Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by cognitive deterioration and changes in behavior. Acetylcholinesterase (AChE), which hydrolyzes acetylcholine, is a key drug target for treating AD. This research aimed to identify new AChE inhibitors using the IMPPAT database. We used known drugs as a basis to search for similar chemicals in the IMPPAT database and created a library of 127 plant-based compounds. Initial screening of these compounds was performed using molecular docking, followed by an analysis of their drug-likeness and ADMET properties. Compounds with favorable properties underwent density functional theory (DFT) calculations to assess their electronic properties such as HOMO-LUMO gap, electron density, and molecular orbital distribution. These descriptors provided insights into each compound’s reactivity, stability, and binding potential with AChE. Promising candidates were further evaluated through molecular dynamics (MD) simulations over 100 ns and MMPBSA analysis for the last 30 ns. Two compounds, Biflavanone (IMPHY013027) with a binding free energy of − 130.394 kcal/mol and Calomelanol J (IMPHY007737) with − 107.908 kcal/mol, demonstrated strong binding affinities compared to the reference molecule HOR, which has a binding free energy of − 105.132 kcal/mol. These compounds exhibited promising drug-ability profiles in both molecular docking and MD simulations, indicating their potential as novel AChE inhibitors for AD treatment. However, further experimental validation is necessary to verify their effectiveness and safety.
Similar content being viewed by others
Introduction
Alzheimer’s disease (AD), the leading form of dementia, is a progressive neurodegenerative illness1. , characterized by the progressive impairment of higher intellectual functions, including changes in cognitive, behavioral, and social activities2. Disturbingly, it is estimated that a new case of dementia emerges every three seconds globally, with approximately 55 million individuals currently living with dementia, predominantly in low- and middle-income countries3. Remarkably, there are approximately 10 million new cases reported each year, and projections suggest that the global number of individuals affected by dementia will reach 82 million by 2030 and an alarming 152 million by 20504. In India, the Dementia India Report 2020 estimates that around 5.3 million people aged 60 and older are currently affected by dementia, with projections suggesting this figure could rise dramatically to 14.32 million cases by 20505.
The exact etiology of AD remains uncertain, although several factors, including β-amyloid plaques6,7, τ-protein aggregation8, oxidative stress9,10, and reduced acetylcholine (ACh) levels11, have been linked to its pathophysiology. Neuropathologically, AD is characterized by early cholinergic neuron degeneration in the basal forebrain, leading to decreased cholinergic signaling. This impairment can be addressed through the use of acetylcholinesterase inhibitors or by modulating muscarinic and nicotinic acetylcholine receptors12. Acetylcholinesterase (AChE) is an enzyme classified as a serine hydrolase, present at neuromuscular junctions and cholinergic synapses, responsible for breaking down acetylcholine (ACh) into acetate and choline, thus terminating cholinergic impulse transmission13. Inhibiting AChE activity leads to reduced breakdown of ACh, resulting in increased ACh concentration in the synapses14. This elevation in ACh levels may enhance cholinergic neurotransmission and potentially improve cognitive function in individuals with AD.
Approved Alzheimer’s treatments include cholinesterase inhibitors (donepezil, rivastigmine, galantamine) and NMDA antagonist memantine, enhancing cognitive function but having uncertain effects on neuropsychiatric symptoms. Side effects can include nausea and dizziness15. Gradually increasing the dosage and taking the medication with food can help minimize side effects. Additional adverse effects of cholinesterase inhibitors include extrapyramidal symptoms, sleep issues, cardiorespiratory problems, muscle cramps, weakness, and urinary incontinence, due to central and peripheral cholinergic over-activity16. On the contrary, traditional Indian medicinal plants, deeply embedded in Ayurveda, Siddha, and Unani systems, present a diverse array of therapeutic flora17. Backed by traditional wisdom, herbal medicines possess the potential to address AD pathophysiology at various sites, operating at both cellular and molecular levels18. Though not fully understood, herbal medicines are believed to protect against cognitive decline through general antioxidant and anti-inflammatory actions, along with specific interactions with AChE, β-amyloid fibrils, and tau aggregation19,20. Indian herbal remedies like Withania somnifera, Centella asiatica, Celastrus paniculatus, and Bacopa monnieri have shown cognitive benefits in experimental Alzheimer’s models when used preventively21,22,23,24,25,26. These findings suggest medicinal plants may offer potential for new Alzheimer’s treatments and cognitive improvement.
The schematic workflow depicts the screening procedure for the two leading virtual hits identified in the study.
This study aims to identify new compounds from the IMPPAT database with promising AChE inhibitory activity using in silico methods, targeting Alzheimer’s disease treatment. This study employed a multi-step approach; initially we used known drugs (Fig. 1) as a basis to search for similar chemicals in the IMPPAT database and created a library of 127 plant-based compounds. Subsequently, virtual screening of the phytochemical library was conducted using rigid receptor-molecular docking to identify compounds capable of effectively inhibiting the activity of the AChE enzyme. The selected compounds from the docking results were further evaluated for their physicochemical properties and ADMET characteristics. These evaluations are key for assessing the drug-likeness and safety profiles of potential drug candidates. Furthermore, to enhance understanding of the binding interactions and electronic properties of the selected compounds with the AChE enzyme, density functional theory (DFT) analysis was employed. DFT is a quantum mechanical technique employed to assess the electronic structure of molecules. Finally, to explore the dynamic behavior of the ligand-enzyme complexes, the selected compounds undergo MD simulations. These simulations were conducted over a 100-nanosecond timescale to evaluate the stability of the complexes and calculate their binding free energies. To check the potential inhibition of butyrylcholinesterase (BChE) as an off-target effect, we conducted additional molecular docking studies and binding mode analyses of our best hit compounds with butyrylcholinesterase (BChE). By integrating these sophisticated in silico techniques, this study endeavors to streamline the identification of potential novel compounds from Indian medicinal plants with promising inhibitory activity against the AChE enzyme (Table 1). Such comprehensive analysis holds great promise for discovering innovative therapeutics for AD and advancing the quest for effective treatments in the fight against this challenging neurodegenerative disorder. Ultimately, This study’s findings could greatly help to alleviating the burden of AD on patients and their caregivers, offering hope for improved quality of life for those affected by this devastating condition.
Beyond the techniques used in our study, recent in silico advancements have introduced a variety of tools for predicting protein function, evaluating mutation impacts, and analysing drug-target interactions. For instance, a study on aminoarylbenzosuberene (AAB) molecules as inhibitors of 11β-hydroxysteroid dehydrogenase (11β-HSD1) utilized conventional molecular dynamics, steered molecular dynamics, and enhanced umbrella sampling simulations, highlighting the effectiveness of these methods in drug discovery27. Sequence and structure-based tools like SIFT and PolyPhen assess amino acid substitutions to predict SNP pathogenicity28,29, while machine learning models like PhD-SNP and MutPred link mutations to disease mechanisms by integrating sequence and structural data28. Homology-based tools, such as Modeller and I-TASSER, support accurate structure prediction for proteins lacking experimental data, even for low-homology cases like taste receptors T2R4 and T2R1429,30.
Other specialized tools like HADDOCK enhance protein-protein interaction modeling by docking complexes with precision, identifying active residues, and applying docking restraints to aid pathway analysis31. Advanced structural analyses like Normal Mode Analysis (NMA) provide additional insights into protein flexibility and potential allosteric sites by modeling atomic vibrational motion, a useful feature for studying conformational changes in proteins like those associated with HIV32. Techniques such as computational solvent mapping offer a unique perspective on ligand binding by identifying binding hotspots beyond primary active sites, which is essential for targeting allosteric regions, as exemplified in studies on Aurora kinase A in cancer research31.
To enhance binding site and ligand mapping, tools like CASTp and the Catalytic Site Atlas (CSA) help locate structural pockets and cavities, pinpointing critical regions for ligand interaction32. PatchDock further complements this by assessing ligand binding affinity through molecular shape complementarity and atomic desolvation energy scoring, essential for cases where shape compatibility underpins binding stability32. These cutting-edge techniques expand the scope of in silico studies, aiding in high-precision research across disease mechanisms and drug design.”
Materials and methods
Structure-based virtual screening
The IMPPAT 2.0 database, which contains a comprehensive collection of phytochemicals from Indian medicinal plants, was chosen as the primary data source for this study33,34. This database provides valuable information on the chemical structures of various plant-derived compounds, making it suitable for the identification of potential AChE inhibitors. In order to assess the structural similarity of chemicals present in the IMPPAT database, two types of molecular fingerprints were utilized: (a) Extended Circular Fingerprints (ECFP4)35 were generated using the Morgan algorithm36 with a radius value of 2, as implemented in RDKit37; and (b) a fingerprint based on MACCS keys. These fingerprints were employed to calculate the Tanimoto coefficient38, enabling an evaluation of the structural resemblance between the chemicals and known drugs39. Drugs known as AChE inhibitors, such as donepezil, galantamine, rivastigmine, and others (Table 1), were chosen to serve as benchmark for chemical similarity search and to aid in the identification of potential novel compounds from Indian medicinal plants database with AChE inhibitory activity. By utilizing IMPPAT 2.0 database, a total of 127 phytochemicals were extracted based on chemical structures similarity. These compounds constituted the initial library for the subsequent screening analyses.
