Abstract
Many RNA molecules, particularly non-coding RNA molecules, fold back on themselves to make basepairs, base stacks, and other contacts, and the 3D structures that they form are essential for the performance of their functions. Experimentally determining the 3D structure of an RNA molecule is difficult and time consuming, so it is desirable to be able to predict the 3D structure that an RNA molecule will fold into based only on the molecule’s sequence. We review RNA 3D structure prediction techniques that have been benchmarked in the most recent RNA-Puzzles competition and survey new tools that have been developed since then. The evolution of tools that predict RNA 3D structures from sequence has been similar to that of tools for the prediction of protein 3D structures, and it seems we might be on the precipice of a leap forward in RNA 3D structure prediction from tools using machine learning and deep neural networks.
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Roll, J., Zirbel, C.L. (2023). Predicting the 3D Structure of RNA from Sequence. In: Sugimoto, N. (eds) Handbook of Chemical Biology of Nucleic Acids. Springer, Singapore. https://doi.org/10.1007/978-981-19-9776-1_14
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