fourth year thesis

1

Using Docking Studies to produce a viable small molecule

Interleukin-1 Receptor Antagonist

Masters in Chemistry (MChem)

O.H. Steele

12110133

Dr Lindsey J. Munro & Dr Alan M Jones

A thesis submitted in partial fulfilment for the degree of Master in Chemistry at

Manchester Metropolitan University.

“I declare that none of the work detailed herein has been submitted for any other

award at Manchester Metropolitan University or any other Institution.”

“I declare that, except where specifically indicated, all the work presented in this

report is my own and I am the sole author of all parts. I understand that any evidence

of plagiarism and/or the use of unacknowledged third part data will be dealt with as a

very serious matter”

Signature……………………………………… Date: 27-APRIL-2016

2

Acknowledgements

The author would like to acknowledge Dr Lindsey Munro & Dr Alan Jones for their

continued support throughout the investigation.

3

Contents

1 Introduction

1.1 What is Interleukin?

1.1.1 Interleukin terminology

1.1.2 Interleukin’s Role in the Cell

1.1.3 Interleukin Agonism

1.1.4 Interleukin Genesis

1.1.5 Interleukin Mechanism of Action

1.2 Interleukin Antagonism as Inflammatory Mitigation

1.2.1 Current Interleukin-1 Antagonists in Medicine

1.2.2 Improving on Anakinra

1.2.3 Small Molecule Antagonism

1.3 Computational Chemistry in Drug Design

1.3.1 Choice of Software

1.3.2 IL-1 on the PDB

2. Aims and Objectives of Research

3 Experimental

3.1 Docking Preparation

3.1.1 Targets on the Receptor

3.1.2 Limitations of the Software Package

3.1.3 Compound Design

3.1.4 Docking Procedure

3.1.5 Optimisation of Geometry

3.1.6 Docking Study

3.1.7 Quantum Scoring

3.2 Data Handling

3.2.1 Visualising the Data

3.3 Developing better Small Molecule Antagonists

3.3.1 Batch Investigation Objectives

4 Results and Discussion

4.1 Issues with the Procedure

4.1.1 Issues with Docking Procedure

4.1.2 Managing Excessive Conformers

4.2 Batch 0

4.2.1 Benzene Functionalities

4.2.2 Aliphatic H-Bond Sites

4.2.3 Tryptophan-like Functionalities

4.2.4 Excessive H-Bond Sites

4.2.5 Batch 0 Summary

4.3 Batch 1

4.3.1 Aromatic and Hydrophobic Functionalities

4.3.2 Extending Toluene R-Groups

4.3.3 Quantum Scoring Problem

4.3.4 Extending Tryptophan Residues

4

4.3.5 Toluene/Isobutane Combinations


4.4 Batch 2

4.4.1 Nitrogen based Functionalities

4.4.2 Different Binding to all previous compounds

4.4.3 Compound 2_6 points to a new, discrete binding site


4.5 Batch 3

4.5.1 Combining the Best Functionalities

4.5.2 Batch 3 produces the best and worst Quantum Score

4.5.3 Selective Targeting Achieved

4.6 Quantum Scoring Assessment

4.6.1 Correlation of Observable Interactions to Scoring

4.6.2 Quantum Scoring Conclusion

4.7 Optimum Ligand Selection

4.7.1 Selection Method

4.7.2 Residues Involved in Binding

4.7.3 Identifying the Best Ligand

4.7.4 No common residues in the Best Two Compounds

5 Conclusions

5.1 Scigress Explorer

5.1.1 Can Scigress Explorer predict Strong Intermolecular Interactions?

5.2 The Ligand-Receptor Interactions

5.2.1 Using the Ligand-Receptor Interactions to Identify the Best Ligand

5.2.2 The Potential for Co-Docking or Fragment Combination

5.3 Interleukin Antagonism in Research

5.3.1 What this means to IL-1 Research

5.3.2 IL-1R Inhibition in Inflammation Mitigation

6 Future Work

6.1 Improvements on this Investigation

6.1.1 The Quantum Scoring

6.1.2 Co-Docking

6.1.3 Fragment Combination

6.2 Testing Observations in vitro

6.2.1 Biological Assay

7 References

8 Appendices

8.1 Ligand Structures

8.2 Docking Scores

8.3 Ligand Interaction Diagrams (Docking)

8.4 Ligand Interaction Diagrams (Quantum)

8.5 Interaction Table

5

Abstract

The Interleukin-1 Receptor (IL-1R) is involved in a number of acute phase

inflammatory responses, and has been linked to a number of inflammatory conditions

such as gout, type-2 diabetes and arthritis. The only IL-1R antagonist on the market is

Anakinra, the recombinant form of the naturally produced antagonist Interleukin-1

Receptor antagonist (IL-1RA), a protein with mass in excess of 17kDa.

This investigation attempts to develop a viable, small molecule antagonist for IL-1R in

by means of a docking study in Fujitsu’s Scigress Explorer. Although the investigation

identifies a limitation in the scoring procedure, it succeeds in identifying new potential

residues involved in binding, as well as recognising two unique regions of the receptor

that favour different compounds based on the dominant heteroatom present on the

chemical structure. When superimposed it was found the two compounds occupied

none of the same space, suggesting that co-docking or fragment combination is possible

in future work.

6

Introduction

1.1 What is Interleukin?

1.1.1 Interleukin terminology

Interleukin-1 (IL-1) refers to a family of cytokines involved in inflammation comprised

primarily of three natural ligands and two receptors. IL-1α & IL-1β are IL1 agonists,

which when bound to the correct receptor exhibit an inflammatory response.1 In

addition to IL-1α & IL-1β, there is a naturally occurring IL-1 receptor antagonist (IL-

1RA) identified in 1991.2

1.1.2 Interleukin’s Role in the Cell

There are two known receptors to facilitate interaction with cells. When IL-1α or IL1β

(the two IL-1 agonists) are bound, IL-1R is the receptor involved in the acute-phase

inflammatory response that characterises the IL-1 cytokines. In addition, for signal

transduction to occur, the agonist must bind to the IL-1 Receptor Accessory Protein

(IL-1RacP). The second IL-1 receptor (IL-1RII) has been described in investigations

as a “decoy” receptor. This was first identified when it was observed that induced

expression of IL-1RII can be facilitated by a different cytokine (IL-4), resulting in an

antagonised action of IL-1.3 This receptor can bind IL-1α & IL-1β, suggesting it is

linked with inhibition of IL-1. The presence of a potent natural antagonist as well as a

decoy receptor implies that not only are interleukins involved in inducing the

inflammatory response, but do so in a controlled fashion. This is not unexpected, when

it is known that there are a 37 different Interleukins4; and IL-1 is most associated with

acute & chronic inflammation out of all the other cytokine families.5

The link between immune response and IL-1 is because all members of the IL-1 family

possess a cytoplasmic domain that is highly homologous to those of all toll-like

receptors (TLRs). This domain was termed the “toll IL-1 receptor (TIR) domain. The

TIR domain signals as do the IL-1 receptors, resulting in inflammation from both

receptors being almost identical.6,7

7

1.1.3 Interleukin Agonism

Of the two active agonists, IL-1β has been more favoured as a point of investigation,

as it is believed – based on greater concentrations in the bloodstream - to have a much

more prominent role in auto inflammatory disease mediation than IL-1α.8 IL-1α is not

commonly detected in circulation except during severe diseases and is thought to be

released primarily due to cell death or to a much lesser extent due to proteolysis. This

is because formation of IL-1α involves formation of a 33kDa form before being

proteolytically processed into its 17kDa form9, through a complex mechanism

discussed later.

