RNA 3D Modeling with FARFAR2, Online

Watkins, Andrew; Das, Rhiju

doi:10.1007/978-1-0716-2768-6_14

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…In conclusion, researchers without access to experienced structural bioinformaticians can now independently utilize one of the top-performing methods, such as Vfold2, DeepFoldRNA, or RhoFold. Nevertheless, experienced bioinformaticians may prefer methods like SimRNA or FARFAR2, which offer enhanced flexibility for integrating both expertise and experimental data ( 63 , 64 ).…”

Section: Discussionmentioning

confidence: 99%

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Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA–ligand interactions

Nithin,

Kmiecik,

Błaszczyk

et al. 2024

Nucleic Acids Research

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Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods—DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3′s performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA–ligand interactions accurately.

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Section: Discussionmentioning

confidence: 99%

“…It features a fragment assembly protocol for modelling. This method offers various customization options such as user provided input template files, used of experimental data such as chemical shift and MOHCA-Seq ( 64 ).…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA–ligand interactions

Nithin,

Kmiecik,

Błaszczyk

et al. 2024

Nucleic Acids Research

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“…This server has been particularly useful for researchers who are less familiar with command-line tools and to those without access to high-performance computing facilities. It is important to emphasize that compared to other web servers for RNA 3D structure prediction, such as RNAComposer ( 4 ), Vfold-Pipeline ( 5 ), FARFAR2 ( 6 ), trRosettaRNA ( 7 ) or DeepFoldRNA ( 8 ), SimRNAweb generates folding trajectories and can be used to study RNA conformational landscapes or carry out unfolding simulations for RNA stability analyses.…”

Section: Introductionmentioning

confidence: 99%

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories

Moafinejad,

de Aquino,

Boniecki

et al. 2024

Nucleic Acids Research

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Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

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