We propose an RNA secondary structure folding path learning algorithm based on deep reinforcement learning combined with Monte Carlo Tree Search (MCTS), called 2dRNA-Fold. 2dRNA-Fold selects residue pairing step by step according to a given RNA sequence until the final secondary structure is formed. Then, we can find a folding path with the largest probability that is considered as the folding path of the RNA. The following diagram illustrates the entire pipeline.
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Figure 1: The diagram of 2dRNA-Fold predicts the secondary structure folding path of a single RNA molecule. |
Here one small RNA with a length of 30 nucleotides is selected and used 2dRNA-Fold to learn and visualize their folding paths (shown as the figure 2). Its sequence secondary structure is:
AAGCGGAACGAAACGUUGCUUUUGCGCCCU
.((.((.(((...)))(((....)))))))
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Figure 2: Learned folding path of an RNA. The top is the folding path tree, and the bottom is the visualized folding path. |
Software Requirments:
For secondary structure folding path of single sequence:
python main.py --model model/PDB_00010 --fold data/PDB_00010.bp
For a single RNA molecule, here we provide some examples including trained models and data, see the model and data folders. This command is used to predict the secondary structure of a single RNA molecule and visualize the predicted folding path. The intermediate output process is shown in Figure 3.
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Figure 3: secondary structure folding path prediction for single sequence. |
For secondary structure prediction of single sequence:
python main.py --model model/bpRNA_21650 --pred GGAGGCUCUGGCAGCUUUC
The following datasets were used for multi-RNA molecules folding path learning:
- bpRNA: training set and testing set