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A new computational tool developed with support from the U.S. National Science Foundation could greatly speed up determining the 3D structure of RNAs, a critical step in developing new RNA-based drugs, identifying drug-binding sites and using RNAs in other biotechnology and biomedicine applications.

The tool, NuFold, leverages state-of-the-art machine learning techniques to predict the structure of a wide variety of RNA molecules from their sequences. This new capability will allow researchers to visualize what a given RNA structure could look like based on its sequence and identify its potential use in drug delivery, disease treatment and other applications.  The research leading to NuFold was published in Nature Communications.

RNAs are critical biological molecules — encoding information, like DNA, and performing cellular functions, like proteins — but relatively few RNA structures have been determined through experimentation thus far, which severely limits understanding of their functions. For example, RNAs in the NSF-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) represent only about 3% of total entries. Experimentally determining RNA structures is often time-consuming and costly. By providing a path to reliably predicting RNA structure from sequence, NuFold could greatly expedite the discovery of RNA function and enable quicker development of RNA-based therapeutics and technologies.