Receptor preparation
The structure of Recombinant Human Acetylcholinesterase in complex with Compound 12 (HOR), an AChE inhibitor (PDB ID: 7D9P), was obtained from the Protein Data Bank at a resolution of 2.85 Å and an R-Value of 0.21940. The structure of human BChE in complex with (S)-2-(butylamino)-N-(2-cycloheptylethyl)-3-(1 H-indol-3-yl)propanamide (HUN), a known BChE inhibitor (PDB ID: 6QAA) is also retrived from Protein Data Bank. For preparing the protein, water molecules, ions, chain B, and unrelated molecules were eliminated from the protein using PyMOL software41. Protein energy minimization was conducted using Swiss-PdbViewer v4.1.042. Subsequently, hydrogen atoms were added to optimize the structure using MGL Tools from AutoDockVina software43, and the resulting structure was saved for further analysis.
Ligand preparation
The 3D structure of compounds screened through the chemical similarity search, along with the co-crystalized ligand (2 S)-2-[[4-fluoranyl-1-[(2-fluorophenyl) methyl] piperidin-4-yl] methyl]-5,6-dimethoxy-2,3-dihydroinden-1-one or HOR (CID: 156583277), which was used as a benchmark in each screening step, were downloaded from PubChem server in their SDF format (https://pubchem.ncbi.nlm.nih.gov). According to the previous study, HOR is a highly potent and selective AChE inhibitor with excellent oral bioavailability and preferential brain distribution. It has also demonstrated significant efficacy in ameliorating cognitive impairments in mouse models at a lower effective dose than donepezil40. This makes HOR a relevant reference compound for evaluating the performance of our novel candidates. To facilitate further processing, OpenBabel (version 2.4.1)44 was utilized to convert the SDF format files into the PDB format. For the preparation of the ligands, hydrogen atoms were added to all compounds. Subsequent to this step, energy minimization was carried out using the UFF force field and a conjugate-gradient algorithm with the PyRx software43. Following these processes, all compounds were saved for further analysis.
Virtual screening by molecular docking
To identify potential candidates against AChE, molecular docking was employed to screen ligands with a high affinity for the enzyme’s binding site using pyVSvina (https://github.com/shuklarohit815/pyVSvina), a Python-based tool for Virtual Screening. This tool utilizes Autodock Vina to screen a library of ligands against a protein receptor.
To find a binding pocket of co-crystalized ligand in the receptor, the PyMol plugin feature41 was utilized to generate a three-dimensional grid box. The dimensions of grid box for AChE were defined as X = 13.9, Y = 43.2, and Z = 27.2 grid points, with corresponding grid spacing for X, Y, and Z coordinates set at 23.1 Å, 16.3 Å, and 21.0 Å, respectively while dimensions of grid box for BChE were defined by dimensions X = 19.0, Y = 42.7, and Z = 39.5 grid points, with grid spacing set at 20.8 Å, 17.5 Å, and 18.6 Å, respectively. An exhaustiveness value of eight was selected to find best ligand pose. To analyze the interactions within protein-ligand complexes, including hydrogen bonds and bond lengths, the LigPlot + v.2.2.5 program45 was utilized for 2D interaction visualization. Furthermore, 3D visualization analysis was performed using the PyMol molecular visualization tool version 2.1.041.
Drug-likeness and ADMET analysis
The compounds that were selected post-molecular docking underwent further analysis to predict their drug-likeness and ADMET properties. ADMET, short for Absorption, Distribution, Metabolism, Excretion, and Toxicity, constitutes crucial components of drug development. It evaluates how a drug interacts within the body. Absorption concerns how drugs enter the bloodstream, while distribution examines their movement to target sites. Metabolism involves enzymatic transformation and potential impact on effectiveness. Excretion focuses on elimination routes, guiding dosage and duration. Toxicity assesses adverse effects, vital for patient safety. The drug-likeness and ADMET properties were evaluated through the online web-server SwissADME (http://www.swissadme.ch), ADMETlab 2.046, pkCSM47 and ADMET SAR48,49.
Density functional theory (DFT)
Subsequently, Only a subset of the 127 Compounds, those showing favorable drug likeness and ADMET properties, underwent further screening through DFT analysis using the “atomistica.online”50 web application and the ORCA 5.0.4 software51,52,53,54. DFT, a computational strategy, employs quantum mechanical calculations to investigate the molecular structure and attributes of compounds, thereby advancing our comprehension of their interactions with target proteins and their potential therapeutic effectiveness. To conduct the DFT analyses, we utilized Becke’s three parameters in conjunction with the Lee-Yang-Parr exchange correlation functional (B3LYP)55,56. Furthermore, a balanced polarized triple-zeta basis set (6-311G**), with the default parameter settings within both atomistica and ORCA 5.0.4, was employed. This choice was based on previous studies demonstrating that this combination effectively balances computational efficiency and the reliability of the results57.
Our investigation primarily centered on analyzing the Frontier Molecular Orbitals (FMO) and the molecular electrostatic potential (MEP) surfaces, along with performing quantum chemical calculations. Initially, we optimized the structures and generated an input file for the ORCA software using atomistica. Subsequently, we carried out a single-point energy calculation using the restricted Self-consistent field (SCF) method in Cartesian format, with the goal of determining the electron orbital energies of HOMO, LUMO, and ΔE. To visualize the generated output data, comprising optimized geometry, HOMO-LUMO gap, and MEP, we utilized software tools like Avogadro 1.2.058, IboView v2015042759,60, and UCSF Chimera v1.861.
Molecular dynamics (MD) simulation
After DFT processes, the two most promising compounds underwent for further examination through MD simulations. MD simulation was also performed for HOR (an AChE inhibitor), serving as a benchmark to compare the simulation results. MD simulations were employed to evaluate the stability of the protein-ligand complex under diverse physiological conditions62. For all MD simulations, we utilized the GROMACS software63. To generate topologies for the protein-ligand complex, we employed the CHARMM 36 force field64, which takes into account various factors including valency, atom groups, and bond connectivity to carry out MD calculation. To mimic the natural environment, the molecules were solvated in water. The TIP3P water model was chosen to establish a water-solvated system with periodic boundary conditions in a dodecahedron shape, defined by box vectors of uniform length measuring 9.81 nm. When placing solutes in the simulation box, a minimum distance of 10 Å (1.0 nm) from the box edges was maintained. To balance the system’s charge, sodium (Na+) and chloride (Cl−) ions were introduced, ensuring periodic boundary conditions. Following ion addition, energy minimization was conducted on the protein-ligand complex to confirm absence of steric clashes and ensure an appropriate initial structure. This step employed a steepest descent algorithm with a 10 kJ/mol energy cutoff, using the Verlet cutoff method. Equilibration of the protein-ligand complex unfolded in two stages. In the first stage, equilibration occurred within the NVT ensemble at 300 K for 100 ps to establish temperature equilibrium. The second stage involved equilibration within the NPT ensemble using the Parrinello–Rahman simulation method, maintaining constant temperature (300 K) and pressure (1 atm), with a time step of 2 fs. MD simulations of the protein-ligand complex were conducted for duration of 100 ns. Following the MD simulations, we employed various tools from the GROMACS 5.0.7 software package, including g_rms, g_rmsf, g_gyrate, and g_sasa, to analyze key features such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) based on the MD trajectories. Hydrogen bond analysis was also utilized to calculate the number of hydrogen bonds formed within the complex and involving the protein during the MD simulation.
Molecular mechanics Poisson–Boltzmann surface area calculation
Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) is a computational approach utilized to predict the binding free energy of protein-ligand complexes. In this study, we employed g_mmpbsa—a GROMACS Tool for High-Throughput MM-PBSA Calculations—to analyze the binding mode and overall free binding energy of our top two ligands62. This approach involved calculating the free energy of solvation, accounting for both polar and nonpolar solvation energies, as well as the potential energy from electrostatic and van der Waals interactions between protein and ligand molecules. The entire MMPBSA process is represented by the following equation:
The MM-G/PBSA values for protein-ligand complexes were calculated by summing contributions from gas-phase electrostatic energy (Eele), van der Waals interactions (Evdw), polar interactions (Gpolar), and nonpolar interactions (Gnonpolar). The MM-PBSA calculations were performed over the final 30 ns of the simulation period. Ultimately, the average binding energy was computed using a Python script provided within the g_mmpbsa toolset.
Results and discussion
Binding site analysis
Human acetylcholinesterase (AChE) is a crucial enzyme in the nervous system, responsible for hydrolyzing the neurotransmitter acetylcholine into acetate and choline65. The active site of AChE is located at the bottom of a deep and narrow gorge approximately 20Å in length66, comprises several key regions. The esteratic site contains the catalytic triad of Ser203, His447, and Glu334, which are essential for hydrolyzing acetylcholine67. The anionic site, formed by the side chains of Glu202, Trp86, and Tyr337 residues, binds the quaternary trimethylammonium tail group of ACh68,69. Additionally, the peripheral anionic site (PAS), located at the entrance of the active site70, includes residues such as TYR 72, ASP 74, TYR 124, TRP 286, and TYR 341, and plays a role in modulating substrate access and enzyme activity67. The acyl pocket, with key residues Phe-295 and Phe-297, determines the substrate specificity for the covalent adducts71,72. The binding site residues of AChE interacting with the reference molecule, HOR, are illustrated in Fig. 2.
Three-dimensional representation of the active binding site of the AChE protein (A), along with the depiction of residues involved in active binding (B).
The key interacting residues within the active site of BChE include HIS 438, GLU 325, and SER 198, which form the catalytic triad; ASP 70 and TYR 332 in the peripheral anionic site; and TRP 82, TYR 128, and PHE 329 in the anionic site73.
Molecular docking and binding mode analysis
Prior to conducting molecular docking, the protocol’s validity was ensured by redocking the reference molecule (HOR) into the active site of the AChE enzyme. The outcome revealed that the docked HOR perfectly aligned with the co-crystallized HOR in the PDB (Fig. 3A). Moreover, the docked HOR exhibited interactions with the same amino acid residues through hydrogen and hydrophobic bonds, as found in the crystal structure in PDB (Fig. 3B).
(A) Superimposed image of native protein with co-crystallized HOR in PDB, (B) Docked HOR showed interaction with the same amino acid residues as found in the crystal structure in PDB.
Subsequently, in the quest for novel compounds targeting AChE, the molecular docking of 127 phytochemicals, was executed with the target protein AChE utilizing pyVSvina. The docking outcomes were then prioritized according to their binding energies, with preference given to compounds exhibiting low binding energy/high binding affinity with AChE compared to HOR. These selected compounds were then subjected to further assessments for drug-likeness and toxicity predictions (Table 2).
The docking results for BChE with our top compounds reveal binding energies of − 8.7 kcal/mol for HUN (the reference inhibitor), − 12.0 kcal/mol for Biflavanone, and − 10.4 kcal/mol for Calomelanol J. These findings indicate that both Biflavanone and Calomelanol J exhibit greater binding affinities to BChE compared to the reference compound. Biflavanone, showing the lowest binding energy, exhibits the highest potential for inhibition, followed closely by Calomelanol J. These findings suggest that both compounds may be more effective BChE inhibitors compared to HUN. LigPlot + v.2.2.5 was utilized to visualize the interactions between the protein and ligands. The docked poses of the two best compounds with AChE are depicted in Fig. 4. As shown in the figure, Biflavanone (IMPHY013027) forms hydrogen bonds with TYR 124 and establishes hydrophobic bonds with HIS447, TYR341, PHE338, TYR337, PHE 297, PHE295, SER203, SER125, TYR124, GLY122, GLY121, ASN 87, TRP86, THR83, and ASP74. Calomelanol J (IMPHY007737) forms hydrogen bonds with PHE295 and engages in hydrophobic interactions with TYR341, PHE338, PHE297, PHE295, VAL294, TRP286, TYR124, LEU76, and TYR72 residues. Analyzing all the complexes, common binding active site residues of Biflavanone and HOR include HIS447, TYR341, PHE338, TYR337, PHE297, PHE295, SER203, TYR124, and TRP86. On the other hand, common binding active site residues of Calomelanol J and HOR consist of TYR341, PHE338, PHE297, PHE295, TRP286, TYR124, and TYR72.
2D interaction profiles of AChE and BChE with their reference inhibitors (HOR and HUN, Respectively) and top ligands (Biflavanone and Calomelanol J), showing hydrogen bonds and hydrophobic interactions. (Dotted green lines represent hydrogen bonds, red ignited arcs indicate hydrophobic interactions, and red circles and ellipses highlight common residues with the reference.)
Conversely, analysis of key interactions within the BChE active site highlights the distinct binding behaviours of Biflavanone and Calomelanol J (Fig. 4). It was observed that Biflavanone did not form hydrogen bonds with any of these crucial residues. Instead, its binding mode is driven by a network of hydrophobic interactions with residues such as PHE 398, TYR 332, PHE 329, LEU 286, PRO 285, SER 198, TYR 128, LEU 125, THR 120, GLY 116, GLY 115, TRP 82 and ASP 70. This reliance on hydrophobic contacts suggests a more transient interaction profile, which may influence its stability and efficacy as a BChE inhibitor.In contrast, Calomelanol J exhibited both hydrogen bonding and hydrophobic interactions, suggesting a more secure attachment to the BChE active site. It formed hydrogen bonds specifically with HIS 438 and SER 198 in the catalytic triad, which could contribute significantly to its binding strength and stability within the active site. Furthermore, Calomelanol J displayed additional hydrophobic interactions with HIS 348, PHE 398, TYR 332, PHE 329, ALA 328, VAL 288, SER 287, LEU 286, PRO 285, TRP 231, SER 198, GLU 197, GLY 117, GLY 116 and TRP 82, bolstering its position in the enzyme’s active pocket. Together, these findings imply that Calomelanol J may offer a more robust inhibition profile due to its combined bonding modes, while Biflavanone, relying solely on hydrophobic contacts, may exhibit a weaker and less specific binding.
Drug-likeness and ADMET properties
The screening of 127 compounds from the IMPPAT database revealed two promising candidates, Biflavanone and Calomelanol J, which exhibited superior binding profiles compared to the reference compound, HOR. Notably, both Biflavanone (− 13.1 kcal/mol) and Calomelanol J (− 12.0 kcal/mol) demonstrated significantly lower binding energies than HOR (− 10.6 kcal/mol), indicating stronger interactions with the AChE, likely correlating with their enhanced biological activity. Structurally, Biflavanone (C30H22O4) has a higher molecular weight (446.49 g/mol) and features four hydrogen bond acceptors but lacks donors, while Calomelanol J (C24H18O5) has a lower molecular weight (386.4 g/mol), five hydrogen bond acceptors, and one donor. The logP values for both compounds (3.3 for Biflavanone and 3.17 for Calomelanol J) indicate a favourable balance between lipophilicity and hydrophilicity, which may enhance their membrane permeability. The presence of a hydrogen bond donor in Calomelanol J suggests it may form stronger interactions with the AChE compared to Biflavanone, which lacks such donors. Additionally, Biflavanone has three rotatable bonds, contributing to greater molecular flexibility, while Calomelanol J, with two rotatable bonds, exhibits more rigidity, potentially enhancing specificity during binding. The topological polar surface area (TPSA) values further indicate that Calomelanol J (72.83 Ų) has a larger polar surface area than Biflavanone (52.60 Ų), which could correlate with improved solubility and bioavailability.
Drug likeness involves assessing the compatibility between a drug’s physicochemical characteristics and the desired biopharmaceutical properties within the human body. Lipinski’s five rules act as crucial criteria for evaluating drug likeness, imposing specific conditions that determine whether a drug is a suitable candidate74,75. These rules include requirements such as a molecular weight below 500, a log P (octanol/water coefficient) value ≤ 5, a maximum of 5 hydrogen bond donors (HBD), and no more than 10 hydrogen bond acceptors76,77. On the other hand, ADMET encompasses the processes of absorption, distribution, metabolism, excretion, and toxicity of drugs. Optimal ADMET properties are essential descriptors used in pharmaceutical characterization, playing a pivotal role in selecting drugs that are compatible with human administration78.
The results of drug likeness and ADMET properties calculations are presented in Table 3. According to the table, the selected ligand adheres to Lipinski’s five rules79 and exhibits potential for use as a drug molecule. Additionally, it satisfies the Veber80 and golden triangle rule81 and does not trigger any PAINS alert82, further confirming its favorable drug likeness properties. Solubility plays a crucial role in achieving optimal pharmacological effects, and low solubility is a common issue when synthesizing new drugs. Based on the standard ranking of drug solubility, the calculated water solubility values for Biflavanone and Calomelanol J are − 3.9268 log mol/L and − 3.7486 log mol/L, respectively. These values classify the studied molecules as highly soluble (0 > soluble > − 4), which is a desirable pharmacokinetic property for absorption and distribution83.
The absorption analysis revealed that both compounds, Biflavanone and Calomelanol J, exhibit high intestinal absorption (HIA+) and excellent permeability in MDCK and Caco-2 cell models. The human colon adenocarcinoma cell line (Caco-2) is widely utilized as a surrogate for human intestinal epithelium to assess in vivo drug permeability due to its morphological and functional resemblance. The predicted Caco-2 permeability, represented as log cm/s, is considered favorable if it exceeds − 5.15 log cm/s46. Both hit compounds demonstrated exceptional Caco-2 permeability with values of − 5.134 log cm/s and − 4.875 log cm/s for Biflavanone and Calomelanol J, respectively. Furthermore, the oral bioavailability of a drug is closely linked to its intestinal absorption, making human intestinal absorption (HIA) a vital indicator. Compounds with an absorbance below 30% are considered poorly absorbed46, whereas both hit compounds showed good absorbance, categorizing them as highly absorbed (HIA+). MDCK cells are frequently employed for studying drug efflux and active transport, particularly mediated by P-glycoprotein (P-gp)84. The predicted MDCK permeability, measured in cm/s, categorizes compounds as having high, medium, or low permeability based on Papp values. Both Biflavanone and Calomelanol J demonstrated excellent MDCK permeability with values of 2.27 × 10–5 cm/s and 1.54 × 10–5 cm/s, respectively. The drug distribution assessment reveals distinct characteristics for Biflavanone and Calomelanol J. Biflavanone exhibits limited ability to traverse the blood–brain barrier (BBB), yet it effectively penetrates the central nervous system (CNS) to reach its molecular target. On the contrary, Calomelanol J demonstrates a high BBB-crossing ability, enabling efficient penetration into the CNS for target interaction. Measuring blood-brain permeability is often challenging due to confounding factors, and the logPS value, derived from in situ brain perfusions, serves as a direct indicator47,85. Compounds with logPS > − 2 are deemed CNS-penetrating, while those with logPS > -3 are considered non-CNS-penetrating85. Both hit compounds, Biflavanone and Calomelanol J, boast logPS values of − 1.456 and − 1.865, respectively, signifying their CNS-penetrating capabilities. The Volume of Distribution (VD), connecting the administered dose with initial concentration in circulation, provides insights into in vivo drug distribution46. Both hit compounds exhibit optimal VD values within the 0.04–20 L/kg range46, indicating favorable distribution characteristics. Concerning metabolism, the ADMET analysis focused on the role of CYP450 enzymes, pivotal in metabolizing diverse substances like drugs, xenobiotics, steroids, fatty acids, bile acids, and carcinogens86. Different models were employed to assess the potential interaction of the compounds with various CYP450 substrates (1A2, 2C9, 2D6, 2C19, and 3A4) and their inhibitory effects on these enzymes. The outcomes revealed that neither compound acted as a substrate for the tested CYP450 enzymes. In terms of inhibition, they displayed no inhibitory capability towards CYP450 enzymes except for 2C9 and 3A4. Consequently, the analysis established that both hit compounds exhibited low CYP Inhibitory Promiscuity, implying their safety concerning pharmacokinetic interactions. Total clearance of a drug serves as a metric for its bioavailability and the required dosing rates to achieve steady-state concentrations. A higher total clearance value indicates a faster excretion process for the molecule87. The concept of half-life for a drug is a combination of clearance and volume of distribution, and having reliable estimates of these two properties is arguably more appropriate46. The studied molecule exhibits an excellent value for its half-life (T1/2). In terms of carcinogenicity, both ligands were found to be non-carcinogenic. The emergence of drug-induced liver injury is a notable apprehension for patient well-being and represents a primary factor leading to the removal of drugs from the market. Observing adverse effects on the liver in clinical trials frequently results in the costly and premature cessation of drug development initiatives. In the case of both compounds, they exhibited no hepatotoxicity in human systems, indicating a lack of harmful effects on the liver. Additionally, the AMES Toxicity assay, which tests the mutagenicity of compounds, revealed that all compounds were non-AMES toxic, further suggesting their suitability as potential drug candidates.
Frontier molecular orbitals
Frontier molecular orbitals (FMOs) are utilized to predict the most active regions within molecular systems and to elucidate various reaction types. The properties associated with these FMOs, specifically the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), are of significant importance in characterizing materials in science. This is due to their ability to discern the chemical reactivity of molecules88. The HOMO represents an electron-rich orbital with a propensity to transfer electrons to unoccupied orbitals, while the LUMO, conversely, denotes an electron-deficient orbital capable of accepting electrons from occupied orbitals.
Figure 5A–C illustrates the gap values between the HOMO and LUMO. The energy gap of the FMOs is presented as follows: HOR (4.567 eV), Biflavanone (4.701 eV), Calomelanol J (4.366 eV). This energy gap offers insights into the optical and electronic properties. In this study, both hit compounds exhibited a comparable orbital electron energy gap to the reference molecule. Consequently, all three compounds are in an excited state with similar interactive modes. The higher energy gap suggests lower chemical reactivity and higher kinetic stability. Therefore, both hit compounds demonstrated similar interactive behavior with AChE compared to the reference molecule. In summary, FMO serves as a drug investigation tool that employs the concept of a molecule’s electronic orbital energy89. The Frontier Molecular Orbital is depicted in Fig. 5, providing insights into the molecular structure. The reference molecule comprises (5, 6-Dihydroxy-2,3-dihydro-1 H-inden-1-one) and (1-Methyl-2-fluorobenzene) linked by a central (4-Fluoro-4-methylpiperidine) ring. Biflavanone is structured as 2-(4-oxo-2-phenyl-3 H-chromen-2-yl)-2-phenyl-3 H-chromen-4-one, and Calomelanol J consists of 5-hydroxy-4,8-diphenyl-3,4,7,8-tetrahydropyrano[3,2-g]chromene-2,6-dione. The HOMO-LUMO Transition for HOR, Biflavanone, and Calomelanol J moieties is presented in the Fig. 5. Notably, in the reference molecule, a transition occurs from HOMO to LUMO, while the hit molecules exhibit a preference for intramolecular charge transfer.
Analysis of frontier molecular orbitals (FMOs) presented as LUMO, HOMO, and their energy gap (ΔE in kcal/mol) for hit compounds (B; Biflavanone, C; Calomelanol J) in comparison with the HOR (A).
Quantum chemical descriptors
Quantum chemical descriptors, derived from Koopmans’ theorem, provide insights into a molecule’s reactivity by evaluating its electronic properties90. Key descriptors include ionization potential (I), electron affinity (A), hardness (η), softness (σ), chemical potential (µ), electronegativity (χ), and electrophilicity (ω). These are defined mathematically as:
Ionization potential, I = -EHOMO
Electron affinity, A = -ELUMO
Hardness, η = \(\:\frac{I-A}{2}\)
Softness, σ = \(\:\frac{1}{\eta\:}\)
Chemical potential, µ = -χ.
Electronegativity, χ = \(\:\frac{I+A}{2}\)
Electrophilicity, ω = \(\:\frac{\chi\:2}{2\eta\:}\)
Table 4 presents HOMO and LUMO energies and associated chemical descriptors for the investigated and reference compounds. The HOMO-LUMO energy gap indicates kinetic stability91, with larger gaps indicating higher stability and smaller gaps implying greater reactivity. Ionization potential (I) is the energy required to remove an electron from a molecule’s ground state, while electronic affinity (A) is the energy released when the molecule captures an electron. Higher I values indicate greater chemical stability, and higher A values suggest a stronger tendency to accept electrons87. According to Table 4, the studied compound has a similar HOMO-LUMO energy gap to the reference, indicating comparable stability. The hit compounds have higher I values, showing superior stability, and slightly higher A values, implying a greater electron-accepting propensity than the reference. Hardness (η) indicates how resistant a molecule is to alterations in its electron distribution, quantifying its resistance to deformation or polarization of the electron cloud92. Conversely, softness (σ), being the inverse of hardness, reflects how readily a molecule can adjust its electron distribution. A softer molecule is more polarizable and can engage in chemical reactions more easily93. The results obtained reveal that both hit compounds demonstrate nearly identical values for hardness and softness compared to the reference molecule. Chemical potential (µ) indicates reaction likelihood. Higher µ (less negative) means easier electron donation, while lower µ (more negative) means greater electron acceptance87. The hit compounds have lower µ values than the reference, indicating a higher tendency to accept electrons. Electrophilicity (ω) values classify the compounds as stronger electrophiles than the reference, indicating their potential as effective electron acceptors.
