Accelerated aptamer selection via SELEX and molecular simulations for lipopolysaccharide detection

Area of Science:

• Biotechnology
• Computational Biology
• Molecular Diagnostics

Background:

• Lipopolysaccharides (LPS) are critical virulence factors in Gram-negative bacteria, triggering severe immune responses and diseases like sepsis.
• Accurate LPS detection is vital for clinical diagnosis, pharmaceutical safety, and food monitoring.
• Aptamers offer a stable, specific, and cost-effective alternative to antibodies for molecular detection.

Purpose of the Study:

• To develop and validate a computationally assisted pipeline for refining aptamer candidates post-SELEX.
• To accelerate the identification of high-affinity aptamers for lipopolysaccharide detection.
• To reduce the experimental burden and cost associated with aptamer selection.

Main Methods:

• Systematic Evolution of Ligands by Exponential Enrichment (SELEX) was performed to generate ssDNA aptamer candidates against LPS from *Klebsiella pneumoniae*.
• A computational workflow integrating molecular docking and molecular dynamics simulations was employed for post-SELEX refinement.
• Candidate aptamers were prioritized based on predicted binding affinity and validated using *in vitro* assays.

Main Results:

• The SELEX process yielded multiple candidate aptamer sequences.
• The computational pipeline successfully prioritized aptamer candidates based on predicted binding affinity.
• Aptamer sequence 5 demonstrated the highest predicted and experimental affinity, with a dissociation constant (Kd) of 6.68 nM.
• The integrated SELEX-simulation approach significantly reduced the number of aptamers requiring experimental validation.

Conclusions:

• The proposed SELEX-simulation pipeline offers an efficient method for aptamer discovery and refinement.
• This approach accelerates the development of aptamers for detecting bacterial lipopolysaccharides.
• The study highlights the potential of computational methods to streamline aptamer-based diagnostics.