Gödel

Recurrent neural networks (RNNs) have been used in a number of studies to improve the accuracy of secondary structure predictions for RNAs.

A query sequence and its anticipated secondary structure (SS) are entered to start the process. An embedding layer uses this data to create crucial sequence and pair representations. These embedded representations are processed by 48 RNA transformer blocks, supplying energy for the following processes.

The predictive outputs from the previous steps, namely the frame vectors and geometric restraints, are amalgamated into a composite potential. This potential is instrumental in guiding RNA structure reconstruction through a gradient-based optimization process. The primary objective of this optimization is to identify the RNA conformation with the lowest energy, which is subsequently selected as the output model.

To enhance the precision and reliability of the models, the coarse-grained representations of the RNA structures generated by the pipeline undergo a meticulous refinement procedure. This procedure leverages a two-step molecular dynamics-based approach, which aids in atomic-level structure reconstruction and fine-tuning.