Molecular electrostatic potential (MEP)
The reactivity of a molecule towards nucleophilic and electrophilic attacks is gauged by the molecular electrostatic potential (MEP), offering valuable insights into the distribution of nuclear and electronic charges. Visualization of the MEP utilizes a color-coded representation, where blue, red, and green signify positive, negative, and neutral electrostatic potentials, respectively. The blue/red regions become particularly favorable for nucleophilic/electrophilic attacks, as positively/negatively charged sites attract nucleophile/electrophile entities poised for reaction94. In Fig. 6 provided, the MEP is depicted for both a reference molecule and the hit compounds. Notably, hydrogen atoms exhibit a positive electrostatic potential (maximum blue color), while oxygen atoms display a negative electrostatic potential (maximum red color) in both the hit compounds and the reference molecule. This consistent pattern suggests that hydrogen atoms serve as the most reactive sites for nucleophilic attack, while oxygen atoms act as focal points for electrophilic attack in both the hit compounds and the reference molecule. The distribution of electrostatic potential varies based on the molecule or drug, attributed to the distinct types and electronic properties of constituent atoms. Consequently, differences in electrostatic potential distribution around the drug can be linked to variations in its biological activity95.
Three-dimensional plot of Molecular Electrostatic Potential (MEP) for the reference HOR (A) and the investigated molecules (B; Biflavanone, C; Calomelanol J).
MD simulation
Following this, we conducted a 100 ns MD simulation to assess the flexibility, structural behavior, and stability of the top two compounds. This information holds particular significance in the field of drug discovery, aiding our understanding of how these compounds interact and behave within a biological system. To establish a comparative baseline, we also conducted the MD simulation on the AChE protein without the ligand, as well as with our reference compound, HOR, complexed with the protein. Moreover, we examined various parameters for each system during the MD simulation, including root mean square deviation (RMSD) for overall structural changes, root mean square fluctuations of the residues (RMSF) indicating the flexibility of individual residues, radius of gyration (Rg) assessing the compactness of the protein structure, and measurements such as solvent-accessible surface area (SASA) and MMPBSA providing insights into the protein’s interaction with its surroundings. Analysis of the MD simulation data revealed that Biflavanone and Calomelanol J demonstrated stability against the AChE enzyme. Subsequently, the results from the MD simulation were visually presented for each analysis using xmgrace, offering a comprehensive representation of the findings.
RMSD of the protein–ligand complex
The structural stability of the protein-ligand complex was evaluated by measuring the root-mean-square deviation (RMSD) throughout a 100 ns simulation. RMSD tracks variations in the protein structure during the simulation, offering insights into its stability. A lower RMSD suggests a more predictable nature of the protein. For reference, the native AChE protein exhibited an RMSD of 0.17 ± 0.01 nm, while the benchmark compound HOR showed a slightly higher RMSD of 0.19 ± 0.01 nm, suggesting good stability for both. Specifically, RMSD values were calculated for the α-carbon of Biflavanone and Calomelanol J bound to AChE. The RMSD trajectory for both ligands, as illustrated in Fig. 7A, indicated equilibration, signifying the attainment of a stable state by the protein complexes. The average RMSD values for the Biflavanone and Calomelanol J complexes were 0.19 ± 0.01 nm and 0.20 ± 0.02 nm, respectively, as presented in the Table 5. These values are comparable to the RMSD observed for the reference compound HOR, suggesting that both Biflavanone and Calomelanol J form similarly stable interactions with AChE. RMSD is a commonly used metric in MD simulations to assess equilibration and mobility of proteins/enzymes and to estimate distances between the protein’s backbone and atoms96. The minimal variations and reduced RMSD observed in both protein-ligand complexes, Biflavanone and Calomelanol J, indicated their stability. Furthermore, the results affirmed that the AChE enzyme maintained a stable interaction with both Biflavanone and Calomelanol J ligands throughout the 100 ns simulation. Researchers have also employed RMSD to investigate variations in protein-ligand complexes during biological processes, underscoring its significance in comprehending complex dynamics97. These results underscore that both Biflavanone and Calomelanol J demonstrate comparable stability to HOR, highlighting their potential as robust AChE inhibitors.
Graphic depiction of RMSD (A) and RMSF (B) profiles for the protein–ligand complex during a 100 ns MD simulation.
RMSF of the protein–ligand complex
To evaluate the movement of atoms within the protein-ligand complexes under specific temperature and pressure conditions, we conducted an analysis of root-mean-square fluctuations (RMSF). The RMSF results offered insights into the flexible regions of the protein, estimating the overall variability of the protein structure during the MD simulation. A lower RMSF value generally signifies greater stability in the protein-ligand complex, while a higher value suggests increased flexibility during the MD simulation. We observed variations in residue components of the enzyme throughout the 100 ns trajectory time for the protein-ligand complexes, as depicted in Fig. 7B. In the active binding region, the RMSF exhibited variations of less than 0.4 –0.2 nm, which is perfectly acceptable (Table 6). The average RMSF values were found to be 0.07 nm for the AChE-HOR reference complex, 0.06 nm for the AChE-Biflavanone complex, and 0.07 nm for the AChE-Calomelanol J complex. The slightly lower RMSF for Biflavanone indicates marginally higher stability in this complex compared to HOR and Calomelanol J. This reduction in fluctuations suggests that the Biflavanone complex maintains a more stable interaction, potentially enhancing the rigidity of the binding region. Overall, the RMSF results reveal that both AChE - Biflavanone and AChE - Calomelanol J complexes exhibit stability comparable to the HOR complex. The minor differences observed in the RMSF values indicate that these ligands do not significantly disrupt the protein’s structural integrity, supporting their potential as stable inhibitors in the active site of AChE.
Radius of gyration
The radius of gyration (Rg) serves as a measure of the compactness of an enzyme-substrate complex and is indicative of protein folding and unfolding. In this study, the Rg values were determined based on the final 100 ns trajectories. For reference, the native AChE protein had an Rg of 1.96 ± 0.02 nm, while the AChE-HOR complex showed a similar Rg of 1.97 ± 0.04 nm, suggesting stable, well-folded structures for both. For Biflavanone and Calomelanol J, the mean Rg values were 1.97 ± 0.02 and 1.97 ± 0.04 nm, respectively, as indicated in Table 5. The stability in Rg values suggests that the enzyme maintained its folded state. Conversely, fluctuations in Rg values typically indicate unfolding of the enzyme98. The Rg results demonstrated that both complexes, AChE-Biflavanone and AChE-Calomelanol J, exhibited Rg values that were comparable and consistent with the reference molecule, as illustrated in Fig. 8A. This suggests that the binding of the ligands did not significantly alter the protein’s folding behavior, and its folded conformation remained stable throughout the MD simulation.
Graphical representations illustrating the Radius of Gyration (Rg) for the protein, protein–ligand complexes, and Hydrogen Bonds (H-bonds) throughout the 100 ns simulation period.
Hydrogen bond analysis
Hydrogen bonding plays a pivotal role in mediating the interaction between enzymes and substrates, influencing substrate selectivity, metabolism, and catalysis99. In this study, hydrogen bonds were continuously monitored throughout a 100 ns simulation to gain insights into ligand–protein interactions at the active site Fig. 8B. For the reference molecule, approximately three hydrogen bonds remained consistent during the simulation. Similarly, both complexes AChE-Biflavanone and AChE-Calomelanol J exhibited three hydrogen bonds throughout the simulation, as illustrated in Table 5. These findings suggest that both compounds demonstrate molecular or structural stability similar to that of the reference molecule when interacting with AChE.
Solvent accessible surface area
Additionally, we assessed the extent of the complexes’ surfaces that interact with the water solvent by calculating the Solvent Accessible Surface Area (SASA). This analysis is crucial for understanding the solvent-like properties (hydrophilic or hydrophobic) of both the protein molecules and the protein-ligand complexes. A higher SASA value suggests a less stable structure, while a lower value indicates a more compact arrangement of water molecules and amino acid residues100,101. In this study, the AChE-HOR (Reference) complex had an average SASA value of 214.77 ± 2.34 nm², demonstrating a compact structure with moderate solvent exposure. Comparatively, the AChE-Biflavanone and AChE-Calomelanol J complexes showed SASA values of 216.85 ± 3.68 nm² and 215.04 ± 3.17 nm², respectively (Table 5). The marginally higher SASA values for Biflavanone and Calomelanol J indicate slightly increased surface exposure to the solvent relative to the HOR complex. The small difference in SASA between HOR and the hit compounds implies that Biflavanone and Calomelanol J do not significantly alter the overall compactness and stability of the AChE structure upon binding. Their SASA values, which remain close to that of HOR, suggest that these complexes retain a similar degree of solvent accessibility, contributing to their stability. This is supported by Fig. 9, where the SASA trajectories over the 100 ns simulation reveal minimal fluctuations, indicating steady solvation profiles for both hit compounds. The slight increase in SASA for Biflavanone (216.85 ± 3.68 nm²) compared to HOR (214.77 ± 2.34 nm²) may reflect minor structural adjustments in the protein that allow for optimized ligand accommodation. Calomelanol J’s SASA value (215.04 ± 3.17 nm²) is even closer to that of HOR, suggesting a particularly stable interaction similar to the reference molecule. Thus, the SASA results indicate that Biflavanone and Calomelanol J maintain stable, compact configurations comparable to HOR, further validating these ligands as promising candidates due to their stability in a solvated environment and minimal impact on the structural compactness of AChE.
