The paper introduces CaCoFold-R3D, a novel computational method and probabilistic grammar for predicting RNA structure. This approach uniquely integrates the prediction of RNA secondary structure (canonical helices and pseudoknots) with the simultaneous identification of recurrent three-dimensional (3D) motifs found in non-helical loop regions. A key feature of CaCoFold-R3D is its use of evolutionary information from RNA alignments, specifically covariation, to constrain and improve the accuracy of both helix and 3D motif prediction. The method is described as computationally efficient and capable of predicting a large number of known motifs, offering a significant advancement over previous sequential or limited prediction methods. Ultimately, the tool is positioned as valuable for guiding RNA design and serving as input for advanced deep learning models in all-atom RNA structure prediction.
References:
