• Computational Investigation of DNA Aptamer-OmpA Protein Interactions for Enhanced Biosensor Development in Klebsiella pneumoniae Detection
  • Aida Arezoumandchafi,1 Maryam Azimzadeh Irani,2,* Hamidreza Mollasalehi,3
    1. Faculty of Life Sciences and Biotechnology, Shahid Beheshti University
    2. Faculty of Life Sciences and Biotechnology, Shahid Beheshti University
    3. Faculty of Life Sciences and Biotechnology, Shahid Beheshti University


  • Introduction: Klebsiella pneumoniae is an opportunistic bacterium and a frequent cause of infections, such as urinary tract infections, bacteremia, or respiratory tract infections that mainly affect immunocompromised patients [1]. Timely and accurate detection of this bacterium is crucial for correct medical intervention [1]. Short strands of DNA or RNA, named aptamers, are currently attracting increasing interest as alternatives to antibodies for their use in biosensing [2]. The higher binding affinity of these molecules towards selected targets may allow detection strategies for pathogens [2]. This article employs computational approaches to explore the recognition process between a DNA aptamer and outer membrane protein A (OmpA), a critical surface protein in Klebsiella pneumoniae, which has been identified as a potential target in previous studies [3]. This interaction was explored to potentially provide key insights into the design of more effective biosensors for bacterial detection.
  • Methods: The DNA aptamer sequence used in this study was previously obtained through the SELEX methodology, specifically for the detection of Klebsiella pneumoniae [1]. The OmpA protein structure was retrieved from Protein Data Bank PDB ID: 7RJJ [4]. To prepare the protein structure for docking, we minimized it with the YASARA web server[5]. Using AlphaFold3 [6], the 3D structure of the DNA aptamer was predicted and the secondary structures of the DNA aptamer were analyzed using NUPACK [7], providing crucial insights into its folding characteristics. HADDOCK2.4 web server [8] was used for docking the aptamer and the protein. The top-rated docking output clusters were chosen according to Haddock score, cluster size, and interaction energy values. Finally, we used PyMOL[9] to examine the polar interactions between aptamer and OmpA protein at a 5Å distance.
  • Results: Docking led to the identification of Cluster 9 as the highest-scored complex, with a Haddock score: 21.9 ±26.4 The calculated for van der Waals and electrostatics energies were −125.8 ± 10.5 and -264.4 ± 79.6 respectively. Calculation of the interaction between aptamer and protein showed a buried surface area of 2960.2 ± 175.7 Ų for this complex. Suggesting a large contact region formed by these two molecules. Extensive mapping of the polar interactions identified multiple interaction sites between aptamer and OmpA protein. Including ARG-535, ARG-453 and LYS-452. These interactions indicate that the aptamer is a good candidate for targeting OmpA, making it feasible for advancing novel biosensing designs.
  • Conclusion: This study demonstrates the value of integrating in silico tools along with experimental SELEX for optimizing DNA aptamer-protein interactions, which is crucial for developing efficient biosensors. By leveraging computational methods before or after SELEX, we can rapidly identify and refine aptamer candidates, thereby accelerating the development of diagnostic approaches for Klebsiella pneumoniae. These findings highlight the importance of incorporating in silico approaches early in the biosensor design process, offering a cost-effective and swift solution for enhancing pathogen detection.
  • Keywords: Klebsiella pneumoniae, DNA aptamer, in silico analysis, biosensor, OmpA protein