Design and synthesis of non-peptide mimetics mapping the immunodominant myelin basic protein (MBP83-96) epitope to function as T-cell receptor antagonists

Article abstract of DOI:10.3390/ijms18061215

Encephalitogenic T cells are heavily implicated in the pathogenesis of multiple sclerosis (MS), an autoimmune demyelinating disease of the central nervous system. Their stimulation is triggered by the formation of a trimolecular complex between the human

Full text of DOI:10.3390/ijms18061215

International Journal of
Molecular Sciences
Article
Design and Synthesis of Non-Peptide Mimetics
Mapping the Immunodominant Myelin Basic Protein
(
MBP83–96) Epitope to Function as T-Cell
Receptor Antagonists
Mary-Patricia Yannakakis 1 , Carmen Simal , Haralambos Tzoupis , Maria Rodi ,
,2
1
1
3
4
4
3
2
Narges Dargahi , Monica Prakash , Athanasia Mouzaki , James A. Platts ,
Vasso Apostolopoulos * and Theodore V. Tselios *
4
, ,†
1, ,†
1
Department of Chemistry, University of Patras, 26504 Rion Patras, Greece;
yannakakism@gmail.com (M.-P.Y.); carmen.simal@gmail.com (C.S.); haralambostz@gmail.com (H.T.)
School of Chemistry, Cardiff University, Park Place, Cardiff CF103AT, Wales, UK; platts@cardiff.ac.uk
Laboratory of Immunohematology, Division of Hematology, Department of Internal Medicine, Medical
2
3
School, University of Patras, Rion, 26500 Patras, Greece; marodi_biol@yahoo.gr (M.R.);
mouzaki@upatras.gr (A.M.)
Centre for Chronic Disease, College of Health and Biomedicine, Victoria University, St. Albans, VIC 3021,
4
Australia; Narges.dargahi@live.vu.edu.au (N.D.); monica.prakash@vu.edu.au (M.P.)
*
Correspondence: vasso.apostolopoulos@vu.edu.au (V.A.); ttselios@upatras.gr (T.V.T.);
Tel.: +61-3-9919-2025 (V.A.); +30-261-099-7905 (T.V.T.)
†
These authors contributed equally to this work.
Academic Editor: Christoph Kleinschnitz
Received: 26 April 2017; Accepted: 2 June 2017; Published: 8 June 2017
Abstract: Encephalitogenic T cells are heavily implicated in the pathogenesis of multiple sclerosis
(
MS), an autoimmune demyelinating disease of the central nervous system. Their stimulation
is triggered by the formation of a trimolecular complex between the human leukocyte antigen
HLA), an immunodominant myelin basic protein (MBP) epitope, and the T cell receptor (TCR).
(
We detail herein our studies directed towards the rational design and synthesis of non-peptide
mimetic molecules, based on the immunodominant MBP83–96 epitope that is recognized by the TCR in
complex with HLA. We focused our attention on the inhibition of the trimolecular complex formation
and consequently the inhibition of proliferation of activated T cells. A structure-based pharmacophore
model was generated, in view of the interactions between the TCR and the HLA-MBP83–96 complex.
As a result, new candidate molecules were designed based on lead compounds obtained through
the ZINC database. Moreover, semi-empirical and density functional theory methods were applied
for the prediction of the binding energy between the proposed non-peptide mimetics and the TCR.
We synthesized six molecules that were further evaluated in vitro as TCR antagonists. Analogues
15 and 16 were able to inhibit to some extent the stimulation of T cells by the immunodominant
MBP83–99 peptide from immunized mice. Inhibition was followed to a lesser degree by analogues 17
and 18 and then by analogue 19. These studies show that lead compounds 15 and 16 may be used for
immunotherapy against MS.
Keywords: multiple sclerosis; trimolecular complex; rational drug design; non-peptide mimetics;
molecular modeling; cell proliferation; T cell antagonism
1
. Introduction
Multiple sclerosis (MS) is an immunologically controlled, inflammatory, demyelinating disease,
described as the destruction of the myelin sheath of the central nervous system, which can lead to
Int. J. Mol. Sci. 2017, 18, 1215; doi:10.3390/ijms18061215
www.mdpi.com/journal/ijms

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paralysis [
1
,
2
]. Although evidence suggests the important role of B-cells (auto-antibodies), T helper
+
+
(Th)-17 cells, and CD8 T cells in disease pathogenesis [
3
], it is well regarded that CD4 Th1 cells
+
contribute to initiation and progression of disease. Indeed, CD4 T cells have been identified in
patients with MS to react to self-peptide epitopes within the myelin sheath, including that of myelin
basic protein (MBP), proteolipid protein, myelin oligodendrocyte glycoprotein, and myelin associated
glycoprotein [
of a trimolecular complex between the T cell receptor (TCR), a short 14–18 amino acid myelin peptide
epitope), and the major histocompatibility complex (MHC) class II. In fact, the MHC class II, human
leukocyte antigen (HLA) locus is the most closely correlated genetic locus to the development of
MS, in particular HLA-DR1, HLA-DR2, and HLA-DR4 [ ]. In humans, the MHC class II (HLA)
10], which present short antigenic peptide epitopes
4,5]. In the context of MS, encephalitogenic T cells are activated through the formation
(
6–8
consists of dimers (the
α chain and the β chain) [9,
+
to CD4 Th cells, resulting in the formation of the trimolecular complex (HLA-peptide-TCR). The TCR
is also composed of two different polypeptide chains (
complementarity determining regions; CDRs). CDRs are implicated in the recognition of the TCR
to HLA-peptide complex, and their structural diversity plays a crucial role in the recognition of the
α and β chains) that consist of variable domains
(
different antigens presented to T cells by antigen presenting cells [11
,
12]. In fact, there are more
7
than 2.5 × 10 unique TCR (CDRs) structures in humans [12]. In addition, the rigorous positive and
negative selection process of T cells in the thymus does not prevent auto-reactive T cells from escaping
thymic deletion [13–15], thus initiating the development of autoimmune disorders such as MS.
In patients with MS, T cell responses are primarily associated with recognition of the 81–105 region
of MBP (QDENPVVHFFKNIVTPRTPPPSQGK) [16], with the MBP83–99 (ENPVVHFFKNIVTPRTP)
peptide epitope displaying the strongest binding to HLA-DR2 [17,18], MBP83–96 being the minimal
recognized epitope. T cell recognition of MBP83–96 has also been shown in healthy individuals, albeit
at relatively low precursor frequencies [19]. Hence, the immunodominant MBP83–96 epitope plays
+
an important role at inducing CD4 T cells, which contribute to the demyelination process, and it is
therefore considered one of the main targets for developing molecular therapeutics [20
,
21]. The primary
binding residues of MBP83–96 to HLA-DR2 are via hydrophobic V87 and F , which anchor the peptide
into pockets P1 and P4, respectively, as noted in the HLA-DR2-peptide-TCR crystal structure [22];
albeit at a low resolution of 3.5 A, this structure served as the basis of all future studies of MBP
peptides interacting with HLA-DR2. Additionally, other crystal structures reported in the RCSB
90
databank [23,24] that address the role of MBP immunodominant epitopes in MS induction contain
the same TCR sequence. Furthermore, it was noted that a second step in the T cell activation process
involves the recognition of His88 and Phe , which are placed in pockets P2 and P3 of the TCR [22],
with secondary binding residues being Val86 and Lys , which are oriented in pockets P-1 and P5 of the
TCR [22]. Thus, a detailed analysis of the interactions between HLA-MBP83–96-TCR complexes would
lead to valuable information towards rational design of non-peptide mimetics with inhibitory activity.
Indeed, a number of studies have shown that using antagonist peptides (1–2 amino acid mutations to
TCR contact residues), or altered peptide ligands, can effectively modulate T cell responses and switch
89
91
from pro- to anti-inflammatory responses [25–35]. In addition, using a computational structure-based
approach, non-peptide mimetics of small organic compounds that were able to bind to MHC class II
and block the presentation of MBP152–185 to auto-reactive T cells were identified [36].
The principal goal of this study was the rational design of non-peptide mimetic molecules that
could bind to the TCR with increased affinity and not to the MHC–peptide complex. Such potential
inhibitors would prevent the formation of the trimolecular complex and consequently the stimulation
of T cells. To this end, robust computational techniques, such as molecular docking, pharmacophore
modeling, and molecular dynamics, were utilized for the design of novel TCR inhibitors. The application
of pharmacophore modeling in the trimolecular complex (HLA-MBP83–96-TCR) allows the differentiation
between the different contributions (e.g., electrostatic and van der Waals interactions, hydrogen donors
and acceptors) involved in the epitope recognition process. By analyzing the variations in these aspects,
it is possible to search through diverse chemical databases and filter the results for the identification of

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potential lead TCR antagonists (hits). Furthermore, molecular docking methodologies can be implemented
in order to identify and isolate common substructures of the top ranking hits. Subsequently, the analogue
with the best docking score (lead molecule) and preferable structural orientation over the TCR is selected
for further optimization and this optimized structure then opts for increased interactions with the TCR.
Molecular dynamics (MD) simulations and molecular orbital calculations were carried out in the optimized
hits in order to evaluate their binding to the TCR. Finally, the proposed analogues were synthesized to
evaluate their biological activity against MBP83–99 primed mouse T cells and to human peripheral blood
T cells.
2
. Results and Discussion
2
.1. Pharmacophore Modeling and Virtual Screening
In computational drug discovery, screening of large databases with chemical property information
obtained from relatively small data is essential. The combination of results from structure- and
ligand-based pharmacophore models allows a thorough search in order to discover potential
antagonists. The proposed pharmacophore model (Figure 1) is based on features such as an aromatic
ring (Aro, green), a hydrogen bond (HB), cation and donor (Cat, magenta), hydrophobic groups (Hyd,
orange), and volume exclusion (V, gray). The detailed parameters utilized for the construction of the
model are described in the Materials and Methods section. The key features are based on residues
88
89
86
85
His and Phe (Aro, Figure 1, green sphere), Val (Hyd, Figure 1 orange sphere), and Pro (Cat,
Figure 1, magenta sphere). The grey spheres in Figure 1 represent residues with bulky side chains,
such as Val87 and Phe , that do not interact with the TCR. These residues are employed to define
the Volume Exclusion (V) feature of the pharmacophore model. This information is important for
excluding residues that interact with the HLA receptor and consequently are not involved in key
interactions with the TCR.
90
Figure 1. The proposed pharmacophore model, based on the myelin basic protein MBP83–96 epitope,
with the relevant features depicted as spheres (Aro: green; Cat: magenta; Hyd: orange; V: gray).
Only the binding cavity of the T cell receptor (TCR) is presented in the figure as surface and ribbons.
The residues of the MBP83–96 are depicted as sticks.
The next step was the implementation of the pharmacophore model for the virtual screening
of chemical databases. As described in the Materials and Methods section; the ZINC database
was employed in the virtual screening process. The combinatorial information yielded from the
pharmacophore model was employed as the starting point of our search. The examination of