1.1.4 Interleukin Genesis

Secretion of IL-1β is managed by Caspase-1 (ICE-1), a protease which cleaves the

IL1β precursor in the same way proteolysis gives rise to IL-1α but in contrast ICE-1

has also been linked to autonomic cell death.10

The link between IL-1 and cell death via ICE-1 proceeds via pyroptosis. Pyroptosis is

a form of apoptosis (cell death) associated with antimicrobial responses due to

inflammation. Immune cells that identify threat within themselves produce cytokines

- in this case ICE-1 producing mature IL-1β - that swell, burst and die. This cell death

is the mechanism that releases the cytokines, which bind to their respective receptors,

which in turn attract other immune cells to combat the infection. This is what

ultimately produces the characteristic inflammation response. If these pyroptosis

processes become deregulated, it would lead to the development of multiple

inflammatory disorders.

Less is known about IL-1α secretion, but it has also been linked to ICE-1 and has been

observed to cosecrete with IL-1β. It was also observed by Groβ et al that although the

pro IL1β doesn’t exhibit binding to IL-1R before proteolysis by ICE-1, pro IL1α

exhibits similar activity to the mature form of IL-1α.11

All of these observations thus far fail to rationalise why IL-1β is observed throughout

the body in cases where inflammation is present, but IL-1α remains scarce except in

cases of massive cell necrosis.

1.1.5 Interleukin Mechanism of Action

Burzynski et al12 have addressed this by investigating IL-1α and the role it has in

chronic graft rejection. They found that the activity of IL-1α in Necrotic Endothelial

Cells (EC) is controlled independently of the level of the protein. A normal EC exists

8

with IL-1α bound to IL-1RII within the cell cytoplasm – the decoy receptor – before

one of two events occur. The first type is abrupt necrosis without prior stimulation –

which does not result in inflammation because although the IL-1α is released, it is

released in a complexed form where it’s bound to IL-1RII. The alternative event is

vessel wall damage, this gives the cell opportunity to become “aware” of damage. IL1α

or IL-1β outside the cell binds to IL-1R on the cell surface, and causes ICE-1 to cleave

the IL-1RII:IL-1α complex. Now we can consider the cell to be “primed” and if the

cell were to undergo necrosis, we would see an outflow of IL-1α that could in turn

stimulate IL-1R, which primes more cells, which produces more of the cytokine which

initiates the cycle again – producing inflammation. This inflammation proves to be

extremely problematic for chronic graft rejection, and Burzynski et al proposed that

an IL-1R antagonist could help therapeutic candidates with atherosclerosis and

allograft rejection.

Since the discovery of IL-1 and its association with inflammation more so than any

other family of cytokine, it has become a therapeutic target to treat conditions such as

arthritis.13,14

1.2 Interleukin Antagonism as Inflammatory Mitigation

1.2.1 Current Interleukin-1 Antagonists in Medicine

Anakinra (see Fig 1) is the recombinant form of the naturally produced IL-1

Antagonist, IL-1RA and emerged in the late 90’s and early 00’s as a potential

candidate in therapeutic IL-1 antagonism15. Since then, Anakinra has been used been

FIG 1 . PDB image of Anakinra, AKA IL - 1 RA dimer from determined crystal structure 20

9

most notably used to successfully treat a number of inflammatory conditions such as

Type-2 diabetes mellitus16, acute gout17, Muckle-Wells Syndrome18 and Still’s

Disease19, as well as being the only FDA-approved treatment for NOMID21. An

additional noteworthy treatment is Anakinra restoring autophagy – the process by

which cells undergo destruction – which provides further evidence for the link

between IL-1, ICE-1 and autonomic cell death22. One of the most positive properties

of Anakinra is its low risk of side effects, frequently referenced in literature, which is

due in large part to the short half-life of the drug. In the case of adverse effect,

stopping treatment leads to a rapid decrease in the levels of Anakinra in the

bloodstream23, however equally this is detrimental due to the need for regular (daily)

administration.

At present, including Anakinra there are four drugs that are used to target the IL-1

family. Anakinra, which is used as an IL-1R antagonist, and inhibits the binding of

IL1β. The soluble receptor Rilonacept and monoclonal antibody Canakinumab24,

which work by trapping IL-1β, and preventing it from binding to form the receptor

complex; and most recently, Gevokizumab, another monoclonal antibody with a

similar ability to neutralise IL-1β. Rilonacept differs from Canakinumab and

Gevokizumab, because while the latter two only bind IL-1β, Rilonacept can bind IL-

1Ra23.

Surprisingly, although IL-1 has been so strongly linked to inflammation, and Anakinra

has success in rapid treatment of a number of inflammatory disorders, at present there

are still no small molecule antagonists that can inhibit binding of IL-1β. Anakinra has

a mass in excess of 16kDa, can only be synthesised via cloning, and is limited to

subdermal injection as a method of administration, so the discovery of a viable small

molecule antagonist could improve upon the already impressively broad track record

of Anakinra.

1.2.2 Improving on Anakinra

Although no small molecule inhibitors exist, the possibility of a high affinity small

molecule antagonist was demonstrated as early as 1997 Yanofsky et al.25 The

investigation involved employing recombinant peptide libraries and attempting to

identify the minimum number of amino acids required to facilitate binding. They

compared their peptides with IL-1α and IL-1β to identify the activity of their

compounds. They identified three different peptides that exhibited an IC50 of < 3nM.

10

Crucially, this investigation challenged the pre-existing belief that the nature of

cytokine receptors preclude the identification of a small molecule agonist or

antagonist, and supported the suggestion that a large proportion of the binding energy

in protein-ligand interactions comes from just a few contacts26 by demonstrating this

high affinity binding in a compound after an 85% reduction in mass.

Although this investigation presents the largest leap forward toward a high affinity

small molecule antagonist, the nature of the compounds presents similar problems to

using IL-1RA: the method of administration is limited to injection, there is no simple

chemical synthesis, and the molecular weight is still high.

1.2.3 Small Molecule Antagonism

To date, there has only been one study that involved using chemically synthesised

compounds in an attempt to antagonise IL-1R27 and this attained an affinity that was

only in the order of micromolar, 1000 times less potent than the compounds

demonstrated by Yanofsky et al. The investigation operated on the premise of

targeting pi-pi interactions with ring systems in what was believed to be the active

site.

In a previous study it was attempted to assess the interactions involved and the

overall quality of binding in an effort to demonstrate the ability of an in silico

method to provide a model for binding. This paper was based on the premise that the

key residues involved in binding of IL-1α and IL-1β were Met-11, Arg-12, Ile-14,

FIG 2. PDB image of 1ITB: IL - 1 R with IL - 1 β bound

11

Ile-64, Lys-96, Trp-109 & Thr-111; & Arg-4, Lys-93, Phe-46, Ile-56, Glu-105,

Lys103 & Glu-105 respectively28,29.

