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. 2024 Jun 12;14(1):13472.
doi: 10.1038/s41598-024-63555-1.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Sidra Aslam  1 , Hossam M Aljawdah  2 , Mutee Murshed  2 , Geidy E Serrano  3
Affiliations

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Sidra Aslam et al. Sci Rep. .

Abstract

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-D-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Keywords: Waddlia chondrophila; Drug targets; Molecular docking; Molecular dynamic simulation; Phytochemicals.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Graphical synopsis representing the outcome of subtractive proteomics approach.
Figure 2
Figure 2
3D Structure visualization of the unique proteins.
Figure 3
Figure 3
Top four drug candidates 2D structure visualization obtained after virtual screening.
Figure 4
Figure 4
3D visualization of receptor and ligand indicating interacting residues. (a) Butanoiic acid in complex with RNA polymerase sigma factor SigA. (b) Astrapterocarpan/ RNA polymerase sigma factor SigA. (c) Betaine/ RNA polymerase sigma factor SigA. (d) Anthraquinone/ RNA polymerase sigma factor SigA.
Figure 5
Figure 5
3D visualization of targeted protein with attached ligand indicating interacting residues. (a) Enterodiol in complex with 3-deoxy-d-manno-octulosonic acid transferase. (b) Colchicine/3-deoxy-d-manno-octulosonic acid transferase. (c) Calycosin/3-deoxy-d-manno-octulosonic acid transferase. (d) 3-Hydroxycinnamic acid/3-deoxy-d-manno-octulosonic acid Transferase.
Figure 6
Figure 6
The MD trajectory analysis of the 3DAT complex. (A) RMSD of C-alpha atoms of proteins and ligand atoms fit on protein. (B) RMSF analysis. (C) Protein–ligand contacts calculated during simulation. (D) Tendency of the interacting residues with the ligand during simulation.
Figure 7
Figure 7
The contribution of the energy components in total binding free energy in Enterodiol/3-deoxy-d-manno-octulosonic acid transferase complex.
Figure 8
Figure 8
The MD trajectory analysis of the SigA complex. (A) RMSD of C-alpha atoms of proteins and ligand atoms fit on protein. (B) RMSF analysis. (C) Protein–ligand contacts calculated during simulation. (D) Tendency of the interacting residues with the ligand during simulation.
Figure 9
Figure 9
The contribution of the energy components in total binding free energy in Butanoic acid/RNA polymerase sigma factor SigA complex.
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