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

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.

arXiv commentSupporting information included in ancillary files

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Cited by in corpus (4)

• Machine learning a model for RNA structure prediction
• Towards Parsimonious Generative Modeling of RNA Families
• adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
• adabmDCA: Adaptive Boltzmann machine learning for biological sequences