Abstract
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.
Highlights
RNA tertiary structure predictionRNA plays a central role in all of life, acting as both a carrier of genetic information and as an active participant in numerous cellular processes, including pre-mRNA splicing and gene regulation via modulation of transcription and translation [1]
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
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
Summary
RNA tertiary structure predictionRNA plays a central role in all of life, acting as both a carrier of genetic information and as an active participant in numerous cellular processes, including pre-mRNA splicing and gene regulation via modulation of transcription and translation [1]. A wide range of computational methods were developed to automatically predict RNA structure These methods include fragment assembly [11,12,13,14], all-atom modeling with constraints from sequence comparison and experimental information using molecular mechanics [15,16], coarse-grained molecular simulation [17,18,19,20,21,22], coarse-grained helix-as-a-stick models [23,24,25], and homology modeling using a known structure [26]. All of these methods can use sparse information from experimental methods or low-resolution computational structure prediction, such as prediction of secondary structure, to reduce the search space of the tertiary structure problem and improve accuracy
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