Abstract
Trypanosoma cruzi, the causative agent of Chagas disease, poses a life-threatening risk in both endemic and non-endemic regions. The parasite's survival depends on the production of sterols via the 14-α-demethylase (CYP51) metabolic pathway. Current treatments for Chagas disease are often associated with undesirable side effects and drug resistance. This study aimed to identify potential inhibitors of CYP51 using bioactive compounds derived from Tinospora cordifolia. A library of 122 compounds from T. cordifolia was screened against CYP51 using the Glide docking model in the Maestro-Schrodinger suite (2022). The top four leads were evaluated through e-pharmacophore modeling, pharmacokinetics (ADMET) analysis and molecular mechanics generalized Born surface area (MM-GBSA) calculations. The top four compounds exhibited superior binding affinity to CYP51 compared to the standard drug, benznidazole, with docking scores ranging from − 11.397 kcal/mol to − 9.539 kcal/mol. ADMET predictions suggested low cytotoxicity for these compounds. Among the leads, epicatechin and n-trans-caffeoyl tyramine showed the greatest stability, reduced flexibility, and compact conformations, making them promising candidates for further investigation. This study identifies potential inhibitors from T. cordifolia with high binding affinity and structural compatibility with CYP51. While these results are encouraging, further in vivo and in vitro studies are necessary to validate their efficacy as anti-Chagas agents.
Supplementary Information
The online version contains supplementary material available at 10.1007/s40203-025-00312-w.
Keywords: Tinospora cordifolia, Chagas disease, Computational models, Phyto-compounds, In-silico, Sterol 14-α-demethylase
Background
American trypanosomiasis, commonly known as Chagas disease (CD), is one of the neglected tropical diseases (NTDs) that is spread by kissing bugs (Omoboyowa 2022). According to Lidani et al. (2019), the illness is a major public health risk in most of the nations where it is prevalent. The World Health Organization (WHO) estimates that 6–7 million individuals worldwide suffer from CD. It is commonly contracted by humans and other mammals in rural Latin American countries through contact with the urine or dung of kissing bugs, also known as triatomine bugs (Conlan and Lal 2015). The disease is currently common outside of Latin American nations as a result of migration from these endemic areas to Japan, the US, Canada, and Australia (Ribeiro et al. 2012). The three stages of Chagas disease—acute, indeterminate, and chronic are fully contracted by the host whenever they come into touch with T. cruzi (Buckner 2008). Since most infected individuals have relatively non-specific clinical symptoms, the acute stage of Chagas disease often appears soon after infection, as the majority of infected people present vague clinical signs. Furthermore, the diagnosis of the condition is rendered more difficult by the use of outdated and subjective methods (Antas et al. 1999). Benznidazole and Nifurtimox were the first treatments for Chagas disease for a long time. Because Chagas disease is considered a neglected ailment, pharmaceutical companies are not competing to meet manufacturing demands. Notably, the high cost of existing and exclusive Chagas disease medications prevents individuals from affording them (Pinheiro et al. 2017). Cruzis life cycle includes the critical step of 14-α-demethylase synthesizing sterols (Omoboyowa 2022). Mammals use the enzyme sterol 14-α-demethylase to biosynthesize cholesterol. T. cruzi can only grow and survive by using physiologically active sterols; it cannot use host cholesterol, which makes its sterol synthesis pathway particularly interesting for therapeutic research (Lepesheva et al. 2011). Due to significant differences from those of human hosts, the sterol biosynthesis pathway in T. cruzi is a desirable target for therapeutic exploration. To maintain membrane structure and function, T. cruzi depends on endogenously produced ergosterol and related sterols, while humans require cholesterol. The conversion of lanosterol into ergosterol analogs, which are vital for membrane stability, fluidity, and signaling within the parasite, is catalyzed by the enzyme sterol 14-α-demethylase (CYP51), a critical player in this process (Lepesheva et al. 2011). Since CYP51 inhibition interferes with sterol production, it is a highly intriguing target for anti-T. cruzi drug development, as it leads to membrane instability, compromised cellular functions, and ultimately parasite mortality. However, recent reports suggest that T. cruzi may develop resistance to CYP51 inhibitors due to mutations in the enzyme's active site or changes in the parasite’s metabolic pathways, emphasizing the need to explore diverse compounds with high binding affinity and unique interaction profiles (Bustamante et al. 2014). Natural compounds, with their structural diversity and potential for multi-target action, offer a valuable source for new CYP51 inhibitors that may overcome or minimize resistance mechanisms. The Menispermaceae family, which includes T. cordifolia, commonly known as Guduchi, is a naturally occurring herbal shrub (Tiwari et al. 2018). Approximately 32 species of climbing shrubs in the genus Tinospora (Menispermaceae) are found in tropical Africa, Madagascar, Australia, and the Pacific Islands (Udayan et al. 2009). In Ayurvedic medicine, T. cordifolia is classified as a "Rasayana" and is used as a tonic and vitalizer, as well as a treatment for diabetes, skin conditions, heart conditions, jaundice, rheumatoid arthritis, allergies, leprosy, urinary tract issues, and dysentery. While the stems