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.从序列预测RNA三级结构是一项重要的研究课题，但即便针对较短的序列，生成精准的结构模型仍颇具挑战。RNA三级结构预测的准确性在环区往往最差，而非经典碱基对对于确定结构的细节至关重要。非经典碱基对可通过基于知识的结构模型进行预测，该模型会对核苷酸环状基序（nucleotide cyclic motifs, NCMs）进行打分。本研究提出了一种配分函数算法，可用于估算经典与非经典碱基相互作用的碱基配对概率。预测得到的高概率碱基对，相较于低概率碱基对，更有可能存在于真实结构中。通过使用TurboFold算法预测多同源序列共有的保守结构，可进一步优化碱基配对概率的估算结果。将这些配对概率与经典二级结构的先验知识相结合，即可实现非经典碱基对的精准推断，这是迈向完整三级结构精准预测的关键一步。用于预测非经典碱基对与配对概率的软件，现已作为RNAstructure软件包的一部分对外发布。