A computational approach to identify phytochemicals as potential inhibitor of acetylcholinesterase: Molecular docking, ADME profiling and molecular dynamics simulations

Inhibition of acetylcholinesterase (AChE) is a crucial target in the treatment of Alzheimer’s disease (AD). Common anti-acetylcholinesterase drugs such as Galantamine, Rivastigmine, Donepezil, and Tacrine have significant inhibition potential. Due to side effects and safety concerns, we aimed to investigate a wide range of phytochemicals and structural analogues of these compounds. Compounds similar to the established drugs, and phytochemicals were investigated as potential inhibitors for AChE in treating AD. A total of 2,270 compound libraries were generated for further analysis. Initial virtual screening was performed using Pyrx software, resulting in 638 molecules showing higher binding affinities compared to positive controls Tacrine (-9.0 kcal/mol), Donepezil (-7.3 kcal/mol), Galantamine (-8.3 kcal/mol), and Rivastigmine (-6.4 kcal/mol). Subsequently, ADME properties were assessed, including blood-brain barrier permeability and Lipinski’s rule of five violations, leading to 88 compounds passing the ADME analysis. Among the rivastigmine analogous, [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate showed interaction with Tyr123, Tyr336, Tyr340, Phe337, Trp285 residues of AChE. Tacrine similar compounds, such as 4-amino-2-styrylquinoline, exhibited bindings with Tyr123, Phe337, Tyr336, Trp285, Trp85, Gly119, and Gly120 residues. A phytocompound (bisdemethoxycurcumin) showed interaction with Trp285, Tyr340, Trp85, Tyr71, and His446 residues of AChE with favourable binding. These findings underscore the potential of these compounds as novel inhibitors of AChE, offering insights into alternative therapeutic avenues for AD. A 100ns simulation analysis confirmed the stability of protein-ligand complex based on the RMSD, RMSF, ligand properties, PCA, DCCM and MMGBS parameters. The investigation suggested 3 ligands as a potent inhibitor of AChE which are [3-(1-methylpiperidin-2-yl)phenyl] N,N-diethylcarbamate, 4-Amino-2-styrylquinoline and bisdemethoxycurcumin. Furthermore, investigation, including in-vitro and in-vivo studies, is needed to validate the efficacy, safety profiles, and therapeutic potential of these compounds for AD treatment.

