MOLECULAR DYNAMICS SIMULATION OF FOUR BIFLAVONOIDS WITH THE LEISHMANOLYSIN (gp63) PROTEIN OF Leishmania major

doi:10.21203/rs.3.rs-2238863/v1

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MOLECULAR DYNAMICS SIMULATION OF FOUR BIFLAVONOIDS WITH THE LEISHMANOLYSIN (gp63) PROTEIN OF Leishmania major

https://doi.org/10.21203/rs.3.rs-2238863/v1

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Leishmanolisyn (gp63) is a 63 kDa surface metalloprotease belonging to the M8 family (subclan MA (M), metzincines) that is found in both promastigote and amastigote of leishmania ssp. It constitutes one of the virulence factors in the pathogenesis of Leishmania spp., being crucial in its entry into the macrophage and in complement activation. Therefore, gp63 may be a therapeutic alternative for potential new drugs needed to combat this disease. Among the many functional groups of naturally occurring compounds, the biflavonoids amentoflavone (A), lanaraflavone (L), podocarpusflavone A (PA), and podocarpusflovone B (PB) are promising candidate ligands for the treatment of all forms of leishmaniasis. In this study we carried out the validated molecular dynamics simulation with GROMACS, using mean square deviation (RMSD), mean square fluctuation (RMSF), the radius of gyration (Rg), and intermolecular hydrogen bonds (H-bonds) between the gp63 protein of L. major (1LML) in complex with the four biflavonoids. In addition, the free binding energies were estimated using the MM-PBSA method, to determine their stability in the active site of the protein. The computational analysis revealed that the interaction of lanaroflavone (ΔG_Bing = -156.75 ± 31.91 Kcal/mol for L. major) remained stable in this enzyme, revealing their remarkable potential as a possible antileishmanial agent to combat the Leishmania parasite. On the other hand, amentoflavone, podocarpusflavone A, and B also showed a good affinity for both metalloproteases according to the ΔG_bind values obtained.

Biflavonoids

MMPBSA

Principal Component (PC)

Leishmania spp.

The development of new antileishmanial agents is challenging due to the complex biology of the Leishmania parasite. First, the drug’s efficacy varies by parasite species, clinical symptoms, and even geographic region (Tiuman et al. 2011). Second, the life cycle of the parasite is another determining factor since Leishmania alternates between the extracellular promastigote found in the vector sandflies and the intracellular amastigote, which is present in macrophages and causes clinical symptoms (Séguin et al. 2016). This metalloprotease has also been found in amastigotes, where it is believed to aid macrophage adherence (Brittingham et al. 1995). Therefore, an antileishmanial compound must enter the host macrophage and eliminate the parasite, without damaging the host macrophage (Mosser et al. 1985). In the search for new biological targets against the parasite, the metalloprotease leishmanolisyn (gp63) constitutes a good target for the design of new antileishmanial drugs (Yao et al. 2003).

The zinc-dependent metalloprotease called glycoprotein 63 (gp63) or leishmanolysin found on the surface of the Leishmania parasite, was first discovered in the 1980s, being genetically and biochemically described as an important surface antigen expressed in Leishmania promastigotes of several species and possess a wide range of substrates including casein, gelatin, albumin, hemoglobin, and fibrinogen (Mosser et al. 1985). This protein, called Leishmanolysin or also MSP (Major Surface Protease) (Yao et al. 2003), is an extremely metalloprotease (0.5-1% of the total cellular protein) and is distributed over the entire surface of the promastigote, including the flagella and flagellar pocket, being crucial in its entry into the macrophage and in complement activation (Martin-Olivier et al. 2014). Since leishmanolysin is a protein that has been used by the parasite as a strategy for host cell entry, it has been suggested as a promising biological target for the development of new chemotherapy to treat leishmaniasis (Rodriguez, Sandobal. 2015).

The screening of plant extracts is employed to discover new compounds for infectious diseases, using natural products to detect secondary metabolites with biological. Biflavonoids have recently come to the forefront of the polyphenol family for their antiviral and antituberculosis activities (Lin et al. 2001). Only a few reports are available regarding their antiprotozoal activity, but antiplasmodial activity has been established for compounds of this class (Ahmed et al. 2001; Suárez et al. 2003).

