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Article

Triose Phosphate Isomerase Structure-Based Virtual Screening and In Vitro Biological Activity of Natural Products as Leishmania mexicana Inhibitors

by
Luis D. González-Morales
1,
Adriana Moreno-Rodríguez
2,
Lenci K. Vázquez-Jiménez
1,
Timoteo Delgado-Maldonado
1,
Alfredo Juárez-Saldivar
1,
Eyra Ortiz-Pérez
1,
Alma D. Paz-Gonzalez
1,
Edgar E. Lara-Ramírez
1,
Lilian Yépez-Mulia
3,
Patricia Meza
3 and
Gildardo Rivera
1,*
1
Laboratorio de Biotecnología Farmacéutica, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa 88710, Mexico
2
Laboratorio de Estudios Epidemiológicos, Clínicos, Diseños Experimentales e Investigación, Facultad de Ciencias Químicas, Universidad Autónoma “Benito Juárez” de Oaxaca, Avenida Universidad S/N, Ex Hacienda Cinco Señores, Oaxaca 68120, Mexico
3
Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias-Pediatría, Instituto Mexicano del Seguro Social, Mexico City 06720, Mexico
*
Author to whom correspondence should be addressed.
Pharmaceutics 2023, 15(8), 2046; https://doi.org/10.3390/pharmaceutics15082046
Submission received: 17 June 2023 / Revised: 25 July 2023 / Accepted: 27 July 2023 / Published: 29 July 2023
(This article belongs to the Special Issue Recent Advances in Drug Discovery and Drug Delivery Systems for Antimicrobial Therapy)
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Versions Notes

Abstract

:
Cutaneous leishmaniasis (CL) is a public health problem affecting more than 98 countries worldwide. No vaccine is available to prevent the disease, and available medical treatments cause serious side effects. Additionally, treatment failure and parasite resistance have made the development of new drugs against CL necessary. In this work, a virtual screening of natural products from the BIOFACQUIM and Selleckchem databases was performed using the method of molecular docking at the triosephosphate isomerase (TIM) enzyme interface of Leishmania mexicana (L. mexicana). Finally, the in vitro leishmanicidal activity of selected compounds against two strains of L. mexicana, their cytotoxicity, and selectivity index were determined. The top ten compounds were obtained based on the docking results. Four were selected for further in silico analysis. The ADME-Tox analysis of the selected compounds predicted favorable physicochemical and toxicological properties. Among these four compounds, S-8 (IC50 = 55 µM) demonstrated a two-fold higher activity against the promastigote of both L. mexicana strains than the reference drug glucantime (IC50 = 133 µM). This finding encourages the screening of natural products as new anti-leishmania agents.

1. Introduction

Leishmaniasis is caused by flagellate parasites belonging to the genus Leishmania and is transmitted through the bites of sandflies of the genus Phlebotomus. Although this disease is endemic in certain regions of America and the African continent, its distribution, favored by environmental, migratory, and climatic factors [1], has recently been reported across Asia and Europe [2]. Three forms of this disease, namely visceral, cutaneous, and mucocutaneous leishmaniasis, have been reported [1].
Cutaneous leishmaniasis (CL) is an infection caused by Leishmania mexicana (L. mexicana). Although not life-threatening, CL is important to recognize and treat as it is associated with permanent scarring, a decreased quality of life, stigmatization, and long-term psychological consequences [3,4].
The standard treatment for CL implicates using two pentavalent antimonial drugs (sodium stibogluconate and meglumine antimoniate). However, their toxicity requires therapy surveillance and monitoring [5,6]. Additionally, resistance to this kind of drug has begun to appear in certain endemic areas, which limits their efficacy [5,7]. Unfortunately, candidate drugs registered in clinical trials to treat Leishmania infections are limited [8]. Therefore, the search for new drugs with less toxicity and more effective leishmanicidal activity is imperative.
Different molecular targets against L. mexicana, such as trypanothione reductase [9], nucleoside hydrolase [10], arginase [11], and several glycolytic enzymes [12], have been considered. Among these, triosephosphate isomerase (TIM) stands out due to its essential role in energy metabolism.
TIM is a dimer that catalyzes the chemical interconversion of dihydroxyacetone phosphate and 3-phosphate glyceraldehyde to form the intermediate cis-enediol (ato) [13]. Additionally, the dimer interface of L. mexicana TIM (LmTIM) compared to the homologous human TIM (HsTIM) reveals a 48% sequence dissimilarity [14], a key factor in their selectivity. Based on the above points, the dimer interface of LmTIM (Figure 1) has been highlighted as a site of interest in discovering specific compounds that inhibit its function by disrupting its interface.
On the other hand, secondary metabolites from natural products have been considered as an option to develop new drugs against Leishmania infections [15,16]. In recent years, significant progress has been made in mitigating the threat of Leishmania parasites. Despite this, promising drugs still do not exist. In this sense, the use of bioactive natural products in the treatment of the disease has appeared as an alternative strategy, as it has long been considered a medicinal source to treat different critical diseases [17,18,19] (Cartuche et al., 2020; Silva-Silva et al., 2021; Yang and Wang, 2021). For example, Das et al. [20] showed that quercetin (6.6 µM), a polyphenolic flavonoid found in several vegetables and fruits, has a strong in vitro leishmanicidal effect. Additionally, Fróes et al. [21] demonstrated that the hexane fraction of Vernonanthura brasiliana exhibits a leishmanicidal effect against L. amazonensis promastigotes (IC50 = 5.76 µg/mL) and a low cytotoxic effect against RAW 264.7 cells (CC50 = 314.8 µg/mL), suggesting that their main secondary metabolites, namely eriodyctiol, luteolin, and apigenin, could act on the lanosterol demethylase enzyme.
In recent decades, virtual screening has become a strategy implemented to obtain compounds with biological activity at a low cost and in a short time [22,23]. Our research group has previously applied this technique to obtain new potential inhibitors of essential targets against parasites [24,25,26]. Recently, a structure-based virtual screening (SBVS) of FDA-approved drugs was performed using the enzyme TIM of Entamoeba histolytica (E. histolytica) and Giardia lamblia (G. lamblia) to identify drugs with antiprotozoal activity. Chlorhexidine, tolcapone, and imatinib inhibited the growth of G. lamblia trophozoites (0.05–4.93 μg/mL), while folic acid exhibited activity against E. histolytica (0.18 μg/mL) [27]. Therefore, in this work, an SBVS of natural compounds from two chemodatabases (BIOFACQUIM and Selleckchem) at the interface of the LmTIM enzyme was performed to find new leishmanicidal agents. Subsequently, in silico physicochemical and toxicological properties were predicted for the selected natural compounds. Finally, the biological activity of four selected compounds was determined in vitro against promastigote forms of two L. mexicana strains and their cytotoxicity on macrophages of the J774.2 cell line.

2. Materials and Methods

2.1. Protein Preparation

The crystallographic structure of the LmTIM protein was obtained from the protein data bank (PDB) (http://www.pdb.org accessed on 25 February 2023) [28] with the code access 1AMK. The protein was prepared for molecular docking with the UCSF Chimera 1.14.1 software through the DockPrep tool [29]. The “prepare_receptor4.py” script from MGLTools 1.5.6 [30] was used to add AutoDock atom types and Gasteiger charges to the protein structures. The HsTIM (PDB ID 4POC) was also prepared according to the same protocol.

2.2. Databases

BIOFACQUIM (https://www.difacquim.com/d-databases/ accessed on 25 February 2023), a database of 423 natural products isolated and characterized from Mexico [31], and the Selleckchem database (https://www.selleckchem.com/ accessed on 25 February 2023), with 2726 compounds that make up the catalog of the natural products library with a wide variety of chemical structures [32], were considered for virtual screening. All ligands obtained were prepared by minimizing their charges and coordinates with the Open Babel program [33]. The structures were then exported in 3D and in SDF format.