demonstrate hepatoprotective, antipyretic, cytotoxic, antidiabetic, and immunomodulatory actions, the entire plant is said to possess hepatoprotective, antiulcer, and antioxidant qualities (Singh and Chaudhuri 2017). It contains a multitude of chemical constituents, including ß-sitosterol, cordifolioside A, B, and C; cordifoliside D and E; tinosporine (S); magnoflorine; giloinsterol (S); and palmatosides C and F. These constituents fall into various classes of bioactive compounds, including lignans, terpenoids, glycosides, aliphatic compounds, and polysaccharides (Upadhyay et al. 2010). Because of the variety of bioactive chemicals it contains, T. cordifolia is an excellent choice for T. cruzi drug discovery. The potential of phytochemicals from Tinospora cordifolia to act as inhibitors against T. cruzi 14-α-demethylase protease was examined using a computer-aided molecular modeling technique. This comprehensive approach employs the pharmacophore hypothesis, MM/GBSA, molecular docking, molecular dynamics (MD) simulation, and ADMETox screening to evaluate a library of phytochemicals from T. cordifolia. In silico techniques are useful for predicting chemical efficacy and safety profiles, but they have limitations (Sliwoski et al. 2014). Environmental factors, enzyme conformation, and off-target effects in biological systems can all influence real-world interactions, which are only approximated by docking scores and molecular dynamics (MD) simulations. Thus, to validate these results and assess the chemicals' safety and effectiveness in real biological environments, experimental validation is necessary.
Methods
Retrieval of receptors
The Protein Data Bank (PDB) (pdb: 4CKA) provided the 3D structures of T. cruzi 14-α-demethylase protease. These PDB files were processed in Maestro, where the protein preparation wizard was used to prepare the protein. The preparation process included strict energy minimization, insertion of absent side chains, elimination of water and unconventional ligands, and modification of hydrogen positions.
Preparation of compounds from Tinospora cordifolia
Tinospora cordifolia has been reported to contain 122 active chemicals, which were gathered from published literature sources (Sharma et al. 2019; Saha and Ghosh 2012; Singh and Chaudhuri 2017). To identify these compounds, we searched PubChem (https://pubchem.ncbi.nlm.nih.gov) for their chemical names or structures. Following that, the compounds were downloaded in SDF format by selecting the SDF file format from the "Download" option on the PubChem compound information page (https://pubchem.ncbi.nlm.nih.gov). Similar methods were used to obtain benznidazole, the reference drug. The LigPrep tool was then used to prepare each compound using the OPLS_2005 force field once they had been imported into the Schrödinger workspace. Furthermore, the T. cruzi 14-α-demethylase protease co-ligand ((1S)-1-(4-Fluorophenyl)-2-(1H-Imidazol-1-Yl)ethyl 4-Isopropylphenylcarbamate) was also looked up and downloaded in SDF format from PubChem via the same procedure. As a reference, this ligand was prepared in the same way as the standard drug, benznidazole.
Receptor grid generation
The size and binding orientation of the active site for protein–ligand docking are determined during the receptor grid generation process. The Grid Generation Module was utilized in this investigation to calculate the receptor grid coordinates of T. cruzi 14-α-demethylase protease binding pockets, taking into account the co-crystallized ligands. For each protein target, a distinct grid box was created and aligned with the location of the corresponding co-crystallized ligands. The receptor grid box was generated with the three-dimensional coordinates x = 0.38 Å, y = 26.32 Å, and z = 460.09 Å.
Molecular docking
Docking was performed on Maestro 13.4 (Schrödinger 2022) using the Glide tool (Friesner et al. 2004). Using three hierarchical docking filtering methodologies, benznidazole (the reference drug), the co-crystallized ligand, and one hundred twenty-two (122) compounds from T. cordifolia were virtually screened. The large number of ligands was quickly screened using high-throughput virtual screening (HTVS) docking precision, which has considerably more limited conformational sampling than existing precision filters. Thus, using HTVS docking, the 122 compounds were screened against T. cruzi 14-α-demethylase protease. The standard precision (SP) filtering method was utilized to conduct additional screening on the top twenty hit compounds. Using the extra precision (XP) scoring algorithm, four (4) hits with docking affinities greater than that of the reference medication (benznidazole) were screened further. The docking protocol was validated through a redocking procedure to ensure the accuracy of the docking parameters (see Supplementary Material for details).
Hits virtually screening with E-pharmacophore model
An energy-optimized pharmacophore model was developed for the crystal structure of T. cruzi 14-α-demethylase protease (4CKA) using the co-crystallized ligand (1S)-1-(4-fluorophenyl)-2-(1H-imidazol-1-yl)ethyl 4-isopropylphenylcarbamate. The protein–ligand option of the Phase-Develop pharmacophore tool in the Schrödinger suite (2022) was used to develop the model (Omoboyowa 2022). The four (4) hit compounds from the docking scores were used for virtual screening based on e-pharmacophore. The pharmacophore-based analysis was performed with a phase module to produce a subset of molecules with chemical features suitable for binding to 4CKA in accordance with the created model. The hit compounds were prepared utilizing macro model minimization, and the best hits were justified based on their fitness scores.