It is clear that understanding protein-protein binding interactions to IL-1R is the key to

developing an effective small molecule inhibitor. This is often explored in literature;

R.J. Evans et al30 used site-directed mutagenesis and suggested the residues

responsible on IL-1R to be Trp-16, Gln-20, Tyr-34, Gln-36 and Tyr-147. The literature

is largely inconsistent about what is actually the binding site in IL-1R. By using

molecular modelling, the statements made about these active sites can be challenged,

by using interactions observed in the ligand-protein complexes.

1.3 Computational Chemistry in Drug Design

1.3.1 Choice of Software

FIG3. PDB image of 1G0Y: IL-1R with AF10847 bound

Using computational chemistry as a model for ligand-receptor interactions is often the

first step in drug discovery. Fujitsus Scigress Explorer is a software package that has

12

been used for binding site mapping31, generating QSAR models of HIV protease

inhibitors32, as well as a number of docking studies33-36.

1.3.2 IL-1 on the PDB

The most important thing before any docking study can be undertaken is the crystal

structure of the receptor being known and published on the PDB. There are two crystal

structures currently on the PDB that pertain to IL-1R: 1ITB37, the IL-1R with IL-1β

bound; and 1G0Y38, which is IL-1R with the aforementioned Yanofsky et al ligand

(AF10847) bound.

A noteworthy observation of the two structures is the flexibility of the receptor (shown

in Fig 4)demonstrated upon replacement of the very bulky IL-1β with the 90% lighter

AF10847 resulting in a large amount of the receptor rotating about a very small axis –

a straight chain of peptides. This suggests that small molecule inhibition may be

energetically favourable, as the receptor site possesses the ability to improve the

relative surface area available to the small molecule for binding interactions.

2. Aims and Objectives

The overall aim is to identify potential functionalities that encourage binding to IL1R.

This investigation investigates the relative quantitative ability of various proposed

ligands by means of the Quantum Scoring (QS) procedure. The investigation aims to

find a small molecule that can exhibit multiple strong intermolecular forces to residues

believed to be part of IL-1R’s active site.

FIG 4. PDB images of 1ITB and 1G0Y with the rotational axis highlighted

13

3. Experimental

3.1 Docking Preparation

3.1.1 Targets on the Receptor

The active site of IL-1R used in this investigation was determined in previous research

that investigated and characterised the binding of AF10847 based on interactions

Fig 5. Intermolecular forces between AF10847 and IL-1R

visualised in Fujitsu’s Scigress Explorer based on the crystal structure from 1G0Y39.

Key

Negative

Positive

Hydrophobic

Special

Uncharged

14

The investigation focused on the individual amino acids in AF10847, by searching

within a radius of 3.5Å for neighbouring atoms. The search radius was small, so as to

exclude as many redundant interactions as possible for the purpose of reducing the

time for docking calculations.

3.1.2 Limitations of the Software Package

Due to the nature of Scigress Explorer, no information on Pi-Pi Stacking or Pi-Cation

interactions was obtained. The intermolecular H-bonding can be visualised in Table 1.

Based on the results of the previous docking study which postulated the necessity of

Pi-Pi stacking, it was decided that a simpler framework richer in heteroatoms was the

best way to proceed to maximise potential for H-bond interactions

3.1.1 Compound Design

Thirteen compounds were drawn in Scigress Explorer based on the framework in an

attempt to find the difference that small changes in R-groups could present. This

compound list is shown fully in Appendix 8.1

Compounds were designed in triplets – generally a set of three compounds will

constitute a small investigation. For example, Cmpd_1_1, Cmpd_1_2 & Cmpd_1_3

are an attempt to explore how a change in carbon chain length has an effect on how

the R-groups interact with the receptor site.

Information obtained in Batch 0 was used to produce Batch 1, and the subsets of

compounds are rationalised in the respective results section for each batch.

3.2 Docking Procedure

3.2.1 Optimisation of Ligand Geometry

Initially, the compounds are optimised by a simple MM2 procedure which utilises the

Allinger classical forcefield approach.40 This approach treats the atoms as balls and the

bonds as springs, and doesn’t take into account valence electrons. While limited, this

approach is sufficient for optimising the geometry of the ligand, as the flexibility of

the ligand in the docking procedure will have makes this step less important, as it is

only to identify conformers that may be energetically similar with very different

physical arrangements in space.

3.2.2 Docking Study

The compounds are then docked by a genetic algorithm with a quick, simple potential

of mean force (PMF).41 PMF uses data from known protein-ligand complexes from the

PDB - such as pairwise atomic potentials – to calculate binding energy.42 At its

simplest, a PMF examines the change in energy of a system when a parameter – such

15

as the distance between the receptor and the ligand in various geometries – is altered,

over a number of generations to generate the lowest-energy product.

3.2.3 Quantum Scoring

The final step is to optimise the binding with a QS procedure. The procedure takes the

docked receptor-ligand complex and adjusts the geometry of the ligand to optimise the

binding. This generates a score usually in the range of -50 to +100 kcal/mol where a

more negative score is better. This also operates using the Allinger MM2.

3.3 Data Handling

3.3.1 Visualising the Data

Although Fujitsu’s Scigress Explorer is the software package used for Docking,

Schrodinger’s Maestro has the advantage of being able to generate 2-dimensional

protein-ligand interaction diagrams as well as visualising Pi-Pi Stacking and Pi-Cation

interactions. The docked and quantum docked compounds are exported as a PDB file

and imported into Maestro. For some reason this removes all the double bonds in the

ligand, and they are required to be redrawn manually. Once this has been completed,

the 2D ligand interaction diagram can be generated, making it possible to see what

specific residues and atoms are involved in binding without having to manipulate the

3D structure awkwardly. This provides a much clearer understanding of how the ligand

binds as opposed to a raw score.

FIG 5. Framework 1 & 2 used for batches 0-2 & 3 respectively

16

3.4 Developing Better Small Molecule Antagonists

Once the initial batch of compounds were completed, three more batches were

created, with each batch investigating different facets of binding. Batch 0 revisits the

work of Year 3 Research, testing the effect of benzene ring systems versus long chain

aliphatic systems to identify a starting point for the investigation. Batch 1 focuses on

varying carbon chain length in aromatic ring systems that feature heteroatoms; Batch

2 explores the effectiveness of nitrogen donors specifically, as well as seeing the

effect of a spirocyclic group behaving as a proline mimic; Batch 3 introduces a new

framework featuring amine groups in the place of the usual carbonyls, in an attempt

to enhance interactions observed in Batch 2.

The process of docking, quantum docking and ligand interaction diagram generation

stayed the same for all compounds.

The investigation featured 38 different chemical structures which led to 51 different

physical structures and concluded with 91 different docks and quantum docks.

4 Results and Discussion

4.1 Issues with the Procedure

4.1.1 Issues with Docking Procedure

Scigress Explorer did not succeed in docking every compound in the library, and of

those that did dock, not all of them successfully quantum docked – so an objective

score of a compound relative to another might not be immediately clear until the

inspection of the protein-ligand complexes is complete.

