Authors
Tong, Y., Taghavi, A., Su, X., Yang, X., Rouse, W., Kovachka, S., Childs-Disney, J. L., Sekioka, R., Zavala, R., Li, Y., Moss, W., Disney, M. D.
Abstract
RNA folds are abundant in mammalian cells yet poorly characterized as small-molecule targets. We present a scalable, unbiased live-cell pipeline that maps where small molecules bind RNA across the human transcriptome and convert those binders into selective degraders. A 200-member fragment library bearing diazirine/alkyne handles yielded 23 RNA-binding candidates. Chem-CLIP-Map-Seq in MDA-MB-231 cells identified 723 RNA targets and their binding sites, revealing a strong bias toward 5'; and 3'; untranslated regions (UTRs) in mRNAs and enrichment at thermodynamically stable structures, with limited binding to non-coding RNAs. Expression level and local stability contributed to engagement. An integrated machine-learning model trained on multiple fingerprints distinguished binders from non-binders, and highlighted chemotypes and physicochemical features that favor RNA recognition. Four fragments were converted to a Ribonuclease Targeting Chimera (RiboTAC) to recruit Rnase L to degrade targeted mRNAs. Despite broad binding, cleavage was highly selective, with X1-RiboTAC degrading MPP7 and SSC4D mRNAs in an RNase L-dependent manner and reducing their protein levels. A competitive profiling workflow quantified in-cell target occupancy and guided optimization of the RNA-binding module to reprogram selectivity: an X1 derivative produced an MPP7-selective RiboTAC that lowered MPP7 mRNA levels and suppressed breast-cancer cell migration, while sparing SSC4D transcripts. This end-to-end framework, including transcriptome-wide mapping, data-driven rules, and tunable degradation, establishes practical principles for ligandable RNA sites in cells and enables rational design of RNA-targeted small molecules and degraders.
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bioRxiv
The authors list and abstract were imported from bioRxiv on 07 Nov 2025.
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