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Introduction
Alzheimer's disease (AD) is a neurological disorder that leads to the deterioration of brain cells.It is the primary cause of dementia, a condition marked by a decline in cognitive abilities and a loss of independence in daily tasks (Breijyeh & Karaman, 2020).AD is characterized by a decline in the cholinergic system, resulting in reduced levels of acetylcholine in brain regions responsible for learning, memory, behaviour, and emotional responses (Anand et al., 2012).AD is neuropathologically defined by the presence of beta-amyloid (Aβ) plaques, neurofibrillary tangles, and degeneration or atrophy of the basal forebrain cholinergic neurons (Roberson & Harrell, 1997).
Acetylcholinesterase (AChE), an enzyme that belongs to the serine hydrolase family, plays a vital role in breaking down acetylcholine (ACh) into choline and acetate.Therefore, maintaining normal cholinergic neurotransmission.In AD patients ACh degradation is amplified by the AChE in early stages.The use of enzymatic inhibition to reduce AChE activity has shown promise as a treatment strategy for AD (Du et al., 2018).The FDA-approved AChE enzyme inhibitors donepezil and rivastigmine are utilized for the treatment of mild to moderate AD.Tacrine was one of the AChE inhibitory drugs which had been banned since 2013.Both medications have adverse effects such as nausea, diarrhoea, loss of appetite, fainting, abdominal pain, and vomiting (Tayeb et al., 2012).
Administration of tacrine (THA) for AD treatment leads to reversible hepatotoxicity in 30-50% of patients, as evidenced by an elevation in transaminase levels (Lagadic-Gossmann et al., 1998).
Therefore, scientists are searching for more effective agents with fewer side effects (Scheltens et al., 2021).
Researchers have investigated natural resources for anti-AChE agents because they are safer than synthetic chemicals (Kim et al., 2010).Galantamine, a natural drug from Galanthus woronowii, is used to treat AD alongside other chemical drugs (Bartolucci et al., 2001).However, none of these medications have proven to be entirely effective in halting the advancement or formation of AD.
To ameliorate the potential side effects and optimize the therapeutic efficacy of enzyme inhibition, compounds possessing structural similarities to FDA-approved drugs emerge as promising candidates (Birks & Harvey, 2003;Olin & Schneider, 2002;Onor et al., 2007).Ongoing research is being conducted to discover novel compounds derived from natural sources or FDA-approved drug-like compounds with anti-AChE properties (Pilger et al., 2001).Natural products derived from different plants are increasingly being recognized globally for their potential as AChE inhibitors (AChEi), making them a promising therapeutic option for the treatment of AD (Taqui et al., 2022).Extensive research has identified a comprehensive list of plant-derived substances that inhibit AChE.The research on AChE inhibition-based treatment of AD has focused on this diverse range of phytochemicals due to the absence of promising, effective, and safe inhibitors (Kim et al., 2010;Sarkar et al., 2021).
Studies have demonstrated that memory-enhancing herbs such as Enhydra fluctuans, Vanda roxburghii, Bacopa monnieri, Centella asiatica, Convolvulus pluricaulis, and Aegle marmelos have acetylcholinesterase inhibitory and antioxidant properties.Acetylcholinesterase, the main cholinesterase in the brain that breaks down acetylcholine, shows greater specificity for acetylcholine.The findings indicate possible advantages for treating Alzheimer's disease (Lopa et al., 2021).This study aims to elucidate how human AChE is inhibited by the current FDAapproved drugs similar to structure analogues, as well as phytochemicals.Our study aimed to assess the in-silico assay results through docking, ADME simulation (RMSD, RMSF, Ligand properties), PCA, and DCCM, comparing them with FDA-approved drugs (donepezil, galantamine, rivastigmine), a selective AChE inhibitor employed in current AD therapy.

Ligand library 1: Similar structure selection
The rationale behind constructing library 1 (Similar structure search) was two-sided.Firstly, compounds with analogous structures might be able to show a similar kind of effect to some extent.
Secondly, studies have reported mild to severe adverse effects upon their administration and among them.Each of the four compounds was used as a query in the PubChem database followed by a similar structure search.

Ligand library 2: Dr. Duke database search for phytochemicals
Phytochemicals, known for their anti-AChE and anti-Butyrylcholinesterase (BChE) activities, were identified through a literature review of medicinal plants.Scientific names were queried in Dr. Duke's Phytochemical and Ethnobotanical Databases (https://phytochem.nal.usda.gov/).
Compound names were then searched in PubChem for 3-D structure retrieval.

Selection of target protein and protein preparation
The RSCB-PDB database (https://www.rcsb.org)was utilized to search for the target protein, human acetylcholinesterase protein (PDB ID: 4M0E) with a lower X-ray resolution (2.00 Å).
Several gaps were spotted while checking the structure with PyMol.Both the docking and simulation processes were vulnerable to interference from missing residues.To avoid any subsequent anomaly in docking and molecular dynamics simulation the spotted missing residues were repaired.To ensure the missing residues I-tasser (https://zhanggroup.org/I-TASSER/) a webbased server was used to predict the 3D structure of protein.The FASTA sequence was retrieved from the RCSB PDB database and used to build the predicted structure.The geometry analysis was performed using the MolProbity server (http://molprobity.biochem.duke.edu/),and the overall geometry and Ramachandran plots were analyzed.

