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A chemoinformatics-guided platform for efficient discovery of RNA-binding small molecules: Proof-of-concept for myotonic dystrophy type 1

Amirhossein TaghaviiD, Jingsong ShaniD, Xiyuan YaoiD, Patrick R. A. ZanoniD, Kisu SungiD, Álvaro Simba-LahuasiiD, Sylwia Gorlach, Henning Labuhn, David Georges Salthouse, Zhen WangiD, Adeline Feri, Matthew David DisneyiD

Abstract

Structured RNAs cause human diseases but remain challenging to target selectively with small molecules. Here, we report a chemoinformatics-guided discovery framework that integrates fingerprint-based molecular design, experimental validation, and mechanistic profiling to identify small molecules that bind highly structured, disease-associated RNAs. Using an RNA-binder fingerprint derived from known ligands, a Tversky similarity screen of >8 million compounds yielded a 150-member library enriched in chemical space for RNA-active scaffolds. Target engagement and cell-based assays identified multiple selective ligands for the pathogenic expanded triplet repeat, r(CUG)exp, that causes myotonic dystrophy type 1 (DM1) by binding and sequestering the RNA-binding protein muscleblind-like 1 (MBNL1). Biophysical and single-molecule analyses revealed that the small molecules bind the 1x1 nucleotide U/U internal loops formed when r(CUG)exp folds, partially block MBNL1 binding, and modulate RNA folding equilibria. Two optimized scaffolds rescued MBNL1-dependent splicing in patient-derived myotubes with micromolar potency and minimal cytotoxicity. This study establishes a generalizable, data-driven platform for discovering drug-like RNA-binding lead small molecules and demonstrates its application to the toxic repeat expansion RNA underlying DM1. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/723748v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1a87b41org.highwire.dtl.DTLVardef@340a14org.highwire.dtl.DTLVardef@81b583org.highwire.dtl.DTLVardef@1b3ba14_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

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02 /Specification & Classification

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