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compounds in databases yielded a total of 340 potential inhibitors (hits). A subsequent visual analysis
revealed 13 molecules (compounds 13, Table 1) with binding conformations that closely resembled
1
–
the positioning of the MBP83–96 epitope inside the TCR binding cavity (Tables 1 and S1).
Table 1. Chemical structure and docking scores of the proposed potential T cell receptor (TCR)
antagonists (compounds 1–19).
∆G d (kcal/mol)
−11.89
Compound Number
Structure
MBP83–96
Seq: ENPVVHFFKNIVTP
1 a
−15.87
2
−
−
−
19.71
14.46
14.43
(
* S/** R)
3
(
* S/** S)
4
* R)
(
5
−10.32
6
7
−15.34
−16.38

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Table 1. Cont.
∆G d (kcal/mol)
Compound Number
Structure
8
* S)
−
13.26
(
9
−15.86
1
0
−
−
21.56
20.85
(
Lead Compound)
1
* R)
1
(
1
1
2
−16.05
3
−
20.65
(
* S)
1
4 b
−23.76
−18.13
−18.03
1
5
6
1
1
7 c
−18.49

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Table 1. Cont.
∆G d (kcal/mol)
−20.70
Compound Number
Structure
1
8
1
9
−21.32
a
13 were obtained from the pharmacophore model. b Compounds 14
Compounds
1–
–
16 were derived through
c
d
modifications of the lead compound 10
.
Compounds 17
–
19 were modified analogues of compound 15
.
Docking
score as calculated by the MOE2016 software at 298 K.
2
.2. Lead Optimization and Molecular Docking Calculations
All the selected molecules were visualized in MOE2010, while their structural orientation and
binding with the TCR were assessed. Each of the potential hits was subjected to molecular docking
calculations, and the results are presented in Table 1. The analysis of the docking experiments showed
that, of the 13 compounds obtained from the pharmacophore screen, compound 10 presented with the
highest docking score (
was reported for compound
the best candidate for lead optimization. The formation of only 2 hydrogen bonds with residues
AspA92 and GlyA96 of the TCR is noted along with the existence of a -stacking interaction between
−
21.56 kcal/mol) inside the TCR binding cavity, while the lowest docking score
5
(
−
10.32 kcal/mol). This suggests that compound 10 may be considered
π
the aromatic rings of the compound and the side chain of TyrA98 in the TCR. Despite the favorable
interactions between analogue 10 and the TCR, the bulky nature of the lead compound prevents the
better positioning of the molecule inside the binding cavity.
The optimization process for target compound 10 included the removal and addition of functional
groups in order to improve the placement of the molecule inside the selected TCR pockets and
subsequently increase the interactions (Figure 2a). As depicted in Figure 2a, the substituted aromatic
ring was removed to decrease the bulky nature of the potential inhibitor. The benzimidazole was
replaced by a guanidino group (Figure 2a) to enhance the hydrogen bonding potential of the designed
inhibitor. This preliminary study led to the identification of compound 14 as drug-target (Table 1 and
Figure 2b). The next step was the setup of a molecular docking simulation for compound 14 in the TCR.
The results of the docking experiments show that the alterations in compound 14 increase its binding
affinity inside the TCR compared to the lead compound 10
(
−
23.76 to
−
21.56 kcal/mol, Table 1).
The ligand pose with the best docking score for compound 14 (Figure 2b) presented the formation
of six hydrogen bonds with residues of the TCR. In addition to the hydrogen bond interactions with
amino acids AspA92 and GlyA96, the optimized compound further interacts with residues AsnA30
and ThrA97 (Figure 2b). The improved interaction, via the increased number of hydrogen bonds,
may further explain the better binding affinity of compound 14, due to the more favorable positioning
inside the binding cavity of the receptor. As expected the π-stacking interaction with TyrA98 in the
TCR is retained in the new optimized compound, further enhancing its binding.
As stated in the Materials and Methods section, the filtering process of the pharmacophore search
was based on Lipinski’s rule of five. Properties such as size (molecular weight, MW), hydrophobicity
content (logP), and Total Polar Surface Area (TPSA) were recorded for the potential candidates
(Table S1). The lead compound (compound 10) was selected due to its high binding affinity (Table 2)
and its better positioning inside the TCR binding cavity. The optimization process that led to the
design of compound 14 aimed to enhance the binding affinity as well as to improve its positioning
deeper within the TCR. Additionally, the modifications in the lead compound were intended to reduce
its hydrophobic content (logP) and increase the polar surface area of the proposed inhibitor (Table 2).
The smaller size of optimized compound 14 (MW = 272.33, Table 2), showed a notable decrease

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in its hydrophobic content (
−
0.84 from 5.25 of compound 10, Table 2) and an increase in its TPSA
(
Table 2). Both of these chemical properties are indicators of compound's membrane/cell permeability.
Compound 14 proved better potential absorption properties than the lead compound, as indicated by
the logP and TPSA values.
Figure 2.
(
a) Optimization process for the lead compound 10. The preserved groups are presented in
blue, while the optimized groups are shown in red and the removed ones in green; (
b
) best docking
pose of compound 14 inside the TCR binding site, showing the different interactions. Green arrow:
Hydrogen Bond (HB) formed with the side chain of the residue; Blue arrow: HB formed with the
backbone; Blue shade: Solvent accessible surface area (SASA) of the ligand; Turquoise halo: SASA of
the receptor residues; Green dotted lines:
π-π interactions between two phenyl rings or interaction of
a cation (+) with a π system (phenyl ring).
Table 2. Properties of lead compound 10 and optimized analogues 14–19.
TPSA (Å2)
Compound
MW (g/mol)
logP
Docking Score (kcal/mol)
94.06 a
97.67
97.67
5.25 b
−0.84
−0.84
−0.71
−1.62
−1.49
−1.42
−21.56
−23.76
−18.13
−18.03
−18.49
−20.70
−21.32
1
1
1
1
0
4
5
6c
495.57
272.33
286.36
286.36
354.40
344.39
344.39
97.67
1
7
152.13
137.34
126.34
1
1
8
9
a,b
Total Polar Surface Area (TPSA) and hydrophobicity content (logP) values are reported as shown on Ambinter
Chemicals catalogue: http://www.ambinter.com/. c Compounds 17–19 are modified analogues of compound 15.
Based on the calculated properties of the compounds 10 and 14 (logP and TPSA, Table 2), we aimed
to further optimize analogue 14 through small changes in the compound’s backbone to explore whether
an additional increase in binding affinity is possible. Thus, two new target molecules 15 and 16 were
obtained (Table 2, Figure 3); the 3–substituted pyrrole ring with an additional methylene group (–CH –)
2
between the amide bond and the guanidino group, compound 15 (Figure 3a), and its 2–substituted
pyrrole ring isomer, compound 16 (Figure 3b). The addition of the methylene group aimed to improve
the positioning of the guanidino group in the P2 pocket of TCR. As expected, this variation increases
the molecular weight but does not affect the hydrophobicity content, and the TPSA values of the
two derivatives in comparison to compound 14 (Table 2). Molecular docking simulations were also
carried out for analogues 15 and 16 in complex with the TCR, and the results are reported in Table 2
and Figure 3. The reported interactions for compounds 15 and 16 show the retention of the hydrogen
bonds with AspA92 and GlyA96 (Figure 3a,b), while the addition of the methylene group prevents
the interactions with residues AsnA30 and ThrA97 reported for compound 14 (Figures 2b and 3a,b).
The absence of these interactions, compared to compound 14, may explain the differences observed for
the binding affinities of the two derivatives 15 and 16 (Table 2).

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The abolition of interactions with residues AsnA30 and ThrA97 for analogue 15 and the
subsequent decrease in the binding affinity compared to compound 14 (Table 2) led to the design of
derivatives 17
–19 (Figure 3c–e). The analogues include meta– (compound 17) and para– (compounds
1
8
and 19) substitutions of the aromatic ring in compound 15. The meta– substitution with the tetrazole
group in compound 17 restores the hydrogen bond with ThrA97 (Figure 3c). Furthermore, the tetrazole
interacts via the formation of a hydrogen bond with TyrA98 (Figure 3c). The new interactions between
the compound and TCR residues are mirrored in the increased docking score of the molecule as
reported in Table 2. The para –CH COOH substitute (compound 18) retains the interactions of
2
analogue 15 with AspA92 and GlyA96, while creating hydrogen bonds with residues AsnB51 and
LysB55 (Figure 3d). The amino acids AsnB51 and LysB55 are located in the TCR binding site, opposite
to AspA92 and GlyA96, thus enhancing the positioning of derivative 18 in the TCR binding cavity.
A similar pattern of interactions inside the TCR cavity is observed for the para– methyl ester substituent
(compound 19, Figure 3e). Again, the methyl ester group allows the compound to be better oriented
inside the binding site. The possible advantageous positioning of compounds 18 and 19 is mirrored in
their docking scores (−20.70 and −21.32 kcal/mol, respectively, Table 2).
Figure 3. Best docking poses inside the TCR binding site, showing the different interactions for
compounds: (a) 15; (b) 16; (c) 17, (d) 18; (e) 19. Green arrow: Hydrogen Bond (HB) formed with the
side chain of the residue; Blue arrow: HB formed with the backbone; Blue shade: Solvent accessible
surface area (SASA) of the ligand; Turquoise halo: SASA of the receptor residues; Green dotted lines:
interaction of C-H with a π system (phenyl ring).

