INTRODUCTION
Fungal pathogens are responsible for 13 million infections and 1.5 million deaths annually [1]. The opportunistic pathogens of the Aspergillus genus have emerged as the most frequent cause of fungal diseases [1]. For aspergillosis, triazole drugs are commonly employed as the first line of treatment in clinical therapy. However, these compounds can cause several adverse clinical effects, such as nausea, vomiting, neurotoxicity, and kidney damage [2].
One of the validated targets against Aspergillosis [3] is the Chitinase enzyme (EC 3.2.1.14), a glycoside hydrolase (GH) superfamily member. These enzymes are responsible for the hydrolysis of ?-1,4 glycosidic bonds in chitin polymers [4]. Among the several GH families, the fungi chitinases belong to the GH18 family, employed for cell wall remodeling and as virulence factors [5]. Chitinase inhibitors (e.g. methylxanthines) can drastically affect the hyphal morphology of fungal pathogens, such as A. fumigatus. The mode of action shows that these compounds act as competitive inhibitors against the chitinase of the fungal family 18 [6].
The identification of potential targets for a known bioactive compound is fundamental for drug design and development. One of the most used techniques is Structure-Based Drug Design (SBDD), applied when 3D structural information on the molecular target is used to simulate intermolecular interactions with another molecule. Recently, our research group identified antileishmanial and antitumoral activity for acridine and spiro-acridine derivatives [7], which have been shown to be potential drug candidates. However, the few therapeutic applications reported in the literature make us wonder about the best activity performance of these compounds against a target.
AIMS
This study focused on identifying rational targets for acridine derivatives as antileishmanial activity by employing inverse virtual screening (IVS) methods.
METHODS
In our research group, a total of 28 acridine derivatives were selected. The two-dimensional structures of compounds in this series were created using the MarvinSketch program, considering a pH of 7.4 for protonation.
The compound 1 was selected to perform an inverse virtual screening (target fishing). A non-redundant library of targets, comprising about 23,000 structures, was created and originally contained ligands bound to them. All bound ligands were removed, and proper inputs were created to perform automated docking simulation with Autodock Vina. Ad hoc scripts were created and used for automation. The data was sorted according to the scores and analyzed by human inspection.
The chitinase structure from Trichoderma harzianum was used as a model organism and obtained from the Protein Data Bank under the access codes 6EPB (Chit42) and 7ZYA (Chit33). The protein structures were subjected to preparation by deleting solvents, adding hydrogens, charges, and replacing the rotamer library with incomplete side chains in the Chimera UCSF program. The redocking results obtained from Vina were used as input data in AutoDockTools 1.5.751, and the crystallized PDB binder was used as a fixed reference. Chitinases from A. fumigatus and T. harzianum had ligands docked to the binding sites with AutoDock Vina 1.1.2 to predict binding poses and scores. Molecular docking was performed according to Rao et al. [8], where the inhibitor caffeine (CFF) position in the reference PDB ID 2A3B is considered as the initial position.
In vitro tests were performed to validate the computational methods. Assays were performed in 96-well coated microplates (Greiner CELLSTAR® Sigma-Aldrich Co.) and consisted of 95 µL McIlvaine buffer pH 6.0, 5 µL of the substrate (0.8 mM), 5 µL Lysing Enzymes from T. harzianum (200 mg/mL in PBS 1 x; Sigma-Aldrich Co.), and 5 µL of the evaluated compound. The inhibitory assays were performed with increasing concentrations of the compounds evaluated (compounds 5, 7, and 9) diluted in DMSO. Quantification of the samples was based on the relative fluorescence units (RFU) using the previously established standard curve [9].
All simulations were carried out using the GROMACS Simulation package version 553 and CHARMM force field for chitinase of A. fumigatus, and T. harzianum. The compounds 5, 7 and 9 had their topology built using SwissParam. The molecular dynamics simulation was performed for a run time of 100 ns. To evaluate protein-ligand interactions we used RMSD (Root Mean Square Deviation), RMSF (Root Mean Square Fluctuation), and number of Hydrogen bonds (H-bond).
RESULTS AND DISCUSSION
This analysis revealed that chitinase enzymes can be potential targets for these compounds. We found 10 chitinase targets with scores between ? 10.6 kcal/mol to ? 9.2 kcal/mol. The best targets for the investigated small compounds were chitinases from A. fumigatus (AfChiB) (?10.6 kcal/mol). For A. fumigatus the position of the acridine ring overlaps with the aromatic ring of the PDB inhibitor (caffeine). The amino acid Tyr29 showed ?–? interactions with acridine fragments, since Gly322, Asp246 and Tyr299 showed an H-bond with the ligand. This conformation may indicate a possible activity of functional mimicry.
Additionally, the alignment shows 61% identity and 90% coverage of T. harzianum (Chit33 and Chit42) with A. fumigatus. It is observed that AfChiB shows greater identity with Chitinase 42 from T. harzianum (Chit42), with the same active site.
Also, all acridine derivatives interact with the DXDXE motif, which is involved in the catalysis and loss of catalytic activity of chitinase. The acridine derivatives showed better energy value with chitinases isoforms from A. fumigatus and T. harzianum, when compared to the caffeine compound.
To perform a consensus analysis between molecular docking, it was observed that compounds 5 (? 10.9 kcal/mol), compound 7 (? 10.1 kcal/mol) and compound 9 (? 10.6 kcal/mol) were shown to be the top hit compounds. These compounds were selected for in vitro activity in the T. harzianum enzyme, the model species for chitinase assay.
All tested compounds inhibited chitinolytic activity in a dose-dependent manner, with compound 5 exhibited inhibition of chitinolytic activity with IC50 of 0.6 ng/µL, the compound 7 showed IC50 of 5,7 ng/µL and compound 9 with 11.3 ng/µL. Notably, the in vitro assays show similar results to those found employing molecular docking, where compound 5 was assigned as the most potent.
Therefore, this study recommends IVS as a powerful tool for drug development. The potential applications are highlighted as this is the first report of spiro-acridine derivatives acting as chitinase inhibitors that can be potentially used as antifungal and antibacterial candidates.
CONCLUSION
The results presented in the inverse virtual screening for acridine derivatives helped us to find that chitinase enzyme is the best target for these derivatives. Three derivatives compounds showed activity prediction and high docking score, being confirmed in vitro, with compound 5 showing the best profile with an IC50 of 0.6 ng/µL. Additionally, molecular dynamic simulation and free energy validated the chosen approach by identifying the stability in the formation of the chitinase-compound 5 complex. Thus, with this analysis, we increase the probability of selecting more potentially active molecules using structure based virtual screening approaches.
ACKNOWLEDGMENT
The authors would like to thank the Federal University of Rio Grande do Norte (UFRN), State University of Paraíba (UEPB) and Federal University of Rio Grande do Sul (UFRGS) for providing the infrastructure and support for this project. The authors are indebted to the High-Performance Computing Center (NPAD) at UFRN for the availability of computational resources and the Instituto Metrópole Digital (IMD) for the support in the realization of this work.
REFERENCES
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[3] Souza, E. S. et al. A plumieridine-rich fraction from Allamanda polyantha inhibits chitinolytic activity and exhibits antifungal properties against Cryptococcus neoformans. Front. Pharmacol. 11, 1–14. https://doi.org/10.3389/fmicb.2020.02058 (2020).
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Comissão Organizadora
Francisco Mendonça Junior
Pascal Marchand
Teresinha Gonçalves da Silva
Isabelle Orliac-Garnier
Gerd Bruno da Rocha
Comissão Científica
Ricardo Olimpio de Moura
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