Design and synthesis of a novel inhibitor of T. Viride chitinase through an in silico target fishing protocol

Article abstract of DOI:10.1016/j.bmcl.2017.06.016

In the last ten years, we identified and developed a new therapeutic class of antifungal agents, the macrocyclic amidinoureas. These compounds are active against several Candida species, including clinical isolates resistant to currently available antifungal drugs. The mode of action of these molecules is still unknown. In this work, we developed an in-silico target fishing procedure to identify a possible target for this class of compounds based on shape similarity, inverse docking procedure and consensus score rank-by-rank. Chitinase enzyme emerged as possible target. To confirm this hypothesis a novel macrocyclic derivative has been produced, specifically designed to increase the inhibition of the chitinase. Biological evaluation highlights a stronger enzymatic inhibition for the new derivative, while its antifungal activity drops probably because of pharmacokinetic issues. Collectively, our data suggest that chitinase represent at least one of the main target of macrocyclic amidinoureas.

Full text of DOI:10.1016/j.bmcl.2017.06.016

Bioorganic & Medicinal Chemistry Letters xxx (2017) xxx–xxx
Contents lists available at ScienceDirect
Bioorganic & Medicinal Chemistry Letters
journal homepage: www.elsevier.com/locate/bmcl
Design and synthesis of a novel inhibitor of T. Viride chitinase through an
in silico target fishing protocol
Giorgio Maccari a, Davide Deodato a, Diego Fiorucci a, Francesco Orofino a, Giuseppina I. Truglio a,
Carolina Pasero a, Riccardo Martini a, Filomena De Luca b, Jean-Denis Docquier b,d, Maurizio Botta a,c,d,
⇑
a Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100 Siena, Italy
b Department of Medical Biotechnology, University of Siena, I-53100 Siena, Italy
c Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, BioLife Science Building, Suite 333, 1900
N 12th Street, Philadelphia, PA 19122, USA
d Lead Discovery Siena s.r.l, Via Vittorio Alfieri 31, I-53019 Castelnuovo Berardenga, Italy
a r t i c l e i n f o
a b s t r a c t
Article history:
In the last ten years, we identified and developed a new therapeutic class of antifungal agents, the macro-
cyclic amidinoureas. These compounds are active against several Candida species, including clinical iso-
lates resistant to currently available antifungal drugs. The mode of action of these molecules is still
unknown. In this work, we developed an in-silico target fishing procedure to identify a possible target
for this class of compounds based on shape similarity, inverse docking procedure and consensus score
rank-by-rank. Chitinase enzyme emerged as possible target. To confirm this hypothesis a novel macro-
cyclic derivative has been produced, specifically designed to increase the inhibition of the chitinase.
Biological evaluation highlights a stronger enzymatic inhibition for the new derivative, while its antifun-
gal activity drops probably because of pharmacokinetic issues. Collectively, our data suggest that chiti-
nase represent at least one of the main target of macrocyclic amidinoureas.
Received 28 April 2017
Revised 1 June 2017
Accepted 3 June 2017
Available online xxxx
Keywords:
Target fishing
Macrocyclic compounds
Antifungal agents
Amidinoureas
Chitinase
Ó 2017 Published by Elsevier Ltd.
Candida strains
In silico target fishing (TF) is a computational procedure able to
identify the biological target of an active compound using its struc-
tural information and data from biological databases. The develop-
ment of this technique has been fuelled by the evergrowing
amount of X-ray data in public databases such as the Protein Data
Bank (PDB).1,2 The identification of the biological target of a com-
pound is an essential information to support the design of new
derivatives with improved pharmacological activity and/or phar-
macokinetic properties. The main limitation of TF is that it may
suggest potential targets only among those present in the source
database, which in most cases is not exhaustive since it contains
only proteins with a published X-ray crystal structure. Neverthe-
less, crystallographic data stored in the PDB can be profitably
exploited by TF approaches in order to identify possible targets
of a compound endowed with a biological effect but lacking phar-
macodynamic characterization. Moreover, the TF procedure can be
used for several other applications, such as off-target prediction,
drug repositioning, drug rescue and polypharmacological investi-
gation.3–5 It is possible to apply TF procedures using different com-
putational approaches such as chemical similarity searching, data
mining/machine learning, bioactivity spectra and panel docking.6,7
All these techniques can be used individually or in combination to
obtain better results.
