Alzheimer's disease (AD), a neurodegenerative disorder, is one of the severe health problems of aged population. A deficit in cholinergic neurotransmission was believed to be one of the major causes of the memory impairments in AD patients in the past decades. The rational approach to treat AD is to restore the acetylcholine (ACh) levels by inhibiting acetylcholinesterase (AChE) with highly selective inhibitors.
In the present study various inhibitors of AChE were screened for specificity using ArgusLab and binding interactions were studied using Autodock to find the best inhibitor for AChE.
INTRODUCTION
Acetylcholinesterase, also known as AChE, is an enzyme that degrades (through its hydrolytic activity) the neurotransmitter acetylcholine, producing choline and an acetate group. It is mainly found at neuromuscular junctions and cholinergic synapses in the central nervous system, where its activity serves to terminate synaptic transmission. AChE has a very high catalytic activity each molecule of AChE degrades about 5000 molecules of acetylcholine per second. The choline produced by the action of AChE is recycled - it is transported, through reuptake, back into nerve terminals where it is used to synthesize new acetylcholine molecules. (Purves et al., 2008)
Acetylcholinesterase is also found on the red blood cell membranes, where it constitutes theYt blood group antigen. Acetylcholinesterase exists in multiple molecular forms, which possess similar catalytic properties, but differ in their oligomeric assembly and mode of attachment to the cell surface.
In humans acetylcholinesterase is encoded by the AChE gene. (Ehrlich G et al., 1992)
2.1 Species distribution:
Acetylcholine is widely expressed in eukaryotes including at least some plants. (Momonoki YS et al., 1992)
2.2AChE gene:
Acetylcholinesterase is encoded by the single AChE gene; and the structural diversity in the gene products arises from alternative mRNA splicing and post-translational associations of catalytic and structural subunits. The major form of acetylcholinesterase found in brain, muscle, and other tissues is the hydrophilic species, which forms disulfide-linked oligomers with collagenous, or lipid-containing structural subunits. The other, alternatively-spliced form, expressed primarily in the erythroid tissues, differs at the C-terminus, and contains a cleavable hydrophobic peptide with a GPI-anchor site. It associates with membranes through the phosphoinositide (PI) moieties added post-translationally.
2.3 Mechanism of action of acetylcholinesterase inhibitors:
There are 3 different types of acetylcholinesterase inhibitors - short-acting, medium-duration and irreversible inhibitors, which differ in their interactions with the active site of acetylcholinesterase. Neostigmine is a medium-duration acetylcholinesterase inhibitor that enhances cholinergic transmission in the central nervous system, autonomic nervous system and at neuromuscular junctions. Acetlycholinesterase inhibitors are an established therapy for Alzheimer's disease and dementia. (Rang HP et al., )
2.4 MODELLING:
Molecular modeling is a collective term that refers to theoretical methods and computational techniques to model or mimic the behavior of molecule. There are three types of molecular modeling methods; they are Homology modeling, threading and Ab initio method.
HOMOLOGY MODELLING:
Homology modeling, also known as comparative modeling refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the "template"). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence. The sequence alignment and template structure are then used to produce a structural model of the target. Because protein structures are more conserved than DNA sequences, detectable levels of sequence similarity usually imply significant structural similarity. The quality of the homology model is dependent on the quality of the sequence alignment and template structure. The approach can be complicated by the presence of alignment gaps (commonly called indels) that indicate a structural region present in the target but not in the template, and by structure gaps in the template that arise from poor resolution in the experimental procedure (usually X-ray crystallography) used to solve the structure. However, the errors are significantly higher in the loop regions, where the amino acid sequences of the target and template proteins may be completely different. . Taken together, these various atomic-position errors are significant and impede the use of homology models for purposes that require atomic-resolution data, such as drug design and protein-protein interaction predictions. Nevertheless, homology models can be useful in reaching qualitative conclusions about the biochemistry of the query sequence, especially in formulating hypotheses about why certain residues are conserved, which may in turn lead to experiments to test those hypotheses. For example, the spatial arrangement of conserved residues may suggest whether a particular residue is conserved to stabilize the folding, to participate in binding some small molecule, or to foster association with another protein or nucleic acid. Homology modeling can produce high-quality structural models when the target and template are closely related, which has inspired the formation of a structural genomics consortium dedicated to the production of representative experimental structures for all classes of protein folds. The chief inaccuracies in homology modeling, which worsen with lower sequence identity, derive from errors in the initial sequence alignment and from improper template selection.. Like other methods of structure prediction, current practice in homology modeling is assessed in a biannual large-scale experiment known as the Critical Assessment of Techniques for Protein Structure Prediction, or CASP.
