INTRODUCTION
Leishmaniasis, classified as a neglected tropical disease (NTD), emerges from protozoan parasites belonging to the genus Leishmania and is transmitted through the bite of infected sandflies. The impact of this disease is substantial, affecting millions of individuals globally, especially in developing nations with limited healthcare infrastructure. Leishmaniasis manifests in primary clinical forms: visceral, cutaneous, and mucocutaneous. According to the World Health Organization (WHO), the global burden of leishmaniasis is estimated to range between 700,000 to 1 million new cases annually, with a staggering 90% occurring in only six countries—Afghanistan, Algeria, Brazil, Colombia, Iran, and Syria [1,2]. The sandflies transmitting leishmaniasis are most active during the night and usually breed in damp soil, organic matter, or animal burrows [3].
In Colombia, 10 out of 20 species capable of infecting both humans and other organisms are prevalent. Cutaneous leishmaniasis (CL) constitutes the most frequent form (98-99%), disproportionately affecting the population under five years old and immunocompromised individuals [4,5]. Colombia reported 4,906 cases of CL in 2022, with the departments of Amazonas, Boyacá, Caquetá, Cesar, Córdoba, Cundinamarca, Putumayo, Santander, and Sucre being the most affected areas [6].
Currently, antimonial compounds constitute the primary treatment for leishmaniasis. However, they exhibit high toxicity and resistance in some endemic regions. To mitigate these challenges, alternative drugs have been developed, including liposomal amphotericin B, which significantly reduces side effects and treatment duration compared to free amphotericin B, albeit at a higher cost [7,8]. Other drugs, such as paromomycin and miltefosine, have been associated with high toxicity, resistance, and teratogenic and abortive effects. This underscores the need for discovering and developing low-cost, highly effective drugs with low toxicity [9].
High-throughput screening (HTS) has been utilized since the early 1990s to evaluate the activity of numerous molecules against various diseases, aiding in the identification of potential hits for drug development [10]. However, the uncertainty of success, along with time and screening costs, limits the widespread use of this technique [11]. In recent years, chemoinformatics tools such as molecular docking and machine learning have gained traction, enabling in-silico studies predicting the interactions between proteins and ligands. This approach reduces the need for actual laboratory experiments, accelerating the drug discovery process in a more efficient and cost-effective manner [10-12].
Traditionally, Leishmaniasis has been treated with plants from the Asteraceae family in traditional medicine. The vast diversity of this family (32,913 species) and the diverse range of phytochemicals they encompass, including alkaloids, coumarins, flavonoids, benzofurans, sterols, and terpenoids, present a promising source of novel leishmanicidal compounds [13]. Some studied secondary metabolites from this family include sesquiterpenoids, triterpenes, phytosterols, and kauranes. However, although these compounds have shown activity in inhibiting the disease, their pIC50 values are not sufficiently high. Compounds that are effective at low concentrations and selective against the parasite are preferred. A subgroup of compounds within the Asteraceae family that remains underexplored, despite records indicating promising in-vitro activity, includes derivatives of cinnamic acid [14-16].
AIM
In the present study, was employed to select potential inhibitors of the bifunctional enzyme dihydrofolate reductase-thymidylate synthase (DHFR-TS) of Leishmania major, a crucial protein in the synthesis of DNA in trypanosomatids, essential for the parasite's reproduction through a computational approach.
METHODS
Cinnamic Acid Derivatives Dataset
A dataset of 314 cinnamic acid derivatives was compiled from 76 scientific articles using specific keywords. ChemAxon MarvinSketch was used to design the structures, and 3D structures were generated using Standardizer software.
Classificatory Machine Learning Models
The dataset was imported into Knime software, and descriptors were generated using Volsurf+ and AlvaDesc programs. Models were created using the random forest (RF) algorithm with a five-fold cross-validation procedure. Various performance metrics like sensitivity, specificity, accuracy, ROC curve, and MCC were used to evaluate model performance.
Molecular Docking Calculations
Molecular docking was performed using Molegro 6.0.1 software, considering the LmDHFR-TS model and cinnamic acid derivatives. Docking scores were used to evaluate ligand binding affinity, and methotrexate (MTX) was used as a control.
Molecular Dynamics Simulations
Simulations were conducted using YASARA Structure v. 19.12.14, employing the AMBER14 force field. The MM-PBSA method was used to calculate binding free energies of the enzyme-ligand complexes.
LmDHFR-TS and HsDHFR Enzymatic Inhibition Assays
Recombinant LmDHFR-TS and HsDHFR proteins were used for in-vitro evaluation of selected compounds. A spectrophotometric assay was conducted under standard DHFR conditions, and IC50 values were determined.
Pharmacokinetic Properties Predictions
ADMET parameters and drug toxicity predictions were calculated for specific compounds using ADMETlab 2.0 and OSIRIS DataWarrior.
