FebRNA: an automated fragment-ensemble-based model for building RNA 3D structures

Abstract

Knowledge of RNA 3-dimensional (3D) structures is critical to understanding the important biological functions of RNAs. Although various structure prediction models have been developed, high accuracy of predicted RNA 3D structures is still limited to the RNAs with short length or with simple topology. In this work, we proposed a new model, namely FebRNA, for building RNA 3D structures through fragment assembly based on coarse-grained (CG) fragment ensembles. Specifically, FebRNA is composed of four processes: establishing the library of different types of CG fragment ensembles, building CG 3D structure ensemble through fragment assembly, identifying top-1 CG structure through a CG scoring function, and rebuilding the all-atom structure from the top-1 CG one. Extensive examination on different types of RNA structures indicates that FebRNA gives consistently reliable predictions on RNA 3D structures including pseudoknots, 3-way junction, 4-way and 5-way junctions, and RNAs in the RNA-Puzzles. FebRNA is available at website: https://github.com/Tan-group/FebRNA.