IsRNAcirc: 3D structure prediction of circular RNAs based on coarse-grained molecular dynamics simulation.

As an emerging class of RNA molecules, circular RNAs play pivotal roles in various biological processes, thereby determining their three-dimensional (3D) structure is crucial for a deep understanding of their biological significances. Similar to linear RNAs, the development of computational methods for circular RNA 3D structure prediction is challenging, especially considering the inherent flexibility and potentially long length of circular RNAs. Here, we introduce an extension of our previous IsRNA2 model, named IsRNAcirc, to enable circular RNA 3D structure predictions through coarse-grained molecular dynamics simulations. The workflow of IsRNAcirc consists of four main steps, including input preparation, end closure, structure prediction, and model refinement. Our results demonstrate that IsRNAcirc can provide reasonable 3D structure predictions for circular RNAs, which significantly reduce the locally irrational elements contained in the initial input. Moreover, for a validation test set comprising 34 circular RNAs, our IsRNAcirc can generate 3D models with better scores than the template-based 3dRNA method. These findings demonstrate that our IsRNAcirc method is a promising tool to explore the structural details along with intricate interactions of circular RNAs. Author summary: Knowledge of the 3D structure of circular RNA molecules can help us better understand their roles in eukaryotic cells. However, experimental determination of circular RNA 3D structure remains challenging, with currently only one complete circular RNA 3D structure available in the Protein Data Bank. Thus, extending from their linear counterparts, computational methods that can predict circular RNA 3D structure are urgently needed. In particular, physics-based de novo 3D structure prediction methods are promising due to their powerful sampling capability in the conformational space, which avoid the problem of limited available templates in template-based approaches. We have developed IsRNAcirc, a new physics-based method that employs an accurate coarse-grained force field and replica-exchange molecular dynamics simulations to predict the 3D structure of circular RNAs. IsRNAcirc is shown to significantly reduce the number of locally irrational structures in the input trials, which are caused by the lack of suitable templates and the improper assembly of fragments. Furthermore, in the absence of experimental reference structures, the predicted 3D models were validated using the popular scoring functions, such as DFIRE-RNA and rsRNASP, demonstrating that IsRNAcirc outperforms the template-based 3dRNA method in generating low-energy conformations. [ABSTRACT FROM AUTHOR]