Active site prediction
The active region on the surface of the protein that performs protein function is known as a proteinligand binding site.To avoid blind docking the specific amino acid residues (Table S1) of proteinligand interaction were predicted using CASTP v3.0 (http://sts.bioe.uic.edu/castp/calculation.html).
Molecular docking of primarily selected molecules.
PyRx 0.8 was used for the initial virtual screening (Dallakyan & Olson, 2015a).The protein was retrieved from the I-tasser website in PDB format after homology modelling and ligands were downloaded from the PubChem of NCBI (https://pubchem.ncbi.nlm.nih.gov)one by one in SDF file format.
The target protein was loaded in Pyrx 0.8 and converted into macromolecules.The similar structures of tacrine, donepezil, rivastigmine and galantamine (considered as controls) along with phytochemicals were loaded in the PyRx virtual screening tool.After energy minimization, it was converted into a pdbqt file.All the parameters and grid box positioned at some standard value (Centre box: X = -0.9600,Y = -38.1677,Z = 34.2085)and the dimensions in Angstrom were X = 58.7652,Y = 60.0782 and Z = 65.867.Later, the docking results were screened for binding affinity and then all the generated possible docked conformations were stored in CSV format (Dallakyan & Olson, 2015b).Only those conformations that interacted specifically with the active-site residues of the target protein targeted protein were selected and further detailed interactions were explored through Discovery Studio and PyMOL.

ADME Profiling
The SwissADME (http://www.swissadme.ch/index.php)server was utilized to conduct ADME profiling.Canonical smiles of ligands were required for conducting ADME analysis.To perform ADME profiling, the canonical smiles of all the ligands were uploaded as input on the SwissADME server.The entirety of the data was acquired in the CSV (comma-separated value) format.The subsequent sorting procedure was conducted according to the permeability of the blood-brain barrier, greater binding affinity, violations of drug-likeness violation (Lipinski, Ghose, Veber, Egan, Muggue), and oral bioactivity (lipophilicity, flexibility, solubility, instability, size) (Daina et al., 2017).

Molecular Re-docking performance
Re-docking was performed by the AutoDock Vina tool for the reliability of the software, and consistency of the docking algorithm.The target protein was converted into pdbqt.The parameters and grid box were positioned at some standard value (Centre box: X =106.848,Y = 43.703,Z = 18.797) and the dimensions of Box in Angstrom were X = 126, Y = 116 and Z = 122.Subsequently, the docking results were screened for binding affinity and generated all possible docked conformations were stored in the pdbqt file.Docking results were reported as a negative score in kcal/mol where the lowest docking score indicates the highest binding affinity (Kuntz, 1992).

Molecular Dynamic Simulation
Protein-ligand interaction stability during macromolecule structure-to-function transitions was studied using molecular dynamics.The Desmond software, developed by Schrödinger LLC, enabled the execution of molecular dynamics (MD) simulations that lasted for a duration of 100 nanoseconds.The simulations, utilizing Newton's classical equation of motion, monitored the path of atoms as they moved through time.The receptor-ligand complex was subjected to preprocessing using Maestro's Protein Preparation Wizard, which included optimization and minimization procedures.The system was prepared using the System Builder tool, employing the Transferable Intermolecular Interaction Potential 3 Points (TIP3P) solvent model within an orthorhombic box.
The simulation was governed by the OPLS 2005 force field, and counter ions were introduced to maintain model neutrality.A 0.15 M sodium chloride (NaCl) solution was added to replicate the conditions found in the body.The simulations were conducted using the Number of particles (N), Pressure (P), and Temperature (NPT) ensemble, with a temperature of 300 K and a pressure of 1 atm.Before the simulation, the models underwent a process of relaxation.The trajectories were recorded at intervals of 100 picoseconds.The stability was evaluated by comparing the root mean square deviation (RMSD), root mean square fluctuation (RMSF), Ligand properties (radius of Gyration, Molecular surface area, hydrogen bond etc.), PCA and DCCM of the protein and ligand during the entire simulation (Malik et al., 2023;Rathod et al., 2023).

Ligand library construction
The number of similar structure compounds were massive; however, considering the facts about drug-likeness several criteria were optimized to select the best suited structures.A total of 2252 similar compounds (library 1) and 18 phytochemicals (library 2) were primarily selected for virtual screening based on the selection criteria (Table 1).

3D structure prediction
The I-tasser gave a modelled structure which is like the 4M0E pdb (Fig. 1).The alignment of the sequence of amino acids is provided to verify the residues, with further sequence alignment and geometry details (Table S2 and Table S3).The Ramachandran plot (Fig. S1) shows the statistical distribution of the combinations of the backbone dihedral angles ϕ and ψ.In theory, the allowed regions of the Ramachandran plot show which values of the Phi/Psi angles are possible for an amino acid, X, in an ala-X-ala tripeptide (Wiltgen, 2019).The Ramachandran plot analysis of protein AChE showed high conformational quality, with no outliers identified.All 537 residues (100%) were in acceptable regions (>99.8%), with 96.6% (519/537) falling within favoured regions (>98%).The findings show the strong structural integrity of AChE (Sobolev et al., 2020).