2.3. Molecular Docking

The conformational search space was determined by setting the coordinates at the center of the interface of LmTIM (X = −5.933, Y = −8.890, and Z = 7.297) along with a box size of 18 × 18 × 18 Å to apply the AutoDock vina 1.1.2 software [34] for the molecular docking simulations. The docking site on HsTIM was established through superimposition between LmTIM (PDB 1AMK) and the HsTIM protein (PDB 4POC) using UCSF Chimera. Following this, the box was centered on the interface residues, as described by Téllez-Valencia et al. [35].
Subsequently, through the web server protein–ligand interaction profiler (PLIP), amino acid interaction profiles were generated for the selected docked ligands [36]. The compound 6,6′-bisbenzothiazole-2,2′diamine (BTZ), a known inhibitor of LmTIM, was used as a control [37].

2.4. Molecular Dynamics Simulation

The analysis of molecular dynamics simulations was performed using the GROMACS version 2018.4 software with a 240 ns trajectory [38]. The topology of each compound was generated with the ACPYPE Antechamber module using the General Amber Force Field. First, the system was solvated by adding water molecules in a dodecahedron with a minimum distance from the wall of 10 Å using the TIP3P water model. After, ions (Na+ and Cl−) were added to neutralize the system. Subsequently, it was prepared at a temperature of 315.10 K and a pressure of 1.01325 bar. The periodic grid box boundary conditions were met in all directions and the simulation chamber was prepared using an automatically calculated orthorhombic box with damping dimensions of 10 Å × 10 Å × 10 Å × 10 Å and NPT assembly. Energy minimization and position moderation of the solvated system of protein–ligand complexes were removed. The Parrinello–Rahman coupling method was used for the equilibrium of the NVT and NPT sets. For the NVT ensemble, a constant number of particles (N), volume (V), temperature (T), and coupling constant of 0.1 ps per 100 ps were maintained throughout the molecular dynamics simulation time. In the NPT assembly, a constant number of particles (N), pressure (P), temperature (T), and the same coupling constant were maintained. The pressure during the stimulations of the molecular dynamics simulation was 1 atm. The stability of the complexes was determined using the GROMACS software tools and the RMSD between the α-carbons and the ligand was obtained, and the RMSF of the α-carbons, together with the two-dimensional structure and Rg, were calculated.

2.5. In Silico Pharmacokinetic Analysis

Selected compounds were analyzed using the SwissADME website (http://www.swissadme.ch/ accessed on 27 February 2023) [39] to determine their physicochemical and pharmacokinetic properties, and the ProTox-II server (https://tox-new.charite.de/protox_II/ accessed on 27 February 2023) [40] to predict their toxicity.

2.6. In Vitro Leishmanicidal Evaluation on Promastigotes

Promastigotes from the reference strain of L. mexicana (MNYC/BZ/62/M379) and the autochthonous isolate of L. mexicana (MHOM/MX/2017/UABJO17FCQEPS) were used in the in vitro studies. Parasites were preserved in RPMI 1640 supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 ug/mL streptomycin, and glutamine (2 mM). The parasites, in their logarithmic growth phase (5 × 105 parasites/mL), were incubated in 96-well plates with the compounds under different concentrations (0.78–100 µM) dissolved in dimethyl sulfoxide (DMSO) in a final volume of 200 µL for 48 h at 26 °C. Parasites in the presence of DMSO (0.2%) were included as a negative control. Glucantime, at the same concentrations, was included as a positive control. The metabolic activity of the cells was determined using the method of 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The half-maximum inhibitory concentration (IC50) was determined using probit analysis. Three independent assays were each performed in triplicate. Biostat software was used to analyze the data for statistically significant data (p < 0.05).

2.7. Cytotoxicity in Murine Macrophages

This assay was performed on mouse macrophages from the J774.2 cell line that were recloned from the original ascites and solid tumor J774.1 (according to the fabricant). The cells were cultured in RPMI medium supplemented with 10% SFB, 100 U µg/mL penicillin, 100 ug/mL streptomycin, and glutamine (2 mM) at 37 °C and in a 5% CO2 atmosphere. The medium was changed at intervals of every 2 to 3 days. For the cytotoxicity assays, 1 × 106 cells were incubated with different concentrations of the compounds (from 0.78 µM to 100 µM, respectively) at 37 °C for 48 h in a 5% CO2 atmosphere. Cells in the presence of the maximum concentration of DMSO (0.2%) were included as a negative control. The metabolic activity of the cells was determined using the MTT method. The percentage of cell viability was calculated, and the mean cytotoxic concentration (CC50) was determined using probit analysis. Three independent assays were each performed in triplicate. The selectivity index (SI) was calculated for the promastigotes of L. mexicana and the autochthonous isolate (CC50/IC50).

3. Results

3.1. Molecular Docking on LmTIM

Molecular docking studies were conducted using AutoDock Vina, which uses an algorithm that predicts the free energy of binding calculated from the intermolecular part of the lowest-scoring conformation. Docking of the ligand 6,6′-bisbenzothiazole-2,2′diamine (BTZ) at the LmTIM interface was performed to determine the cut-off value based on the vina score, which means establishing a value of predicted binding affinity to select the potential inhibitors that are better than the ligand reference BTZ. BTZ has been described as an LmTIM inhibitor [14]. The cut-off value was set at −5.6 Kcal/mol to select potential LmTIM inhibitors from the natural products databases. The amino acid interaction profile mainly hydrophobic for the BTZ compound (as shown in Figure 2A) was also considered for the selection of the predicted inhibitors.
Subsequently, 3149 compounds from the BIOFACQUIM (423) and Selleckchem (2726) databases were screened through molecular docking as potential LmTIM inhibitors. Twenty-three compounds had a docking vina score better than BTZ (See Supplementary Materials). The top ten compounds based on the vina scores from each database are shown in Table 1. The compounds from the BIOFACQUIM database had a vina score from −8.3 to −6.5 Kcal/mol, while the vina score for the compounds in the Selleckchem database was from −8.2 to −5.8 Kcal/mol, respectively. The main interactions at the LmTIM interface were hydrophobic with Phe75, Ile69, and Lys71.
An analysis of the predicted binding poses of the best ligands and their commercial availability led to the detection of the candidate compounds for subsequent studies. These compounds B-3, S-3, S-7, and S-8, were identified as ursolic acid, glycyrrhetinic acid, sorafenib, and indacaterol, respectively. Figure 2 shows the interactions of B-3, S-3, S-7, and S-8 at the LmTIM interface. To investigate the effects of the charge on the −COOH group in B-3 and S-3, docking runs were performed. The docking results revealed a slight change in the interactions. For B-3, the carboxylate group formed a new interaction type hydrophobic with Tyr103 and a H-bond with Gln112. Conversely, S-3 lost two interactions with Ile69 and Ile109. In both cases, the observed -COO− interactions were with the -NH group (Figures S1 and S2).

