Essential oil components interacting with insect odorant-binding proteins: a molecular modelling approach.

Essential oils (EOs) are natural products currently used to control arthropods, and their interaction with insect odorant-binding proteins (OBPs) is fundamental for the discovery of new repellents. This in silico study aimed to predict the potential of EO components to interact with odorant proteins. A total of 684 EO components from PubChem were docked against 23 odorant binding proteins from Protein Data Bank using AutoDock Vina. The ligands and proteins were optimized using Gaussian 09 and Sybyl-X 2.0, respectively. The nature of the protein-ligand interactions was characterized using LigandScout 4.0, and visualization of the binding mode in selected complexes was carried out by Pymol. Additionally, complexes with the best binding energy in molecular docking were subjected to 500 ns molecular dynamics simulations using Gromacs. The best binding affinity values were obtained for the 1DQE-ferutidine (-11 kcal/mol) and 2WCH-kaurene (-11.2 kcal/mol) complexes. Both are natural ligands that dock onto those proteins at the same binding site as DEET, a well-known insect repellent. This study identifies kaurene and ferutidine as possible candidates for natural insect repellents, offering a potential alternative to synthetic chemicals like DEET.

High-Throughput Molecular Modeling and Evaluation of the Anti-Inflammatory Potential of Açaí Constituents against NLRP3 Inflammasome.

The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.

Permeation mechanisms of hydrogen peroxide and water through Plasma Membrane Intrinsic Protein aquaporins.

Hydrogen peroxide (H2O2) transport by aquaporins (AQP) is a critical feature for cellular redox signaling. However, the H2O2 permeation mechanism through these channels remains poorly understood. Through functional assays, two Plasma membrane Intrinsic Protein (PIP) AQP from Medicago truncatula, MtPIP2;2 and MtPIP2;3 have been identified as pH-gated channels capable of facilitating the permeation of both water (H2O) and H2O2. Employing a combination of unbiased and enhanced sampling molecular dynamics simulations, we investigated the key barriers and translocation mechanisms governing H2O2 permeation through these AQP in both open and closed conformational states. Our findings reveal that both H2O and H2O2 encounter their primary permeation barrier within the selectivity filter (SF) region of MtPIP2;3. In addition to the SF barrier, a second energetic barrier at the NPA (asparagine-proline-alanine) region that is more restrictive for the passage of H2O2 than for H2O, was found. This behavior can be attributed to a dissimilar geometric arrangement and hydrogen bonding profile between both molecules in this area. Collectively, these findings suggest mechanistic heterogeneity in H2O and H2O2 permeation through PIPs.

(-)-Epicatechin metabolites as a GPER ligands: a theoretical perspective.

Diet habits and nutrition quality significantly impact health and disease. Here is delve into the intricate relationship between diet habits, nutrition quality, and their direct impact on health and homeostasis. Focusing on (-)-Epicatechin, a natural flavanol found in various foods like green tea and cocoa, known for its positive effects on cardiovascular health and diabetes prevention. The investigation encompasses the absorption, metabolism, and distribution of (-)-Epicatechin in the human body, revealing a diverse array of metabolites in the circulatory system. Notably, (-)-Epicatechin demonstrates an ability to activate nitric oxide synthase (eNOS) through the G protein-coupled estrogen receptor (GPER). While the precise role of GPER and its interaction with classical estrogen receptors (ERs) remains under scrutiny, the study employs computational methods, including density functional theory, molecular docking, and molecular dynamics simulations, to assess the physicochemical properties and binding affinities of key (-)-Epicatechin metabolites with GPER. DFT analysis revealed distinct physicochemical properties among metabolites, influencing their reactivity and stability. Rigid and flexible molecular docking demonstrated varying binding affinities, with some metabolites surpassing (-)-Epicatechin. Molecular dynamics simulations highlighted potential binding pose variations, while MMGBSA analysis provided insights into the energetics of GPER-metabolite interactions. The outcomes elucidate distinct interactions, providing insights into potential molecular mechanisms underlying the effects of (-)-Epicatechin across varied biological contexts.

Multiscale simulations of DNA from electrons to nucleosomes: 22 years of the Ascona B-DNA Consortium.

This commentary provides a retrospective on the Ascona B-DNA Consortium (ABC) initiative and on the conference held in April 2023 at Ascona, Switzerland, where we celebrated 22 years of the consortium, sharing the latest advances in simulations and experiments of the effects of sequence on the mechanical properties of DNA from electrons to nucleosomes.

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques.

