The intricate network of RNA-RNA interactions, crucial for orchestrating essential cellular processes like transcriptional and translational regulation, has been unveiling through high-throughput techniques and computational predictions. With the emergence of deep learning methodologies, the question arises: how do these cutting-edge techniques for base-pairing prediction compare to traditional free-energy-based approaches, particularly when applied to the challenging domain of interaction prediction via chain concatenation? In this study, we employ base pairs derived from three-dimensional RNA complex structures as the gold standard benchmark to assess the performance of 23 different methods, including recently developed deep learning models. Our results demonstrate that the deep-learning-based methods, SPOT-RNA can be generalized to previously unseen RNA structures and are capable of making accurate zero-shot predictions of RNA-RNA interactions.
cd SPOT-RNAc
python3 SPOT-RNAc.py --I1 inputs/seq_1/6XJQ_A.fasta --I2 inputs/seq_2/6XJQ_A.fasta --device cpu --ncpu 4 --output outputs
The following data is 64 RNA-RNA interaction pairs:
RNA-RNA-Interaction_DataSet_64.csv
the ./data also contain the the PDB 3D file and parse DSSR file that used in paper: