, since this means that it will be locked in a more rigid conformation.31

3 “The beauty of a living thing is not the atoms that go into it, but the way those atoms are put together.” —Carl Sagan 1.2. Multicomponent System I: Focus on Class II MHC Proteins and Rheumatoid Arthritis

1.2.1. The Immune System The immune system is crucial for the protection of the body against diseases, and has the ability to differentiate between the body’s own substituents and foreign antigens. Immunity is divided into the innate and the adaptive immunity. Innate immunity is a less specific but much faster response, which takes minutes to hours to eliminate infectious agents. The adaptive immunity is more antigen specific, but an immune response against a pathogen is not initiated until five to six days after initial exposure. The recognition process relies on B- and T-cell receptors generated in B- and T-cells, respectively. After clearance of an infection, residual B- and T-cells can remain as memory cells for decades, leading to a more rapid and effective response in case of a repeated infection. Tolerance against the body’s own constituents is essential to prevent the immune system from attacking the host itself. Thus, developing B- and T-cells are subjected to an innate test of non-responsiveness against structures of self-origin. Destruction of any cell that does not pass this test, thus possessing the ability to harm the host, will occur.32, 33

1.2.2. Rheumatoid Arthritis Sometimes the recognition by the immune system goes wrong and it will start to attack the body itself, which will result in autoimmune diseases such as rheumatoid arthritis (RA). RA is mainly affecting the peripheral joints of hands and feet, leading to a chronic inflammation that destroys cartilage and bone through degradation of type II collagen. Current treatment involves three different types of drugs. The initial use of nonsteroidal anti-inflammatory drugs (NSAIDs, e.g. ibuprofen) and glucocorticoids (e.g. prednisone), are meant to supress the inflammation and quickly relieve the pain and swelling. They are distributed together with disease-modifying antirheumatic drugs (DMARDs, e.g. methotrexate) that are slower to act (weeks to months), but relieve the symptoms and slow down the disease progression (Figure 3). Finally, biological-response modifiers, including for instance tumor necrosis factor-alpha (TNF-α) inhibitors (e.g. infliximab), target the inflammatory mediators that lead to tissue damage.34, 35 However, the current treatment, which is focusing on treating the

Figure 3. Chemical structures of three substances used in the treatment of symptoms of RA: the NSAID ibuprofen, the glucocorticoid prednisone, and the DMARD methotrexate.

symptoms of the disease, includes side effects and does not offer a cure. In addition, the effect of the treatments varies between patients. Thus, there is a great need for new therapeutics. Even though the cause of the disease is not known, it has been genetically linked to expression of two class II major histocompatibility complex (MHC) proteins: human leukocyte antigen-D related 1 and 4 (HLA-DR1 and -DR4, abbreviated as DR1 and DR4 throughout this thesis).36, 37

1.2.3. The Structure and Function of Class II MHC Proteins Compared to B-cell receptors that recognize lone antigens, T-cell receptors (TCRs) recognize fragments of antigens positioned on MHC proteins presented on the surface of cells. There are two classes of MHC proteins. Class I MHC proteins are present on all nucleated cells and present intracellular antigens to CD8+ T-cells that kill the cells in case of foreign material. Class II MHC proteins are mainly expressed in antigen- presenting cells (APC). They are associated with endocytosed extracellular antigens that are then presented to CD4+ T cells, forming a multicomponent system. The T-cell will be activated and initiate immunity towards extracellular invaders (Figure 4).33, 38- 42

Figure 4. Class II MHC proteins are expressed in the endoplasmic reticulum (ER) of APC and are associated with the invariant chain (Ii). The Ii will stabilize the protein as well as preventing peptides from binding during the transport to the endosome through the Golgi. After arriving to the endosome, the Ii will be degraded, leaving the Class II-associated invariant chain peptide (CLIP) in the binding groove. With the aid of HLA-DM chaperones the CLIP will be exchanged with endocytosed antigens. Thereafter the entire complex will be transported to the surface of the APC, where the peptide is presented to TCRs.

Class II MHC proteins are membrane bound glycoproteins. The first X-ray crystal structure was published in 1994, in complex with an influenza virus peptide.43 It consists of two domains (the α- and β-chains) including a transmembrane segment and a cytoplasmic anchor region. The binding groove consists of eight β-strands, and

two α-helices. Peptides of 13–18 amino acids can bind to the extended binding groove that is open in both ends, positioned between the two α-helices. The peptides will be anchored both with the aid of a hydrogen bond network and a number of binding pockets with preferences for different amino acid side chains (Figure 5).43

Figure 5. Three-dimensional structure of a class II MHC protein and a TCR. A) The structure of the class II MHC protein DR4 (α-domain in purple and β-domain in cyan) in complex with a peptide (green). B) The structure of a multicomponent system, consisting of the class II MHC protein DR4 in complex with a peptide and a TCR (α-domain in pink and β-domain in orange, PDB 1J8H44).

1.2.4. The Structure and Function of the T-Cell Receptor TCRs belong to the immunoglobulin superfamily of proteins. A TCR consists of an α- and a β-chain (or a γ- and δ-chain) containing a variable domain (V) composed of the six complementarity-determining region (CDR) loops, and a constant Ig like domain (Figure 5). It also has a transmembrane domain and a short cytoplasmic tail. The class II MHC●peptide complex will interact with the variable domain of the TCR, leading to the formation of a multicomponent system. Mainly, the peptide will be in contact with the CDR3-loops, while the class II MHC protein is instead interacting with the CDR1- and CDR2-loops. The signal transduction between the class II MHC●peptide complex and the TCR is dependent on intracellular receptor associated molecules (a complex of proteins referred to as CD3) and CD4 co-receptors.45 They will aid in transmitting the signals into the cell as well as secure the binding between the T-cell and the APC. In addition, in case of naïve T-cells that have not been in previous contact with an antigen, binding of the CD28 co-receptor to the co- stimulatory molecules CD80 or CD86 on the APC are also required. Finally, cytokine production is initiated, directing T-cell differentiation into effector cell types (Figure 6).33, 46

Figure 6. A schematic figure of the presentation of an antigen by a class II MHC protein to a TCR. Three signals are required for naïve T-cell activation to occur: 1) Antigen specific signalling from the interaction with the MHC●peptide complex, together with the CD3 and CD4 co-receptors. 2) Co-stimulation from CD28 interacting with CD80 or CD86. 3) Signal 1 and 2 leads to production of cytokines that direct T-cell proliferation and differentiation into effector cell types.

1.2.5. Modulation of the T-cell Response as a Strategy for Finding a Vaccine against Rheumatoid Arthritis One strategy for finding a vaccine against RA is to target T-cells and modulate the response, aiming at introducing tolerance against the self-antigen they recognize.47 Collagen induced arthritis (CIA) is a validated mouse model used for studying RA.48 It can be induced in animals by immunizing with type II collagen derived from cartilage in rats and complete Freund’s adjuvant, and will lead to inflammation of synovial joints resembling RA in humans.49 Compared to RA that is genetically linked to the expression of class II MHC proteins DR1 and DR4, CIA is instead linked to the class II MHC protein H-2Aq (Aq).50 Clinical trials have been conducted, where oral or nasal administration of type II collagen has been performed to patients, but only with marginal improvements.51 However, administration of soluble type II collagen,52, 53 or fragments of thereof, 54, 55 to mice has been shown to supress CIA and induce tolerance. Modifications of these fragments, even very small ones, can lead to a change in T-cell response.56 Immunization can lead to unresponsive T-cells (anergy)57 or alter the pattern of cytokine production, from inflammatory to anti-inflammatory.58

1.2.6. A Glycopeptide Fragment from Type II Collagen Protects against Collagen Induced Arthritis A glycopeptide fragment derived from type II collagen — CII259–273 (Figure 7) — is an antigen that has been linked to RA. It has shown promising results as a vaccine against CIA, when administered alone or as a complex with the class II MHC protein Aq. It can reduce the disease progression in CIA and lead to disease regression,55, 59 and CII259–273 also binds to DR4. It is recognized by T-cells isolated from patients suffering from RA, and by T-cell hybridomas from transgenic mice expressing human DR4.60

Figure 7. Chemical structure of the glycopeptide CII259–273, derived from type II collagen.

In agreement with the general binding mode for peptides, CII259–273 binds to the MHC protein between the two α-helices with the aid of a hydrogen bond network mainly formed to the N-terminus backbone of the glycopeptide. In case of murine Aq, Ile260 and Phe263 are important anchor residues positioned in the P1 and P4 pocket respectively. The minimal epitope has been determined to Ile260–Gln267.61 In case of human DR4, the binding of CII259–273 is similar, but the peptide has been shifted three amino acids towards the N-terminus, so that Phe263 is anchored in P1 while Glu266 is positioned in the now shallow P4 pocket.62-64 The carbohydrate moiety is essential for the specific biological activity of this glycopeptide.64-66 It is attached to hydroxylated Lys264 (GalHyl264), and is protruding out of the binding site serving as a contact point for the TCR (Figure 8). Modification of CII259–273 by exchange of amino acids67 is of importance to improve binding to the class II MHC protein, which could affect the presentation to the TCR and result in an altered T-cell response. Furthermore, the use of peptides as therapeutics is problematic due to their metabolic instability, thus introduction of amide bond isosteres68-70 is of interest. Investigation of such modifications has been previously done and a SAR has been established. The importance of the hydrogen bond network formed to the backbone of CII259–273 has been confirmed, together with the effect of the dynamics of the complex on a resulting T-cell response.

Figure 8. Three-dimensional structure of CII259–273 binding to Aq (A) or DR4 (B), respectively. A surface has been added to the protein to display the binding pockets. The glycopeptide (green) is displayed with a stick model, with the carbohydrate moiety in ball and stick. A) Only Gly259–Lys270 is experimentally determined in the X-ray crystal structure,71 and the carbohydrate moiety has been computationally modelled onto the glycopeptide due to poor electron density. B) The entire glycopeptide has been computationally modelled into DR4 (based on PDB 1J8H72).

9 “There is nothing like looking, if you want to find something. You certainly usually find something, if you look, but it is not always quite the something you were after.” —John Ronald Reuel Tolkien

1.3. Multicomponent System II: Focus on Acetylcholinesterase and Nerve Agent Antidotes

1.3.1. The Nervous System The nervous system is acting as a network, transmitting signals between different parts of the body. It is responsible for coordination, communication and regulation. It is divided into the central nervous system (CNS) that includes the brain and spinal cord, and the peripheral nervous system (PNS) that consist of motor nerves and sensory nerves that carry messages between the CNS and the rest of the body. The communication between neurons, or between neurons and their target tissue, is central for the function of the nervous system. The communication is mediated by different neurotransmitters, e.g. dopamine, serotonin, or acetylcholine (ACh). The ACh dependent cholinergic signalling system is an important part for both the CNS and the PNS. It is also of importance for this thesis and will therefore be described in more detail. For a signal to be transmitted between two neurons, ACh is synthesized in the presynaptic neuron from choline (Ch) and acetylcoenzyme A (AcCoA). The reaction is catalyzed by the enzyme choline acetyltransferase (CA). With the aid of transport proteins ACh will thereafter be incorporated in vesicles. The vesicles will fuse with the cell membrane, releasing ACh into the synaptic cleft. ACh will cross the cleft and bind to cholinergic receptors on the postsynaptic nerve thus triggering the opening of

Figure 9. The mechanism for signalling between neurons by ACh. ACh is synthesized in the presynaptic neuron and released into the synaptic cleft where it binds to cholinergic receptors, transferring a nerve signal. ACh will thereafter be hydrolyzed by AChE into Ch and acetate to terminate nerve signalling.

ion-channels in the downstream nerve. To terminate signalling, ACh is thereafter hydrolyzed by the enzyme acetylcholinesterase (AChE), into Ch and acetate. Ch will be taken up by the presynaptic neuron with the aid of a transport protein, and once again used in the synthesis of ACh (Figure 9).10

1.3.2. The Structure and Function of Acetylcholinesterase The first X-ray crystal structure of AChE was published in 1991, where AChE from the pacific electric ray (Torpedo californica AChE, TcAChE) had been crystallized.73 Since then crystallization has been performed both for Mus musculus AChE (mAChE)74 and Homo sapiens AChE (hAChE)75 among others. AChE is an α/β protein that consists of a 12-stranded mixed β sheet with 14 α-helices (Figure 10).73 It possesses a large dipole moment that is aligned with the active site gorge.76 This gorge is 20 Å deep and lined with aromatic amino acids. The rim of this gorge, consisting of Tyr72, Asp74, Tyr124, Trp286, and Tyr341 (residue numbering from mAChE is used throughout this thesis), is referred to as the peripheral site (PS). It is believed that the positively charged ACh will bind in the PS and rapidly diffuse down to the catalytic site (CAS). Hydrolysis of ACh is performed by the catalytic triad (Ser203, Glu334, His447) at the bottom of the gorge. The quaternary amine of ACh will bind to the choline-binding site (Trp86, Glu202, Tyr337) that enables an optimal positioning of the carbonyl for a subsequent nucleophilic attack from Ser203. During the hydrolysis, the transition state carbonyl oxygen will be stabilized by the oxyanion hole (formed by the backbone nitrogens of Gly121, Gly122, and Ala204), while the

Figure 10. The structure of AChE and mechanism of hydrolysis of ACh. A) Structure of AChE in complex with donepezil (PDB 1EVE77), a drug used in the treatment of Alzheimer’s disease. A surface has been added to the active site gorge. The PS, and the CAS where the catalytic triad responsible for hydrolysis of ACh is located, have been marked. B) The mechanism of hydrolysis of ACh into Ch and acetate performed by AChE, involving the catalytic triad consisting of Ser203, Glu334, and His447.

acyl group is placed in the acyl pocket (Phe295, Phe297, Phe338) (Figure 11).78-81 Due to the fact that the catalytic site is located at the bottom of this narrow gorge, one could expect that substrate and product trafficking to and from the active site would be slow. Surprisingly this is not the case, and the fact is that AChE is one of the most efficient enzymes with a turnover number ~ 10 000 s-1.82, 83

Figure 11. The different subsites of the apo form of AChE, (PDB 1J0674). Amino acids belonging to the PS (Tyr72, Asp74, Tyr124, Trp286, and Tyr341) are coloured in green, the acyl pocket (Phe295, Phe297, Phe338) in magenta, the oxyanion hole (Gly121, Gly122, Ala204) in yellow, the choline binding site (Trp86, Glu202, Tyr337) in cyan, and the catalytic triad (Ser203, Glu334, His447) in pink.

1.3.3. Inhibitors of Acetylcholinesterase Small organic molecules that inhibit AChE can be divided into two groups depending on their mode of action: non-covalent inhibitors and covalent inhibitors. Non-covalent inhibitors bind in a reversible manner, and are of interest for treatment of cholinergic disorders. A quaternary amine or basic nitrogen is generally included, resembling ACh with the possibility to form cation-aromatic interactions to AChE. One example

is donepezil (Figure 13), used for treating the symptoms of Alzheimer’s disease, where cholinergic neurons in the brain are reduced.84 Treatment with donepezil will decrease the rate of hydrolysis of ACh through inhibition of AChE, thus increasing the concentration of ACh. The other group of inhibitors of AChE constitutes carbamates and organophosphorus compounds that mimics the ester bond in ACh. They will react with the catalytic Ser203 of AChE and form a covalent bond to the enzyme. In case of carbamates, e.g. rivastigmine that is also used for the treatment of Alzheimer’s disease, the inhibition is reversible due to hydrolysis. For most organophosphorus compounds however, AChE will be essentially irreversibly inhibited due to the slow rate of hydrolysis.85

1.3.4. Nerve Agents as Chemical Weapons One important use of organophosphorus compounds (and carbamates) are as insecticides. Another class of more toxic compounds, classified as chemical warfare agents (i.e. organophosphorus nerve agents86), have also been developed. Nerve agents are extremely toxic compounds that enter the body through inhalation or via skin contact. When AChE is exposed to nerve agents, the catalytic Ser203 will be phosphonylated (Figure 12). Dephosphonylation through hydrolysis does occur, but as previously stated it is a very slow process. Inhibition of AChE will lead to accumulation of ACh at the synapses of the CNS and PNS. This will lead to overstimulation of the nervous system, and eventually death due to acute cholinergic crisis causing respiratory failure. Apart from acute symptoms — e.g. respiratory arrest, arrhythmia, or convulsions86 — exposure to nerve agents can also lead to long- term neurological effects.87 Today, nerve agents are strictly prohibited by the chemical weapons convention (see below).88 The first nerve agent, tabun, was discovered in 1936. This was followed by sarin in 1938, soman in 1944, and cyclosarin in 1948. In 1950 the more stable and toxic VX was developed, followed by RVX in 1963, and CVX (Figure 13). During World War II, nerve agents were produced in large scale. Fortunately, they were never used on the battlefield. However, since then they have been used on multiple occasions, including the Iraq-Iran war (1980–1988), the sarin terrorist attack in the Tokyo subway in 1995, but also more recently during the civil war in Syria 2013 and 2017.85, 89

Figure 12. Inhibition, aging, and reactivation of AChE, exemplified with the nerve agent sarin and a general oxime.

