Biology subjects

Childs-Disney, J. L.

Publications and source records attributed to Childs-Disney, J. L..

8 recordsLinked to original sources

bioRxiv2026

SMARTFlexDB: a paired apo-holo RNA structure database and analysis platform for RNA conformational flexibility and small-molecule recognition

RNA structures can undergo substantial conformational changes upon small-molecule binding, yet existing structural resources provide only static RNA-ligand snapshots without connecting ligand-bound (holo) RNA structures to corresponding ligand-free (apo) conformations. SMARTFlexDB is a curated database and analysis platform that uses paired apo-holo RNA structures to characterize conformational differences and RNA-small-molecule recognition. Candidate pairs were identified from the Protein Data Bank by matching RNA chains with at least 90% sequence identity and coverage, and fully conserved binding pocket nucleotides. The current release contains 434 binding pockets from 266 holo structures, linked to 186 apo structures to yield 1,187 pocket-level apo-holo pairs organized into 734 database entries, spanning 150 small molecules and 23 RNA functional classes. Each entry integrates RNA sequence and secondary-structure annotations, apo and holo three-dimensional structures, RNA-ligand atomic interactions, and quantitative descriptors of global and local conformational changes. An interactive web interface enables users to explore sequence alignments and structural overlays, compare secondary structures and RNA motifs, and upload paired apo-holo structures for customized analysis. By linking ligand-bound and ligand-free RNA structures, SMARTFlexDB provides a robust foundation for investigating RNA structural flexibility and supporting RNA-targeted small-molecule drug discovery. The database and analysis platform are available at https://aidd.rc.ufl.edu/app/smartflexdb/.

bioinformatics↗

bioRxiv2026

Geometric Deep Learning Reveals Ligandable and Cryptic RNA Binding Small Molecule Pockets (SMARTPocket)

RNAs are important therapeutic targets, however identifying ligandable small-molecule binding pockets remains a major barrier to RNA-targeted drug discovery. Here, SMARTPocket, an atomic-level geometric deep learning framework for predicting RNA-small molecule binding pockets directly from three-dimensional structure is introduced. SMARTPocket represents RNA as full-atom point clouds and uses transfer learning from more than 110,000 protein binding interface structures to overcome the limited number of experimentally elucidated RNA-ligand complexes. Across four established single-chain benchmarks and three broader curated benchmarks, SMARTPocket consistently outperforms existing RNA pocket predictors and general biomolecular modeling approaches. The model generalizes to apo RNA structures when conformational changes are modest, identifies cryptic ligandable pockets, and recapitulates experimentally validated binding sites in the SARS-CoV-2 frameshifting element and an RNA aptamer evolved to bind small molecules. SMARTPocket-guided docking further improves near-native RNA-ligand pose recovery and computational efficiency compared with blind docking. These results establish SMARTPocket as a generalizable framework for structure-based identification of ligandable RNA pockets and for accelerating discovery of RNA-targeted small molecules.

bioinformatics↗

bioRxiv2025

Live-Cell Covalent Profiling Reveals Principles of RNA-Small Molecule Recognition across the Human Transcriptome

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 RiboTACs; 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. TEASERLive-cell mapping reveals ligandable RNA sites and guides design of selective RNA degraders. TOC graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=61 SRC="FIGDIR/small/686775v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@a216c8org.highwire.dtl.DTLVardef@185c59eorg.highwire.dtl.DTLVardef@17131e2org.highwire.dtl.DTLVardef@81ffa2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Small Molecule Approach to RNA Targeting Binder Discovery (SMARTBind) Using Deep Learning Without Structural Input

Accurate identification of small molecule binders to RNA is critical for chemical probes and therapeutics. Computational approaches offer a cost-effective strategy to identify small molecules targeting RNA but are often limited by poor predictive accuracy and high computational demands. Here, we introduce Small Molecule Approaches to RNA Targeting Binder Discovery (SMARTBind), a structure-agnostic ligand discovery framework that combines an RNA large language model with contrastive learning and a ligand-specific decoy enhancement strategy. The RNA language model, pre-trained on millions of RNA sequences, together with the decoy enhancement strategy, addresses data scarcity and improves model generalizability. SMARTBind uses only RNA primary sequence to identify small molecule binders and their binding sites accurately. Across multiple benchmarks and case studies, SMARTBind outperforms existing data-driven and docking-based methods while significantly reducing computational cost. In a real- world application, SMARTBind identified novel small molecules targeting the precursor of oncogenic microRNA-21, validated by in vitro and cellular assays. These results highlight SMARTBinds potential as a scalable, accurate, and structure-independent platform for RNA-targeted small molecule discovery.