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

Exploring structural and computational contrasts in Myoglobins: Implications for thermal treatment-induced Sulfmyoglobin formation.

Greening of tuna metmyoglobin (MetMb) by thermal treatment (TT) and free cysteine is associated with sulfmyoglobin (SulfMb) production. This greening reaction (GR) was once thought to occur only in tuna species. However, recent research has revealed that not all tuna species exhibit this behavior, and it can also occur in horse MetMb. Thus, the present study aimed to compare the GR-reactive (Katsuwonus pelamis and Equus caballus) and GR-unreactive (Sarda chiliensis and Euthynnus lineatus) MetMb using UV-vis spectrometry during TT (60 °C/30 min and free cysteine) to monitor the GR. We used molecular dynamics (MD) simulation to assess the stability of the heme group during TT. We discovered that using GR-unreactive MetMb resulted in an incomplete GR without producing SulfMb. Additionally, our MD simulations indicated that Met85 presence in the heme cavity from GR-unreactive is responsible for the heme group instability and displacement of distal His during TT.

Binding of green tea epigallocatechin gallate to the arginine kinase active site from the brown recluse spider (Loxosceles laeta): A potential synergist to chemical pesticides.

Loxosceles spp. spiders can cause serious public health issues. Chemical control is commonly used, leading to health and environmental problems. Identifying molecular targets and using them with natural compounds can help develop safer and eco-friendlier biopesticides. We studied the kinetics and predicted structural characteristics of arginine kinase (EC 2.7.3.3) from Loxosceles laeta (LlAK), a key enzyme in the energy metabolism of these organisms. Additionally, we explored (-)-epigallocatechin gallate (EGCG), a green tea flavonoid, as a potential lead compound for the LlAK active site through fluorescence and in silico analysis, such as molecular docking and molecular dynamics (MD) simulation and MM/PBSA analyses. The results indicate that LlAK is a highly efficient enzyme (K m Arg 0.14 mM, K m ATP 0.98 mM, k cat 93 s-1, k cat/K m Arg 630 s-1 mM-1, k cat/K m ATP 94 s-1 mM-1), which correlates with its structure similarity to others AKs (such as Litopenaeus vannamei, Polybetes pythagoricus, and Rhipicephalus sanguineus) and might be related to its important function in the spider's energetic metabolism. Furthermore, the MD and MM/PBSA analysis suggests that EGCG interacted with LlAK, specifically at ATP/ADP binding site (RMSD <1 nm) and its interaction is energetically favored for its binding stability (-40 to -15 kcal/mol). Moreover, these results are supported by fluorescence quenching analysis (K d 58.3 µM and K a 1.71 × 104 M-1). In this context, LlAK is a promising target for the chemical control of L. laeta, and EGCG could be used in combination with conventional pesticides to manage the population of Loxosceles species in urban areas.

How Domain Segregation in Ionic Liquids Stabilizes Nanoparticles and Establishes Long-Range Ordering─A Computational Study.

Due to their physical properties including high thermal stability, very low vapor pressure, and high microwave absorption, ionic liquids have attracted great attention as solvents for the synthesis of nanomaterials, being considered as greener alternatives to traditional solvents. While usual solvents often need additives like surfactants, polymers, or other ligands to avoid nanoparticle coalescence, some ionic liquids can stabilize nanoparticles in dispersion without any additive. In order to quantify how the ionic liquids can affect both the aggregation thermodynamics and kinetics, molecular dynamics simulations were performed to simulate the evolution of concentrated dispersions and to compute the potential of mean force between nanoparticles of both hydrophilic and hydrophobic natures in two imidazolium-based ionic liquids, which differ from each other by the length of the cation alkyl group. Depending on the nature of the nanoparticle, structured layers of the polar and apolar regions of the ionic liquid can be formed close to its surface, and those layers lead to activation barriers for dispersed particles to get in contact. If the alkyl group of the ionic liquid is long enough to lead to domain segregation between the ionic and apolar portions of the solvent, the layered structure around the particle becomes more structured and propagates several nanometers away from its surface. This leads to stronger barriers close to the contact and also multiple barriers at larger distances that result from the unfavorable superposition of solvent layers of opposing nature when the nanoparticles approach each other. Those long-range solvent-mediated forces not only provide kinetic stability to dispersions but also affect their dynamics and lead to a long-range ordering between dispersed particles that can be explored as a template for the synthesis of complex materials.

In Silico Comparative Analysis of Ivermectin and Nirmatrelvir Inhibitors Interacting with the SARS-CoV-2 Main Protease.

