Zacco, E.; Gilodi, M.; Armaos, A.; Waldron, F.; Rupert, J.; Schneider, N.; Gregory, J.; Tartaglia, G. G.

FUS is a multifunctional RNA/DNA-binding protein whose mislocalization and aggregation into insoluble inclusions are defining features of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Conventional antibody-based detection methods are often hindered by epitope masking and the structural heterogeneity of FUS aggregates. In this study, we present a computational pipeline for the in silico design of RNA aptamers capable of targeting FUS, including its aggregated forms. By integrating CLIP data, de novo motif enrichment, secondary structure prediction, and catRAPID-based interaction scoring, we screened over 28,000 RNA sequences and identified high-affinity aptamer candidates predicted to bind solvent-exposed, aggregation-resistant regions of FUS. The top candidates adopted stable hairpin structures favorable for interaction with the RNA recognition motif (RRM) and contained a GGU motif in the 3' region to enhance binding to the zinc finger domain. Biolayer interferometry confirmed strong binding affinities in the low nanomolar range, with a high correlation to computational predictions. Circular dichroism analysis further validated the presence of stable hairpin conformations. When tested in post-mortem brain tissue from ALS patients harboring the P525L FUS mutation, the aptamers selectively labeled pathological FUS aggregates - including nuclear inclusions and nucleolar localization patterns - while demonstrating greater specificity than conventional antibodies. Together, these findings establish a rapid, cost-effective, and structure-guided approach for designing RNA aptamers that reliably detect FUS pathology with high sensitivity and specificity.