Recently, we have reported the in vivo efficacy and in vitro char-
acterization data of a series of macrocyclic amidinoureas repre-
sented by compound 1 (Fig. 1), which proved to be active against
various Candida species, including drug-resistant strains.8–16
The mode of action of these molecules is still unknown, but evi-
dences coming from the biological assays against resistant
strains,14,15 suggest that the molecular target is different compared
to those of azoles and macrolides. Moreover, small changes in the
structure lead to valuable changes in the activity, suggesting the
interaction with a specific cellular structure such as a receptor or
an enzyme. For this reason, we developed a TF protocol aimed to
identify a putative target for our hit compound based on several
ranking factors such as: shape similarity between our compound
to the co-crystallized molecules in PDB; inverse docking proce-
dure; scoring using a consensus score rank-by-rank. As a result,
the computational protocol suggested chitinase as possible target
⇑
Corresponding author at: Department of Biotechnology, Chemistry and Phar-
macy, University of Siena, Via Aldo Moro 2, I-53100 Siena, Italy. Tel.: +39 0577
234306.
E-mail address: botta.maurizio@gmail.com (M. Botta).
http://dx.doi.org/10.1016/j.bmcl.2017.06.016
0960-894X/Ó 2017 Published by Elsevier Ltd.
Please cite this article in press as: Maccari G., et al. Bioorg. Med. Chem. Lett. (2017), http://dx.doi.org/10.1016/j.bmcl.2017.06.016

2
G. Maccari et al. / Bioorganic & Medicinal Chemistry Letters xxx (2017) xxx–xxx
sensus score using ComboScore as scoring function. The whole
NH
NH
complex was used to create a receptor object for the docking of
compound 1 with the Hybrid method.30 The best ten binding
modes obtained for the studied compound were further optimized
by energy minimization and calculation of the binding energy with
the Szybki and Zap toolkits respectively. The pose with the best
binding energy was saved for the consensus score. The final rank-
ing list was produced by applying a consensus scoring on the
results obtained with the shape comparison and the docking. Com-
plete results are reported in Table S1 in Supplementary data.
Among the top fifteen ranked structures the two most repre-
sented enzymes belonged to the families of the xylanase and the
chitinase. Xylanase is an enzyme involved in the degradation of
b-1,4-xylan. Chitinases are involved in the remodelling of the fun-
gal cell wall by degrading the chitin, its main component. Both
enzymes belong to the glycosidase superfamily, but the three crys-
tal structures of chitinase B from Aspergillus fumigatus were
resolved in complex with the natural compound Argifin (IC50 and
Ki respectively of 27 and 17 nM)31,32 (Fig. 2) and two oligopeptides
synthesized during a molecular simplification study,32,33 which
look similar to our compounds. We have thus hypothesized that
this enzyme could represent a possible target for our compound.
Argifin is the fermentation product of Gliocladium fungal cul-
ture.34–36 Chitin is the main component of the fungal cell wall; it
is a polymer of N-acetylglucosamine (GlcNAc) with a b-1,4 linkage
between monomers. Chitinases are the enzymes responsible for
chitin degradation and have been validated as a promising target
for the design of new therapeutic agents active against fungal
infections.37,38 Moreover, the inhibition of chitinase in insects is a
mechanism exploited in the production of new pesticides.39
To support the chitinase inhibition hypothesis, we performed
enzymatic assays against the homologue protein extracted from
Trichoderma viride. This protein was chosen for its commercial
availability and for the high degree of identity, being our attempts
to express the Candida chitinase unproductive. The enzyme was
incubated with different concentrations of inhibitor directly into
the reading plate at room temperature for three hours. At the
HN
NH
O
N
N
N
H
H
1
Fig. 1. Compound 1.
for our compounds. To confirm our hypothesis, we decided to opti-
mize the series of compounds in order to improve activity toward
the chitinase enzyme.
We setup the TF protocol by using a computational approach
consisting of the following six steps:
1. Selection of a set of ligand/protein complexes whose crystal
structures have been resolved;
2. Preparation of each complex by separating the protein from the
ligand and relaxing the system by means of energy
minimization;
3. Shape based comparison between the crystallized ligands and
our compound;
4. Docking of our compound in the binding site of the crystallo-
graphic structures;
5. Scoring of the candidate targets by means of a consensus score
rank-by-rank using both the results of the shape matching and
the docking;
6. Selection of the possible targets among the highest ranked
structures.
