A Modular Approach to the Discovery and Affinity Maturation of Sequence-Selective RNA-Binding Compounds

Miller, Benjamin L.

doi:10.1007/7355_2016_23

Cited by 2 publications

(1 citation statement)

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“…The Miller laboratory also used a similar approach. A dynamic combinatorial library initially designed for DNA using building blocks from DNAinteracting natural products was later successfully employed for RNA targets (69,70).…”

Section: Scaffold-based Synthesismentioning

confidence: 99%

Frameworks for targeting RNA with small molecules

Juru

Hargrove

2021

Journal of Biological Chemistry

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Since the characterization of mRNA in 1961, our understanding of the roles of RNA molecules has significantly grown. Beyond serving as a link between DNA and proteins, RNA molecules play direct effector roles by binding to various ligands, including proteins, DNA, other RNAs, and metabolites. Through these interactions, RNAs mediate cellular processes such as the regulation of gene transcription and the enhancement or inhibition of protein activity. As a result, the misregulation of RNA molecules is often associated with disease phenotypes, and RNA molecules have been increasingly recognized as potential targets for drug development efforts, which in the past had focused primarily on proteins. Although both small molecule–based and oligonucleotide-based therapies have been pursued in efforts to target RNA, small-molecule modalities are often favored owing to several advantages including greater oral bioavailability. In this review, we discuss three general frameworks (sets of premises and hypotheses) that, in our view, have so far dominated the discovery of small-molecule ligands for RNA. We highlight the unique merits of each framework as well as the pitfalls associated with exclusive focus of ligand discovery efforts within only one framework. Finally, we propose that RNA ligand discovery can benefit from using progress made within these three frameworks to move toward a paradigm that formulates RNA-targeting questions at the level of RNA structural subclasses.

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Section: Scaffold-based Synthesismentioning

confidence: 99%