A computational approach for the identification of distant homologs of bacterial riboswitches based on inverse RNA folding

Abstract

Riboswitches are conserved structural ribonucleic acid (RNA) sensors that are mainly found to regulate a large number of genes/operons in bacteria. Presently, >50 bacterial riboswitch classes have been discovered, but only the thiamine pyrophosphate riboswitch class is detected in a few eukaryotes like fungi, plants and algae. One of the most important challenges in riboswitch research is to discover existing riboswitch classes in eukaryotes and to understand the evolution of bacterial riboswitches. However, traditional search methods for riboswitch detection have failed to detect eukaryotic riboswitches besides just one class and any distant structural homologs of riboswitches. We developed a novel approach based on inverse RNA folding that attempts to find sequences that match the shape of the target structure with minimal sequence conservation based on key nucleotides that interact directly with the ligand. Then, to support our matched candidates, we expanded the results into a covariance model representing similar sequences preserving the structure. Our method transforms a structure-based search into a sequence-based search that considers the conservation of secondary structure shape and ligand-binding residues. This method enables us to identify a potential structural candidate in fungi that could be the distant homolog of bacterial purine riboswitches. Further, phylogenomic analysis and evolutionary distribution of this structural candidate indicate that the most likely point of origin of this structural candidate in these organisms is associated with the loss of traditional purine riboswitches. The computational approach could be applicable to other domains and problems in RNA research.

Identification of potential riboswitch elements inHomo SapiensmRNA 5’UTR sequences using Positive-Unlabeled Machine learning

“…7 and 8). Our approach provides a complementary strategy to the that taken in Mukherjee et al (39), where the authors began with a known riboswitch sequence and selectively mutated nucleotides while preserving structure, and then searched genomics data for a sequence match. In contrast, our approach starts with genomics data to learn our classifier and any given sequence can be assessed for riboswitch probability.…”

“…Notably, many of these have difficulty extrapolating to unknown riboswitches and rely heavily on a previous knowledge base. Recent breakthroughs have been achieved via reverse homology searching approaches (mutate a sequence without disturbing secondary structure, search for the new mutant in a genome wide fashion), which recently helped to identify a list of potential purine riboswitches in fungi (39) – however this approach once again suffers from a lack of extrapolation and requires a known starting point structure.…”