ArticleBMC bioinformatics2019
DIRECT: RNA contact predictions by integrating structural patterns.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Advances in Methods for Accurate Prediction of RNA-Small Molecule Binding Sites: From Isolated to AI-Integrated Strategies.Pharmaceuticals (Basel, Switzerland) · 2025Review
- AI-integrated network for RNA complex structure and dynamic prediction.Biophysics reviews · 2024Review
- lociPARSE: A Locality-aware Invariant Point Attention Model for Scoring RNA 3D Structures.Journal of chemical information and modeling · 2024Article
- lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.bioRxiv : the preprint server for biology · 2024Article
- Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.Bioinformatics (Oxford, England) · 2022Article
- RPpocket: An RNA-Protein Intuitive Database with RNA Pocket Topology Resources.International journal of molecular sciences · 2022Article
- RPocket: an intuitive database of RNA pocket topology information with RNA-ligand data resources.BMC bioinformatics · 2021Article
- RNA inter-nucleotide 3D closeness prediction by deep residual neural networks.Bioinformatics (Oxford, England) · 2021Article
- RNAStat: An Integrated Tool for Statistical Analysis of RNA 3D Structures.Frontiers in bioinformatics · 2021Article
- Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement.Frontiers in molecular biosciences · 2021Article
- RBinds: A user-friendly server for RNA binding site prediction.Computational and structural biotechnology journal · 2020Article
- Advances in RNA 3D Structure Modeling Using Experimental Data.Frontiers in genetics · 2020Review
- HKPocket: human kinase pocket database for drug design.BMC bioinformatics · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundIt is widely believed that tertiary nucleotide-nucleotide interactions are essential in determining RNA structure and function. Currently, direct coupling analysis (DCA) infers nucleotide contacts in a sequence from its homologous sequence alignment across different species. DCA and similar approaches that use sequence information alone typically yield a low accuracy, especially when the available homologous sequences are limited. Therefore, new methods for RNA structural contact inference are desirable because even a single correctly predicted tertiary contact can potentially make the difference between a correct and incorrectly predicted structure. Here we present a new method DIRECT (Direct Information REweighted by Contact Templates) that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural features in contact inference.
resultsBenchmark tests demonstrate that DIRECT achieves better overall performance than DCA approaches. Compared to mfDCA and plmDCA, DIRECT produces a substantial increase of 41 and 18%, respectively, in accuracy on average for contact prediction. DIRECT improves predictions for long-range contacts and captures more tertiary structural features.
conclusionsWe developed a hybrid approach that incorporates a Restricted Boltzmann Machine (RBM) to augment the information on sequence co-variations with structural templates in contact inference. Our results demonstrate that DIRECT is able to improve the RNA contact prediction.
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