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In Silico Drug Design of Biofilm Inhibitors of Staphylococcus epidermidis

by Al-mulla, Aymen Faraoun, MS


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Lipinski rule:
Lipinski rule of five is a set of criteria for predicting wether a compound
will be orally bioavailable. This rule state that the molecule shoud have: no
more than 5 H-donors, no more than 10 H-acceptors, logP must be ≤ 5,
molecular weight must be ≤ 500 and rotatable bonds must be ≤ 5.

Molecular mechanics:
It is a generic method for the simulation of molecules. Molecular
mechanics provides an equation for computing the energy of a molecule.
With the use of this energy expression, various algorithms can be used to
determine the preferred shape of a molecule, the energetics of its
interaction with other molecules, and the way in which it can move.

Orientation:
Orientation of a molecule refers to rotations around the Cartesian axes
while keeping the center of mass at a fixed point in space.

Perl:
Perl is a family of high-level, general-purpose, interpreted, dynamic
programming languages. Perl languages borrow features from other
programming languages including C, shell scripting (sh), AWK, and sed
and provide powerful text processing facilities.

Pharmacophore:
A pharmacophore is a hypothetical collection of functional features that
describes the properties needed for a compound to bind in an active site. A
pharmacophore describes a three-dimensional arrangement of molecular
features: hydrogen bond donors and acceptors, bulky and hydrophobic
groups.


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PHP:
PHP is a server-side scripting language designed for web development but
also used as a general-purpose programming language. PHP is now
installed on more than 244 million websites and 2.1 million web servers.

Pose:
Pose is used here to encompass every detail of the ligand’s shape and
location, including orientation, translation, and conformation.

Python:
Python is a widely used general-purpose, high-level programming
language. Its design philosophy emphasizes code readability, and its syntax
allows programmers to express concepts in fewer lines of code than would
be possible in languages such as C.

Quantum mechanics:
Quantum mechanics is the correct mathematical description of the
behavior of electrons in atoms and molecules. As such, it is the correct
mathematical method for computing molecular geometries, spectra and
enthalpies.

Scoring function:
Scoring functions are fast approximate mathematical methods used to
predict the strength of the non-covalent interaction (also referred to as
binding affinity) between two molecules after they have been docked.


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SOAP interface:
It is a protocol specification for exchanging structured information in Web
Services in computer networks. It relies on XML Information Set for its
message format, and usually relies on other Application Layer protocols,
most notably Hyper Text Transfer Protocol (HTTP) or Simple Mail
Transfer Protocol (SMTP), for message negotiation and transmission.

Virtual screening:
Virtual screening is a computational technique used in drug discovery to
search libraries of small molecules in order to identify those structures
which are most likely binding to a drug target, typically a protein receptor
or enzyme.
The reference of all the definitions is (young, 2009).


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Chapter one

Introduction


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Introduction:
Drug discovery process is a critical issue in the pharmaceutical industry
since it is a very costy and time consuming process to produce new drug
potentials and enlarge the scope of diseases incurred (Rao and Srinivas,
2011). Two different methods are widely used in the pharmaceutical
industry for finding hits are: high throughput screening and virtual
screening.
In high throughput screening (HTS), the chemical compounds are
synthesized, and screened against protein based or cell based assays. This
process is commonly used in all major pharmaceutical industries.
However, the cost in synthesis of each compound, in vitro testing and low
hit rate are posing huge problems for pharmaceutical industries. Current
efforts within the industry are directed to reduce the timeline and costs.
Besides, HTS campaigns to identify compounds exerting a desired
phenotype or entire pathways, many of these drugs are failing in clinical
development either because of poor pharmacokinetic characteristics or to
intolerable side effects, which may reflect insufficient specificity of the
compounds (Böhm et al., 2000). At present, hundreds of thousands to
millions of molecules have to be tested within a short period for finding
novel hits, therefore, highly effective screening methods are necessary for
today's researchers.

In view of the above problems in finding new drugs by HTS; cost
effective, reliable in Silico screening procedures are in practice. The socalled
in Silico approaches, using computational environments as their
experimental laboratories (Noori and Spanagel, 2013). These new in Silico
approaches were used in this study to predict compounds for treatment of
the major problems of Staphylococcus epidermidis biofilms.