Graphic depiction of the SASA profile for the protein–ligand complexes.
Interaction energy analysis
To confirm the binding scores from molecular docking studies, we performed a thorough analysis of the free energy of interactions in protein-ligand complexes using the Parrinello–Rahman parameter in GROMACS. The average interaction energy for all complexes was found to be between − 100 and − 200 kJ/mol. During the 100 ns simulation, the AChE-Biflavanone and AChE-Calomelanol J complexes achieved the highest interaction energies of − 200.272 kJ/mol and − 153.885 kJ/mol, respectively, both exceeding the reference molecule’s energy of − 150.666 kJ/mol (Table 5). The average interaction energy remained consistently within the acceptable range throughout the simulation (Fig. 10), validating the docking results and highlighting a strong affinity of these compounds for AChE.
Graphical representations illustrating the free energy of interactions in protein–ligand complexes.
Binding free energy calculations
The assessment of binding free energies included examining the last 30 nanoseconds (ns) of the MD trajectories. By applying the MM-PBSA method, we evaluated non-polar, polar, and non-bonded interaction energies, covering both electrostatic interactions and Van der Waals forces for each complex. Remarkably, The AChE-Biflavanone and AChE-Calomelanol J complexes recorded the highest binding free energies, with values of − 130.394 kJ/mol and − 107.908 kJ/mol, respectively. These results were considerably higher than the reference molecule’s value of − 105.132 kJ/mol (refer to Table 7). The MM-PBSA analysis underscores the efficient binding of both compounds, signifying a significant binding affinity towards the target protein AChE.
To validate computational predictions about acetylcholinesterase (AChE) inhibition, various in vitro assays are widely used. The Ellman’s colorimetric method remains a primary technique, providing quantitative metrics such as IC50 and Ki values, which are essential for comparing the efficacy of different inhibitors102. Fluorescence-based assays like Förster Resonance Energy Transfer (FRET) offer an alternative with high sensitivity and real-time monitoring, making them ideal for high-throughput screening103. Another effective method is the thiocholine iodide assay, which uses thiocholine as a substrate to produce a measurable colour change upon reaction with iodide, thus simplifying AChE inhibition quantification104. For high-capacity testing, microplate assays with 5,5′-dithiobis(2-nitrobenzoic acid) (DTNB) facilitate efficient, multi-sample analysis similar to Ellman’s method105,106. Complementing these in vitro assays, in vivo studies using animal models, like zebrafish107,108 and rodents109,110, provide insights into the cognitive and behavioral impacts of AChE inhibition, essential for assessing therapeutic potential. Zebrafish models, due to their genetic resemblance to humans and transparent larvae, allow real-time neurobehavioral observation, while rodent models, particularly those with scopolamine-induced cognitive impairment, help evaluate memory and learning improvements following AChE inhibitor administration111,112. These assays will contribute to a comprehensive approach in future studies aimed at evaluating AChE inhibitors for their potential therapeutic applications in neurodegenerative diseases.
Conclusion
This study successfully identified potential acetylcholinesterase (AChE) inhibitors from a library of 127 phytochemicals sourced from the IMPPAT database. Using molecular docking, ADMET analysis, and DFT calculations, two compounds, Biflavanone (IMPHY013027) and Calomelanol J (IMPHY007737), demonstrated strong binding affinities and promising drug-like properties. The subsequent MD simulations, along with post-MDS analysis, further confirmed their strong binding affinity to AChE. Additional docking studies for butyrylcholinesterase (BChE) revealed that both compounds exhibited higher binding affinities than the reference inhibitor, highlighting their potential efficacy while also taking into account their specificity and possible off-target effects These findings highlight the potential of these natural compounds as novel AChE inhibitors, emphasizing the importance of exploring plant-based compounds in the quest for new therapeutic options for Alzheimer’s disease.
Data availability
All data are included in the manuscript.
References
Yoshita, M. A clinical role for [123I]MIBG myocardial scintigraphy in the distinction between dementia of the Alzheimer’s-type and dementia with Lewy bodies. J. Neurol. Neurosurg. Psychiatry. 71 (5), 583–588 (2001).
Rajesh, V. et al. Memory enhancing activity of Lawsonia inermis Linn. Leaves against scopolamine induced memory impairment in Swiss albino mice. Orient. Pharm. Exp. Med. 17 (2), 127–142 (2017).
Patterson, C. The State of the Art of Dementia Research: New Frontiers. An Analysis of Prevalence, Incidence, Cost and Trends (Alzheimer’s Disease International, 2018).
WHO. https://www.who.int/news-room/fact-sheets/detail/dementia
Dementia-in-India-. https://dementiacarenotes.in/dcnfiles/Dementia-in-India-2020.pdf (2020).
Castro, A. & Martinez, A. Targeting beta-amyloid pathogenesis through acetylcholinesterase inhibitors. Curr. Pharm. Des. 12 (33), 4377–4387 (2006).
Karran, E., Mercken, M. & Strooper, B. D. The amyloid cascade hypothesis for Alzheimer’s disease: an appraisal for the development of therapeutics. Nat. Rev. Drug Discov. 10 (9), 698–712 (2011).
Alonso, A. D. C., Grundke-Iqbal, I. & Iqbal, K. Alzheimer’s disease hyperphosphorylated tau sequesters normal tau into tangles of filaments and disassembles microtubules. Nat. Med. 2 (7), 783–787 (1996).
Andersen, J. K. Oxidative stress in neurodegeneration: cause or consequence? Nat. Med. 10 (S7), S18–S25 (2004).
Dumont, M. & Beal, M. F. Neuroprotective strategies involving ROS in Alzheimer disease. Free Radic Biol. Med. 51 (5), 1014–1026 (2011).
Cummings, J. L. Treatment of Alzheimer’s disease: current and future therapeutic approaches. Rev. Neurol. Dis. 1 (2), 60–69 (2004).
Mangialasche, F. et al. Alzheimer’s disease: clinical trials and drug development. Lancet Neurol. 9 (7), 702–716 (2010).
Manavalan, P., Taylor, P. & Curtis Johnson, W. Circular dichroism studies of acetylcholine sterase conformation. Comparison of the 11 S and 5.6 S species and the differences induced by inhibitory ligands. Biochim. Biophys. Acta BBA Protein Struct. Mol. Enzymol. 829 (3), 365–370 (1985).
Colovic, M. B. et al. Acetylcholinesterase inhibitors: pharmacology and toxicology. Curr. Neuropharmacol. 11 (3), 315–335 (2013).
Kobayashi, H. et al. The comparative efficacy and safety of cholinesterase inhibitors in patients with mild-to‐moderate Alzheimer’s disease: a bayesian network meta‐analysis. Int. J. Geriatr. Psychiatry. 31 (8), 892–904 (2016).
Inglis, F. The tolerability and safety of cholinesterase inhibitors in the treatment of dementia. Int. J. Clin. Pract. Suppl. (127), 45–63 (2002).
Ahmad, S. et al. Indian Medicinal plants and formulations and their potential against COVID-19-Preclinical and clinical research. Front. Pharmacol. 11, 578970 (2020).
Mehla, J. et al. Indian medicinal herbs and formulations for Alzheimer’s disease, from traditional knowledge to scientific assessment. Brain Sci. 10 (12), 964 (2020).
Peterson, D. W. et al. Cinnamon extract inhibits tau aggregation associated with Alzheimer’s disease in vitro. J. Alzheimers Dis. 17 (3), 585–597 (2009).
Youdim, K. A. & Joseph, J. A. A possible emerging role of phytochemicals in improving age-related neurological dysfunctions: a multiplicity of effects. Free Radic. Biol. Med. 30 (6), 583–594 (2001).
Holcomb, L. A. et al. Bacopa monniera extract reduces amyloid levels in PSAPP mice. J. Alzheimers Dis. JAD. 9 (3), 243–251 (2006).
