3dRNA: 3D Structure Prediction from Linear to Circular RNAs. Issue 11 (15th June 2022) - Zhang, Yi; Wang, Jun; Xiao, Yi
Graphical abstract: Workflow of 3dRNA in prediction of RNA 3D structure. Highlights: The 3D structures of RNAs are very limited at present. 3dRNA was proposed to predict 3D structure of RNAs. A score function 3dRNAscore was built to evaluate predicted 3D structures. Information of coevolution in the RNA sequences was incorporated into 3dRNA. 3dRNA is now extended to predict the 3D structures of circular RNAs. Abstract: 3D structures of RNAs are the basis for understanding their biological functions. However, experimentally solved RNA 3D structures are very limited. Therefore, many computational methods have been proposed to solve this problem, including our 3dRNA. 3dRNA is an automated template-based method of building RNA 3D structures from sequences and secondary structures by using the smallest secondary elements (SSEs) (http://biophy.hust.edu.cn/new/3dRNA ). The first version of 3dRNA simply predicts an assembled structure for a target RNA. Later, it is improved to generate a set of assembled models and a method to further optimize them using experimental or theoretical restraints. In particular, pseudoknot base pairings are treated as restraints to solve the problem of no 3D templates for pseudoknots. Here 3dRNA is further extended to predict the 3D structures of circular RNAs since thousands of circular RNAs have been found recently but no 3D structures of them have been determined up to now. We show that circular RNAs can be divided into four types and two types show similar 3D structures with their linear counterparts while two types very different. We also show that the predicted structures of circular RNAs can bind to their ligands more stable than those of their linear counterparts, consistent with experimental results.