The prediction of tertiary RNA structures is significant to the field of medicine (e.g. mRNA vaccines, genome editing), and the exploration of viral transcripts. Though many RNA folding software exist, few studies have condensed their locus of attention solely to viral pseudoknotted RNA. These regulatory pseudoknots play a role in genome replication, gene expression, and protein synthesis. This study explores five RNA folding engines that compute either the minimum free energy (MFE) or the maximum expected accuracy (MEA). These folding engines were tested against 26 experimentally derived short pseudoknotted sequences (20-150nt) using metrics that are commonly applied to software prediction accuracy (e.g. F 1 scoring, PPV). This paper reports higher accuracy RNA prediction engines, such as pKiss, when compared to previous iterations of the software, and when compared to older folding engines. They show that MEA folding software does not always outperform MFE folding software in prediction accuracy when assessed with metrics such as percent error, sensitivity, PPV, and F 1 scoring when applied to viral pseudoknotted RNA. Moreover, the results suggest that thermodynamic model parameters will not ensure accuracy if auxiliary parameters such as Mg 2+ binding, dangling end options, and H-type penalties are not applied. The observations reported in this paper highlight the quality between different ab initio prediction methods while enforcing the idea that a better understanding of intracellular thermodynamics is necessary for a more efficacious screening of RNAs.
Importance
The importance of accurately predicting RNA structures cannot be overstated, particularly in the context of viral biology and the development of therapeutic interventions such as mRNA vaccines and genome editing. Our study addresses the gap in the existing literature by concentrating solely on viral pseudoknotted RNA, which plays a crucial role in viral replication, gene expression, and protein synthesis. Our study sheds light on the debate surrounding minimum free energy (MFE) versus maximum expected accuracy (MEA) models in RNA folding predictions. Contrary to existing beliefs, we found that MEA models do not consistently outperform MFE models, especially in the context of viral pseudoknotted RNAs. Our research contributes to advancing the field of computational biology by providing insights into the efficacy of different prediction methods and emphasizing the need for a deeper understanding of intracellular thermodynamics to improve RNA structure predictions.