Document Text (Pages 11-20) Back to Document

In Silico Drug Design of Biofilm Inhibitors of Staphylococcus epidermidis

by Al-mulla, Aymen Faraoun, MS


Page 11

List of Figures

Figure Title
Page
No.

Chapter Two: Literature Review

2.1 Schematic representation of virtual screening
methods
15

2.2 Pharmacophore model 15

2.3 Molecular docking 16

2.4 Schematic overview of PIA synthesis 31

Chapter Four: Results and Discussion

4.1 Effect of sarA on many proteins and transcriptional
factors
47

4.2 Bacterial strains containing sarA protein in Uniprot 48

4.3 Alignment of sarA protiens from different
Staphylococcus epidermidis strains
4.4 WebLogo Create results of comparison between 25
sarA sequences

49

50


Page 12

4.5 Primary structure of sarA protein in Staphylococcus
epidermidis VCU144
51

4.6 sarA protein modelling by Raptorx website 52

4.7 sarA 3D Homolog of Staphylococcus epidermidis 53

4.8 Z-score for sarA model from QMEAN website 54

4.9
A,B
Hypothetical pharmacophores generated in
Discovery Studio
60

4.10 Pharmacophore features in Zinc Pharmer 61

4.11 Acetaminophen docking results 69

4.12 Acetylsalicylic Acid docking results 70

4.13 Acetic Acid docking results 71

4.14 Diacetyl docking results 72

4.15 Ferric Ammonium Citrate docking results 73

4.16 Ibuprofen docking results 74
4.17 Thymol docking results 75

4.18
A,B
Antibiotic Sensitivity and Biochemical tests 76

4.19 Biofilm production using TM by different isolates 78


Page 13

4.20 Biofilm production using CRA method 78

4.21 Biofilm production by using TCP method 79

4.22 Growth curve for biofilm producer isolate No. 56
using VC
83

4.23 Growth curve for biofilm producer isolate No. 56
using OD
83

4.24 Slime production for biofilm producer isolate No. 56 84

4.25 Linear regression between VC and OD for biofilm
producer isolate No.56
84

4.26 Growth curve for biofilm producer isolate No.58
using VC
85

4.27 Growth curve for biofilm producer isolate No.58
using OD
85

4.28 Slime production for biofilm producer isolate No.58 86

4.29 Linear regression between VC and OD for biofilm
producer isolate No.58
86

4.30 Growth curve for non producer isolate No.10 using
VC
87


Page 14

4.31 Growth curve for non producer isolate No.10 using
OD
87

4.32 Slime production for non producer isolate No.10 88

4.33 Linear regression between VC and OD for non
producer isolate No.10
88

4.34
A,B
Effect of gradient concentrations of Acetaminophen
on:

A- bacterial growth; B- slime production
96

4.35
A,B
Effect of gradient concentrations of Acetylsalicylic
Acid on: A-bacterial growth; B-slime production
98

4.36
A,B
Effect of gradient concentrations of Acetic Acid on:

A-bacterial growth; B-slime production
99

4.37
A,B
Effect of gradient concentrations of Diacetyl on:

A-bacterial growth; B-slime productin
101

4.38
A,B

4.39
A,B
4.40
A,B

Effect of gradient concentrations of Ferric
Ammonium Citrate on: A-bacterial growth; B-slime
productin
Effect of gradient concentrations of Ibuprofen on: A-
bacterial growth; B- slime production
Effect of gradient concentrations of Thymol on: A-
bacterial growth; B-slime production

102

104

105


Page 15

List of Tables

Table Title
Page
No.