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2
.3. Molecular Dynamics Simulations
Molecular dynamics (MD) simulation experiments were performed on the optimized compounds
(14
–
19). The best docking poses were utilized as the starting conformations in the different MD
simulation runs. The conformational changes observed for the TCR are similar in the different MD
simulation runs (Figure S1a). This pattern is also observed in the atomic positional fluctuations of the
residues of the TCR (Figure S1b). The different amino acids of the receptor show an identical pattern
of deviation from their original position in the complexes with different analogues. Furthermore,
the conformational analysis of the ligands showed that there are no extensive conformational
changes (Figure S1c) during the simulation time. The average RMS value (1.97 Å
compound 14 presents the greatest deviation from its starting conformation compared to compounds
±
0.1) for
1
1
5 (1.90 Å ± 0.37)
,
16
(
1.72 Å ± 0.20)
,
17
(
1.82 Å ± 0.63), and 18 (1.01 Å
±
0.13). Only analogue
0.47) to all the other derivatives (Figure S1c).
9
presents a higher average RMS value (2.01 Å
±
These deviations in the RMS values for the designed analogues reflect very small changes in their
conformation during MD simulations.
The clustering analysis for the different MD simulations showed that compound 14 presents
two dominant conformational groups throughout the simulation (Figure 4a, blue and yellow).
The difference between the two conformations is in the positioning of the aromatic ring inside the P3
pocket of the TCR (Figure 4b). In one instance, the aromatic ring is facing towards TyrA98 (Figure 4b,
green) and in the other it faces away from TyrA98 and towards PheB34 (Figure 4b, yellow). In both
cases, though, the docking pose is not retained throughout the MD simulation and the guanidino
group is facing away from the binding cavity of the TCR (Figure 4b). The modification of compound
1
4
in which an additional methylene group (analogues 15 and 16) is introduced might lead to a better
positioning inside the TCR binding cavity. The clustering analysis for the two modified analogues
and 16 revealed the presence of only one dominant conformation for both compounds (Figure 5a,
1
5
black and salmon respectively). The positioning of the two analogues 15 and 16 inside the binding
cavity of the TCR is very similar (Figure 5a, black and salmon, respectively). The most pronounced
difference between them is the positioning of the aromatic ring. In 2–substituted pyrrole analogue
16, the aromatic ring during the MD simulations points away from the binding pockets (Figure 5a,
salmon). On the other hand, 3–substituted pyrrole analogue 15 adopts a more optimal conformation
inside the binding pockets of TCR (Figure 5a, black). While analogues 14 and 16 have a portion of their
structure pointing away from the TCR receptor (Figures 4b and 5a), the addition of the methylene
group in compound 15 allows for the conformation of the molecule to create a bent, thus optimizing
the orientation inside pockets P–1, P2, and P3 of the TCR (Figure 5a, black).
Figure 4.
clustering analysis in the molecular dynamics (MD) simulations and (
representative conformations (yellow and green) inside the TCR binding pockets.
(
a) Representative conformations of compound 14 inside the TCR as reported by the
b
) the positioning of analogue 14

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Figure 5. Conformations of compounds 15–19 inside the TCR binding pockets. The common backbone
features have been implemented for the superimposition of the derivatives on analogue 15 (black);
a) with 16 (salmon); (b) with 17 (cyan); (c) with 18 (magenta); (d) with 19 (green).
(
As previously mentioned, the best possible positioning of compound 15 inside the binding pocket
observed in the docking experiments led to the design of derivatives 17 19. The clustering analysis of
the particular simulations confirmed the results obtained from the RMS analysis (Figure S1c). Likewise,
with compounds 15 and 16, the derivatives 17 19 present only one dominant conformation throughout
the MD simulations. The structural similarities of analogue 15 with compounds 17 19 led to the
–
–
–
supposition that the derivatives would adopt a similar positioning inside the TCR. The superimposition
of the representative conformations with that of compound 15 (Figure 5) confirmed the above
supposition. Compounds 17 and 18 present identical positioning inside pockets P2 and P3 with
that of compound 15 (Figure 5b,c), suggesting that the guanidino group firmly anchors the analogues
inside the receptor. At the opposite end of the derivatives though the substitutions with the tetrazole
(
compound 17) and the –CH COOH (compound 18) groups do not greatly improve the conformational
2
positioning of the designed analogues in the binding cavity. Additionally, the –CH COOH substituent
2
in compound 18 orients the aromatic ring of the derivative away from the pockets of TCR (Figure 5c).
On the other hand, the positioning of compound 19, which has a para– methyl ester substitution, in the
binding site of the receptor closely resembles that of analogue 15 (Figure 5d, green). The substitution
seems to position the analogue inside the TCR between pockets P3 and P–1 in an even better way
(Figure 5d, red and black).

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Hydrogen Bond Interactions
Analysis of the hydrogen bond interactions for all compounds (14
–
19) was performed during the
MD simulations. The results are outlined in Table 3 and compared with the interactions reported from
the molecular docking experiments. The changes in the orientation of the molecules inside the TCR
are mirrored in the observed differences of the interactions for each molecule. As mentioned above,
compound 14 creates hydrogen bonds with residues in pockets P2 and P3 (Figure 2b and Table 3)
with the guanidino group anchoring the compound in pocket P2 (AsnA30) and pocket P3 (ThrA97).
During the MD simulation time, these interactions are not retained, and the terminal nitrogens of the
guanidino group do not create stable interactions with the TCR. Instead the only interactions are those
with residues of P2 pocket of TCR (AsnB104 and GluB106). The same pattern is observed for compound
16, where the interactions with residues AspA92 and GlyA96 (P2 pocket of TCR) are not retained
during the MD simulations. Instead, analogue 16 is involved in hydrogen bonding interactions with
residues TyrA98 and AlaB103, both in the P3 pocket of the receptor (Table 3).
Table 3. Hydrogen bonds for all optimized analogues (14–19) as reported in the docking and MD
simulation experiments.
Compounds
TCR
Residues
1
4
15
16
17
18
19
Dock
MD
Dock
MD
Dock
MD
Dock
MD
Dock
MD
Dock
MD
a
AsnA30
AspA92
ThrA93
GlyA96
ThrA97
TyrA98
TyrA100
AsnB51
LysB55
SerB101
AlaB103
AsnB104
GluB106
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
a
presence of hydrogen bonds.
In contrast to the previous two analogues, compound 15 retains the hydrogen interactions reported
in the molecular docking experiments (Figure 3a, Table 3). The hydrogen bonds with residues AsnA30
and GlyA96 in the P2 pocket of the TCR are conserved, while the orientation of the molecule inside
the cavity allows for interaction with ThrA97 in the P3 pocket (Table 3). Furthermore, the anchoring
of the compound 15 inside the two pockets (P2 and P3, Figure 5a), in combination with the bent
conformation of the molecule, allows better positioning of the aromatic ring inside the P–1 pocket
(
Figure 5a). This may lead to increased interactions between the potential inhibitor and the receptor.
Similarly to compound 15, the three derivatives (17 19) present comparative interaction patterns
Table 3). The guanidino group of these analogues retains the interaction with AspA30 and GlyA96 in
–
(
the P2 pocket of the TCR observed for compound 15 (Table 3), while there are small changes in the
interaction patterns with the neighboring amino acids. Compound 17 further interacts with ThrA97
and SerB101 in the P2 pocket, while compound 18 further interacts with AsnA30 in the P3 pocket and
TyrA98 in the P2 pocket of the receptor. Finally, the very similar positioning of compounds 15 and 19
(
Figure 5d) points to the conservation of the interactions between the two designed analogues (Table 3).
The only difference is the hydrogen bond of compound 19 with ThrA93 instead of GlyA96 in the P2
pocket of the receptor.
2
.4. Chemistry
Initial studies on the synthesis of pyrrole-based TCR antagonists provided candidates 15 and 16
via a six-step synthetic procedure with a total yield of 14% and 21%, respectively (Route A, Scheme 1).

Int. J. Mol. Sci. 2017, 18, 1215
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N-alkylation of commercially available 3- or 2-methyl pyrrolecarboxylates, with benzyl bromide in the
presence of sodium hydride, afforded the 3-/2-substituted N-benzylpyrroles 15a 16a 37]. Subsequent
hydrolysis of the methyl ester, followed by standard procedure for DCC/DMAP amide coupling with
N-Boc-ethylenediamine, gave the corresponding pyrrole carboxamides 15c 16c. N-Boc-deprotection
with TFA followed by N-iodosuccinimide-mediated guanylation reaction [38] with di-Boc-thiourea
/
[
/
furnished the di-Boc-guanidino derivatives 15d/16d, which allowed final molecules 15/16, after
Boc cleavage.
Upon further investigations, a rapid and simple three-step protocol (Route B, Scheme 1) was
developed to expand the scope and utility of this synthetic methodology and readily prepare diverse
pyrrole analogues. Thus, the guanidine moiety 20
pyrrole-3-carboxylic acid to provide a common structural core 21, after amidation reaction. Subsequent
pyrrole-N-protection [37] with primary alkyl bromides 17a 19a, followed by removal of the Boc-groups,
produced target compounds 17–19 in a shorter sequence and an 11–27% overall yield.
[39] was first synthesized and then reacted with
–
Scheme 1. Synthesis of 2-/3-substituted pyrrole analogues 15
) BnBr, NaH, DMF; ( ) i. KOH 30%, MeOH–H O, reflux; ii. 6 M HCl; (
DCM; ( ) BocNHC (S) NHBoc, NIS, DIPEA, MeOH–DCM; then (
DMF; (f) DCC/HOBt, DIPEA, DMF–DCM; (d) TFA, DCM.
–
19. Reagents and conditions: Route A:
) DCC, DMAP, DCM; ( ) TFA,
) TFA, DCM. Route B: ( ) BnBr, NaH,
(a
b
c
d
2
e
d
a

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2
.5. Molecular Orbital Calculations
From the three analogues (14–16) reported in this study, compound 15 presents a high docking
score ( 18.13 kcal/mol) coupled with a preferred orientation inside the binding cavity of the TCR
−
(
Figure 5a). This, in combination with the compound’s favorable pharmacokinetic properties (TPSA
and logP, Table 2), inspired us to explore the analogue 15/TCR complex by employing molecular
orbital methods.
2
.5.1. Semi-Empirical Simulation Method
In order to better estimate the interaction energy of the system, a number of different approaches
were employed. The results (Table S2) show that the PM7 (parameterization method 7) [40] approach
best reproduces the density functional theory (DFT) calculations for the selected residues. All other
semi-empirical (SE) methods tested present considerable errors compared to PM7 despite the inclusion
of dispersion correction. Based on these observations, the PM7 method was used as the most
appropriate for further calculations on the entire receptor–ligand complex (Table S2). Two protocols
were utilized for our calculations. In the first one, the ligand along with the same residues used in
the DFT calculations was preferred. The interaction energy of the particular system was calculated
to
−
24.09 (kcal/mol). The larger value compared to the DFT calculations (
−
31.63/ 42.85 kcal/mol)
−
may be attributed to the level of accuracy for the SE methodologies and the treatment of the electron
density of the various atoms in the system.
The second approach employed in our calculations involved the ligand with the whole receptor.
In order to explore the effect of the different TCR residues, amino acids within a cutoff distance of
4.5 Å from the ligand were initially elected. Subsequent rounds of interaction energy calculations
followed, by augmenting the selected area per 4.5 Å each time until the entire receptor was included in
our calculations (Figure 6a). The interaction energy calculated for the TCR in complex with compound
1
5
is
−
34.39 kcal/mol. In order to further study the interaction energy of compound 15, different
snapshots were taken from the MD simulation run (the last 20 ns of the simulation). For each snapshot,
the interaction energy was calculated with the PM7 method to monitor the fluctuations in the energy
(Figure 6b and Table S3). The mean value over the 20 snapshots for the interaction energy was
−
47.26 kcal/mol. The low interaction energy calculated from both the best docking pose and the
different MD snapshots (Tables S2 and S3) suggests that derivative 15 interacts strongly with the TCR
and thus may be competitive with native ligands. The interaction energy calculated for compounds
1
−
7
–
19 with the SE methodology are reported in Table S5. The values range between
−
35.39 and
37.20 kcal/mol, higher than the value reported for analogue 15 (−47.26 kcal/mol).
2
.5.2. DFT Calculations
The large size of the TCR (341 amino acids) hinders the use of DFT methodologies on the
entire complex [41 42]. Thus, to calculate the interaction energy for the complex, the best docking
,
pose was selected. The selection of the receptor residues (total of nine amino acids) was based
on the interactions formed with compound 15 and their distance from it (<3.5 Å). As reported in
the Materials and Methods section, Section 3.6, different methodologies were employed, and the
results are outlined in Table S2. The analysis of the calculations showed variability depending on
the method and the basis set selected. In fact, the methods that include dispersion either explicitly
or implicitly (e.g., M06, M06-2X, B97D, BHandH, and B3LYP-D) calculate more negative interaction
energies (Table S4) [43,44]. In contrast, the choice of basis set does not have such an extensive impact
in the calculation of the interaction energy. Therefore, in order to obtain a more accurate result, the
inclusion of dispersion functions was considered in our calculations [45]. Based on this, the interaction
energy between compound 15 and the selected residues of the TCR was calculated between
and 42.85 kcal/mol (Table S4). Compared to the SE methodologies, DFT techniques allow for a more
accurate prediction of interaction energy between the ligand and the residues that are directly involved
−
31.63
−