Figure 13. Chemical structure of compounds binding to AChE. A) Donepezil and rivastigmine are used for the treatment of symptoms of Alzheimer’s disease. B) Tabun, sarin, soman, cyclosarin, VX, RVX, and CVX are classified as organophosphorus nerve agents. C) Atropine, diazepam, and pyridostigmine are used for treatment in case of nerve agent exposure, in combination with a pyridinium oxime. D) The structure of four pyridinium oximes that have been in clinical use: pralidoxime, obidoxime, TMB-4, and HI-6.

1.3.5. The Chemical Weapons Convention Throughout the years, there have been multiple attempts to prevent chemical warfare. Already in 1675, a treaty was signed between France and the Holy Roman Empire, which prohibited the use of poisoned bullets. This was followed by the Brussels convention 1874, prohibiting the use of poison or poisoned weapons, and the Hague agreement in 1899, once again banning the use of poisoned weapons. Following the events during World War I, where chemical weapons such as chlorine and mustard gas were extensively used, the Geneva protocol was composed and signed in 1925, prohibiting the use of chemicals in war. However, this agreement did not include any prohibitions against development, production, and stockpiling, which was still performed. Finally, on April 29, 1997, the chemical weapons convention entered into force after years of negotiations. The purpose of the convention is to prohibit development, production, acquisition, stockpiling, retention, transfer, or use of chemical weapons. For most countries in the world, all research focusing on chemical warfare agents are today regulated by this convention. The organization of the prohibition of chemical weapons, independent from the United Nations, has the authority to monitor, inspect, and verify that the convention is followed.88

1.3.6. Treatment of Nerve Agent Intoxication Despite the chemical weapons convention, nerve agents still pose a threat due to their continued use as chemical weapons. In order to protect both the civilian population as well as the military, research focus on developing medical treatment against nerve agent intoxication. The carbamate pyridostigmine can be taken as a prophylaxis; it covalently inhibits AChE, thus preventing the enzyme from reacting with nerve agents. Spontaneous reactivation will thereafter occur, restoring the function of AChE.90 Post-exposure treatment typically consists of administration of atropine, a pyridinium oxime, and diazepam. Atropine is an antimuscarinic drug that will act as an antagonist and block muscarinic ACh receptors, thereby reducing the effect of nerve agents by preventing accumulated ACh to bind. Diazepam is used as an anticonvulsant drug. Pyridinium oximes will react with the phosphonylated serine residue, cleaving the covalent bond and reactivate AChE (i.e. restoring AChE to its active form, Figure 12). 85, 91, 92

1.3.7. Pyridinium Oximes — Reactivators of Nerve Agent Inhibited Acetylcholinesterase In the 1950’s, it was discovered that hydroxylamine93 and nicotinhydroxamic acid94 could reactivate phosphonylated AChE. Continued research led to the discovery of oximes, in particular 2-pyridine aldoxime (2-PAM), as successful reactivators.95-97 Over the years, this has been followed by bispyridinium compounds,98, 99 leading to variations of the length and structure of the linker,99-108 number of quaternary pyridinium rings,109-112 and variation of the oxime113-119 (Figure 13).120, 121 The antidotes bind non-covalently to the multicomponent system consisting of AChE

phosphonylated by a nerve agent. This is followed by a nucleophilic attack from the oxime moiety on the phosphorous atom of the nerve agent adduct, which will result in cleavage of the covalent bond and reactivation of the enzyme. Administration of an antidote needs to be performed as soon as possible after exposure to nerve agents. This is because of the rapid onset of toxicity, but also because of a process called aging. Aging refers to dealkylation, leading to an even more stable complex that cannot be reactivated by any antidote that is available today (Figure 12).122 The aging half time varies from a few minutes for soman, to days for VX.108 An additional issue is that reactivation with oximes will lead to reactive phosphonylated oximes that can reinhibit AChE.123 Also, the pyridinium oximes contain a quaternary ammonium with a permanent positive charge, which will limit their ability to freely diffuse through the blood brain barrier to reach the CNS.124 Attempts have been made to increase the access to the CNS, e.g. by fluorination,125, 126 attachment of glucose,127 use of prodrugs,128 or by using non-permanently charged reactivators.113-119 The efficiency of reactivation depends on both the structure of the antidote and the nerve agent. Despite all effort at developing new antidotes, there is no broad-spectrum antidote available that can efficiently reactivate AChE independently of the kind of nerve agent,86, 129 and the details of the molecular mechanism of reactivation is not known.108

16 “The art and science of asking questions is the source of all knowledge.” —Thomas Berger

2. Scope of the Thesis

This thesis combine areas within the field of medicinal chemistry, to explore design strategies for drugs targeting multicomponent systems. Two complex biological systems are investigated, for which the dynamics plays a crucial role for molecular recognition.

2.1. Towards a Vaccine against Rheumatoid Arthritis The explicit objective of the first part of this thesis is to develop an approach for the design of drugs for which simultaneous interaction with multiple proteins, thus forming a multicomponent system, are essential. The long-term goal is to develop a vaccine against RA. The specific goals are:

 Dynamic studies of CII259–273 in complex with class II MHC proteins to identify properties essential for binding and TCR recognition, for this specific antigen.  Modification of CII259–273 (i.e. introduction of non-native amino acids and amide bond isosteres) to improve binding to class II MHC proteins, alter the T-cell response, and increase stability against degradation.  Connect structural and dynamic changes in the class II MHC protein to experimentally determined biological responses, to understand and predict the effect of modifications of CII259–273.

2.2. Towards a Broad-Spectrum Nerve Agent Antidote The explicit objective of the second part of this thesis is to develop an approach for the design of reactive drugs, with the long-term goal of developing an efficient broad- spectrum antidote against nerve agent inhibited AChE. The specific goals are:

 Design and synthesis of a set of non-covalent inhibitors of AChE, to identify a fragment with a similar affinity for the different forms of a multicomponent system consisting of AChE covalently inhibited by different nerve agents.  Design and synthesis of a small set of potential nerve agent antidotes, by transforming the previously identified fragment into reactive compounds.  Structural studies of an identified nerve agent antidote in complex with AChE covalently inhibited by a nerve agent, to further investigate prerequisites for a broad-spectrum antidote, as well as the mechanism of reactivation.

17 “There never was a good knife made of bad steel.” —Benjamin Franklin

3. Methods and Techniques

A medicinal chemistry project involves many different areas of expertise, and the different methods used in this thesis are briefly described.

3.1. Organic Synthesis in Drug Design Organic synthesis can be described as the practice of building organic molecules, by applying chemical reactions to connect building blocks or transform functional groups. It has been used to synthesize specific compounds or libraries of analogous compounds throughout the work of this thesis. Typically, the synthetic process consists of four sequential steps, as described below.

3.1.1. Retrosynthesis The organic chemist must first come up with a synthetic pathway. This is performed by retrosynthesis of the target compound, where the structure of the compound is transformed into precursor structures (i.e. synthons) in a stepwise manner, until commercially available building blocks are identified. Thereafter, the synthetic pathway is planned, including selection of reagents, solvents, and conditions for each step that is to be performed (Figure 14).

Figure 14. A schematic figure describing a retrosynthetic analysis, where the target compound is transformed into synthons (A) followed by the planning of a synthetic pathway (B).

3.1.2. Organic Synthesis After deciding upon a plausible synthetic pathway, the actual synthesis of the compound is performed. Solution phase synthesis is the most common procedure, applicable to most organic reactions (Paper I–IV). Reagents are mixed and allowed to react in solvent, usually followed by purification after each step of the synthesis towards the target compound. Monitoring of the reaction to know when it has reached completion can in most cases easily be done using thin layer chromatography (TLC). Solid phase peptide synthesis (SPPS)130, 131 is another technique that has been applied, where the compound of interest is connected to a resin to be systematically transformed into the target compound. This technique has been utilized for the synthesis of glycopeptides132 (Paper I and II). A Fmoc-Thr(tBu)-PHB-S-Tentagel resin has been used, onto which amino acids have been sequentially added, followed by cleavage from the solid support and deprotection. The desired target compound is

thereafter purified. The method has the advantage that reagents can be used in large excess, and purification will not be necessary until after the final step. Monitoring of reactions that are performed with solid phase synthesis is not always as trivial compared to solution phase synthesis. Here, bromophenol blue has been used as an indicator, enabling detection of the free amine, which will give rise to a blue colour, compared to yellow as for the Fmoc-protected version (Figure 15).

Figure 15. A schematic figure describing SPPS. A) Stepwise preparation of a peptide using SPPS, performed in DMF. The Fmoc-protecting group is cleaved off, followed by addition of the next amino acid that has been preactivated using DIC and HOBt or HOAt. B) Chemical structure of the Fmoc-Thr(tBu)-PHB-S-Tentagel resin. C) Cleavage of the glycopeptide from the solid phase, subsequent deprotection of the amino acids, and deacetylation of the carbohydrate moiety, to yield CII259–273.

3.1.3. Purification Purification of the compounds is essential. There are a number of different techniques to choose from, the most common including recrystallization and column chromatography. Recrystallization normally involves dissolving a compound mixture in a warm solvent. Upon cooling, the desired compound will precipitate while impurities stay in solution. Chromatographic purification methods rely on separation of the desired compound from impurities based on properties such as polarity or size (depending on the stationary phase) when passing the compound mixture through a column.

3.1.4. Characterization The final step of organic synthesis typically involves characterization, to ensure that the correct structure has been obtained. This includes for instance nuclear magnetic resonance (NMR), which will give information about the chemical environment that can be interpreted to elucidate the structure. Mass spectroscopy will give the mass of the compound, chemical formula, and fragmentation pattern. Infrared (IR) spectroscopy will reveal the presence of specific functional groups.

3.2. Computational Chemistry in Drug Design Nowadays, many different techniques within the field of computational chemistry are utilized during the drug design process. Both prior to synthesis, e.g. to predict biological responses and prioritize compounds that are more likely to bind to a target, or afterwards to e.g. explain biological responses or chemical outcomes of a reaction.

3.2.1. Statistical Molecular Design In order to select a representative set of molecules, where the properties believed to be of importance for a biological response are varied in a systematic way, we have used statistical molecular design (SMD) (Paper III). The set of molecules that are subsequently synthesized will give varied biological responses, essential for a reliable (quantitative) structure-activity relationship ((Q)SAR) model (see below). One example of a method that can be used in SMD is factorial design. In case of a full factorial design, the properties that are investigated will be varied at fixed levels. The total number of molecules will be NK, where K is the number of properties and N is the number of levels. In addition, centre points should be added to detect non- linearity and investigate reproducibility of the experiments. A list of all the combinations of the different properties will thereafter be compiled. Obviously, if many different properties are varied this will quickly lead to a huge number of combinations. Thus, we have used a D-optimal design (Paper II and III) where one aims at selecting a specified number of molecules from a multi-dimensional property space, in such a way that the selected molecules will span a maximum volume in the space. It gives the possibility of including restrictions, so that the user can include specific molecules that are of interest, or exclude certain combinations (Figure 16).133

Figure 16. A geometrical representation of two SMDs. A) A 32 full factorial design, where two properties (x and y) are investigated at three levels (-1, 0, and 1), resulting in 9 combinations. B) A D-optimal design where three combinations are selected for investigation, in such a way that they span a maximum volume in the property space.

3.2.2. Quantitative Structure-Activity Relationship Modelling (Q)SAR modelling (Paper III and IV) has been applied to correlate the structure of a set of molecules to their biological activity. This method has given us an idea about which properties that are most important as well as enabled predicting the activity of molecules prior to synthesis. The structure of the molecule can be described with chemical descriptors, for instance molecular weight, polarity, rigidity, or charge. We have used partial least square projection to latent structures (PLS) modelling, where an X matrix containing molecular properties and a Y matrix containing a biological effect are correlated via linear regression, according to Eq. 2.

∑ (2)

Where yi is the ith biological response, xik is the ith molecule described by k = 1,

2, … K properties, bk is the model coefficient for each property k, and fi is the residual for the ith biological response. The aim of using an SMD when selecting the set of molecules is to get a large variation of the properties, which will lead to a minimized error in the QSAR model coefficient (bk), resulting in a more robust model. In PLS, a number of PLS-components will be computed. Each PLS component describes variance in the X- and Y-matrix. Each property and biological response will be projected onto each PLS component, thus obtaining a score value (t1, t2, ….tn and u1, u2, ….un for the X- and Y-matrix respectively), so that a good correlation is found between the scores t1 and u1 (Figure 17). It is of importance to evaluate the quality of a QSAR model, by for instance evaluation of R2 and Q2 that responds to the amount of explained variation and the cross-validated R2 (i.e. amount of predicted variation) respectively.134 Additional evaluation methods for QSAR models have been investigated in Paper III.

Figure 17. A geometric representation of PLS regression, where each observation is represented by a dot, with three properties (x1–x3) and three responses (y1–y3). The score vector in the property space and the response space is oriented to optimise the correlation between t1 and u1, resulting in the first PLS component.

3.2.3. Molecular Dynamics Simulations We have studied the dynamics of the class II MHC●glycopeptide complexes using molecular dynamics (MD) simulations (Paper I and II). This technique is based on molecular mechanics, which relies on classical physics where atoms and bonds are

defined as “balls and springs”. During MD simulations the position and velocity of the atoms in the system are calculated over time, based on Newton´s equations of motion, separated into short time steps. The forces on the atoms are recalculated after each step. This will give a trajectory that can be analyzed to see how the positions and velocities of particles, i.e. the dynamics, of the system vary with time.135, 136 The protocol when running the MD simulations is divided into five steps. 1) First, the system (e.g. X-ray crystal structure or homology model) is prepared; missing atoms are added, charges and atom types are assigned, counter ions are added, and the system is solvated. 2) This is followed by an energy minimization, to remove bad contacts and get a low energy structure. 3) Thereafter, the system is heated to the desired temperature. 4) A subsequent equilibration step is performed, to ensure the desired pressure within the system. 5) Finally, the production MD simulation is initiated. The Langevin thermostat has been used to keep a constant temperature, by modifying Newton’s equation of motion, while isotropic position scaling has been used to control the pressure, by adjusting the volume of the unit cell. To ensure that the MD simulation has converged, we have analyzed the root-mean-square deviation (RMSD) values for each frame compared to the initial one. Converged RMSD values have been used as a guide to know when to terminate the MD simulation. In addition, energy, temperature, pressure, volume, and density is monitored over time. This is followed by analysis of the trajectory, including visual inspection of the complex to ensure that the secondary structure is retained. Thereafter more detailed analyses are performed including study of structural differences over time, and mapping of interactions between the ligand and the protein.

3.3. Structure Determination in Drug Design The availability of a three-dimensional structure of the target protein in a medicinal chemistry project opens up the possibility of atomic-resolution structural studies. X- ray crystallography is presently the most common and accurate method of obtaining such a structure, and has been used throughout this thesis (Paper I–IV), aided by homology modelling (Paper II).