MM/GBSA
The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) continuum solvent model was used to calculate the binding free energy of the docked protein–ligand complex. Prime's rotamer search techniques were integrated with the OPLS3 force field and the VSGB solvent model.
Molecular dynamic (MD) simulation
Molecular dynamics is a vital computational technique for examining protein dynamics and fully ascertaining the binding mechanism of receptor-ligand interaction systems. The binding free energy (BFE) of the protein system was determined using the generalized Born surface area/molecular mechanics (MM/GBSA) approach (Genheden and Ryde 2015). The Desmond module of the Schrödinger suite was used to carry out the molecular dynamics (MD) simulation for 4CKA. MD simulation tests were performed on APO (unbound protein), as well as the docked complexes of the hit compounds: epicatechin, N-trans-caffeoyltyramine, N-trans-feruloyltyramine, paprazine, standard drug, and co-crystallized ligand. All three distances were set at 10 Å; the box size was calculated as a buffer, and the volume of the box was reduced before binding the protein–ligand complexes in an orthorhombic box. The solvent model employed was the TIP3P water model. The system's overall charge was neutralized by adding Cl⁻ ions, and the salt content was adjusted to 0.15 M to replicate physiological conditions. An orthorhombic boundary box with dimensions of 10 × 10 × 10 Å was utilized to prepare the system using the System Builder module. The box was reduced using the OPLS3e force field (Omirin et a l. 2023). First, normal Maestro environment procedures were used to minimize and prepare the system. An NPT ensemble was used to perform system relaxation at 300 K and 1.01325 bar of pressure. Trajectory sampling was set at 100 ps intervals with 1000 frames, and the MD simulation lasted for 100 ns, allowing for a thorough sampling of conformational space (Ejeje et al. 2024). To determine the radius of gyration (RoG) (Lobanov et al. 2008), trajectory snapshots, solvent-accessible surface area (SASA) (Roe and Cheatham 2013), Cα root mean square deviation (RMSD), and root mean square fluctuation (RMSF) (Dong et al. 2018) were acquired from simulation trajectories. These MD parameters captured exact atomic interactions between the protein and ligand (Adekunle et al. 2024). MS-MD trajectory analysis and a simulation interaction diagram were used to illustrate and interpret the simulation outputs. The data were plotted using Origin version 6.0.
ADME/Tox screening
The pharmacokinetic profile, drug-likeness, and toxicity of the hit compounds were evaluated along with their absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties using the AdmetSar web server, SwissADME web server, and QikProp module within Schrödinger's Maestro interface.
Results
Docking/MMGBSA
The docking scores and MM/GBSA screening outcomes for the hit compounds are graphically represented in Fig. 1 and Table 1, respectively, with their chemical structures depicted in Fig. 4. To assess the potential inhibitory effects of the selected T. cordifolia ligands, the target protein's T. cruzi 14-α-demethylase protease binding pocket was docked with enhanced precision (XP). In addition to examining the structural interactions of the top compounds, the significant amino acid interactions at the T. cruzi 14-α-demethylase protease binding site were identified.
Fig. 1.
Graphical representation of the binding free energy (docking score) and Prime/MMGBSA (∆G bind) binding energy for docking complexes
Table 1.
The binding affinity (kcal/mol) and MMGBSA of the top-ranked Tinospora cordifolia phytochemical constituents and standard drug against the T. cruzi 14-α-demethylase protease target
| s/n | Compound name | PubChem Cid | docking score (kcal/mol) | mmgbsa (∆G bind) |
|---|---|---|---|---|
| 1 | Epicatechin | 107905 | − 11.397 | − 23.88 |
| 2 | N-trans-caffeoyltyramine | 9994897 | − 10.918 | − 42.15 |
| 3 | Co-crystallized ligand | 76871876 | − 10.653 | − 24.92 |
| 4 | N-trans-feruloyl tyramine | 5280537 | − 10.254 | − 39.3 |
| 5 | Paprazine | 5372945 | − 9.539 | − 45.53 |
| 6 | Benznidazole (standard drug) | 31593 | − 5.892 | − 38.62 |
Fig. 4.
Chemical structures of hit compounds from Tinospora cordifolia:a Epicatechin, bN-trans-caffeoyl tyramine, c co-crystallized ligand, dN-trans-feruloyl tyramine, e Paprazine, and f Benznidazole (standard drug). Structures were sourced from PubChem (https://pubchem.ncbi.nlm.nih.gov)
Figure 1 and Table 1 show a range of docking scores for the four chosen ligands. At − 11.397 kcal/mol, epicatechin exhibited the highest docking score, suggesting a high projected binding affinity. Paprazine and N-trans-feruloyl tyramine had docking values of − 9.539 kcal/mol and − 10.254 kcal/mol, respectively, whereas N-trans-caffeoyl tyramine had a docking score of − 10.918 kcal/mol. In contrast, the standard medication, benzotriazole, had a docking score of − 5.892 kcal/mol, while the co-crystallized ligand showed a value of − 10.653 kcal/mol.