Biflavonoids are dimers of C4-carbonyl flavonoids (chalcones, flavanones, flavones, flavonoids, flavonols, aurons, and isoflavones) that vary in the oxygenation pattern of their components (Sagrera et al. 2011). Most natural biflavonoids contain an interflavonyl bond between the ring B and A of the other flavonoid part (type AB) or two A rings (type AA). The interflavonyl bond may involve ring A (at positions 5, 6, 7, or 8), ring B (at positions 2 ', 3', 4 ', 5', or 6 '), or ring C (at positions 2 or 3), through C-C or C-O-C bonds. In this way, biflavonoids can be classified by indicating the rings involved in the interflavonyl bond (AA, BB, AB, CC, etc.) (Swamy et al. 2006).

Biflavonoids show various biological activities, namely, antifungal, antiviral, antibacterial, antioxidant, antitumor, antiplasmodial, antiallergic, anti-inflammatory, hepatoprotective, vasodilatory, and hypotensive, sometimes better than the corresponding monomers (Sakasai et al. 2007; Kim et al. 2008). It has been found antiprotozoal activity in 7,4´, 7´´-tri-O methylamentoflavone, amentoflavone, and lanaroflavone, among others (Olaf Kunert et al. 2008). These results suggest that the evaluation of its antiprotozoal activity should be extended to other structural types of bioflavonoids (Weniger et al. 2006).

Latterly our group published an article, where we expose the interaction by molecular docking of flavonoids with leishamolysis, showing goodly binding of these compounds to the protein; being the biflavonoids lanaroflavone, amentoflavone, podocarpusflavone A and B as the most promising to be used in the design of novel drugs with antileishmanial activity (Mercado-Camargo et al. 2020). The purpose of this second work using molecular dynamics (MD) is to expand the studies of the behavior of these compounds against their interaction with L. major. Molecular dynamics (MD) simulations were performed to accurately analyze the ligand-protein interaction and determine the binding strength of the biflavonoids to the critical amino acids of the gp63 proteins.

Preparation of the studied systems

The crystallized structure of L. major leishmanolysin (gp63) was retrieved from the Protein Data Bank with code 1LML. The docking and Molecular Dynamics (MD) were carried out according to what was published by Mercado-Camargo et al., 2020.

The GROMACS program was used for performing the basic structural analysis (Abraham et al. 2015). Subsequently, the trajectories were converted to dcd format by VMD (Humphrey et al. 1996). The PCA (principal component analysis), was analyzed through the Bio3d program (Barry et al. 2021), using the method described before (Dash et al. 2019). Discovery Studio software (Accelrys Inc., http://www.accelrys.com) was mainly used as a visualizer in this study (Dassault Systemes BIOVIA, 2016).

Molecular Dynamics Simulation

Molecular dynamics (MD) simulations of 500 ns were performed, the parameters for the ligands and proteins were generated using the Amber99s (f99SB) force field (Hornak et al. 2006), the topologies for 1LML were generated using pdb2gmx included in GROMACS, while the SwissParm (Zoete et al. 2011) online program was used for the ligands (https:/https://www.swissparam.ch/), using the GROMACS 2021.4 package; the protein alone and the protein-ligand complexes were solvated by a cubic periodic box in which each protein was solvated with TIP3P water under periodic boundary conditions (PBCs) (Mark et al. 2001). The system was neutralized, and the ionic strength (0.1 mol L^− 1) of the medium was adjusted by adding Na⁺ and Cl⁻ ions, keeping the number of particles constant. After these steps, energy minimization of the systems was performed until convergence, which was followed by equilibration with pressure and temperature (NVT and NPT ensembles) kept constant at 300 K and 1.0 bar, respectively; equilibration periods were 1.0 ns, production runs were of 10 ns duration, and the V-rescale thermostat and Parrinello − Rahman thermostat were used. The LINCS (Hess et al. 1997) and SETTLE (Miyamoto et al. 1997) algorithms were employed to determine bond lengths of hydrogen atom distance constraints, respectively, whereas long-range interactions were calculated employing the particle-mesh Ewald (PME) method (Darden et al. 1993; York et al. 1994), used to constrain the geometry of the water molecules. The equilibrated system was subjected to the final MD production run of 10 ns, applying the periodic boundary conditions (PBCs) and integrating the equation of motion every 2 fs.