3.3.1. X-ray Crystallography The process of determining an X-ray crystal structure of a protein is dependent on a number of critical steps. First, the protein of interest is expressed and purified. This is followed by crystallization of the protein, typically involving time consuming investigation of many different conditions before the first crystal is finally obtained. In case a protein●ligand complex is desired, one can either co-crystallize the ligand with the protein (Paper I) or soak the protein crystal in a saturated solution of the ligand (Paper IV) prior to flash-freezing and subsequent data collection. During data collection, the crystal is rotated in an X-ray beam. The electronic cloud of the molecules in the crystal will interact with the X-rays. Under certain conditions, the diffracted X-rays are amplified by the highly ordered molecules in the crystal resulting in a diffraction pattern. With the aid of a software, the diffraction pattern is converted

into electron density maps, where the electron density of the molecules in the crystal is visualized in three dimensions. The electron density is thereafter interpreted (modelled) to fit atoms of the ligand or protein into the correct positions. This is an iterative process involving both manual and computational refinement of atomic coordinates.137

3.3.2. Homology Modelling In cases when no three-dimensional structure has been available, we have used homology modelling to computationally predict the structure (Paper II). To enable homology modelling, one or multiple templates, i.e. structures related to the desired target, must first be identified. Thereafter the amino acid sequence of the target protein is aligned to the template structure, followed by the construction of a model based on information from the template structure(s).138

3.4. Biochemical and Biological Evaluation in Drug Design The ability to investigate the potency of novel compounds is an essential part of the drug design process. A variety of biochemical and biological assays have been utilized in this project.

3.4.1. Evaluation of Glycopeptides Targeting Class II MHC Proteins

3.4.1.1. Binding to Class II MHC Proteins In order to determine whether a peptide bind to the class II MHC protein, a competitive inhibition assay is applied (Paper I and II). A fixed concentration of a biotinylated CLIP tracer is incubated with purified class II MHC protein and different concentrations of the peptide of interest that will compete for binding. The mixture is transferred to microtiter plates that are pre-coated with antibodies that will capture the class II MHC●peptide complex. Any non-specific binding sites are blocked. A washing step is performed to remove excess peptide, before incubating with europium labelled streptavidin that has a high affinity for biotin. Another washing step is performed before the chelated europium is released from the streptavidin, which leads to the formation of a highly fluorescent micellar solution (Figure 18). Thus, the amount of bound biotinylated CLIP peptide can be quantified by measuring the fluorescence using the dissociation-enhanced lanthanide fluoroimmunoassay (DELFIA) system.41, 63, 139, 140

Figure 18. Schematic figure describing the assay used to determine binding of the glycopeptides.

3.4.1.2. T-Cell Receptor Recognition Determination of T-cell activation has been performed with different T-cell hybridomas in a cell based assay (Paper I and II). In case the T-cell hybridoma recognize a class II MHC●glycopeptide complex, secretion of the cytokine interleukin-2 (IL-2) will be initiated, which can be quantified in a sandwich enzyme- linked immunosorbent assay (ELISA). T-cell hybridomas are incubated in microtiter plates with antigen presenting spleen cells expressing class II MHC molecules and increasing concentrations of the glycopeptides of interest. The supernatant is removed from the plate, and any transferred T cell hybridomas are killed by freezing the samples. The supernatant is thereafter incubated in plates that have been pre-coated with IL-2 capturing antibodies, followed by incubation with biotin labelled IL-2 detection antibodies. After a washing step europium labelled streptavidin is added, enabling measurements of the fluorescence after releasing the europium as described above, thereby quantifying the amount of secreted IL-2 using the DELFIA system (Figure 19).60, 141

Figure 19. Schematic figure describing the sandwich linked ELISA used to determine TCR recognition.

3.4.2. Evaluation of Small Organic Molecules Targeting Acetylcholinesterase

3.4.2.1. Binding to Acetylcholinesterase — Ellman Assay The potency of ligands binding to AChE has been determined using the well established colorimetric Ellman assay142 (Paper III and IV). AChE is allowed to react with acetylthiocholine (an analogue of ACh) that will be degraded to acetate and thiocholine. Thiocholine will thereafter react with 5,5’-dithiobis-(2-nitrobenzoic acid) (DTNB), cleaving the disulfide bond, leading to the formation of 3-thi-6- nitrobenzoate (TNB), enabling measurements of absorbance corresponding to the activity of AChE (Figure 20). The yellow colour is monitored over time, with different

concentrations of the inhibitor of interest added. Thus, a dose response curve can be constructed, giving an IC50 value corresponding to half of the maximum inhibitory concentration (Figure 21).

Figure 20. Schematic representation of the Ellman assay. ACh is hydrolyzed into acetate and thiocholine that will react with DTNB, thus enabling quantification of the activity of AChE by measuring absorbance.

Figure 21. Determination of an IC50 value from a dose response curve. The determined activity of the enzyme is plotted against the logarithm of the concentration of inhibitor, and the IC50 value is indicated by dotted lines.

3.4.2.2. Binding to Phosphonylated Acetylcholinesterase — Time-Resolved Fluorescence Decay Measurements If AChE is covalently inhibited, the Ellman assay will no longer be useful since it depends on an active enzyme. Thus, time-resolved fluorescence decay measurements using time-correlated single photon counting (TCSPC) have been utilized to determine the potency of compounds binding to AChE that is covalently inhibited by different nerve agents (Paper IV). The fluorescence lifetime for propidium iodide (PI) differs depending on if it is in solution, or if it is in complex with AChE. When a ligand is added, it will compete with PI for binding. Thus, the fraction of free PI compared to the fraction of PI bound to AChE can be quantified.143, 144

3.4.2.3. Reactivation of Nerve Agent Inhibited Acetylcholinesterase The reactivation of nerve agent inhibited AChE proceeds according to Figure 22A, and the efficiency of reactivation by oximes can be described by a number of kinetic constants (Paper IV). The association and dissociation constants (Ka and KD) corresponds to the affinity between an antidote and AChE inhibited by a nerve agent, at the transition state of reactivation. The first-order rate constant (kr) describes the rate of cleavage of the covalent bond between Ser203 and the nerve agent. The ratio of kr and KD gives the second-order rate constant (kr2) that describes the overall efficiency of reactivation, compared to the observed first-order rate constant (kobs) that describes the efficiency of reactivation at one single concentration. The Ellman assay has been utilized to determine these kinetic constants. In short, purified AChE will first be inhibited by a nerve agent. Excess nerve agent is removed prior to the addition of a potential reactivator at different concentrations. The activity of the potentially reactivated AChE is measured over a time period. From the resulting graph (Figure 22B) kobs at the different concentrations can be derived from the slope.

Thereafter, kr and KD can be derived by plotting kobs against the different oxime concentrations, as displayed in Figure 22C.107

Figure 22. Determination of the efficiency of a reactivator. A) A schematic figure for the reactivation of nerve agent inhibited AChE. [AChE•NA] = phosphonylated AChE, [OX] = oxime, [AChE•NA•OX] = Michaelis type AChE•nerve agent•oxime complex, [AChE] = free

AChE, [NA•OX] = phosphonylated oxime. B) Determination of kobs at different concentrations from the slope of the curves. C) Determination of KD and kr, and subsequently kr2.

26 “If the human brain were so simple we could understand it, we would be so simple we couldn’t” —Emerson Pugh

4. Towards a Vaccine against Rheumatoid Arthritis: Focus on Class II MHC Proteins

4.1. A Dynamic Study of Class II MHC•Glycopeptide Complexes

4.1.1. Aim of the Study The exact mechanism for the initiation of an immune response is unknown, but the importance of the dynamics of the signalling class II MHC•peptide complex and the responding TCR has been extensively studied with MD simulations, as discussed in several reviews.145-147 In Paper I we have studied the dynamics of CII259–273 in complex with murine Aq, to investigate the effect of this specific antigen. In Paper II the focus is shifted towards the more relevant human class II MHC protein DR4, followed by studies of the dynamics of CII259–273 in complex with DR4.

4.1.2. Modelling of CII259–273 in complex with Aq or DR4 For the murine class II MHC protein Aq in complex with CII259–273 we have solved an X-ray crystal structure.71 Only Gly259–Lys270 was experimentally determined, due to weak electron density for the remaining amino acids of the glycopeptide. The electron density for the carbohydrate moiety was also too weak to be reliable, indicating a high mobility. However, the carbohydrate moiety is crucial for the TCR recognition,64-66 thus it was modelled onto Lys264 in silico, followed by a conformational search in the binding groove and subsequent energy minimization. The X-ray crystal structure of the human class II MHC protein DR4 in complex with CII259–273 has not been determined. However, there are multiple X-ray crystal structures of DR4 in complex with other peptides.45, 72, 148-151 Since the binding mode and backbone conformation of peptides are well conserved, CII259–273 could be computationally modelled. The backbone was manually aligned to an influenza peptide in complex with DR4 (PDB 1J8H72) so that Phe263 of the glycopeptide overlapped with Tyr308 of the influenza peptide, positioned in the P1 pocket of DR4.64 The positioning of the side chains in the generated models were compared to the remaining X-ray crystal structures to aid in selecting plausible conformations. Solvent exposed conformations of Lys264 were favored since the carbohydrate moiety was to be computationally modelled onto this residue. This was followed by an energy minimization, and thereafter Lys264 was manually hydroxylated and galactosylated in silico, as for Aq.

4.1.3. Dynamic Studies of Gly259–Lys270 in Complex with Murine Aq Two 60 ns MD simulations were conducted for the Gly259–Lys270 glycopeptide in complex with murine Aq, with different initial velocities, using the Amber12152 simulation package. The analysis was mainly focusing on the binding mode and mobility of the glycopeptide, in addition to the hydrogen bond network formed from Aq to the backbone of Gly259–Lys270, and the stability of the protein.

Throughout the MD simulations, Ile260 and Phe263 were both firmly anchored in their respective binding pocket, and a hydrogen bond network was formed mainly to the N-terminus backbone of the glycopeptide. Thus, the N-terminus of the peptide backbone displayed a low mobility, while the C-terminus displayed a slightly increased mobility. This is consistent with the fact that the minimal epitope required for binding to Aq and subsequent T-cell response has been determined to Ile260– 61 Gln267. The α1- and β1-helix as well as the β-sheet that forms the binding groove for the glycopeptide were intact throughout the MD simulations (Figure 23).

Figure 23. Superposition of representative frames from a cluster analysis of the MD q simulations of Gly259–Lys270 in complex with A . The α1-domain is represented in purple, the β1-domain in cyan, and the glycopeptide in green. The conformation of the glycopeptide in the X-ray crystal structure is included as a reference, in orange.

The hydrogen bond network formed to the backbone of Gly259–Lys270 was analysed during the last 20 ns of the MD simulations. In total 8–13 hydrogen bonds were formed, out of which 4–5 were considered strong. 8 out of the 13 hydrogen bonds were formed to the N-terminus, Gly259–Gly265, out of which 5 were strong in at least one of the MD simulations. In particular, there were two bonds that consistently had a high occupancy in both the simulations, also when compared to previous MD simulation of the same complex.69 Thus, these hydrogen bonds were considered of high importance (Table 1).

Table 1. Summary of the MD simulations of Aq•Gly259–Lys270. Investigated Property Analysis Anchor residues Anchored in P1 or P4 respectively (Ile260 and Phe263) N-terminus of Gly259–Lys270 Low mobility C-terminus of Gly259–Lys270 Increased mobility q α1- and β1-helix of A Conserved Number of hydrogen bondsa ● Total: 10.5±2.5 ● Strong: 4.5±0.5

Importantb hydrogen bonds ● Ala261(a)c–βAsn82(s)d Occupancy: 68.5±1.5 ● Gln267(a)–βTrp61(s) Occupancy: 60.5±0.5 aHydrogen bonds were defined as having an acceptor-donor length of <3.3 Å and an angle of >160◦, calculated with the aid of the hydrogen bond extension tool in VMD.153 Only hydrogen bonds with an occupancy >10% are considered, and a strong hydrogen bond is defined as having an occupancy >40%. bHydrogen bonds are considered “important” in case they consistently have a high occupancy in multiple MD simulations. ca = acceptor, ds = side chain.

4.1.4. Dynamic Studies of CII259–273 in complex with Human DR4 Initial MD simulations of CII259–273 in complex with human DR4 resulted in an immediate unfolding of the α1-helix. This was likely an artifact originating from the exclusion of the TCR during the MD simulation. No unfolding is indicated in the X- ray crystal structure that the homology model is based on (PDB 1J8H72). Thus, to further investigate this phenomenon, the ternary X-ray crystal structure of the influenza peptide in complex with DR4 and the TCR was studied (PDB 1j8H72). A 40 ns MD simulation of the ternary complex displayed a conserved α1-helix, while unfolding immediately occurred in case of MD simulations of the binary complex where the TCR was excluded. Thus, to mimic the presence of a TCR, a constraint of

1 kJ/mol was added to the backbone of the α1-helix of DR4 (αAla5–αTyr79) in complex with CII259–273. This is further supported by a another study154 demonstrating that the MHC protein is more rigid in presence of a TCR. Six 60 ns MD simulations were conducted for the DR4•CII259–273 complex, and analyzed similar to Aq as described above. The findings in the MD simulations performed with DR4 corresponded to the ones for Aq. Phe263 was firmly anchored in the P1 binding pocket, and the hydrogen bond network formed to the backbone of CII259–273 led to a stable N-terminus of the peptide, while the C-terminus was highly mobile. The minimal epitope required for binding and T-cell response has not been determined for DR4, but likely the peptide q can be truncated from each end corresponding to the one determined for A . The α1- and β1-helix in DR4, as well as the β-sheet, remain intact throughout the MD simulations (Figure 24).

The hydrogen bond network formed to the backbone of CII259–273 was analysed during the last 30 ns of the MD simulations. In total 10–17 hydrogen bonds were formed, out of which 6–8 were considered strong. 6–13 out of all the hydrogen bonds were formed to the N-terminus, Gly259–Gly268, out of which 5–7 were considered strong. Compared to the Aq system, for the complex with DR4 there were four hydrogen bonds that in general have a high occupancy in multiple simulations, and were thus considered of high importance (Table 2).

Figure 24. Superposition of representative frames from a cluster analysis of the MD simulations of CII259–273 in complex with DR4. The α1-domain is represented in purple, the

β1-domain in cyan, and the glycopeptide in green. The conformation of the glycopeptide in the homology model is included as a reference, in orange.

Table 2. Summary of the MD simulations of DR4•CII259–273. Investigated Property Analysis Anchor residue (Phe263) Anchored in P1 N-terminus of CII259–273 Low mobility C-terminus of CII259–273 High mobility q α1- and β1-helix of A Conserved Number of hydrogen bondsa ● Total: 13.5±2.2 ● Strong: 7.0±0.8 Importantb hydrogen bonds ● Gly262(a)c–βHis81(s)d Occupancy: 63.0±31.6 ● Phe263(d)e–αSer53(m)f Occupancy: 79.5±18.6 ● GalHyl264(a)–βAsn82(s) Occupancy: 88.7±7.1 ● GalHyl264(d)–βAsn82(s) Occupancy: 89.5±10.4 aHydrogen bonds were defined as having an acceptor-donor length of <3.4 Å and an angle of >140◦, calculated with the aid of the hydrogen bond extension tool in VMD.153 Only hydrogen bonds with an occupancy >10% are considered, and a strong hydrogen bond is defined as having an occupancy >40%. bHydrogen bonds are considered “important” in case they consistently have a high occupancy in multiple MD simulations. ca = acceptor, ds = side chain, ed = donor, fm = main chain.

4.1.5. Summary and Concluding Remarks Multiple MD simulations have been performed for the class II MHC proteins Aq and DR4, to sample the conformational space. It was obvious that the different MD simulations represented different time points or scenarios. This is in agreement with other studies that have demonstrated the importance of multiple simulations to identify systematic differences in a reliable manner.145, 155 Noteworthy is that the two complexes have been subjected to somewhat different conditions during the MD simulations and analysis. Thus, a detailed comparison between the two systems has not been performed. The MD simulations showed that even though Aq and DR4 are derived from different species, they behaved in a similar manner, further supporting the use of CIA as a model system for RA. Different MHC proteins have previously been extensively studied with MD simulations.145 In agreement with the results presented in this thesis it demonstrates that these are highly dynamic systems, in contrast to the static X-ray crystal structure. In particular, the N-terminus of the glycopeptide displayed a low mobility, while the C-terminus displayed an increased mobility. This could explain why the system is more sensitive towards modifications occurring around the N- terminus of the peptide, compared to the C-terminus.69, 151, 156 A dynamic hydrogen bond network was formed from the class II MHC proteins to the backbone of CII259– 273. The class II MHC•glycopeptide•TCR complex is an excellent example for demonstrating the difficulties when studying multicomponent systems. There are many prerequisites for a successful immune signalling process. Not only does the peptide need to bind to the class II MHC protein, the binding must also lead to the formation of an epitope that is recognized by the TCR. Studies have demonstrated the importance of including additional components by performing MD simulations of a membrane embedded class II MHC•peptide•TCR•CD4 co-receptor complex.157 Yet another study have applied steered MD simulations to investigate the association and dissociation of the class I MHC•peptide•TCR complex,158, 159 demonstrating that different TCRs can have different binding strategies. This would be of value to study for CII259-273 as well. However, there is currently no X-ray crystal structure available for Aq or DR4 in complex with CII259–273 or any other glycosylated peptide that also contains a TCR. Thus, one important component of this multicomponent system is missing. Efforts at either crystallizing the TCR or using homology modelling would be of great value for future studies. These findings are significant for the understanding how the class II MHC proteins and the subsequent TCR recognition process work, which is essential for the design of new drugs. Previous research within the field has focused on investigation of modified peptides.68-70, 151, 156, 160, 161 This has demonstrated that it is possible to increase the binding affinity to the class II MHC protein as well as affecting the subsequent T-cell response. The MD simulations described in this chapter are used as a reference for further studies, where modifications of CII259–273 have been performed (Chapter 4.2 and 4.3).

