ArticleStructure (London, England : 1993)2019
Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking.
Article in Structure (London, England : 1993), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Modeling binding of the conserved Csr/Rsm protein family across species of the γ-proteobacteria reveals niche-specific adaptation of the post-transcriptional regulon.bioRxiv : the preprint server for biology · 2026Article
- Navigating protein-nucleic acid sequence-structure landscapes with deep learning.Current opinion in structural biology · 2025Review
- Thermodynamic modeling of RsmA - mRNA interactions capture novel direct binding across theFrontiers in molecular biosciences · 2025Article
- Advances and Challenges in Scoring Functions for RNA-Protein Complex Structure Prediction.Biomolecules · 2024Review
- HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses.BMC bioinformatics · 2024Article
- DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design.PLoS computational biology · 2023Article
- RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study.International journal of molecular sciences · 2023Article
- Adaptation of HIV-1/HIV-2 Chimeras with Defects in Genome Packaging and Viral Replication.mBio · 2022Article
- In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials.Biology direct · 2022Article
- Conformational variability in proteins bound to single-stranded DNA: A new benchmark for new docking perspectives.Proteins · 2022Article
- Three critical regions of the erythromycin resistance methyltransferase, ErmE, are required for function supporting a model for the interaction of Erm family enzymes with substrate rRNA.RNA (New York, N.Y.) · 2022Article
- Sequence- and structure-selective mRNA mNational science review · 2021Article
- Integrating single-molecule FRET and biomolecular simulations to study diverse interactions between nucleic acids and proteins.Essays in biochemistry · 2021Review
- Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis.Nature communications · 2021Article
- Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.Biochemistry · 2021Review
- FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds.Structure (London, England : 1993) · 2020Article
- Macromolecular modeling and design in Rosetta: recent methods and frameworks.Nature methods · 2020Review
- How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms.Molecular cell · 2020Review
- Article
- Blind tests of RNA-protein binding affinity prediction.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
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2 authors.
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Abstract
RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe RNP-denovo, a Rosetta method to simultaneously fold-and-dock RNA to a protein surface. On a benchmark set of diverse RNA-protein complexes not solvable with prior strategies, RNP-denovo consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges involving the spliceosome, telomerase, and a methyltransferase-ribosomal RNA complex in which previous methods gave poor results. When coupled with the same sparse FRET, crosslinking, and functional data used previously, RNP-denovo gave models with significantly improved accuracy. These results open a route to modeling global folds of RNA-protein complexes from low-resolution data.
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