Chapter Two: Literature Review

2.1 Typical Costs of Experiments 6

Chapter Four: Results and Discussion

4.1 Values of binding energies for all docked molecules 62

4.2 Biofilm producing isolates according to (TM) , (CRA)
and (TCP)
77

4.3 Biofilm producing strains with their OD from ELISA
reader
80

4.4 Statistical analysis values for TM and CRA method 81

4.5 Linear regression and correlation coefficient for VC and
OD curves of the three isolates
89

4.6 Generation times 91

4.7 Viable count and slime production after treating with
different concentrations of the study molecules
92


Page 16
List of Abbreviations
Symbol Definition
3D-QSAR Three Dimentional Quantitative Structure Activity
Relationship
Aap Accumulation-associated protein
ADME Absorption, Distribution, Metabolism, Excretion
AI Auto-Inducer
Ajax Asynchronous JavaScript and XML
Bhp Biofilm-associated protein homologue
CADD Computer Aided Drug Design
CFU Colony Forming Unit
CHARMM Chemistry at Harvard Macromolecular Mechanics
CPU Central Processor Unit
CRA Congo Red Agar
CRBSI Catheter Related Blood Stream Infection
ELISA Enzyme-Linked Immuno-Sorbent Assay
EPS Extracellular Polymeric Substance
HSL Homo Serine Lactone
HTML HyperText Markup Language
HTS High Throughput Screening
kDa Kilo Dalton

Page 17
LBVS Ligand Based Virtual Screening
NCBI National Center for Biotechnology Information
NMR Nuclear Magnetic Resonance
NSAID Non-Steroidal Anti-Inflamatory Drug
NVE Native Valve Endocarditis
OD Optical Density
PBS Phosphate Buffer Saline
PDB Protein Data Bank
PIA Polysaccharide Intercellular Adhesion
PMF Potential of Mean Force
PNAG Poly-N-Acetyl Glucosamine
PVE Prosthetic Valve Endocarditis
QS Quorum Sensing
RMSD Root Mean Standard Deviation
S. aureus Staphylococcus aureus
S. epidermidis Staphylococcus epidermidis
SBDD Structure Based Drug Design
SBVS Structure Based Virtual Screening
SOAP Simple Object Access Protocol
TCP Tissue Culture Plate
TetR Tetracycline Repressor
TM Tube Method

Page 18

TSB
UniProt

UTI
VC
VS

Trypticase Soy Broth
Universal Protein Resource

Urinary Tract Infection

Viable Count
Virtual Screening


Page 19

Definitions of Key Terms

3D-QSAR:
It is a computational procedure for predicting how a drug will interact with
an active site when the geometry of the active site is not known. This is
done by computing the electrostatic and steric interactions that an
imaginary probe atom would have if it were placed at various positions
surrounding a known active compound.

Algorithm:
It is a code which is used to write the programs. An algorithm is a step-bystep
procedure for calculations. Algorithms are used for calculation, data
processing, and automated reasoning.

CHARMM:
A widely used molecular simulation program with broad applications to
many-particle systems. It has been developed for the study of molecules of
biological interest. It provides a large suite of computational tools that
encompass numerous conformational and path sampling methods, free
energy estimates, molecular minimization, dynamics, and analysis
techniques, and model-building capabilities.

Conformation:
Conformation is the typical definition of the 3D shape of the molecule
being used in drug design studies.


Page 20

Drug likeness:
Druglikeness may be defined as a complex balance of various molecular
properties and structure features which determine whether particular
molecule is similar to the known drugs. These properties, mainly
hydrophobicity, electronic distribution, hydrogen bonding characteristics,
molecule size, flexibility and the presence of various features influencing
the behavior of the molecule in a living organism, including bioavailability,
transport properties, affinity to proteins, reactivity, toxicity, metabolic
stability and many others.

High throughput screening:
It is a drug-discovery process widely used in the pharmaceutical industry.
It is an automated process to quickly assay the biological or biochemical
activity of a large number of drug-like compounds. Typically, these assays
are performed in "automation-friendly" microtiter plates with a 96, 384 or
1536 well format.

Lead:
A lead compound (i.e. the "leading" compound, not lead metal) in drug
discovery is a chemical compound that has pharmacological or biological
activity and whose chemical structure is used as a starting point for
chemical modifications in order to improve potency, selectivity, or
pharmacokinetic parameters.

Ligand:
A ligand is an ion or molecule (functional group) that binds to a central
metal atom to form a coordinated complex. In drug design it is the molecule
of choice that can bind to a biological target and perform an effect.

© 2009 OpenThesis.org. All Rights Reserved.