Structure-Guided Optimization of a LipL32-Targeting DNA Aptamer through Molecular Dynamics and Free-Energy Analysis

Accurate detection of leptospirosis requires high-affinity recognition of the conserved LipL32 biomarker. Although LipL32-targeting DNA aptamers have been identified through systematic evolution of ligands by exponential enrichment (SELEX), the molecular basis of their target recognition remains unclear, limiting rational post-SELEX optimization. Here, we investigated the molecular recognition mechanism of the LepDapt-5 aptamer family using molecular dynamics (MD) simulations and binding free-energy analysis. We generated three-dimensional aptamer models using AlphaFold 3 (AF3) and constructed protein-aptamer docking complexes with HADDOCK, showing a stable G-quadruplex (GQ) motif within the aptamer core. Structural and energetic analyses identified key domains contributing to LipL32 binding, while per-residue energy decomposition highlighted nucleotides suitable for rational sequence refinement. Guided by these insights, we introduced targeted single-point mutations, evaluated their effects computationally, and validated their performance experimentally using enzyme-linked aptamer sorbent assays (ELASA). Relative binding free energies (ΔΔG_bind) calculated using nonequilibrium alchemical MD showed strong agreement with ELASA measurements: T19A reduced binding affinity, G7C produced minimal change, and G24C significantly enhanced affinity. Experimental equilibrium dissociation constants (K _D) further supported these predictions, identifying G24C as the highest-affinity variant. Additionally, the G24C mutant retained strong binding to LipL32 while exhibiting no detectable binding to the NS1 protein of dengue virus 2 and the E2 protein of Chikungunya virus.