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“DISCOVERY OF POTENTIAL MTOR INHIBITORS : A COMBINATION
OF VIRTUAL SCREENING AND MOLECULAR DOCKING STUDIES” 1Manikandan.A,
2T. Jayalakshmi
1Assistant Professor,
2 Associate Professor
Dept. of Genetic Engineering
BIHER, BIST, Bharath University
Chennai- 600073.
ABSTRACT
Lung cancer is the leading cause of death in many countries. Non-small cell lung
cancer more common it generally grows and spreads more slowly. Recent, rapid
advances in Molecular Biology have lead to the development of many new agents
that inhibit the activities of specific molecules related to tumor growth, invasion or
metastasis. Several of the protein kinases have been directly implicated in human
ontogenesis by virtue of being over expressed or mutationally activated in cancer
cells. Hence, the protein kinases have been widely considered to represent an
important class of candidates as drug targets for cancer therapy. The mammalian
target of rapamycin (mTOR) also known as mechanistic target of rapamycin.
Rapamycin is a bacterial product that can inhibit mTOR by associating with its
intracellular receptor FKBP12. The FKBP12-rapamycin complex binds directly to
the FKBP12-Rapamycin Binding (FRB) domain of mTOR. Side effects in patients
consuming rapamycin drug severe mood swings, small purple spots over the body
retention, water face allergy and anger. Drug like molecules from drugbank were
used to screen for potential inhibitors by docking in to the active site of mTOR and
also with yielded several docked structures using GLIDE tool. Ten top scoring
ligands, based on the glide score, were selected from the virtual screening .These
drug molecules of are complexed with mTOR was subjected to molecular
simulation studies shows that DB00094 drug molecule can bind effectively into the
International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 2225-2238ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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binding site of mTOR. Thus it is proposed that DB00094 can be a better substitute
of rapamycin.
Introduction
Bioinformatics:
Bioinformatics is the application of information science to the field of
biology. Bioinformatics was applied in the creation and maintenance of a database
to store biological information at the beginning of the “genomic revolution”; such
as nucleotide and amino acid sequences.[1-7] Common activities in Bioinformatics
include mapping and analyzing DNA and Protein sequences, aligning different
DNA and Protein sequences to compare them and creating and viewing 3-D
models for protein structure.
According to the definition given by NCBI, It says that there are three
important sub-disciplines within Bioinformatics: the development of new
algorithms and statistics with which to asses relationships among members of large
data sets; the analysis and interpretation of various types[8-11] of data including
nucleotide and amino acid sequences, protein domains, and protein structures; and
the development and implementation of tools that enable efficient access and
management of different types of information.
Drug design:
Drug discovery research today relies heavily on Bioinformatics to manage the
databases of small molecules that are potential lead compounds, to search Drug
design, also sometimes referred to as rational drug ,[12-15]it is the inventive
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process of finding new medications based on the knowledge of the biological
target .
Traditional methods require nearly $800 million and 12-15 years for
introducing a new successful drug into the market. These methods are rapidly
being replaced nowadays with rational drug designing which aims to generate
novel pharmacophores which may act as new successful leads. Rational drug
design in Bioinformatics refers to the use of specialized molecular modeling,
molecular visualization and molecular docking software to accelerate the drug
discovery process insilico based on the exploitation [16-20]of the 3D structural
information of the target molecules and/or the hits obtained via high throughput
screening. Steps involved in rational drug discovery are:
MATERIALS AND METHODS
Maestro Environment
Schrodinger develops stat-of-the-art chemical simulation software for use in
pharmaceutical, biotechnology, and materials science research. since its founding
in 1990.Scrodinger has earned a reputation for its leadership in scientific
environment. Schrodinger products range frrom general molecular modelling
programs to a complete suite of drug design software including both ligand and
structure based methods.[21-26]
Maestro is the linchpin of Schrodinger's computational technology. Far more than
just a visualization program, Maestro also helps researchers organize and analyze
data. Maestro's intuitive interface makes setting up calculations easy and straight
forward. Computed results are automatically returned and incorporated into
International Journal of Pure and Applied Mathematics Special Issue
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projects for further study. Maestro's vast array of visualization options makes it
possible to glean insight into molecular properties as well as detailed
intermolecular interactions. Maestro is a powerful and versatile molecular
modelling environment,[27-29] and the portal to the most advanced science in
computational chemistry.