4.1.2 Managing Excessive Conformers

Some compounds, for example Cmpd_1_6 produced a number of different conformers

that would’ve been inefficient to dock individually. For the purpose of comparison, the

highest and lowest-energy conformers were docked to see if a large difference in

energy in the secondary structure led to a large discrepancy in the binding energy, this

observation is visible when comparing the conformers of Cmpd_0_4. The effect on

docking score was minimal, so for the intent of maximum diversity in the compound

library, especially in later batches that generated larger volumes of conformers, a three

conformer maximum was set per compound.

17

4.2 Batch 0

4.2.1 Benzene Functionalities

Cmpd R- Group

R1 R2 R3

Cmpd_0_1

-H

-H

Cmpd_0_2

-H

Cmpd_0_3

Cmpd_0_4

-H

-H

Cmpd_0_5

-H

Cmpd_0_6

-NH2

Cmpd_0_7

-H

-H

Cmpd_0_8

-H

-H

18

Cmpd_0_9

-H

-H

Cmpd_0_1 0

-H

TABLE 1. R-Group list for Batch 0 (for framework see Fig. 5)

The intent of batch 0 was to investigate the opposite of the work conducted by Sarabu

et al. They postulated that the key interactions involved in binding for IL-1R were pi

based interactions. Cmpd_0_0 was just the first framework (Fig 5), an attempt to

establish a baseline that other docking scores can be compared against.

Cmpd_0_1, 0_2 and 0_3 were investigating an increasing number of benzene groups,

akin to their work, with a low number of heteroatoms. While the number of ring

systems increases, ultimately even 0_3 doesn’t have as strong a Quantum Score (QS)

as the framework alone with no R-groups attached. Inspection of the ligand interaction

diagrams reveals that 0_3_1_Q exhibits pi-stacking as well as a H-bond, while 0_0_Q

doesn’t even indicate the presence of H-bonding.

4.2.2 Aliphatic H-Bond Sites

The second triplet of compounds (0_4, 0_5, 0_6) explored flexible straight chain

carboxylic acids in an attempt to test the opposite of the focus of Sarabu et al. This

triplet yielded conflicting data, because while 0_4_1 has exhibited two H-bonds while

simultaneously generating the poorest score. Overall as a set, this triplet demonstrated

poor quantum scores (See Table 2).

Chemical

Sample

Docking

Score

Quantum Score

(kcal/mol)

Cmpd_0_0_0 -86.891 -29.799

Cmpd_0_1_1 -141.956 101.001

Cmpd_0_1_2 -143.659 16.109

Cmpd_0_2_1 -162.733 44.488

Cmpd_0_3_1 -182.535 -23.129

Cmpd_0_4_1 -121.516 54.742

Cmpd_0_4_2 -117.074 9.312

19

Cmpd_0_4_3 -105.555 10.221

Cmpd_0_4_4 -116.931 23.191

Cmpd_0_4_5 -113.126 26.487

Cmpd_0_5_1 -126.951 36.271

Cmpd_0_6_1 -156.596 43.845

Cmpd_0_7_1 -109.855 28.989

Cmpd_0_8_1 -147.063 -54.88

Cmpd_0_9_1 -158.824 -23.503

Cmpd_0_10_1 -182.855 -55.412

TABLE 2. Quantum Score Data for Batch 0

4.2.3 Tryptophan-like Functionalities

The final triplet in batch 0 was exploring the potency of nitrogen donors based on

tryptophan analogues, with just a single R-group being edited so the binding is clearer

without multiple competing groups. The first uses just the five-membered nonaromatic

system, with the latter two being a comparison of aromatic versus aliphatic. The first

structure yielded nothing of note, but both others exhibited hydrogen bonding. The

rationale behind the highly negative (strong) quantum score is believed to be the large

numbers of contact forces from the aromatic ring structures as both approach the

aromatic ring based side chain R-groups of Phe-130 and Tyr-127. 0_8 shows no pi

based interactions in the QS structure, but it should be noted that pi-stacking is present

in the docked compound but not in the QS before it undergoes the QS procedure. This

is likely due to the MM2 approach not prioritising valence electron based interactions

in its refinement and is not uncommon for the remainder of the investigation.

Inspection of the 3D structure confirms that the aromatic system has been moved out

of an alignment that would have allowed pi-stacking (Fig. 6).

FIG 6. Comparison of 0_8_1 (left) and 0_8_1_Q (right) where the QS procedure has prevented pi

stacking from occurring by altering the alignment of the aromatic groups

20

4.2.4 Excessive H-Bond Sites

Cmpd_0_10 was a wild card, with a very long, non-aromatic carbon chain featuring

alternating nitrogen and oxygen heteroatoms. The purpose of this tenth compound was

to identify what types of heteroatoms would produce hydrogen bonding and which

residues on the receptor those would target. Just two hydrogen bonds are exhibited,

but it demonstrates the highest QS we have observed so far.


Batch 0 has suggested the aromatic ring systems are indeed the correct route to take

for R-groups due to the presence of pi-stacking to Tyr-127, but introducing

heteroatoms to the structures should be considered to encourage multiple interactions

from a single R-group, and this was to be the objective of Batch 1.

21

4.3 Batch 1

4.3.1 Aromatic and Hydrophobic Functionalities

Cmpd_1_ 10

22

Cmpd_1_ 11

Cmpd_1_ 12

Table 3. Batch 1 Compound List

Batch 1 consists of mostly tryptophan and toluene R groups, plus a few isobutene

functionalities to see whether the success of 0_10 was in fact the ability of the flexible

carbon chains to probe into hydrophobic clefts of the active site.

4.3.2 Extending Toluene R-Groups

The first three compounds (1_1, 1_2 and 1_3) all feature toluene as every R-group,

with the carbon chain length to the ring system increasing by one carbon unit with each

new compound, the rationale being the increased chain length would allow flexibility

to encourage the ring systems to reach multiple aromatic side chains in the receptor.

Compound 1_3 failed to undergo any docking procedure due to an unknown error in

the software. 1_1 managed to exhibit a negative docking score, but 1_2 had no such

success, demonstrating a positive docking score.

FIG 7. Ligand interaction diagrams of Cmpd_1_2_1 before and after the QS procedure. Costs the

ligand-receptor complex two pi-stack interactions possibly due to ignorance of valence electrons.

23

4.3.3 Quantum Scoring Problem

Chemical

Sample

Docking

Score

Quantum Score

(kcal/mol)

Cmpd_1_1_1 -225.009 -32.614

Cmpd_1_1_2 -207.171 -27.65

Cmpd_1_2_1 -237.498 45.537

Cmpd_1_2_2 -221.27 37.288

Cmpd_1_5_1 -260.407 -48.869

Cmpd_1_6_1 -271.497 -52.285

Cmpd_1_6_2 -264.937 -49.24

Cmpd_1_6_3 -243.028 -42.166

Cmpd_1_7_1 -145.984 N/A

Cmpd_1_8_1 -169.787 N/A

Cmpd_1_9_1 -202.99 N/A

Cmpd_1_10_1 -204.226 N/A

Cmpd_1_11_1 -179.942 N/A

Cmpd_1_12_1 -173.993 N/A

Table 4. Docking and Quantum Scoring of Batch 1

Conflicting QS and ligand interaction diagrams produces a large problem. When

compared with all other compounds from all other batches, compound 1_2 emerges as

one of the most viable candidates for assay due to its large number of interactions from

a diverse range of residues; five interactions (excluding contact forces), from four

different binding candidates (two from R1, one from R3, one from framework oxygen

2 (FO2) and one from framework oxygen 3 (FO3)) to three different residues on the

receptor (Phe-111, Tyr-127 and Glu-129). When compared to all of the docks over the

course of the investigation, 1_2 is second only to one other compound with respect to

the ligand interaction diagrams, so to exhibit a poor QS implies again that using this

procedure as a scoring system may be unhelpful, and the ligand interaction diagrams

are more effective. The QS procedure also removes two previously identified

interactions from the Docking procedure, but since the Docking procedure involves a

higher level of theory, it could prove to be the more accurate at predicting the in vivo

system (Fig 7).