PDB (Protein Data Bank):
The PDB archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies. As a member of the wwPDB, the RCSB PDB curates and annotates PDB data according to agreed upon standards. The RCSB PDB also provides a variety of tools and resources. Users can perform simple and advanced searches based on annotations relating to sequence, structure and function. These molecules are visualized, downloaded, and analyzed by users who range from students to specialized scientists.
2.5 DOCKING:
Docking is the interaction of one molecule to another molecule usually between a drug and a receptor. It is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex.
It is widely accepted that drug activity is obtained through the molecular binding of one molecule i.e., the ligand (brown color) to the pocket of another, usually larger, molecule which is called receptor (green color), commonly a protein. In their binding conformations, the molecules exhibit geometric and chemical complementarity, both of which are essential for successful drug activity.
Docking is frequently used to predict the binding orientation of small molecule drug candidates to their protein targets in order to in turn predict the affinity and activity of the small molecule. Hence docking plays an important role in the rational design of drugs.
REVIEW OF LITERATURE
The gene encoding acetylcholinesterase (AChE) was cloned from common carp muscle tissue. The full-length cDNA was 2368 bp that contains a coding region of 1902 bp, corresponding to a protein of 634 amino acids. The deduced amino acid sequence showed a significant homology with those of ichthyic AChEs and several common features among them, including T peptide encoded by exon T in the C-terminus. Three yeast expression vectors were constructed and introduced into the yeast Pichia pastoris. The transformant harboring carp AChE gene lacking exon T most effectively produced AChE activity extracellularly. The replacement of the native signal sequence with the yeast α-factor prepro signal sequence rather decreased the production. A decrease in cultivation temperature from 30 to 15 °C increased the activity production 32.8-fold. The purified recombinant AChE lacking T peptide, eluted as a single peak with a molecular mass of about 230 kDa on the gel filtration chromatography, exhibited the specific activity of 4970 U/mg. On the SDS-PAGE, three proteins with molecular masses of 73, 54, and 22 kDa were observed. These proteins were N-glycosylated, and their N-terminal sequence showed that the latter two were produced from the former probably by proteolytic cleavage at the C-terminal region. Thus, the recombinant AChE is homotrimer of three identical subunits with 73 kDa. The optimal temperature and pH of the recombinant were comparable to those of the native enzyme purified previously, but the values of kinetic parameters and the sensitivities to substrate inhibition and inhibitors were considerably different between them. (Ryohei Sato., et al )
RNA interference is an effective means of regulation of gene expression both in vitro and in vivo. We studied the effect of siRNA on larval development by selective targeting of the acetylcholinesterase (AChE) gene ofHelicoverpa armigera. Chemically synthesized siRNA molecules were directly fed to H. armigera larvae along with the artificial diet. The siRNA treatment resulted in specific gene silencing of AChE and consequently brought about mortality, growth inhibition of larvae, reduction in the pupal weight, malformation and drastically reduced fecundity as compared to control larvae. Our studies suggest some novel roles for AChE in growth and development of insect larvae and demonstrate that siRNA can be readily taken up by insect larvae with their diet. (Maneesh Kumar., et al)
