ArticleNAR genomics and bioinformatics2020
Machine learning a model for RNA structure prediction.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Small non-coding RNAMicrobiology spectrum · 2026Article
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Incorporating mRNA therapeutics into biological treatments of hematologic malignancies.Frontiers in immunology · 2025Review
- Advances in nucleic acid probe-based detection of gene point mutations: a review.Frontiers in chemistry · 2025Review
- Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.Frontiers in cellular and infection microbiology · 2024Review
- RNA contact prediction by data efficient deep learning.Communications biology · 2023Article
- Molecular Dynamics Simulations with Grand-Canonical Reweighting Suggest Cooperativity Effects in RNA Structure Probing Experiments.Journal of chemical theory and computation · 2023Article
- High-throughput biochemistry in RNA sequence space: predicting structure and function.Nature reviews. Genetics · 2023Review
- In silico methods for predicting functional synonymous variants.Genome biology · 2023Review
- Machine learning for RNA 2D structure prediction benchmarked on experimental data.Briefings in bioinformatics · 2023Review
- RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling.Nucleic acids research · 2023Article
- Article
- Review
- Automatic Learning of Hydrogen-Bond Fixes in the AMBER RNA Force Field.Journal of chemical theory and computation · 2022Article
- Predicting higher-order mutational effects in an RNA enzyme by machine learning of high-throughput experimental data.Frontiers in molecular biosciences · 2022Article
- Deep Learning in RNA Structure Studies.Frontiers in molecular biosciences · 2022Review
- In Vitro Methods to Decipher the Structure of Viral RNA Genomes.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Review of machine learning methods for RNA secondary structure prediction.PLoS computational biology · 2021Review
- Prediction of the RNA Tertiary Structure Based on a Random Sampling Strategy and Parallel Mechanism.Frontiers in genetics · 2021Article
- Advances in RNA 3D Structure Modeling Using Experimental Data.Frontiers in genetics · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless complemented by auxiliary experimental data. Here, we build a set of models that combine thermodynamic parameters, chemical probing data (DMS and SHAPE) and co-evolutionary data (direct coupling analysis) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window combines neighboring reactivities, enlightening their structural information content and the contribution of local conformational ensembles. Regularization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensemble populations for native structures and more accurate predictions in an independent validation set. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.