Assessing the accuracy of direct-coupling analysis for RNA contact prediction
- 1Scuola Internazionale Superiore di Studi Avanzati, International School for Advanced Studies, 34136 Trieste, Italy
- 2Center for Complexity and Biosystems and Department of Physics, Università degli Studi di Milano and INFN, 20133 Milano, Italy
- Corresponding author: bussi{at}sissa.it
Abstract
Many noncoding 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, the idea of exploiting the covariance of mutations within a family to predict the protein structure using the direct-coupling-analysis (DCA) method has emerged in the last decade. 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, pseudolikelihood, 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.
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Article is online at http://www.rnajournal.org/cgi/doi/10.1261/rna.074179.119.
- Received November 29, 2019.
- Accepted February 26, 2020.
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