The set of complexes was taken from the PDB. In a first attempt,
we focused our attention on the crystal structures of protein from
the genus Candida, co-crystallized with at least one compound
(inhibitors or physiological substrates). This led to the selection
of only 95 crystal structures. To increase the chances of finding
an ideal target for our compounds, we enlarged the set of reference
structures by including all the protein of the Ascomycota phylum,
obtaining an initial database of 3636 structures. The second step,
i.e. preparation of the complexes, was performed using an in-house
python script that makes use of the OpenEye toolkits.17 The script
recognizes as ligand each molecule with at least four carbon atoms
and no more than 75 heavy atoms; compounds too far from the
protein atoms were ignored. Each ligand was processed by perceiv-
ing the bond orders and assigning the tautomerization and proto-
nation state at pH 7.4. The last two operations were performed
using the Quacpac libraries of the OpenEye toolkit.17,18 The protein
was prepared by adding the explicit hydrogens and calculating the
most probable protonation calling the external program
Reduce.19,20 Following the pre-treatment of both ligand and pro-
tein, the script recreates the complex and relaxes the system by
means of energy minimization, using the Szybki libraries included
in the OpenEye toolkit.17,21 In case of complexes with more than
one ligand two different files were saved. To improve the efficiency
of the computation the whole script was parallelized using the
mpi4py Python libraries22–24 interfaced with the openMPI
libraries.25 As a result, we obtained 2177 optimized complexes.
Steps from 3 to 5 were implemented in another python script
that makes use of the OEDocking, Shape, Quacpac, Szybki and
Zap libraries of the OpenEye toolkit.17,18,21,26–28 The script accepts
as input a list of pre-processed complexes and the studied com-
pound, assuming that the reference is a multi-conformer file con-
sisting of conformations calculated using Omega2.29 For each
complex the bound ligand was used as a reference for a shape
and colour atoms comparison against every conformer of com-
pound 1. The best values were stored to be used in the final con-
end of the incubation, the substrate (4-nitrophenyl N-acetyl-b-D-
glucosaminide) was added and the reading was conducted by
recording the values up to a maximum time of 10 min. The sub-
strate concentration was measured by reading UV absorption at
300 nm (
sis was measured at 400 nm (
result, both IC50 and Ki could be calculated.
e
M300: 11,100 MÀ1ÁcmÀ1), while the product of hydroly-
D
e
M400: 2,120 MÀ1ÁcmÀ1). As a
The assay has been performed on compound 1 and on other
compounds of the same family, either previously described14 or
prepared with the same procedure (Scheme S1 Supplementary
data). The results are reported in Table 1. The compounds were
active against chitinase with IC50 values of the same order of mag-
nitude of the MIC measured in cells.
O
O
O
N
HN
O
OH
HN
NH
O
NH
O
HN
O
NH
NH
NH
OH
O
Argifin
Fig. 2. Structures of one of the three inhibitors co-crystallized with the complex
1W9V.
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G. Maccari et al. / Bioorganic & Medicinal Chemistry Letters xxx (2017) xxx–xxx
3
Table 1
Enzymatic assays on compound 1 and analogous compounds.
Cpd
112
n
6
Scaffold
A
R
IC50 [mM]
Ki [mM]
157.0
50.8
214
6
A
130.0
42.0
3
4
4
4
A
A
111.0
108.0
35.8
34.8
5
6
4
4
A
A
140.0
97.0
45.3
31.3
714
814
6
6
B (m = 2)
B (m = 3)
55.0
35.5
17.8
14.1
Remarkably, all the tested derivatives showed inhibition of the
enzyme with Ki values below 50 mM, confirming chitinase as one of
the main target of this series of macrocyclic amidinoureas. The best
inhibition (Ki = 14–18 mM) was obtained with compounds 7–8,
bearing an aromatic ring into the macrocycle.
Encouraged by these results, we decided to use the data
obtained from the computational procedure in order to design a
new derivative able to bind and inhibit the chitinase enzyme with
higher potency. The comparison of shape similarity, docking pose
and crystallographic data between our reference compound 1
and Argifin are shown in Fig. 3. The macrocyclic portion and the
linear chain assume similar orientation in both 1 and Argifin, and
both compounds interact with Glu177. However, the amidinourea
moiety of Argifin is able to form two additional interactions with
carboxylic group of Asp 175 and phenolic OH of Tyr 245.
Based on these observations we decided to merge our best chiti-
nase inhibitor (compound 8) with the Argifin terminal amidi-
nourea moiety in order to improve its activity through additional
interactions with the chitinase active site (see Fig. 4).
Compound 9 was prepared with a convergent synthesis, accord-
ing to what previously reported.14 (Scheme 1) Briefly, salicylalde-
hyde was alkylated, then the aldehyde group was converted to
the corresponding primary amine and finally guanylated, furnish-
ing 11. The linker moiety 12 was prepared starting from
O
NH
O
O
N
H
N
H
N
N
H
NH
H
N
O
O
NH
HN
O
O
O
HN
H
N
OH
H
N
NH HN
O
N
6
O
NH
OH
8
Argifin
H
NH
N
N
O
HN
O
NH
O
N
H
N
N
H
Fig. 3. Binding mode of compound 1 in the active site of chitinase (PDBID: 1W9V).