Exploring therapeutic options is crucial in the ongoing COVID-19 pandemic caused by SARS-CoV-2. Nirmatrelvir, which is a potent inhibitor that targets the SARS-CoV-2 Mpro, shows promise as an antiviral treatment. Additionally, Ivermectin, which is a broad-spectrum antiparasitic drug, has demonstrated effectiveness against the virus in laboratory settings. However, its clinical implications are still debated. Using computational methods, such as molecular docking and 100 ns molecular dynamics simulations, we investigated how Nirmatrelvir and Ivermectin interacted with SARS-CoV-2 Mpro(A). Calculations using density functional theory were instrumental in elucidating the behavior of isolated molecules, primarily by analyzing the frontier molecular orbitals. Our analysis revealed distinct binding patterns: Nirmatrelvir formed strong interactions with amino acids, like MET49, MET165, HIS41, HIS163, HIS164, PHE140, CYS145, GLU166, and ASN142, showing stable binding, with a root-mean-square deviation (RMSD) of around 2.0 Å. On the other hand, Ivermectin interacted with THR237, THR239, LEU271, LEU272, and LEU287, displaying an RMSD of 1.87 Å, indicating enduring interactions. Both ligands stabilized Mpro(A), with Ivermectin showing stability and persistent interactions despite forming fewer hydrogen bonds. These findings offer detailed insights into how Nirmatrelvir and Ivermectin bind to the SARS-CoV-2 main protease, providing valuable information for potential therapeutic strategies against COVID-19.

Probing Self-Assembly of Ammeline in Chloroform and Aqueous Media: Interplay Between Hydrogen Bonding Diversity and Dimerization.

Ammeline (AM) is a molecule with a very low reputation in the field of supramolecular community, but with a recently proven potential both experimentally and theoretically. In this work, dispersion-corrected density functional theory (DFT-D) computations and molecular dynamics (MD) simulations were employed to understand the aggregation mechanism of AM in chloroform and water media. Our DFT-D and MD analyzes show that the most important interactions are those formed by the amine groups (-NH2) with both the pyridine-type nitrogen atoms and the carbonyl groups (C=O). In the more polar solvent, the interactions between water molecules and the C=O group prevent the AM from forming more interactions with itself. Nevertheless, four types of dimers involving N-H∙∙∙O interactions were found to exist in water solutions. The overlooked tetrel bond between endocyclic N and C atoms can also stabilize dimers in solution. Moreover, while most AM dimers are enthalpy-driven, our results indicate that the unique DD-AA dimer (D=donor, A=acceptor) that originates cyclic rosettes is entropy-driven.

Molecular dynamics simulations in pre-polymerization mixtures for peptide recognition.

CONTEXT: Molecularly imprinted polymers (MIPs) have promising applications as synthetic antibodies for protein and peptide recognition. A critical aspect of MIP design is the selection of functional monomers and their adequate proportions to achieve materials with high recognition capacity toward their targets. To contribute to this goal, we calibrated a molecular dynamics protocol to reproduce the experimental trends in peptide recognition of 13 pre-polymerization mixtures reported in the literature for the peptide toxin melittin. METHODS: Three simulation conditions were tested for each mixture by changing the box size and the number of monomers and cross-linkers surrounding the template in a solvent-explicit environment. Fully atomistic MD simulations of 350 ns were conducted with the AMBER20 software, with ff19SB parameters for the peptide, gaff2 parameters for the monomers and cross-linkers, and the OPC water model. Template-monomer interaction energies under the LIE approach showed significant differences between high-affinity and low-affinity mixtures. Simulation systems containing 100 monomers plus cross-linkers in a cubic box of 90 Å3 successfully ranked the mixtures according to their experimental performance. Systems with higher monomer densities resulted in non-specific intermolecular contacts that could not account for the experimental trends in melittin recognition. The mixture with the best recognition capacity showed preferential binding to the 13-26-α-helix, suggesting a relevant role for this segment in melittin imprinting and recognition. Our findings provide insightful information to assist the computational design of molecularly imprinted materials with a validated protocol that can be easily extended to other templates.

Removal of phenazopyridine from water, synthetic urine, and real sample by adsorption using graphene oxide: A DFT theoretical/experimental approach.

