Improved RNA pseudoknots prediction and classification using a new topological invariant

TL;DR

This paper introduces a novel topological approach to RNA pseudoknot prediction by combining genus and crossing invariants into the free energy model, improving structural classification accuracy.

Contribution

It presents a new topological invariant-based model for RNA pseudoknot prediction, integrating topology into free energy calculations for better structural analysis.

Findings

Enhanced accuracy in RNA pseudoknot classification

Incorporation of topological invariants improves prediction models

Better understanding of RNA pseudoknot topology diversity

Abstract

We propose a new topological characterization of RNA secondary structures with pseudoknots based on two topological invariants. Starting from the classic arc-representation of RNA secondary structures, we consider a model that couples both I) the topological genus of the graph and II) the number of crossing arcs of the corresponding primitive graph. We add a term proportional to these topological invariants to the standard free energy of the RNA molecule, thus obtaining a novel free energy parametrization which takes into account the abundance of topologies of RNA pseudoknots observed in RNA databases.