3.2. Molecular Dynamics Simulation on the LmTIM protein

Molecular dynamics simulations were conducted at 240 ns using GROMACS version 2018.4 applying the AMBER03 force field. The analysis of the apo-LmTIM protein revealed a root mean square deviation (RMSD) with a minimum of 0.30 Å and a maximum of 2.75 Å of fluctuation throughout the MD trajectory (Figure 3A). The BTZ–LmTIM complex had an RMSD with a minimum of 0.87 Å, a maximum of 8.43 Å, and a fluctuation of 7.56 Å. In comparison, its mean was 3.36 Å (Figure 3A). The B-3–LmTIM complex had an RMSD value from 0.90 Å to 6.15 Å, respectively, with this being the most stable of the complexes analyzed with a fluctuation of 5.25 Å and a mean of 4.06 Å (Figure 3A). On the other hand, the S-3–LmTIM complex had an RMSD value from 1.32 Å to 30.10 Å, respectively, and the fluctuation was 28.78 Å with a mean of 21.33 Å, with this being the most unstable of the five complexes analyzed (Figure 3A). The RMSD values for the S-7–LmTIM complex ranged from 1.02 Å to 23.92 Å, respectively, with a fluctuation of 22.90 Å and a mean of 17.13 Å, achieving the highest stability after 25 ns. Finally, the S-8–LmTIM complex predicted RMSD values from 0.86 Å to 7.39 Å, respectively, along with a fluctuation of 6.54 Å and a mean of 5.94 Å, with this complex being the second most stable of the five complexes analyzed (Figure 3A).
The root mean square fluctuation (RMSF) of the apo-LmTIM protein was between 0.41 Å and 2.75 Å, respectively, with a fluctuation of 2.35 Å and a mean of 0.82 Å (Figure 3B). The BTZ–LmTIM complex showed an RMSF with a minimum of 0.43 Å, a maximum of 3.86 Å, a fluctuation of 3.44 Å, and a mean of 0.94 Å. The B-3–LmTIM complex had an RMSF value from 0.47 Å to 4.13 Å, respectively, with a fluctuation of 3.66 Å and a mean of 1.0 Å. The S-3–LmTIM complex presented an RMSF value from 0.45 Å to 3.96 Å, respectively, with a fluctuation of 3.51 Å and a mean of 0.93 Å. The S-7–LmTIM complex presented an RMSF value from 0.43 Å to 4.13 Å, respectively and a fluctuation of 3.71 Å. Finally, the RMSF of the S-8–LmTIM complex was from 0.46 Å to 3.40 Å, respectively, and the fluctuation was 2.93 Å. The results of the RMSF analysis showed similar fluctuation patterns between the analyzed complexes and apo-LmTIM (Figure 3B).
Finally, a radius of gyration (Rg) analysis of apo-LmTIM in complex with the compounds B-3, S-3, S-7, S-8, and the control BTZ ligand was performed (Figure 3C). The folding of apo-LmTIM maintained a fluctuation between 24.83 Å and 25.67 Å, respectively, a difference in oscillation of 0.84 Å, and a mean of 25.28 Å during the 240 ns analyzed. The BTZ–LmTIM control complex showed a Rg from 24.82 Å to 25.73 Å, respectively, a fluctuation of 0.91 Å, and a mean of 25.23 Å. The B-3–LmTIM complex showed an almost constant fluctuation from 24.52 Å to 25.58 Å, respectively. The difference was 1.06 Å and the mean was 25.02 Å. The S-3–LmTIM complex presented a Rg between 24.68 Å and 25.87 Å, respectively, a fluctuation of 1.19 Å, and a mean of 25.30 Å. The S-7–LmTIM and S-8–LmTIM complexes showed values between 24.60 Å to 25.62 Å and 24.49 Å to 25.65 Å, respectively, with fluctuations of 1.01 Å and 1.16 Å, respectively (Figure 3C).

3.3. Molecular Docking and Molecular Dynamics Simulation on HsTIM

On the other hand, the hit compounds B-3, S-3, S-7, and S-8 were docked on the HsTIM interface to investigate in silico selectivity (Figure 4). The control ligand BTZ showed a vina score of −5.1 Kcal/mol and only four interactions with residues of the HsTIM interface (Arg17, Ser20, Lys84, and Glu119). The docking score was −5.5, −6.6, −6.3, and −6.6 Kcal/mol, respectively, for B-3, S-3, S-7, and S-8. In contrast, poor interactions were observed with the HsTIM residues.
The results of the molecular dynamics simulation analysis showed a constant RMSD value for apo-HsTIM from 0.30 to 3.07 Å, respectively, with a fluctuation of 2.19 Å and a mean of 1.94 Å (Figure 5A). The BTZ–HsTIM complex presented an RMSD value from 0.63 to 29.45 Å, respectively; the oscillation difference was 28.82 Å and the mean was 19.05 Å. Meanwhile, the complexes of the compounds B-3, S-3, S-7, and S-8 with HsTIM presented RMSD values between 0.81 Å and 25.79 Å, respectively, with fluctuations between 15.52 Å and 24.94 Å and averages between 9.70 Å and 21.86 Å, respectively (Figure 5A). Figure 5B shows the RMSF graph where an RMSF from 0.47 Å to 4.30 Å, respectively, with a fluctuation of 3.82 Å and a mean of 1.09 Å are observed. The complexes of the compounds (B-3, S-3, S-7, S-8, and BTZ) with the protein (HsTIM) showed similar values to the free protein between 0.43 Å and 4.89 Å, respectively, with fluctuations between 3.39 Å and 4.46 Å and means between 0.94 Å and 1.10 Å, respectively. The Rg for apo-HsTIM was obtained with a minimum of 24.29 Å, a maximum of 25.62 Å, a fluctuation of 1.34 Å, and a mean of 24.85 Å (Figure 5C). The Rg was between 24.27 Å and 25.42 Å for the complexes of compounds B-3, S-3, S-7, S-8, and BTZ, respectively, while the fluctuations remained less than 1.46 Å with a mean from 24.77 Å to 25.13 Å, respectively.

3.4. In Silico Prediction of Pharmacokinetic Properties

The compounds B-3, S-3, S-7, and S-8 were also evaluated to define their physicochemical properties (Lipinski’s rule of five) using the SwissADME server (Table 2). Compounds S-7 and S-8 had an optimal lipophilicity (LogP), while B-3 and S-3 obtained results slightly above the allowed value of five. All compounds showed adequate values in terms of their molecular weight (<500 g/mol) and polar surface area (TPSA), with values within the established range (90 Å2) from 57.53 to 85.35, respectively, except for S7 (92.35 Å2), which is essential for good penetration through biological membranes [41]. Furthermore, all compounds were within the normal range of the number of hydrogen bond acceptors (≤10) and donors (≤5) according to Lipinski’s rule of five.
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were also determined using SwissADME and ProTox II (Table 3). The analyzed compounds showed a low-to-moderate water solubility. The analysis also predicted that gastrointestinal (GI) absorption would be high for S-3 and S-8, while for B-3 and S-7, it was low. None of the compounds exhibited blood–brain barrier (BBB) permeability, and only S-3 and S-8 showed inhibition towards P-glycoprotein. Compounds B-3 and S-3 did not show inhibition for any CYP450 metabolizing enzyme; contrary to S-7, which was predicted to be an inhibitor of the five isoforms CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4. Compound S-8 only showed inhibition of the CYP2D6 enzyme with low hepatotoxic, carcinogenic, mutagenic, and cytotoxic effects. In addition, pKa values for the -COOH group in B-3 and S-3 were calculated (Figures S3 and S4). Minimal differences were observed in both compounds; however, S-3 was able to reach the ionized form before B-3.
Based on the previous computational analysis, along with the cost and availability in Mol-Port (https://www.molport.com), compounds B-3, S-3, S-7, and S-8 were selected and acquired for in vitro analysis.

3.5. Biological Activity against Promastigotes of L. mexicana and Cytotoxicity

Compounds B-3 (ursolic acid), S-3 (glycyrrhetinic acid), S-7 (sorafenib), and S-8 (indacaterol) (Figure 6) were evaluated against the promastigotes of the L. mexicana strains MNYC/BZ/62/M379 and MHOM/MX/2017/UABJO17FCQEPS. Their leishmanicidal activity is shown in Table 4. Compounds S-7, S-3, and S-8 demonstrated better activity (IC50 = 24.91, 41.18, and 55.13 µM, respectively) against the L. mexicana M379 strain than glucantime (IC50 = 133.96 µM), with S-7 being five times more active. Compounds B-3 and S-8 were the most active compounds (IC50 = 87.16 and 55.97 µM, respectively) against the FCQEPS strain, compared to glucantime (IC50 = 125.23 µM).
The cytotoxic effect of the four compounds against murine J774.2 macrophages and their SI were determined (Table 4). Compounds B-3, S-3, S-7, and S-8 showed a moderate level of cytotoxicity (with CC50 values between 49.16–100 µM). However, all compounds had SI values against L. mexicana FCQEPS strain that were lower than for glucantime (SI = 2.03). Compounds S-3, S-7, and S-8 had SI values against the L. mexicana M379 strain that were similar to that obtained with glucantime.

