Machine Learning to Enhance Biophysical Models for Riboswitch Discovery.

Abstract

Riboswitches are RNA-based genetic control elements that provide a unique mechanism of gene regulation. They function without the participation of proteins and are believed to represent ancient regulatory systems on the evolutionary timescale. To expand the search for novel riboswitches of significant interest, such as eukaryotic riboswitches beyond the purine riboswitch class, the integration of machine learning into RNA design offers considerable potential to improve the effectiveness of traditional search methods. Many riboswitch aptamers that could be targeted in such searches are larger than purine riboswitch aptamers, making their design time-consuming (e.g., SAM, lysine, FMN, glycine, and cobalamin riboswitches). In an example problem unrelated to the discovery of novel riboswitches, namely challenges in the eteRNA game, it has been shown that a transformer encoder-decoder model is effective in solving difficult inverse RNA folding challenges. In the outlined approach, machine learning is used as a preprocessing step before RNA design, rather than relying on random inputs to the Monte Carlo method, and the RNA design step crucially depends on a biophysical model for the forward problem of RNA folding prediction. It is therefore suggested to optimize the training of the machine learning model and then utilize this approach for riboswitch discovery to make searches for riboswitches larger than purine aptamers more efficient and robust.