  RNA structure determination and prediction can promote RNA-targeted drug
development and engineerable synthetic elements design. But due to the
intrinsic structural flexibility of RNAs, all the three mainstream structure
determination methods (X-ray crystallography, NMR, and Cryo-EM) encounter
challenges when resolving the RNA structures, which leads to the scarcity of
the resolved RNA structures. Computational prediction approaches emerge as
complementary to the experimental techniques. However, none of the \textit{de
novo} approaches is based on deep learning since too few structures are
available. Instead, most of them apply the time-consuming sampling-based
strategies, and their performance seems to hit the plateau. In this work, we
develop the first end-to-end deep learning approach, E2Efold-3D, to accurately
perform the \textit{de novo} RNA structure prediction. Several novel components
are proposed to overcome the data scarcity, such as a fully-differentiable
end-to-end pipeline, secondary structure-assisted self-distillation, and
parameter-efficient backbone formulation. Such designs are validated on the
independent, non-overlapping RNA puzzle testing dataset and reach an average
sub-4 \AA{} root-mean-square deviation, demonstrating its superior performance
compared to state-of-the-art approaches. Interestingly, it also achieves
promising results when predicting RNA complex structures, a feat that none of
the previous systems could accomplish. When E2Efold-3D is coupled with the
experimental techniques, the RNA structure prediction field can be greatly
advanced.
