# Shedding light on the dark matter of the biomolecular structural universe: Progress in RNA 3D structure prediction

## TL;DR

This paper reviews current computational methods for RNA 3D structure prediction, highlighting recent advances with coevolutionary data, and discusses future challenges in understanding RNA structural stability.

## Contribution

It provides a comprehensive overview of state-of-the-art RNA 3D prediction methods, emphasizing recent developments and open challenges in the field.

## Key findings

- Coevolutionary information enhances prediction accuracy
- Current methods have strengths and weaknesses in different contexts
- Future research needs to address stability and accuracy challenges

## Abstract

Structured RNA plays many functionally relevant roles in molecular life. Structural information, while required to understand the functional cycles in detail, is challenging to gather. Computational methods promise to complement experimental efforts by predicting three-dimensional RNA models. Here, we provide a concise view of the state of the art methodologies with a focus on the strengths and the weaknesses of the different approaches. Furthermore, we analyzed the recent developments regarding the use of coevolutionary information and how it can boost the prediction performances. We finally discuss some open perspectives and challenges for the near future in the RNA structural stability field.