In this study, we evaluated the effects of hopeahainol A, a novel acetylcholinesterase inhibitor (AChEI) from Hopea hainanensis, on H2O2-induced cytotoxicity in PC12 cells and the possible mechanism. Exposure of PC12 cells to 200 μM H2O2 caused cell apoptosis, reduction in cell viability and antioxidant enzyme activities, increment in malondialdehyde (MDA) level, and leakage of lactate dehydrogenase (LDH). Pretreatment of the cells with hopeahainol A at 0.1-10 μM before H2O2 exposure significantly attenuated those changes in a dose-dependent manner. Moreover, hopeahainol A could mitigate intracellular accumulation of reactive oxygen species (ROS) and Ca2+, the loss of mitochondrial membrane potential (MMP), and the increase of caspase-3, -8 and -9 activities induced by H2O2. These results show that hopeahainol A protects PC12 cells from H2O2 injury by modulating endogenous antioxidant enzymes, scavenging ROS and prevention of apoptosis. There was potential for hopeahainol A to be used in treating Alzheimer's disease (AD) that involved acetylcholinesterase, free radical, oxidative damage and cell apoptosis .(Da Hua Shi., et al)
An electrochemical biosensor for the determination of pesticides: methyl parathion and chlorpyrifos, two of the most commonly used organophosphorous insecticides in vegetable crops, is described. The self assembled monolayers (SAMs) of single walled carbon nanotubes (SWCNT) wrapped by thiol terminated single strand oligonucleotide (ssDNA) on gold was utilized to prepare nano size polyaniline matrix for acetylcholinesterase (AChE) enzyme immobilization. The key step of this biosensor was AChE-acetylcholine enzymatic reaction which causes the small changes of local pH in the vicinity of an electrode surface. The pesticides were determined through inhibition of enzyme reaction. The dynamic range for the determination of methyl parathion and chlorpyrifos was found to be in between 1.0 Ã- 10−11 and 1.0 Ã- 10−6 M (0.6 < SD < 3.5) with good reproducibility and stability. The detection limit of the biosensor for both pesticides was found to be 1 Ã- 10−12 M. The biosensor has been applied for the determination of methyl parathion and chlorpyrifos in spiked river water samples. (Subramanian Viswanathan., et al)
RNA interference is an effective means of regulation of gene expression both in vitro and in vivo. We studied the effect of siRNA on larval development by selective targeting of the acetylcholinesterase (AChE) gene of Helicoverpa armigera. Chemically synthesized siRNA molecules were directly fed to H. armigera larvae along with the artificial diet. The siRNA treatment resulted in specific gene silencing of AChE and consequently brought about mortality, growth inhibition of larvae, reduction in the pupal weight, malformation and drastically reduced fecundity as compared to control larvae. Our studies suggest some novel roles for AChE in growth and development of insect larvae and demonstrate that siRNA can be readily taken up by insect larvae with their diet. (Maneesh Kumar., et al)
Migration of plant-parasitic nematode infective larval stages through soil and invasion of roots requires perception and integration of sensory cues culminating in particular responses that lead to root penetration and parasite establishment. Components of the chemoreceptive neuronal circuitry involved in these responses are targets for control measures aimed at preventing infection. Here we report, to our knowledge, the first isolation of cyst nematode ace-2 genes encoding acetylcholinesterase (AChE). The ace-2 genes from Globodera pallida (Gp-ace-2) and Heterodera glycines (Hg-ace-2) show homology to ace-2 of Caenorhabditis elegans (Ce-ace-2). Gp-ace-2 is expressed most highly in the infective J2 stage with lowest expression in the early parasitic stages. Expression and functional analysis of the Globodera gene were carried out using the free-living nematode C. elegans in order to overcome the refractory nature of the obligate parasite G. pallida to many biological studies. Caenorhabditis elegans transformed with a GFP reporter construct under the control of the Gp-ace-2 promoter exhibited specific and restricted GFP expression in neuronal cells in the head ganglia. Gp-ACE-2 protein can functionally complement its C.elegans homologue. A chimeric construct containing the Ce-ace-2 promoter region and the Gp-ace-2 coding region and 3′ untranslated region was able to restore a normal phenotype to the uncoordinated C. elegans double mutant ace-1; ace-2. This study demonstrates conservation of AChE function and expression between free-living and plant-parasitic nematode species, and highlights the utility of C. elegans as a heterologous system to study neuronal aspects of plant-parasitic nematode biology. (Joana C.Costa., et al)
