# Unknotting RNA: A method to resolve computational artifacts

## TL;DR

This paper introduces a method to fix entanglements in RNA 3D structure predictions, allowing previously discarded models to be used effectively.

## Contribution

The first systematic method to classify and remove RNA entanglements while preserving structural integrity.

## Key findings

- The method resolved over 70% of interlaces and 40% of lassos in RNA models with minimal geometric distortion.
- It achieved an 81% efficiency in untangling conformations classified as artifacts.
- The approach was validated on 195 entangled RNA models from CASP15 and RNA-Puzzles.

## Abstract

RNA 3D structure prediction often encounters entanglements, computational artifacts that complicate structural models, resulting in their exclusion from further studies despite the potentially accurate prediction of regions outside the entanglement. This study presents a protocol aimed at resolving such issues in RNA models while preserving the overall 3D fold and structural integrity. By employing the SPQR coarse-grained model and short Molecular Dynamics simulations, the protocol imposes energy terms that enable selective modifications to disentangle structures without causing significant distortions. The method was validated on 195 entangled RNA models from CASP15 and RNA-Puzzles, successfully resolving over 70% of interlaces and approximately 40% of lassos, with minimal impact on the original geometry but notable improvement in ClashScore. The efficiency of untangling conformations that are unequivocally classified as artifacts is 81%. Certain cases, particularly those involving dense packing of atoms or complex secondary structures, posed challenges that limited the efficiency of the method. In this paper, we present quantitative results from the application of the protocol and discuss examples of both successfully disentangled and unresolved structures. We show a viable approach for refining models previously deemed unsuitable due to topological artifacts.

Most algorithms for 3D RNA structure prediction, including a recent version of AlphaFold, in the pool of solutions return models with entanglements, which turn out to be artifacts of computational modeling. Excluding the entanglement region, these models can reflect the native conformation quite well; however, due to the presence of the artifact, they are not considered promising and are therefore discarded from further study. In this paper, we present the first method that enables the systematic classification and removal of RNA entanglements while preserving the model’s consistency and its global 3D fold. Tests on the in silico models submitted to RNA-Puzzles and CASP15 RNA show that the system can automatically solve a significant number of issues, including 81% of entanglements considered incorrect conformations.