31 “Life can only be lived forwards, but must be understood backwards.” —Søren Kierkegaard

4.2. Hydroxyethylene and Ketomethylene Isosteres Introduced in CII259–273 Affect the Dynamics of Aq

4.2.1. Aim of the Study In order to further explore the interactions between Aq and CII259–273, requirements for binding and subsequent TCR recognition, hydroxyethylene isosteres were introduced at the position of Ala261–Gly262 of the glycopeptide (Paper I). A previous study70 had shown that after incorporation of ketomethylene, methyleneamine, and (E)-alkene isosteres in this position, only the ketomethylene isostere modified glycopeptide displayed Aq-binding and TCR recognition similar to CII259–273. Thus, the conclusion was that the hydrogen bond acceptor was essential for binding and TCR recognition, while the hydrogen bond donor had minimal effect and could be excluded. Here, this is further investigated by introduction of hydroxyethylene isosteres in the same position. The hydrogen bond acceptor possibility was retained, although a chiral center was introduced altering its orientation. In addition, hydrogen bond donor possibilities have been introduced via the hydroxyl group (Figure 25).

Figure 25. Chemical structure of the antigen CII259–273 including the three glycopeptide isosteres, 1–3, that have been investigated in this study.

4.2.2. Hydroxyethylene Isosteres Affect Binding to Aq and Subsequent TCR Recognition The hydroxyethylene amide bond isosteres were synthesized as Fmoc protected dipeptide building blocks (Scheme 1) that were then incorporated into the glycopeptide by Fmoc-based SPPS. The first step involved diastereoselective reduction of the ketone followed by exchange of the protecting group and subsequent lactonization. After conversion to the thioester, the compounds were silylated followed by hydrolysis into the target dipeptide building blocks. The ketomethylene isosteric glycopeptide was synthesized in a previous study.70

Scheme 1. Synthesis of the R-isosteric dipeptide building block 8 to be incorporated into

CII259–273 to give glycopeptide isostere 2. Reagents and conditions; a) LiAlH(O-tBu)3, EtOH,

-78 ºC, 75%; b) (i) TFA, DCM, (ii) FmocOSu, Na2CO3, dioxane, H2O, 0 ºC to r.t., 86%; c) (i) tBuSH, AlMe3, CH2Cl2, 0 ºC, (ii) TBDMSCl, imidazole, DMF, r.t., 55%; d) LiOH, H2O2, THF, 0 ºC to r.t., 75%. The S-isosteric dipeptide building block was synthesized using similar conditions, except that reduction of aldehyde 4 was performed with (S)-Alpine-Hydride. This resulted in a diastereomeric ratio of S:R 2:1. The diastereomers were not separated until after step b.

The binding to Aq and subsequent T-cell response was thereafter determined. Evidently, no binding could be detected for either one of the S- and R- hydroxyethylene isosteres 1 or 2 (Figure 26). However, a weak response was elicited when measuring the TCR recognition for two out of four hybridomas (Figure 27). Thus, 1 and 2 do bind to Aq, but with an affinity that was too low to be detected in assay, possibly since they could not compete with the CLIP tracer for binding. The ketomethylene isostere 3 binds to Aq with a strength comparable to CII259–273, and gave an intermediate or strong T-cell response for three out of the four hybridomas that were investigated.70

Figure 26. Binding of CII259–273 and the S- and R-hydroxyethylene isosteres 1 and 2, determined with a competitive assay. Different concentrations of the glycopeptides have competed for binding against a fixed concentration of a biotinylated CLIP tracer.

Figure 27. TCR recognition determined for four different T-cell hybridomas: HM1R.2, HCQ.3, HCQ.10, and 22a1-7e. The hybridomas have been incubated with APCs that express Aq and different concentrations of the glycopeptides CII259–273, 1, or 2. In case of a T-cell response IL-2 will be secreted, which is quantified in a sandwich ELISA.

4.2.3. The Dynamics of Aq in Complex with Hydroxyethylene- and Ketomethylene Isosteric Glycopeptides To investigate the reason for the effect on binding and T-cell response the S- and R- hydroxyethylene (1, 2) and ketomethylene (3) isosteric glycopeptides were modelled into Aq in silico. The modelling was based on the previously prepared Aq•Gly259– Lys270 complex, followed by an energy minimization. The resulting complexes displayed glycopeptides with a binding mode almost identical to Gly259–Lys270. Thus, the complexes were subjected to three 60 ns MD simulations each, which were then compared to the MD simulations of Aq•Gly259–Lys270 as a reference (Chapter 4.1.3). Aq in complex with the S- and R-hydroxyethylene glycopeptides (1 and 2) displayed a dynamic behavior differing from Aq in complex with Gly259–Lys270. The N-terminus of the S-hydroxyethylene isostere 1 was highly mobile, and the anchor residues Phe263, and occasionally Ile260, were displaced from the P4 and P1 pocket respectively. In addition, the β1-helix was unfolding close to the modification of the glycopeptide. Similar to 1, the N-terminus of the R-hydroxyethylene isosteric glycopeptide 2 was also mobile, with both anchor residues displaced from their respective pockets, including unfolding of the β1-helix (Figure 28). In comparison, the ketomethylene isostere 3 had a dynamic behavior similar to Gly259–Lys270. The N-

terminus displayed a low mobility in the binding groove, the anchor residues were firmly anchored in the P1 or P4 binding pocket, and no unfolding of the β1-helix occurred.

Figure 28. Snapshots from the MD simulations of glycopeptide 1 and 2 in complex with Aq, where superposition of representative frames from a cluster analysis has been performed. A) Glycopeptide 1, B) Glycopeptide 2. During parts of the MD simulations of the hydroxyethylene isosteric glycopeptide (1, 2) Ile260 and Phe263 have left the P1 pocket, suggesting that the peptide is diffusing from the binding groove. The α1-domain is represented in purple, the β1- domain in cyan, and the glycopeptide in green. The conformation of the glycopeptide in the homology model is included as a reference, in orange.

4.2.4. The Effect on the Hydrogen Bond Network by Introduction of Hydroxyethylene- and Ketomethylene Isosteres The entire hydrogen bond network formed to the hydroxyethylene isostere 1 and 2 was disrupted compared to Gly259–Lys270. The introduction of the amide bond isosteres resulted in a decreased number of strong hydrogen bonds (Table 3). Occasionally, there was a hydrogen bond formed to the hydroxyl group of 1 corresponding to Ala261(a)–βAsn82(s), but with a low occupancy. There were also other hydrogen bonds formed to the hydroxyl group of both 1 and 2, but they were consistently weaker than the ones formed to the carbonyl oxygen in Gly259–Lys270. The ketomethylene isosteric glycopeptide 3, however, did form a strong hydrogen bond between Ala261 and βAsn82, and in general the hydrogen bond pattern was comparable to Gly259–Lys270. Furthermore, the second hydrogen bond judged to be of high impact (Gln267–βTrp61) is retained for all glycopeptides 1–3, with an in general high occupancy.

Table 3. Summary of the MD simulations of Aq in complex with the S- and R-hydroxyethylene (1, 2) and ketomethylene (3) isosteric glycopeptides. Analysis Investigated Property 1 2 3 Potency of binding to Aq Not detected Not detected Comparable (compared to CII259–273) TCR recognition Decreased Decreased Comparable (compared to CII259–273) Anchor residues Displaced Displaced Anchored (Ile260 and Phe263)

N-terminus of Gly259–Lys270 High mobility High mobility Low mobility

C-terminus of Gly259–Lys270 High mobility High mobility High mobility

q α1-helix of A Conserved Conserved Conserved q β1-helix of A Unfolding Unfolding Conserved ● Total: ● Total: ● Total: 7.3±0.5 9.0±0.8 9.7±1.2 Number of hydrogen bondsa ● Strong: ● Strong: ● Strong: 1.7±1.2 3.3±2.1 4.3±0.5 Ala261(a)– Occupancy: Occupancy: Occupancy: Importantb βAsn82(s)c 11.0±15.6d 0±0d 68.7±3.8 hydrogen Gln267(a)– Occupancy: Occupancy: Occupancy: bonds βTrp61(s) 38.0±11.4 63.0±4.3 41.7±29.5 aHydrogen bonds were defined as having an acceptor-donor length of <3.3 Å and an angle of >160◦, calculated with the aid of the hydrogen bond extension tool in VMD.153 Only hydrogen bonds with an occupancy >10% are considered, and a strong hydrogen bond is defined as having an occupancy >40%. bHydrogen bonds were considered “important” in case they consistently had a high occupancy in multiple MD simulations of Aq in complex with Gly259–Lys270. ca = acceptor, s = side chain. dReferring to the hydrogen bond formed to the hydroxyl group in the amide bond isostere, corresponding to Ala261(a)– βAsn82(s).

4.2.5. Summary and Concluding Remarks The disrupted hydrogen bond network formed to the backbone of the hydroxyethylene isosteric glycopeptides 1 and 2 resulted in an increased mobility. Even though the second hydrogen bond of high impact (Gln267–βTrp61) was retained for both the hydroxyethylene isosteric glycopeptides, with an in general high occupancy, it could not compensate for the loss of the first important hydrogen bond. This resulted in displacement of the anchor residues Ile260 and Phe263 from their respective pockets and unfolding of the β1-helix, suggesting that the glycopeptide was about to dissociate from the binding groove. Thus, the loss of even just one important hydrogen bond will disrupt the entire hydrogen bond network, affecting the stability of the entire complex.

The reduced T-cell response observed for 1 and 2 was most likely a result of the poor binding. In addition, the epitope presented to the TCR was altered due to the increased mobility of the peptide, in combination with the unfolding of the β1-helix. The MD simulations of 1 and 2 clearly demonstrated the importance of performing multiple simulations for the same complex, in order to sample a large conformational space of the energy landscape, in agreement with other studies.145, 155 In some cases the glycopeptides displayed a behavior similar to Gly259–270, suggesting a good binder that should elicit a T-cell response similar to Gly259–Lys270. Other simulations differed significantly and instead indicated a high mobility of the glycopeptide and unfolding of Aq, and a T-cell response was not to be expected. Only the combination of multiple MD simulations could clarify why the glycopeptides displayed a poor binding and T-cell response. In addition, once again this study underlined the importance of studying the dynamics145 of the complexes, rather than simply the static structure, to understand how even small modifications of the glycopeptide CII259–273 can have a high impact on this multicomponent system. Introduction of amide bond isosteres have been previously performed to probe important interactions in the class II MHC•peptide•TCR complex.68-70, 160-162 The ability to introduce amide bond isosteres into CII259–273 would be a way to make the glycopeptide more resistant to degradation, which is of importance from a vaccination perspective. However, these findings suggested that when incorporating such modifications great care needs to be taken not to disrupt important hydrogen bonds formed between the class II MHC protein and the backbone of the glycopeptide, since if even just one hydrogen bond is disrupted the entire network can be affected, in agreement with previous studies.41, 163 Other studies164-166 where amide bond isosteres have been introduced have also indicated that class II MHC proteins are highly sensitive towards such modifications, decreasing potency of binding to the protein and TCR recognition. Even though the MD simulations have been performed in the absence of a TCR, the resulting T-cell response could be explained by studying the dynamics of the class II MHC•glycopeptide epitope. Thus, the inclusion of a TCR was not essential, even though it would be preferable in future simulations.

37 “Small things were important. Seconds were small things, and if you heaped enough of those on top of one another, they became a man's life.” —Brandon Sanderson

4.3. Modifications of Anchor Residues and the Backbone of CII259– 273 to Probe Interactions in DR4

4.3.1. Aim of the Study Previously, the main focus when studying the effect on modifications of CII259–273 has been on the murine class II MHC protein Aq. In this study (Paper II) the focus is shifted from the model system Aq to the more clinically relevant human class II MHC protein DR4, which enables more reliable detailed studies. The requirements for binding to DR4 and obtaining a subsequent T-cell response was investigated through introduction of non-native amino acids and amide bond isosteres into CII259–273.

4.3.2. Exchange of Anchor Residues in CII259–273

4.3.2.1. Design of Anchor-modified Glycopeptides The residues Phe263, Gly268, and Gly271 in CII259–273 were exchanged with non- native amino acids for which both the size and polarity were varied. The selection of which amino acids to introduce was guided by previous studies67 and X-ray crystal structures of DR4 in complex with other peptides,45, 72, 148, 151 thus increasing the probability of obtaining glycopeptides with a high potency of binding towards DR4. Phe263 is an important anchor residue positioned in P1, essential for binding to DR4. Previously,67 both cyclohexylalanine (Cyh) and m-methyl phenylalanine were tolerated in this position. We decided to investigate Cyh further, in addition to m- fluoro phenylalanine (FPh) and Trp. Gly268 and Gly271 are positioned next to P6 and P9, thus we hypothesized that larger side chains would be placed in their respective pockets, decreasing the mobility of the glycopeptide binding. Val or Thr, and Leu or Gln, respectively, were introduced in these two positions. A D-optimal design was applied to select a representative subset of 12 glycopeptides (Figure 29) out of the 36 possible combinations, which were then synthesized by Fmoc-based SPPS.

4.3.2.2. DR4 Binding and T-Cell Response for Anchor-modified CII259–273 All of the glycopeptides 9–20 bind to DR4 with a potency comparable to CII259–273, determined in a competitive binding assay. The subsequent T-cell response was quantified by measuring the amount of secreted IL-2, revealing that 10 and 15 gave a similar response as CII259–273, 13, 14, and 16 gave a weaker response, 9 a much weaker response, and the remaining ones did not elicit any response (Figure 30). Introduction of FPh263 or Cyh263 in the P1 position resulted in glycopeptides that could retain an equal T-cell response compared to CII259–273, while it was absent in case of Trp263. For all the glycopeptides where Gly268 in the P6 position has been exchanged with Val268 the subsequent T-cell response was weaker than for CII259– 273, but complete loss was observed when introducing Tyr268. Finally, exchange of the residue Gly271 in the P9 position to Leu271 or Gln271 resulted in peptides that could still elicit an equal T-cell response compared to CII259–273.

Substitutions Substitutions ID ID R1 R2 R3 R1 R2 R3

9 15

10 16

11 17

12 18

13 19

14 20

Figure 29. The structure of CII259–273 and the anchor modified glycopeptides 9–20. Native amino acids are in gray, non-native in black.

Figure 30. Binding to DR4 and T-cell response. A) Binding of CII259–273 compared to glycopeptides 9–20, determined in a competitive binding assay. B) T-cell response by the DR4 restricted hybridoma mDR1.1, for CII259–273 compared to the anchor-modified glycopeptides 9–20, quantified in a sandwich ELISA where the amount of secreted IL-2 was measured.

