Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.09.06.749718

Deep Mutational Scanning of structure-switching aptamers identifies both base requirement and base-pairing and uncovers regions required for either sensitivity or specificity

Tan, J. H.·Fraser, A. G.
View original source ↗Export metadata ↓

Abstract

Structure-switching aptamers (SSAs) are short oligonucleotides that undergo a large conformational change on binding their specific target. This change can be used to read out target levels and SSA-based sensors have been developed for targets including drugs, metabolites, and toxins. Despite this potential as molecular sensors, we cannot predict how an SSA folds, how it binds its target, or the final SSA:target conformation and there are very few solved SSA structures. Here we use deep mutational scanning (DMS) to probe SSA structure and function, focusing on three published SSAs that detect cortisol. We show that DMS delivers rich data: it identifies which bases are required for activity but also maps base-pairing either in the presence or absence of target. We find all three SSAs have a similar structure when bound to their target despite having different predicted structures. To broaden our understanding of how SSAs can see cortisol, we selected many new cortisol-binding SSAs. Surprisingly, we identify diverse sequences that all have similar cores to the published cortisol SSAs - many diverse sequences thus converge on the same mode of target recognition. Finally, we use DMS to show we can change target specificity with a small number of mutations and map key specificity determinants. We conclude DMS can provide rich information on how SSAs work and that generating this for many SSAs could lead to greatly improved models for SSA mechanism.

Explore related subjects

genomicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tan, J. H., Fraser, A. G.. 2026-09-09. Deep Mutational Scanning of structure-switching aptamers identifies both base requirement and base-pairing and uncovers regions required for either sensitivity or specificity. https://doi.org/10.64898/2026.09.06.749718

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

System-Specific Epigenetic Aging Signatures in Autistic Adults

Background: Autism is a lifelong neurodevelopmental condition, but the biological processes shaping aging are poorly understood. DNA methylation-based measures index physiological decline and mortality risk independently of chronological age. We tested whether biological aging differs in autistic adults, whether differences are system-specific, and which factors predict within-autism variation. Methods: Saliva DNA methylation (Illumina EPIC) was assayed in 37 autistic adults (31 male, 6 female; mean age 29.8 years), followed from early childhood, spanning a broad ability range including intellectual disability, and 188 non-autistic adults from four public datasets, processed jointly. Fifteen measures were calculated: four global (Horvath, GrimAge, DunedinPACE, SystemsAge) and 11 system-specific SystemsAge subscores. Models adjusted for age, sex, and epithelial-cell proportion. Results: Global aging measures did not differ between groups (all q > .34). However, autistic adults had higher Brain ({beta} = 0.81), Blood ({beta} = 0.65), and Liver SystemsAge ({beta} = 0.58; all q < .01), with Brain and Blood robust across sensitivity analyses. Within autism, aging measures were unrelated to symptom severity, IQ, co-occurring diagnoses, or medication use. Instead, elevations were associated with psychosocial circumstances, including a lack of structured daytime activity, greater loneliness, and lower quality of life. Conclusions: Differences in aging biology in autism appeared system-specific rather than global, with no evidence of generalized epigenetic age acceleration. This vulnerability appeared more closely tied to psychosocial factors than core clinical features. System-specific DNAm measures may therefore provide a more sensitive framework for identifying biological heterogeneity and potentially modifiable contributors to healthy aging in autistic adulthood.

genomics↗
bioRxiv2026

ssJSD: A fusion of sparsity and spatial information for HiC single-cell clustering

Single-cell high-throughput chromatin conformation capture (scHiC) enables profiling three dimensional genome architecture at cellular resolution, providing insights into cell-to-cell variability and cellular functions. Recent frameworks utilize spatial interaction patterns to derive dissimilarity measures for downstream tasks such as cell clustering. However, the inherent sparsity and ultra-high dimensionality of scHiC contact matrices pose significant challenges. A central hurdle is that existing measures typically treat all zeros without distinction, failing to differentiate biologically meaningful structural zeros (SZs) from technical dropouts. Here, we introduce ssJSD (spatial and sparsity informed Jensen-Shannon Divergence), a computational framework designed to explicitly account for scHiC-specific sparsity patterns. By integrating band-wise contact frequency profiles with SZ-induced sparsity matrices, ssJSD leverages both spatial interaction patterns and biological absence of contacts. We adopted two complementary integration strategies: an early fusion approach that concatenates information into a single representation, and a late fusion approach that integrates JSD-based dissimilarities through diverse averaging methods. Through simulations and applications to human cell lines and prefrontal cortex data, we demonstrate that ssJSD improves clustering accuracy and effectively distinguishes cell types. Our study indicates that integrating SZ patterns is important for accurately quantifying cell-to-cell variability in 3D genomics.

genomics↗
bioRxiv2026

An atlas of transcription factor cooperation reveals how motif readers shape regulatory output

Regulatory motifs are conventionally associated with named transcription factors (TFs), yet a motif label need not identify the protein that reads the sequence or the regulatory consequence that follows in a given cell. We analyzed 1,552 TF binding datasets in 10 cell types using ARES, a multi-agent system that tests competing mechanisms of TF-motif dependencies in a specific cellular context against multi-omic data. We found that the inferred mechanisms converged on three operating routes: direct sequence recognition, protein-mediated recruitment or exclusion, and regulatory context. Importantly, the predictive motifs of the target TF binding were read by their conventionally "canonical" TFs in only one third of resolved dependencies, and these "canonical" TFs were expressed much less often than the inferred readers. Furthermore, we observed that motif similarity was associated with shared regulatory region type but not shared transcriptional outcome, whereas reader identity was associated with both and the only feature among the examined associated with outcome. In validation case studies where an inferred reader was perturbed, target TF occupancy fell in proportion to reader binding before perturbation, and a natural variant disrupting the predictive motif altered target TF binding at every intermediate step of the inferred mechanism. These observations were further supported by single-cell perturbation, in vitro cooperativity and evolutionary constraint. Together, these results separate motif identity from reader identity and regulatory output, suggesting that a motif acts as an address whose regulatory consequence is shaped in trans by the protein that interprets it.

genomics↗