Herein, graphene oxide was used as the highly efficient phenazopyridine adsorbent from aqueous medium, synthetic, and human urine. The nanoadsorbent was characterized by different instrumental techniques. The adsorption capacity (1253.17 mg g-1) was reached at pH 5.0, using an adsorbent dosage of 0.125 g L-1 at 298 K. The Sips and Langmuir described the equilibrium data well. At the same time, the pseudo-second order was more suitable for fitting the kinetic data. Thermodynamic parameters revealed the exothermic nature of adsorption with an increase in randomness at the solid-liquid interface. The magnitude of the enthalpy variation value indicates that the process involves the physisorption phenomenon. At the same time, ab initio molecular dynamics data corroborated with the thermodynamic results, indicating that adsorbent and adsorbate establish hydrogen bonds through the amine groups (adsorbate) and hydroxyl groups on the adsorbent surface (weak interactions). Electrostatic interactions are also involved. Additionally, the adsorption assays conducted in simulated medium and human urine showed the excellent performance of adsorbent material to remove the drug in real concentrations excreted by the kidneys (removal values higher than 60%).

In vivo movement of retinoblastoma-related protein (RBR) towards cytoplasm during mitosis in Arabidopsisthaliana.

Retinoblastoma protein is central in signaling networks of fundamental cell decisions such as proliferation and differentiation in all metazoans and cancer development. Immunostaining and biochemical evidence demonstrated that during interphase retinoblastoma protein is in the nucleus and is hypophosphorylated, and during mitosis is in the cytoplasm and is hyperphosphorylated. The purpose of this study was to visualize in vivo in a non-diseased tissue, the dynamic spatial and temporal nuclear exit toward the cytoplasm of this protein during mitosis and its return to the nucleus to obtain insights into its potential cytosolic functions. Using high-resolution time-lapse images from confocal microscopy, we tracked in vivo the ortholog in plants the RETINOBLASTOMA RELATED (RBR) protein tagged with Green Fluorescent Protein (GFP) in Arabidopsis thaliana's root. RBR protein exits from dense aggregates in the nucleus before chromosomes are in prophase in less than 2 min, spreading outwards as smaller particles projected throughout the cytosol during mitosis like a diffusive yet controlled event until telophase, when the daughter's nuclei form; RBR returns to the nuclei in coordination with decondensing chromosomal DNA forming new aggregates again in punctuated larger structures in each corresponding nuclei. We propose RBR diffused particles in the cytoplasm may function as a cytosolic sensor of incoming signals, thus coordinating re-aggregation with DNA is a mechanism by which any new incoming signals encountered by RBR may lead to a reconfiguration of the nuclear transcriptomic context. The small RBR diffused particles in the cytoplasm may preserve topologic-like properties allowing them to aggregate and restore their nuclear location, they may also be part of transient cytoplasmic storage of the cellular pre-mitotic transcriptional context, that once inside the nuclei may execute both the pre mitosis transcriptional context as well as new transcriptional instructions.

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.

Molecular Dynamic Simulations for Biopolymers with Biomedical Applications.

Computational modeling (CM) is a versatile scientific methodology used to examine the properties and behavior of complex systems, such as polymeric materials for biomedical bioengineering. CM has emerged as a primary tool for predicting, setting up, and interpreting experimental results. Integrating in silico and in vitro experiments accelerates scientific advancements, yielding quicker results at a reduced cost. While CM is a mature discipline, its use in biomedical engineering for biopolymer materials has only recently gained prominence. In biopolymer biomedical engineering, CM focuses on three key research areas: (A) Computer-aided design (CAD/CAM) utilizes specialized software to design and model biopolymers for various biomedical applications. This technology allows researchers to create precise three-dimensional models of biopolymers, taking into account their chemical, structural, and functional properties. These models can be used to enhance the structure of biopolymers and improve their effectiveness in specific medical applications. (B) Finite element analysis, a computational technique used to analyze and solve problems in engineering and physics. This approach divides the physical domain into small finite elements with simple geometric shapes. This computational technique enables the study and understanding of the mechanical and structural behavior of biopolymers in biomedical environments. (C) Molecular dynamics (MD) simulations involve using advanced computational techniques to study the behavior of biopolymers at the molecular and atomic levels. These simulations are fundamental for better understanding biological processes at the molecular level. Studying the wide-ranging uses of MD simulations in biopolymers involves examining the structural, functional, and evolutionary aspects of biomolecular systems over time. MD simulations solve Newton's equations of motion for all-atom systems, producing spatial trajectories for each atom. This provides valuable insights into properties such as water absorption on biopolymer surfaces and interactions with solid surfaces, which are crucial for assessing biomaterials. This review provides a comprehensive overview of the various applications of MD simulations in biopolymers. Additionally, it highlights the flexibility, robustness, and synergistic relationship between in silico and experimental techniques.