4. Discussion

4.1. Molecular Docking on LmTIM

In general, compounds from the BIOFACQUIM database displayed a better vina score (from −8.3 to −6.5 Kcal/mol, respectively) than those from the Selleckchem database (from −8.2 to −5.8 Kcal/mol, respectively). The main interactions at the interface of LmTIM were hydrophobic with Phe75, Ile69, and Lys71. These data suggests that these residues are important to accommodate compounds containing a central hydrocarbon rings nucleus. Several hydrogen bond interactions with Ala70, Tyr103, Gln112, and Lys71 were identified, which stabilized the complexes.
Compounds from the Selleckchem database had a similar interaction pattern to BIOFACQUIM. These compounds possess several rings that allow for the formation of hydrophobic contacts with Ile60, Ala70, and Lys71. The main hydrogen bonds were formed with Ala70 and Lys71, which may be attributed to their side chains containing hydrogen donor groups.
These interactions can be attributed to the fact that aromatic rings, alkyl chains, and hydrogen bond donors or acceptors form the side chains of the interface. The control BTZ compound presented interactions with the residues Gln112, Ile109, Tyr102, Tyr103, Ile69, Phe75, Lys71, and Ala70, which are also present in the complexes of the four selected compounds (Figure 2). In addition, interactions with these residues have been described in the binding of compounds at the TIM interface of other species, such as Trypanosoma cruzi [42,43,44]. Hence, our molecular docking predictions are suited in this context.

4.2. Molecular Dynamics Simulation on LmTIM

Molecular dynamics simulation analysis was performed to predict the stability of the LmTIM protein in complex with the compounds B-3, S-3, S-7, S-8, and BTZ. The apo-LmTIM-free protein was also analyzed with these results, being comparable to those previously reported by our work group [45]. In general, compounds B-3 and S-8 in complex with LmTIM showed a more stable behavior than the complex with the control ligand (BTZ–LmTIM) due to their low RMSD and minimal differences in oscillations since they have been generally described in most molecular dynamics simulations with RMSD values < 2 Å with fluctuations between 0.7 and 9 Å, respectively [46,47,48].
The RMSF is a measure of the variation in the structure of a protein over time, which is why it is also analyzed during molecular dynamics simulations [49]. RMSF calculations revealed that compounds B-3 and S-8 in complex with LmTIM were the least fluctuating, while compounds S-3 and S-7 in complex with LmTIM showed a high level of fluctuation in some regions according to the RMSD pattern. The Rg analysis allowed for the prediction of the structural variations that the protein can present during the molecular dynamics simulation, observing structural compactness, and suggesting that the interactions do not affect the structure of this protein [26,50,51].

4.3. Molecular Docking and Molecular Dynamics Simulation on HsTIM

As LmTIM presents a human homolog, molecular docking and molecular dynamics simulations were carried out to predict whether there was a potential selectivity of compounds (B-3, S-3, S-7, S-8, and BTZ) towards this protein [52]. The results predicted a low vina score (between −5.5 and −6.6 Kcal/mol, respectively) and fewer interactions over the HsTIM interface than the LmTIM interface. These results suggest a lower affinity by the analyzed compounds against HsTIM, supported by the results from the molecular dynamics simulation where unstable complexes were predicted during the 240 ns analyzed (Figure 6).

4.4. In Silico Prediction of Pharmacokinetic Properties

The molecular, physicochemical, and pharmacokinetic properties of the four selected compounds were predicted. In general, these compounds did not violate or have characteristics similar to those of drugs [53]. In silico analyses indicated a good level of permeability through the intestinal membrane for compounds S-3 and S-8. None of the four compounds demonstrated blood–brain barrier permeability but did show potential for good oral absorption without harming the gut [41]. Compound B-3 did not show inhibition of any CYP450 metabolizing enzyme, while compound S-8 did not exhibit any form of hepatotoxicity, carcinogenicity, mutagenicity, or cytotoxicity. Therefore, these compounds can be administered orally with few adverse effects.

4.5. Biological Activity against L. mexicana Promastigotes and Cytotoxicity

Interestingly, compounds B-3 (ursolic acid) and S-3 (glycyrrhetinic acid) have a similar chemical structure (pentacyclic triterpenoid carboxylic acid), but cause a different biological effect, even between strains. A structure–activity relationship analysis showed that a steric effect of the -COOH group (a weak acidic group capable of hydrogen bonding by both donating and accepting a proton) [54] attached at the C-17 position in B-3 and the 2-position in S-3 is the origin of the differences in biological activity observed in vitro. Additionally, the incorporation of an oxo group at C-13 in S-3 favors its activity. Unfortunately, both compounds have a LogP > 5, which is unfavorable for drug likeness [55]. However, an easy esterification reaction could improve the solubility profile and activity of both compounds.
Sorafenib (S-7) has a moiety of picolinamide, a monocarboxylic acid amide derivative of picolinic acid [56]. Sorafenib inhibits the RAF/MEK/ERK pathway and receptor tyrosine kinases in unresectable liver carcinoma [57]. In Leishmania species, families of protein kinases are involved in cell survival [58]. Therefore, this information and our prediction as a ligand of LmTIM suggest that sorafenib could cause a dual effect (kinase and TIM inhibitor) against L. mexicana.
Indacaterol (S-8), a monohydroxyquinolinone derivative used as a β-adrenergic agonist, had the best leishmanicidal activity in both strains. Indacaterol has a quinolin-2-one scaffold (Figure 6). It is a natural product derivative found in plants, like Glycosmis pentaphylla, Houttuynia cordata, and Aconitum ferox. The mechanism of action associated with indacaterol includes the stimulation of intracellular adenyl cyclase, which catalyzes the conversion of ATP into cyclic adenosine monophosphate (cAMP), increasing cAMP levels [59]; a mechanism of action reported for other Leishmania species [60]. Therefore, the biological activity of indacaterol against L. mexicana could also be explained by a dual effect.
Finally, the different biological behaviors of ursolic acid (B-3), glycyrrhetinic acid (S-3), and sorafenib (S-7) in both strains can be attributed to resistance or sensibility mechanisms in L. mexicana strains [61].

5. Conclusions

In this work, an SBVS identified four compounds derived from natural products as potential ligands of LmTIM with leishmanicidal activity. Compounds S-3, S-7, and S-8 displayed a significant effect (p < 0.05) against promastigotes of the L. mexicana strain MNYC/BZ/62/M379, with S-7 (sorafenib) highlighted as the best compared to the drug reference glucantime. Interestingly, compound S-8 (indacaterol) displayed a significant leishmanicidal activity against both strains of L. mexicana. These findings suggest that compounds targeting the LmTIM interface are disrupting it and exerting a leishmanicidal effect. Additionally, this study revealed that S-7 (sorafenib) and S-8 (indacaterol) can be used as scaffolds to develop new and more potent anti-L. mexicana agents. Finally, future structural optimization studies, like esterification for the carboxylic triterpenoids B-3 and S-3, may improve biological activity and decrease toxicity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pharmaceutics15082046/s1. Figure S1. 3D interactions of compounds B-3 and S-3 in their ionized form at LmTIM interface. Figure S2: 3D interactions of compounds B-3 and S-3 in their ionized form. Figure S3: Determination of pKa B-3 using in silico prediction. Figure S4: Determination of pKa S-3 using in silico prediction.