RESULTS AND DISCUSSION
Initially, an in-house library containing 314 specialized metabolites derived from cinnamic acid was virtually screened in two predictive classification models which were developed using experimental information on the IC50 values retrieved from in-vitro assays of reported compounds against Leishmania. According to the parameters displayed in quality parameters of the RF models, good values were obtained for both AlvaDesc (AUC: 0.863 and 0.906, MCC: 0.554 and 0.645) and VolSurf (AUC: 0.855 and 0.884, MCC: 0.539 and 0.598) molecular descriptors.
Regarding precision, recall, and F1-score, good and similar values were obtained for both models, except for the recall for inactive compounds in the model created using VolSurf descriptors, which was low with a value of 0.69. Sensitivity and specificity measures were also calculated to assess the performance of the RF model. For AlvaDesc, the values were 0.807 and 0.752, while for VolSurf, the values were 0.843 and 0.690, respectively. These results indicate a tendency to have few false negatives, a higher value of true negatives, and a lower false-positive rate for both descriptors. The applicability domain was also determined, confirming that the regression model can provide reliable predictions.
Ligand-based virtual screening (VS) was then employed to predict the potential inhibitory activity of 314 compounds derived from cinnamic acid in the Asteraceae family, as documented in the literature. The five best compounds classified using AlvaDesc descriptors were (E)-2-hydroxy-3',6'-dimethoxychalcone (103), apigenin 7-O-(6´´-caffeoyl)-glucoside (235), montamine (63), 3-O-p-coumaroyl-betulinic acid (150), and Cordoin (202). In addition, the top five compounds predicted using VolSurf descriptors were: 6,8-di-C-?-glucopyranosylchrysin (242), montamine (63), dihydrocubebin (305), prebalanophonin (312), and 4-O-feruloyl 5-O-caffeoylquinic acid (96).
In parallel, using a hybrid LmDHFR-TS model constructed based on its amino acid sequence, a structure-based ranking, through molecular docking calculations, was performed using the investigated specialized metabolite database. The results showed that energy-based scoring values were lower for the cinnamic acid derivatives than for the reference ligands, suggesting that the studied compounds have a better affinity with the LmDHFR-TS active site in the molecular recognition process. Additionally, the docking results showed that 24.5% of the 314 cinnamic acid derivatives dataset had PSB values above 0.5, and 64 of these top-ranked compounds had a lower docking score than methotrexate, which obtained -114.15 kJ/mol.
Three of the top-ranked molecules predicted to have high ligand-based probability values based on the RF model also demonstrated high structure-based probability values. Specifically, compound 242, ranked fourth in the structure-based classification was the best classified in the ligand-based VS model with VolSurf descriptors. Compounds 235 and 63, positioned among the top ten compounds in structure-based VS with docking scores of -161.4 kJ/mol and -160.1 kJ/mol, respectively, also showed high ligand-based probabilities. Compound 235 was predicted to be the second-best structure with high potential for inhibition using the model built with AlvaDesc descriptors, while Compound 63 was classified in the top three for both RF models (AlvaDesc and VolSurf molecular descriptors).
The analysis of residues for the best poses in the top three compounds showed that the residues responsible for ligand binding (Val30, Val31, Ala32, Ile45, Trp47, Asp52, Met53, Phe56, Val87, Pro88, Phe91, Leu94, Val156, Tyr162, and Thr180) have been previously reported in the literature as part of the active site. Certain characteristics of these residues, such as accessibility and charge distribution, enable selective drug design against these protozoans without affecting human enzymes [17]. The highest ranked structure (compound 241) possesses heterocyclic rings like the reference ligands, with oxygen atoms replacing the nitrogen atoms present in the reference ligands. However, due to the similar electronegativities of nitrogen and oxygen, these atoms favor nearly identical interactions with the enzyme's active site.
Through a consensus analysis, molecules with the highest probability of being inhibitors by both approaches were classified as possible hits. The consensus analysis identified 110 compounds with combined-approach probability values greater than 0.5; however, only 47% of these compounds (52) were classified as active through the three in-silico models used in this study. Compound 63 (montamine) was the top-ranked compound. Montamine is an indole alkaloid that has been isolated from Asteraceae species, such as Centaurea schischkinii and Centaurea montana. Previous studies have reported its anticancer properties [18, 19], but its efficacy against Leishmania has not been investigated.
Among the tested compounds, apigenin 7-O-rutinoside (39), lithospermic acid (237), diarctigenin (306), and isolappaol A (308) – four cinnamic acid derivatives that previously exhibited moderate values in both RF models and the molecular docking calculations (all classified as active) appeared among the top ten ranked compounds in the combined approach. Hence, these compounds emerge as interesting antileishmanial candidates, as they exhibit activity across all models and maintain consistency in their probability values. Notably, consensus scoring methods are known to enhance hit rates by diminishing the likelihood of false positives.