Acetylcholinesterase (AChE) is postulated to play a nonenzymatic role in the development of neuritic projections. We gave the specific neurotoxin, 6-OHDA to rats on postnatal day (PN) 1, a treatment that destroys noradrenergic nerve terminals in the forebrain while producing reactive sprouting in the brainstem. AChE showed profound decreases in the forebrain that persisted in males over the entire phase of major synaptogenesis, from PN4 through PN21; in the brainstem, AChE was increased. Parallel examinations of choline acetyltransferase, an enzymatic marker for cholinergic nerve terminals, showed a different pattern of 6-OHDA-induced alterations, with initial decreases in both forebrain and brainstem in males and regression toward normal by PN21; females were far less affected. The sex differences are in accord with the greater plasticity of the female brain and its more rapid recovery from neurotoxic injury; our findings indicate that these differences are present well before puberty. These results support the view that AChE is involved in neurite formation, unrelated to its enzymatic role in cholinergic neurotransmission. Further, the results for choline acetyltransferase indicate that early depletion of norepinephrine compromises development of acetylcholine systems, consistent with a trophic role for this neurotransmitter. (Theodore A., et al)
MATERIALS AND METHODS
4.1. Modeling and screening ligands:
The structures of lead compounds were obtained from literature. Lead compounds were selected based on IC50 values and other information provided in literature. The structure of each molecule was built using Chemsketch. After successful building of the structures, the geometry optimization and energy minimization were done. Energy minimization process was carried out for 100 cycles using chimera.
Lead database for the selected thirty four compounds were built using VegaZZ and Screening was done using ArgusLab. Screening was done for PDB structure (2V96) for Hsp90.(Colletier, J-.P., et al).
From the top ranked ligand molecules first four were selected and ligand receptor interactions were analyzed with the help of docking studies using Autodock
4.2. Docking Ligand with AChE:
Ligand Preparation:
Initially the hydrogens were added to all the atoms in the ligand and ensured that their valences were completed. This was done using this molecular modeling package (ADT). It was made sure that the atom types were correct before adding hydrogens. Depending on whether charged or neutral carboxylates and amides are desired the PH was specified automatically.
Next, partial atomic charges were assigned to the ligand molecule. AMPAC or MOPAC was used to generate partial atomic charges for the ligand. These charges were written in 'pdbq' format, which had the same columns as a Brookhaven PDB format, but with an added column of partial atomic charges.
Ligand Flexibility: To allow flexibility in the ligand, the rotatable bonds were assigned. AutoDock can handle up to MAX_TORS rotatable bonds: this parameter is defined in "autodock.h", and is ordinarily set to 32. If this value is changed, AutoDock must be recompiled.
Protein Preparation:
When modeling hydrogen bonds, polar hydrogens are added to the target protein -Hsp90. Then the appropriate partial atomic charges were assigned. The charged protein is converted to 'pdbqs' format so that AutoGrid can read it. It was noted that in most modeling systems polar hydrogens were added in a default orientation, assuming each new torsion angle was 0° or 180°. Without some form of refinement, this would lead to spurious locations for hydrogen-bonds. One option is that the hydrogens were relaxed and a molecular mechanics minimization would be performed on the structure. Another one is that a program like "pol_h" is used where the default-added polar hydrogen structure, was taken as input favourable locations for each movable proton, were sampled and the best position for each was selected. This "intelligent" placement of movable polar hydrogens would be particularly important for tyrosine, serine and threonine amino acids.
