In Silico Study of Newly Designed Tetracycline Derivatives for Its Antibiotic Activity

When antibiotics were first developed, they completely changed the pharmaceutical industry. The discovery of penicillin by Sir Alexander Fleming in 1928 marked the beginning of the antibiotic era.These medications treat bacterial infections and save countless lives by either killing or stopping the growth of bacteria. Antibiotic-resistant bacteria have become more prevalent over time as a result of antibiotic overuse and abuse, which presents a serious threat to global health. Tetracyclines are a vital class of antibiotics known for their broad-spectrum activity. However, the increasing emergence of tetracycline-resistant bacterial strains has posed significant therapeutic challenges. This study utilizes an in silico approach to design, evaluate, and optimize novel tetracycline derivatives with improved efficacy against TetR—a regulatory protein responsible for resistance. A total of 19 derivatives were subjected to ADMET screening, toxicity profiling, and molecular docking using PyRx, SwissADME, and Discovery Studio. Compounds C1, C11, C12, C14, C16, and C17 demonstrated superior binding affinity with ΔG values ranging from -7.7 to -8.9 kcal/mol. C1, with a fluorine substitution at the 7th position, exhibited the highest affinity. These molecules also fulfilled drug-likeness criteria and showed favorable safety profiles, suggesting potential as next-generation antibacterial agents. Further validation via molecular dynamics and biological assays is recommended.

3. Drug Design Strategy

Rational drug design involves the development of bioactive molecules that can interact specifically and effectively with biological targets. Our study focused on modifying the tetracycline scaffold to enhance interaction with TetR's binding site. Structure-based drug design (SBDD) was applied using computational modeling, where small molecule ligands were generated with optimal steric and electronic complementarity to the TetR binding pocket.

Key considerations included:

- Lipophilicity (LogP)

- Hydrogen bond donor/acceptor balance

- Molecular weight and size

- Conformational flexibility

- Binding site topology and electrostatics

4. Drug Target: Tet Repressor Protein (TetR)

The TetR protein acts as a transcriptional repressor in the resistance mechanism, regulating the expression of the TetA efflux pump. It binds to tetracycline and undergoes conformational changes, releasing the DNA operator sequence and allowing transcription of resistance genes. The crystal structure (PDB ID: 2TCT) reveals a helix-turn-helix DNA-binding motif and an antibiotic binding pocket.

Target Binding Site Coordinates:
x = 21.2214, y = 36.7432, z = 35.1532

Figure 1: Crystal structure of TetR protein (2TCT), Chain A

5. Molecular Docking and Binding Analysis

5.1 Preparing Proteins and Ligands

• From RCSB-PDB, the protein structure (2TCT) was obtained.
• Heteroatoms and water molecules were eliminated.
• The addition of polar hydrogens.
• Ligands (19 derivatives) were created with ChemDraw and then transformed into 3D conformations using Chem3D.
• SDF formats were used for docking, and SMILES were used for ADMET prediction.

5.2 Docking Protocol

Docking simulations were conducted using Docking Protocol PyRx 0.8 with AutoDock Vina.
• Search area centered on the active site; grid box size: 25 Å

• Output: Hydrogen bonding, interaction residues, and binding energies (ΔG)

6.MATERIALS METHOD: -

1. Protein and Ligand Preparation:

• The RCSB Protein Data Bank (PDB) provided the target proteins' three-dimensional (3D) crystal structures in PDB format.
• The PubChem database provided the selected ligands. Ligand structures were downloaded in SDF format, and Open Babel was then used to convert them to PDB format.
• To find the most stable conformers, ligand structures were optimized by energy minimization using the MM2 force field and ChemSketch software.

2. Protein Optimization:

• UCSF Chimera was used to refine the protein structures. Ions, co-crystallized ligands, and water molecules were eliminated.
• Gasteiger charges were allocated to the proteins after hydrogen atoms were added.
  If required, side chains and missing residues were fixed.

3. Molecular Docking:

• Protein and ligand files were converted to the PDBQT format using AutoDock Tools (ADT).
• To guarantee that important binding residues were covered, the docking grid box was   positioned around the active site region.
• The docking was carried out using AutoDock Vina, which produced several binding positions for every ligand.
• To determine the most advantageous interactions, the docking poses and binding affinity (in kcal/mol) were examined.

4. Visualization of Docked Complexes:

• PyMOL and Discovery Studio Visualizer were used to visualize docked conformations.
• Important molecular interactions were found and examined, including π-π stacking, hydrophobic interactions, and hydrogen bonding.
• The structural compatibility of the ligands' fitting and binding orientation within the active pocket was assessed.

5.ADMET and Drug-Likeness Prediction:

• Online tools like SwissADME and pkCSM were used to predict the ADMET characteristics of the top-ranked ligands.
• Absorption, distribution, metabolism, excretion, and toxicity were among the parameters that were assessed.
• The drug-likeness and oral bioavailability were evaluated using Lipinski's Rule of Five.

Step-by-Step Workflow:

1. Finding the target: TetR from Escherichia coli, PDB ID: 2TCT
2. Building a ligand library: 19 derivatives made by changing the tetracycline core
3. Making proteins and ligands: with Discovery Studio and Chem3D
4. Virtual Screening: PyRx docking based on how well they stick together
5. Discovery Studio 2021:for 2D/3D interactions lets you see how things work together.
6. Tools for ADMET Prediction: SwissADME and DataWarrior

Figure 3. 2D chemical structure of tetracycline derivatives.

6. ADMET and Toxicity Analysis