According to these findings, the co-crystallized ligand and the standard drug have weaker projected binding affinities than epicatechin and N-trans-caffeoyl tyramine. We utilized the MM/GBSA method to obtain a more precise assessment of binding free energy and further validate these docking results. Because rigid docking simulations do not account for solvation effects and ligand flexibility, MM/GBSA is a reliable technique for evaluating the binding stability of protein–ligand complexes (Omoboyowa et al. 2022, Tripathi et al. 2013). The MM/GBSA binding free energies for the chosen compounds are also displayed in Fig. 1 and Table 1. Intriguingly, paprazine showed the lowest binding free energy of − 45.53 kcal/mol, indicating a more stable interaction with the target protein than the standard medication (benznidazole, − 38.62 kcal/mol) and the co-crystallized ligand (− 24.92 kcal/mol).
The reason for this disparity between the MM/GBSA energy and the docking score for paprazine could be due to several factors not considered during the initial docking process, including ligand flexibility, solvation effects, or favorable conformational changes during MM/GBSA calculations. Despite having the best docking score, epicatechin's MM/GBSA binding energy was not the best among all the ligands. This indicates that even though epicatechin has a strong initial binding position, it may not form the most stable association when solvent effects and protein flexibility are taken into account. The findings highlight how crucial it is to integrate docking and MM/GBSA analyses to gain a thorough understanding of binding affinities and stabilities. The disparity in ranking between MM/GBSA free energies and docking scores underscores the need for MM/GBSA to improve the assessment of potential inhibitors and highlights the limitations of docking as a stand-alone technique.
The T. cruzi 14-α-demethylase protease's binding pocket was used for extra precision (XP) docking to better understand the inhibitory potential of the selected T. cordifolia ligands (PDB ID: 4CKA). The standard molecule, co-crystallized ligand, and leading compounds all showed structural correlations when critical amino acid interactions were analyzed for each ligand. This investigation clarifies the stability and binding mechanisms of each ligand–protein complex, supporting the potential of the identified hits as strong inhibitors.
Figure 2 displays two-dimensional molecular interactions, whereas Fig. 3 demonstrates three-dimensional molecular interactions. Table 2 and Fig. 2 show that two hydrogen bonds were formed by epicatechin with ALA291 and MET358, along with pi-pi stacking with TYR103 and HEM1480. N-Trans-caffeoyltyramine formed two hydrogen bonds with MET358 and TYR116, as well as pi-pi stacking with TYR103, while the co-crystallized ligand formed one hydrogen bond with TYR116 and pi-pi stacking with HEM1480 and TYR103. N-Trans-feruloyl tyramine formed one hydrogen bond with amino acid TYR116 and no pi-pi stacking. Paprazine formed two hydrogen bonds with amino acids MET358 and TYR116, along with pi-pi stacking with TYR103. Additionally, the standard drug benznidazole formed two hydrogen bonds with amino acids TYR103 and TYR116 and exhibited pi-pi stacking with TYR103 (Fig. 4).
Fig. 2.
2D molecular interaction of hit compounds (Tinospora cordifolia) with binding pocket of T. cruzi 14-α-demethylase protease a Epicatechin bN-trans-caffeoyltyramine c Co-crystallized Ligand dN-trans-feruloyl tyramine e Paprazine f Benznidazole(standard)
Fig. 3.
3D molecular interaction of hit compounds (Tinospora cordifolia) with binding pocket of T. cruzi 14-α-demethylase protease a Epicatechin bN-trans-caffeoyl tyramine c Co-crystallized Ligand dN-trans-Feruloyl tyramine e Paprazine f Benznidazole(standard)
Table 2.
