sciphy Volume 2, Issue 1, Page 8-16, 2023
e-ISSN 2962-553X
p-ISSN 2962-5793
DOI 10.58920/sciphy02010008
Nahar Uddin Barbhuyan1, Dipak Chetia1, Malita Sarma Barthakur1, Dubom Tayeng2, Neelutpal Gogoi2, Lima Patowary3
1Department of Pharmaceutical Sciences, Dibrugarh University; 2Department of Pharmaceutical sciences, Dibrugarh University; 3Girijananda Chowdhury Institute of Pharmaceutical sciences, Guwahati
Corresponding: sarma.malita0@gmail.com (Malita Sarma Barthakur).
Tetracyclines are a significant group of broad-spectrum antibiotics that
act by obstructing protein production to stop bacterial growth. Compounds of
this broad family have bacteriostatic action and are used to treat a variety of
bacterial infections, including Gram-positive and Gram-negative infections as
well as diseases brought on by protozoan parasites and intracellular organisms
[1]. Tetracyclines have a hydronaphthacene nucleus and four linearly
condensed benzene rings as their primary structural component.
Despite the notable success of penicillin, several industries, and
academic institutions have been concerned with finding novel antibiotics
synthesized by microorganisms by analyzing a large number of soil samples
submitted from all over the world. Actinomycete bacteria were found to produce
a yellow colony that had a remarkable inhibitory effect against a variety of
pathogenic strains, including rickettsia and Gram-positive and Gram-negative
bacteria. Aureomycin (syn. chlortetracycline) was the first tetracycline
extracted from Streptomyces aureofaciens. With the isolation of Streptomyces
rimosus by Pfizer, the antibiotics terramycin (syn. oxytetracycline) and
aureomycin were obtained. Based on the chemical structure of Chlortetracycline
by catalytic hydrogenation, tetracycline was discovered in 1953, with an
improved pharmacokinetic profile. After discovering the first generation of
tetracyclines (Chlortetracycline, oxytetracycline, and tetracycline),
scientists have begun the development of newer tetracyclines with better
pharmacokinetic properties, broader antimicrobial spectrum, and lower toxicity
[2]. The basic structure of tetracycline is given in figure 1,
substitution on the different positions can lead to different compounds. The
classification of a different generations of tetracyclines has shown in table
1.
Figure 1 General structure of tetracycline
Table 1 Classification of the different generations of tetracyclines
Generations |
Obtaining method |
Drugs |
First |
Biosynthesis |
Chlortetracycline,
Oxytetracycline, Tetracycline, Demeclocycline |
Second |
Semi-synthesis |
Doxycycline, Minocycline,
Lymecycline, Meclocycline, Methacycline, Rolitetracycline |
Third |
Semi-synthesis Synthetic |
Tigecycline, Omadacycline,
Sarecycline Eravacycline |
For
extended spectrum β-lactamase (ESBL)-producing Escherichia coli and Klebsiella
species (spp.), the prevalence of tetracycline resistance was found to be 66.9%
and 44.9%, respectively. For methicillin-resistant Staphylococcus aureus
(MRSA) and Streptococcus pneumonia, the prevalence was 8.7% and 24.3%,
respectively [3]. Tetracycline resistance is typically attributed to
one or more of the following factors: chromosomal mutations that increase the
expression of intrinsic resistance mechanisms, the acquisition of mobile
genetic elements carrying tetracycline-specific resistance genes, and/or
ribosomal binding site mutations. Computational approaches in drug design,
discovery, and development process gain very rapid exploration, implementation,
and administration. Introducing a new drug into a market is a very complex,
risky, and costly process in terms of money and manpower. For reducing time,
cost, and risk-born factors, the computer aided drug design (CADD) method is
widely used as a new drug design approach [4]. There are mainly two
types of approaches for drug design through CADDS: Structure-based drug
design/direct approach and Ligand-based drug design/ indirect approach. Docking
is a valuable approach for studying the many features connected to
protein-ligand interactions. In the field of molecular modelling, it predicts
the preferred orientation of one molecule to another when they are linked
together to form a stable complex. In this study, different compounds have been
designed based on the general structure of the tetracycline and performed basic
in-silico studies to find the best compound derivatives that can be
analysed in resistant bacterial cells [5].
