Insilico screening to identify novel inhibitors targeting 30S ribosomal protein S12 in meningitis-causing organism ‘Elizabethkingia meningoseptica’

Abstract

The current trend in biomedical research is on prioritizing infections based on multidrug resistance. Elizabethkingia meningoseptica, a nosocomial infection-causing organism emerging from Neonatal Intensive Care Units (NICUs), leads to neonatal meningitis and sepsis resulting in severe illness, and, in some cases, fatal. Finding a solution remains challenging due to limited prior work. Translational S12 ribosomal proteins play a crucial role in decoding the codon-anticodon helix, which is essential for the survival of E. meningoseptica. These proteins do not exhibit significant similarity with humans, making them potential drug targets. An in silico study aims to identify specific inhibitors for E. meningoseptica ribosomal proteins among known bioactive compounds targeting prokaryotic 30S ribosomal protein. A 3D model of the 7JIL_h protein from Flavobacterium johnsoniae, showing 90% sequence similarity with the target protein was generated using SWISS-MODEL software. The model was validated through Molprobity v4.4, VERIFY 3D, Errata, and ProSA analysis, confirming conserved residues of the target protein. Insilico screening of known bioactive compounds and their analogs identified potential ligands for the target protein. Molecular Docking and post-docking analysis assessed the stability of the protein-ligand complexes among the shortlisted compounds. The top two compounds with high Gold fitness scores and low predicted binding energy underwent MD simulation and further estimation of free binding energy using the MM_PBSA module. These computationally shortlisted compounds, namely chEMBL 1323619 and chEMBL 312490 may be considered for future in-vivo studies as potential inhibitors against the modeled 30S ribosomal protein S12 of E. meningoseptica.

Communicated by Ramaswamy H. Sarma

Keywords:

• Elizabethkingia meningoseptica
• ribosomal protein
• antibiotic
• nosocomial
• molecular dynamics simulation

Acknowledgments

The authors acknowledge Dr. Manish (School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi) for the technical support.

Ethical approval

The authors declare that no ethical approval is required for the current study as all the research studies performed are virtual (computational).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Genome Sequence is freely available at the NCBI site.

Additional information

Funding

The authors reported there is no extramural funding associated with the work featured in this article. The authors acknowledge the support provided by the Bioinformatics Infrastructure facility at JNU, funded by the Department of Biotechnology, Government of India.