Principal components analysis

The trajectory files generated by the molecular dynamic simulation contain a large amount of information that helped to understand the great contributions to the movement of proteins. The eigenvectors and eigenvalues for the protein systems and the protein-ligand complexes were obtained by calculating a covariance matrix of coordinates of all Cα atoms (Kollman et al. 2000). Principal Component Analysis (PCA) aims to reduce the complexity of dynamic data and extract collective movements and correlate the atoms of the protein (Shukla et al. 2020). The PCA analysis was carried out with snapshots storing every 1 ps in the 500 ns simulation. Covariance matrices of the Cα atoms were constructed to capture the essential collective movements of proteins with and without ligands. The PCA is calculated to establish the relationship between statistically significant conformations (significant global movements) during the trajectory. For entries in the covariance matrix, the positive sign means correlated movements, while the negative sign indicates anti-correlated movement between two Cα atoms. The covariance matrices were then diagonalized to produce a set of eigenvectors with the respective eigenvalues. The eigenvalues represent the relevance of their corresponding eigenvectors in the dynamics of the systems, whereas the eigenvectors with larger eigenvalues represent more relevant motions (Zhe et al. 2019).

Cross-correlation Dynamics Analysis

To interpret the most appropriate dynamic behavior of the simulated systems, a DCCM analysis was performed to evaluate the cross-correlation shift of the protein backbone atoms (Cα). In the MD simulation process, the correlated movements between two paired protein residues can be presented by a DCCM plot. The cross-correlation matrix (Cij) between residues i and j was calculated by implementing the Bio3D package in R.34 The Cij was determined by the following equation:

\({C}_{ij}=\frac{<{\varDelta r}_{i}{\varDelta r}_{j}}{\sqrt{<{\varDelta r}_{i}^{2 }{\varDelta r}_{j}^{2}>}}\) Eq. (1)

where Δr_i or Δr_j represents the displacement from the mean position of the i_th or j_th atom, and the angle bracket denotes an average over the sampled period (Simon et al. 2020; Skjaerven et al. 2014).

MMPBSA free energy calculations

The Poisson-Boltzmann Surface Area Molecular Mechanics (MM-PBSA), implemented in the GROMACS g_mmpbsa tool, was used to estimate the relative free binding energy of each protein-ligand (Michielin et al. 2017). Briefly, the free energy binding (ΔG_bind) of a protein-ligand in the solvent is calculated as the free energy difference between the complex (ΔG_complex) and the sum of the free energy of the protein (ΔG_Protein) and ligand (ΔG_ligand):

ΔG_bind = Δ_Gcomplex - (ΔG_protein + ΔG_ligand) Eq. (2)

The binding free energies of the complexes between the ligands and the proteins were analyzed during the equilibrium phase by capturing 200 snapshots in a 50 ps interval in the last 40 ns MD simulations.

Physicochemical, pharmacokinetic, and toxicity properties prediction

Physicochemical and pharmacokinetic properties were estimated using the free SwissADME server and toxicity was performed using the PtoTox-II-Prediction of Toxicity of Chemical platform (Michielin et al. 2017; Banerjee et al. 2018). The compounds were then studied for molecular weight, log P, topological polar surface area (TPSA), solubility, H-donor, H-acceptor, drug-likeness, and total drug score. Drug absorption, including intestinal absorption, bioavailability, and blood-brain barrier penetration, has been identified by topological polar surface area (TPSA) (Gobind et al. 2021). The predicted toxicity parameters were Hepatotoxicity, Carcinogenicity, Immunotoxicity, Mutagenicity, and Cytotoxicity.