Int. J. Mol. Sci. 2017, 18, 1215
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in the binding to TCR. The application of DFT incorporates the effect of all atoms, without any of the
approximations (empirical data) applied during the SE calculations.
Figure 6.
(
a) Interaction energies for derivative 15 and the residues of the binding cavity, extended by
4
.5 Å until the entire TCR is included, calculated by the PM7 method (red) in solvent and (
b
) graphical
representation of the interaction energy calculated via the PM7 method for the different MD snapshots.
2
.6. Biological Assays
2
.6.1. Human Peripheral Mononuclear Cells
Blood samples were drawn from two healthy subjects for biological assays and contained: 1st
3
person: 2.84
×
10 lymphocytes/
µ
L of blood (42.9% of total leukocytes) and 410 monocytes/
µ
L
3
of blood (6.2% of total leukocytes); 2nd person: 1.83
total leukocytes) and 330 monocytes/
×
10 lymphocytes/
µL of blood (34.6% of
µ
L of blood (6.2% of total leukocytes). The peripheral blood
mononuclear cells (PBMCs) isolated from the blood samples were cultured in the presence of various
concentrations of the MBP83–96 peptide to estimate the optimal concentration for inducing T-cell
proliferation. It is noteworthy that the specific culture conditions used in this work, i.e., allo-peptidic
antigens and anti-CD28 antibody, target T-cell responses [46]. T-cell proliferation was measured by flow
cytometry. The highest T-cell proliferation was noted at 0.1 nM MBP83–96 (Figure 7a). PBMC cultures
were then repeated with 0.1 nM MBP83–96 and 0.1 mM of each of the fifteen analogues (Figure 7, Table 1:
compounds
was the most effective TCR antagonist, i.e., it conferred the highest inhibition of T cell proliferation
Figure 7b).
1–13, 15, and 16) per experimental point, in triplicate. The results show that analogue 15
(

Int. J. Mol. Sci. 2017, 18, 1215
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Figure 7.
were cultured with several concentrations of the MBP83–96 peptide for 3 days; cell proliferation was
measured by flow cytometry. Data are shown as median of triplicate measurements. ( ) Proliferation of
(a) T cell proliferation in the presence of MBP83–96. Peripheral blood mononuclear cells (PBMC)
b
PBMC in the presence of 0.1 nM MBP83–96 and 0.1 mM of each of the 15 analogues/point, in triplicate.
Control: MBP83–96 peptide alone. Data are shown as mean ± standard error of the mean.
2
.6.2. Mouse MBP83–99 Specific T Cell Assays
+
Autoimmune CD4 T cells can be stimulated in mice following immunization with MBP8
3–99
peptide together with Mycobacterium, which results in experimental autoimmune encephalomyelitis
EAE), an animal model for MS [47]. Characteristics of EAE are comparable to those of MS in humans
where Th1 phenotype (IFN- ) and Th17 cells contribute to pathogenesis of disease in mice. Similar to
(
γ
MS, EAE susceptibility is dependent on the mouse (MHC class II background) and diverse peptides
s
are immunogenic in different mouse strains. The SJL/J mouse strain (MHC class II H-2 haplotype)
is commonly used for EAE, since numerous histopathological, clinical, and immunological features
resemble those of human MS [48]. In the SJL/J mouse strain, the peptide MBP81–100 has been shown
s
to bind with high affinity to MHC class II, H-2 . In fact, the minimum epitope required for binding is
MBP83–99 [48], similar to human HLA-DR2 binding. Hence, the epitope MBP83–99 could be used as an

Int. J. Mol. Sci. 2017, 18, 1215
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+
agonist peptide to immunize mice to activate CD4 T cells, as we previously demonstrated [26,30–32].
Here, mice were immunized with MBP83–99 peptide conjugated to the carrier reduced mannan. Following
three immunizations, spleen cells were isolated and mixed with recall peptide MBP83–99 for six days
in vitro. In addition, compounds 15
to each well in order to determine whether T cell proliferation to the recall peptide MBP8
–
19 or AMB (lead compound 10) were added at 100
×
molar excess
could
3–99
be inhibited. The particular compounds (15
–19), due to their increased calculated binding affinity
(Table 1) to TCR, were employed in order to assess the potency in mouse MBP83–99 specific T cell
assays. Compound 15 and 16 showed the greatest % inhibition of MBP83–99-specific T cell proliferation,
followed by compound 17 and 18; compound 19 showed the least inhibition, and lead compound AMB
was able to inhibit proliferation comparable to that of the other compounds (Figure 8). Compounds 15
and 16 have simpler structures compared to 17
19 analogues, compared to 15 and 16, may be due to an inappropriate position of the acidic/esteric
group. Even though complete inhibition of T cell proliferation is not noted, compounds 15 and 16
–19 and AMB. It is likely that the reduced activity of
17–
,
based on in silico conformational studies, show promise for further optimization studies in order to
develop new improved TCR antagonists with improved activity.
Figure 8. Specific MBP83–99 epitope T cell proliferation using MTT as a readout. Mice were immunized
three times with reduced mannan conjugated to MBP83–99 peptide. Ten days following the last
immunization, mice were sacrificed and spleen cells isolated, and MBP83–99 peptide was added for
6
days. In addition, compounds 15
–19 and lead compound AMB (lead compound 10) were added
at 100 molar excess. The percent inhibition of T cell proliferation to MBP83–99 of each compound is
×
shown. The mean of three individual mice are shown in triplicate wells
±
standard error of the mean.
3
. Materials and Methods
3
.1. Structure Preparation
The X-ray crystallographic coordinates contained in PDB entry 1YMM were obtained from
the Protein Data Bank [22]. The particular PDB entry was selected because it contains the main
immunodominant epitope MBP83–96 involved in MS, as well as a human TCR from a patient with MS.
The Molecular Operating Environment (MOE2010) software [49] was utilized for the preparation of the
complex. The peptide–TCR complex was isolated, and the residues were protonated accordingly with
all hydrogen positions optimized using the AMBER94 force field [50]. All the possible protonation
states for the histidine (His) residues were explored and evaluated with the use of the PROpKa [51,52]
and AMBER94 tools in MOE2010. The analysis supports the prevalence of neutral His in all cases,
in agreement with the results reported by Wucherpfenning et al. [53,54].

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3
.2. Pharmacophore Modeling
The pharmacophore model was designed based on the MBP83–96 key residues [55] involved
in the binding with the HLA receptor and the TCR. A combination of features from structure- and
ligand-based pharmacophore models was utilized in the development of the model presented in this
study. According to the crystal structure of the binding cavity of the TCR, an analysis of its chemical
features was carried out using the MOE2010 software [49]. The development of the ligand-based
pharmacophore model relied on features such as aromaticity (Aro), a hydrogen bond cation (Cat) and
8
8
89
donor, and hydrophobic groups (Hyd). The Aro motifs were modeled on the His and Phe residues
86
85
of the epitope, the Hyd feature on Val , and the Cat feature on Pro , respectively, all residues that
interact with the TCR. The volume exclusion (V) features of the pharmacophore model were developed
based on Val87 and Phe that interact with the HLA.
90
Virtual Screening
The pharmacophore hypothesis based on the TCR active site as well as the MBP83–96 epitope
were utilized to scan 500,000 compounds from the ZINC database [56]. The compounds were filtered
according to Lipinski’s rule of five [57] and their commercial availability. Finally, the molecules were
sorted based on their fitness to the selected hypothesis.
3
.3. Molecular Docking
Molecular docking simulations were performed on the TCR using MOE2010 [49]. The ligand,
as well as the TCR residues in a radius of 4.5 Å surrounding the ligand, was considered flexible.
The definition of the TCR binding site was performed manually by selecting the area including the
residues involved in the main binding pockets. The ligands were allowed to move freely in the
vicinity of the active site. The top 500 poses for each ligand were ranked using the London
∆G
scoring function [49]. Subsequently, a maximum of 10 poses were retained based on their docking
scoring function, and the poses were rescored using the GBVI/WSA (Generalized-Born Volume
Integral/Weighted Surface Area) scoring function [58].
3
.4. Lead Optimization
Thirteen potential inhibitors (hits) were directly purchased for additional in vitro biological
evaluation, as TCR antagonists. Based on their properties and binding scores with the TCR, compound
was selected as a lead compound for further optimization. Chemical groups were modified to
1
0
improve the binding properties, such as orientation of the molecule inside the TCR. Additionally, new
chemical groups were added to lengthen the carbon chain and optimize the pocket fit.
3
.5. Molecular Dynamics (MD) Simulation
The construction of the TCR parameters was performed using the AMBER force field ff14SB [59],
while the parameters for the organic molecules were constructed using the general Amber force field
(
GAFF) [60]. The TIP3P water model [61] was utilized for the solvation of the system, and the total
−
charge was neutralized by the addition of Cl ions. Truncated octahedral periodic boundary conditions
were applied to the system with a cutoff distance of 10 Å. The next step involved minimization,
followed by the heating of the system, under a constant volume, to 300 K for 100 ps using the Langevin
dynamics temperature scaling [62]. This was followed by equilibration for another 100 ps under
− −2
1
constant pressure. Both heating and pressure equilibration were carried out using a 10 kcal
restraint on the solute. The equilibration step under constant pressure was prolonged for a further
00 ps, after removing all restraints. The MD production run was performed under constant pressure
and temperature conditions (NPT ensemble) for 50 ns. The temperature was kept constant with the
·
mol
·
Å
2
−
1
use of the Langevin thermostat (using a collision frequency of 2 ps ). All bonds involving hydrogen
atoms were kept to their equilibrium distance with the SHAKE algorithm (allowing for a 2 fs time step

