1. 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. 2
Acknowledgements
The author would like to acknowledge Dr Lindsey Munro & Dr Alan Jones for their
continued support throughout the investigation.
3. 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
4.3.5 Toluene/Isobutane Combinations
4.3.6 Batch 1 Summary
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
4.4.4 Batch 2 Summary
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. 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. 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. 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. 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 imageof Anakinra, AKA IL-1RA dimer from determined crystal structure20
9. 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. 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-1R with IL-1β bound
11. 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. 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. 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. 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. 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. 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.
18. 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. 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. 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.
4.2.5 Batch 0 Summary
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.
22. 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. 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. 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. 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.
4.3.6 Batch 1 Summary
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. 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. 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. 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.
4.4.4 Batch 2 Summary
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. 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. 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. 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. 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. 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 0Docking Score (AU)
Total Interactions. vs Docking Score
34. 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
Quantum Score (kcal/mol)
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
Quantum Score (kcal/mol)
Total Interactions. vs Quantum Score
35. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