Molecular contact profiling of the top Tinospora cordifolia phytochemicals, the standard drug, and co-crystallized ligand
| Compound name | H-bond interaction | Hydrophobic amino acid | Other interaction |
|---|---|---|---|
| Epicatechin | ALA291, MET358, | ALA291, PHE290, ALA388, ALA287, LEU130, MET123, PHE110, LEU127, TRY103, TYR116, VAL213, ALA115, MET106, VAL359, MET358, LEU356, MET460, VAL461 | PI–PI STACKING:TYR103, HEM1480 |
| N-trans-caffeoyltyramine | MET358, TYR116 | ALA291, PHE290, ALA288, ALA287, MET284, LEU130, LEU127, PHE110, ALA115, TYR116, MET123, TYR103, MET106, MET360, VAL359, MET358, LEU356 | PI–PI STACKING:TYR103 |
| Co-crystallized Ligand | TYR116 | LEU356, ALA391, PHE290, PHE110, ALA288, ALA287, TYR116, ALA115, VAL114, MET123, MET284, LEU127, ILE423, LEU130, MET106, ILE105, LEU208, MET460, TYR103 | PI-PI STACKING:HEM1480, TYR103 |
| N-trans-feruloyl tyramine | TYR116 | MET123, MET284, ALA287, ALA288, PHE110, PHE290, ALA291, MET360, VAL359, MET358, VAL213, LEU356, TYR103, LEU208, MET460, ILE105, LEU130, LEU127, TYR116, MET106, ALA115 | PI–PI STACKING: NONE |
| Paprazine | MET358, TYR116 | ALA291, PHE290, ALA288, ALA287, MET284, LEU130, LEU127, PHE110, ALA115, TYR116, MET123, TYR103, MET106, VAL359, MET358, LEU256 | PI–PI STACKING: TYR103 |
| standard drug Benznidazole | TYR116, TYR103 | LEU130, ALA291, ALA288, LEU127, ALA287, MET123, PHE110, ALA115, TYR116, TYR103, MET106, VAL359, MET358, LEU356 | PI–PI STACKING: TYR103 |
Generation of E-pharmacophore model
The ligand-based pharmacophore approach is an essential computational model for drug development that does not require an understanding of macromolecular protein structure. This approach describes a set of steric and electronic characteristics necessary to ensure molecular interactions with specific biological molecules and to either activate or inhibit the signaling pathways of these proteins (Yang 2010). To discover important interaction properties, the co-crystallized protein complex was used as the reference for the e-pharmacophore model created for this investigation. Figure 5 illustrates the derived hypothesis’s three aromatic rings (orange), two hydrophobic areas (green), and one hydrogen bond donor (blue). A compound's degree of alignment with the pharmacophore model is gauged by its fitness score. Higher fitness scores imply that the molecule may interact with the target protein efficiently, as they indicate a stronger alignment with the key characteristics needed for biological activity. With the highest fitness score of 2.32, the co-crystallized ligand which acts as a benchmark shows the best fit to the model. The two hit compounds with the highest fitness scores are paprazine (1.128) and N-Trans-feruloyl tyramine (1.051). Even though the co-crystallized ligand has higher scores, these still suggest the possibility of biological activity. The pharmacophore hypothesis was generated using the co-crystallized ligand as a reference, highlighting important pharmacophore properties, including hydrophobic contacts (H5, H6), aromatic rings (R8, R9, R10), and a hydrogen bond donor (D4). Figure 6 illustrates the pharmacophore hypothesis in complex with the co-crystallized ligand, demonstrating its role as a reference for evaluating the fit of the identified hit compounds.
Fig. 5.
The H5 and H6 is Hydrophobic (green), R8,R10,R9 are Aromatic rings (orange) hydrogen bond donor (D4: blue) of the pharmacophore hypothesis
Fig. 6.
The pharmacophore hypothesis generated in complex with the reference ligand
Pharmacokinetic screening
An important pharmacokinetic screening test is the measurement of blood–brain barrier (BBB) permeability, which serves two functions: it determines the degree to which potential medications are bioavailable to the central nervous system (CNS) and keeps harmful compounds out of the brain. Despite potential accuracy issues with computer-aided assessments of BBB permeability, the discovery of novel therapeutic candidates has proven to be a time- and cost-saving approach (Table 3).
Table 3.
Fitness scores of hit compounds from the E-pharmacophore model
| s/n | Compound name | PubChem Cid | Fitness scores |
|---|---|---|---|
| 1 | Epicatechin | 107905 | 0.961 |
| 2 | N-trans-caffeoyltyramine | 9994897 | 0.967 |
| 3 | Co-crystallized ligand | 76871876 | 2.32 |
| 4 | N-trans-feruloyl tyramine | 5280537 | 1.051 |
| 5 | Paprazine | 5372945 | 1.128 |
Drug-drug interactions (DDIs) can result from drugs that inhibit specific CYP isoforms, such as CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4, which are involved in the metabolism of many pharmaceuticals. The top compound in Table 4 and Fig. 7, epicatechin, did not show any inhibitory effect on various CYP isoforms, indicating that it is unlikely these compounds will impact the metabolism of other medications.
Table 4.
