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In Silico Drug Design of Biofilm Inhibitors of Staphylococcus epidermidis

by Al-mulla, Aymen Faraoun, MS

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1. Acetaminophene
2. Acetic Acid
3. Albendazole
4. Acetylsalicylic Acid
5. Diacetyl
6. Eugenol
7. Piroxicam
8. Ibuprofen
9. Ferric ammonium citrate
12.Methyl dopa

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4.1.3 Docking:
Most life science processes involve, at the atomic scale, recognition
between two molecules. The prediction of such interactions at the
molecular level, by the so-called docking software, is a non-trivial task.
Docking programs have a wide range of applications ranging from protein
engineering to drug design.
SwissDock, a web server dedicated to the docking of small molecules on
target proteins. It is based on the EADock DSS engine, combined with
setup scripts for curating common problems and for preparing both the
target protein and the ligand input files. It also uses calculations performed
in the CHARMM force field. An efficient Ajax/HTML interface was
designed and implemented, so that workers can easily submit dockings and
retrieve the predicted complexes. For automated docking tasks, a
programmatic SOAP interface has been set up and template programs can
be downloaded in Perl, Python and PHP. The web site also provides an
access to a database of manually curated complexes, based on the Ligand
Protein Database (Grosdidieret al., 2011).
The 20 molecules chosen from the previous section were entered the
EADock DSS engine in the Swissdock Website. The result was positive
(i.e. their minimum binding energy was negative) for only 8 molecules:

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1. Acetaminophen

2. Aspirin

3. Ibuprofen

4. Acetic acid

5. Diacetyl

6. Ferric Ammonium Citrate

7. Thymol

8. Pentazocine
Then only the molecules with five lowest binding free energy score (Saha
et al., 2013) were chosen for screening ligands (acetaminophen, ibuprofen,
acetic acid, diacetyl, ferric ammonium citrate).
4.1.4 Pharmacophore Virtual Screening:
The goal of virtual screening is to select, relatively rapidly and cheaply, a
small subset of compounds predicted to have activity against a given

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biological target out of a large database of compounds. While it is possible
to screen large databases using automated high-throughput screening
methods, this is expensive and requires a substantial investment in
infrastructure and assay development. The idea of virtual screening is to
test compounds computationally in order to reduce the number of
compounds to be screened experimentally, with the additional advantage
that the number of compounds in the final set can easily be adjusted
according to the resources available for assaying (Peachand Nicklaus,
2009). A pharmacophore model is an ensemble of steric and electronic
features that is necessary to ensure the optimal supramolecular interactions
with a specific biological target and to trigger (or block) its biological
response (Yang, 2010). In this study, Discovery Studio was used for
pharmacophore modelling. The Discovery Studio is a well-known suite of
software for simulating small molecule and macromolecule systems. It is
developed and distributed by Accelrys, a company specialized in scientific
software products covering computational chemistry, computational
biology, cheminformatics, molecular simulations and Quantum
Mechanics. Discovery Studio uses Hip Hop generator for pharmacophore
Because sarA in S.epidermidis has no previous studies to be used as drug
target, a new strategy was used to build a pharmacophore for this target.
The five molecules with the best score selected from the previous section
were entered in Discovery Dtudio program to build a pharmacophore to
be used in drugs like molecule screening. The Hip Hop hypothesis in the
Discovery Studio gave 2 hypothysed pharmacophores (Figures 4.9 A and

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Figure 4.9 A, B Hypothetical pharmacophores generated in Discovery Studio (red

colored groups represent H-acceptors while blue groups represent H-donors)

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Then these pharmacophores were entered in the ZincPharmer to screen
more than 180 million different conformations in Zinc database (Figure

Group Aromatic




Figure 4.10 Pharmacophore features in Zinc Pharmer

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One hundred seventy seven (177) molecules were obtained from Zinc
database after filtering through Lipinski rule of five and through analyzing
them with the T.E.S.T program for their mutagenicity.
All molecules were entered in the Swissdock to estimate their binding
affinity to the protein. Many criteria from docking results can be used for
estimating binding affinity including, binding free energy, full fitness,
hydrogen bonding and total free energy. Binding free energy was used as
the main criterion for ranking the best powerful ligands. The final result
was 37 molecules having positive docking results after docking through
Swissdock (Table 4.1).

Table 4.1 Values of binding energy for all docked molecules

Molecules Minimum
or Binding Free Energy
Zinc Accession No. (Kcal/Mol)

1 Acetaminophene -5.369
2 Acetic Acid -23.01
3 Albendazole +ve
4 Acetylsalicylic Acid -0.0485
5 Diacetyl -12.626
6 Eugenol +ve
7 Piroxicam +ve
8 Ibuprofen -11.998
9 Ferric ammonium citrate -4.455
10 Indomethacin +ve
11 Levamisole +ve
12 Methyl dopa +ve
13 Niclosamide +ve
14 Pentazocine -5.222

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15 Rifampicin +ve
16 Thymol -5.444
17 Vancomycine +ve
18 Diclofenac +ve
19 zinc_1768401 -4.065
20 zinc_11851832 -7.218
21 Zinc_20136958 +ve
22 Zinc_5234894 +ve
23 Zinc_613454 +ve
24 Zinc_73647912 +ve
25 Zinc_19129671 +ve
26 Zinc_55161158 +ve
27 Zinc_254936 -6.810
28 Zinc_53791504 +ve
29 Zinc_53792818 -19.662
30 Zinc_55106978 +ve
31 Zinc_55106979 +ve
32 Zinc_55112784 +ve
33 Zinc_55112847 +ve
34 Zinc_55128530 +ve
35 Zinc_55227938 +ve
36 Zinc_55288598 +ve
37 Zinc_55290057 +ve
38 Zinc_55320092 +ve
39 Zinc_55739862 +ve
40 Zinc_65042260 +ve
41 Zinc_65044324 +ve
42 Zinc_65054752 -2.869
43 Zinc_65629848 +ve
44 Zinc_65635719 +ve
45 Zinc_65636814 +ve
46 Zinc_65642133 +ve
47 Zinc_66941713 +ve
48 Zinc_67155881 +ve

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49 Zinc_67177467 +ve
50 Zinc_67177468 +ve
51 Zinc_68844542 +ve
52 Zinc_69348970 +ve
53 Zinc_69532296 +ve
54 Zinc_71763859 -13.979
55 Zinc_71770235 +ve
56 Zinc_75161634 +ve
57 Zinc_75177692 +ve
58 Zinc_75200618 +ve
59 Zinc_75207701 +ve
60 Zinc_75207779 +ve
61 Zinc_75438198 +ve
62 Zinc_76480271 +ve
63 Zinc_76690397 +ve
64 Zinc_76992901 +ve
65 Zinc_77209716 +ve
66 Zinc_77444799 +ve
67 Zinc_80101271 +ve
68 Zinc_80102393 +ve
69 Zinc_80102396 +ve
70 Zinc_3074344 -11.67
71 Zinc_3077159 -8.5
72 Zinc_4675592 -6.29
73 Zinc_4988457 +ve
74 Zinc_53800017 +ve
75 Zinc_55108824 +ve
76 Zinc_55158315 +ve
77 Zinc_55225113 +ve
78 Zinc_55225115 +ve
79 Zinc_55248465 +ve
80 Zinc_55250118 +ve
81 Zinc_55259445 +ve
82 Zinc_55318551 +ve

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