4.3.2.3. Dynamic Studies of Anchor-modified CII259–273 in Complex with DR4 The dynamics of the DR4•glycopeptide complexes was correlated to the T-cell response with the aid of MD simulations. Glycopeptide 9 and 10 were included as positive controls. None of the peptides where Trp263 or Thr268 have been introduced elicit any T-cell response despite the fact that they bind to DR4, therefore 11 and 12 were selected as negative controls. Thus, each non-native amino acid has been included in the MD simulations once. The anchor-modified glycopeptides were modelled in silico in complex with DR4, based on the structure of DR4•CII259–273. Six 60 ns MD simulations were conducted for each complex, and the MD simulations of CII259–273 in complex with DR4 were used as a reference (Chapter 4.1.4.). The behavior of DR4 in complex with glycopeptide 9 or 10 was similar to CII259– 273, with the N-terminus positioned between the α1- and β1-helix and FPh263 or Cyh263 firmly anchored in the P1 pocket. As expected, the larger side chains introduced in the P6 or P9 position were placed in their respective pocket during most of the simulation time, leading to a decreased mobility of the C-terminus. Interestingly, despite the fact that DR4•9 was the most stable complex according to the MD simulations, a much weaker T-cell response is elicited. Possibly this is originating from a too rigid epitope, not compatible with the T-cell receptor. We have q previously seen that a more rigid α1-helix of A could be connected to a decreased T- cell response.69 The behavior of DR4 in complex with 11 or 12 differed substantially compared to CII259–273, with an increased mobility for both the N- and C-terminus of the glycopeptide. An increased mobility was also observed for the α1- and β1-helix of DR4, although the secondary structure was intact. (Figure 31).

Figure 31. Superposition of representative frames from each cluster of the MD simulations of anchor-modified glycopeptides in complex with DR4. A) Glycopeptide 9. B) Glycopeptide 10.

C) Glycopeptide 11. D) Glycopeptide 12. The α1-domain is represented in purple, the β1- domain in cyan, and the glycopeptide in green. The conformation of native CII259–273 from the homology model is included as a reference, in orange.

4.3.2.4. Anchor-modified CII259–273 Affect the Hydrogen Bond Network to DR4 The total number of hydrogen bonds formed between DR4 and the glycopeptide backbone, as well as the occupancy for the four hydrogen bonds judged to be of high importance, has decreased for DR4•11 and DR4•12 compared to DR4•CII259–273 (Table 4). Compared to murine Aq in complex with 1 and 2 the decrease in occupancy for the hydrogen bonds is not as profound, possibly explaining the retained potency of binding to DR4. Further analysis of the MD simulations revealed that introduction of Trp263 in 11 result in a repositioning of the peptide backbone (Figure 32), leading to the decreased occupancy for the four hydrogen bonds considered to be of high importance. The introduction of Thr268 also led to a loss of T-cell response, possibly due to the hydrogen bonds that were formed between the hydroxyl group of Thr268 and αAsn62 or αAsp66 in DR4, affecting the positioning of the backbone, leading to an increased mobility.

Figure 32. One representative snapshot from the MD simulations of DR4 in complex with glycopeptide 11 (A) or 12 (B) respectively. A) Introduction of Trp263 cause a repositioning of the glycopeptide backbone, weakening the hydrogen bonds considered to be of high importance, marked with dotted lines. B) Hydrogen bonds are formed to the hydroxyl group in

Thr268 from αAsn62 or αAsp66, marked with dotted lines. The α1-domain is represented in purple, the β1-domain in cyan, and the glycopeptide in green. The conformation of native CII259–273 from the homology model is included as a reference, in orange.

In conclusion, four properties appear to be of importance for a retained biological response. 1) The positioning and stability of the glycopeptide backbone should not be altered in the N-terminus of CII259-273. 2) The side chain of the amino acid in the P1 position must be placed in the pocket. 3) The secondary structure of DR4 should be intact. 4) The hydrogen bond network formed to the backbone of the peptide is highly sensitive to alterations, in particular the four hydrogen bonds considered of high importance

Table 4. Summary of the MD simulations of DR4 in complex with anchor-modified CII259– 273 (9–12). Analysis Investigated Property 9 10 11 12 Potency of binding to DR4 Comparable Comparable Comparable Comparable (compared to CII259–273) TCR recognition Decreased Comparable Not detected Not detected (compared to CII259–273) Anchor residues Anchored Anchored Displaced Displaced (Phe263, Gly268, Gly271) N-terminus of Low Low High High CII259–273 mobility mobility mobility mobility C-terminus of Low Low High High CII259–273 mobility mobility mobility mobility Increased Increased β1-helix of DR4 Conserved Conserved mobility mobility ● Total: ● Total: ● Total: ● Total: 13.5±3.1 13.3±3.0 9.3±3.9 11.2±3.2 Number of hydrogen bondsa ● Strong: ● Strong: ● Strong: ● Strong: 9.3±1.8 7.7±1.9 4.8±3.5 6.0±2.9 Gly262(a)c 74.3±7.1 65.7±24.2 37.2±36.0 34.7±37.5 –βHis82(s)d Phe263(d)e Importantb 70.0±32.1 77.8±20.6 50.8±41.2 40.8±35.1 –αSer53(m)f hydrogen GalHyl263(a) bonds 95.3±3.7 64.8±35.8 61.8±44.2 58.2±41.9 –βAsn82(s) GalHyl263(d) 96.3±2.4 67.7±35.2 61.2±43.4 62.5±44.3 –βAsn82 (s) aHydrogen bonds were defined as having an acceptor-donor length of <3.4 Å and an angle of >140◦, calculated with the aid of the hydrogen bond extension tool in VMD.153 Only hydrogen bonds with an occupancy >10% are considered, and a strong hydrogen bond is defined as having an occupancy >40%. bHydrogen bonds were considered “important” in case they consistently had a high occupancy in multiple MD simulations of DR4 in complex with CII259–273. ca = acceptor, ds = side chain, ed = donor, fm = main chain.

4.3.2.5. MD Simulations to Predict the Biological Response In order to attempt prediction of the biological response, three new glycopeptides were designed and synthesized. Phe263 and Gly268 were exchanged with benzothiophenealanine (Bta) and m-bromo phenylalanine (BPh), or cyclopropylalanine (Cyp) respectively, to investigate increasing size. Weak hydrogen bonds were formed to the sidechain of Gln271 during the MD simulations of glycopeptide 10, 17, and 19. Thus, we wanted to introduce alternative structures able

to form hydrogen bonds, and 3-pyridylalanine (Pyr) or His were selected for this position (Figure 33). MD simulations of the resulting DR4•glycopeptide complexes were conducted as described above. The four properties previously specified were considered to predict the subsequent biological response.

Substitutions ID R1 R2 R3

Figure 33. The structure of CII259–273 and the three modified glycopeptides (21–23) synthesized to attempt predicting the biological response by conducting MD simulations.

Both the N- and C-terminus of the glycopeptide, as well as DR4, displayed a low mobility during the MD simulations of the DR4•21 complex. Bta263, Cyp268, and Pyr271 were placed in their respective binding pockets. In comparison, DR4•22 was more similar to CII259–273 with a highly mobile C-terminus, and Pyr271 was not firmly placed in P9. Finally, in case of DR4•23 the N-terminus displayed a low mobility with Bta263 firmly anchored in P1, while the C-terminus was mobile and neither Cyp268 nor His271 were placed in their respective pockets throughout all of the MD simulations. In addition, the secondary structure of the β1-helix of DR4 had started to unfold in the area around the C-terminus of the peptide (Figure 34). The number of strong hydrogen bonds formed to the backbone of the three glycopeptides were in the same range as for CII259–273, even though the total number had decreased (Table 5). Noteworthy is that only weak hydrogen bonds were formed to the side chain of Pyr271 or His271. Thus, glycopeptide 21 and 22 were expected to bind and likely elicit a T-cell response, while the unfolding of the β1-helix in case of 23 was expected to affect both binding and subsequent T-cell response.

Figure 34. Superposition of representative frames from each cluster extracted from a cluster analysis of the MD simulations of anchor-modified glycopeptides in complex with DR4. A)

Glycopeptide 21. B) Glycopeptide 22. C) Glycopeptide 23. The α1-domain is represented in purple, the β1-domain in cyan, and the glycopeptide in green. The conformation of native CII259–273 from the homology model is included as a reference, in orange.

The potency towards DR4 and the subsequent T-cell response was measured. This confirmed that the less mobile glycopeptides according to the MD simulations (21, 22) also displayed a higher potency towards DR4 (Figure 35). However, despite the unfolding of the β1-helix, 23 has a similar potency towards DR4 as CII259–273. Possibly, this could be explained by the fact that the unfolding occurred around the C-terminus of the glycopeptide, which is less important for binding, in combination with the strong hydrogen bonds formed to the backbone that are retained. Even though 21 displayed a higher potency towards DR4, it was not recognized by the TCR at the concentrations investigated, once again suggesting that a more rigid epitope is not able to adapt to the TCR. Both 22 and 23 elicited a T-cell response, but it was weak compared to CII259–273. This was not surprising in case of 23, due to the unfolding of the β1-helix.

Figure 35. Binding to DR4 and subsequent T-cell response. A) Binding of CII259–273 compared to anchor-modified glycopeptides 21–23, determined in a competitive binding assay. B) T-cell response by the DR4 restricted hybridoma mDR1.1, for CII259–273 compared to the anchor-modified glycopeptides 21–23, quantified in a sandwich ELISA where the amount of secreted IL-2 is measured.

Table 5. Summary of the MD simulations of DR4 in complex with anchor-modified CII259– 273 (9–12). Analysis Investigated Property 21 22 23 Potency of binding to DR4 Comparable Comparable Comparable (compared to CII259–273) TCR recognition Not detected Decreased Decreased (compared to CII259–273) ● Bta263: ● BPh263: ● Bta263 anchored anchored anchored Anchor residues ● Cyp268: ● Cyp268: ● Cyp268: (Phe263, Gly268, Gly271) anchored anchored displaced ● Pyr271: ● Pyr271 ● His271: anchored displaced displaced N-terminus of Low mobility Low mobility Low mobility CII259–273 C-terminus of Low mobility High mobility High mobility CII259–273

β1-helix of DR4 Conserved Conserved Unfolding ● Total: ● Total: ● Total: 10.2±2.1 11.2±2.3 10.2±2.8 Number of hydrogen bondsa ● Strong: ● Strong: ● Strong: 8.5±2.5 7.5±2.8 8.3±2.1 Gly262(a)c 53.0±37.6 65.5±27.2 74.2±18.8 –βHis82(s)d Phe263(d)e Importantb 82.0±18.2 63.5±40.9 77.7±34.8 –αSer53(m)f hydrogen GalHyl263(a) bonds 77.7±35.1 54.0±36.9 92.3±3.2 –βAsn82(s) GalHyl263(d) 79.5±35.7 95.0±3.3 95.8±1.7 –βAsn82(s) aHydrogen bonds were defined as having an acceptor-donor length of <3.4 Å and an angle of >140◦, calculated with the aid of the hydrogen bond extension tool in VMD.153 Only hydrogen bonds with an occupancy >10% are considered, and a strong hydrogen bond is defined as having an occupancy >40%. bHydrogen bonds were considered “important” in case they consistently had a high occupancy in multiple MD simulations of DR4 in complex with CII259–273. ca = acceptor, ds = side chain, ed = donor, fm = main chain.

4.3.3. Introduction of Amide Bond Isosteres in the C-terminus of CII259–273

4.3.3.1. Design and Synthesis of Backbone Modified CII259–273 The MD simulations of the anchor-modified glycopeptides suggested that the C- terminus of the peptide is of minor importance for binding to DR4. Thus, two positions in the backbone of the C-terminus of CII259–273 were modified by introduction of alkane and alkene amide bond isosteres (Figure 36). Not only will this increase the stability against degradation as previously mentioned, it is also of value to probe important interactions between the glycopeptide and DR4 as well as the TCR. Hydrogen bond donor and acceptor possibilities to the backbone are removed, and for the alkane isostere the backbone gain an increased flexibility. The dipeptide isosteres were synthesized as Fmoc-protected dipeptide building blocks, as exemplified with 31 and 32 (Scheme 2). The carboxylic acid 27 was converted into the aldehyde 28 via a Weinreb amide, and thereafter allowed to react with the triphenylphosphine salt 29 under Wittig reaction conditions to yield the (Z)- alkene 30. This was followed by hydrogenation, Fmoc-protection, and subsequent deprotection of the carboxylic acid and amide, finally yielding 31. Despite attempts with different Wittig reaction conditions the desired (E)-alkene could not be obtained, resulting in three final dipeptide building blocks that were then introduced into CII259–273 via Fmoc-based SPPS, resulting in the backbone-modified glycopeptides 24–26.

Figure 36. The structure of CII259–273 and the three amide bond isosteres that have been introduced (24–26).

Scheme 2. Synthesis of the Gln267–Gly268 dipeptide building blocks incorporated into glycopeptide 24 and 25. The dipeptide building block incorporated into 26 was synthesized using a similar procedure. Reagents and conditions: a) TEA, HATU, N,O- Dimethylhydroxylamine hydrochloride, DCM, 92%; b) DIBAL-H, THF, -78 ºC, 90%; c)

KHMDS, THF, -30 ºC to r.t., 62%; d) (i) Pd/C (10%), H2, MeOH; (ii) Fmoc-OSu, Na2CO3, aq.

10%:MeCN 1:1, 68%; e) tBuOH:H2O 1:1, acetyl chloride, 0 ºC to r.t., 83%; f) (i) tBuOH:H2O

1:4, acetyl chloride, 0 ºC to r.t.; (ii) Fmoc-OSu, Na2CO3, aq. 10%:MeCN 1:1, 82%.

4.3.3.2. Binding and T-Cell Response for Backbone Modified CII259–273 Despite the belief that the C-terminus of the glycopeptide CII259–273 would not be highly sensitive to introduction of amide bond isosteres, neither binding nor subsequent T-cell response was detected for the backbone modified glycopeptides 24– 26 at the measured concentrations (Figure 37).

Figure 37. Binding to DR4 and subsequent T-cell response. A) Binding of CII259–273 compared to glycopeptide 24–26, determined in a competitive assay. B) T-cell response by the DR4 restricted hybridoma mDR1.1, for CII259–273 compared to backbone-modified glycopeptides 24–26, quantified in a sandwich ELISA, measuring the amount of secreted IL-2.

4.3.3.3. Dynamic Studies of Backbone Modified CII259–273 in Complex with DR4 To investigate why an increased flexibility in the C-terminus led to a loss of binding and subsequent T-cell response, MD simulations were conducted for 24 and 26 in complex with DR4, as previously described. The MD simulations of DR4•26 displayed a highly unstable system due to introduction of an alkane isostere at

Lys270–Gly271. Phe263 was displaced from the P1 pocket and the β1-helix of DR4 was unfolding in the area around the N-terminus of the glycopeptide, indicating that 26 was about to diffuse from the binding groove. Isosteric glycopeptide 24, with an alkane isostere at Gln267–Gly268, was instead displaying a dynamic pattern similar to native CII259–273. The N-terminus was firmly anchored, the C-terminus highly mobile, and the secondary structure of DR4 was intact (Figure 38).

Figure 38. Superposition of frames from a cluster analysis of the MD simulations of 24 (A) or

26 (B) in complex with DR4. The α1-domain is represented in purple, the β1-domain in cyan, and the glycopeptide in green. The conformation of native CII259–273 from the homology model is included as a reference, in orange.

4.3.3.4. Modification of the C-Terminus Backbone of CII259–273 Affect the Hydrogen Bond Network When studying the hydrogen bond network it was evident that both the total number of hydrogen bonds and the number of strong hydrogen bonds were decreased for 26, including the four bonds considered important that are severely weakened. For 24 the effect is not as obvious, although the number of hydrogen bonds were less consistent, where occasionally only two or three strong bonds were formed. Thus, while the occupancy for the important hydrogen bonds reported in Table 6 were comparable to native CII259–273, the overall hydrogen bond pattern was affected by the increased flexibility introduced into the backbone of the glycopeptide. We speculate that because of the disrupted hydrogen bond pattern, a more flexible C-terminus led to an increased mobility that manifested towards the N-terminus, leading to the observed loss of binding. Another possibility that should be considered is that the backbone modification could cause problems during the loading event of the peptide to DR4.