Running AutoGrid:
The pre-calculated grid maps, one for each atom type present in the ligand being docked were required for Autodock to make the docking calculations extremely fast. These maps were calculated by AutoGrid. A grid map was created with a three dimensional lattice of regularly spaced points, surrounding (either entirely or partly) and centered on the active site of the macromolecule. Typical grid point spacing varies from 0.2Å to 1.0Å, although the default was 0.375Å (roughly a quarter of the length of a carbon-carbon single bond). The potential energy of a 'probe' atom or functional group that is due to all the atoms in the macromolecule was stored in each point with in the grid map. An even number of grid points in each dimension, nx, ny and n was specified as AutoGrid adds a central point and AutoDock requires an odd number of grid points.
An input grid parameter file, which usually has the extension ".gpf" was required for Autogrid. The maximum and minimum energies found during the grid calculations were given in the log file. The grid maps were written in ASCII form by Autogrid, for readability and portability; AutoDock reads ASCII format grid maps.
With these important features of Autogrid, the grid was set exactly on the active site of the human Hsp90 (pdbid: 2VCi) and the grid parameter file is written as a result of this process.
Running AutoDock:
Once the grid maps have been prepared by AutoGrid and the docking parameter file, or 'dpf', is ready, the user is ready to run an AutoDock job. The docking results were viewed using "get-docked", a PDB formatted file was created. It was called "lig.macro.dlg.pdb" and will contain all the docked conformations output by AutoDock in the "lig.macro.dlg" file.
4.3. Hardware & Software Used:
Hardware Environment:
Pentium 4 - 3.20 GHz
512 MB of RAM
40 GB Hard Disk Drive
1 MB cache
1.44" Floppy Disk Drive
17" Color Monitor
128 MB AGP Card
Software:
Operating System: Linux Enterprise Edition 4 (RHEL4)
Molecular Docking Software: AutoDock version-3.0, ArgusLab
Molecular Modeling Tool: Chimera, Vegazz
Visualization Tools: PyMOL
Databases: PDB and PMC (PubMed Central)
Chemical Drawing Tool: ChemSketch
A. PyMOL: (Colletier, J-.P., et al)
PyMOL is an open-source, user-sponsored, molecular visualization system created by Warren Lyford DeLano and commercialized by DeLano Scientific LLC, which is a private software company dedicated to create useful tools that become universally accessible to scientific and educational communities. It is well suited for producing high quality 3D images of small molecules and biological macromolecules such as proteins. PyMOL is one of the few open source visualization tools available for use in structural biology. The 'Py' portion of the software's name refers to the fact that it extends, and is extensible by, the Python Programming Language.
B. PROTEIN DATA BANK :( Helen M.Berman., et al)
The Protein Data Bank (PDB) is a repository for 3-D structural data of proteins and nucleic acids. The data, obtained by X-ray crystallography or NMR spectroscopy and submitted by biologists and biochemists from around the world, is submitted to this public domain and can be accessed free. The WorldWide Protein Data Bank (wwPDB) consists of organizations that act as deposition, data processing and distribution centers for PDB data. The founding members are Research Collaboratory for Structural Bioinformatics (RCSB PDB,USA), Macromolecular structure Database-European Bioinformatics Institute (MSD-EBI,Europe) and Protein Data Bank Japan (PDBj,Japan). The Biological Magnetic Resonance Bank (BMRB, USA) group joined the wwPDB in 2006.
The mission of the wwPDB is to maintain a single Protein Data Bank Archive of macromolecular structural data that is freely and publicly available to the global community. The PDB is a key resource in structural biology and is critical to more recent work in structural genomics This database stores information about the exact location of all the atoms in a large biomolecule (although, usually without the hydrogen atoms, as their positions are more of a statistical estimate) If one is only interested in sequence data, such as amino acid sequence of a particular protein or the nucleotide sequence as a particular nucleic acid, the much larger databases from Swiss-Prot and the International Nucleotide Sequence Database Collaboration should be used. Each structure published in PDB receives a four-character alphanumeric identifier, its PDB ID. This should not be used as an identifier for biomolecules, since often several structures for the same molecule (in different environments or conformations) are contained in PDB with different PDB IDs
C. Chemsketch: www.acdlabs.com
Visualize a chemically intelligent drawing interface that provides a portal to an entire range of analytical tools, and facilitates the transformation of structural or analytical data into professional, easy-to-decipher reports or presentations.