GLIDE
GLIDE is a ligand binding program provided by Schrodinger for predicting protein
ligand binding modes. It provides a complete solution for ligand receptor docking.
Glide offers the full spectrum of speed and accuracy from high-throughput virtual
screening of millions of compounds to extremely [30-34]accurate binding mode
predictions,providing consistently high enrichment for every level.
GLIDE is designed to assist in high throughput screening of potential ligands
based on binding mode and affinity for a given receptor molecule.We can compare
ligand scores with those of other test ligands or compare ligand geometries with
those of reference ligand.GLIDE can be used to generate one or more possible
binding modes for a newly designed ligand.protein preparation[35-39] is required
for GLIDE calculation.It can be performed for most protein ligand complex.PDB
structures using protein preparation wizard panel in Maestro.
PREPARING A WORKING DIRECTORY
A working directory is created to keep all the input and output files.After creating
the working directory,start MAESTRO and set the MAESTRO working directory.
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RECEPTOR GRID GENERATION
The receptor grid generation panel helps in grid calculation job.Grid files represent
physical properties of a volume of the receptor(specifically the active site)that is
searched when attempting to dock a ligand.Grid files calculated are used to dock
ligands using GLIDE.[17-21]
STARTING AND MONITORING GRID CALCULATION
After defining the ligand and the active site and setting up constraints,the grid
generation can be started.
LIGAND DOCKING
The glide is used to screen a multiple-ligand file for structures that interact
favorablywith a receptor active site.The receptor grid files calculated is used to
dock ligands.[26-29]Here the ligands used are collected from National Cancer
Institute drug Database.
Discovery Studio
Accelrys has released Discovery Studio 2.5, an advanced computational
chemistry and biology software environment for drug discovery.Accelrys'
scientific operating platform, Discovery Studio 2.5 is an extensible virtual
discovery environment that lets researchers integrate any tool they need to create
solutions that are uniquely tailored to fit their research process.Discovery Studio is
a life science modeling and simulation suite of applications focused on optimizing
the drug discovery process. Discovery Studio makes it easier to examine the
properties of large and small molecules, study systems, identify leads and optimize
candidates. Discovery Studio 2.5 streamlines collaboration and increases research
productivity, addressing areas such as new scientific developments in the area of
International Journal of Pure and Applied Mathematics Special Issue
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small molecule modelling, fragment-based design, transmembrane protein
analysis, and antibody modelling.
RESULT AND DISCUSSION
DOCKING RESULT
Docking studies were performed to look for potential novel inhibitors of
mTOR.Docking studies were performed using SP GLIDE which do docking of
various conformations of ligand molecules by taking into consideration of various
factors such as ligand binding energy,hydrophobic intractions,hydrogen
bonding,polarity,entropy,RMSD etc.Thus SP Glide score is an overall sum of
scores for favourable as well as unfavourable interaction between the ligand and
the amino acids residues in the binding pocket of Mtor.[31-36]
Based on the SP Glide Score drug bank molecules docked into mTOR were
ranked .It is interesting to find that ten hit molecules were found to be common in
both docking studies. [40-45]
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CONCLUSION
The main goal of the project was to find best common drug for patients affected
with lung cancer due to mTOR abnormality. The target protein selected was
mTOR which was found to be over expressed in majority of the cancers, especially
in lung cancer.
The structure of mTOR was available in Protein Data Bank PDB ID:1NSG. Ligand
molecules for docking were selected from drug bank and prepared for docking.
Docking study was performed using GLIDE. Protein was pre processed and grid
was generated using the structural features of receptor bonding site. The prepared
drug molecules were docked into the grid and binding scores were obtained.
Molecules with best binding score were subjected to simulation studies using
discovery studio.DB00094 was found to have energetically found to bind
effectively with optimal binding confirmations. In fact rest of the nine drug
molecules obtained top scores in docking study also produced significant binding
confirmations with an mTOR receptor site. On the basis of the result can be
proposed direct DB00094 as well as the rest of nine top scoring drug molecules
can be a better substitute of rapamycin which needs to be further confirmed using
clinical studies.
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