Author Contributions

Conceptualization, G.R.; Methodology, L.D.G.-M., A.M.-R., L.K.V.-J., T.D.-M., A.J.-S., E.O.-P., A.D.P.-G., E.E.L.-R., L.Y.-M., P.M. and G.R.; Validation, L.K.V.-J. and A.J.-S.; Formal analysis, L.D.G.-M., T.D.-M., A.J.-S., E.O.-P., A.D.P.-G., E.E.L.-R. and P.M.; Data curation, T.D.-M.; Writing—original draft, L.D.G.-M. and G.R.; Writing—review & editing, A.M.-R., E.O.-P., E.E.L.-R. and L.Y.-M.; Funding acquisition, A.M.-R., L.Y.-M. and G.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Secretaria de Investigacion y Posgrado del Instituto Politecnico Nacional (SIP-20230935). Gildardo Rivera Sánchez holds a scholarship from the “Comisión de Operación y Fomento de Actividades Académicas” (COFAA-IPN) and the “Programa de Estímulos al Desempeño de los Investigadores” (EDI-IPN).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article and supplementary material.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mokni, M. Leishmanioses cutanées [Cutaneous leishmaniasis]. Ann. Dermatol. Venereol. 2019, 146, 232–246. [Google Scholar] [CrossRef] [PubMed]
  2. Gurel, M.S.; Tekin, B.; Uzun, S. Cutaneous leishmaniasis: A great imitator. Clin. Dermatol. 2020, 38, 140–151. [Google Scholar] [CrossRef] [PubMed]
  3. Croft, S.L.; Coombs, G.H. Leishmaniasis–current chemotherapy and recent advances in the search for novel drugs. Trends Parasitol. 2003, 19, 502–508. [Google Scholar] [CrossRef]
  4. Yanik, M.; Gurel, M.S.; Simsek, Z.; Kati, M. The psychological impact of cutaneous leishmaniasis. Clin. Exp. Dermatol. Clin. Dermatol. 2004, 29, 464–467. [Google Scholar] [CrossRef]
  5. Handler, M.Z.; Patel, P.A.; Kapila, R.; Al-Qubati, Y.; Schwartz, R.A. Cutaneous and mucocutaneous leishmaniasis: Differential diagnosis, diagnosis, histopathology, and management. J. Am. Acad. Dermatol. 2015, 73, 911–926, 927–928. [Google Scholar] [CrossRef]
  6. Burza, S.; Croft, S.L.; Boelaert, M. Leishmaniasis. Lancet 2018, 392, 951–970. [Google Scholar] [PubMed]
  7. Kedzierski, L. Leishmaniasis. Hum. Vaccin. 2011, 7, 1204–1214. [Google Scholar] [CrossRef] [Green Version]
  8. Safety and Efficacy of Azithromycin to Treat Cutaneous Leishmaniasis (PCL01). Available online: https://clinicaltrials.gov/ct2/show/NCT00682656?cond=Leishmaniasis&draw=3&rank=55 (accessed on 16 March 2023).
  9. Ilari, A.; Baiocco, P.; Messori, L.; Fiorillo, A.; Boffi, A.; Gramiccia, M.; Di Muccio, T.; Colotti, G. A gold-containing drug against parasitic polyamine metabolism: The X-ray structure of trypanothione reductase from Leishmania infantum in complex with auranofin reveals a dual mechanism of enzyme inhibition. Amino Acids 2012, 42, 803–811. [Google Scholar]
  10. Figueroa-Villar, J.D.; Sales, E.M. The importance of nucleoside hydrolase enzyme (NH) in studies to treatment of Leishmania: A review. Chem. Biol. Interact. 2017, 263, 18–27. [Google Scholar] [CrossRef]
  11. Garcia, A.R.; Oliveira, D.M.P.; Claudia, F.; Amaral, A.; Jesus, J.B.; Rennó Sodero, A.C.; Souza, A.M.T.; Supuran, C.T.; Vermelho, A.B.; Rodrigues, I.A.; et al. Leishmania infantum arginase: Biochemical characterization and inhibition by naturally occurring phenolic substances. J. Enzyme Inhib. Med. Chem. 2019, 34, 1100–1109. [Google Scholar] [CrossRef] [Green Version]
  12. Gomes, M.T.; Paes-Vieira, L.; Gomes-Vieira, A.L.; Cosentino-Gomes, D.; da Silva, A.P.P.; Giarola, N.L.L.; Da Silva, D.; Sola-Penna, M.; Galina, A.; Meyer-Fernandes, J.R. 3-Bromopyruvate: A new strategy for inhibition of glycolytic enzymes in Leishmania amazonensis. Exp. Parasitol. 2021, 229, 108154. [Google Scholar] [CrossRef]
  13. Kelpšas, V.; Lafumat, B.; Blakeley, M.P.; Coquelle, N.; Oksanen, E.; von Wachenfeldt, C. Perdeuteration, large crystal growth and neutron data collection of Leishmania mexicana triose-phosphate isomerase E65Q variant. Acta Crystallogr. F Struct. Biol. Commun. 2019, 75, 260–269. [Google Scholar]
  14. Olivares-Illana, V.; Rodríguez-Romero, A.; Becker, I.; Berzunza, M.; García, J.; Pérez-Montfort, R.; Cabrera, N.; López-Calahorra, F.; de Gómez-Puyou, M.T.; Gómez-Puyou, A. Perturbation of the dimer interface of triosephosphate isomerase and its effect on Trypanosoma cruzi. PLoS Negl. Trop. Dis. 2007, 1, e1. [Google Scholar]
  15. Zin, N.N.I.N.M.; Rahimi, W.N.A.W.M.; Bakar, N.A. A Review of Quercus infectoria (Olivier) Galls as a Resource for Anti-parasitic Agents: In Vitro and In Vivo Studies. Malays. J. Med. Sci. 2019, 26, 19–34. [Google Scholar]
  16. Kingston, D.G.I.; Cassera, M.B. Antimalarial Natural Products. Prog. Chem. Org. Nat. Prod. 2022, 117, 1–106. [Google Scholar]
  17. Cartuche, L.; Sifaoui, I.; López-Arencibia, A.; Bethencourt-Estrella, C.J.; San Nicolás-Hernández, D.; Lorenzo-Morales, J.; Piñero, J.E.; Díaz-Marrero, A.R.; Fernández, J.J. Antikinetoplastid Activity of Indolocarbazoles from Streptomyces sanyensis. Biomolecules 2020, 10, 657. [Google Scholar] [PubMed] [Green Version]
  18. Silva-Silva, J.V.; Moragas-Tellis, C.J.; Chagas, M.S.S.; Souza, P.V.R.; Moreira, D.L.; de Souza, C.S.F.; Teixeira, K.F.; Cenci, A.R.; de Oliveira, A.S.; Almeida-Souza, F.; et al. Carajurin: A anthocyanidin from Arrabidaea chica as a potential biological marker of antileishmanial activity. Biomed. Pharmacother. 2021, 141, 111910. [Google Scholar] [CrossRef] [PubMed]
  19. Yang, L.; Wang, Z. Natural Products, Alone or in Combination with FDA-Approved Drugs, to Treat COVID-19 and Lung Cancer. Biomedicines 2021, 9, 689. [Google Scholar] [CrossRef] [PubMed]
  20. Das, S.S.; Dubey, A.K.; Verma, P.R.P.; Singh, S.K.; Singh, S.K. Therapeutic Potential of Quercetin-Loaded Nanoemulsion against Experimental Visceral Leishmaniasis: In Vitro/Ex Vivo Studies and Mechanistic Insights. Mol. Pharm. 2022, 19, 3367–3384. [Google Scholar] [PubMed]
  21. Fróes, Y.N.; Araújo, J.G.N.; Gonçalves, J.R.d.S.; Oliveira, M.d.J.M.G.d.; Everton, G.O.; Filho, V.E.M.; Silva, M.R.C.; Silva, L.D.M.; Silva, L.A.; Neto, L.G.L.; et al. Chemical Characterization and Leishmanicidal Activity In Vitro and In Silico of Natural Products Obtained from Leaves of Vernonanthura brasiliana (L.) H. Rob (Asteraceae). Metabolites 2023, 13, 285. [Google Scholar] [CrossRef]
  22. Adasme, M.F.; Bolz, S.N.; Adelmann, L.; Salentin, S.; Haupt, V.J.; Moreno-Rodríguez, A.; Nogueda-Torres, B.; Castillo-Campos, V.; Yepez-Mulia, L.; De Fuentes-Vicente, J.A.; et al. Repositioned Drugs for Chagas Disease Unveiled via Structure-Based Drug Repositioning. Int. J. Mol. Sci. 2020, 21, 8809. [Google Scholar]
  23. Juárez-Saldivar, A.; Barbosa-Cabrera, E.; Lara-Ramírez, E.E.; Paz-González, A.D.; Martínez-Vázquez, A.V.; Bocanegra-García, V.; Palos, I.; Campillo, N.E.; Rivera, G. Virtual Screening of FDA-Approved Drugs against Triose Phosphate Isomerase from Entamoeba histolytica and Giardia lamblia Identifies Inhibitors of Their Trophozoite Growth Phase. Int. J. Mol. Sci. 2021, 22, 5943. [Google Scholar] [CrossRef] [PubMed]
  24. Herrera-Mayorga, V.; Lara-Ramírez, E.E.; Chacón-Vargas, K.F.; Aguirre-Alvarado, C.; Rodríguez-Páez, L.; Alcántara-Farfán, V.; Cordero-Martínez, J.; Nogueda-Torres, B.; Reyes-Espinosa, F.; Bocanegra-García, V.; et al. Structure-Based Virtual Screening and In Vitro Evaluation of New Trypanosoma cruzi Cruzain Inhibitors. Int. J. Mol. Sci. 2019, 20, 1742. [Google Scholar] [PubMed] [Green Version]
  25. Matadamas-Martínez, F.; Hernández-Campos, A.; Téllez-Valencia, A.; Vázquez-Raygoza, A.; Comparán-Alarcón, S.; Yépez-Mulia, L.; Castillo, R. Leishmania mexicana Trypanothione Reductase Inhibitors: Computational and Biological Studies. Molecules 2019, 24, 3216. [Google Scholar] [PubMed] [Green Version]
  26. Vázquez-Jiménez, L.K.; Juárez-Saldivar, A.; Gómez-Escobedo, R.; Delgado-Maldonado, T.; Méndez-Álvarez, D.; Palos, I.; Bandyopadhyay, D.; Gaona-Lopez, C.; Ortiz-Pérez, E.; Nogueda-Torres, B.; et al. Ligand-Based Virtual Screening and Molecular Docking of Benzimidazoles as Potential Inhibitors of Triosephosphate Isomerase Identified New Trypanocidal Agents. Int. J. Mol. Sci. 2022, 23, 10047. [Google Scholar]
