5.2.3. Cytotoxicity assays
performed to predict the binding mode. Consequently, the
complexes with the lowest interaction energy were evaluated.
The interactions between LaARG, in both forms, and each
inhibitor were analyzed using the ligand map algorithm, a
standard algorithm in the MVD program.26 The usual threshold
values for H-bonds and steric interactions were used. All figures
for LaARG modeling and molecular docking results were edited
using the Visual Molecular Dynamics 1.9.3 (VMD) program
Cytotoxicity assays using mammalian host cells: The toxic
aspects of each compound on mammalian cells was performed by
using primary cultures of peritoneal macrophages obtained from
Swiss male mice (18-20 g) previously inoculated with 1 mL 3%
thioglycolate. After 96 h of thioglycolate stimulation, the
peritoneal cells were harvested by rinsing the animals’
peritoneum with RPMI 1-640. The peritoneal cells were then
plated into 96-well microplates at a cell density of 105 cells/well
as reported.34 The cultures were then sustained in RPMI 1640
medium (pH 7.2 to 7.4) without phenol red (Gibco BRL)
supplemented with 10% fetal bovine serum and 2 mM
glutamine.33 After 24 h of platting, the compounds (up to 200
µM) were added and the cultures incubated for 48 h at 37 ºC in
an atmosphere of 5% CO2 and air. Then, MTT solution was
added to the treated cultures (0.45 mg/mL), and after 4 h of
incubation, the O.D. read at 570 nM by a UV spectrophotometer.
The results were calculated according to the manufacturer’s
instructions and the concentration that reduced host cell viability
by 50% (LC50) calculated. All assays were run at least 2 times in
triplicate.34 Ethics: All procedures were carried out in accordance
with the guidelines established by the FIOCRUZ Committee of
Ethics for the Use of Animals (CEUA LW16/14).
(available
for
download
at
http://www.ks.uiuc.edu/Research/vmd/vmd-1.9.3/).
Acknowledgments
This study was financed in part by the Coordenacao de
Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES)
- Finance Code 001. The authors thank the Coordination of
Superior Level Staff Improvement (CAPES) and the National
Council of R&D of Brazil (CNPq) for the fellowships granted to
the authors. JAASSC is a fellowship of Ministério da Ciência e
Tecnologia Ensino Superior
e
Técnico-Profissional de
Moçambique (MCTESTP). We also thank the Foundation for
Research of the State of Rio de Janeiro (FAPERJ), Technological
Development Program on Products for Health (PDTIS) and São
Paulo Research Foundation (FAPESP, #17/06917-4) for financial
support. ERS, NB, MNCS, and LCSP are recipient of research
productivity fellowships from the CNPq and NB and MNCS
thank the (“Cientista do Nosso Estado”) from FAPERJ.
5.3. Molecular Modeling
5.3.1. Comparative modeling
References and notes
The amino acid sequence of L. amazonensis arginase
(LaARG, UniProtKB ID: O96394) was obtained from the
ExPASy server.35 The region between Glu12-Thr318, the portion
of the LaARG sequence that includes the whole catalytic core,
was considered to construct the models in monomeric and
trimeric forms, using the MODELLER v9.19 program
4ITY as templates.24 Subsequently, the models were refined
using the same program. Thus, the final models were validated
using two programs: PROCHECK and VERIFY3D.21,22
PROCHECK analyzes the stereochemical quality and
VERIFY3D performs compatibility analysis between the 3D
model and its own amino acid sequence by assigning a structural
class based on its location and environment, and by comparing
the results with crystal structures with good resolution.21,22
1.
World Health Organization (WHO). Leishmaniasis. Available at:
Croft, S. L.; Yardley, V. Curr. Pharm. Des. 2002, 8, 319–342.
Aronson, N.; Herwaldt, B. L.; Libman, M.; Pearson, R.; Lopez-
Velez, R.; Weina, P.; Carvalho, E.; Ephros, M.; Jeronimo, S.;
Magill, A. Clin. Infect. Dis. 2016, 63, 1539–1557.
Boechat, N.; Pinheiro, L. C. S. An Overview of New Synthetic
Antileishmanial Candidates, 2014, 63-121. In Adilson Beatriz and
Dênis Pires de Lima (Eds) Organic Compounds to Combat
Neglected Tropical Diseases Chapter 3 Bentham Science
Publishers 2014.