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Biofilms consist mostly of Extracellular Polymeric Substance (EPS), 90
%, whereas the cells account for only 10 %. The formation of a biofilm
gives the bacteria several advantages: it immobilizes the cells while
maintaining a comfortable architecture allowing the cells to communicate,
it creates a reservoir of nutrients from lysed cells including DNA, which
makes horizontal gene transfer more likely to occur. Of most clinical
importance, it also protects the cells from the surroundings such as host
immune defence, many antibiotics, ultraviolet radiation and oxidizing or
charged biocides (Flemming and Wingender, 2010).
The biofilm mode of life is a central infection mechanism and is
recognized as the causing or exacerbating feature in many medical
infections including dental caries, nosocomial infections, pneumonia,
cystic fibrosis, urinary tract infections, and infections related to catheters
and medical implants. According to the US National Institutes of Health,
biofilms are medically important and account for 80 % of human bacterial
infections (Jacobsen, 2013).
Aims of this study are:
1. Choosing biological target suitable for bioassay.
2. Modelling the selected target protein.
3. In Silico search for suitable anti-target molecules.
4. Application and studying the effect of designed molecules on selected
target.


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Chapter two

Literature Review


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Literature Review

Drug Discovery and Design

Drug Discovery
Drugs are chemicals that prevent disease or assist in restoring health to
diseased individuals. As such they play an indispensable role in modern
medicine. Medicinal chemistry is that branch of science that provides these
drugs either through discovery or through design. The classical drugs of
antiquity were primarily discovered by empirical observation using
substances occurring naturally in the environment. During the last two
centuries, drugs increasingly were also prepared by chemical alteration of
natural substances. In the century just past many novel drugs were
discovered entirely by chemical synthesis. In the third millennium, all of
these techniques are still in use and a researcher of drug design and
development must appreciate their relative value. Added to this picture are
novel opportunities made possible by deeper understanding of cell biology
and genetics (Madsen et al., 2002).
Drug discovery is one of the most crucial components of the
pharmaceutical industry's Research and Development (R&D) process and
is the essential first step in the generation of any robust, innovative drug
pipeline (Arlington, 2000).
The process of drug development aims towards the identification of
compounds with pharmacological interest to assist in the treatment of
diseases and ultimately to improve the quality of life. The compounds used
in pharmacology are mainly small organic molecules (ligands) which
interact with specific biomolecules (receptors) (Plewczynski et al., 2010).


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In the distant past, designing a new drug by changing the molecular
structure of an existing drug was a slow process of trial and error. Now, a
computer can display the molecular structure of any drug from a list of
thousands in a database. With only very slight molecular changes, the
original drug may be significantly changed in a variety of ways that
influence absorption, metabolism, half-life, therapeutic effect, or side
effects. The computer can also identify those chemicals that would
probably not be successful in treating a particular disease before time and
money are invested in extensive testing. Using computers to manipulate
chemicals at the molecular level and design new drugs is based on
molecular pharmacology, the study of the chemical structures of drugs and
their interactions at the molecular level within a cell and even within DNA
in the nucleus. Traditionaly, drugs are discovered by synthesizing
compounds in a time consuming multi-step process against a battery of in
vivo biological screens and further investigating the promising candidates
for their pharmacokinetic properties, metabolism and potential toxicity.
Such a development process has resulted in high attrition rates with failures
attributed to poor pharmacokinetics (39%), lack of efficacy (30%), animal
toxicity (11%), adverse effects in humans (10%) and various commercial
and miscellaneous factors(Gunjan et al., 2013).
Traditional Drug Discovery Limitations
There are an estimated 35,000 open reading frames in the human
genome, which, in turn, generate an estimated 500,000 proteins in the
human proteome. About 10,000 of those proteins have been characterized
crystallographically. In the simplest terms, that means that there are about
490,000 unknowns that may potentially foil any scientific effort.
This means that drug design is a very difficult task. A pharmaceutical
company may have from 10 to 100 researchers working on a drug design
project, which may take from 2 to 10 years to get to the point of starting


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animal and clinical trials. Even with every scientific resource available, the
most successful pharmaceutical companies have only one project in ten
succeed in bringing a drug to market. Drug design projects can fail for a
myriad of reasons. Some projects never even get started because there are
no adequate assays or animal models to test for proper functioning of
candidate compounds. Some diseases are so rare that the cost of a
development effort would never be covered by product sales (as in the case
of orphan drugs). Even when the market exists, and assays exist, every
method available may fail to yield compounds with sufficiently high
activity. On the other hand, compounds that are active against the disease
may be too toxic, not bioavailable, or too costly to manufacture. Recent
estimates of how much it costs to bring a drug to the market have ranged
from $300 million to $1.7 billion. A single laboratory researcher’s salary,
benefits, laboratory equipment, chemicals, and supplies can cost in the
range of $200,000 to $300,000 per year. Some typical costs for various
types of experiments are listed in Table 2.1, owing to the enormous costs
involved, the development of drugs is primarily undertaken by big
pharmaceutical companies. Indeed, the dilution of investment risk over
multiple drug design projects pushes pharmaceutical companies to
undertake many mergers in order to form massive corporations. Because
of all these reasons, it is necessary to effectively leverage every
computational tool that can help to achieve successful results (Young ,
2009).

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