After, molecular dynamics (MD) studies were conducted to assess the protein–ligand stabilities, considering factors such as solvent, ions, pressure, and temperature for compounds 237, 306, and 308. These three compounds were identified as potential inhibitors of LmDHFR-TS through the consensus analysis of the approaches outlined in this study. Methotrexate (MTX) was also used as the reference ligand.
The structural stability was evaluated through root-mean-square deviation (RMSD) measurements. During the simulated time (100 ns), all tested compounds showed similar behavior concerning the apoenzyme of LmDHFR-TS (apoLmDHFR-TS, the protein devoid of a ligand) and the LmDHFR-TS-MTX complex. After 20 ns, all ligands (including the reference ligand) displayed diminished disturbances, reaching values ranging from 0.23 to 0.27 nm. The derivative 237 exhibited a higher disturbance between 60 and 85 ns, with RMSD values varying close to 0.1 nm.
Subsequently, we analyzed the root-mean-square fluctuation (RMSF) plot, allowing us to examine residue flexibility in the presence of various ligands. The RMSF plot revealed higher fluctuations in the N-terminus region, reaching approximately 0.35 nm, while the C-terminal residues exhibited lower fluctuations with values close to 0.20 nm. Regions characterized by well-defined tertiary structures, such as ?-helices or ?-sheets, consistently displayed RMSF values ranging from 0.10 to 0.20 nm.
In general terms, all evaluated compounds exhibited the same behavior; however, we identified some specific cases. The residues Glu218 and Thr410, located in the protein's loop regions, showed the highest fluctuations for the apoenzyme, with Glu218 having approximately twice the RMSF value compared to the complexes with MTX and the tested cinnamic acid derivatives. Among the selected compounds, compound 237 displayed higher fluctuations in the loop regions than the other derivatives and MTX, with Gly118, Arg254, and Arg380 being the most variable amino acids. Compounds 306 and 308 exhibited a similar behavior throughout the simulation, with reduced flexibility when complexed with LmDHFR-TS.
The structural compactness and level of mobility of the protein-ligand complexes throughout the simulation period were evaluated using the radius of gyration (RoG) plot. In the case of LmDHFR-TS, during the initial half of the 50 ns simulation, the complexes with cinnamic acid derivatives displayed RoG values that were indistinguishable from those of the control MTX and apoLmDHFR-TS (ranging from 2.64 nm to 2.70 nm). This finding suggests a high level of stability and low fluctuations in the tertiary structure. However, after 60 ns, compounds 237, 306, and 308 exhibited a similar behavior (varying between 2.64 nm to 2.70 nm) with increased perturbation compared to the DHFR-TS-MTX complex and the apoenzyme, which maintained a consistent mean value with fluctuations ranging from 2.62 to 2.64 nm.
The structures 237, 306, and 308 were then evaluated through in-vitro assays using the recombinant LmDHFR-TS. The IC50 values were determined by analyzing concentration-response curves in the 0.1–128 ?M range, using spectrophotometric monitoring of enzymatic activity under a standard DHFR assay. This analysis yielded a spectrum of values spanning from 6.1 to 53.2 ?M, corresponding to pIC50 values ranging from 4.27 to 5.21. The compounds 237, 306, and 308 were the most active molecules against LmDHFR-TS. Hesperidin (IC50 = 21.6 ?M) showed high activity against the target among the three tested flavonoids, with observed IC50 values of 53.2 ?M and 41.7 ?M for isovitexin 4'-O-glucoside and rutin, respectively.
Subsequently, based on the obtained results from in-vitro tests using the recombinant protein of Homo sapiens (Hs) DHFR, the selectivity index (SI) was calculated. The IC50 values obtained against HsDHFR showed a different pattern, suggesting different mechanisms of action for these two proteins. Moderate SI values were revealed, with both benzylbutyrolactone-type lignans (compounds 306, and 308) showing the highest SI values at 4.6 and 4.4, respectively. Both lignans exhibited higher SI values than MTX, which was used as a positive control. Finally, ADMET properties were calculated, and none of the three studied compounds possess mutagenic, tumorigenic, reproductive, or irritant effects.
CONCLUSIONS
The lignans 237, 306, and 308 emerged as an interesting alternative as hits against LmDHFR-TS; however, specific assays against the parasitic forms of Leishmania major are required to extend a clearer prospect for fighting this neglected tropical disease.
ACKNOWLEDGES
Authors thank Universidad ECCI, UMNG and CNPq for their financial support. This study is a result of the Program + Woman + Science + Equity, an initiative of the Ministry of Science, Technology, and Innovation of Colombia in collaboration with the Organization of Ibero-American States for Education, Science, and Culture (OEI). Additionally, this study is a cooperative activity within the Research Network on Natural Products against Neglected Diseases (ResNet NPND, www.resnetnpnd.org).
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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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