  27. Juárez-Saldivar, A.; Schroeder, M.; Salentin, S.; Haupt, V.J.; Saavedra, E.; Vázquez, C.; Reyes-Espinosa, F.; Herrera-Mayorga, V.; Villalobos-Rocha, J.C.; García-Pérez, C.A.; et al. Computational Drug Repositioning for Chagas Disease Using Protein-Ligand Interaction Profiling. Int. J. Mol. Sci. 2020, 21, 4270. [Google Scholar]
  28. Berman, H.M.; Kleywegt, G.J.; Nakamura, H.; Markley, J.L. How community has shaped the Protein Data Bank. Structure 2013, 21, 1485–1491. [Google Scholar] [PubMed] [Green Version]
  29. Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605–1612. [Google Scholar] [PubMed] [Green Version]
  30. Ravi, L.; Kannabiran, K. A handbook on protein-ligand docking tool: AutoDock 4. Innovare J. Med. Sci. 2016, 4, 28–33. [Google Scholar]
  31. Pilón-Jiménez, B.A.; Saldívar-González, F.I.; Díaz-Eufracio, B.I.; Medina-Franco, J.L. BIOFACQUIM: A Mexican compound database of natural products. Biomolecules 2019, 9, 31. [Google Scholar]
  32. Flores-Padilla, E.A.; Juárez-Mercado, K.E.; Naveja, J.J.; Kim, T.D.; Alain Miranda-Quintana, R.; Medina-Franco, J.L. Chemoinformatic characterization of synthetic screening libraries focused on epigenetic targets. Mol. Inform. 2022, 41, 2100285. [Google Scholar]
  33. O'Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminformatics 2011, 3, 33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [Green Version]
  35. Téllez-Valencia, A.; Olivares-Illana, V.; Hernandez-Santoyo, A.; Perez-Montfort, R.; Costas, M.; Rodriguez-Romero, A.; López-Calahorra, F.; de Gómez-Puyou, M.T.; Gómez-Puyou, A. Inactivation of triosephosphate isomerase from Trypanosoma cruzi by an agent that perturbs its dimer interface. J. Mol. Biol. 2004, 341, 1355–1365. [Google Scholar] [CrossRef] [PubMed]
  36. Adasme, M.F.; Linnemann, K.L.; Bolz, S.N.; Kaiser, F.; Salentin, S.; Haupt, V.J.; Schroeder, M. PLIP 2021: Expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res. 2021, 49, W530–W534. [Google Scholar] [CrossRef] [PubMed]
  37. Olivares-Illana, V.; Pérez-Montfort, R.; López-Calahorra, F.; Costas, M.; Rodríguez-Romero, A.; Tuena de Gómez-Puyou, M.; Gómez Puyou, A. Structural differences in triosephosphate isomerase from different species and discovery of a multi-trypanosomatid inhibitor. Biochemistry 2006, 45, 2556–2560. [Google Scholar] [CrossRef]
  38. Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1, 19–25. [Google Scholar] [CrossRef] [Green Version]
  39. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef] [Green Version]
  40. Banerjee, P.; Eckert, A.O.; Schrey, A.K.; Preissner, R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018, 46, W257–W263. [Google Scholar] [CrossRef] [Green Version]
  41. Ibrahim, Z.Y.; Uzairu, A.; Shallangwa, G.A.; Abechi, S.E. Application of QSAR Method in the Design of Enhanced Antimalarial Derivatives of Azetidine-2-carbonitriles, their Molecular Docking, Drug-likeness, and SwissADME Properties. Iran. J. Pharm. Res. 2021, 20, 254–270. [Google Scholar]
  42. Kurkcuoglu, Z.; Ural, G.; Demet Akten, E.; Doruker, P. Blind Dockings of Benzothiazoles to Multiple Receptor Conformations of Triosephosphate Isomerase from Trypanosoma cruzi and Human. Mol. Inform. 2011, 30, 986–995. [Google Scholar] [CrossRef] [PubMed]
  43. Velázquez-López, J.M.; Hernández-Campos, A.; Yépez-Mulia, L.; Téllez-Valencia, A.; Flores-Carrillo, P.; Nieto-Meneses, R.; Castillo, R. Synthesis and trypanocidal activity of novel benzimidazole derivatives. Eur. J. Med. Chem. Lett. 2016, 26, 4377–4381. [Google Scholar] [CrossRef] [PubMed]
  44. Vázquez-Jiménez, L.K.; Moreno-Herrera, A.; Juárez-Saldivar, A.; González-González, A.; Ortiz-Pérez, E.; Paz-González, A.D.; Palos, I.; Ramírez-Moreno, E.; Rivera, G. Recent Advances in the Development of Triose Phosphate Isomerase Inhibitors as Antiprotozoal Agents. Curr. Med. Chem. 2022, 29, 2504–2529. [Google Scholar] [CrossRef]
  45. Vázquez-Jiménez, L.K.; Juárez-Saldivar, A.; Chan-Bacab, M.J.; Delgado-Maldonado, T.; González-Morales, L.D.; Palos, I.; Ortiz-Pérez, E.; Lara-Ramírez, E.E.; Ramírez-Moreno, E.; Rivera, G. Virtual Screening of Benzimidazole Derivatives as Potential Triose Phosphate Isomerase Inhibitors with Biological Activity against Leishmania mexicana. Pharmaceuticals 2023, 16, 390. [Google Scholar] [CrossRef] [PubMed]
  46. Bhowmik, D.; Jagadeesan, R.; Rai, P.; Nandi, R.; Gugan, K.; Kumar, D. Evaluation of potential drugs against leishmaniasis targeting catalytic subunit of Leishmania donovani nuclear DNA primase using ligand based virtual screening, docking and molecular dynamics approaches. J. Biomol. Struct. Dyn. 2021, 39, 1838–1852. [Google Scholar] [CrossRef]
  47. Kumari, M.; Subbarao, N. Virtual screening to identify novel potential inhibitors for Glutamine synthetase of Mycobacterium tuberculosis. J. Biomole Struct. Dyn. 2020, 38, 5062–5080. [Google Scholar] [CrossRef]
  48. Méndez-Álvarez, D.; Herrera-Mayorga, V.; Juárez-Saldivar, A.; Paz-González, A.D.; Ortiz-Pérez, E.; Bandyopadhyay, D.; Pérez-Sánchez, H.; Rivera, G. Ligand-based virtual screening, molecular docking, and molecular dynamics of eugenol analogs as potential acetylcholinesterase inhibitors with biological activity against Spodoptera frugiperda. Mol. Divers. 2022, 26, 2025–2037. [Google Scholar] [CrossRef]
  49. Collier, T.A.; Piggot, T.J.; Allison, J.R. Molecular Dynamics Simulation of Proteins. Methods Mol. Biol. 2020, 2073, 311–327. [Google Scholar]
  50. Singh, S. Dynamics of heroin molecule inside the lipid membrane: A molecular dynamics study. J. Mol. Model. 2019, 25, 121. [Google Scholar] [CrossRef]
  51. Wolf, A.; Kirschner, K.N. Principal component and clustering analysis on molecular dynamics data of the ribosomal L11·23S subdomain. J. Mol. Model. 2013, 19, 539–549. [Google Scholar] [CrossRef] [Green Version]
  52. Minini, L.; Álvarez, G.; González, M.; Cerecetto, H.; Merlino, A. Molecular docking and molecular dynamics simulation studies of Trypanosoma cruzi triosephosphate isomerase inhibitors. Insights into the inhibition mechanism and selectivity. J. Mol. Graph. Model. 2015, 58, 40–49. [Google Scholar] [CrossRef]
  53. Benet, L.Z.; Hosey, C.M.; Ursu, O.; Oprea, T.I. BDDCS, the Rule of 5 and drugability. Adv. Drug Deliv. Rev. 2016, 101, 89–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Klecker, C.; Nair, L.S. Matrix Chemistry Controlling Stem Cell Behavior. In Biology and Engineering of Stem Cell Niches; Academic Press, Elsevier: Cambridge, MA, USA, 2017; pp. 195–213. [Google Scholar]
  55. Lobo, S. Is there enough focus on lipophilicity in drug discovery? Expert Opin. Drug Discov. 2020, 15, 261–263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Murali, K.; Machado, L.A.; Carvalho, R.L.; Pedrosa, L.F.; Mukherjee, R.; Da Silva Júnior, E.N.; Maiti, D. Decoding Directing Groups and Their Pivotal Role in C-H Activation. Chemistry 2021, 27, 12453–12508. [Google Scholar] [CrossRef] [PubMed]
  57. Abdelgalil, A.A.; Alkahtani, H.M.; Al-Jenoobi, F.I. Sorafenib. In Profiles of Drug Substances, Excipients and Related Methodology; Elsevier: Amsterdam, The Netherlands, 2019; Volume 44, pp. 239–266. [Google Scholar]
  58. Efstathiou, A.; Smirlis, D. Leishmania Protein Kinases: Important Regulators of the Parasite Life Cycle and Molecular Targets for Treating Leishmaniasis. Microorganisms 2021, 9, 691. [Google Scholar] [CrossRef]
  59. McKeage, K. Indacaterol: A review of its use as maintenance therapy in patients with chronic obstructive pulmonary disease. Drugs 2012, 72, 543–563. [Google Scholar] [CrossRef]
  60. Sebastián-Pérez, V.; Hendrickx, S.; Munday, J.C.; Kalejaiye, T.; Martínez, A.; Campillo, N.E.; de Koning, H.; Caljon, G.; Maes, L.; Gil, C. Cyclic Nucleotide-Specific Phosphodiesterases as Potential Drug Targets for Anti-Leishmania Therapy. Antimicrob. Agents Chemother. 2018, 62, 00603–00618. [Google Scholar] [CrossRef]