Kuboyama, T., Tohda, C. & Komatsu, K. Withanoside IV and its active metabolite, sominone, attenuate Abeta(25–35)-induced neurodegeneration. Eur. J. Neurosci. 23 (6), 1417–1426 (2006).
Kumar, M. H. V. & Gupta, Y. K. Antioxidant property of Celastrus Paniculatus willd.: a possible mechanism in enhancing cognition. Phytomed.Int. J. Phytother. Phytopharm. 9 (4), 302–311 (2002).
Muralidhara, G. K. S. & Bharath, M. S. Exploring the role of Brahmi (Bacopa monnieri and Centella asiatica) in brain function and therapy. Recent. Pat. Endocr. Metab. Immune Drug Discov. 5 (1), 33–49 (2011).
Uabundit, N. et al. Cognitive enhancement and neuroprotective effects of Bacopa monnieri in Alzheimer’s disease model. J. Ethnopharmacol. 127 (1), 26–31 (2010).
Veerendra Kumar, M. H. & Gupta, Y. K. Effect of Centella asiatica on cognition and oxidative stress in an intracerebroventricular streptozotocin model of Alzheimer’s disease in rats. Clin. Exp. Pharmacol. Physiol. 30 (5–6), 336–342 (2003).
Singh, R. et al. Identification of 11β-HSD1 inhibitors through enhanced sampling methods. Chem. Commun. 58 (32), 5005–5008 (2022).
Kumar, A. & Purohit, R. Computational investigation of pathogenic nsSNPs in CEP63 protein. Gene 503 (1), 75–82 (2012).
Purohit, K. B. Mutational analysis of TYR gene and its structural consequences in OCA1A. Gene 513 (1), 184–195 (2013).
Bhardwaj, V. et al. Elimination of bitter-off taste of stevioside through structure modification and computational interventions. J. Theor. Biol. 486, 110094 (2020).
Tanwar, G. & Purohit, R. Gain of native conformation of Aurora A S155R mutant by small molecules. J. Cell. Biochem. 120 (7), 11104–11114 (2019).
Purohit, R. et al. Studies on flexibility and binding affinity of Asp25 of HIV-1 protease mutants. Int. J. Biol. Macromol. 42 (4), 386–391 (2008).
Mohanraj, K. et al. IMPPAT: a curated database of Indian medicinal plants, phytochemistry and therapeutics. Sci. Rep. 8 (1), 4329 (2018).
Vivek-Ananth, R. P. et al. IMPPAT 2.0: an enhanced and expanded phytochemical atlas of Indian medicinal plants. ACS Omega. 8 (9), 8827–8845 (2023).
Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50 (5), 742–754 (2010).
Morgan, H. L. The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. J. Chem. Doc. 5 (2), 107–113 (1965).
RDKit Open-source cheminformatics. http://www.rdkit.org
Tanimoto, T. T. IBM Internal Report. (1957). http://en.wikipedia.org/wiki/Jaccard_index for details.
Bajusz, D., Rácz, A. & Héberger, K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J. Cheminform. 7, 20 (2015).
Zhou, Y. et al. Kinetics-driven drug design strategy for next-generation acetylcholinesterase inhibitors to clinical candidate. J. Med. Chem. 64 (4), 1844–1855 (2021).
Schrödinger, L. L. C. The PyMOL molecular graphics system.
Guex, N. & Peitsch, M. C. SWISS-MODEL and the Swiss‐pdb viewer: an environment for comparative protein modeling. Electrophoresis 18 (15), 2714–2723 (1997).
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31 (2), 455–461 (2010).
O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform.. 3 (1), 33 (2011).
Laskowski, R. A. & Swindells, M. B. LigPlot+: multiple ligand–protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 51 (10), 2778–2786 (2011).
Xiong, G. et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 49 (W1), W5–W14 (2021).
Pires, D. E. V., Blundell, T. L. & Ascher, D. B. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem. 58 (9), 4066–4072 (2015).
Yang, H. et al. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Wren J, editor. Bioinformatics. 35(6), 1067–1069 (2019).
Cheng, F. et al. admetSAR: a Comprehensive Source and Free Tool for Assessment of Chemical ADMET properties. J. Chem. Inf. Model. 52 (11), 3099–3105 (2012).
Armaković, S. & Armaković, S. J. Atomistica.online – web application for generating input files for ORCA molecular modelling package made with the Anvil platform. Mol. Simul. 49 (1), 117–123 (2023).
Al-Wahaibi, L. H. et al. Crystal structure, Hirshfeld surface analysis and DFT studies of 5-(adamantan-1-yl)-3-[(4-chlorobenzyl)sulfanyl]-4-methyl-4H-1,2,4-triazole, a potential 11β-HSD1 inhibitor. Sci. Rep. 9 (1), 19745 (2019).
Neese, F. The ORCA program system. WIREs Comput. Mol. Sci. 2 (1), 73–78 (2012).
Neese, F. Software update: the ORCA program system, version 4.0. WIREs Comput. Mol. Sci. 8 (1), e1327 (2018).
Neese, F. et al. The ORCA quantum chemistry program package. J. Chem. Phys. 152 (22), 224108 (2020).
Hertwig, R. H. & Koch, W. On the parameterization of the local correlation functional. What is Becke-3-LYP? Chem. Phys. Lett. 268 (5–6), 345–351 (1997).
Raghavachari, K. Perspective on density functional thermochemistry. III. The role of exact exchange. Theor. Chem. Acc. Theory Comput. Model. Theor. Chim. Acta. 103 (3–4), 361–363 (2000).
Xiao, H., Xie, D. & Xue, Y. Density functional theory studies on the structures and vibrational spectra of 3,6-dichlorocarbazole and 3,6-dibromocarbazole. Spectrochim Acta Mol. Biomol. Spectrosc. 58 (10), 2227–2235 (2002).
Avogadro. an open-source molecular builder and visualization tool. http://avogadro.cc/
Knizia, G. Intrinsic atomic orbitals: an unbiased bridge between quantum theory and chemical concepts. J. Chem. Theory Comput. 9 (11), 4834–4843 (2013).
Knizia, G. & Klein, J. E. M. N. Electron flow in reaction mechanisms—revealed from First principles. Angew Chem. Int. Ed. 54 (18), 5518–5522 (2015).
Pettersen, E. F. et al. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25 (13), 1605–1612 (2004).
Karplus, M. & McCammon, J. A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9 (9), 646–652 (2002).
Jorgensen, W. L. et al. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79 (2), 926–935 (1983).
Kumari, R., Kumar, R. & Open Source Drug Discovery Consortium. g_mmpbsa—A GROMACS Tool for High-Throughput MMcalculationsations. J. Chem. Inf. Model. 54 (7), 1951–1962 (2014).
McHardy, S. F. et al. Recent advances in acetylcholinesterase inhibitors and reactivators: an update on the patent literature (2012–2015). Expert Opin. Ther. Pat. 27 (4), 455–476 (2017).
Parlar, S. et al. Synthesis, biological evaluation and molecular docking study of hydrazone-containing pyridinium salts as cholinesterase inhibitors. Chem. Pharm. Bull. (Tokyo). 64 (9), 1281–1287 (2016).
Ramachandran, A. K. et al. E-Pharmacophore modelling, molecular docking and dynamics approaches for in silico identification of acetylcholinesterase inhibitors from natural products against Alzheimer’s disease. https://www.researchsquare.com/article/rs-3475912/v1 (2023).
Kua, J. et al. Studying the roles of W86, E202, and Y337 in binding of acetylcholine to acetylcholinesterase using a combined molecular dynamics and multiple docking approach. Protein Sci. 12 (12), 2675–2684 (2003).
Quinn, D. M. et al. Delineation and decomposition of energies involved in quaternary ammonium binding in the active site of acetylcholinesterase. J. Am. Chem. Soc. 122 (13), 2975–2980 (2000).
Johnson, G. & Moore, S. W. The peripheral anionic site of acetylcholinesterase: structure, functions and potential role in rational drug design. Curr. Pharm. Des. 12 (2), 217–225 (2006).
Ordentlich, A. et al. Dissection of the human acetylcholinesterase active center determinants of substrate specificity. Identification of residues constituting the anionic site, the hydrophobic site, and the acyl pocket. J. Biol. Chem. 268 (23), 17083–17095 (1993).
Shafferman, A. & Velan, B. (eds) Multidisciplinary Approaches to Cholinesterase Functions. http://link.springer.com/ (Springer US, 1992). https://doi.org/10.1007/978-1-4615-3046-6
Macdonald, I. R. et al. Probing the peripheral site of human butyrylcholinesterase. Biochemistry 51 (36), 7046–7053 (2012).
Benet, L. Z. et al. BDDCS, the rule of 5 and drugability. Adv. Drug Deliv Rev. 101, 89–98 (2016).
