# Assessing the accuracy of direct-coupling analysis for RNA contact prediction

## TL;DR

This paper evaluates the effectiveness of direct-coupling analysis (DCA) in predicting RNA contacts, comparing various DCA methods and covariance analysis to determine the most accurate approach for RNA structure prediction.

## Contribution

It provides a comprehensive assessment of DCA methods for RNA contact prediction, introducing a stochastic Boltzmann learning approach that outperforms existing techniques.

## Key findings

- Boltzmann learning outperforms other DCA methods in contact prediction.
- DCA methods show promise but vary in accuracy for RNA.
- Comparison with mutual information and R-scape highlights strengths and limitations.

## Abstract

Many non-coding RNAs are known to play a role in the cell directly linked to their structure. Structure prediction based on the sole sequence is however a challenging task. On the other hand, thanks to the low cost of sequencing technologies, a very large number of homologous sequences are becoming available for many RNA families. In the protein community, it has emerged in the last decade the idea of exploiting the covariance of mutations within a family to predict the protein structure using the direct-coupling-analysis (DCA) method. The application of DCA to RNA systems has been limited so far. We here perform an assessment of the DCA method on 17 riboswitch families, comparing it with the commonly used mutual information analysis and with state-of-the-art R-scape covariance method. We also compare different flavors of DCA, including mean-field, pseudo-likelihood, and a proposed stochastic procedure (Boltzmann learning) for solving exactly the DCA inverse problem. Boltzmann learning outperforms the other methods in predicting contacts observed in high resolution crystal structures.