Int. J. Mol. Sci. 2017, 18, 1215
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to be used) [63]. The long range electrostatic interactions were calculated with the Particle Mesh Ewald
PME) method [64]. The different systems were subjected to all-atom unrestrained MD simulations in
(
explicit solvent using AMBER12 [65]. The cpptraj module [66] of AMBER12 was implemented for the
trajectory analysis (clustering, RMSD, hydrogen bonds).
3
.6. Chemistry
Reactions involving moisture sensitive reagents were carried out under an argon atmosphere
in addition to oven-drying glassware and anhydrous solvents. Room temperature (rt) refers to
◦
2
0–25 C. Crude products were purified by flash chromatography on 230–400 mesh silica gel in the
solvents system stated. Analytical thin layer chromatography was performed on pre-coated aluminium
plates (Merck 60G F254 silica). TLC visualization was performed out with ultraviolet light (254 nm).
1
13
1
13
The yields were calculated in w/w. H and C nuclear magnetic resonance ( H NMR, C NMR)
spectra (Figures S2–S14) were acquired on Bruker Avance 400 and Bruker Ascend 600 spectrometer at
ambient temperature in the deuterated solvent stated. All chemical shifts are quoted in parts per million
(
ppm) relative to the internal standard (TMS). All coupling constants, J, are quoted in Hz. Multiplicities
are indicated by s (singlet), d (doublet), t (triplet), q (quartet), and m (multiplet). The abbreviation Ar
is used to denote aromatic, br to denote broad, and app to denote apparent. Mass spectrometry (m/z)
data were acquired on an Electrospray Ionization Platform spectrometer (ESI-MS) by Micromass and
a MassLynx NT 2.3 operating system (Waters Corporation, Milford, MA, USA).
3
.6.1. General Procedure A: N-Alkylation of Pyrroles
To a solution of 1H-pyrrole analogue (1.00 equiv) in DMF (5–10 mL/mmol), under argon at 0 C
◦
was added sodium hydride 60% (1.50–2.50 equiv), and the resulting mixture was stirred at the same
temperature for 10–20 min. Then, a solution of the corresponding alkyl bromide (1.00–1.50 equiv)
in DMF (5–10 mL/mmol) was added dropwise, and the reaction mixture warmed to rt over ~2 h
(
monitored by TLC). It was quenched with water (20 mL) and extracted with EtOAc (3
×
20 mL).
The combined organics were washed with brine (20 mL), dried (Na SO ), filtered, and concentrated
2
4
in vacuo. Purification of the residue by column chromatography on silica gel (using the appropriate
mixture of eluents) allowed pyrroles N-protected 15a, 16a, and 17b–19b.
3
.6.2. General Procedure B: Hydrolysis of Methyl Pyrrole-2/3-Carboxylates
To a solution of methyl N-benzyl pyrrole 3- or 2-carboxylate 15a or 16a (1.00 equiv) in MeOH–H O
2
3
:1 v:v (15.0 mL/mmol), an aq solution of KOH 30% (15.0 mL/mmol) was added. The resulting reaction
mixture was refluxed and monitored by TLC (10% MeOH–DCM) until completion (~2 h). Then, it was
allowed to attain rt and acidified pH = l via the addition of 6.0 M HCl (until cloudiness persisted).
The white precipitate was filtered off and washed with ice-water to give the crude of 15b or 16b
,
respectively, which was used in the next step without further purification.
3
.6.3. General Procedure C: Amidation Reaction
To a solution of the required pyrrole 3- or 2-carboxylic acid, 15a or 16a (1.00 equiv)
in dichloromethane (DCM) (20.0 mL/mmol), 4-dimethylaminopyridine (DMAP) (20 mol %),
0
N-Boc-ethylenediamine (1.00 equiv), and then N,N -dicyclohexylcarbodiimide (DCC) (1.50 equiv)
◦
at 0 C were added. The resulting mixture was warmed to rt and stirred for a further 16 h (monitored
by TLC, 10% MeOH–DCM). After completion of the reaction, dicyclohexylurea (DCU) formed was
◦
filtered off and washed with DCM (5 mL) at 0 C. The organic layer was quenched with 0.1 M
HCl (20 mL) and extracted with EtOAc (3
×
20 mL). The combined organics were washed with
brine (20 mL), dried (Na SO ), filtered, and concentrated in vacuo. Purification of the residue by
2
4
column chromatography on silica gel (using the appropriate mixture of eluents) allowed pyrrole
-/2-carboxamides 15c or 16c.
3

Int. J. Mol. Sci. 2017, 18, 1215
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3
9
.6.4. General Procedure D: Removal of the Boc-Group
The corresponding N-Boc analogue (1.00 equiv) was dissolved in trifluoroacetic acid (TFA)–DCM
5:5 v/v DCM (20–30 mL/mmol) (and added triethylsilane (TES, 1.00 equiv) if required). The reaction
mixture was stirred at rt, and the progress was monitored by TLC (10% MeOH–DCM) until complete
consumption of the starting material.
3
.6.5. General Procedure E: Guanylation Reaction
The amine salt 15c’ or 16c’ (as crude derived from N-Boc deprotection of 15c or 16c
)
was dissolved in a mixture of MeOH–DCM 4:1 v:v (20.0 mL/mmol), under argon. Then,
0
N,N -di-(tert-butoxycarbonyl)thiourea (1.50 equiv), N,N-diisopropylethylamine (DIPEA) (4.00 equiv)
and N-iodosuccinimide (1.50 equiv) in one portion were added at rt. The reaction mixture was stirred
at rt under argon, and monitored by thin layer chromatography (TLC) (20% MeOH–DCM) until
completion (~24 h). It was next quenched with an aq solution of 1 M sodium thiosulfate solution
(
(
20 mL), and the resulting solution was then diluted in water (20 mL) and extracted with EtOAc
3 × 20 mL). The combined organic layer was washed with brine (20 mL), dried (Na SO ), filtered, and
2
4
concentrated in vacuo. Purification of the residue by column chromatography on silica gel (using the
appropriate mixture of eluents) allowed the corresponding di-Boc-guanidino derivatives 15d or 16d.
3
.6.6. Synthesis of Methyl 1-Benzyl-1H-Pyrrole-3-Carboxylate 15a [67]
From methyl 1H-pyrrole-3-carboxylate (98.4 mg, 0.79 mmol) and NaH 60% (38.0 mg, 1.58 mmol)
in dimethylformamide (DMF) (4.0 mL), and a solution of benzyl bromide (0.14 mL, 1.18 mmol) in
DMF (6.0 mL), following the general procedure A (2 h) and after chromatographic purification (DCM),
1
1
5a (144 mg, 85%) was obtained as a clear gum. Data for 15a: H NMR (400 MHz, CDCl ) δ 7.31–7.37
3
(
m, 4H, Ph, Ar), 7.13–7.15 (m, 2H, Ph), 6.60–6.63 (m, 2H, Ar), 5.06 (s, 2H, CH Ph), 3.79 (s, 3H, OMe);
2
+
ESI-MS m/z found for C H NO : 216.32 [M + H] ; RP-HPLC gradient separation from 30% to 100%
13
13
2
acetonitrile at 30 min, flow rate: 1 mL/min, t = 10.8 min.
R
3
.6.7. Synthesis of 1-Benzyl-1H-Pyrrole-3-Carboxylic Acid 15b [68]
From methyl 1-benzyl-1H-pyrrole-3-carboxylate 15a (144 mg, 0.67 mmol) in MeOH–H O (10.0
2
mL) and an aq solution of KOH 30% (10.0 mL), following the general procedure B (2 h) and after
precipitation, the crude of 15b (90.0 mg, 67%) was used in the next step without further purification.
1
Data for 15b: proton nuclear magnetic resonance ( H NMR) (400 MHz, CDCl )
δ 7.29–7.40 (m, 4H, Ph,
3
Ar), 7.15 (d, 2H, J = 7.2 Hz, Ph), 6.63-6.66 (m, 2H, Ar), 5.07 (s, 2H, CH Ph); electrospray ionization
2
+
+
mass spectrometry (ESI-MS) m/z found for C H NO : 425.41 [2M + Na] , 202.25 [M + H] ; RP-HPLC
12
11
2
gradient separation from 5% to 100% acetonitrile at 30 min, flow rate: 1 mL/min, t = 14.2 min.
R
3
.6.8. Synthesis of 1-Benzyl-1H-N-[2-(Tert-Butoxycarbonyl)Aminoethyl]Pyrrole-3-Carboxamide 15c
From 1-benzyl-1H-pyrrole-3-carboxylic acid 15b (90.0 mg, 0.45 mmol) in DCM (9.0 mL), DMAP
(
10.9 mg, 0.09 mmol), N-Boc-ethylenediamine (0.07 mL, 0.45 mmol), and then DCC (138 mg, 0.67 mmol),
following the general procedure C (16 h) and after chromatographic purification (20% MeOH–DCM),
1
1
5c (126 mg, 82%) was obtained as a clear gum. Data for 15c
:
H NMR (400 MHz, CDCl3) δ
7
6
3
2
.29–7.35 (m, 3H, Ph), 7.25–7.26 (m, 1H, Ar), 7.12–7.14 (m, 2H, Ph), 6.62 (app t, 1H, J = 2.4 Hz, Ar),
.48 (br s, 1H, NH)
.32–3.35 (m, 2H, CH2), 1.41 (s, 9H, 3
,
6.41 (br s, 1H, Ar)
,
5.04 (s, 2H, CH Ph), 4.98 (br s, 1H, NH), 3.51–3.45 (m, 2H, CH ),
CH t-Bu); ESI-MS m/z found for C H N O : 344.33 [M + H] ,
3 19 25 3 3
2
2
+
×
+
88.32 [(M–Ph) + Na] ; reversed phase high-performance liquid chromatography (RP-HPLC) gradient
separation from 5% to 100% acetonitrile at 30 min, flow rate: 1 mL/min, t = 17.0 min.
R

Int. J. Mol. Sci. 2017, 18, 1215
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3
.6.9. Synthesis of 1-Benzyl-1H-N-[2-(2,3-Di-Tert-Butoxycarbonyl)Guanidinoethyl]Pyrrole-3-
Carboxamide 15d
From N-Boc analogue 15c (120 mg, 0.35 mmol) in TFA–DCM 95:5 (7.0 mL), following the
general procedure D (1 h), the crude of 2-(1-benzyl-1H-pyrrole-3-carboxamido)ethanaminium
0
2
,2,2-trifluoroacetate 15c’ was dissolved in MeOH–DCM (7.0 mL). Then, N,N -di-(tert-butoxycarbonyl)
thiourea (145 mg, 0.52 mmol), DIPEA (0.24 mL, 1.40 mmol, 4.00 equiv), and N-iodosuccinimide
(
(
118 mg, 0.52 mmol), following the general procedure E (24 h) and after chromatographic purification
20% MeOH–DCM), 15d (56.6 mg, 33%) was obtained as a clear gum. Partial data for 15c’: ESI-MS m/z
+
found for C H N O: 244 [M] ; RP-HPLC gradient separation from 5% to 100% acetonitrile at 30 min,
flow rate: 1 mL/min, t = 10.3 min. Data for 15d: H NMR (400 MHz, CDCl ) δ 11.50 (s, 1H, NH)
1
4
17
3
1
,
R
3
8
5
.71 (s, 1H, NH), 7.28–7.41 (m, 5H, Ph, Ar, NH), 7.11–7.13 (m, 2H, Ph), 6.56–6.59 (m, 2H, Ar),
.04 (s, 2H, CH Ph), 3.67–3.71 (m, 2H, CH ), 3.53–3.57 (m, 2H, CH ), 1.51 (s, 9H, 3 × CH t-Bu)
,
2
2
2
3
+
1
.49 (s, 9H, 3× CH t-Bu); ESI-MS m/z found for C H N O : 486.34 [M + H] ; RP-HPLC gradient
3
25 35
5
5
separation from 30% to 100% acetonitrile at 30 min, flow rate: 1 mL/min, t = 13.4 min.
R
3
.6.10. Synthesis of 1-Benzyl-1H-N-(2-Guanidinoethyl)Pyrrole-3-Carboxamide 15
From di-Boc guanidine analogue 15d (50.0 mg, 0.10 mmol) in TFA–DCM 95:5 (3.0 mL), following
the general procedure D (1 h) and after chromatographic purification (0.5% NH OH, 19.5% MeOH, 80%
4
1
DCM), final product 15 (26.5 mg, 91%) was obtained as a white solid. Data for 15: H NMR (400 MHz,
CD OD) δ 7.27–7.36 (m, 4H, Ph, Ar), 7.20–7.22 (m, 2H, Ph), 6.78 (dd, 1H, J = 2.8, 2.4 Hz, Ar), 6.52 (dd, 1H,
3
13
J = 2.8, 2.0 Hz, Ar), 5.13 (s, 2H, CH Ph), 3.46 (t, 2H, J = 6.3 Hz, CH ), 3.35 (t, 2H, J = 6.3 Hz, CH2)
;
C
2
2
NMR (100 MHz, CD OD)
δ
168.6 (C=O), 159.0 (C=NH), 139.0 (C Ph), 129.8 (2× CH Ph)
, 129.0 (CH Ph),
3
1
3
28.5 (2
×
CH Ph), 125.4 (CH Ar), 123.6 (CH Ar), 120.1 (C Ar), 109.0 (CH Ar), 54.5 (CH Ph), 42.4 (CH ),
2
2
+
+
9.4 (CH ); ESI-MS m/z found for C H N O: 286.66 [M + H] , 243.21 [M–(C(NH)NH ) + H] , 214.16
2
15 19
5
2
+
[
M–(CH NHC(NH)NH ) + H] ; RP-HPLC gradient separation from 10% to 100% acetonitrile at 30 min,
2 2
flow rate: 1 mL/min, t = 19.3 min, R = 0.46 (MeOH–DCM 2:8).
R
f
3
.6.11. Synthesis of Methyl 1-Benzyl-1H-Pyrrole-2-Carboxylate 16a [67]
From methyl 1H-pyrrole-2-carboxylate (151 mg, 1.21 mmol) and NaH 60% (58.1 mg, 2.42 mmol)
in DMF (6.0 mL), and a solution of benzyl bromide (0.21 mL, 1.80 mmol) in DMF (9.0 mL), following
the general procedure A (3 h) and after chromatographic purification (DCM), 16a (234 mg, 90%)
1
was obtained as a pale yellow oil. Data for 16a
:
H NMR (400 MHz, CDCl3) δ 7.23–7.34 (m,
3
6
H, Ph), 7.11 (d, 2H, J = 7.2 Hz, Ph), 7.02 (dd, 1H, J = 3.4, 1.6 Hz, Ar), 6.89 (app t, 1 H, J = 1.6 Hz, Ar),
.19 (app t, 1H, J = 3.4 Hz, Ar)
,
5.57 (s, 2H, CH Ph)
,
3.77 (s, 3H, OMe); ESI-MS m/z found for
2
+
+
C H NO : 216 [M + H] , 138 [(M–Ph) + H] ; RP-HPLC gradient separation from 30 to 100%
13
13
2
acetonitrile at 30 min, flow rate: 1 mL/min, t = 13.9 min.
R
3
.6.12. Synthesis of 1-Benzyl-1H-Pyrrol-2-Carboxylic Acid 16b
From methyl 1-benzyl-1H-pyrrole-2-carboxylate 16a (230 mg, 1.07 mmol) in MeOH–H O (16.0 mL)
2
and an aq solution of KOH 30% (16.0 mL), following the general procedure B (3 h) and after
precipitation, the crude of 16b (172 mg, 80%) was used in the next step without further purification.
1
Data for 16b: H NMR (400 MHz, CDCl )
δ
7.24–7.33 (m, 3 H, Ph), 7.14 (dd, 1H, J = 3.8, 2.0 Hz, Ar),
3
7
.11 (d, 2H, J = 6.8 Hz, Ph), 6.93 (app t, 1H, J = 2.0 Hz, Ar), 6.21 (dd, 1H, J = 3.8, 2.8 Hz, Ar), 5.56 (s, 2H,
+
+
+
CH Ph); ESI-MS (EI) m/z found for C H NO : 240 [M + K] , 224 [M + Na] , 202 [M + H] ; RP-HPLC
2
12 11
2
gradient separation from 5% to 100% acetonitrile at 30 min, flow rate: 1 mL/min, t = 16.2 min.
R
3.6.13. Synthesis of 1-Benzyl-1H-N-[2-(Tert-Butoxycarbonyl)Aminoethyl]Pyrrole-2-Carboxamide 16c
From 1-benzyl-1H-pyrrole-2-carboxylic acid 16b (172 mg, 0.86 mmol) in DCM (17 mL), DMAP
(
21.0 mg, 0.17 mmol), N-Boc-ethylenediamine (0.13 mL, 0.86 mmol), and then DCC (266 mg, 1.29 mmol),

Int. J. Mol. Sci. 2017, 18, 1215
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following the general procedure C (16 h) and after chromatographic purification (20% MeOH-CH Cl ),
2
2
1
1
6c (248 mg, 84%) was obtained as a clear gum. Data for 16c: H NMR (400 MHz, CDCl ) δ 7.20–7.30
3
(
(
m, 5H, Ph, Ar, NH), 7.11 (d, 2H, J = 7.2 Hz, Ph), 6.79 (br s, 1H, Ar), 6.64 (br d, 1H, J = 2.0 Hz, NH), 6.13
app t, 1H, J = 3.2 Hz, Ar), 5.60 (s, 2H, CH Ph), 3.42 (t, 2H, J = 5.6 Hz, CH ), 3.29 (app t, 2H, J = 5.6 Hz,
2
2
+
+
CH ), 1.43 (s, 9H, 3
[
×
CH t-Bu); ESI MS m/z found for C H N O : 367 [M + Na] , 344 [M + H] , 288
3 19 25 3 3
2
+
+
(M–Ph) + Na] , 244 [(M–Boc) + H] ; RP-HPLC gradient separation from 30% to 100% acetonitrile at
3
0 min, flow rate: 1 mL/min, t = 13.3 min.
R
3
.6.14. Synthesis of 1-Benzyl-1H-N-[2-(2,3-Di-Tert-Butoxycarbonyl)Guanidinoethyl]Pyrrole-2-
Carboxamide 16d
From N-Boc analogue 16c (248 mg, 0.72 mmol) in TFA–DCM 95:5 (14.4 mL), following the
general procedure D (1 h), the crude of 2-(1-benzyl-1H-pyrrole-2-carboxamido)ethanaminium
0
2
,2,2-trifluoroacetate 16c’ was dissolved in MeOH–DCM (14.4 mL). Then, from N,N -di-(tert-
butoxycarbonyl)thiourea (299 mg, 1.08 mmol), DIPEA (0.50 mL, 2.88 mmol), and N-iodosuccinimide
(
(
243 mg, 1.08 mmol), following the general procedure E (~24 h) and after chromatographic purification
20% MeOH-CH Cl ), 16d (136 mg, 39%) was obtained as a clear gum. Partial data for 16c’: ESI-MS
2
2
+
+
+
+
m/z found for C H N O: 267 [M + Na] , 244 [M] , 227 [M–NH ] , 184 [M–HN(CH )NH ] , 158
[
1
4
17
3
2
2
2
+
M–(CO)HN(CH )NH ] ; RP-HPLC gradient separation from 5% to 100% acetonitrile at 30 min, flow
2
2
1
rate: 1 mL/min, t = 11.1 min. Data for 16d: H NMR (400 MHz, CD OD)
δ
7.28–7.18 (m, 3H, Ph), 7.07
d, 2H, J = 6.8 Hz, Ph), 6.92 (dd, 1 H, J = 2.4, 1.6 Hz, Ar), 6.76 (dd, 1H, J = 3.6, 1.6 Hz, Ar), 6.12 (dd, 1H,
J = 3.6, 2.4 Hz, Ar), 5.57 (s, 2H, CH Ph), 3.29–3.32 (m, 2H, CH ), 3.16 (t, 2H, J = 6.2 Hz, CH ), 1.52 (s, 9 H,
R
3
(
2
2
2
+
3
×
CH t-Bu), 1.42 (s, 9H, 3
×
CH t-Bu); ESI-MS m/z found for C H N O : 486 [M + H] ; RP-HPLC
3 25 35 5 5
3
gradient separation from 5% to 100% acetonitrile at 30 min, flow rate: 1 mL/min ,t = 21.9 min.
R
3
.6.15. Synthesis of 1-Benzyl-1H-N-(2-Guanidinoethyl)-Pyrrole-2-Carboxamide 16
From di-Boc guanidine analogue 16d (114 mg, 0.23 mmol) in TFA–DCM 95:5 (7.0 mL), following
the general procedure E (1 h) and after chromatographic purification (0.5% NH OH, 19.5% MeOH, 80%
4
1
DCM), final product 16 (58.6 mg, 88%) was obtained as a white solid. Data for 16: H NMR (600 MHz,
CD OD)
δ
7.19–7.27 (m, 3 H, Ph), 7.07 (d, 2H, J = 7.8 Hz, Ph), 6.97–6.98 (m, 1H, Ar), 6.79–6.80 (m, 1H,
3
Ar), 6.14–6.15 (m, 1 H, Ar), 5.59 (s, 2H, CH Ph), 3.40 (t, 2 H, J = 6.3 Hz, CH ), 3.26 (t, 2H, J = 6.3 Hz,
2
2
CH2); 13C NMR (100 MHz, CD OD)
δ
165.2 (C=O), 159.9 (C=NH), 140.6 (C Ph), 129.5 (2
×
CH Ph), 129.3
3
(
4
CH Ph), 128.3 (CH Ar), 127.9 (2
×
CH Ph), 126.0 (C Ar), 114.9 (CH Ar), 109.0 (CH Ar), 52.7 (CH Ph),
2
+
2.4 (CH ), 39.3 (CH ); ESI-MS m/z found for C H N O: 286 [M + H] ; RP-HPLC gradient separation
2
2
15 19
5
from 5% to 100% acetonitrile at 30 min, flow rate: 1 mL/mi, t = 20.2 min, R = 0.46 (MeOH–DCM 2:8).
R
f
3
.6.16. Synthesis of N-[2-(2,3-Di-Tert-Butoxycarbonyl)Guanidinoethyl]-1-(m-(1-Trityl-Tetrazol-5-
yl)Benzyl)-1H-Pyrrole-3-Carboxamide 17b [24]
From 1H-pyrrole 21 (65.1 mg, 0.26 mmol), NaH 60% (15.4 mg, 0.39 mmol) in DMF (2.6 mL), and
a solution of 17a [69] (124 mg, 0.26 mmol) in DMF (2.6 mL), following the general procedure A (2 h)
and after chromatographic purification (10–100% AcOEt-Et O), 17b (81.8 mg, 40%) was obtained as
2
1
a white solid. Data for 17b: H NMR (600 MHz, CDCl )
δ 11.48 (s, 1H, NH), 8.06 (d, 1H, J = 7.8 Hz,
3
Ar’), 7.99 (s, 1H, Ar’), 7.32–7.42 (m, 13H, Ar, Ar’, Trt, NH), 7.13–7.16 (m, 8H, Ar, Ar’, Trt), 6.60 (d, 1H,
J = 1.8 Hz, Ar), 5.09 (s, 2H, CH Ar), 3.76 (br s, 2H, CH ), 3.59 (br s, 2H, CH ), 1.51 (s, 9H, 3
×
CH t-Bu),
2
2
2
3
+
+
1.49 (s, 9H, 3
×
CH t-Bu); ESI-MS m/z found for C H N O : 796.30 [M + H] , 696.27 [(M–Boc) + H] ,
3
45 49
9
5
+
+
341.71 [(17a–Trt) + Boc + H] , 243 [Trt] ; RP-HPLC gradient separation from 60% to 100% acetonitrile
at 30 min, flow rate: 1 mL/min, t = 15.8 min.
R

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3
.6.17. Synthesis of 1-(2-(1-(m-(1H-Tetrazol-5-Yl)Benzyl)-1H-Pyrrole-3-Carboxamido)Ethyl)
Guanidinium 2,2,2-Trifluoroacetate 17
From 17b (20 mg, 0.025 mmol) and TES (0.004 mL, 0.025 mmol) in TFA–DCM 95:5 (0.75 mL),
following the general procedure D (5 h) and after purification by semi-preparative HPLC (10–60%
ACN, 45 min), then lyophilization, final product 17 (7.89 mg, 68%) was obtained as a white solidwith
1
9
7
1
9% purity. Data for 17: H NMR (600 MHz, CD OD)
δ
7.95 (d, 1H, J = 7.8 Hz, Ar’), 7.90 (s, 1H, Ar’),
3
.57 (t, 1H, J = 7.8 Hz, Ar’), 7.43 (d, 1H, J = 7.8 Hz, Ar’), 7.41 (app dd, 1H, J = 2.4, 1.8 Hz, Ar), 6.85 (dd,
H, J = 3.0, 2.4 Hz, Ar), 6.56 (dd, 1H, J = 3.0, 1.8 Hz, Ar), 5.26 (s, 2H, CH Ar’), 3.47 (t, 2H, J = 6.0 Hz,
CH ), 3.36 (t, 2H, J = 6.0 Hz, CH ); C NMR (100 MHz, CD OD)
40.8 (C Ar’), 131.5 (CH), 131.1 (CH), 127.7 (CH), 127.2 (CH), 126.3 (C Ar’), 125.4 (CH), 123.7 (CH),
20.5 (C Ar), 109.4 (CH Ar), 54.0 (CH Ar’), 42.4 (CH ), 39.5 (CH ); ESI-MS m/z found for C H N O:
54.53 [M + H] ; RP-HPLC gradient separation from 10% to 100% acetonitrile at 30 min, flow rate:
mL/min, t = 17.7 min.
2
13
δ
168.6 (C=O), 159.0 (2
×
C=NH),
2
2
3
1
1
3
1
2
2
2
16 19
9
+
R
3
.6.18. Synthesis of N-[2-(2,3-Di-Tert-Butoxycarbonyl)Guanidinoethyl]-1-(p-Tert-Butoxycarbonyl
Methyl)Benzyl-1H-Pyrrole-3-Carboxamide 18b
From 1H-pyrrole 21 (168 mg, 0.66 mmol), NaH 60% (39.8 mg, 1.66 mmol) in DMF (6.6 mL), and
a solution of 18a, (189 mg, 0.66 mmol) in DMF (6.6 mL), following the general procedure A (2 h) and
after chromatographic purification (50–100% AcOEt-Et O), 18b (87.6 mg, 22%) was obtained as a white
2
1
solid). Data for 18b: H NMR (400 MHz, CDCl )
δ 11.49 (s, 1H, NH), 8.78 (s, 1H, NH), 7.46 (s, 1H, NH),
3
7
.31 (s, 1H, Ar), 7.22 (d, 2H, J = 8.0 Hz, Ar’), 7.07 (d, 2H, J = 8.0 Hz, Ar’), 6.57 (d, 2H, J = 2.0 Hz, Ar), 5.02
(
s, 2H, CH Ar), 3.67–3.71 (m, 2 H, CH ), 3.56 (br s, 2H, CH ), 3.50 (s, 2H, CH CO t-Bu), 1.51 (s, 9H, 3×
2
2
2
2
2
CH t-Bu),1.49 (s, 9H, 3× CH t-Bu), 1.43 (s, 9H, 3× CH t-Bu).
3
3
3
3
.6.19. Synthesis of 1-(2-(1-(p-(Carboxymethyl)Benzyl)-1H-Pyrrole-3-Carboxamido)Ethyl)
Guanidinium 2,2,2-Trifluoroacetate 18
From 18b (50 mg, 0.083 mmol) and TES (0.01 mL, 0.083 mmol) in TFA–DCM 95:5 (2.50 mL),
following the general procedure D (5 h) and after purification by semi-preparative HPLC (10–60%
ACN, 45 min), then lyophilization, final product 18 (23.2 mg, 61%) was obtained as a white
1
solidwith 98% purity. Data for 18
1
6
:
H NMR (600 MHz, CD OD)
δ
7.33 (app dd, 1H, J = 2.4,
6.78 (dd, 1H, J = 3.0, 2.4 Hz, Ar)
.52 (dd, 1H, J = 3.0, 1.8 Hz, Ar), 5.11 (s, 2H, CH Ar’), 3.59 (s, 2H, CH CO H), 3.46 (t, 2H, J = 6.0 Hz,
3
.8 Hz, Ar), 7.27 (d, 2H, J = 8.1 Hz, Ar’)
,
7.17 (d, 2H, J = 8.1 Hz, Ar’)
,
,
2
2
2
+
CH ), 3.35 (t, 2H, J = 6.0 Hz, CH ); ESI-MS m/z found for C H N O : 344.66 [M + H] ; RP-HPLC
2
2
17 21
5
3
gradient separation from 10% to 100% acetonitrile at 30 min, flow rate: 1 mL/min, t = 16.6 min.
R
3
.6.20. Synthesis of N-[2-(2,3-Di-Tert-Butoxycarbonyl)Guanidinoethyl]-1-(P-Methoxycarbonyl)
Benzyl-1H-Pyrrole-3-Carboxamide 19b
From 1H-pyrrole 21 (109 mg, 0.43 mmol), NaH 60% (25.8 mg, 0.65 mmol) in DMF (4.3 mL), and
a solution of 19a, (98.5 mg, 0.43 mmol) in DMF (4.3 mL), following the general procedure A (2 h)
and after chromatographic purification (50–100% AcOEt-Et O), 19b (103 mg, 44%) was obtained as
2
1
a white solid. Data for 19b: H NMR (400 MHz, CDCl )
δ 11.49 (s, 1H, NH), 8.73 (s, 1H, NH), 7.98
3
(d, 2H, J = 8.2 Hz, Ar’), 7.49 (s, 1H, NH), 7.31 (t, 1H, J = 1.8 Hz, Ar), 7.15 (d, 2H, J = 8.2 Hz, Ar’),
6
2
3
.56–6.60 (m, 2 H, Ar), 5.10 (s, 2H, CH Ar), 3.90 (s, 3H, OCH ), 3.67–3.71 (m, 2H, CH ), 3.55–3.57 (m,
2
3
2
H, CH ), 1.50 (s, 9 H, 3
×
CH t-Bu), 1.49 (s, 9H, 3
×
CH t-Bu); ESI-MS m/z found for C H N O :
3 27 37 5 7
2
3
+
44.60 [(M–2
×
Boc) + H] ; RP-HPLC gradient separation from 5% to 100% acetonitrile at 30 min, flow
rate: 1 mL/min, t = 25.7 min.
R