The target site is the regulator (Tet R) of a membrane-associated
protein (Tet A) that exports the antibiotic out of the bacterial cell before it
attaches to the ribosomes and inhibits polypeptide elongation. The X-Ray
crystal structure of Tet R was downloaded from RCSB-PDB in PDB file format with
a PDB ID of 2TCT. The crystal structure of the target protein is shown in
figure 2. The co-crystal inhibitor (CTC) was also identified and downloaded in
SDF format.
Figure 2 Crystal structure of Tet Repressor
The protein was prepared by the use of open-source BIOVIA Discovery Studio
2021 software [6]. The water molecules and heteroatoms were also
removed from the target protein. The active binding sites are defined with the
‘Define and edit binding site’ feature of the Discovery Studio software and the
active site co-ordinates (x= 21.221424; y= 36.743182; z= 35.153182) were
noted and saved for future use. Finally, polar hydrogen was added to the target
protein and saved in PDB file format for future use.
The structure of twenty compounds of tetracycline was prepared manually
using Chem Draw Professional 16.0.0.82(68) software. The SMILES ID of these
structures was also saved for future use. The compounds prepared with the
software were saved in MDL SD File (*SDF) format.
Molecular docking
simulation studies using Autodock Vina on PyRx 0.8 virtual screening platform
were conducted to predict the binding affinity between the target proteins and
the produced drugs [7]. The prepared protein was inserted into the
3D scene in the software’s virtual platform and converted into PDBQT file
format when processed into macromolecules [8]. The downloaded
proteins were reloaded. On enlarging Chain A of the corresponding protein, the
amino acid sequence and the structure of the co-crystal ligand were made
visible. To pinpoint precisely where the co-crystal inhibitors were located at
the protein's binding site, the atoms of the co-crystal ligand were tagged. The
pre-determined active binding site coordinates were utilized to modify the
alignment of the 3D affinity grid box in the Vina search space of the PyRx 0.8
tool such that all of the amino acids are covered at the protein's active
binding site. The default 3D affinity grid box size of 25 was maintained. And
finally, MDSS was performed by the PyRx tool's predefined protocols [9].
Using the Discovery
Studio Visualizer Software, the 2D interaction of the compounds with the
highest binding affinities to the protein was viewed. Additionally, the
co-crystal-protein ligand interactions in two dimensions were seen. The
compound was excluded from the investigation if it failed to create any regular
hydrogen bonds with the active site residues.
The ADME
and toxicity prediction was done with Swiss ADME [10] and Data Warrior tools
respectively. The SMILES ID of the compounds were entered in the required box
and ADME and toxicity profiles (mutagenicity, carcinogenicity, reproductive
toxicity, irritant) were generated.
The RCSB-PDB website
was used to get the crystal structure of protein chain A, shown in figure 3.
The protein is made up of Chain A, which is complexed with co-crystal inhibitor
viz. CTC (Chlortetracycline). The 2D chemical structure of nineteen
tetracycline derivatives is given in figure 4.
Figure 3 Chain A of 2TCT (Tet Repressor) with co-crystal
inhibitor
Figure 4 2D
chemical structure of Tetracycline derivatives
The ADME characteristics and toxicity of the compounds were established
after the binding affinity of the tetracycline derivatives was established. Due
to weak ADME qualities, substances that are active in in-vitro
environments may not be as active in-vivo. Therefore, it is necessary to
research a compound's bioavailability. The Lipinski Rule of Five, also referred
to as Pfizer's Rule of Five or simply the Rule of Five is a general guideline
used to assess a chemical compound's drug-likeness or to determine whether it
possesses chemical and physical characteristics that would likely make it an
orally active drug in humans.
Drugs are frequently taken off the market from clinical use due to
toxicity. Many researchers rely on Data Warrior, a trustworthy program, to
forecast the toxicity of substances. We conducted an in-silico toxicity
study, to ascertain the toxicity of the compounds. The ADME and toxicity study
analysis is given in table 2.