In-silico drug-likeness, oral bio-availability, pharmacokinetic properties, toxicity profile, and Cytochrome P450 metabolizing enzymes inhibitory potentials of selected Tinospora cordifolia phytochemical constituent, Co-crystallized ligand and standard drug
| Compound name | Epicatechin | N-trans-caffeoyltyramine | Co-crystallized ligand | N-trans-feruloyl tyramine | Paprazine | standard drug |
|---|---|---|---|---|---|---|
| MW g/mol | 442.37 | 299.32 | 367.42 | 313.35 | 283.32 | 260.25 |
| BBB permeant | No | No | Yes | No | Yes | No |
| GI absorption | Low | High | High | High | High | High |
| PGP Substrate | No | No | No | No | No | No |
| CYP1A2 inhibitor | No | No | Yes | No | No | No |
| CYP2C19 inhibitor | No | No | Yes | No | No | No |
| CYP2C9 inhibitor | No | Yes | Yes | No | No | No |
| CYP2D6 inhibitor | No | Yes | Yes | Yes | Yes | No |
| CYP3A4 inhibitor | No | Yes | Yes | Yes | Yes | No |
| Bioavailability Score | 0.55 | 0.55 | 0.55 | 0.55 | 0.55 | 0.55 |
| Hepatotoxicity | active | inactive | active | Inactive | Inactive | active |
| Carcinogenicity | inactive | inactive | inactive | inactive | inactive | Inactive |
| Nephrotoxicity | inactive | inactive | Inactive | inactive | Inactive | inactive |
| Rule of five (5) | 1 | 0 | 1 | 0 | 0 | 0 |
Fig. 7.
The heat map of the ADMET parameter of selected potentials of selected Tinospora cordifolia phytochemical constituent, co-crystallized ligand and standard drug
Drug-drug interactions (DDIs) may also occur due to similarities between CYP3A4 substrate medications and P-glycoprotein substrate pharmaceuticals. P-glycoprotein substrates are excreted in bile and urine, which reduces their bioavailability and increases P-gp clearance (Finch and Pillans 2014). The in silico research conducted for this study indicates that every molecule examined in Table 4 is a P-gp substrate. In pharmacology, "bioavailability" refers to the extent and rate of absorption and transformation of a bioactive molecule into a form that can be utilized (Kim et al. 2014). Drug discovery is frequently hampered by limited oral bioavailability, which can lead to molecules exhibiting excellent performance in vitro and in vivo but failing in clinical trials as a result (Erhirhie et al. 2018). A bioavailability score above 0.5 indicates high oral bioavailability, while a score below 0.5 indicates low oral bioavailability, according to research by Rath et al. (2021). With a bioavailability score of 0.55 for each compound in Table 4, excellent oral bioavailability is indicated. (-) Epicatechin and the co-crystallized ligand are active in hepatotoxicity, whereas N-trans-feruloyl and N-trans-caffeoyl tyramine are not. On the other hand, the standard drug exhibits hepatotoxic activity, indicating possible liver toxicity issues. There is no indication that any of the compounds, including standard drugs, might cause cancer because they are all inert regarding carcinogenicity. Aside from (-) epicatechin, none of the compounds exhibit any nephrotoxicity, suggesting a low probability of kidney toxicity linked to these compounds.
Molecular dynamic (MD) simulation
Root mean square deviation (RMSD)
The seven systems we studied were evaluated for structural stability and system convergence using Root Mean Square Deviation (RMSD). The conformational changes resulting from the atomic deviations were estimated using the C-α backbone. RMSD simulations are necessary for a protein system to achieve stability. Ndagi et al. (2017) calculated the deviation of C-α atoms during the simulation to assess the stability of 4CKA upon chemical binding. While a system with a low RMSD value indicates a protein with decreased structural deviation and is therefore highly stable, an increase in RMSD value indicates greater atomic deviation and a protein with an unstable structure (Oluyemi et al. 2022). Epicatechin, N-trans-caffeoyltyramine, N-trans-feruloyltyramine, paprazine, the standard drug, and the co-crystallized ligand were among the ligands for which RMSD data were obtained in this investigation. The APO state had an RMSD of 2.30 ± 0.01 Å, which is significantly higher than that of the ligand–bound complexes. This is expected given that the lack of a stabilizing ligand allows for more flexibility and fluctuation within the protein structure and acts as a baseline for stability, according to the values displayed in Fig. 8A and Table 5. Epicatechin had the lowest RMSD (1.45 ± 0.02 Å) of all the compounds examined, suggesting the strongest structural stability and the least variation from the reference structure throughout the course of the simulation. In contrast, the ligands N-trans-caffeoyltyramine (1.57 ± 0.02 Å) and co-crystallized ligand (1.52 ± 0.02 Å) have comparable RMSD values, indicating stable interaction with the receptor. As the empirically determined binding pose, the co-crystallized ligand serves as a reference point. Epicatechin and N-trans-caffeoyltyramine, in particular, exhibit outstanding binding stability, making them promising candidates for further drug discovery optimization. The fact that their RMSD values are close to those of the co-crystallized ligand supports these compounds' potential effectiveness and structural compatibility with the target protein.
Fig. 8.
MD simulation of 4CKA in the APO state and complexed to epicatechin, N-trans-caffeoyltyramine, N-trans-feruloyl tyramine, paprazine, the standard drug, and the co-crystallized ligand. A RMSD graphical illustration plot; B RMSF graphical illustration plot; C Radius of Gyration (RoG) representation; D SASA diagram. All simulations were carried out using Schrödinger’s Maestro suite
Table 5.