Md Simulation

The MD was carried out from the docking between the flavonoids that presented the best score and the gp63 proteins (apo form) of L. major and with a simulation time of 500 ns. The RMSD, RMSF, Radius of gyration, and Solvent Accessible Surface Area (SASA) among other values were used to evaluate the stability of apo-protein and protein-ligand complexes.

The mean square deviation (RMSD) of the Cα atoms concerning the movements of their initial position was used to determine the stability of the complexes formed by the four biflavonoids amentoflavone (A), lanaroflavone (L), podocarpusflavone A (PA), and podocarpusflavone B (PB) with the leishmanolysin (gp63) (Fig S1) from L. major. The RMSD data of the protein, the ligand-protein, and the ligand alone are shown in Fig. 1. The complexes 1LML-A, 1LML-L, 1LML-PA, and 1LML-PB, follow a similar trend to the apo-1LML form (1.66 ± 0.21 Å), with RMSD values of 2.28 ± 0.27 Å, 1.79 ± 0.13 Å, 2.04 ± 0.14 Å, and 2.30 ± 0.16 Å, respectively. All proteins and complexes converge after 20 to 80 ns, indicating that all simulated systems of L. major reach stability at 500 ns simulation.

To look at the positional stability of the compounds, the RMSD of each of the biflavonoids was determined in the pocket of the 1LML protein (Fig. 2), during the 500 ns trajectory the fluctuations are kept below 2 Å. The average values in the 1LML for ligand were 1.55 ± 0.21 Å, 2.25 ± 0.36 Å, 1.43 ± 0.20 Å and 2.17 ± 0.60 Å, for A, L, PA, and PB respectively. Principally, the four biflavonoids reach their convergence in the protein studied, although certain fluctuations will depend on the protein-ligand complex.

The RMSF was calculated to reveal the differences in the fluctuation of the local regions for the two systems. The RMSF for the biflavonoids A (black), L (purple), PA (orange), and PB (green) in complex with the proteins 1LML (blue) has been calculated from the Cα shown in Fig. 3. In the case of biflabonoids, show fluctuations in the RMSF values in the complex 1LML-A regions comprising residues 125–143, and 218–252. The complex formed by 1LML-PA presents the greatest fluctuations between amino acids 320–336, while in 1LML-PB the fluctuations are little, the most relevant being residues 317–335 and 490–505. There are a few variations for the 1LML-L complex that show the conformational position of each of the molecules under study at different times of the simulation, where it is observed most of the time, that they remained in the binding sites of the 1LML during the simulation.

Radius Of Gyration (Rg) Analysis

The compactness measure was calculated to determine possible changes in the folding and conformation of the structure of the proteins under study. The mean radius of gyration for 1LML is 2.34 ± 0.10 nm, 2.35 ± 0.18 nm, 2.34 ± 0.29 nm, 2.33 ± 0.01 nm, and 2.34 ± 0.38 nm, for the 1LML, 1LML-A, 1LML- L, 1LML-PA, and 1LML-PB, respectively, Fig. S2. Based on the average values observed, biflavonoids produce compact and stable complexes compared to apo-1LML.

Solvent Accessible Area (Sasa)

All protein-biflavonoid complexes show a low SASA score when compared with the apo form of L. major leishmanolysin, the tendency is to maintain up to 500 ns of the simulation, achieving its stability, Fig. S3. The SASA values of 204.89 ± 2.31 nm², 209.63 ± 2.98 nm², 206.00 ± 2.64 nm², 207.54 ± 2.37 nm², and 205.29 ± 2.24 nm² for apo-1LML, 1LML-A, 1LML-L, 1LML-PA, and 1LML-PB, respectively.

