In silico modeling and molecular docking of recombinant target proteins for multidrug-resistant bacteria and DNA aptamers

Introduction: Multidrug-resistant bacteria pose a critical global threat and may become a leading cause of mortality by 2050.Acinetobacter baumannii (carbapenem-resistant) and Staphylococcus aureus (methicillin-resistant, MRSA) rank among the World Health Organization's (WHO) 2024 priority pathogens list, highlighting the need for novel therapeutic strategies.DNA aptamers provide a specific and stable alternative to antibodies, enabling targeted protein inhibition, biosensing and drug delivery.Structural modeling and molecular docking aid in biomolecular interaction analysis, supporting rational target selection for in vitro validation.Objectives: Predict and validate structural models of a recombinant MRSA target protein (rMRSAProtein), a recombinant A. baumannii protein (rAcinetoProtein), and DNA aptamers for docking prediction.Methodology: rProteins were modeled using SWISS-MODEL Server and aptamers using 3dRNA/DNA Server.Ramachandran plot validation was performed, followed by molecular docking to identify the most stable aptamer-protein interactions.Four aptamers were tested against rAcinetoProtein and one against rMRSAProtein, generating five structural models each.Validation was performed via the SA VES Server, and UCSF Chimera was used for structural visualization and refinement.Molecular docking was conducted using HADDOCK 2.4 to assess binding analysis through the HADDOCK score, which evaluates aptamer-protein complex stability.Results: Ramachandran plot showed 89.7% of amino acids in favored regions for rMRSAProtein and 92% for rAcinetoProtein.Among the four aptamers evaluated against rAcinetoProtein, aptamer 4 model 1 (-70.8 8.7) and aptamer 2 model 1 (-58.8+/-5.8)showed the most stable interaction.For rMRSAProtein, models 5 (-70.3 2.0) and 2 (-53.1 +/-9.2) formed the most stable model. Conclusion:This study provides insights into the structural integrity of two key proteins from multidrugresistant bacteria and identifies DNA aptamers with high binding potential.Computational modeling is a valuable tool for silico aptamer selection, laying the groundwork for further in vitro validation with potential therapeutic and diagnostic applications.