Virtual Screening with PyRx
Using PyRx 0.8 docking tools, the original phytochemicals, and four others with a similar structure were docked.The affinity of tacrine, donepezil, galantamine, and rivastigmine binding was considered as positive control which is -9.0 kcal/mol, -7.3 kcal/mol, -8.3 kcal/mol and -6.4 kcal/mol, and the value (kcal/mol) greater than that was considered as the target ligand.The primary screening was performed by compounds with greater binding affinity than tacrine, rivastigmine, donepezil, and galantamine.A total of 620 molecules have exhibited higher binding affinity than the control molecules (tacrine, donepezil, rivastigmine, and galantamine), including 18 phytochemicals sourced from the Dr. Dukes database (https://phytochem.nal.usda.gov/)(Table S4).

ADME profiling of Screened phytochemicals
The SwissADME (http://www.swissadme.ch/index.php)was utilized to examine the ADME profile and ability to traverse the blood-brain barrier for the selected 638 phytochemicals.During this phase of the investigation, most of the phytochemicals did not meet the drug-likeness property that was assessed.Lipinski's rule states that, historically, 90% of orally absorbed drugs had fewer than 5 H-bond donors, less than 10 H-bond acceptors, molecular weight of less than 500 Daltons and XlogP values of less than 5 (Dai et al., 2016).Due to their high solubility, many phytochemicals may struggle to penetrate the blood-brain barrier (BBB).Therefore, compounds with a blood-brain barrier permeability (BBB) equal to or higher than 0.477 (Log 3) were prioritized for analysis as potentially potent BBB-permeable candidates.Additionally, high gastrointestinal (GI) absorption was assessed.A comprehensive analysis of the ADME (absorption, distribution, metabolism, and excretion) and docking results for similar chemical and phytochemical structures was performed (Tables 2, 3, 4 and 5).These tables provide valuable insights into the compounds' pharmacokinetic properties and their potential interactions with target proteins.A total of 89 compounds along with phytochemicals were found to possess the properties that were assessed (Table S5).

Computational molecular docking with AutoDock
Outperforming control compounds tacrine, donepezil, galantamine, and rivastigmine, 88 identified molecules exhibit enhanced binding affinity in molecular docking via AutoDock Vina-1.5.7.These findings suggest their potential as promising acetylcholinesterase inhibitors, warranting further investigation, this study establishes a benchmark for assessing the comparative efficacy of the identified molecules with the positive control.The docking and redocking outcomes for the remaining compounds are comprehensively presented in the accompanying tables, encapsulating a comprehensive overview of their binding characteristics for further analytical consideration.This nuanced evaluation contributes to the burgeoning discourse surrounding potential therapeutic candidates for the development of novel acetylcholinesterase inhibitors (Motebennur et al., 2023).
The binding affinities of rivastigmine analog compounds, which exhibit both blood-brain barrier (BBB) permeability and favorable drug-likeness characteristics, were further investigated (Table 2).Notably, three rivastigmine analogs, such as 10989924 exhibited superior docking affinities as compared to rivastigmine.This observation suggests a potential enhancement in the binding interactions of these molecules with the target receptor.The binding affinities of tacrine and its structurally analogous exhibited the highest binding 241 affinities in the entirety of the conducted docking study (Table 3).Notably, 2-naphthalen-2-242 ylquinolin-4-amine emerges as the most promising candidate, displaying a substantial binding 243 affinity of -10.3 kcal/mol (PyRx) and -10.7 kcal/mol (AutoDock).The overall binding affinities 244 observed collectively underscore the potential of these compounds for further exploration and 245 development.Conversely, the galantamine similar structures presents only two compounds, and 246 among them 4, 14-dimethyl-11-oxa-4 azatetracyclo [8.7.1.01,12.06,18]Phytochemicals meeting the criteria of the blood-brain barrier (BBB) permeability and favourable drug-likeness were subjected to further investigation through molecular docking (Table 5).Among these, berberine exhibited a notable binding affinity of -9.3 kcal/mol, huperzine B demonstrated -8.3 kcal/mol, bisdemethoxycurcumin revealed -9.3 kcal/mol, and curcumin displayed a binding affinity of -9.2 kcal/mol.These findings highlight the substantial potential of these phytochemicals as candidates for acetylcholinesterase inhibition.

