AlignmentFold and AlignmentPartition: Improving the align-then-fold approach for RNA secondary structure prediction

Abstract

The combination of free energy minimization with sequence covariation analysis is a popular approach for predicting conserved secondary structure using a fixed alignment of homologous RNA sequences. In this work we revisit this approach by developing the new AlignmentFold and AlignmentPartition programs. AlignmentFold and AlignmentPartition predict consensus minimum free energy structures and base pairing probabilities, respectively. We determined new nearest neighbor thermodynamic parameters for gaps and non-canonical base pairs that align with base pairs. These frequently occur for structure prediction with a fixed input alignment. We assessed the impact of alignment prediction method and size on the prediction accuracy. We also assessed the improvement in prediction accuracy conferred by sequence covariation analysis. In contrast to previous solutions, we do not include covariation in the scoring of structure quality and achieve prediction accuracy as good as other tools. AlignmentFold and AlignmentPartition are freely available as part of the RNAstructure software package at https://rna.urmc.rochester.edu/RNAstructure.html.