Int. J. Mol. Sci. 2017, 18, 1215
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3
.6.21. Synthesis of 1-(2-(1-(p-(Methoxycarbonyl)Benzyl)-1H-Pyrrole-3-Carboxamido)Ethyl)
Guanidinium 2,2,2-Trifluoroacetate 19
From 19b (20 mg, 0.037 mmol) in TFA–DCM 95:5 (0.74 mL), following the general procedure D
(
5 h) and after purification by semi-preparative HPLC (10–60% ACN, 45 min), then lyophilization,
1
final product 19 (13.3 mg, 79%) was obtained as a white solid with 99% purity. Data for 19: H NMR
(
400 MHz, CD OD)
δ
7.98 (d, 2H, J = 8.0 Hz, Ar’), 7.36 (app t, 1H, J = 2.0 Hz, Ar), 7.28 (d, 2H, J = 8.0 Hz,
3
Ar’), 6.81–6.82 (m, 1H, Ar), 6.55 (dd, 1H, J = 2.8, 2.0 Hz, Ar), 5.23 (s, 2H, CH Ar’), 3.89 (s, 3H, OCH ),
2
3
3
.47 (t, 2H, J = 6.4 Hz, CH ), 3.35 (t, 2H, J = 6.4 Hz, CH ); ESI-MS m/z found for C H N O : 344.68
2 2 17 21 5 3
+
[
M + H] ; RP-HPLC gradient separation from 5% to 100% acetonitrile at 30 min, flow rate: 1 mL/min,
tR = 18.9 min.
3
.6.22. Synthesis of N-(2,3-Di-(Tert-Butyloxycarbonyl)Guanidinoethyl)Pyrrole-3-Carboxamide 21
To a solution of 1H-pyrrole-3-carboxylic acid (85.5 mg, 0.77 mmol, 1.00 equiv) in DCM (5 mL) and
◦
DMF (1 mL), at 0 C, HOBt (178 mg, 1.16 mmol, 1.50 equiv) and DCC (239 mg, 1.16 mmol, 1.50 equiv)
were added. The mixture was stirred at the same temperature for 10 min, and was then supplemented
with a solution of 20
.16 mmol, 1.50 equiv). The reaction mixture warmed to rt over 3 h and monitored by TLC (10%
MeOH in DCM). The solvents were removed in vacuo, and the remaining residue was purified by
[39] (350 mg, 1.16 mmol, 1.50 equiv) in DCM (13 mL) followed by DIPEA (0.20 mL,
1
column chromatography (5% EtOH in Et O) to yield 21 (170 mg, 0.67 mmol, 87%) as a beige solid.
2
1
Data for 21: H NMR (400 MHz, CDCl )
δ
11.51 (s, 1H, NH), 8.82 (s, 2H, NH), 7.57 (s, 1H, NH), 7.43–7.44
3
(
m, 1H, Ar), 6.73–6.75 (m, 1H, Ar), 6.63–6.65 (m, 1H, Ar), 3.71–3.75 (m, 2H, CH ), 3.57–3.60 (m, 2H,
2
CH ), 1.55 (s, 9 H, 3
×
CH t-Bu), 1.51 (s, 9 H, 3
×
CH t-Bu); ESI-MS m/z found for C H N O : 341
2
3
3
18 29
5
5
+
2+
[M−tBu + H] , 381 [M–tBu + K + H] ; RP-HPLC gradient separation from 5% to 100% acetonitrile at
3
0 min, flow rate: 1 mL/min, t = 21.4 min.
R
3
.7. Molecular Orbital Calculations
Two different approaches were applied in order to calculate the binding energy of the compounds
inside TCR, namely density functional theory (DFT) [70] and semi-empirical (SE) methods [71]. For the
application of DFT, several variants [72] differing in choice of functional [73] and basis set were
implemented in order to calculate the interaction. This procedure was followed to select the most
appropriate method for our complex (see Supporting Information). The self-consistent reaction
field (SCRF) was used with DFT energies, optimizations, and frequency calculations to model the
system in solution (H O). All DFT calculations were performed with Gaussian09 [74]. A similar
2
protocol was applied for the calculation of the interaction energy including the whole TCR with
SE methodologies. The MOPAC2012 [71] software was used for the SE calculations. Due to the
large size of the protein–ligand systems, the keyword MOZYME [75] was employed to accelerate the
calculations, and the COSMO [76] function was used to estimate the effect of the solvent. For the
methods including dispersion (D), the optimized parameters for H, N, C, and O, as reported by
McNamara and Hillier [72,73], were used. Semi-empirical calculations were performed on the whole
complex (ligand–TCR), while DFT on the ligand and selected TCR residues.
3
.8. In Vitro Evaluation of the Analogues Using Human PBMC
Peripheral blood samples (10 mL) were drawn from two healthy volunteers (one 24-year-old
male and one 35-year-old female) and were analyzed in a CELL-DYN Sapphire hematology analyzer
Abbot Diagnostics, Lake Forest, IL, USA) to determine the absolute numbers and percentages of
(
leukocytes, in particular lymphocytes and monocytes. Peripheral blood mononuclear cells (PBMCs)
were isolated by centrifugation over a Ficoll–Paque gradient (Biochrom AG, Berlin, Germany) and
washed
×
3 with ice-cold RPMI1640 culture medium (Gibco BRL, Waltham, MA, USA). The cells were
stained with CellTrace CFSE for flow cytometry (Invitrogen-Thermo Fisher Scientific Inc., Waltham,

Int. J. Mol. Sci. 2017, 18, 1215
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MA, USA) as described and cultured in RPMI1640 (with 10% Fetal Bovine Serum, 50 IU/mL penicillin,
−
5
100
µ
g/mL streptomycin, and 5
×
10 mol/L mercaptoethanol) (Invitrogen-Thermo Fisher Scientific
6
Inc., Waltham, MA, USA) at a concentration of 10 cells/mL. PBMCs were cultured for three days in
the presence of an anti–CD28 antibody (5 µg/mL) (BD Biosciences/Pharmingen, San Diego, CA, USA)
and different concentrations of peptide MBP83–96 (0.01 nM, 0.1 nM, 1 nM, 10 nM, and 100 nM) to
estimate the optimal concentration that induces T cell proliferation. When the optimal MBP83–96
concentration was determined, the cultures were repeated as previously with the addition of the
same concentration of each of the studied analogues per point, in triplicate. T cell proliferation was
monitored and quantified by flow cytometry. Flow cytometric acquisition and analysis were performed
on at least 10,000 acquired events per sample using the BD FACSCalibur™ platform.
3
.9. In Vitro Evaluation of the Analogues Using Mouse-Specific MBP83–99 T Cells
Mice, SJL/J females, aged 4–9 weeks were purchased from the Animal Resource Centre (Perth
Australia). All mice had free access to food and water, and were housed in a temperature-controlled
environment with 12-h day/night cycles at the animal holding room Werribee Campus Animal Facility
(Melbourne, Australia). They were allowed to acclimatize for at least 7 days before immunizations.
All experiments were completed according to the guidelines of the Australian Code of Practice for
the Care and Use of Animals for Scientific Purposes and were approved by Victoria University
Animal Experimentation Ethics Committee (AEC15/013). Mice were subcutaneously injected with
5
0
µ
g/100
described [34 77]. This conjugate has been shown to induce T cell proliferation to native peptide
MBP83–99 26,27,30–32,34,77]. Spleen cells from 3 immunized SJL/J mice were isolated 10 days after
µL reduced mannan conjugated to MBP83–99 via a 10 amino acid linker (KG) as previously
5
,
[
immunization and assessed by T cell proliferation assay. As we have previously shown that the
native peptide MBP83–99 conjugated to mannan induces strong proliferative T cells to recall MBP83–99
peptide, we used 3 mice/group to test each of the compounds’ ability to inhibit this T cell proliferation.
Hence, 3 mice/group in this screening process are adequate for determining the optimal compound
5
for inhibiting T cell proliferation. Spleen cells at 2
9
×
10 in 100
µ
L of culture media were seeded into
◦
6 well U-bottom plates and incubated for 1–6 days at 37 C in the presence of recall MBP
83 99
–
peptide
(10 nM) with or without 100x molar excess of compounds 15–19 or AMB. Proliferation was assessed
by the addition of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, a tetrazole)
for 6 hours and proliferation assessed via spectrophotometry (Biorad microplate reader, 6.0) using
a wavelength of 570 nm. All experiments were conducted in triplicate. The percentage of inhibition of
cell proliferation in the presence of compound was calculated and plotted.
4
. Conclusions
A ligand-based pharmacophore model was developed based on the conformational properties of
the dominant MBP83–96 epitope in complex with the TCR. The resulting model was employed for the
virtual screening of the ZINC database for potential hits. A subset of the database, containing 500,000 all
clean/ commercially available compounds, were screened, and the search yielded 13 hits. The potential
inhibitors were ranked according to their inhibitory activity against TCR with the employment of
molecular docking simulations. The compound with the highest docking score (compound 10) was
selected as lead and was subjected to optimization via chemical modifications. The resulting optimized
molecule (compound 14) presented increased docking score to the TCR and improved chemical
properties such as TPSA and logP (Table 2).
The conformational analysis and the positioning of compound 14 in the TCR binding pocket
led to the further modification with the addition of a methylene group and the organic synthesis
of two isomers (compounds 15 and 16). The analysis of the conformational properties of the three
analogues via MD simulation experiments showed that analogue 15 has the most optimal positioning
inside the TCR binding cavity and is better tethered within the receptor (Figure 5a). Extensive MD
simulations may offer a deeper understanding of the interactions between the designed analogues

Int. J. Mol. Sci. 2017, 18, 1215
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and the receptor, and prove to be a valuable tool in drug design. Furthermore, the interaction energy
between the potential inhibitor (compound 15) and the TCR was explored by employing a variety
of molecular orbital approaches. DFT and SE methodologies were used in order to calculate the
interaction energy between selected residues of the TCR, as well as the entire TCR, and the proposed
inhibitor 15. The combination of the two methodologies allows us to identify whether only certain
residues have the greatest impact in the binding of compound 15 or other conformational aspects of
the TCR are important in its binding. The agreement between the DFT and the SE methods show that
the binding of the potential inhibitor to the TCR is attributed only to the residues surrounding the
binding cavity and not to other conformational changes observed in the TCR. The results of the in vitro
evaluation (Figure 8) suggest that both analogues 15 and 16 may serve as good candidate antagonists
to be developed further for the inhibition of proliferation of T-cells that recognize the MBP83–96 antigen.
Supplementary Materials: Supplementary materials can be found at www.mdpi.com/1422-0067/18/6/1215/s1.
Acknowledgments: We wish to thank the pharmaceutical company Vianex in Greece for supporting the study
against Multiple Sclerosis (MS). This work was financially supported by the “Cooperation” program 09SYN-609-21,
(
Competitiveness & Entrepreneurship (EPAN II), ROP Macedonia—Thrace, ROP Crete and Aegean Islands, ROP
Thessaly–Mainland Greece–Epirus, ROP Attica). We also thank FICYT and the European Union (7th WP) for
a Marie Curie CLARIN-cofund fellowship to Carmen Simal. The authors, also acknowledge the assistance of
Minos Timotheos Matsoukas for the selection of compounds from the ZINC database. Vasso Apostolopoulos
was supported by Vianex Greece (Specific task agreement MS immunotherapeutics) and the Centre for Chronic
Disease, Victoria University, VIC, Australia.
Author Contributions: Theodore Tselios conceived and designed the experiments; Mary-Patricia Yannakakis,
Carmen Simal, Haralambos Tzoupis, Maria Rodi, Narges Dargahi, Monica Prakash, Athanasia Mouzaki,
James Platts, and Vasso Apostolopoulos performed the experiments; All authors analyzed the data;
Theodore Tselios and Vasso Apostolopoulos wrote the paper together with all co-authors.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
CDRs
DCC
DCM
DIPEA
DCU
DMAP
DMF
ESI MS
HLA
HOBt
MBP
complementarity determining regions
N,N -dicyclohexylcarbodiimide
dichloromethane
N,N-diisopropylethylamine
dicyclohexylurea
4-dimethylaminopyridine
dimethylformamide
electrospray ionization mass spectrometry
human leukocyte antigen
1-hydroxybenzotriazole
myelin basic protein
0
MD
molecular dynamics
MHC
MS
major histocompatibility complex
multiple sclerosis
MW
molecular weight
1
H NMR
C NMR
proton nuclear magnetic resonance
carbon-13 nuclear magnetic resonance
peripheral blood mononuclear cells
reversed phase high-performance liquid chromatography
T cell receptor
1
3
PBMC
RP-HPLC
TCR
TES
triethylsilane
TFA
trifluoroacetic acid
Th
T helper
TLC
TPSA
thin layer chromatography
total polar surface area

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