4.3.4 Extending Tryptophan Residues

The second triplet is the same carbon chain experiment as for the toluene, but instead

with tryptophan groups. 1_4 failed to undergo docking for an unknown reason. The

QS is roughly similar for 1_5 and 1_6, but 1_5 is superior by a large margin in the

24

ligand interaction diagram, with five binding interactions even after the QS

procedure – joint second over the entire study, but with poorer ligand and receptor

diversity than 1_2_1 (Fig 8).

The problem of counting the Pi-Cation interactions from R1 is one dealt with later by

introducing ligand and receptor diversity. The issue is that manual inspection of the

3D ligand-receptor complexes show that - for example - a Trp analogue gives two pi

stack interactions – one from the five membered ring, one from the six – and it is

unclear whether this is actually one combined interaction, two separate pi stacks, or

something at an energetic midpoint of both these scenarios. In addition, sometimes

aromatic ring systems experience two pi-stacks from above the system, and it is

unclear if both of these would contribute towards the strength of the binding

interaction. By counting the unique numbers from each receptor and R group in the

ligand coordinate data it can be used to correct for such instances when identifying

the most viable docking candidate. The receptor diversity is a simple count of the

FIG 8. Ligand interaction diagram for Cmpd_1_5_1_Q, counting of the Pi-Cation interactions is

unclear whether to count the two from the same Trp as a single interaction or independently.

number of different residues on the receptor involved in ligand binding.

4.3.5 Toluene/Isobutane Combinations

All of the remaining compounds in Batch 1 failed to complete the QS procedure. All

six investigate all the possible combinations of toluene/isobutane as R groups to try

25

and see if a trend emerges with the isobutane targeting the same hydrophobic pockets

in the active site. No clear trend for the isobutane is observed, but the toluene groups

are consistently pi-stacking with the side chain of Tyr-127, with the exception of 1_9

and 1_12, with the hydroxyl group showing potential of behaving as a H-bond donor

to Asp-23 or on one occasion Val-24.


Batch 1 raised further questions about the QS validity, but suggested the hypothesis

about heteroatomic functionalities on ring systems was the right way to improve the

binding. Batch 1 was expanded upon in Batch 2 by favouring nitrogen as the

heteroatom on the ring systems, as well as trying to use proline structures to mimic the

proline group involved in binding in AF10847. The rationale was to try and target

residues in the active site previously not encountered.

4.4 Batch 2

4.4.1 Nitrogen based Functionalities

Cmpd R- Group

R1 R2 R3

Cmpd_2_1

Cmpd_2_2

Cmpd_2_3

Cmpd_2_4

Cmpd_2_5

-H

26

Cmpd_2_6

-H

Table 5. Batch 2 Compound List

Batch 2 features ligands not used in the investigation before with the exception of the

tryptophan (Trp) analogue. Building on the strong ability of toluene to participate in

simultaneous Pi stacking and H-bonding, aniline R groups are involved in half of the

structures.

Unlike the previous two batches there are no triplet trend compounds for batch 2, it’s

just a broad spectrum approach to review the potential of nitrogen to form hydrogen

bonds with the residues.

Chemical

Sample

Docking

Score

Quantum Score

(kcal/mol)

Cmpd_2_1_1 -67.313 -8.119

Cmpd_2_2_1 -79.127 -39.169

Cmpd_2_3_1 -83.57 -47.447

Cmpd_2_4_1 -71.044 -42.83

Cmpd_2_5_1 -69.432 -26.521

Cmpd_2_6_1 -78.59 -48.378

Cmpd_2_6_2 -79.55 -32.002

Table 6. Batch 2 Docking Score

4.4.2 Different Binding to all previous compounds

The most notable thing about this series of compounds is the total absence of a number

of residues frequently involved in binding up to this point. Tyr-127, Gln-129, Phe-130,

Asp-23 and Leu-15 all vanish from the ligand interaction diagrams. The ligands target

a completely different series of residues such as Lys-112,Lys-114 and Arg-208;

predominantly those with positively charged sidechains due to more pi-cation

interactions between the aromatic groups and carbonyl oxygens on the framework

acting as hydrogen bond acceptors from Lys-114 exclusively. Lys-114 was also

involved in a number of Pi-cation interactions. Overall this set of compounds appear

to target a region of the active site the other compounds failed to reach

4.4.3 Compound 2_6 points to a new, discrete binding site

All of the compounds in Batch 2 had negative docking scores (see Table 6), though

none achieve the best QS. 2_6 comes close, but inspection of the ligand interaction

27

FIG 9. Ligand interaction diagram of 2_6_1 and 3D view of the receptor-ligand complex from top view

– showing binding to an anomalous residue.

diagram does not support this observation with just a single pi-cation interaction, and

no pi stacking or h bonding. The rationale behind the anomalously poor docking

score might be attributed to a narrow crevice that introduces strong contact forces,

but the low number of residues in the ligand interaction diagram does not support this

hypothesis. Interestingly, inspection of the 3D structure reveals that where

compounds previously resided in the centre of the Taurus shape (see Fig. 10) of the

receptor, 2_6_1_Q is on the underside of the receptor, targeting Arg-208 (see Fig.

9) – a residue that has not been involved in docking in any previous compound, or

FIG 10. Top view of 2_6_1_Q (left) and 2_3_1 (right) demonstrating the structure of the latter reaching

into the negative space of the Taurus

any of the binding for AF10847 (the improved combinatorial ligand). Direct

comparison of 2_6_1_Q with 2_3_1_Q illustrates this – although the nitrogen

28

residues cause the ligand to favour the underside of the receptor, the steric bulk of

2_3_1 seems to prevent it from binding to Arg-208.

The large absence of common residues with previous compounds present opportunities

for co-binding from multiple ligands. A fragment based approach between the best

compound from Batch 2 and the best compound for Batch 0/1 also presents an

opportunity by devising the optimum length for a carbon linker. Building on this

possibility, Batch 3 was devised, by changing from a framework featuring carbonyl

groups to one containing amines instead (see Fig. 5). The primary objective is to target

the Arg-208 residue, get H-bonding and then use the remaining compound bulk to

reach into the ring system to access Tyr-127.


Batch 2 has provided a new target for the investigation, by providing evidence that

there are two discrete binding regions that can be targeted by selective use of

heteroatoms. They all provide moderately good docking scores, but with the exception

of 2_6 which undergoes pi-cation interaction with the new residue Arg-208, the ligand

interaction diagrams show an unremarkable number of interactions.

