bioRxivBioinformaticsPreprint
Therapeutic Relevance of NLPA Lipoprotein to Combat Biofilm-Associated infection in Acinetobacter baumannii
Vahinipati U Brahma, Sharon MunagalasettyiD, Vasundhra BhandariiD
Abstract
Acinetobacter baumannii is a leading multidrug-resistant critical priority pathogen in healthcare settings, where biofilm formation confers survival and antibiotic tolerance. Targeting virulence associated proteins offers an alternative to conventional bactericidal strategies. Here, the inner membrane anchored lipoprotein NLPA, implicated in biofilm associated adaptation, was studied as a putative anti-virulence target using an integrated in silico pipeline and complementing the computational findings. The Alpha fold-derived structure of NLPA served as the basis for virtual screening of approximately 1.6 million compounds, with subsequent prioritization guided by MM/GBSA calculated binding free energies to highlight the top promising candidates. Molecular dynamics simulations demonstrated stable NLPA ligand complexes, as indicated by equilibrated RMSD, low residue fluctuations in the binding region, and persistent interaction networks over time. Pharmacokinetic evaluation indicated that the compounds satisfied Lipinskis Rule of Five and had overall acceptable ADMET characteristics. Two compounds, NLPA-6 and NLPA-3, showed the most favourable predicted binding free energies, suggesting strong and stable interactions within the NLPA binding site. NLPA-3 was evaluated in vitro against A. baumannii to validate the computational outcomes. The compound displayed moderate antibacterial activity with a MIC of 125 g/mL and demonstrated 55.75% inhibition of biofilm formation at 4x MIC. In addition, in macrophage infection studies, NLPA-3 decreased intracellular bacterial survival to 19.25% at 50 g/mL, suggesting that it may disrupt virulence pathways linked to persistence. In whole, these findings identify promising NLPA targeting compounds and support the feasibility of NLPA as an anti-virulence target.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: statistical model
- approach: simulation
- Citations
- 0
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