Acetylcholinesterase plasma and brain that have a wide range

Acetylcholinesterase (AChE) and
butyrylcholinesterase (BuChE) are two cholinesterase enzymes in the human brain
1. The BuChE has discovered about 80 years ago 2. The soluble form of this
enzyme exists in both the plasma and brain that have a wide range of substrates
and low affinity for acetylcholine. The pouch is attached to cell membranes of
the brain in a hydrophobic form 3. BuChE is important in aspects of
toxicological and pharmacological, due to its hydrolyzes ester-containing drugs
and clean up cholinesterase inhibitors, including potent organophosphporus
nerve agents before they act on their synaptic targets 4. Investigation in
BuChE in recent years there has been growing due to its possible function in Alzheimer’s
disease and the presentation of anticholinesterase therapy for this disorder. In
Alzheimer disease, a cholinergic deficiency and neurological disorder within
the brain have been reported 5-6. Degeneration of the cholinergic neurons and
the loss of cholinergic transmission represent consistent features of
Alzheimer’s disease. The reduction in choline acetyltransferase provides a
decrease in acetylcholine and acetylcholinesterase activity, which commonly
leads to an increase in BChE activity 7. Limited acetylcholine levels that
have been protected with potent cholinesterase inhibitor therapeutics, acting
on both AChE and BChE. Favorite BChE inhibitors barricade the generation of new
beta-amyloid plaques that cleave amyloid precursor protein to beta-amyloid
protein, which are created by BChE 8. Computational methods have developed into
effective tools in facilitating and simplifying new drug discovery 9-11. By
applying these procedures, the biological activity of the candidate compounds
can be evaluated before experimental trials. Thus, these methods are simple and
non-expensive that leads to acceleration to design compounds with favorable
biological activity 12-13. Two computational methods which mostly used in
drug design are Quantitative structure-activity relationship (QSAR) 14-18 and
docking procedure 19-22. In QSAR methods, a mathematical equation, provide a
relationship between chemical structure and the biological potency of
compounds. Classification and regression tree (CART) method can be used for
QSAR studies in comparison to multiple linear regression (MLR). CART analysis
is a statistical method that explains the variation of a response variable
using a set of explanatory variables, so-called predictors. The method is based
on a recursive binary splitting of the data into mutually exclusive subgroups
containing objects with similar properties 23. Medical diagnosis and
prognosis 24-26, ecology 27, agricultural 28, and chemistry 29-31 are
several areas is extensive, which used for modeling and classification by CART
method. CART provides a graphical representation, which makes the
interpretation of the results easier. Therefore, we felt that CART could be a
very interesting method to select and relate molecular descriptors with the
properties of molecules. The CART analysis includes of three steps: (I) maximal-tree
building (II) tree ”pruning”, which consists of the cutting-off of nodes to
produce a sequence of simpler trees, and (III) the optimal tree selection, as
it has minimized cross-validation error 23-31. In docking studies, different
search algorithms such as genetic algorithm and simulated annealing in
composition with a scoring function such as molecular mechanic calculations are
used to study the binding of the candidate compounds (ligands) to a protein
with known structure. Via docking procedures, not only new biologically active
ligands are recognized, as well as the chemistry of the interactions between
ligand and protein is well recognized. The outcomes from this study should be
beneficial in modeling new inhibitors for Alzheimer’s disease.