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From enrichment to inference: a multi-target framework for scalable aptamer discovery

ABSTRACT: SELEX has greatly advanced aptamer discovery; however, traditional workflows face considerable challenges in scaling due to the labor-intensive screening and validation processes. More critically, in conventional SELEX, sequencing abundance does not always correlate directly with binding properties due to amplification bias, matrix adsorption, and initial copy-number advantages. We establish an automated framework that converts enrichment readouts into affinity- and specificity-informed candidate prioritization. The framework integrates two key innovations: a distribution-reset secondary screening and a relative abundance ratio for candidate selection. Furthermore, the 96-target workflow generates a systematically organized sequence-target dataset that can be utilized as a valuable resource for analyzing SELEX mechanisms and facilitating AI-assisted ap-tamer discovery. Here, we release the sequencing data of the enriched libraries. Within each sequence-protein matrix, the dataset integrates raw read counts, relative abundance ratios, sequence ranks, and target identities into a unified format. This structure preserves both successful and unsuccessful screening results, which are crucial for com-prehending target screenability and distinguishing genuine target-specific enrichment from general back-ground retention.

ORGANISM(S): synthetic construct

PROVIDER: GSE336208 | GEO | 2026/06/23

REPOSITORIES: GEO

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