Table 2 ADMET analysis of the compounds
Compound |
Molecular
weight (g/mol) |
H-bond
acceptor |
H-bond
donor |
iLogP |
Lipinski
violation |
Toxicity |
|||
Mutagenicity |
Tumorigenicity |
Reproductive |
Irritant |
||||||
C1 |
463.41 |
11 |
6 |
1.62 |
Yes |
No |
No |
No |
No |
C2 |
524.32 |
10 |
6 |
2.04 |
No |
No |
No |
No |
No |
C3 |
470.43 |
11 |
6 |
0.68 |
No |
No |
No |
No |
No |
C4 |
513.42 |
13 |
6 |
1.48 |
No |
No |
No |
No |
No |
C5 |
487.46 |
11 |
6 |
1.20 |
No |
No |
No |
No |
No |
C6 |
460.43 |
10 |
7 |
0.25 |
No |
No |
No |
No |
No |
C7 |
474.46 |
10 |
7 |
1.37 |
No |
No |
No |
No |
No |
C8 |
488.49 |
10 |
6 |
1.93 |
No |
No |
No |
No |
No |
C9 |
461.42 |
11 |
7 |
1.59 |
No |
No |
No |
No |
No |
C10 |
488.49 |
10 |
6 |
1.93 |
No |
No |
No |
No |
No |
C11 |
459.45 |
10 |
6 |
2.03 |
Yes |
No |
No |
No |
No |
C12 |
473.47 |
10 |
6 |
2.02 |
Yes |
No |
No |
No |
No |
C13 |
459.45 |
10 |
6 |
2.03 |
Yes |
No |
No |
No |
No |
C14 |
487.50 |
10 |
6 |
2.33 |
Yes |
No |
No |
No |
No |
C15 |
501.53 |
10 |
6 |
1.42 |
No |
No |
No |
No |
No |
C16 |
471.46 |
10 |
6 |
2.05 |
Yes |
No |
No |
No |
No |
C17 |
469.44 |
10 |
6 |
1.85 |
Yes |
No |
No |
No |
No |
C18 |
521.52 |
10 |
6 |
2.35 |
No |
No |
No |
No |
No |
C19 |
469.44 |
10 |
6 |
1.85 |
Yes |
No |
No |
No |
No |
The molecular docking method enables us to define the behaviour of small molecules in the binding site of target proteins and to better understand basic biological processes by simulating the interaction between a small molecule and a protein at the atomic level. To evaluate the binding potential of ligands toward a protein, a docking study using the PyRx program can produce a binding affinity value (kcal/mol) for each ligand. Table 3 consists of a list of binding affinities of the compounds to the protein binding site.
Table 3 Binding affinities of the compounds with the protein
binding site
Sl.
No. |
Compounds |
Binding
affinities (-kcal/mol) |
1. |
CTC (Co-crystal
inhibitor) |
7.7 |
2. |
C1 |
8.9 |
12. |
C11 |
8.4 |
13. |
C12 |
8.5 |
14. |
C13 |
7.5 |
15. |
C14 |
7.7 |
17. |
C16 |
7.7 |
18. |
C17 |
8.6 |
20. |
C19 |
7.6 |
Visualization of the 2D ligand interactions was done with Discovery Studio Visualizer software. The images of the 2D ligand interactions of the compounds with the Ter A protein are given in Figure 5. The summary of the ligand interactions of each drug with the amino acids of the protein is also given in Table 4.