Table 6. Summary of the MD simulations of DR4 in complex with backbone modified CII259– 273 (24, 26). Analysis Investigated Property 24 26 Potency of binding to DR4 Not detected Not detected (compared to CII259–273) TCR recognition Not detected Not detected (compared to CII259–273) Anchor residue (Phe263) Anchored Displaced N-terminus of CII259–273 Low mobility High mobility C-terminus of CII259–273 High mobility High mobility β1-helix of DR4 Conserved Unfolding ● Total: 9.8±3.0 ● Total: 8.0±1.5 Number of hydrogen bondsa ● Strong: 5.5±2.4 ● Strong: 2.3±1.7 Gly262(a)c 57.3±29.2 31.2±31.2 –βHis82(s)d Phe263(d)e 88.7±5.1 50.2±35.9 Importantb –αSer53(m)f hydrogen bonds GalHyl263(a) 81.0±36.2 16.3±36.5 –βAsn82(s) GalHyl263(d) 80.0±35.8 16.3±36.5 –βAsn82 (s) aHydrogen bonds were defined as having an acceptor-donor length of <3.4 Å and an angle of >140◦, calculated with the aid of the hydrogen bond extension tool in VMD.153 Only hydrogen bonds with an occupancy >10% are considered, and a strong hydrogen bond is defined as having an occupancy >40%. bHydrogen bonds were considered “important” in case they consistently had a high occupancy in multiple MD simulations of DR4 in complex with CII259–273. ca = acceptor, ds = side chain, ed = donor, fm = main chain.

4.3.4. Summary and Concluding Remarks In order for a peptide to be presented to a T-cell receptor, it must first bind to a class II MHC protein. Studies have shown that the affinity between the TCR and the class II MHC•glyopeptide complex is low, with slow association rates and fast dissociation rates. Furthermore, it has been shown that low-potency ligands in general also have a faster dissociation rate, which has been connected to a weaker T-cell response.167-171 The effect of modifications of CII259–273 has been investigated with the aid of MD simulations. Non-native amino acids have been introduced151, 156, 160 with the aim to improve the binding to DR4 and decrease the mobility of the C-terminus, aiming at increasing the potency and decreasing the dissociation rate. Evidently, it is possible to introduce non-native amino acids into CII259–273 without decreasing the potency to DR4. The same is true for a subsequent T-cell response, but this recognition process is more sensitive. A decreased mobility of the glycopeptide in the binding groove can lead to a decreased T-cell response. The reason could be an altered positioning of the peptide in the binding groove resulting in a T-cell epitope that is incompatible with,

and too rigid to be able to adapt to, an approaching TCR. MD simulations of glycopeptides with a complete lack of T-cell response displayed an increased mobility of the entire complex, in particular the N-terminus of the peptide, and a disrupted hydrogen bond pattern. Furthermore, MD simulations have been used to design three new glycopeptides that were predicted to bind to DR4. The T-cell response was, as expected, more challenging to predict demonstrating the difficulty when designing drugs for this multicomponent system. The MD simulations of DR4•CII259–273, as well as other studies,69, 151, 156, 163 indicated that the C-terminus of the glycopeptide would not be of high importance for the binding to DR4. Despite this, the backbone-modified glycopeptides 24–26 proved that this is not the case. It is possible that the increased mobility caused by introducing an alkane isostere in the C-terminus will manifest towards the N-terminus, resulting in a disrupted hydrogen bond pattern, and DR4 is unable to stabilize the peptide. Alternatively, the loading event to DR4 could also be affected. In general, as for Aq, the hydrogen bond network formed to the backbone of the glycopeptide was of high importance, and four bonds seemed to be essential. In case the hydrogen bond network is disrupted, especially these four bonds, the T-cell response and binding to DR4 is affected, in agreement with previous studies.41, 163 Similar to Aq, the importance of studying the dynamics145 of the complexes — rather than the static models — is essential to correlate the structure of the peptides to binding and T-cell response. The study displayed a highly sensitive multicomponent system, where even an apparently minor change can have a tremendous effect, and simply because the binding to DR4 is retained, one cannot expect a T-cell response to be elicited. These are all important findings for the investigation of how to introduce tolerance against the self-antigen CII259–273, on the road towards developing a vaccine against RA. Finally, it is important to note that the assay that was used for determining a potential T-cell response is highly complex. Many different components are involved, and it is impossible to elucidate which part of the recognition process that cause a decreased response. Furthermore, variations can occur between replicates since it is a cell-based assay. Thus, care has been taken to always include the native CII259–273 when measuring the response for novel peptides, and the response has thereafter been presented in comparison to CII259–273. Thus, more specific assays, for instance the ability to determine binding between the class II MHC•glycopeptide and a T-cell receptor, would be of high value.

50 “The man who moves a mountain begins by carrying away small stones” —Confucius

5. Towards a Broad-Spectrum Nerve Agent Antidote: Focus on Acetylcholinesterase

5.1. Development of Inhibitors of Acetylcholinesterase

5.1.1. Aim of the Study The use of (Q)SAR studies in medicinal chemistry to correlate the structural features of compounds to the activity they elicit is a common tool to aid the understanding and prediction of a biological response. Which compounds to include in a (Q)SAR study should be carefully considered, since this will affect the usefulness of both interpretations and predictions. In Paper III, a set of compounds used in (Q)SAR modelling of AChE inhibitors was selected by an SMD, to minimize inherent dependencies that may lead to errors when interpreting the model. Furthermore, a QSAR model should be properly evaluated, here performed with the aid of test sets and comparison to simple reference models, to ensure the reliability, robustness, and ultimately usefulness. The aim of this study has been to define molecules with a peripheral fragment that possesses an affinity towards the PS of AChE (Figure 10). The peripheral fragment can be further developed into antidotes against nerve agent inhibited AChE, as described in Chapter 5.2.

5.1.2. Design, Synthesis, and Evaluation of a Balanced Set of AChE Inhibitors The SMD of inhibitors of AChE was based on a hit, 33, from a previous HTS campaign172 (Figure 39). The chemical structure was divided into three building blocks: I (peripheral fragment, binding in the PS), II (linker), and III (CAS binding moiety). This resulted in a total of 144 possible combinations, from which a

Figure 39. Chemical structure of the selected building blocks based on the hit 33 from an HTS campaign. The peripheral fragment, I, has been divided into two parts, Ia and Ib, to further increase the diversity of the final compounds.

representative subset of 18 molecules was selected with the aid of a D-optimal design. In this design, each building block was included at least twice, leading to a combination enabling a subsequent SAR analysis to explain the influence of each building block. The selected 18 compounds were synthesized according to Scheme 3, starting with an amide coupling with sulfonyl or acid chlorides to give the desired compounds 33– 35, 37, 40–45, and 47–48. To obtain the piperazine containing compounds (38, 46, 49) Boc-deprotection of 53 was performed under acidic conditions. Finally, alkylation of pyridine with the alkyl halides (54) yielded the pyridine containing compounds (36, 39, 50). Purification of the target compounds was performed by precipitation of the hydrochloride salt, column chromatography, or preparative HPLC.

Scheme 3. General synthetic pathway for the 18 compounds 33–50, selected with the aid of an SMD. Reagents and conditions: a) TEA, DCM, r.t., 28–99%; b) 4 M HCl, EtOH, r.t., 28–87%; c) pyridine, 120 ºC, 42–83%. n = 2–3.

The resulting inhibition potency, determined with the Ellman assay, displayed a large diversity of IC50 values as desired for (Q)SAR studies, mainly ranging from low- to mid-micromolar range (Table 7).

5.1.3. SAR Studies of the Balanced Set of AChE Inhibitors

In the SAR study of the 18 compounds, the pIC50 values (represented in a Y matrix) were correlated with the absence or presence of the building blocks (represented in the X matrix) that were varied in the SMD. A resulting PLS model described 79% of the variation in the Y matrix, with an internal prediction capacity of 26%. A low internal prediction capacity is to be expected due to the immensely reduced set of compounds selected by the SMD. The PLS model revealed that a benzothiophene or a nitrobenzene in the PS, and N-dimethyl, N-diethyl, or in particular pyridine in the CAS, were advantageous for binding to AChE. In contrast, morpholine was highly disadvantageous in CAS, and the length of the linker was of no significance. The observation that a pyridinium moiety was highly favored in CAS was consistent with the fact that many AChE inhibitors contain a positively charged amine in this position, enabling the formation of cation-aromatic interactions with AChE.

Table 7. Determined inhibition potency to hAChE, for the designed set of 18 compounds (33– 50).

IC50 IC50 ID Structure ID Structure (μM) (μM)

33 13.0 42 12.8

34 445 43 12.0

35 >1000a 44 4250

36 25.0 45 78.0

37 323 46 136

38 4204 47 2430

39 22.2 48 67.7

40 69.5 49 109

41 101 50 6.6

a Solubility issues resulted in unreliable IC50 determinations.

5.1.4. QSAR Studies of the Balanced Set of AChE Inhibitors Parameters that describe the structure and properties of the designed set of 18 compounds were calculated to enable QSAR modelling. The descriptors were calculated for the entire structure, the peripheral fragment (I), and the CAS binding moiety (III). Both the neutral and protonated form of the morpholine and piperazine building blocks were included, due to the possibility that these compounds can bind in the neutral state as well as the charged one (35, 38, 44, 46, 47, 49).

A covariance matrix was calculated for the resulting descriptors to determine potential correlations, since this will affect the quality of interpretation and predictions by the model. Based on this, together with knowledge of what type of properties that is important for ligands binding to AChE, 14 descriptors (Table A1) were selected for the QSAR model. The resulting model described 79% of the variation in Y with an internal prediction capacity of 60%. Based on the QSAR model, it could be elucidated that potent inhibitors had a high logP, were more globular, and had a higher number of rotable bonds. The CAS binding moiety had smaller and more polar van der Waals (vdW) areas, a lower dipole moment, and lower energy of HOMO or LUMO. The peripheral fragment was asymmetric with a lower LUMO energy. The model displayed only a small difference for the compounds where two protonation states are included, suggesting that in this case, the protonation state in which they bind was not the reason for their low potency (Figure 40).

5.1.5. Validation of the QSAR Model

5.1.5.1. Test Sets To validate the QSAR model, three external test sets were prepared (Table A2). Test set 1 (55–59) include combinations of the building blocks in the SMD that were beneficial for a high potency, as well as compounds that were prepared as a prediction set for the original design. Test set 2 (60–66) include building blocks that were not present in the SMD. Test set 3 (67–86) contain compounds where only the peripheral fragment is varied, while the linker and the CAS binding moiety is kept constant. The potency for the combined test sets is ranging between 0.3–162 µM. Test set 1 was correctly predicted to have a high potency, but the QSAR model could not predict the difference in potency within the set. This is not surprising since it ranges between only 2.6–19 µM. Test set 2, judged to be most difficult since it contains structures not included in the training set, was correctly predicted to have a higher potency (0.3–1.3 µM). Again, the potency within the set could not be well predicted. Finally, test set 3 has a moderate potency of 68–162 µM, with the exception of 86 that is significantly more potent (0.7 µM), all correctly predicted by the QSAR model (Figure 40). Due to the small span of potency for the three separate test sets, they were combined into one for further analysis, referred to as test set 4.

Figure 40. Predicted vs measured pIC50 for the compounds in the QSAR model and the three test sets, with an overall root-mean-square error of the prediction (RMSEP) = 0.67. The training set (33–50) is displayed in grey, test set 1 (55–59) in cyan, test set 2 (60–66) in gold, and test set 3 (67–86) in magenta.

5.1.5.2. Reference Models Reference models refer to simple models that were compared to the QSAR model. The aim was to evaluate the quality of the QSAR model and investigate whether or not the predictive capacity of the reference model is inferior. Seven separate reference models were prepared based on the original training set. Two of the models corresponded to the average or median of the pIC50 values of the training set. The “nearest neighbour” was based on the assumption that similar molecules are expected to have similar potencies. In addition, four linear regression models were calculated, based on logP, TPSA, vdW area, or a PLS model including all three descriptors, respectively (Table 8).

The reference models were used to predict the pIC50 values for the test sets 1–4, revealing that they were inferior to the QSAR model, as demonstrated with RMSEP- values (Table 8). No correlation was detected between the potency and the TPSA or vdW area of the compounds (based on R2). Only a weak correlation could be detected to logP or the PLS model based on the three descriptors.

In order to analyze whether or not the pIC50 values predicted by the reference models were significantly different from the measured ones, an Anderson-Darling test173 was used to test for the probability of them being drawn from a normal distribution. In case they were, a paired student t-test and an F-test were used to test for equal mean and variance respectively. In case of non-normal data Kolmogorov- Smirnov174, 175 and Mann-Whitney176, 177 U tests were instead used. These tests

revealed that it was only the predictions made by the QSAR model that were not significantly different from the measured values, (except for the “nearest neighbor” reference model for test set 3). Together with the RMSEP values in Table 8, this clearly showed that the reference models were inferior to the QSAR model, thus validating the usefulness of constructing the QSAR model.

Table 8. Statistics for the QSAR model compared to the reference models. RMSEP Model R2 Test set 1 Test set 2 Test set 3 Test set 4 QSAR 0.77 0.68 0.67 0.50 0.57 Average – 1.52 2.44 0.95 1.49 Median – 1.23 2.14 0.70 1.25 Nearest neighbor – 0.44 2.10 0.69 1.13 logP 0.38 1.32 0.88 1.03 1.05 TPSA 0.01 1.63 2.35 0.95 1.48 vdW area 0.03 1.33 2.60 0.93 1.51 PLS 0.41 0.93 0.73 1.14 1.03

5.1.6. Summary and Concluding Remarks An SMD prior to construction of (Q)SAR models has been performed, resulting in a set of diverse structures with a span in the activity range. The importance of proper validation of the resulting QSAR model before using it for interpretation and predictions has been demonstrated. The resulting SAR model revealed fragments that gave a high or low potency for ligands binding to AChE. A subsequent QSAR model was based on 14 descriptors selected with the aid of knowledge of properties important for ligands binding to AChE, as well as ensuring that they did not correlate.

The QSAR model could properly predict pIC50 values for external test sets, and we were able to design compounds with an increased potency compared to the ones included in the training set. Finally, to further validate the QSAR model, it was compared to simple reference models. The reference models were proven to possess an inferior predicting capability for pIC50 values for the different test sets compared to the QSAR model, supporting the construction of a proper QSAR model. This study focused on inhibitors targeting the enzyme AChE, but the importance of an SMD, followed by covariance analysis and proper validation of the QSAR models, can be adapted to any medicinal chemistry project aiming at determining (Q)SAR for a set of compounds. Furthermore, this study formed the basis for the design of antidotes against nerve agents, as described in Chapter 5.2, where the focus has once again been shifted towards a more complex multicomponent system.

56 “The trick is to never stop looking. There's always another secret.” —Brandon Sanderson

5.2. The Transformation of an Inhibitor into a Reactivator of Nerve Agent Inhibited Acetylcholinesterase

5.2.1. Aim of the Study Despite decades of research since the 1950’s, focusing on developing antidotes (i.e. AChE-reactivators) against nerve agents, an efficient broad-spectrum reactivator is yet to be found. Many components need to be considered during the design of a reactivator. The compound must possess a suitable affinity for the multicomponent system consisting of AChE phosphonylated by different nerve agents. A further complication is that the drug must contain a fragment with an affinity towards AChE as well as a reactive fragment. The reactive fragment must, upon binding, be positioned in such a way that a nucleophilic attack on the phosphorous of the nerve agent is possible, resulting in reactivation. In order to obtain a broad-spectrum reactivator, the compound must be able to adapt to the different conformational states of the active site gorge of AChE that are induced depending on what nerve agent that is bound to Ser203. Preferably, the affinity towards the native (i.e. non- phosphonylated) form of AChE should be low to avoid toxic side effects. The potency of reactivators is complicated to evaluate, and the use of an HTS campaign is not an option. Commercial compound libraries are intended for non- covalent inhibitors. Thus, they will in general not contain reactive nucleophilic moieties essential for a reactivator. In Paper IV, we present our approach for the development of nerve agent reactivators, based on a previously designed non-covalent inhibitor of AChE that is transformed into a reactivator.

5.2.2. Potency for Different Forms of Phosphonylated AChE The potency for the set of 18 AChE inhibitors (33–50) previously designed and synthesized (Chapter 5.1) was measured for native hAChE and hAChE phosphonylated by three different nerve agents (VX, RVX, and tabun, Table A3), using TCSPC. By applying PLS regression, the chemical structure of the compounds was correlated to their potency for native hAChE or for the three phosphonylated forms of hAChE, respectively. The resulting models revealed that the part of the compounds binding in CAS was most influential for binding to native hAChE, and that this fragment should be positively charged. For phosphonylated hAChE it was instead the peripheral fragment that is of high importance (Figure 41). Relatively small and more asymmetric shapes were favoured, together with a polar vdW area. The different forms of phosphonylated hAChE had a similar SAR. Thus, from a binding perspective it was reasonable to believe that it is possible to design a more general reactivator than the existing ones. In addition, the SAR differed compared to native hAChE and the structure of the peripheral fragment present in 40–42 (Figure 41) displayed the general trend of higher potency towards the phosphonylated form of hAChE. This would be beneficial for a reactivator, since it would decrease the risk of toxic side effects.