Interactive properties of MDs of the 4CKA receptors and protein–ligand interactions
| Complex | RMSD Å (mean ± SEM) | RMSF Å (mean ± SEM) |
|---|---|---|
| Epicatechin | 1.45 ± 0.02 | 0.89 ± 0.02 |
| N-trans-caffeoyltyramine | 1.57 ± 0.02 | 0.93 ± 0.02 |
| N-trans-feruloyl tyramine | 1.72 ± 0.02 | 0.93 ± 0.02 |
| Paprazine | 1.84 ± 0.02 | 0.95 ± 0.02 |
| Standard drug | 1.81 ± 0.01 | 1.03 ± 0.02 |
| Co-crystallized ligand | 1.52 ± 0.02 | 0.99 ± 0.03 |
| APO 4CKA | 2.30 ± 0.01 | 1.20 ± 0.02 |
Root mean square fluctuation (RMSF)
The remaining flexibility after a ligand binds is also described by RMSF. While MD simulations are running, it displays variations in the protein residues and reveals how flexible different areas of protein residues are when ligands attach to them (Adewumi et al. 2020). At the atomic level, RMSF analysis demonstrates the importance of molecular dynamics and provides a clearer understanding of the conformational changes that occur upon ligand binding. When determining RMSF, the MD simulations' trajectories show how residues' flexibilities differ with and without a ligand. Higher RMSF values signify more flexible movements, whereas lower fluctuation values indicate fewer conformational changes throughout the simulation (Oluyemi et al. 2022). A baseline for assessing the impact of ligand binding was provided by the unbound apo state, which had an RMSF of 1.20 ± 0.02 Å, as indicated in Fig. 8B and Table 5. The results of this investigation show that standard drug has the maximum flexibility among the ligands at 1.03 ± 0.02 Å, indicating more structural variability during the simulation. In contrast, epicatechin exhibits the least amount of flexibility (0.89 ± 0.02 Å), indicating a stable conformation.
Radius of gyration (RoG)
RoG calculates the overall compactness of a protein structure during the course of simulation. According to Oluyemi et al. (2022), a protein's physiological properties can be affected by how tight or loose its structure is. A high RoG value indicates a loss in structural compactness, whereas a low RoG value indicates a structurally compact protein. Figure 8C and Table 6 demonstrate that molecules N-trans-feruloyltyramine (5.35 ± 0.01 Å) had the largest RoG value, suggesting a longer structure. Given that the standard medication shows the lowest RoG values, this could indicate greater flexibility while suggesting that these molecules maintain a more compact shape throughout the simulation.
Table 6.
Interactive properties of MDs of the 4CKA receptors and protein–ligand interactions
| Complex | RoG Å (mean ± SEM) | SASA Å (mean ± SEM) |
|---|---|---|
| Epicatechin | 4.08 ± 0.01 | 21.19 ± 0.75 |
| N-trans-caffeoyltyramine | 5.32 ± 0.01 | 24.18 ± 1.04 |
| N-trans-feruloyl tyramine | 5.35 ± 0.01 | 29.83 ± 1.17 |
| Paprazine | 5.21 ± 0.01 | 40.09 ± 0.93 |
| Standard drug | 3.49 ± 0.03 | 35.47 ± 0.94 |
| Co-crystallized ligand | 4.77 ± 0.01 | 16.44 ± 0.72 |
Values are represented as mean ± SEM measured in Armstrong units (Å)
RMSD root mean square deviation, RMSF root mean square fluctuation, RoG radius of gyration, MolSA molecular surface area, SASA solvent accessibility surface area
SASA
Furthermore, the solvent-accessible surface area (SASA) was computed to illustrate changes to the 4CKA binding domain upon binding further. During simulations, it displays residue mobility throughout the solvent region (Adekunle et al. 2024). According to Table 6 and Fig. 8D, paprazine has the highest SASA value (40.09 ± 0.93 Å). The co-crystallized ligand (16.44 ± 0.72 Å), epicatechin (21.19 ± 0.75 Å), and N-trans-caffeoyltyramine (24.18 ± 1.04 Å) have lower SASA values, indicating that these compounds have substantial surface areas exposed to solvent. These low values suggest that these compounds are either very compact or contain sizable areas protected from solvent exposure.
Discussion
This study explored the inhibitory potential of bioactive compounds from T. cordifolia against sterol 14-α-demethylase (CYP51), a key enzyme in the life cycle of Trypanosoma cruzi, the pathogen responsible for Chagas disease. Among the compounds, epicatechin exhibited the highest docking score (− 11.397 kcal/mol), surpassing the standard treatment drug benznidazole (− 5.892 kcal/mol) and the co-crystallized ligand (− 10.653 kcal/mol). This suggests that epicatechin may have a stronger binding affinity, positioning it as a promising candidate for further investigation (Ejeje et al. 2024). Similarly, paprazine demonstrated a notable binding energy of − 45.53 kcal/mol, surpassing both the standard drug and co-crystallized ligand, indicating a robust interaction with CYP51.
