Аннотация:The structure of noncoding RNAs largely determines their functions. With the rapid growth of experimental data on the RNA secondary structures, the task of predicting its spatial structure becomes the most urgent task of RNA bioinformatics. The ability to predict tertiary base pairs from data on the secondary structure could significantly reduce the operating time and improve the quality of the RNA spatial structure prediction algorithms. In this work, we applied the machine learning algorithm for the problem of RNA tertiary base pairs prediction from data on the RNA sequence and secondary structure. A group of local base pairs was identified that can be predicted with high quality (80% precision, 80% recall). It was also shown that more than 70% of all long-range noncanonical base pairs in RNA are the base pairs of geometric classes Sugar-Edge/Sugar-Edge and Sugar-Edge/Watson-Crick-Edge that correspond to ribose zipper and A-minor tertiary motifs.
