A Massively Parallel Selection of Small Molecule-RNA Motif Binding Partners Informs Design of an Antiviral from Sequence

Childs‐Disney, Jessica L.; Tran, Tuan Anh; Vummidi, Balayeshwanth R.; Velagapudi, Sai Pradeep; Haniff, Hafeez S.; Matsumoto, Yasumasa; Crynen, Gogce; Southern, Mark R.; Biswas, Avik; Wang, Zi-Fu; Tellinghuisen, Timothy L.; Disney, Matthew D.

doi:10.1016/j.chempr.2018.08.003

Cited by 45 publications

(52 citation statements)

References 63 publications

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“…These interactions fine-tune the specificity of RNA interactions by determining structure at the 3D level [22]. Interestingly, non-canonical pairs were also found to be involved in ligand binding sites [8,19], which corroborates with further findings showing that some secondary structure motifs can specify ligand binding [6,48].…”

Section: Rna Structuresupporting

confidence: 74%

Augmented base pairing networks encode RNA-small molecule binding preferences

Oliver

Mallet

Gendron

et al. 2019

Preprint

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Recent studies have identified small RNA-binding molecules with potential for therapeutic applications including novel antibiotics, antivirals, protein synthesis controllers, and CRISPR activators. As RNA binding data accumulates, machine learning methods are becoming attractive approaches to accelerate the discovery of RNA-targeting drugs. While atomic level representations of molecular systems are regarded as a critical step for physics-based and data-driven drug design, the singularity and hierarchical organization of RNA structures challenges this paradigm. In this work, we present a machine learning framework for assisting in the discovery of small RNA-binding molecules from graphical representations of RNA structures. A key feature of our tool is that it does not rely on manual feature engineering or costly physical simulations. Instead, we extract molecule-binding information directly from known RNA-ligand complexes by combining graph embedding methods with machine learning models. Given only the graph representation of an RNA site as input, our tool is able to reliably predict chemical features, also known as fingerprints, of the observed ligands. The resulting fingerprints can be used to classify RNA sites according to their binding preferences, screen databases for promising ligands, or act as constraints for generating novel ligands. We show consistent performance for across ligand classes in enriching the known binder from a larger ligand library. As a validation, we applied this method to successfully identify binding residues in unbound RNA structures for three different riboswitches. These results also suggest that small molecule binding preferences in RNA can be extracted directly from the nucleotide pairing level.

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Section: Rna Structuresupporting

confidence: 74%

Augmented base pairing networks encode RNA-small molecule binding preferences

Oliver

Mallet

Gendron

et al. 2019

Preprint

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“…Dysregulation of AP-1 is hallmark of viral pathogenesis, neoplastic transformation and tumor progression (35,46). Likewise, dysregulation of dhx9/rha is associated with productive viral infection (31,37,38,45,(47)(48)(49)(50) and tumor survival (51)(52)(53)(54). The recent finding NCBP3 is essential to mount a precise antiviral response (34) posits dysfunction in a RHA-NCBP1-NCBP3 translation pathway contributes to deficient innate response.…”

Section: Discussionmentioning

confidence: 99%

The mRNA encoding the JUND tumor suppressor detains nuclear RNA-binding proteins to assemble polysomes that are unaffected by mTOR

Singh

Fritz

Seufzer

et al. 2020

Journal of Biological Chemistry

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One long-standing knowledge gap is the role of nuclear proteins in mRNA translation. Nuclear RNA helicase A (DHX9/RHA) is necessary for the translation of the mRNAs of JunD proto-oncogene AP-1 transcription factor subunit (JUND) and HIV-1 genes, and nuclear cap-binding protein 1 (NCBP1)/CBP80 is a component of HIV-1 polysomes. The protein kinase mTOR activates canonical messenger ribonucleoproteins (mRNPs) by posttranslationally downregulating the eIF4E inhibitory protein, 4E-BP1. We posited here that NCBP1 and DHX9/RHA (RHA) support a translation pathway of JUND RNA that is independent of mTOR. We present evidence from reciprocal immunoprecipitation experiments indicating that NCBP1 and RHA both are components of mRNPs in several cell types. Moreover, tandem affinity and RT-qPCR results revealed that JUND mRNA is a component of a previously unknown ribonucleoprotein complex. Results from the tandem IP indicated that another component of the JUND-containing ribonucleoprotein complex is NCBP3, a recently identified ortholog of NCBP2/CBP20. We also found that NCBP1, NCBP3, and RHA, but not NCBP2, are components of JUND-containing polysomes. Mutational analysis uncovered two dsRNA-binding domains of RHA that are necessary to tether JUND-NCBP1/NCBP3 to polysomes. We also found that JUND translation is unaffected by inhibition of mTOR, unless RHA was down-regulated by siRNA. These findings uncover a noncanonical cap-binding complex consisting of NCBP1/NCBP3 and indicate that RHA substitutes for eukaryotic translation initiation factor 4E (eIF4E) and eIF4G in activating mTOR-independent translation of the mRNA encoding the tumor suppressor JUND.

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“…Tertiary modeling of polypeptide conformations has been integral to elucidating the functional mechanisms of enzymes, chaperones, many structural proteins and receptor-ligand interactions and for in silico modeling of small molecule therapeutics. Tertiary modeling of RNA structures has robust potential to guide the rational design of antiviral therapeutics [ 58 , 59 , 60 , 61 ]. Supportive evidence that alternative nt–nt pairings propagate structural changes throughout the HIV-1 leader RNA includes TAR-binding compounds exert structural effects outside TAR [ 62 , 63 ] and small molecule binding within the CES reduce virus titer [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

A New Approach to 3D Modeling of Inhomogeneous Populations of Viral Regulatory RNA

Osmer

Singh

Boris‐Lawrie

2020

Viruses

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Tertiary structure (3D) is the physical context of RNA regulatory activity. Retroviruses are RNA viruses that replicate through the proviral DNA intermediate transcribed by hosts. Proviral transcripts form inhomogeneous populations due to variable structural ensembles of overlapping regulatory RNA motifs in the 5′-untranslated region (UTR), which drive RNAs to be spliced or translated, and/or dimerized and packaged into virions. Genetic studies and structural techniques have provided fundamental input constraints to begin predicting HIV 3D conformations in silico. Using SimRNA and sets of experimentally-determined input constraints of HIVNL4-3 trans-activation responsive sequence (TAR) and pairings of unique-5′ (U5) with dimerization (DIS) or AUG motifs, we calculated a series of 3D models that differ in proximity of 5′-Cap and the junction of TAR and PolyA helices; configuration of primer binding site (PBS)-segment; and two host cofactors binding sites. Input constraints on U5-AUG pairings were most compatible with intramolecular folding of 5′-UTR motifs in energetic minima. Introducing theoretical constraints predicted metastable PolyA region drives orientation of 5′-Cap with TAR, U5 and PBS-segment helices. SimRNA and the workflow developed herein provides viable options to predict 3D conformations of inhomogeneous populations of large RNAs that have been intractable to conventional ensemble methods.

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A Massively Parallel Selection of Small Molecule-RNA Motif Binding Partners Informs Design of an Antiviral from Sequence

Cited by 45 publications

References 63 publications

Augmented base pairing networks encode RNA-small molecule binding preferences

Augmented base pairing networks encode RNA-small molecule binding preferences

The mRNA encoding the JUND tumor suppressor detains nuclear RNA-binding proteins to assemble polysomes that are unaffected by mTOR

A New Approach to 3D Modeling of Inhomogeneous Populations of Viral Regulatory RNA

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