Figure 41. Measured vs estimated pIC50 values for native hAChE (A) and hAChE inhibited by tabun (B). A) RMSEE = 0.30, RMSEcv = 0.31. B) RMSEE = 0.30, RMSEcv = 0.31. Compounds marked in green contain a permanently positively charged pyridinium ring favored by native hAChE. Compounds marked in magenta contain a peripheral fragment consisting of an aromatic ring with a nitro substituent, favored by the phosphonylated forms of hAChE.

5.2.3. Structural Studies of Compound 42 in Complex with mAChE The X-ray crystal structure of 42 in complex with native mAChE revealed a binding mode where the peripheral fragment binding to the PS form a sandwich interaction with Trp286 and Tyr124, similar to the well-known reactivator HI-6.178 Furthermore, the hydrogen bond formed from the nitro group in 42 to the backbone amide in Ser298 corresponded to the hydrogen bond formed to the amide in HI-6. The tertiary amine of 42 was placed in the CAS (Figure 42).

Figure 42. Binding mode of 42 (magenta) in complex with mAChE, compared to HI-6 (cyan, PDB 5FPP178) and the apo form of mAChE (yellow, PDB 1J0674). The hydrogen bond formed from the nitro group of 42 to the backbone amide of Ser298 is marked with dotted lines. Blue represents nitrogen and red represents oxygen.

5.2.4. Design of Pyridinium Oximes as Reactivators of Nerve Agent Inhibited AChE The design of reactivators originates from the non-covalent inhibitor 42 and its analogues, mainly because TCSPC data revealed that these analogues are in general more potent towards the phosphonylated form of hAChE, which would be of advantage for a reactivator. Furthermore, the potency towards all the different phosphonylated forms of hAChE is similar. Thus, the peripheral fragment of 42 was maintained to retain the affinity to AChE, while the amine was exchanged with a nucleophilic oxime able to break the covalent bond between AChE and a nerve agent. Current antidotes that are in clinical use (Figure 13) all contain variants of quaternary pyridinium oximes, and typically also an aromatic part binding to the PS, including a flexible linker connecting the two. Based on this, a pyridinium oxime was selected to be attached to inhibitor 42. The position of the oxime on the pyridinium ring as well as the length of the linker was varied, giving a set of pyridinium oxime analogues (Scheme 4).

5.2.5. Synthesis of Pyridinium Oximes The pyridinium oximes 87a–e and 88a–e were synthesized according to Scheme 4. Amide couplings with 89 and varying amino alcohols, followed by Appel reaction conditions,179 gave iodo compounds 90a–e. The iodo compounds were thereafter heated using MWI together with pyridine-4-aldoxime, or refluxed together with pyridine-2-aldoxime,180 to provide the target compounds 87a–e or 88a–e, respectively. The 4-substituted pyridinium oximes generally proceeded with good yields. However, iodo compound 90b was prone to ring closing with the amide oxygen, thus decreasing the yield substantially. Compounds 87a–d precipitated upon cooling and the pure products could be filtered off, while 87e instead required HPLC purification. Purification of the ortho-substituted pyridinium oximes 88a–e were more demanding, requiring HPLC, and subsequent recrystallization for 88a–b, resulting in low yields of 17–37%. This could partly be explained by the formation of the oxime ether that was indicated by LC-MS. It has been shown in the literature that the steric hindrance of the oxime, in addition to formation of the oxime ether and quaternary nitriles, give low yields.99, 180-184 Attempts at alkylating the pyridinium aldehyde96, or other oxime precursors, were unsuccessful. The use of triflate as the leaving group61b or protecting groups for the oxime also failed.

Scheme 4. General synthetic scheme describing the synthesis of the pyridinium oximes 87a–e and 88a–e. Reagents and conditions: a) Amino alcohol, TEA, DCM, 77–96%; b) PPh3, I2, imidazole, DCM, 76–90%; c) pyridine oxime, MeCN, MWI or reflux, 46–89% for para substituted pyridinium oximes, 17–37% for ortho substituted pyridinium oximes.

5.2.6. Activity Screening of the New Reactivators The ability of 87a–e and 88a–e to reactivate phosphonylated hAChE was screened by determining kobs at a single oxime concentration of 10 μM. Five different nerve agents of varying size were investigated: VX, sarin, RVX, cyclosarin, and tabun (Figure 13). HI-6 and obidoxime, two well-known and in general efficient clinically used reactivators, were used as reference substances. The resulting kobs values revealed that the ortho substituted oximes 88a–e were more efficient than the para substituted analogues. In general, there was a preference for the longer linker, in agreement with previous studies (Figure 43).99, 185 As expected, the reference substance obidoxime could reactivate hAChE inhibited by tabun (tabun•hAChE) only to a small extent, while HI-6 does not result in any reactivation at all. Interestingly, compound 88c–e displayed an increased reactivation efficiency compared to obidoxime for hAChE inhibited by this nerve agent. Furthermore, the reactivation capacity was comparable to obidoxime in case of cyclosarin•hAChE and RVX•hAChE. Even though obidoxime was more potent for VX•hAChE and sarin•hAChE, and HI-6 was efficient for all nerve agents except for tabun, it is important to note that the ortho oximes with a longer linker could reactivate all the five nerve agents to at least some extent.

Figure 43. Determined kobs values for 87a–e, 88a–e, HI-6, and obidoxime, at 10 µM. hAChE inhibited by five nerve agents (VX, sarin, RVX, cyclosarin, and tabun) were included in the reactivation experiments. A) Comparison between 87a–e and 88a–e. B) Comparison between

87a–e, 88a–e, HI6, and obidoxime, for tabun•hAChE. The kobs values for HI-6 and obidoxime were obtained from previous studies.186

The pKa values for the oxime moiety were determined to 8.1–8.4 for the para oximes 87a–e, by spectrophotometric titration. Slightly lower values of 7.8–7.9 were obtained for the ortho oximes 88a–e, compared to 7.3 and 7.5 for HI-6 and obidoxime respectively. It has been debated that the pKa of the oxime is of importance and should be within the range of 7.5–8.0, with an optimal value around 7.8, to efficiently enable deprotonation followed by a nucleophilic attack.187-192

5.2.7. Reactivation Capacity of Reactivator 88d

Based on the kobs value of 88d, this compound was selected for more thorough kinetic studies. The dissociation constant at the transition state, and the rate of reactivation, were investigated by determining KD and kr for hAChE phosphonylated by the five nerve agents (Table 9, Figure 22). Compound 88d has a similar affinity towards hAChE phosphonylated by all the five investigated nerve agents, in contrast to the reference obidoxime for which the difference is substantially larger. This is in agreement with the QSAR model, as well as beneficial for a broad-spectrum reactivator. Furthermore, RVX•hAChE has the highest first-order reactivation rate constant, followed by cyclosarin•hAChE and tabun•hAChE, and thereafter sarin•hAChE and VX•hAChE.

The second-order reactivation rate constant (kr2), which describes the overall efficiency of reactivation, confirmed that 88d is a more efficient reactivator than obidoxime for tabun•hAChE. Even though the rate of the reactivation was comparable, the increased potency accelerates the overall reactivation. For RVX•hAChE and cyclosarin•hAChE the overall efficiency of the reactivation was comparable to obidoxime, where the increased potency compensated for a loss of reactivation rate.

The IC50 for 88d and native hAChE was determined to 0.77 μM, demonstrating that it has a high potency for this form of hAChE as well. However, this was not surprising considering that the inhibitors in Chapter 5.2.2. with a pyridinium moiety binding in the CAS had the highest potency for native hAChE.

Table 9. Dissociation constants (KD), reactivation rate constants (kr), and the second order rate constants (kr2), determined for 88d and obidoxime, for hAChE phosphonylated by the agents VX, sarin, RVX, cyclosarin, or tabun. -3 -1 -1 -1 Phosphonylated KD (µM) kr (×10 , min ) kr2 (mM min ) hAChE 88d Obidoxime 88d Obidoxime 88d Obidoxime VX 26.4±9.81 –a 9.29±9 – a 0.35 –a Sarin 56.0±7.82 – 10.5±49.7 – 0.19 – RVX 24.7±5.85 611±154 44.7±3.0 443.4±50.6 1.81 0.73 Cyclosarin 54.0±6.49 738±156 14.6±6 163±12.3 0.27 0.22 Tabun 27.1±6.34 225±87.6 16.4±1.1 15.2±2.02 0.60 0.06 aNot determined.

5.2.8. Crystallographic Studies of Reactivator 88d

5.2.8.1. The Peripheral Fragment Obtain Multiple Binding Modes In order to further investigate the structural basis for the potency of 88d, the X-ray crystal structures of 88d in complex with native mAChE and mAChE inhibited by VX, sarin, RVX or tabun were determined. Interestingly, three different binding modes of the peripheral fragment were observed (Figure 44). For the complex with native mAChE, as well as RVX•mAChE or tabun•mAChE, the same binding mode as for 42 was evident, where the peripheral fragment was forming a sandwich interaction with Trp286 and Tyr124. In contrast, for 88d in complex with VX•mAChE, Trp286 had obtained an alternative conformation, and the ligand formed an arene-arene interaction with this residue. We speculate that it is advantageous for an antidote to be able to bind in multiple conformations to become a broad-spectrum reactivator, since it could then adapt to the different conformations of AChE induced by different nerve agents. Finally, for sarin•mAChE, 88d was also forming arene-arene interactions with Trp286, but the ligand has rotated, directing the oxime moiety out of the binding pocket and towards the solvent. Apparently, this is a low energy conformation occurring at the crystallization conditions that have been utilized. There was no electron density indicating that it binds with the oxime positioned in the CAS, despite the fact that it must occasionally bind in this conformation, proven by the fact that 88d does reactivate sarin•mAChE.

5.2.8.2. The Nerve Agent Adduct Affect Binding Mode and Affinity We have previously observed that in the X-ray crystal structures of VX•mAChE, sarin•mAChE, or tabun•mAChE193, the alkoxy side chain of the nerve agent is directed towards the entrance of the gorge, thus resulting in a closed conformation where the phosphorous is blocked from a nucleophilic attack by an oxime. In case of RVX•mAChE144, the alkoxy side chain has rotated, resulting in an open conformation. For the ternary complexes of 88d and RVX•mAChE or tabun•mAChE, the same structural change of the alkoxy side chain has been induced, enabling a nucleophilic attack. This structural change is also indicated by weak electron density for 88d in complex with VX•mAChE, but for 88d in complex with sarin•mAChE only the closed conformation is observed. The conformation of the alkoxy side chain could explain why compound 88d had a somewhat higher affinity towards VX•hAChE, RVX•hAChE, and tabun•hAChE, compared to sarin•hAChE and cyclosarin•hAChE. It could also be the reason for the third binding mode observed for 88d in complex with sarin•mAChE.

5.2.8.3. The Linker and Pyridinium Moiety Possess a High Mobility Finally, for all the complexes with 88d and different phosphonylated forms of mAChE the electron density was weak or absent for the linker and pyridinium oxime, suggesting that this part is highly mobile. Previously, it has been shown that inhibition

Figure 44. The X-ray crystal structures of 88d (magenta) in complex with A) the native form of mAChE, B) VX•mAChE, C) sarin•mAChE, D) RVX•mAChE, or E) tabun•mAChE. Electron density maps are included, where the positive (green) electron density indicates that there are atoms missing (i.e. the linker and pyridinium oxime that could not be reliably modelled). Blue represents nitrogen, red oxygen, and orange phosphorous.

of AChE by RVX, cyclosarin and tabun causes Phe338 and His447 to adopt an ensemble of conformations.194-199 This will lead to a steric hindrance for the binding of reactivators. It has also been seen that it will affect the accessibility to the phosphorous atom for HI-6 binding to AChE inhibited by tabun, shielding it from an attack. Thus, a more flexible reactivator might have an increased chance of evading the steric hindrance induced by different nerve agents, essential to enable reactivation.

5.2.9. Modelling of the Pyridinium Moiety of Reactivator 88d in Complex with AChE Inhibited by Tabun Because of the improved reactivation efficiency that 88d displayed towards tabun•hAChE compared to HI-6 and obidoxime, molecular modelling was applied to investigate possible conformations of the complete ligand. The peripheral fragment was well defined in the X-ray crystal structure. Thus it was fixed while a conformational analysis of the pyridinium oxime and linker was performed in the binding groove, using molecular mechanics. This was followed by geometry optimization using density functional theory. This resulted in three low energy conformations, displaying the possibility of proton-transfer between the oxime and His447, followed by reorientation of the pyridinium oxime to enable a nucleophilic attack on the phosphorous atom (Figure 45). As previously mentioned, the mechanism of reactivation is not known. However, it is likely that deprotonation of the oxime is performed with the aid of His447 as demonstrated by the determined conformations,

Figure 45. The reactivator 88d (magenta) in complex with mAChE where the oxime moiety and linker has been modelled in three different conformations. A) The protonated oxime is positioned towards His447. B) The deprotonated oxime is positioned towards the protonated His447. C) The conformation of the deprotonated oxime moiety has rearranged so that it is now approaching tabun, enabling a nucleophilic attack on the phosphorous and subsequent breaking of the covalent bond. Blue represents nitrogen, red oxygen, and orange phosphorous. The distances between the oxime and His447, or the phosphorous of tabun, have been marked with dotted lines.

in agreement with previous studies.200 This can explain why ortho substituted oximes with a longer linker are more beneficial for reactivation, since a shorter linker and para-substituted pyridinium oximes are more difficult to correctly position for proton- transfer to occur. The determined conformations can be used as starting points for future mechanistic studies of the reactivation.

5.2.10. Summary and Concluding Remarks A set of reactivators of nerve agent inhibited AChE has been developed by the transformation of an inhibitor. The inhibitor was selected based on its similar potency towards AChE phosphonylated by different nerve agents, in combination with a lower potency towards native AChE. Determined kobs values for the reactivators revealed that a longer linker is beneficial, in agreement with previous studies.185 Furthermore, ortho-substituted pyridinium oximes are more efficient reactivators than the para- substituted analogues. Further investigation of 88d revealed that it is a more efficient reactivator than obidoxime and HI-6 for tabun•hAChE, due to a more favorable affinity. In addition, 88d has a similar efficiency as obidoxime for reactivation of VX•hAChE and cyclosarin•hAChE. X-ray crystallographic studies revealed that 88d obtains different binding conformations, depending on which nerve agent that is bound to AChE. We speculate that this might be a prerequisite for a broad-spectrum reactivator. Furthermore, the weak and noisy electron density for the linker and pyridinium moiety suggested that this part is highly mobile. This is likely an important factor that enables reactivation and binding to different conformational states of the inhibited enzyme.194-199 Molecular modelling was used to determine three conformations, displaying the possibility of proton-transfer between the oxime and His447. This was followed by a reorientation of the oxime moiety, enabling a subsequent nucleophilic attack on the phosphorous atom, which would result in reactivation. As previously mentioned, many components need to be considered for the development of a broad-spectrum reactivator of nerve agent inhibited AChE. Within this study, we have successfully developed reactivators that can adapt to and possess a high affinity towards the different conformational states of phosphonylated AChE. The reactivators can perform a nucleophilic attack and reactivate AChE inhibited by the five different investigated nerve agents. The developed reactivators can now serve as a starting point for further optimization. In order to address the poor membrane permeability of these compounds, preventing them from freely diffusing through the blood brain barrier and entering the CNS, it would be of interest to exchange the pyridinium oxime with non-permanently charged analogues.113-119 This would likely also decrease the potency towards native AChE, resulting in reactivators with a higher potency towards the phosphonylated form as would be preferable. Furthermore, it 187-192 would likely also affect the pKa of the oxime moiety. The impact of varying the linker could also be of value,99-107 as well as variation of the peripheral fragment109, 112, 113 to investigate the impact on multiple binding modes as well as varying potency of binding.

