Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops

The accurate base pairing in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar's advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.

Automated summary

This paper aims to create a pioneering framework for predicting specific RNA structures, particularly focusing on H-type pseudoknots including bulges and internal loops. The proposed framework, Knotify+, leverages the power context-free grammar (CFG) along with maximum base pairing and minimum free energy to effectively address this ambiguous task. Knotify+ outperforms state-of-the-art frameworks in terms of accuracy in core stems prediction, providing higher accuracy in small sequences and comparable accuracy in larger ones, while requiring less execution time compared to well-known platforms.