2.
3.
4.
5.
6.
7.
8.
Fairlamb, A. H.; Cerami, A. Annu. Rev. Microbiol. 1992, 46, 695–
729.
Mukbel, R. M.; Patten, C. Jr.; Gibson, K.; Ghosh, M.; Petersen.
C.; Jones, D. E. Am. J. Trop. Med. Hyg. 2007, 76, 669–675.
Bocedi, A.; Dawood, K. F.; Fabrini, R.; Federici, G.; Gradoni, L.;
Pedersen, J. Z.; Ricci, G. FASEB J., 2010, 24, 1035–1042.
Da Silva, E. R.; Da Silva, M. F.; Fischer, H.; Mortara, R. A.;
Mayer, M. G.; Framesqui, K.; Silber, A. M.; Floeter-Winter, L. M.
Mol. Biochem. Parasitol. 2008, 159, 104–11.
5.3.2. Molecular Docking
The inhibitor structures (1 and 6) were built suing Spartan’14
software (Wavefunction, Inc., Irvine, CA). The docking of the
two inhibitors into the monomeric and trimeric LaARG models
were performed using the Molegro Virtual Docker 6.0 (MVD)
program (CLC bio, Aarhus, Denmark),26 which uses a heuristic
search algorithm that combines differential evolution with a
cavity prediction algorithm. The MolDock scoring function used
is based on a modified piecewise linear potential (PLP) with new
hydrogen bonding and electrostatic terms included. A full
description of the algorithm and its reliability compared to other
common docking algorithms has been described.26 As no
satisfactory cavities were found by the cavity prediction
algorithm using MVD, the whole enzyme, in the monomeric
form, and the whole three chains of the enzymes, in the trimeric
form, were set as the center of the searching space. The search
algorithm MolDock optimizer was used with a minimum of 100
runs, and the parameter settings were: population size = 500;
maximum iteration = 2000; scaling factor = 0.50; offspring
scheme = Scheme 1; termination scheme = variance-based;
crossover rate = 0.90. Due to the stochastic nature of the
algorithm search, ten independent simulations per ligand were
9.
Riley, E.; Roberts, S. C.; Ullman, B. Int. J. Parasitol. 2011, 41,
545–552.
10. Iniesta, V.; Gómez-Nieto, L. C.; Corraliza, I. J. Exp. Med. 2001,
193, 777–784.
11. D'Antonio, E. L.; Ullman, B.; Roberts, S. C.; Dixit, U. G.; Wilson,
M. E.; Hai, Y.; Christianson, D. W. Arch. Biochem. Biophys.
2013, 535, 163–176.
12. Baggio, R.; Elbaum, D.; Kanyo, Z. F.; Carroll, P. J.; Cavalli, R.;
Ash, D. E.; Christianson, D. W. J. Am. Chem. Soc. 1997, 119,
8107–8108.
13. Kim, N. N.; Cox, J. D.; Baggio. R. F.; Emig, F. A.; Mistry, S. K.;
Harper, S. L.; Speicher, D. W.; Morris, S. M. Jr; Ash, D. E.;
Traish, A.; Christianson, D. W. Biochemistry. 2001, 40, 2678–88.
14. Boechat, N.; Pinheiro, L. C. S.; Silva, T. S.; Aguiar, A. C. C.;
Carvalho, A. S.; Bastos, M. M.; Costa, C. C. P.; Pinheiro, S.;
Pinto, A. C.; Mendonça, J. S.; Dutra, K. D. B.; Valverde, A. L.;
Santos-Filho, O. A.; Ceravolo, I. P.; Krettli, A. U. Molecules.
2012, 17, 8285–8302.
15. Boechat. N.; Lages, A. S.; Santos-Filho, O. A.; Genestra, M.;
Bastos, M. M.; Kover, W. B. J. Microbiol. Antimicrob. 2013, 5,
72–86.
16. Da Silva, E. R.; Boechat, N.; Pinheiro, L. C. S.; Bastos, M. M.;
Costa, C. C.; Bartholomeu, J. C.; da Costa, T. H. Chem. Biol.
Drug Des. 2015, 86, 969–978.