Discoveries of RNA roles in cellular physiology and pathology are increasing the need for new tools that modulate the structure and function of these biomolecules, and small molecules are proving useful. In 2017, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND) and discovered distinguishing physicochemical properties of RNA-targeting ligands, leading us to propose the existence of an “RNA-privileged” chemical space. Biennial updates of the database and the establishment of a website platform (rbind.chem.duke.edu) have provided new insights and tools to design small molecules based on the analyzed physicochemical and spatial properties. In this report and R-BIND 2.0 update, we refined the curation approach and ligand classification system as well as conducted analyses of RNA structure elements for the first time to identify new targeting strategies. Specifically, we curated and analyzed RNA target structural motifs to determine the properties of small molecules that may confer selectivity for distinct RNA secondary and tertiary structures. Additionally, we collected sequences of target structures and incorporated an RNA structure search algorithm into the website that outputs small molecules targeting similar motifs without a priori secondary structure knowledge. Cheminformatic analyses revealed that, despite the 50% increase in small molecule library size, the distinguishing properties of R-BIND ligands remained significantly different from that of proteins and are therefore still relevant to RNA-targeted probe discovery. Combined, we expect these novel insights and website features to enable the rational design of RNA-targeted ligands and to serve as a resource and inspiration for a variety of scientists interested in RNA targeting.
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Distance Profiles of Optimal RNA Foldings
Predicting the secondary structure of RNA is an important problem in molecular biology, providing insights into the function of non-coding Rn As and with broad applications in understanding disease, the development of new drugs, among others. Combinatorial algorithms for predicting RNA foldings can generate an exponentially large number of equally optimal foldings with respect to a given optimization criterion, making it difficult to determine how well any single folding represents the entire space. We provide efficient new algorithms for providing insights into this large space of optimal RNA foldings and a research software tool, toRNAdo, that implements these algorithms.
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- Award ID(s):
- 2231150
- PAR ID:
- 10436135
- Editor(s):
- Bansal, M
- Date Published:
- Journal Name:
- Bioinformatics Research and Applications: 18th International Symposium, ISBRA 2022, Haifa, Israel, November 14–17, 2022, Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-23198-8_29
