RNA molecules carry out various cellular functions, and understanding the mechanisms behind their functions requires the knowledge of their 3D structures. Different types of computational methods have been developed to model RNA 3D structures over the past decade. These methods were widely used by researchers although their performance needs to be further improved. Recently, along with these traditional methods, machine-learning techniques have been increasingly applied to RNA 3D structure prediction and show significant improvement in performance. Here we shall give a brief review of the traditional methods and recent related advances in machine-learning approaches for RNA 3D structure prediction.

Considerable progress has been made in the prediction methods of 3D structures of RNAs. In contrast, no such methods are available for DNAs. The determination of 3D structures of the latter is also increasingly needed for understanding their functions and designing new DNA molecules. Since the number of experimental structures of DNA is limited at present, here, we propose a computational and template-based method, 3dDNA, which combines DNA and RNA template libraries to predict DNA 3D structures. It was benchmarked on three test sets with different numbers of chains, and the results show that 3dDNA can predict DNA 3D structures with a mean RMSD of about 2.36 Å for those with one or two chains and fewer than 4 Å with three or more chains.

This article describes how to use 3dRNA/DNA to predict RNA and DNA 3D structures from their sequences and secondary (2D) structures. The predicted RNA and DNA 3D structures can be used to predict or understand their functions and can also be used to find the interactions between the RNA/DNA and other molecules. © 2023 Wiley Periodicals LLC. Basic Protocol: RNA/DNA 3D structure prediction using 3dRNA/DNA web server Alternate Protocol: Using 3dRNA/DNA and 3dRNAscore locally Support Protocol: Installing NSP