4.5 Batch 3

4.5.1 Combining the Best Functionalities

Cmpd R- Group

R1 R2 R3 Cmpd_3_ 1

-H

Cmpd_3_ 2

Cmpd_3_ 3

29

Cmpd_3_ 4

Cmpd_3_ 5

Cmpd_3_ 6

Cmpd_3_ 7

Table 7 Batch 3 Compound List – this is the only Batch that employs the second framework (Fig 5)

Batch 3 doesn’t limit itself to just oxygen/nitrogen donors in the R-groups, and features

the R-groups involved in some of the most effective binding interactions; toluene and

tryptophan as well as some isobutane and a pair of five membered aromatic ring

systems to see if they can exhibit pi stacking in the small crevice where Arg-208 resides

while the remaining groups reach into the negative space of the Taurus. Compounds

3_1 to 3_4 all feature the same groups on functionality R1 and R2 as they’ve both

demonstrated a strong ability to give rise to pi interactions, as well as the extended

toluene being linked to H-bonding. The variation is R3 which scales from a hydrogen

(3_1), to a methyl alcohol (3_2) before 3_3 and 3_4 feature aromatic five membered

ring systems with a heteroatom in the ring system itself.

4.5.2 Batch 3 Quantum Score

Compound 3_1 produced three conformers. All docked, and two successfully gave a

QS. Compound 3_1_3_Q (see Fig 11) produced the most thermodynamically stable

QS of the investigation, and the ligand interaction diagram of that compound

demonstrated the greatest number of non-contact intermolecular forces of any

compound also; targeting Lys-114, Asp-304 and Arg-208. Compound 3_2 also

30

demonstrates a negative docking score, but 3_3 fails to give a QS and 3_4 produces

the least thermodynamically favourable QS of the whole investigation, even with three

FIG 11. The most viable compound according to the QS procedure as well as from interaction count

in the ligand interaction diagrams. 3_1_3_Q forms a claw to reach multiple aromatic side chains

Pi interactions targeting Arg-208 and Lys-114. The remaining compounds in Batch 3

provide no unique interactions, no high counts of binding interactions, and no

exceedingly good docking scores.

4.5.3 Selective Targeting Achieved

Batch 3 succeeds in the objective of targeting the Arg-208 residue on the protein and

expanding into other residues to produce a better binding affinity, but the smaller ring

systems had no effect on non-contact binding at all. It would seem that the high

presence of nitrogen atoms in the structures causes the binding to favour interactions

like Batch 2, targeting charged residues instead of the hydrophobic residues seen in

Batches 0 and 1.

The full binding coordinate data is present in Appendix 8.4. What is most notable

immediately is the clear distinction between the first and second sets of Batches

Chemical

Sample

Docking

Score

Quantum Score

(kcal/mol)

Cmpd_3_1_1 -87.572 N/A

Cmpd_3_1_2 -86.641 -48.891

Cmpd_3_1_3 -88.886 -67.964

Cmpd_3_2_1 -86.402 -27.264

Cmpd_3_2_2 -91.361 N/A

Cmpd_3_2_3 -90.038 -35.233

Cmpd_3_3_1 -92.213 N/A

31

Cmpd_3_4_1 -93.034 134.727

Cmpd_3_4_2 -70.452 N/A

Cmpd_3_5_1 -78.69 -38.124

Cmpd_3_5_2 -81.32 -44.001

Cmpd_3_6_1 -78.319 -30.21

Cmpd_3_7_1 -70.72 29.979

Cmpd_3_7_2 -72.579 N/A

Table 8. Docking and Quantum Scoring from Batch 3

FIG 12. Zoomed out image of the binding interactions of all the ligands in the investigation. Visibly

different binding pattern introduced when Nitrogen systems take over post Batch 2.

(overview in Fig 12). There is a degree of overlap with Batch 1, especially with the

early Trp R groups targeting residues Phe-111 through Lys-114.

The residue at the crux of this seems to be the Arg-208, as no compound exhibits pi

interactions with Arg-208 and Tyr-127 simultaneously. A small number of compounds

closely approach Tyr-127, but no pi or H interactions are exhibited, and this

observation is even more extreme with all the residues from Met-128 to Ile-196.

4.6 Quantum Scoring Assessment

4.6.1 Correlation of Observable Interactions to Scoring

The overall potential of the QS procedure to accurately represent the in vivo systems

was researched in a previous investigation39 with no clear trend visible based on the

IC50 data of Sarabu et al. When comparing QS or even docking score with the number

of binding interactions observed in Maestro no trend arises. Even by the standards of

32

computational chemistry the R2 of the graphs is poor, but most surprising is the

extremely poor correlation and R2 between H-bonding and the docking score. It

suggests that the pi interactions are more responsible for a high docking score, but a

large number of compounds with more than one instance of pi interactions exhibit very

poor docking scores. The only redeeming quality is that at the large negative docking

scores are where we would hope to find them with larger values for binding

interactions.

See overleaf for data correlating observed strong intermolecular forces and Docking

Score/QS.

33

FIG 13 Correlation Graph for H-bonds to Docking Score

FIG 14 Correlation Graph for Pi Interactions to Docking Score

FIG 15 Correlation Graph for Total Interactions vs Docking Score

y = - 0.0001x + 0.535 R² = 8E

- 05

0

0.5

1

1.5

2

2.5

3

3.5

-300 -250 -200 -150 -100 -50 0

Docking Score (AU)

H - Bonds vs Docking Score

y = - 0.0068x + 0.0663 R² = 0.1589

0

1

2

3

4

5

-300 -250 -200 -150 -100 -50 0

Docking Score (AU)

Pi Interactions. vs Docking Score

y = - 0.0069x + 0.6013 R² = 0.1384

0

1

2

3

4

5

6

-300 -250 -200 -150 -100 -50 0 Docking Score (AU)

Total Interactions. vs Docking Score

34

FIG 16 Correlation Graph for Pi Interactions to Quantum Score

FIG 17 Correlation Graph for H-bonds to Quantum Score

FIG 18 Correlation Graph for Total Interactions vs Quantum Score

The suspicions about the ability of the QS procedure to predict the stronger interactions

are confirmed in figures 13-18 with all exhibiting extremely low R2 data. The nature

y = - 0.0056x + 0.7534

R² = 0.0391

-1

0

1

2

3

4

5

6

-100 -50 0 50 100 150

Quantum Score (kcal/mol)

Pi Interactions. vs Quantum Score

y = - 0.0016x + 0.7368 R² = 0.0088

0

0.5

1

1.5

2

2.5

-100 -50 0 50 100 150


H - Bonds vs Quantum Score

y = - 0.0072x + 1.4902 R² = 0.0445

0 1 2 3 4 5 6 7 8

-100 -50 0 50 100 150


Total Interactions. vs Quantum Score

35

of this is likely due to the level of theory involved in the quantum docking procedure,

with MM2 not taking valence electrons into account. Since all of these stronger

interactions are based on electrons, it stands to reason that a procedure that fails to take

those factors into consideration would show a low correlation.

4.6.2 Quantum Scoring Conclusion

With this in mind, selection of the most viable compound for an in vivo assay cannot

be accomplished by use of QS. This led to the generation of the ligand-receptor binding

coordinate spreadsheet shown briefly in Fig. 12. For the full spreadsheet, see Appendix

8.5.