  61. Alpizar-Sosa, E.A.; Ithnin, N.R.B.; Wei, W.; Pountain, A.W.; Weidt, S.K.; Donachie, A.M.; Ritchie, R.; Dickie, E.A.; Burchmore, R.J.S.; Denny, P.W.; et al. Amphotericin B resistance in Leishmania mexicana: Alterations to sterol metabolism and oxidative stress response. PLoS Negl. Trop. Dis 2022, 16, 0010779. [Google Scholar] [CrossRef] [PubMed]
Pharmaceutics 15 02046 g001 550
Figure 1. Comparison of TIM structures. (A) Superimposition of LmTIM (yellow, PDB 1AMK) and HsTIM (blue, PDB 4POC). The interface residues are highlighted in green, and the conservated residues are indicated in the zoom view. (B) Alignment of the amino acid residues of the TIM sequences.
Figure 1. Comparison of TIM structures. (A) Superimposition of LmTIM (yellow, PDB 1AMK) and HsTIM (blue, PDB 4POC). The interface residues are highlighted in green, and the conservated residues are indicated in the zoom view. (B) Alignment of the amino acid residues of the TIM sequences.
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Figure 2. Three-dimensional representation of the interaction profile of BTZ (A) and the compounds S-3 (B), B-3 (C), S-7 (D), and S-8 (E) at the LmTIM interface. Hydrogen bonds are shown as blue lines and halogen bonds are depicted as cyan lines. The π-stacking interactions are indicated as green dashed lines. Gray dashed lines represent hydrophobic contacts.
Figure 2. Three-dimensional representation of the interaction profile of BTZ (A) and the compounds S-3 (B), B-3 (C), S-7 (D), and S-8 (E) at the LmTIM interface. Hydrogen bonds are shown as blue lines and halogen bonds are depicted as cyan lines. The π-stacking interactions are indicated as green dashed lines. Gray dashed lines represent hydrophobic contacts.
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Figure 3. Molecular dynamics simulation analysis of B-3, S-3, S-7, S-8, and BTZ in complex with apo-LmTIM. (A) Plot of RMSD values of the five complexes and apo-LmTIM. (B) Plot of RMSF values of the five complexes and apo-LmTIM. (C) Plot of Rg values of the five complexes and apo-LmTIM.
Figure 3. Molecular dynamics simulation analysis of B-3, S-3, S-7, S-8, and BTZ in complex with apo-LmTIM. (A) Plot of RMSD values of the five complexes and apo-LmTIM. (B) Plot of RMSF values of the five complexes and apo-LmTIM. (C) Plot of Rg values of the five complexes and apo-LmTIM.
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Figure 4. Predicted interaction profile of the control ligand BTZ (A) and the compounds B-3 (B), S-7 (C), S-8 (D), and S-3 (E) at the HsTIM interface. Hydrogen bonds are shown as blue lines and halogen bonds as cyan lines. The π-stacking interactions are indicated as green dashed lines. Gray dashed lines represent hydrophobic contacts.
Figure 4. Predicted interaction profile of the control ligand BTZ (A) and the compounds B-3 (B), S-7 (C), S-8 (D), and S-3 (E) at the HsTIM interface. Hydrogen bonds are shown as blue lines and halogen bonds as cyan lines. The π-stacking interactions are indicated as green dashed lines. Gray dashed lines represent hydrophobic contacts.
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Figure 5. Molecular dynamics simulation analysis of B-3, S-3, S-7, S-8, and BTZ in complex with apo-HsTIM. (A) Plot of RMSD values of the five complexes and apo-HsTIM. (B) Plot of RMSF values of the five complexes and apo-HsTIM. (C) Plot of Rg values of the five complexes and apo-HsTIM.
Figure 5. Molecular dynamics simulation analysis of B-3, S-3, S-7, S-8, and BTZ in complex with apo-HsTIM. (A) Plot of RMSD values of the five complexes and apo-HsTIM. (B) Plot of RMSF values of the five complexes and apo-HsTIM. (C) Plot of Rg values of the five complexes and apo-HsTIM.
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Figure 6. Natural products from the BIOFACQUIM and Selleckchem databases with leishmanicidal activity identified via SBVS.
Figure 6. Natural products from the BIOFACQUIM and Selleckchem databases with leishmanicidal activity identified via SBVS.
Pharmaceutics 15 02046 g006
Table
Table 1. Vina score and the structures of the top ten compounds obtained from the Biofacquim and Selleckchem databases as potential ligands of LmTIM.
Table 1. Vina score and the structures of the top ten compounds obtained from the Biofacquim and Selleckchem databases as potential ligands of LmTIM.
BiofacquimSelleckchem
IDStructureVina Score (Kcal/mol)IDStructureVina Score (Kcal/mol)
B-1Pharmaceutics 15 02046 i001−8.3S-1Pharmaceutics 15 02046 i002−8.2
B-2Pharmaceutics 15 02046 i003−7.8S-2Pharmaceutics 15 02046 i004−8.1
B-3Pharmaceutics 15 02046 i005−7.6S-3Pharmaceutics 15 02046 i006−7.7
B-4Pharmaceutics 15 02046 i007−7.6S-4Pharmaceutics 15 02046 i008−7.7
B-5Pharmaceutics 15 02046 i009−7.6S-5Pharmaceutics 15 02046 i010−7.3
B-6Pharmaceutics 15 02046 i011−7.5S-6Pharmaceutics 15 02046 i012−6.8
B-7Pharmaceutics 15 02046 i013−7.2S-7Pharmaceutics 15 02046 i014−6.7
B-8Pharmaceutics 15 02046 i015−7.1S-8Pharmaceutics 15 02046 i016−6.5
B-9Pharmaceutics 15 02046 i017−6.5S-9Pharmaceutics 15 02046 i018−6.0
B-10Pharmaceutics 15 02046 i019−6.5S-10Pharmaceutics 15 02046 i020−5.8
Table
Table 2. Physicochemical properties of the compounds B-3, S-3, S-7, and S-8.
Table 2. Physicochemical properties of the compounds B-3, S-3, S-7, and S-8.
CompoundMolecular Weight
(g/mol) < 500		Hydrogen Bond Acceptors < 10Hydrogen Bond Donors < 5Rotatable Bonds < 10a TPSA (Ų) < 140pKa
(-COOH)		b Log P < 5c Log S
B-3456.732157.534.745.88Poor
S-3470.6842174.604.445.17Moderate
S-7464.8273992.35–4.10Moderate
S-8392.4944685.35–3.53Soluble
a TPSA: polar surface area, b Log P: partition coefficient, and c Log S: solubility coefficient. pKa calculation using MarvinSketch (https://chemaxon.com/marvin accessed 13 April 2023).
Table
Table 3. Pharmacokinetic and toxicological properties of the compounds B-3, S-3, S-7, and S-8.
Table 3. Pharmacokinetic and toxicological properties of the compounds B-3, S-3, S-7, and S-8.
Pharmacokinetic and
Toxicological Properties		Compound
B-3S-3S-7S-8
GI absorptionLowHighLowHigh
BBB permeantNoNoNoNo
P-gp substrateNoYesNoYes
CYP1A2 inhibitorNoNoYesNo
CYP2C19 inhibitorNoNoYesNo
CYP2C9 inhibitorNoNoYesNo
CYP2D6 inhibitorNoNoYesYes
CYP3A4 inhibitorNoNoYesNo
HepatotoxicityActive 52%Inactive 69%Active 82%Inactive 99%
CarcinogenicityActive 57%Active 55%Inactive 50%Inactive 91%
MutagenicityInactive 85%Inactive 90%Inactive 79%Inactive 97%
CytotoxicityInactive 99%Inactive 91%Active 77%Inactive 66%
Table
Table 4. Leishmanicidal activity, cytotoxicity, and the selectivity index of four natural products and glucantime against the promastigote of two L. mexicana strains.
Table 4. Leishmanicidal activity, cytotoxicity, and the selectivity index of four natural products and glucantime against the promastigote of two L. mexicana strains.
CompoundLeishmania mexicana
c IC50 (µM ± SD)		J774.2 Cell Line
d CC50 (µM ± SD)		e SI
M379		e SI
FCQEPS
a M379b FCQEPS
B-3 (ursolic acid)>20087.16 ± 7.05 *49.16 ± 5.530.240.56
S-3 (glycyrrhetinic acid)44.18 ± 5.03 *144.52 ± 12.13>1002.260.69
S-7 (sorafenib)24.91 ± 3.08 *166.23 ± 18.1364.89 ± 9.582.600.39
S-8 (indacaterol)55.13 ± 2.34 *55.97 ± 5.87 *>1001.811.78
Glucantime133.96 ± 2.79125.23 ± 11.64>273.202.032.18
a M379: MNYC/BZ/62/M379. b FCQEPS: MHOM/MX/2017/UABJO17FCQEPS. c IC50: compound concentration that produced a 50% reduction in parasites. d CC50: compound concentration that produced a 50% reduction in the J774.2 cell line. e SI: selectivity index (CC50/IC50). The * bold indicates statistically significant differences with the drug reference (p < 0.05).
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González-Morales, L.D.; Moreno-Rodríguez, A.; Vázquez-Jiménez, L.K.; Delgado-Maldonado, T.; Juárez-Saldivar, A.; Ortiz-Pérez, E.; Paz-Gonzalez, A.D.; Lara-Ramírez, E.E.; Yépez-Mulia, L.; Meza, P.; et al. Triose Phosphate Isomerase Structure-Based Virtual Screening and In Vitro Biological Activity of Natural Products as Leishmania mexicana Inhibitors. Pharmaceutics 2023, 15, 2046. https://doi.org/10.3390/pharmaceutics15082046

