Computationally Guided Design and Experimental Validation of ssDNA Aptamers Targeting the SARS-CoV-2 Spike Protein

Aptamer development has traditionally relied on iterative, labor-intensive experimental approaches. Recent advances in bioinformatics and computational power now enable the rational in silico design of high-affinity nucleic acid ligands, drastically reducing time and cost. Here, we present an integrated computational pipeline for designing ssDNA aptamers targeting the receptor-binding domain (RBD) of the SARS-CoV-2 Spike (S) protein. A library of 4000 randomly generated 20-nucleotide sequences with defined base ratios (C:G:A:T = 3:3:2:2) was screened based on secondary structure and free energy. The 100 most stable sequences (ΔG ≤ −4.1 kcal/mol) were modeled in three dimensions, yielding 1000 conformations. The 100 structures with the lowest potential energies (≤−78,051.7 kcal/mol) were selected for molecular docking with the RBD in the closed, open, and transitional states. Two aptamers—model-2-seq_2714 (seq_2714) and model-5-seq_1397 (seq_1397)—were selected for experimental assessment based on their favorable predicted docking scores against the RBD. Western blot and dot blot assays showed detectable recognition of the recombinant RBD by both aptamers under the experimental conditions tested. Exploratory testing in a limited number of clinical and environmental samples further showed that detectable signals could be obtained in selected complex matrices. These findings provide an initial proof-of-principle assessment of a computational-first strategy for prioritizing candidate aptamers for subsequent experimental testing.