Figure 5 Visualization of 2D Interactions of the compounds with the Ter A protein: (A) CTC (Co-crystal inhibitor), (B) C1, (C) C11, (D) C12, (E) C14, (F) C16, (G) C17
Table
4 Summary of target-ligand
interactions
Compounds |
Conventional
Hydrogen bond |
Other binding
sites |
CTC (Co-crystal
inhibitor) |
HIS100 (2.70Å), HIS103
(2.15Å), HIS139 (2.23Å) |
-- |
C1 |
ASN82 (2.09Å), GLN116
(1.71Å), HIS100 (2.40Å), SER135 (1.78Å, 2.54Å) |
THR103 (2.79Å), GLN109
(3.58Å) |
C11 |
ASN82 (2.57Å), HIS100
(2.29Å), GLN116 (1.75Å), SER135 (1.73Å) |
GLN109 (3.51Å) |
C12 |
HIS64 (2.91Å), ASN82
(2.62Å, 2.72Å), HIS100 (2.92Å), THR103 (3.00Å), GLN116 (1.78Å, 2.49Å), SER135
(1.80Å) |
GLN109 (3.65Å), HIS139
(4.86Å) |
C14 |
ARG104 (4.12Å), HIS139
(2.03Å) |
VAL113 (4.49Å), ILE134
(3.90Å) |
C16 |
GLY102 (2.33Å), ARG104
(1.95Å), HIS139 (2.57Å) |
GLY102 (3.44Å), VAL112
(4.27Å, 5.13Å), LEU117 (4.26Å), ILE134 (4.42Å) |
C17 |
GLY102 (1.93Å), ARG104
(2.58Å), GLN116 (2.11Å), HIS139 (2.10Å) |
PRO105 (5.43Å), VAL113
(4.27Å), LEU117 (4.27Å, 5.17Å), LEU131 (4.35Å), ILE134 (5.16Å) |
A compound's ADMET characteristics relate to how it is absorbed,
distributed, metabolized, excreted, and processed by and through the human
body. To assess a drug's pharmacodynamic activity, it is crucial to consider
ADMET, which makes up the pharmacokinetic profile of the drug molecule [11].
The computational technique named molecular docking is used to find a ligand
that will fit energetically and physically at the binding site of a protein. A
stronger bond between a substance and a protein is indicated by a binding
affinity value that is more negative [12]. Low binding affinity values also
suggest that protein-ligand binding requires less energy. The first pose is
always regarded as the ideal pose since it has the highest binding affinity and
the last pose has the lowest binding affinity to the target protein [13].
All the 19 derivatives were studied for ADMET analysis and found that
only 8 compounds were shown to retain the Lipinski rule and no toxicity has
been reported. Further, the screened 8 compounds were subjected to docking
study and visualization of ligand interactions. In comparison to the binding
affinity of the co-crystal inhibitor, 6 tetracycline derivatives have exhibited
a greater affinity for chain A of the Tet R protein. These 6 variants
include C1 with fluoro substituent at R4 position (-8.9kcal/mol),
C11 with methyl group at R4 (-8.4kcal/mol), C12 with ethyl group at
R4 (-8.5kcal/mol), C14 with isopropyl substituent at R4 (-7.7
kcal/mol), C16 with ethenyl substituent at R4 (-7.7kcal/mol), C17
with ethynyl substituent at R4 (-8.6kcal/mol). These substances are
further studied for the visualization of ligand interactions. It can be seen
from the ligand interactions in figure 4 and table 5 that all 6 compounds have
formed traditional H-bonds with various amino acids in the active site of the
Tet R protein. Among these 6 tetracycline derivatives, C1 is found to have a
better binding affinity towards the target protein.
Researchers now apply to trim methods that differ from the traditional
approaches used to examine synthetic medicines. In-silico techniques
like molecular docking and molecular dynamics simulations have been employed
more and more in drug discovery research to locate promising synthetic
compounds for the treatment of various ailments. But certain medications have
poor oral bioavailability. To address the issues with drug bioavailability,
many researchers have adopted novel drug delivery strategies. Scientific
developments have led to the use of novel techniques in drug discovery programs
by pharmaceutical researchers [14].
The results of the current in-silico analysis indicate that six compounds can inhibit the Tet R protein of the tetracycline resistance element. Compared to the co-crystal inhibitor, they have shown a greater binding affinity for the Tet R's active binding site. Among the six compounds, Compound 1 (Fluorine at 7th position) has shown a better binding affinity towards the target protein. Additionally, these molecules have a favorable ADME profile and no toxicity. To effectively evaluate the compound's inhibitory potential against Tet R protein of the tetracycline resistance element, however, additional molecular dynamic simulation study and (in-vitro/in-vivo) research is required as the current work is limited to only molecular docking and ADMET in-silico model.