Advanced Chemistry Development, Inc., (ACD/Labs) has developed such an interface, and has integrated it with every desktop software module they produce. To date, over 800,000 chemists have incorporated ACD/Labs' chemical drawing and graphics package, ACD/ChemSketch, into their daily routines. Academic institutions worldwide have adopted this software as an interactive teaching tool to simplify and convey chemistry concepts to their students, and publishing bodies such as Thieme, the publisher of Science of Synthesis, consider it to be "...supportive of the organic chemistry publisher's role, both in the construction of compounds and their basic analysis."
ACD/ChemSketch is an advanced chemical drawing tool and is the accepted interface for the industries best NMR and molecular property predictions, nomenclature, and analytical data handling software.
D Chimera: www.cgl.ucsf.edu/chimera/
UCSF Chimera is a highly extensible program for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories, and conformational ensembles. High-quality images and animations can be generated.
D.VEGA ZZ: www.ddl.unimi.it/vega /
VEGA ZZ is the evolution of the well known VEGA OpenGL package and includes several new features and enhancements making your research jobs very easy. VEGA was originally developed to create a bridge between most of the molecular software packages only, but in the years, enhancing its features, it's evolved to a complete molecular modelling suite.
E. Argus lab: www.arguslab.com
Argus lab will help how to set up docking calculations. Docking calculations attempt to place Ligands into Binding Sites. Before dock a molecule, first need to define the atoms that make up the Ligand (drug, inhibitor, etc.) and the Binding Site on the protein where the drug binds.
The structure we will use is from the Protein Databank and already contains a co-crystallized. We will make a copy of the inhibitor and dock it to the protein and compare the docked structure with the x-ray structure.
E. AutoDock3.0 :
AutoDock3.0 (Morris., et al) is an example of unbiased type and its version 3.0 has a ligand mobilized by a generic algorithm method and evaluates a rapid grid-based energy.
What is AutoDock ?
AutoDock is a suite of automated docking tools. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure. Auto Dock actually consists of two main programs:
AutoGrid pre-calculates these grids.
AutoDock performs the docking of the ligand to a set of grids describing the target protein.
In addition to using them for docking, the atomic affinity grids can be visualized. This can help to guide organic synthetic chemists design better binders.
What is ADT?
AutoDock Tools, or ADT, is the free GUI for AutoDock developed by the same laboratory that develops AutoDock. You can use it to set up, run and analyze AutoDock dockings and isocontour AutoGrid affinity maps, as well as compute molecular surfaces, display secondary structure ribbons, compute hydrogen-bonds, and do many more useful things.
ADT is the ultimate GUI to set up, launch and analyze AutoDock runs, With ADT can be used for the following tasks :
View molecules in 3D, rotate and scale in real time.
Add all hydrogens or just non-polar hydrogens
Assign partial atomic charges to the ligand and the macromolecule (Gasteiger or Kollmann United Atom charges).
Merge non-polar hydrogens and their charges with their parent carbon atom.
Set up rotatable bonds in the ligand using a graphical version of AutoTors.
Set up the AutoGrid parameter File (GPF) using a visual representation of the grid box, and slider-based widgets.
Set up the AutoDock parameter File (DPF) using forms.
Launch AutoGrid and AutoDock.
Read in the results of an AutoDock job and graphically display them.
View isocontoured AutoGrid affinity maps, and much more.
ADVANCES IN AUTODOCK 3.0
Major advancements that are included in the new release of AutoDock, version 3 are as described below.
Addition of three new search methods:
A Genetic Algorithm, (GA)
A local search method, (LA)
A novel, adaptive global-local search method based on Lamarckian genetics, the Lamarckian Genetic algorithm, (LGA).