H-bonds Analysis

The docking carried out for the biflavonoids reveals the particular interactions are hydrophobic, H-bonds, metal-contact, and π-stacking (π-π T-shaped). The MD simulation reveals the number of hydrogen bonds during the trajectory for the four biflavonoids studied. In the 1LML, a small number of conformations exhibited between 1 and more than 7 H-bonds (Fig. 4). Amentoflavone, lanaroflavone, and podocarpusflavones A and B form an average of 2.0, 3.0, 2.0, and 1.0 with the 1LML, respectively. Among the amino acid residues at the end of the simulated 100 ns form H-bond between leismanoliysin and the biflavonoids, Gly222, Val223, Leu224, Ala225, His264, Glu265, Ala346, Ala350, Ser421, Ser462, and Ser465 stand out. The biflavonoids form hydrogen bonds that stabilize the complex during the simulation. Being the lanoroflavone that best stabilizes the structure of the 1LML protein.

Principal component analysis

A dynamic cross-correlation matrix (DCCM) was generated to identify correlated or anti-correlated movements with the L. major gp63 protein bound to the studied biflavonoids. Figure 5 shows the DCCM maps for the protein gp63 alone and the gp63 in complexes with ligands.

The correlation ranges from − 1 to 1, corresponding to the degree of correlation or anti-correlation as indicated by the respective color intensities. The color cyano represents positive correlation values in the range between 0.25 and 1. Magenta color represents negative correlation values ranging from − 0.25 to − 1; and the colors cyano light or light magenta represent a weak or no correlation, for values between − 0.25 and + 0.25. The cyano, and magenta comparing the regions for 1LML alone and 1LML-PB show anti-correlation movements (blue box), being more intense in the complex. In a similar region the other complexes show a negative but less intense correlation compared to 1LML alone and the 1LML-PB complex, in the 1LML-A, 1LML-L, and 1LML-PA complexes the same areas compared to the protein native anti-correlation movements are minor. On the other hand, a positive correlation (red boxes) is present in all systems, being less intense in the 1LML-PB complex.

Movement changes between the residues in the proteins are observed, when the complexes and the apo-proteins are compared, suggesting that ligand interaction affects the movements in the residues. A Principal Component Analysis (PCA) through an MD simulation initially reveals the changes in motion during the trajectory of the protein. This is performed by calculating the eigenvectors (Total movement of the atoms) and the eigenvalues (atomic contribution for each movement) to better understand the structural and conformational changes of the gp63 due to the presence of the ligands.

To investigate and analyze the effect of biflavonoids on the conformational states of 1LML protein in an in-phase principal component (PC) space, PCA analysis was performed for all Cα atoms in the protein of L.major gp63 and their complexes with the biflavonoids in the 500 ns of the trajectory, respectively. The PCA was commonly used to reduce the dimensionality of MD simulation data and identify conformational transformations.

To highlight the differences in the collective movement of the 1LML protein in the system studied. In theory, the first three principal components (PC1, PC2, and PC3) of PCA capture most of the variance in the original distribution of conformational sets in molecules. The CA indicated that the first three PCs represented 22.6%, 50.2%, 46.7%, 39.5%, and 31.3% in the movement variance observed in the trajectories of apo-1LML, 1LML-A, 1LML-L, 1LML-PA, and 1LML-PB, respectively. The 1LML-A, had the highest value followed by the 1LML-L, while the 1LML-PB was the lowest even than the 1LML alone, Fig. S4. The simulated conformations in each system were dynamic and fluctuated during the 500 ns MD simulations, eventually stabilizing in a dominant state. The conformational changes between the 1LML protein with or without ligand were different, aligning with their respective structures (Fig. 6).

The RMSD, RMSF, Rg, and SASA values were stable throughout the MD simulation for the two proteins. Slight differences in these values occur between 1LML when they interact with biflavonoids. The H-bond number for each protein, 1LML, varies slightly when this parameter is compared, with lanaroflavone that shows the highest values at the end of the 500 ns simulation H-bonds for both proteins. The PCA carried out shows the conformational changes produced by the interaction between the biflavonoids and the 1LML protein. Each value is associated with an eigenvalue that reflects the direction of movement and the corresponding magnitudes of fluctuation. The first 20 PCA reflect 60% for 1LML, showing that the three eigenvectors were representative of the fluctuation between atoms in the conformational changes for the two proteins interacting with the tested ligands.

