Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.Author summary: Three dimensional RNA structure prediction methods are not yet able to accurately model the base pairs that are not in standard A-form helices, called non-canonical pairs. Non-canonical base pairs are crucial in determining the conformations of structures, but available algorithms to identify them have limited accuracy. We developed a new method, CycleFold, that can identify non-canonical base pairs using statistical methods that have proven successful in predicting A-form helices. Additionally, CycleFold incorporates evolutionary conservation to further improve accuracy. CycleFold provides a dramatic improvement in accuracy over previously available methods, and its output could be used to refine three dimensional structure predictions from any modeling software.