High specificity and affinity aptamer selection for hydrocortisone and testosterone
We introduce Deep Learning-assisted Systematic Evolution of Ligands by EXponential Enrichment (DL-SELEX) to accelerate small-molecule aptamer selection. This is the first approach to integrate deep learning into SELEX library design, significantly enhancing SELEX workflows. DL-SELEX employs two variational autoencoders (VAEs): AptaVAE, which utilizes transfer learning to generate optimized, ligand-specific libraries, and AptaClux, which extracts consensus structural features from next-generation sequencing (NGS) data to identify high-performing aptamers. Applying DL-SELEX to two key steroid hormones, hydrocortisone (CS) and testosterone (TES), we demonstrated that shared structural characteristics among steroid molecules can effectively guide aptamer selection, leading to significantly enhanced specificity and binding affinity. Experimentally, our method generated CS and TES aptamers with up to 450-fold higher affinities than previously reported sequences while reducing SELEX cycles by approximately 80%. These findings lay the foundation for integrating structural insights with machine learning, enabling de novo aptamer discovery for challenging small-molecule targets.