Binding energy estimation

In silico drug, design has been an active area of research in recent years (Kumari et al. 2014). Virtual screening, docking, molecular dynamics, as well as MMPBSA free energy calculation of compounds have been widely used in recent years for the design of new drugs (Jürgen Bajorath. 2022). This study was based on the interaction between biflavonoids and the Leishmanolisin (gp63) proteins of L. major, a metalloprotease that belongs to class M8 (subclan MA (M), metzincins) (Russo et al. 2020). The free energy of binding of the complexes in the active site for the leismanolysin protein of L. major was calculated according to the MMPBSA method. To estimate the binding of the ligands with the proteins along the trajectory of the 500 ns simulation, the calculation of the free binding energy of these compounds was determined whose values are summarized in Table 1. The ΔG_bind for amentoflavone (-163.39 ± 60.09 Kcal/mol), lanaroflavone (-156.75 ± 31.91 Kcal mol), podocarpuflavone A and B (-134.20 ± 33.26 Kcal/mol and − 112.06 ± 36.23 Kcal/mol, respectively). The negative values of the van der Waals contribution for each ligand are highlighted, being the lowest value for lanaroflavone (-226.80 ± 31.91) and the highest for podocarpusflavone B (-203.94 ± 40.33), in comparison with the electrostatic interaction energies, the latter being higher, evidenced by the low formation of hydrogen bridges as should be expected for these polyhydroxylated compounds.

Table 1

Summary of protein–compound binding energies according to MMPBSA
Ligand	van der Waals energy	Electrostatics energy	Polar solvation energy	SASA energy	Binding energy
Amentoflavone	-199.88 ± 43.24	-49.14 ± 22.91	105.18 ± 31.18	-19.55 ± 5.62	-163.39 ± 60.09
Lanaroflavone	-226.80 ± 31.91	-72.39 ± 18.29	164.23 ± 41.98	-21.79 ± 3.56	-156.75 ± 31.91
Podocarpusflavone A	-215.69 ± 22.44	-83.79 ± 51.76	178.26 ± 36.05	-12.98 ± 1.26	-134.20 ± 33.26
Podocarpusflavone B	-203.94 ± 40.33	-42.45 ± 14.69	146.31 ± 19.02	-11.98 ± 2.37	-112.06 ± 36.23
All values are in Kcal mol^− 1

Important differences between the 1LML protein and the complexes formed with the ligands under study were observed. The analysis of the contribution of residues to the binding energy for each complex is crucial for the stabilization of complex protein-biflavonoids. The contribution per-residues are shown in Fig. 7 for each 1LML-biflavonois complex, it can be observed the most favorable residues in the interaction such as Leu224, Asp255, and Leu257. On the other hand, for the 1LML-Lanoroflavone complex, the active site residues His264, Glu265, and His334 are unfavorable to the interaction with the ligand, on the contrary case for the other bioflavonoids where these same residues are favored in their interaction with the protein.

Further, the time scale was calculated for each biflavonoids in complex with leihsmanolysin protein. The Overlap for 0 ns, 100 ns, 250 ns, and 500 ns are shown in Fig. 8, lanaroflavone moved away from the active site amino acids of the protein at the end of the simulation (green color, 500 ns). In the case of the other compounds, the orientation they take at the end of the simulation is very similar.

Physicochemical, pharmacokinetic, and toxicity properties

The analysis of pharmacokinetic characteristics (ADME) reveals that all four compounds violate drug-likeness in terms of molecular weight and logP (Table S1), according to the analysis performed in SwissADME, taking into consideration physical and pharmacokinetic properties. It can be observed that the studied biflavonoids present high logP values above the stipulated values. As for the pharmacokinetic values, the molecules studied show low GI absorption and none is permeable to the blood-brain barrier penetration (BBB), which contrasts with the poor solubility of the four compounds, in addition to not being good substrates for the P-gp protein. As for lanaroflavone and podocarpusflavone A, both exhibit inhibition of metabolism by CYP2C9 liver enzymes, while amentoflavone and podocarpusfalvone B do not exhibit inhibition of any of the metabolizing enzymes.

