Molecules (Basel, Switzerland)Sep 26, 2026

Small Activating RNAs: A Organized Database and Guide to Designing Them

mRNA Technology Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Sep 27, 2026

Abstract

An updated database of small activating RNA (saRNA) sequences has been compiled to support the rational design of RNA activation therapeutics.

RNA activation (RNAa) utilizes small activating RNAs to enhance gene expression by interacting with various RNA components.
Three saRNA therapeutics are currently undergoing clinical trials, indicating growing interest in this approach.
Existing design guidelines for saRNAs are limited, making it challenging to predict their activity compared to other RNA therapies like siRNAs.
The new database pairs saRNA sequences with RNAa principles, offering a comprehensive overview of their therapeutic landscape.
Machine learning techniques may be employed to analyze curated saRNA datasets, potentially improving the identification of effective saRNA candidates.

Simplified

RNA activation (RNAa), mediated by small activating RNAs (saRNAs), upregulates endogenous gene expression through recruitment of the Argonaute proteins to the promoter, natural antisense transcripts, or other noncoding RNAs. This mechanistically distinct modality addresses an unmet need, as few RNA therapeutics currently function to activate endogenous gene expression. Three saRNA therapeutics are now in clinical trials; however, broader application remains constrained by largely empirical candidate screening and limited design guidelines, particularly in terms of saRNA targets. Unlike siRNAs, for which extensive sequence-activity relationships and computational design strategies exist, saRNA activity remains comparatively difficult to predict. Here, we assembled an updated and annotated database of publicly available saRNA sequences and paired it with an overview of the RNAa principles, the clinical and preclinical landscape of saRNA utility and current saRNA design guidelines. Finally, we outline opportunities for artificial intelligence-assisted rational design, highlighting how machine learning trained on curated saRNA datasets could accelerate discovery of novel saRNA candidates. Consolidation of saRNA records into a unified resource will hopefully enable systematic analysis of sequence features associated with activity and lay a foundation for moving saRNA design from largely empirical screening toward more rational design.

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Small Activating RNAs: A Organized Database and Guide to Designing Them

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