4.7 Optimum Ligand Selection

4.7.1 Selection Method

A deductive approach was taken where compounds were eliminated by various

selection criteria. From the raw data, all the residues not involved in binding or contact

forces are deleted. From that point, all of the cells containing data that represented

anything stronger than simple contact forces (London Dispersion Forces) were

highlighted, and all the residues that were not included in this criteria were eliminated.

This left ten residues on the receptor that demonstrated strong intermolecular forces in

this investigation; Asp-23, Val-24, Phe-111, Lys-112, Lys-114, Tyr-127, Glu-129,

Phe-130, Arg-208 and Asp-304.

4.7.2 Residues Involved in Binding

The residues predominantly involved in binding are those with electrically charged

side chains. When compared with the residues observed in Table 1, it is clear that there

are similarities between the binding of AF10847 and the compounds in this

investigation.

23 24 111 112 114 127 129 130 208 304

ASP VAL PHE LYS LYS TYR GLU PHE ARG ASP FIG 19. The colour coded residues from the ligand-receptor coordinate data (see Appendix 8.4)

Lys-112, Lys-114 and Glu-129 feature in both, which is unsurprising because the

groups in AF10847 responsible for the binding are Trp and Tyr. Tyr-127 is also

probable, due to the polar uncharged side chains on AF10847 being similar to the

highly polar carbon chains produced by N and O heteroatoms.

The most interesting observation is that none of the compounds docked managed to

get any interactions with residues with polar uncharged side chains on the receptor. A

36

rationale for this may be that in small molecule docks, the ligand is more susceptible

to stronger interactions from charged species.

4.7.3 Identifying the Best Ligand

Next, an interaction count was done, and any ligand with two or fewer non-contact

force binding interactions were eliminated. Next the idea of diversity is introduced,

FIG 20. Standard and expanded (top and bottom) ligand interaction diagram for Cmpd_3_1_3_Q

37

FIG 21. Standard and expanded (top and bottom) ligand interaction diagram for Cmpd_1_2_1 and

ligands with fewer than two receptor residues involved in ligand-protein binding were

eliminated. The final step is to remove the uncertainty introduced by compounds that

had a double interaction, i.e. 3_1_2_Q has a Trp functionality that acts as a double Pi

stack with Arg-208 that it is difficult to be certain about the likelihood of the

38

occurrence in vivo if it is possible at all. In this step, the interaction count of any ligand

with such interaction is reduced by the number of double stacks, i.e. the count of

3_1_3_Q is reduced from 7 to 6. From this point any ligand with three or fewer

interactions is eliminated, leaving just three ligands: 1_2_1 (see Fig 21), 3_1_2_Q and

3_1_3_Q (see Fig 20). Since the latter two are conformers, the only two structures

remaining are 1_2 and 3_1.

Compound 3_1_3_Q proves to be the most viable, with Lys-114 and Arg-208 forming

multiple interactions with the R-groups.

1_2_1 is shown to be in a hydrophobic pocket in the ligand interaction diagram, and

these types of green ribbons indicative of a hydrophobic region vanish from the ligand

interaction diagrams in the latter pair of batches when the nitrogen heteroatoms

become more prevalent over the oxygen heteroatoms. Due to this duality, the binding

coordination data for the two compounds was compiled for comparison.

4.7.4 No common residues in the best two compounds

The only residue that shows in the ligand interaction diagrams for both compounds is

Lys-112, and this is only for a contact force. The next step is to identify whether the

two compounds when docked occupy the same space, and therefore whether co-

docking could occur. The two ligand-receptor complexes are shown superimposed in

Schrödinger’s Pymol (see Fig 22).

The superimposed structures revealed that the two ligands occupy none of the same

space, and bind to completely different regions of the active site, providing the

potential for co-docking or the possibility of combining the two structures in a

fragment based approach. Although this is promising, even the cumulative number of

H-bonds is less than a third of that of ligand AF10847 that Yanofsky et al, and there is

no data was obtained on the number of pi interactions in their ligand-receptor complex,

because it is not possible to produce a ligand interaction diagram due to the size of

their ligand.

39

FIG 22. Superimposed structures of 1_2_1 (left green) and 3_1_3_Q (right green) ligand receptor

Nonetheless, the total molecular weight of the proposed 1_2/3_1 co-dock is 786.955

AU, a 69.59% reduction in mass from AF10847, which was already a reduction from

the natural ligand of over 85%.

5 Conclusions

5.1 Scigress Explorer

5.1.1 Can Scigress Explorer predict Strong Intermolecular Interactions? This

investigation showed that molecular modelling produced data that provided new

information about IL-1R and the regions on the receptor that have the potential to be

involved in binding. The major limitation of the work undertaken is the poor ability of

the MM2 QS procedure to accurately correlate the score with observable interactions

in the receptor-ligand interaction diagrams. The PM6 docking score suggested that the

score calculated by is much more heavily influenced by pi interactions, with H-

bonding having surprisingly negligible contributions to the quantitative analysis. The

40

failure of the QS procedure is ultimately due to insufficient level of theory, and the

stronger ability of the PM6 docking procedure reinforces this conclusion.

5.2 The Ligand-Receptor Interactions

5.2.1 Using the Ligand-Receptor Interactions to Identify the Best Ligand The

selection process concluded that the two most viable compounds are 3_1 and 1_2,

based on the analysis of receptor binding in the 2D ligand interaction diagrams.

Compound 1_2 succeeds in targeting the hydrophobic residues that were

commonplace in the first two batches of compounds. It manages to achieve a diverse

range of interactions, to a broad spectrum of residues on the protein. Cmpd 3_1

achieves the objective of targeting the Arg-208 residue on the small crevice in the

underside of the residue, without pervading too far into the Taurus.

5.2.2 The Potential for Co-Docking or Fragment Combination

The two distinct binding regions on the receptor have been discussed extensively, with

superposition indicating that the two ligands occupy none of the same space. Co-

docking in Scigress Explorer may present problems, but from a synthetic standpoint it

would be advantageous to assay the two compounds individually as well as in tandem

to see if co-binding is indeed possible.

5.3 Interleukin Antagonism in Research

5.3.1 What this means to IL-1 Research

This research presents a new avenue for small molecule drug design for IL-1R

antagonism, by allowing two discrete routes of inhibition to be open for development.

The key development is that even these two compounds combined constitute a 69%

reduction in mass from the aforementioned compound AF10847, which was already

an 85% reduction in mass from IL-1RA, which is the same antagonist that is the active

ingredient in Anakinra.

5.3.2 IL-1R Inhibition in Inflammation Mitigation

Anakinra has paved the way for IL-1R as a viable target for inflammation reduction,

due to its very low risk since it has a very low half-life for drug clearance.

Combinatorial Chemistry has succeeded in developing a more potent compound than

IL-1RA, proving that it is possible to improve upon the natural antagonist the body

produces. This docking study shows that the possibility of IL-1R inhibition by a small

molecule antagonist can be possible, but it would be useful to consider multiple

binding sites to enhance inhibition of IL-1R.