Docking site analysis
To conduct a more comprehensive investigation, a total of eight compounds (Table 6) have been chosen for a molecular dynamics (MD) simulation lasting 100 nanoseconds Based on the docking analysis and ADME profiling.Utilizing BioVia Discovery Studio, it is feasible to visually observe the interaction between protein ligands and active site residues, as well as to overlay all proteins and ligands, based on their highest binding affinity and respective segments.The common residues involved in the positive controls tacrine, galantamine, rivastigmine, and donepezil are-Tyr340, Phe296, Trp285, Phe337, and Tyr123, and there was Tyr123 with a hydrogen bond and Trp285, Tyr340, and Phe296 with Pi-allyl interaction.However, the residues involved in the interaction and the binding sites exhibit similarities, as do the bonding characteristics.This suggests that the binding location and residues are congruent to those to which tacrine, donepezil rivastigmine galantamine bind.The 2D interaction analysis elucidates the nature of binding interactions (Fig. 2), revealing the presence of pi-alkyl and pi-sigma interactions while notably excluding electrostatic bonds.
Notably, TYR123 exhibits hydrogen bonding, and TRP285 displays pi-alkyl interaction across all complexes.These residue interactions demonstrate a consistent pattern, underscoring the reproducibility of specific binding motifs within the studied complexes.

Molecular Dynamics Simulation analysis
The simulation was performed in a Desmond environment.There were 8 compounds primarily selected for MD simulation in the Desmond simulation environment.The overall simulation results were interpreted in RMSD, RMSF, Ligand properties, DCCM and PCA values.The binding grooves (Fig. 1) of the examined chemicals were superimposed, revealing a remarkable degree of similarity in their spatial arrangements.Additionally, the residues involved in interactions exhibited striking congruence among the compounds.This congruency in binding grooves and interacting residues suggests a conserved mode of binding, reinforcing the likelihood of a shared molecular mechanism or target engagement.Nevertheless, complex_1 and 3 demonstrate persistent stability, suggesting that the interaction between the protein and ligand remains intact throughout the entire duration.Complex_6 exhibits a deviation of 30ns, indicating inferior stability compared to the other 2 complexes.Nevertheless, the overall binding interaction is not significantly unfavourable, and further investigation is required for the other parameters.
A ligand exhibiting a moderate degree of compactness, as measured by a moderate gyration value, could potentially achieve a harmonious equilibrium between sufficient molecular surface area (SASA) for interaction purposes and accessibility for binding.The combination of moderate gyration and a larger molecular surface area may provide numerous binding interaction sites, whereas a moderate SASA may indicate a stable structure with restricted solvent exposure (Fig. 5).The gyration results indicate that Complex_1 and Complex_3 is located within a range of 3.5-4.00 Armstrong, while Complex_6 is situated between 5.0-5.5 Å (Fig. 6A).A higher value of the radius of gyration indicates a greater dispersion of atoms and a longer molecule.This metric quantifies the degree of elongation of a ligand and is equal to its primary moment of inertia.The SASA analysis reveals superior ligand characteristics, specifically in Complex_3 and Complex_6, with a surface area ranging from 50 to 100 Armstrong square units (Fig. 6B).Reduced solvent-accessible surface area (SASA) leads to increased binding stability.The polar surface area and the molecular surface area exhibit significant differences.Complex_1 exhibits lower levels of PSA and higher levels of MolSA, whereas Complex_6 displays higher levels of both PSA and MolSA (Fig. 6, C and D).Complex_6 exhibits reduced levels of PSA and MolSA.Elevated PSA levels can potentially impact binding employing electrostatic interactions.A greater MolSA value signifies an increased number of sites available for interacting with other molecules or receptors.

