Sequence optimization of a DNA aptamer inhibiting COVID-19 infection guided by analysis of secondary structure distribution.

The post-SELEX optimization of nucleic acid aptamers is often hindered by conformational ambiguity, as the specific functional structure is typically unknown. This study presents a combined computational and experimental framework to address this challenge, using an aptamer that inhibits the SARS-CoV-2/ACE2 interaction as a model system. We analyzed the secondary structure ensembles of a pool of aptamer candidates from an initial screening, employing kinetic and structural clustering alongside a novel nucleotide-structure similarity score. By correlating structural similarity patterns with measured inhibition data, we identified a specific structural cluster likely responsible for the inhibitory function. This insight guided the rational design of a new, truncated aptamer providing a length reduction from 76 to 67 nucleotides, engineered to maximize its similarity to this functional cluster. Experimental validation using a label-free biosensor confirmed that the novel aptamer, although shorter, maintained or enhanced inhibition against multiple SARS-CoV-2 RBD variants compared with the original aptamer, yielding a relative inhibition increase of 71 % for the WT variant. This work demonstrates an effective strategy to guide aptamer optimization by integrating computational structural analysis with initial functional screening data, thereby reducing experimental efforts and accelerating the development of high-performance aptamers.