41

6 Future Work

6.1 Improvements on this Investigation

6.1.1 The Quantum Scoring

The largest limitation to this investigation has been the poor QS procedure, and if the

research was revisited, the primary concern would be to reattempt every QS for every

successful dock, before reassessing the correlation between the score and the

interactions observed in the ligand interaction diagrams. Calculations of the formation

energy of the docked ligand-protein complexes should be more than sufficient

considering the scale of the ligands involved in this docking procedure.

6.1.2 Co-Docking

Attempting to co-dock not only the best pair of compounds described in this

investigation but variations of compounds that have been shown to bind to the two

distinct regions could prove a novel route for investigation.

6.1.3 Fragment Combination

Based on the superimposition of the two strongest binding compounds, a well-

informed effort to link the two compounds with a carbon chain can be attempted. While

a single inhibitor is more satisfying than a pair, it adds increasing levels of complexity

to any chemical synthesis for the purposes of biological assays.

6.2 Testing Observations in vitro

To truly validate the conclusions from this investigation, the compounds need to be

synthesised before attempting a biological assay. If this can be attempted for a broad

range of compounds from this investigations, it may be possible to prove a link

between Docking Score/Quantum Score and IC50.

42

7 References

1. C.A Dinarello, R.C. Thompson, Immunology Today, 1991, 12, 404-410.

2. S. P. Eisenberg, R.J. Evans, W. P. Arend, E. Verderber, M. T. Brewer, C. H.

Hannum & R. C. Thompson

3. Francesco Colotta, Fabio Re, Marta Muzio, Riccardo Bertini, Nadia

Polentarutti, Marina Sironi, Judith G. Giri, Steven K. Dower, John E. Sims,

Alberto Mantovani, Science, 1993, 261, 472-475.

4. M. Akdis, S. Burgler, R. Crameri, T. Eiwegger, H. Fujita, E. Gomez, S.

Klunker, N. Meyer, L. O'Mahony, O. Palomares, C. Rhyner, N. Ouaked, A.

Schaffartzik, W. Van De Veen, S. Zeller, M. Zimmermann, C. A. Akdis, J

Allergy Clin Immunol, 2011, 127, 701-721.

5. C. A. Dinarello, Blood, 2011, 117, 3720-3732.

6. C. A. Dinarello, Molecular Medicine, 2014, 20, S43-S56

7. J. L. Mitcham et al, J. Biol Chem. 1996, 271, 5777-83

8. C. A. Dinarello, Annu Rev Immunol, 2009, 27, 519-50.

9. N Watanabe, Y. Kobayashi, Cytokine, 1994, 6, 597-601

10. A Genes, G Lopez-Castejon, D Brough, Cell Death & Disease, 2012, 3, e338

11. O. Groβ, A.S Yazdi, C. J. Thomas, M. Masin, L. X. Heinz, G. Guarda, M.

Quadroni, S. K. Drexler, J. Tschopp, J. Immunol, 2012, 36, 388-400

12. L.C. Burzynski, M. Humphry, M.R. Bennett, M.C.H. Clarke, J. Biol Chem,

2015, 290, 25188-96

13. D. Burger, J.M. Dayer, G. Palmer, C. Gabay, Best Pract. Clin. Rheumatol,

2006, 20, 879-96

14. J. Kay, L. Calabrese, Rheumatology, 2004, 40, iii2-iii9

15. A.A. Schuna, C. Megeff, Am K Health Sys Pharm, 2000, 57, 225-34

16. C.M. Larsen et al, N Engl J Med, 2007, 356, 1517-26

17. A. So, T. D. Smedt, S. Revaz, J. Tschopp, Arthritis Res Ther, 2007, 9, 1-6

18. P.N. Hawkins, H. J. Lachmann, E. Aganna, M.F. McDermott, Arthritis Rheum,

2004, 50, 607-12

43

19. A.A. Fitzgerald, S.A Leclercg, A. Yan, J.E Homik, C.A. Dinarello, Arthritis

Rheum, 2005, 52, 1794-803

20. H.A Schreuder, J.M. Rondeau, C. Tardif, A. Soffientini, E. Sarubbi, A.

Akeson, S. Yanofsky, R.W. Barrett, Eur J Biochem, 1995, 227, 838-47

21. B. Neven, I. Marvillet et al, Arthritis Rheum, 2010, 62, 258-67

22. A.D. Luca, S.P. Smeekens. A. Casagrande et al, Proc Natl. Acad. Sci. U.S.A,

2014, 18, 3526-31

23. L. Cantari, A. Vitale, P. Scalini et al, Clin Rheumatol, 2015, 34, 1293-301

24. E. A. Dubois, R. Rissman, A.F. Cohen, Br J Clin Pharmacol, 2011, 71, 639641

25. S.F Yanofsky, D. N. Baldwin, J. H. Butler et al,Proc Natl Acad Sci U S A,

1996, 93, 7381-7386.

26. T. Clackson, J.A. Wells, Science, 1995, 267, 383-386.

27. R. Sarabu, J. P. Cooper, C. M. Cook, P. Gillespie, A. V. Perrotta & G. L. Olson,

Drug Des Discov, 1997, 15, 191-198.

28. E. Labriola-Tompkins, C. Chandran, T.A. Varnell, V.S. Madison, G. Ju,

Protein Eng, 1993, 6, 535-39

29. E. Labriola-Tompkins, C. Chandran, K.L. Kaffka, D. Biondi, B.J. Graves, M.

Hatada, V.S. Madison, P.L. Killian, G. Ju, Proc Natl Acad Sci U.S.A, 1991,

88, 11182-86

30. R.J Evans, J. Bray, G.P. Vigers, B.J. Brandhuber, J.J. Skalicky, R.C.

Thompson, S.P. Eisenberg, J Biol Chem, 1995, 270, 11477-83

31. A.K. Kahlon, S. Roy, A. Sharma, J Biomol Struct Dyn., 2010, 28, 201-210

32. H.K. Srivastava, C. Choudhury, G. N. Sastry, Medicinal Chemistry, 2012, 8,

811-25

33. R.S Bahare, S. Ganguly, Medicinal Chemistry Research, 2014, 23, 1300-08

34. S. Ganguly, N. Panigrahi, International Journal of ChemTech Research, 2009,

1, 974-84

35. S. Ganguly, R.S. Bahare, Medicinal Chemistry Research, 2013, 22, 3350-63

36. N. Kaur, Monika, K. Singh, Research Journal of Pharmacy and Technology,

2014, 7, 760-70

44

37. G.P. Vigers, L.J. Anderson, P. Caffes, B. J. Brandhuber, Nature, 1997, 386,

190-94

38. G.P. Vigers, D.J. Dripps, C.K. Edwards 3rd, B.J. Brandhuber, J. Biol. Chem.,

2000, 275, 36927-33

39. O. H. Steele, Year 3 Research

40. N.L. Allinger, J. Am. Chem. Soc., 1977, 99, 8127-34

41. I. Muegge & Y. Martin, J. Med. Chem., 1999, 42, 791-804

42. I. Muegge, Med. Chem. Res., 1999, 9, 490-500

8 Appendices

8.1 Ligand Structures

8.1 .pdf

8.2 Docking Scores

8.2.xlsx

8.3 Ligand Interaction Diagrams (Dock)

8.3.pdf

8.4 Ligand Interaction Diagrams (Quantum)

8.4.pdf

8.5 Interaction Table

8.5.xlsx

fourth year thesis

Documents