PCA analysis
Principal Component Analysis (PCA) is a mathematical technique that identifies the most significant components in a dataset by analyzing the covariance or correlation matrix.In the context of protein analysis, PCA utilizes atomic coordinates to define the protein's available degrees of freedom (DOF).The result of those three results PCAs has been performed (Fig. 7).
PCA analysis of each of the component percentage indicate each of the parameters, PC1 might indicate how strongly the ligand binds to the protein, PC2 could represent something like the flexibility of the protein-ligand complex and PC3 might capture variations in the shape complementarity between the protein and ligand.The highest percentage of variance explained is indicated by the Single Component with the Highest Variance (PC1), as determined by the PCA analysis.Complex_1 PCA yields the most favourable outcomes, followed by complex_3 and complex_6.By considering the amalgamation of constituents that capture substantial variation in contrast to the summaries of 46.18% and 41.54% for both complexes, Complex_1 exhibits a sum of 53% (Table 7).It exhibits improved variances.Complex 1 exhibits superior performance in both analyses, whether a singular component with the highest variance is considered or a collection of components that collectively account for a substantial proportion of the data's variance is considered (David & Jacobs, 2014a).

DCCM analysis
The DCCM analysis method was applied in a novel way to assist in the identification of potential protein domains.During the implementation of this novel approach, multiple DCCM maps were computed, each utilizing a distinct coordinate reference frame to determine the boundaries of protein domains and the constituents of protein domain residues (Nascimento et al., 2022).

Discussion
The therapeutic intervention of Alzheimer's disease (AD) using acetylcholinesterase inhibitors (AChEi) has been demonstrated by a wide range of plant-based compounds (Santos et al., 2018).
Given the absence of reliable, efficient, and secure inhibitors, investigating structurally similar compounds could be a promising field for researchers to explore (Čolović et al., 2013).In this study, we analyzed the chemical structures of tacrine, donepazel, galantamine, and rivastigmine to identify potential alternative drugs that are safer (Ahmed et al., 2021).Computer aid drug design (CADD) methodologies have been discovered to expand the repositories of chemical compounds for the identification of potential inhibitors.The assessment of the binding affinity between a protein and a vast collection of ligands is frequently accomplished through the application of molecular docking techniques (Baig et al., 2018).The molecules within the applicability domain of the constructed-in silico model were screened to assess their drug-likeness and ADME properties.Drug likeness provides a highly valuable criterion for determining the minimum requirements that a compound must meet to be considered suitable for drug development (Gleeson et al., n.d.).This criterion helps in the objective selection of new drug candidates that have desirable bioavailability (Hefti, 2008).
Molecular docking is a highly effective approach in CADD that utilizes specific algorithms to determine the affinity scores based on the positioning of ligands within the binding pocket of a target.In molecular docking, the lowest docking score corresponds to the highest affinity, indicating that the complex remains in contact for a longer period with good stability (Agu et al., 2023;Meng et al., 2011).Rigorously examine the protein-ligand binding to identify compounds with higher binding affinity and potentially improved hydrogen bonding characteristics (Du et al., 2016).The analysis of the docking results confirmed the binding of the final three compounds, negative associations (Avti et al., 2022).
Exploring the potential of computationally screened compounds in comparison to established drugs for Alzheimer's disease shows a promising direction for future research (Ahmed et al., 2021).
Experimental validation using in vitro and in vivo studies is essential to confirm the effectiveness and safety characteristics of these identified compounds.Recognizing the constraints of the computational approach is crucial, including the inherent approximations in modelling, the possibility of false positives, and the requirement for experimental verification.The intricate characteristics of AD pathophysiology pose difficulties in identifying specific inhibitors that efficiently target the progression of the disease (Golriz Khatami et al., 2020).The combination of computational screening and molecular dynamics simulations provides an initial yet insightful view on potential inhibitors for AD (Lemkul & Bevan, 2012).The identified compounds show potential as candidates for further investigation and confirmation in preclinical and clinical studies.
Nevertheless, the practical application of these compounds as effective treatments necessitates thorough experimental verification (Siddiqui et al., 2017).