AMA Style

González-Morales LD, Moreno-Rodríguez A, Vázquez-Jiménez LK, Delgado-Maldonado T, Juárez-Saldivar A, Ortiz-Pérez E, Paz-Gonzalez AD, Lara-Ramírez EE, Yépez-Mulia L, Meza P, et al. Triose Phosphate Isomerase Structure-Based Virtual Screening and In Vitro Biological Activity of Natural Products as Leishmania mexicana Inhibitors. Pharmaceutics. 2023; 15(8):2046. https://doi.org/10.3390/pharmaceutics15082046

Chicago/Turabian Style

González-Morales, Luis D., Adriana Moreno-Rodríguez, Lenci K. Vázquez-Jiménez, Timoteo Delgado-Maldonado, Alfredo Juárez-Saldivar, Eyra Ortiz-Pérez, Alma D. Paz-Gonzalez, Edgar E. Lara-Ramírez, Lilian Yépez-Mulia, Patricia Meza, and et al. 2023. "Triose Phosphate Isomerase Structure-Based Virtual Screening and In Vitro Biological Activity of Natural Products as Leishmania mexicana Inhibitors" Pharmaceutics 15, no. 8: 2046. https://doi.org/10.3390/pharmaceutics15082046

APA Style

González-Morales, L. D., Moreno-Rodríguez, A., Vázquez-Jiménez, L. K., Delgado-Maldonado, T., Juárez-Saldivar, A., Ortiz-Pérez, E., Paz-Gonzalez, A. D., Lara-Ramírez, E. E., Yépez-Mulia, L., Meza, P., & Rivera, G. (2023). Triose Phosphate Isomerase Structure-Based Virtual Screening and In Vitro Biological Activity of Natural Products as Leishmania mexicana Inhibitors. Pharmaceutics, 15(8), 2046. https://doi.org/10.3390/pharmaceutics15082046

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