TT2NE: A novel algorithm to predict RNA secondary structures with pseudoknots

TL;DR

TT2NE is a new algorithm that accurately predicts RNA secondary structures with pseudoknots by classifying structures based on topological genus, offering a powerful tool within certain sequence length limits.

Contribution

It introduces TT2NE, a novel algorithm that guarantees minimum free energy structure prediction with pseudoknots, advancing RNA structure prediction methods.

Findings

TT2NE guarantees minimum free energy structures regardless of pseudoknot topology.

Comparison shows TT2NE outperforms existing algorithms within its sequence length limits.

Analysis highlights sterical constraints affecting pseudoknot formation.

Abstract

We present TT2NE, a new algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. TT2NE guarantees to find the minimum free energy structure irrespectively of pseudoknot topology. This unique proficiency is obtained at the expense of the maximum length of sequence that can be treated but comparison with state-of-the-art algorithms shows that TT2NE is a very powerful tool within its limits. Analysis of TT2NE's wrong predictions sheds light on the need to study how sterical constraints limit the range of pseudoknotted structures that can be formed from a given sequence. An implementation of TT2NE on a public server can be found at http://ipht.cea.fr/rna/tt2ne.php.