Conclusion
The treatment of Alzheimer's disease through acetylcholinesterase inhibitors has been showcased by various plant-derived compounds.Considering the scarcity of dependable, effective, and safe inhibitors, exploring compounds with comparable structures holds promise as a potential avenue for investigation.One of the quickest and most economical methods is computational techniques.
Computational biology has shown that different types of chemicals from plants and marine sources have been identified and found to possess strong inhibitory effects against cholinesterase.In this study, we performed a virtual screening to discover new cholinesterase inhibitors from similar structures and plant compounds that interact with cholinesterase.Docking and molecular simulation tools were employed to investigate the significance of binding interactions of potentially new molecules for Alzheimer's disease treatment.
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Figure 1 .
Figure 1.The alignment between the RCSB PDB structure and the 3D predicted structure of the 4M0E protein is depicted.The resolved missing residues and the conservation of the protein structure compared to its actual PDB sequence are shown.

Figure 2 :
Figure 2: A visual representation of Protein-ligand interaction.The protein-ligand interaction of Complex_1 (A), Complex_2 (B), Complex_3 (C), Complex_4 (D), Complex_5 (E), Complex_6 (F), Complex_7 (G), and Complex_8 (H).All the interactions have common Tyr123 with a hydrogen bond and Trp85 with Pi-allyl interaction.The rest of the interactions have Pi-sigma with similar residues of the active side.

Figure 3 :
Figure 3: A visual representation of the binding pocket and ligand interaction.(A) The 3d Structure of protein-ligand complex and protein hydrophobicity mapping.Close view of Complex_1 (B), Complex_3 (C), Complex_6 (D).The protein pocket region is slightly bluish which indicates partially hydrophilic.All the ligands bind to the same side of the protein.

Figure 4 :
Figure 4: A 100-nanosecond simulation is conducted to measure the root mean square deviation (RMSD).Results of four complexes.Complexes 1, 2, and 3 are subjected to a 100nanosecond molecular dynamics simulation using the Desmond software.A) RMSD of Complex_1.B) RMSD of Complex_3.C) RMSD of Complex_6.The root means square deviation (RMSD) between the ligand and protein exhibits temporal constancy, thereby ensuring stability.

Figure 5 :
Figure 5: The root means square fluctuation (RMSF) of all the simulation complexes over a 100-nanosecond simulation.A-Root Mean Square Fluctuation (RMSF) of Complex_1, B-RMSF of Complex_3, C-RMSF of Complex_6.The interpretation of the results is justified.Several significant fluctuations.The fluctuation primarily arises when the ligand interacts with the protein residues.Complex_1 exhibits three significant fluctuations on the green vertical bar, which signify the contact between the ligand molecule and the protein.Complex_3 and Complex_6 exhibit significant temporal fluctuations.The overall comparison reveals significant fluctuations, although they do not exceed 4.8 Å.

Figure 7 :
Figure 7: PCA analysis of Three Complexes.The PCA of Complex_1 (A), complex_2 (B), and complex_3 (C).The White dot here mentioning the transition state of protein ligand simulation confirmation, the blue dot with a scattered indicates energetically unstable conformational states and red dots indicate the stable conformational state.

Figure 8 :
Figure 8: The cross-correlation map of the C α atom pairs within the monomers of AChE is analyzed for dynamics.The DCCM of Complex_1 (A), complex_2 (B), and complex_3 (C).The correlation coefficient (C ij) was represented using various colours.The values of Cij, ranging from 0 to 1, indicate positive correlations.Positive correlations indicate that these pairs of atoms tend to move in similar directions or have comparable behaviours during the simulation.On the other hand, negative correlations are represented by Cij values ranging from -1 to 0. Negative correlations indicate that these pairs of atoms tend to migrate in opposite directions or have contrasting behaviours during the simulation.

Table 1 :
Primary selection Criteria for similar structure compounds

Table 2 .
The docking, redocking and ADME results of Rivastigmine's similar structure with CID 239 and chemical name.240

Table 3 .
The Docking and redocking results of tacrine's similar structures with CID and chemical 253

Table 4 .
The Docking and redocking results of galantamine similar structure with CID and chemical name.

Table 5 .
The Docking results of phytochemicals with CID and chemical name.

Table 6 .
Docking site analysis for selected chemicals.