The toxicity of the biflavonoids was predicted using the ProTox-II platform. The LD50 predicted for the four molecules was high according to the toxicity scale of the program, which was higher than 7.27 mM Kg^− 1. Hepatoxicity, immunotoxicity, carcinogenicity, and cytotoxicity were evaluated, being all inactive except for podocarpusflavone A and B which presented a high probability of immunotoxicity.

Numerous publications have presented biflavonoids as potential antiprotozoal candidates. Thus, lanaroflavone Weniger et al. have reported that this biflavonoid shows moderate antiplasmodial activity in vitro. The same author reports that lanroflavone has activity on L. donovani amastigotes of IC₅₀ 3.9 µg/mL (Wenige et al. 2020). In the case of amentoflavone, activity has been reported for this compound which was active against L. amazonenis amastigotes in murine macrophages. In this case, an IC₅₀ of 2.3 ± 0.93 µM is reported, reducing total amastigotes by 57%. It also reduced lesions in a subcutaneous treatment at 21 days compared to the control group (Silva et al. 2020). On the other hand, the biflavonoids podocarpusflavone A and B showed little activity on the promastigote of L. donovani (Del Rayo et al. 2000).

In this in silico study, they reveal that naturally occurring biflavonoid compounds such as amentoflavone, lanaroflavone, podocarpusflavone A, and B can bind to the 1LML, inhibiting them of these, and therefore possibly preventing the entrance of the promastigote into the host.

Using in silico analysis and statistical tools demonstrated that these biflavonoids could be used as a possible inhibit the leishmania parasite's growth. After performing a molecular dynamic to evaluate de stability of the complex, the biflavonoid lanaroflavone was identified as a potential inhibit leishmanolysin protease (estimated binding affinity using MMPBSA − 156.75 ± 31.91 Kcal mol). The four compounds show fluctuations in the ligand trajectories of molecular dynamics that are attributable to high hydrophobic and H-bonds were crucial for the recognition interaction between gp63 protein and the ligand under the study. All selected drugs interact with both (His and Glu residues) or at least one of the catalytic residues. Besides, these ligands' MMPBSA-free energy profiles suggest strong binding to the 1LML protein. This study detected significant changes in the residual flexibility of the protein 1LML, as explained in the RMSF and PCA analyses.

Acknowledgments All authors acknowledge the Vice-Rectory for Research at the University of Cartagena. Faculty of Pharmacy-University of Cartagena, Center for Tropical Diseases of the University of Salamanca (CIETUS), and Ministry of Science and Technology of Colombia (Minciencias).

Funding. The Program to Support Research Groups, Vice-Rectory for Research at the University of Cartagena (N° 031–2021). This work was supported by the National Program for Doctoral Formation Minciencias (L.C.C: 727- 2015).

Conflicts of interest/Competing interests. The authors declare no conflicts of interest.

Availability of data and material. All data generated during this study are included in the article and are available from the corresponding author upon reasonable request.

Authors' contributions. Jairo Mercado-Camargo., Harold Gómez-Estrada., and Leonor Cervantes-Ceballos, conceived and designed the experiments; Jairo Mercado-Camargo, Harold Gómez-Estrada., Ester de Olmos-Fernandez, and Ricardo Vivas-Reyes., analyzed the data. All authors discussed, edited, and approved the final version of the manuscript.

Ethics approval. No available

Consent to participate. No available

Consent for publication. All authors revised the manuscript and gave final approval for publication

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MOLECULAR DYNAMICS SIMULATION OF FOUR BIFLAVONOIDS WITH THE LEISHMANOLYSIN (gp63) PROTEIN OF Leishmania major

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Abstract

Figures

Introduction

Materials And Methods

Preparation of the studied systems

Molecular Dynamics Simulation

Principal components analysis

Cross-correlation Dynamics Analysis

MMPBSA free energy calculations

Physicochemical, pharmacokinetic, and toxicity properties prediction

Results And Discussion

Md Simulation

Radius Of Gyration (Rg) Analysis

Solvent Accessible Area (Sasa)

H-bonds Analysis

Principal component analysis

Binding energy estimation

Physicochemical, pharmacokinetic, and toxicity properties

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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