ArticleNucleic acids research2015
Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- A spectral framework for measuring diversity in multiple sequence alignments.PLoS computational biology · 2026Article
- Detecting and quantifying overparametrization in RNA language models with REDIAL.bioRxiv : the preprint server for biology · 2026Article
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Enhancing RNA 3D Structure Prediction in CASP16: Integrating Physics-Based Modeling With Machine Learning for Improved Predictions.Proteins · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Influence of contact map topology on RNA structure prediction.Nucleic acids research · 2025Article
- NucleoSeeker-precision filtering of RNA databases to curate high-quality datasets.NAR genomics and bioinformatics · 2025Article
- Generating Artificial Ribozymes Using Sparse Coevolutionary Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Towards parsimonious generative modeling of RNA families.Nucleic acids research · 2024Article
- Residue coevolution and mutational landscape for OmpR and NarL: You can teach old dogs new tricks.Biophysical journal · 2024Article
- pycofitness-Evaluating the fitness landscape of RNA and protein sequences.Bioinformatics (Oxford, England) · 2024Article
- Multiple sequence alignment-based RNA language model and its application to structural inference.Nucleic acids research · 2024Article
- Advancing RNA 3D structure prediction: Exploring hierarchical and hybrid approaches in CASP15.Proteins · 2023Article
- trRosettaRNA: automated prediction of RNA 3D structure with transformer network.Nature communications · 2023Article
- RNA contact prediction by data efficient deep learning.Communications biology · 2023Article
- Thirteen dubious ways to detect conserved structural RNAs.IUBMB life · 2023Article
- Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.PLoS computational biology · 2022Article
- Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.Bioinformatics (Oxford, England) · 2022Article
- Coupling a Live Cell Directed Evolution Assay with Coevolutionary Landscapes to Engineer an Improved Fluorescent Rhodopsin Chloride Sensor.ACS synthetic biology · 2022Article
- Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data.Frontiers in molecular biosciences · 2022Article
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7 authors.
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Abstract
Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone.
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