Incorporation of an empirical binding free energy force field that allows the prediction of binding free energies and hence binding constants, for docked ligands.
Addition of new keywords to AutoDock to assist in setting up of docking parameters using the new methods. Those keywords that pertain to the genetic algorithm are prefixed with the letters "ga", those specific to local search have the prefix "ls" and those specific to Solis and Wets and pseudo-Solis and Wets have the prefix "sw".
For example: To use the GA, the "set_ga" directive must be given.
RESULTS AND DISCUSSIONS
5.1 SCREENING:
Screening for the ligand H. Wen et al molecules specific for the target AChE was done using ArgusLab and results can be summarized as follows:
Table 5.1: Screening result
Compound
File Name
Rank
Structure
Dock Score
Lead1
Tacrine
1
tacrine
-9.19142
Lead2
Rivastigmine
2
rivastigmine
-8.8264
Lead3
8
3
8
-7.93564
Lead4
galanthamine
4
galanthamine
-7.73393
Lead5
6d
5
6d
-7.13007
5.2 DOCKING:
From the screening results first four ligands were chosen and docked with AChE to find the interaction. The molecular docking was performed using Genetic Algorithm - Least Square (GA-LS) algorithm optimized with autodock tool. From the several poses of docking, the complex formed with least energy and with the top rank chosen as the stable conformation. The clustering histogram, RMSD and rank lists were collected form docking log file.
5.2.1 Lead1:
Table 1: Clustering Histogram for lead1
Table 2: RMSD table for lead 1
From the Clustering histogram run 12 has the least energy and is ranked first. But run 21 has 18 clusters and moreover the energy difference between two was very less. The conformation with higher clusters and less deviation in docked energy with the one having very least must be chosen as stable conformation. In this context, with reference to the tables 1.1 and 1.2, the complex formed in run 18 was better than that formed in run12 which was ranked first.
Apart from docked energy, interactions between lead1 and AChE was studied using pymol and was depicted as follows
tacrine
Figure 1: Interaction between AChE and lead1
S.No
Interaction
1
N1H O( PHE'331/2.94 A0)
5.2.2 Lead2:
Table 3: Clustering Histogram for lead 2
Table 4: RMSD table for lead 2
From the Clustering histogram run 1 has the least energy and was ranked first. Hence with reference to the tables 2.1 and 2.2, the complex formed in run1 was considered to be better.
Apart from docked energy, interactions between lead2 and AChE was studied using pymol and was depicted as follows
riv
Figure 2: Interaction between AChE and lead2.
S.No
Interaction
1
O2H O( ARG'289/3.04 A0)
2
O2H N( ARG'289/2.97 A0)
3
O2H N( PHE'288/2.88 A0)
Table 5.Lead 3:
Table 6: RMSD table for lead 3
From the Clustering histogram run 1 has the least energy and was ranked first. Hence with reference to the tables 2.1 and 2.2, the complex formed in run1 was considered to be better.
Apart from docked energy, interactions between lead3 and AChE was studied using pymol and was depicted as follows
8
Figure 3: Interaction between AChE and lead3
S. No
Interaction
1
O4H O( ARG'289/3.01 A0)
2
O4H O( ARG'289/2.76 A0)
3
O3H N( ARG'289/2.95 A0)
CONCLUSION
Alzheimer's disease (AD), a neurodegenerative disorder, is one of the severe health problems of aged population. A deficit in cholinergic neurotransmission was believed to be one of the major causes of the memory impairments in AD patients in the past decades. The rational approach to treat AD is to restore the acetylcholine (ACh) levels by inhibiting acetylcholinesterase (AChE) with highly selective inhibitors.
Screening was done using ArgusLab and from the library of twenty five compounds five compounds were listed with accepted pose and negative dock score. From the result first three compounds were selected for docking studies using Autodock.
From the docking studies the second and third compounds, lead2 and lead3 showed better interactions than other compounds and hence lead2 and lead3 was considered as the best from other twenty five compounds.