65 “You can never know everything and part of what you know is always wrong. Perhaps even the most important part. A portion of wisdom lies in knowing that. A portion of courage lies in going on anyway.” —James Oliver Rigney, Jr. 6. Summary and Future Directions

The field of medicinal chemistry is constantly evolving, nowadays including more complex and non-traditional drug targets such as multicomponent systems. This thesis is focusing on two such systems: I) The class II MHC•CII259–273•TCR complex, essential for the initiation of an immune response, and connected to the autoimmune disease RA. For a successful design of new therapeutics by modifying CII259–273, one must consider not only the effect such modifications have on binding to the class II MHC protein, but also how the resulting structural and dynamic changes will affect the subsequent T-cell response. II) Reactivators of nerve agent inhibited AChE. During the design process of a new broad-spectrum reactivator, one must identify compounds that bind to all the different phosphonylated forms of AChE. It must possess the ability to adapt to different structural changes caused by these nerve agents, to reach the phosphorous atom to enable a nucleophilic attack leading to reactivation. Even though the research in this thesis is focusing on two completely different biological systems, a common factor is the importance of the dynamics of the investigated complexes, studied with different methods.

6.1. Towards a Vaccine against Rheumatoid Arthritis Currently, there is no cure for the autoimmune disease RA, and the cause is unknown. Current drugs used in the treatment of RA aim at relieving the symptoms and slow down disease progression, rather than countering the underlying cause. Therefore, there is a great need for the development of new therapeutics, and even more importantly to increase the knowledge of the mechanism leading to RA.

6.1.1. Modifications of CII259–273 Affect the Dynamics of Class II MHC Proteins The first part of this thesis focus on increasing the understanding of the recognition process in a multicomponent system. This system is formed by class II MHC proteins that present the antigen CII259–273 to TCRs, determining whether an immune response is to be initiated. Since we have seen that traditional methods such as docking or calculation of binding affinity cannot accurately describe the important interactions, we have performed dynamic studies of CII259–273 in complex with the class II MHC proteins Aq or DR4, using MD simulations. This has revealed highly dynamic systems, in contrast to the static complex indicated by X-ray crystal structures, in agreement with other studies.145 Modifications of CII259–273 by introduction of non-native amino acids and amide bond isosteres have been performed to probe essential interactions. Previous studies have shown that the system is highly sensitive to such modifications.164-166 Changes in the resulting dynamic pattern of the peptide was connected to the potency of binding and T-cell response. This has clearly revealed that one must be careful not to disrupt the hydrogen bond network formed to the backbone of CII259–273, as well as

the conformation the peptide in the binding groove. This will lead to an increased dynamics of the entire complex and change the epitope presented to the T-cell receptor, affecting the subsequent T-cell response. Furthermore, the studies indicate that the binding to class II MHC proteins and the resulting T-cell response is mainly sensitive towards changes occurring around the N-terminus of the glycopeptide, in comparison to the C-terminus. This is consistent with previous studies69, 151, 156 that have also shown that the C-terminus of the peptide is of less importance for a biological response. Even so, introduction of amide bond isosteres in the C-terminus affected both binding to DR4 and subsequent TCR recognition.

6.1.2. TCR Recognition is Highly Sensitive to Alterations of the Epitope We have seen that the T-cell response is more sensitive towards modifications of CII259–273 than class II MHC binding is. Thus, the T-cell response is more difficult to predict than the potency of binding. Furthermore, even though binding to the class II MHC protein is a prerequisite for T-cell response, the strength or stability is not directly correlated. This clearly demonstrates the difficulties when studying such multicomponent systems, where both binding and recognition must be considered. As seen in Chapter 4.3, a decreased mobility of the peptide binding to DR4 can result in an epitope that is incompatible with, and too rigid to adapt to, the TCR. This results in a decreased TCR signalling. At the same time, an increased mobility and dynamics of the epitope also result in a loss of T-cell response. Even so, the ability to modify CII259–273 is a prerequisite for the development of a successful vaccine and must be further investigated, since the use of peptides as drugs is complicated due to their high rate of degradation. This is a difficult task since even apparently small changes in this multicomponent system can lead to tremendous changes of the dynamics.

6.1.3. Future Directions Future studies focusing on this multicomponent system would greatly benefit from a three-dimensional model of a TCR. In case no X-ray crystal structure is available, homology modelling could be utilized. Inclusion of the TCR during MD simulations would be of value, as well as other additional components that are important during the recognition process, as has been previously demonstrated.157 This would also enable applying steered MD simulations to investigate association and dissociation of the class II MHC•peptide•TCR complex.158, 159 Furthermore, developing more specific assays to measure for instance binding of the TCR to the class II MHC•glycopeptide complex would be of value and aid during detailed analysis of the MD simulations. All of these findings are valuable and must be considered in the development of new therapeutics, and hopefully this has brought us one step closer towards understanding the TCR-signalling process and towards the development of a vaccine against RA, which is the long-term goal of the project.

6.2. Towards a Broad-Spectrum Nerve Agent Antidote Nerve agents were developed over 80 years ago, and are still used in chemical warfare. Oximes have been known nearly as long as the nerve agents, and yet there is no reactivator that is efficient enough to serve as a broad-spectrum antidote.86, 129 Increasing the understanding of the structural preferences and mechanism of reactivation would greatly aid during the design process of a new broad-spectrum antidote. As mentioned in the introduction of this thesis, there is an increased interest in the rational design of selective covalent drugs,24, 25 and we have presented our approach for design of reactive, nucleophilic drugs.

6.2.1. Design Strategy for New Nerve Agent Reactivators The second part of this thesis focuses on the identification of a peripheral fragment that can adapt and bind to AChE independently of the kind of nerve agent inhibiting the enzyme. The peripheral fragment was identified with the aid of a statistically balanced set of non-covalent inhibitors of AChE, followed by QSAR studies of the potency of the resulting compounds towards AChE inhibited by different nerve agents. The selected peripheral fragment had a potency that was only marginally affected by which nerve agent that was bound to AChE. Furthermore, the affinity was higher for the phosphonylated form of the enzyme than the native form. The peripheral fragment was thereafter transformed into a reactivator by attaching an oxime functionality, and a set of analogues was synthesized. This led to the discovery of reactivator 88d that possess the ability to reactivate AChE inhibited by all the five nerve agents that were investigated.

6.2.2. Kinetic and Structural Studies of the Developed Nerve Agent Reactivator For tabun•hAChE, 88d is substantially more efficient than obidoxime and HI-6. The increased efficacy was due to an improved affinity. For example, the KD of obidoxime is 225 M, and for 88d the KD is 27.1 M. For RVX•hAChE and cyclosarin•hAChE the reactivation potency was comparable to obidoxime, while the compound was less efficient than obidoxime for VX•hAChE and sarin•hAChE. Important to note is that a successful broad-spectrum reactivator does not need to be the most efficient reactivator for all nerve agents, rather it must be efficient enough for all nerve agents. Thus, reactivator 88d will serve as a promising precursor for development of new, efficient broad-spectrum antidotes. X-ray crystallographic studies reveal multiple binding modes for the peripheral fragment of 88d, likely a beneficial property of a broad-spectrum reactivator that must be able to adapt to the different conformations of AChE induced by different nerve agents. Furthermore, the linker and oxime moiety is highly dynamic, likely a prerequisite for reactivation to occur.194-199 Subsequent modelling led to the identification of possible conformations, suggesting that the reactivator will bind to AChE followed by deprotonation of the oxime and subsequent attack on the phosphorous. However, further investigations using quantum mechanical studies are of importance to validate the suggested mechanism.

6.2.3. Future Directions There is still a long way to go before a successful broad-spectrum antidote is obtained for clinical use. As previously mentioned, further development based on 88d would initially focus on varying the peripheral fragment109, 112, 113 to investigate the effect on potency and binding mode. In addition, introduction of a non-permanently charged oxime moiety113-119 would be of interest to study the effect on blood brain barrier penetration that is currently a drawback for all current pyridinium oxime reactivators. Given the QSAR, this would likely also decrease the potency for the native form of AChE, reducing the risk of toxicity. Furthermore, variations of the linker could be of interest.99-107 Investigating the molecular mechanism of reactivation using quantum mechanical methods, as previously mentioned, is of high relevance and would greatly aid in design of novel antidotes. Other issues that we have not yet considered are the inability to reactivate the aged form201 of phosphonylated AChE, which would be of value in case of nerve agents where this process occur rapidly, such as soman. The possibility of reinhibition caused by the phosphonylated antidotes is another issue. Furthermore, it should be noted that there are other, non-clinically used reactivators, that have an even higher efficiency of reactivation for tabun, such as ortho-7.185 Hopefully, the approach described will bring us one step closer towards understanding the mechanism of reactivation of nerve agent inhibited AChE, as well as on the path towards developing a broad-spectrum reactivator, which is the long term goal of the second project included in this thesis.

6.3. Concluding Remarks The research included in this thesis is focusing on two specific multicomponent systems, but the presented approaches can be applied to other systems. One prerequisite for the success is the combination of many different areas of expertise within medicinal chemistry, including structural biology, computational chemistry, synthetic chemistry, biochemistry, and biology. Since the field of medicinal chemistry is continuously growing, including more complex and unconventional drug targets and ligands, the development of rational strategies targeting such systems is of high relevance to decrease the time and cost for designing novel drugs. In parallel with increasing computer power enabling dynamic studies of relatively large systems, the importance of protein dynamics are nowadays being acknowledged, essential to fully understand the mechanism of different complex biological systems. Hopefully, the research presented in this thesis will be of aid as the field of medicinal chemistry continues to evolve.

69 ”If I have seen further it is by standing on the shoulders of Giants” —Isaac Newton

7. Acknowledgements

De år som slutligen lett fram till den här avhandlingen har inte bara varit otroligt lärorika, de har också varit oerhört roliga och medfört att jag fått lära känna många fina vänner och förebilder, och jag vill tacka er alla! Speciellt vill jag näma:

Anna, stort tack för att du trodde på mig i början och för att du har gett mig möjligheten att utföra alla dessa roliga projekt. För att du alltid har uppmuntrat mig att utforska alla olika områden jag velat, och för att du alltid lyckas inspirera till nya idéer. Du är en fantastisk handledare!

Mikael, för att du gav mig den första vägen in i forskningsvärlden och lät mig upptäcka hur fantastiskt roligt det är.

Fredrik, för att ingen annan kan ge mig sådant självförtroende, och för att du ser till så att jag inser hur bra vi faktiskt är! Tack till alla er andra på FOI för ett fenomenalt samarbete. Särskilt vill jag tacka Nina, för allt jobb med kristallstrukturerna, och för att du tillsammans med Fredrik tålmodigt har förklarat och lärt mig i princip allt jag kan om kristallografi. Christine, för allt jobb med reaktiveringsdatat, och för att du glatt tagit dig tid att förklara experimenten.

Jan, för ett roligt samarbete och alla goda råd, ända från studenttiden. Many thanks to Johan and Mohit, for all the work with the peptide synthesis. Rickard and everyone at the medical inflammation research at the Karolinska Institute. A special thanks to Changrong, Naru, Sabrina, and Ida for quickly running all the assays and patiently explaining the outcome. Doreen, for all your work with the crystal structure.

Karina och Dan, för att ni funnits med på alla uppföljningsmöten där ni gett goda råd och sett till så att jag hållit det hela på rätt spår.

Many thanks to the entire Linusson Group, both previous and current members. You are amazing researchers, collaborators, and friends. I especially want to mention: David, för att du alltid tar dig tid vad det än gäller, du är gruppens klippa! Tomas, för att du ser till att syntesen inte glöms bort. Rashmi, for your kindness and all the valuable input regarding MD simulations. Mina doktorandkollegor, som jag lärt mig så mycket av och med under dessa år. Cecilia, för att du har varit en superb kontorskompis från allra första början, för att du alltid tar dig tid för mini- diskussionspauser som kan handla om allt och inget, skratt och gnäll, och för att du förstår vikten av julpynt! Sofie, för att du alltid kan inspirera och har nya bra idéer och förslag, vare sig det gäller jobb eller hobbyprojekt, när jag själv har kört fast.

Lotta, för din positiva energi som verkligen smittar av sig, och för alla erfarenheter och tips som du givmilt delat med dig av som har gjort sådan skillnad.

Everyone in the lab, and all other organic chemists who have kindly taken the time to answer questions and help me with the synthesis over the years. For showing me all the tips and tricks that makes all the difference (work related or not) and for welcoming a medicinal chemist to deep mechanistic discussions at geek meetings and problem sessions.

Mattias och David, för att ni har så otroligt god smak när det kommer till böcker, serier och filmer! Och för att ni aldrig har något emot att diskutera detta oerhört viktiga ämne och tipsa om sådant man inte får missa.

Pelle, Mattias, Tobias, Marcus, Weixing, Anders, Christoffer, Maria, Sara, Rosita, alla andra på administrationen, LG, Åke, och alla på avdelningen, som ser till att hålla maskiner i trim, håller koll på allt det administrativa, sköter allt det tekniska, ser till att alla program fungerar, och helt enkelt gör så att allt kan flyta på som det ska.

Elin och Mari, för erat fina välkomnande den där första dagen, och för att ni fortsatte att göra studenttiden så rolig med filmkvällar, julmiddagar, promenader, konserter, labbrapporter, bowling, fotografering, långa samtal och allt annat. Mari, för att vi alltid kan ta vid där vi senast slutade när vi äntligen ses, och för din varma omtänksamhet. Elin, för att du förstår mig så bra och för att du är den mest godhjärtade mäniska jag någonsin träffat.

Jennie, för att du har varit en så fantastisk fin och lojal vän så länge jag kan minnas, och för att du alltid har ställt upp i vått och torrt. Tack för att vi kan prata om allt, för alla kloka råd, och för att det är så lätt att vara din vän.

Katarina och Viktoria, för att ni alltid hejar på, för att ni aldrig tvekar att ställa upp, och för att ni är så otroligt fina vänner.

Mamma och Pappa, för att ni alltid finns där, för allt skoj vi gör ihop, och för er oändliga kärlek som betyder så ofattbart mycket mer än vad jag med ord kan beskriva. Storebror, för att du envisas med att tro på mig när jag envisas med att inte göra det, för alla interna skämt som bara du förstår det hysteriskt roliga i (Blubb!), men mest av allt för att du är min bästa vän.

71 “Those who cannot remember the past are condemned to repeat it.” —George Santayana

8. References

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“A real scientist solves problems, not wails that they are unsolvable.” —Anne McCaffrey

9. Appendix

Table A1. The 14 descriptors that the QSAR model in Chapter 5.1 is based on. Global descriptors b_1rotR logP(o/w) TPSA vdw_area rgyr Descriptors describing the peripheral fragment VSA_FPPOS AM1_LUMO npr1 Descriptors describing the CAS binding moiety VSA_FPNEG VSA_FPOS VSA_FPPOS AM1_HOMO AM1_LUMO dipole

Table A2. Structure and potency towards AChE for the compounds included in the three test sets, used to evaluate the QSAR model in Chapter 5.1. ID Structure Test set IC50 (μM)

55 1 4.9

56 1 10

57 1 19

58 1 12

59 1 2.6

60 2 0.3

61 2 0.6

62 2 1.3

H N 63 O 2 1.3 O N

64 2 0.6

65 2 8.1

66 2 0.7

67 3 51

68 3 19

69 3 135

O2N H N 70 S N 3 162 O O

71 3 118

72 3 91

73 3 134

74 3 94

75 3 6.8

76 3 51

77 3 65

78 3 42

79 3 42

80 3 17

81 3 11

82 3 33

83 3 24

84 3 10

85 3 38

86 3 0.7

Table A3. IC50 values determined by TCSPC measurements for ligands binding to native hAChE and hAChE inhibited by three different nerve agents, Chapter 5.2.

IC50 (μM) Native ID Structure Phosphonylated hAChE hAChE VX RVX Tabun

33 39 324 117 223

34 135 291 548 365

35 -a - - -

36 42 439 2003 155

37 119 105 9487 255

38 277 278 653 100

39 7 44 43 40

40 95 72 65 43

41 32 22 26 14

42 24 11 24 6

43 20 152 245 72

44 - - - -

45 49 72 120 26

46 30 92 91 45

47 1001 >1000000 >1000000 2900

48 31 65 91 31

49 41 98 101 58

50 9 33 42 33

aNegligible inhibition