Erol, M., Celik, I., Kuyucuklu, G., Synthesis, M. Docking molecular dynamics, DFT and antimicrobial activity studies of 5-substituted-2-(p-methylphenyl)benzoxazole derivatives. J. Mol. Struct. 1234, 130151 (2021).
Doak, B. C. & Kihlberg, J. Drug discovery beyond the rule of 5 - opportunities and challenges. Expert Opin. Drug Discov. 12 (2), 115–119 (2017).
Obu, Q. S. et al. Synthesis, spectra (FT-IR, NMR) investigations, DFT study, in silico ADMET and molecular docking analysis of 2-amino-4-(4-aminophenyl)thiophene-3-carbonitrile as a potential anti-tubercular agent. J. Mol. Struct. 1244, 130880 (2021).
Wang, Y. et al. In silico ADME/T modelling for rational drug design. Q. Rev. Biophys. 48 (4), 488–515 (2015).
Lipinski, C. A. et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 3–25. 1. Adv. Drug Deliv. Rev. 46(1–3), 3–26 (1997).
Veber, D. F. et al. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45 (12), 2615–2623 (2002).
Johnson, T. W., Dress, K. R. & Edwards, M. Using the golden triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett. 19 (19), 5560–5564 (2009).
Baell, J. B. & Holloway, G. A. New substructure filters for removal of Pan Assay Interference Compounds (PAINS) from screening libraries and for their exclusion in Bioassays. J. Med. Chem. 53 (7), 2719–2740 (2010).
Avdeef, A. Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database. ADMET DMPK. 8 (1), 29–77 (2020).
Jin, X. et al. Comparison of MDCK-MDR1 and Caco-2 cell based permeability assays for anti-malarial drug screening and drug investigations. J. Pharmacol. Toxicol. Methods. 70 (2), 188–194 (2014).
Suenderhauf, C., Hammann, F. & Huwyler, J. Computational prediction of blood-brain barrier permeability using decision tree induction. Mol. Basel Switz. 17 (9), 10429–10445 (2012).
Anzenbacher, P. & Anzenbacherová, E. Cytochromes P450 and metabolism of xenobiotics. Cell. Mol. Life Sci. 58 (5), 737–747 (2001).
El-Shamy, N. T. et al. DFT, ADMET and molecular docking investigations for the antimicrobial activity of 6,6′-Diamino-1,1′,3,3′-tetramethyl-5,5′-(4-chlorobenzylidene)bis[pyrimidine-2,4(1H,3H)-dione]. Molecules 27 (3), 620 (2022).
Ibnaouf, K. H. et al. Spectral, structural, stability characteristics and frontier molecular orbitals of tri-n-butyl phosphate (TBP) and its degradation products: DFT calculations. J. Ovonic Res. 17, 23–30 (2021).
Hagar, M. et al. Investigation of some antiviral N-Heterocycles as COVID 19 drug: Molecular Docking and DFT calculations. Int. J. Mol. Sci. 21 (11), 3922 (2020).
Tsuneda, T. et al. On Koopmans’ theorem in density functional theory. J. Chem. Phys. 133 (17), 174101 (2010).
Rezaeivala, M. et al. Experimental and theoretical investigation of corrosion inhibition effect of two piperazine-based ligands on carbon steel in acidic media. Colloids Surf. Physicochem. Eng. Asp. 641, 128538 (2022).
Pearson, R. G. Absolute electronegativity and hardness: application to inorganic chemistry. Inorg. Chem. 27 (4), 734–740 (1988).
Pearson, R. G. Recent advances in the concept of hard and soft acids and bases. J. Chem. Educ. 64 (7), 561 (1987).
Sheena Mary, Y. et al. DFT, molecular docking and SERS (concentration and solvent dependant) investigations of a methylisoxazole derivative with potential antimicrobial activity. J. Mol. Struct. 1232, 130034 (2021).
Matta, C. F. & Arabi, A. A. Electron-density descriptors as predictors in quantitative structure–activity/property relationships and drug design. Future Med. Chem. 3 (8), 969–994 (2011).
Sargsyan, K., Grauffel, C. & Lim, C. How molecular size impacts RMSD applications in molecular dynamics simulations. J. Chem. Theory Comput. 13 (4), 1518–1524 (2017).
Joshi, T. et al. Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae Pv. Oryzae by targeting peptide deformylase. J. Biomol. Struct. Dyn. 39 (3), 823–840 (2021).
Lobanov MYu, Bogatyreva, N. S. & Galzitskaya, O. V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol. 42 (4), 623–628 (2008).
Chen, D. et al. Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci. Adv. 2 (3), e1501240 (2016).
Grossoehme, N. E. et al. Coronavirus N protein N-Terminal domain (NTD) specifically binds the transcriptional regulatory sequence (TRS) and melts TRS-cTRS RNA duplexes. J. Mol. Biol. 394 (3), 544–557 (2009).
Rajpoot, S., Alagumuthu, M. & Baig, M. S. Dual targeting of 3CLpro and PLpro of SARS-CoV-2: a novel structure-based design approach to treat COVID-19. Curr. Res. Struct. Biol. 3, 9–18 (2021).
Ferreira, J., Santos, S. & Pereira, H. In vitro screening for acetylcholinesterase inhibition and antioxidant activity of Quercus suber Cork and corkback extracts. Evid-Based Complement. Altern. Med. ECAM. 2020, 3825629 (2020).
Suo, Z. et al. Ratiometric assays for acetylcholinesterase activity and organo-phosphorous pesticide based on superior carbon quantum dots and BLGF‐protected gold nanoclusters FRET process. ChemistrySelect 5 (29), 9254–9260 (2020).
Sun, Y., Tan, H. & Li, Y. A colorimetric assay for acetylcholinesterase activity and inhibitor screening based on the thiocholine–induced inhibition of the oxidative power of MnO2 nanosheets on 3,3′,5,5′–tetramethylbenzidine. Microchim. Acta. 185 (10), 446 (2018).
Liu, C. & Gomez, F. A. A microfluidic paper-based device to assess acetylcholinesterase activity. Electrophoresis 38 (7), 1002–1006 (2017).
Santillo, M. F. & Liu, Y. A fluorescence assay for measuring acetylcholinesterase activity in rat blood and a human neuroblastoma cell line (SH-SY5Y). J. Pharmacol. Toxicol. Methods. 76, 15–22 (2015).
De Lima, D., Roque, G. M. & De Almeida, E. A. In vitro and in vivo inhibition of acetylcholinesterase and carboxylesterase by metals in zebrafish (Danio rerio). Mar. Environ. Res. 91, 45–51 (2013).
Koenig, J. A. et al. Zebrafish as a model for acetylcholinesterase-inhibiting organophosphorus agent exposure and oxime reactivation. Ann. N Y Acad. Sci. 1374 (1), 68–77 (2016).
Geerts, H. et al. Brain levels and acetylcholinesterase inhibition with galantamine and donepezil in rats, mice, and rabbits. Brain Res. 1033 (2), 186–193 (2005).
Kwon, K. J. et al. Effects of donepezil, an acetylcholinesterase inhibitor, on neurogenesis in a rat model of vascular dementia. J. Neurol. Sci. 347 (1–2), 66–77 (2014).
He, D. et al. Effects of harmine, an acetylcholinesterase inhibitor, on spatial learning and memory of APP/PS1 transgenic mice and scopolamine-induced memory impairment mice. Eur. J. Pharmacol. 768, 96–107 (2015).
Bhuvanendran, S. et al. Amelioration of cognitive deficit by embelin in a scopolamine-induced Alzheimer’s disease-like condition in a rat model. Front. Pharmacol. 9, 665 (2018).
Acknowledgements
The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Largege Research Project under grant number RGP2/499/45. The authors would like to thanks the Head of the Botany Department, Soban Singh Jeena University, S.S.J Campus, Almora, India, for providing necessary support and facilities to conduct this research work.
Author information
Authors and Affiliations
Contributions
All authors contributes significantly.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Rawat, K., Tewari, D., Bisht, A. et al. Identification of AChE targeted therapeutic compounds for Alzheimer’s disease: an in-silico study with DFT integration. Sci Rep 14, 30356 (2024). https://doi.org/10.1038/s41598-024-81285-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-024-81285-2
Keywords
This article is cited by
-
Ameliorative role of Polyscias fruticosa leaf extract in aluminum chloride-induced neurotoxicity flies possibly mediated by N-methyl-d-aspartate receptor antagonistic and anticholinesterase active compounds
Journal of Natural Medicines (2025)
-
Mechanistic study on ligustilide modulation of the TLR4/NF-κB pathway in ameliorating Scopolamine-Induced cognitive impairment
Metabolic Brain Disease (2025)