Pharmacokinetic profiling supported the drug-like properties of these lead compounds. All compounds demonstrated favorable oral bioavailability, with scores of 0.55, exceeding the minimum threshold for acceptable oral absorption (Erhirhie et al. 2018). Furthermore, the absence of significant carcinogenicity and nephrotoxicity in most of the lead compounds bolsters their safety profile, suggesting that natural compounds from T. cordifolia could serve as viable alternatives to conventional treatments for parasitic diseases. Additionally, comprehensive information about the structural stability and flexibility of the 4CKA protein–ligand complexes is provided by molecular dynamics (MD) simulations, which include RMSD, RMSF, RoG, and SASA analyses. According to the RMSD results, epicatechin has the lowest deviation (1.45 ± 0.02 Å) among the ligands tested, indicating strong structural stability that is comparable to that of the co-crystallized ligand (1.52 ± 0.02 Å). It also has the lowest SASA value (21.19 ± 0.75 Å), indicating significant regions that are shielded from solvent interaction. Considering their stability, decreased flexibility, and compact conformations, the results generally point to epicatechin and N-trans-caffeoyltyramine as promising candidates for additional drug discovery. These findings support their consideration for further optimization and experimental validation by highlighting their potential effectiveness and structural compatibility with 4CKA.
The bioactive substances from T. cordifolia that have been identified, especially epicatechin, may have effects on Trypanosoma cruzi that extend beyond merely inhibiting enzymes. These substances may also impact cellular pathways vital to the parasite's life cycle; for instance, while CYP51 inhibition impairs sterol production and affects membrane fluidity and structural integrity—both crucial for parasite survival—epicatechin might increase oxidative stress in the parasite, taking advantage of its weak antioxidant defenses to make T. cruzi more susceptible to host immune responses. Furthermore, these compounds may alter kinase pathways that interfere with cellular signaling necessary for T. cruzi differentiation and proliferation (Soares-Silva et al. 2016), further impeding the course of infection. The therapeutic potential of T. cordifolia compounds is highlighted by these multi-targeted actions. Although these in silico results are encouraging, in vitro and in vivo investigations are still necessary for experimental validation to verify the drug's safety and effectiveness in biological systems. To translate these computational results into potential medicinal applications, further experimental testing is essential.
Conclusions
One important therapeutic target for the development of drugs to treat Chagas disease is sterol-14-α-demethylase. The purpose of this study was to use computational models to predict strong sterol-14-α-demethylase antagonists from T. cordifolia compounds that can inhibit the protein's active site. The compounds were subjected to virtual ADME screening, pharmacophore modeling, MM/GBSA calculations, molecular docking, and molecular dynamics (MD) simulation.
Certain compounds found in T. cordifolia, including ( −) epicatechin, N-trans-caffeoyltyramine, N-trans-feruloyl tyramine, and paprazine, have been identified by the study as possible therapies that can target T. cruzi 14-α-demethylase protease in the treatment of Chagas disease. However, further investigation is required to evaluate and verify these findings, including the extraction of these compounds for in vitro testing to validate and fully realize their therapeutic potential.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We acknowledge the management of the research unit (MOLS & SIMS) for the provision of the needed facilities for this research work.
Abbreviations
- CYP51
Sterol 14a-demethylase
- MM-GBSA
Molecular mechanics generalized born surface area
- PDB
Protein database
- CYP
Cytochrome
- ADMET
Absorption, distribution, metabolism, excretion and toxicity
- BBB
Blood brain barrier permeability negative
- MW
Molecular weight
Author contributions
K.T.M. conceptualized the study, wrote the original draft, and contributed to the methodology. P.C.O. contributed to formal analysis, reviewing, and conceptualization. I.M.A. and Q.O.I. were responsible for methodology. D.S.B. performed visualization, validation, result analysis, and contributed to editing and reviewing the manuscript. A.S.B. and W.K.A. contributed to editing and reviewing the manuscript. S.Y.J. and A.M.A. contributed to reviewing. M.S.M. performed visualization. O.I.O. supervised the project and provided validation. All authors have read and approved the final manuscript.
Funding
This research received no external funding.
Data availability
All results, discussions, and details regarding the methods and materials used in this study are available within the submitted manuscript. This includes the 3D structures of T. cruzi 14-α-demethylase protease (PDB: 4CKA), molecular docking results, e-pharmacophore models, molecular dynamic (MD) simulation, and ADMET/Tox screening data. If additional information or data is required and not available within the manuscript, readers may request it from the corresponding author.
Declarations
Conflict of interest
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All results, discussions, and details regarding the methods and materials used in this study are available within the submitted manuscript. This includes the 3D structures of T. cruzi 14-α-demethylase protease (PDB: 4CKA), molecular docking results, e-pharmacophore models, molecular dynamic (MD) simulation, and ADMET/Tox screening data. If additional information or data is required and not available within the manuscript, readers may request it from the corresponding author.