Mooney-Rivlin Parameter Determination Model as a Function of Temperature in Vulcanized Rubber Based on Molecular Dynamics Simulations.

The automotive industry is entering a digital revolution, driven by the need to develop new products in less time that are high-quality and environmentally friendly. A proper manufacturing process influences the performance of the door grommet during its lifetime. In this work, uniaxial tensile tests based on molecular dynamics simulations have been performed on an ethylene-propylene-diene monomer (EPDM) material to investigate the effect of the crosslink density and its variation with temperature. The Mooney-Rivlin (MR) model is used to fit the results of molecular dynamics (MD) simulations in this paper and an exponential-type model is proposed to calculate the parameters C1(T) and C2T. The experimental results, confirmed by hardness tests of the cured part according to ASTM 1415-88, show that the free volume fraction and the crosslink density have a significant effect on the stiffness of the EPDM material in a deformed state. The results of molecular dynamics superposition on the MR model agree reasonably well with the macroscopically observed mechanical behavior and tensile stress of the EPDM at the molecular level. This work allows the accurate characterization of the stress-strain behavior of rubber-like materials subjected to deformation and can provide valuable information for their widespread application in the injection molding industry.

Rational Approach toward COVID-19's Main Protease Inhibitors: A Hierarchical Biochemoinformatics Analysis.

This study investigated the potential of selected compounds as inhibitors of SARS-CoV-2 Mpro through pharmacokinetic and toxicological analyses, molecular docking, and molecular dynamics simulations. In silico molecular docking simulations revealed promising ligands with favorable binding affinities for Mpro, ranging from -6.2 to -9.5 kcal/mol. Moreover, molecular dynamics simulations demonstrated the stability of protein-ligand complexes over 200 ns, maintaining protein secondary structures. MM-PBSA analysis revealed favorable interactions between ligands and Mpro, with negative binding energy values. Hydrogen bond formation capacity during molecular dynamics was confirmed, indicating consistent interactions with Mpro catalytic residues. Based on these findings, selected ligands show promise for future studies in developing COVID-19 treatments.

Schwarzites and Triply Periodic Minimal Surfaces: From Pure Topology Mathematics to Macroscale Applications.

Schwarzites are porous (spongy-like) carbon allotropes with negative Gaussian curvatures. They are proposed by Mackay and Terrones inspired by the works of the German mathematician Hermann Schwarz on Triply-Periodic Minimal Surfaces (TPMS). This review presents and discusses the history of schwarzites and their place among curved carbon nanomaterials. The main works on schwarzites are summarized and are available in the literature. Their unique structural, electronic, thermal, and mechanical properties are discussed. Although the synthesis of carbon-based schwarzites remains elusive, recent advances in the synthesis of zeolite-templates nanomaterials have brought them closer to reality. Atomic-based models of schwarzites are translated into macroscale ones that are 3D-printed. These 3D-printed models are exploited in many real-world applications, including water remediation and biomedical ones.

Targeting Leishmania infantum Mannosyl-oligosaccharide glucosidase with natural products: potential pH-dependent inhibition explored through computer-aided drug design.

Visceral Leishmaniasis (VL) is a serious public health issue, documented in more than ninety countries, where an estimated 500,000 new cases emerge each year. Regardless of novel methodologies, advancements, and experimental interventions, therapeutic limitations, and drug resistance are still challenging. For this reason, based on previous research, we screened natural products (NP) from Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB), Mexican Compound Database of Natural Products (BIOFACQUIM), and Peruvian Natural Products Database (PeruNPDB) databases, in addition to structural analogs of Miglitol and Acarbose, which have been suggested as treatments for VL and have shown encouraging action against parasite's N-glycan biosynthesis. Using computer-aided drug design (CADD) approaches, the potential inhibitory effect of these NP candidates was evaluated by inhibiting the Mannosyl-oligosaccharide Glucosidase Protein (MOGS) from Leishmania infantum, an enzyme essential for the protein glycosylation process, at various pH to mimic the parasite's changing environment. Also, computational analysis was used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profile, while molecular dynamic simulations were used to gather information on the interactions between these ligands and the protein target. Our findings indicated that Ocotillone and Subsessiline have potential antileishmanial effects at pH 5 and 7, respectively, due to their high binding affinity to MOGS and interactions in the active center. Furthermore, these compounds were non-toxic and had the potential to be administered orally. This research indicates the promising anti-leishmanial activity of Ocotillone and Subsessiline, suggesting further validation through in vitro and in vivo experiments.