The protein is represented in lines and cartoon. Argifin is represented as green
sticks, while the shape comparison pose and the docked pose are represented as
cyan and magenta sticks respectively. Hydrogen bonds are represented as green
dashes.
H
9
Fig. 4. Rational optimization of compound 8 with terminal amidinourea moiety to
improve Chitinase inhibition.
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4
G. Maccari et al. / Bioorganic & Medicinal Chemistry Letters xxx (2017) xxx–xxx
Scheme 1. Synthesis of compound 9. Reagents and conditions: (i.) 5-Bromo-1-pentene, K2CO3, NaI, EtOH, reflux, 16 h; (ii.) Hydroxylamine hydrochloride, Pyridine, EtOH,
reflux, 2 h; (iii.) Zn0, 2 M HCl, THF, reflux, 6 h; (iv.) Pyrazole-di-Boc-Guanidine, DIPEA, THF, r.t., 16 h; (v.) Benzylchloroformate, K2CO3, THF, r.t, 3 h. (vi.) Allylamine, EDC, HOBt,
DIPEA, DMF, 0 °C to r.t., 3 h; (vii.) DIBAL-H, DCM, À78 °C, 6 h; (viii.) TEA, dry THF, reflux, 16 h; (ix.) Grubbs’ catalyst II generation, degassed DCM, reflux, 16 h; (x.) Pd/C 10%, H2,
cat. HCl, 2-Propanol, r.t., 4 h; (xi.) Pirazole-di-Boc-Guanidine, DIPEA, THF, r.t., 16 h; (xii.) MeNH2, DIPEA, THF, 80 °C, sealed tube, 16 h; (xiii.) 20% CF3COOH in DCM, r.t., 8 h.
results from the poor membrane penetration of 9, due to the trans-
formation of the positive-charged terminal guanidine (pKa ꢀ 12) to
a neutral amidinourea (pKa ꢀ 6). It is known that many cationic
molecules use polyamine transporter to cross the fungal mem-
brane,40 and in our case the loss of the positive charge at physio-
logical pH may reduce the affinity for the transporter. It is also
possible that chitinase represents only one of the targets for this
class of compounds, and these modifications reduced the affinity
for the other targets.
In summary, we successfully identified a putative target for
antifungal macrocyclic amidinoureas using an in silico target fish-
ing protocol. Enzymatic assays confirm the computational results
but it is still controversial if the inhibition of chitinase represent
the only mechanism of action of these molecules. Nevertheless,
we believe that the acquired knowledge about this family of
enzymes and the inhibition of the T. Viride chitinase could be pre-
Fig. 5. Ki [mM] and IC50 [mM] of the tested compounds.
cious for future work, being these enzymes involved in many dif-
ferent biological mechanisms concerning not only fungal
infections but also human diseases such as tumors, respiratory,
8-aminooctanoic acid, by protecting the amino group with Cbz,
coupling the carboxylic acid with allylamine and finally reducing
it with DIBAL-H. The two fragments were joined together and the
resulting compound 13 underwent ring-closing metathesis to form
the macrocycle. Subsequent hydrogenation and a guanylation
afforded the intermediate 15, which was reacted with methy-
lamine and trifluoroacetic acid, furnishing the desired compound
9 as trifluoroacetate salt.
The optimized derivative was tested against T. viride chitinase
and it exhibited a potent enzymatic inhibition, with IC50 and Ki val-
ues (0.68 mM and 0.22 mM respectively) over 50-fold lower than
compound 8 (Fig. 5), confirming the robustness of our computa-
tional model.
cardiovascular and immune system diseases.41
Acknowledgments
This Work was partially supported by Bakker Medical s.r.l. Prof.
Maurizio Sanguinetti and Dr. Francesca Bugli from Università Cat-
tolica del Sacro Cuore (Rome, Italy) are acknowledged for the anti-
fungal MIC assays.
A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.bmcl.2017.06.
016. These data include MOL files and InChiKeys of the most
important compounds described in this article.
On the other hand, cellular assays against Candida isolates
showed
a reduction of anti-fungal activity for compound 9
(Table S2 Supplementary data). We assume that this behaviour
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G. Maccari et al. / Bioorganic & Medicinal Chemistry Letters xxx (2017) xxx–xxx
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21. OpenEye Scientific Software Szybki, 1.8.0.1; Santa Fe, NM, 2013.
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