bioinformatics↗

bioRxiv2024

NMR structures and magnetic force spectroscopy studies of small molecules binding to models of an RNA CAG repeat expansion

RNA repeat expansions fold into stable structures and cause microsatellite diseases such as Huntingtons disease (HD), myotonic dystrophy type 1 (DM1), and spinocerebellar ataxias (SCAs). The trinucleotide expansion of r(CAG), or r(CAG)exp, causes both HD and SCA3, and the RNAs toxicity has been traced to its translation into polyglutamine (polyQ; HD) as well as aberrant pre-mRNA alternative splicing (SCA3 and HD). Previously, a small molecule, 1, was discovered that binds to r(CAG)exp and rescues aberrant pre-mRNA splicing in patient-derived fibroblasts by freeing proteins bound to the repeats. Here, we report the structures of single r(CAG) repeat motif (5CAG/3GAC where the underlined adenosines form a 1x1 nucleotide internal loop) in complex with 1 and two other small molecules via nuclear magnetic resonance (NMR) spectroscopy combined with simulated annealing. Compound 2 was designed based on the structure of 1 bound to the RNA while 3 was selected as a diverse chemical scaffold. The three complexes, although adopting different 3D binding pockets upon ligand binding, are stabilized by a combination of stacking interactions with the internal loops closing GC base pairs, hydrogen bonds, and van der Waals interactions. Molecular dynamics (MD) simulations performed with NMR-derived restraints show that the RNA is stretched and bent upon ligand binding with significant changes in propeller-twist and opening. Compound 3 has a distinct mode of binding by insertion into the helix, displacing one of the loop nucleotides into the major groove and affording a rod-like shape binding pocket. In contrast, 1 and 2 are groove binders. A series of single molecule magnetic force spectroscopy studies provide a mechanistic explanation for how bioactive compounds might rescue disease-associated cellular phenotypes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/608150v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@145398org.highwire.dtl.DTLVardef@7c37caorg.highwire.dtl.DTLVardef@132cbbeorg.highwire.dtl.DTLVardef@1de59d3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

NMR structures of small molecules bound to a model of an RNA CUG repeat expansion

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntingtons disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntingtons disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG)exp. The RNA folds into a hairpin structure with a periodic array of 1x1 nucleotide UU loops (5CUG/3GUC; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5CUG/3GUC motifs in complex with three different small molecules, a diguandinobenzoate (1), a derivative of 1 where the guanidino groups have been exchanged for imidazole (2), and a quinoline with improved drug-like properties (3). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1, 2, and 3 formed stacking and hydrogen bonding interactions with the 5CUG/3GUC motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG)exp. As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

biochemistry↗

bioRxiv2024

Transcriptome-wide mapping of small-molecule RNA-binding sites in live cells

Small molecules targeting RNA can be valuable chemical probes and potential therapeutics. The interactions between small molecules, particularly fragments, and RNA, however, can be difficult to detect due to their modest affinities and short residence times. Here, we describe the procedures for mapping the molecular fingerprints of small molecules in vitro and throughout the human transcriptome in live cells, identifying both the targets bound by the small molecule and the sites of binding therein. For complete details on the use and execution of this protocol, please refer to 1.

biochemistry↗

bioRxiv2019

The RNA encoding the microtubule-associated protein tau has extensive structure that affects its biology

Tauopathies are neurodegenerative diseases that affect millions of people worldwide including those with Alzheimers disease. While many efforts have focused on understanding the role of tau protein in neurodegeneration, there has been little done to systematically analyze and study the structures within taus encoding RNA and their connection to disease pathology. Knowledge of RNA structure can provide insights into disease mechanisms and how to affect protein production for therapeutic benefit. Using computational methods based on thermodynamic stability and evolutionary conservation, we identified structures throughout the tau pre-mRNA, especially at exon-intron junctions and within the 5' and 3' untranslated regions (UTRs). In particular, structures were identified at twenty exon-intron junctions. The 5' UTR contains one structured region, which lies within a known internal ribosome entry site. The 3' UTR contains eight structured regions, including one that contains a polyadenylation signal. A series of functional experiments were carried out to assess the effects of mutations associated with mis-regulation of alternative splicing of exon 10 and to identify regions of the 3' UTR that contain cis-regulatory elements. These studies defined novel structural regions within the mRNA that affect stability and pre-mRNA splicing and may lead to new therapeutic targets for treating tau-associated diseases.

bioinformatics↗