Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction
Table 9
RMSD comparison (in Å) of models for 13 targets predicted by methods that take secondary structure (ss) as input.
The methods compared are RNAcomposer (rnac), 3dRNA (3dRNA), DRFold (drfold) and trRosettaRNA (trr). The columns without ‘_ss’ suffix indicate the predicted models where the input ss is the default one predicted by the respective associated ss prediction method for each tool (RNAfold for RNAComposer and 3RNA; SPOT-RNA for trRosettaRNA; PETfold + RNAfold for DRFold). The columns with ‘_ss’ suffix are the ones where the input ss is the one extracted from the native PDB file using the RNAPDBee tool. We observe that for both the fragment-assembly-based methods i.e. RNAComposer and 3dRNA, the average RMSD for predicted models is much lower with the native ss compared to the default ones (22.64 Å vs 13.72 Å for RNAComposer and 24.81 Å vs 20.65 Å for 3dRNA). For the ML-based methods i.e. DRFold and trRosettaRNA, we don’t see much difference between the two scenarios. We actually observe a slight increase in the average RMSD for DRFold (17.27 Å vs 18.19 Å) and a slight reduction in the average RMSD for trRosettaRNA (19.80 Å vs 18.63 Å). The probable reason for this could be that the ML-based methods are actually trained on ss input from their respective ss prediction methods so ss extracted from native PDBs might not offer much improvement in the quality of the predicted model and native ss as input rather creates restraints that these tools are not able to handle, thus resulting in models with higher RMSDs.