RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints

Chojnowski, Grzegorz; Zaborowski, Rafał; Magnus, Marcin; Mukherjee, Sunandan; Bujnicki, Janusz M

doi:10.1093/bioinformatics/btad527

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…14 Chojnowski et al developed a method to model 3D structures of short RNA polymers featuring base-paired strands as well as unpaired strands involved in loops by assembling RNA fragments from the PDB with the option to include experimental restraints. 31 Ensembles of flexible biopolymers can be improved by integrating available experimental data. Approaches such as Bayesian/Maximum Entropy (BME) 32−35 and Bayesian inference 36−40 have been shown to work well in applications to ensembles of disordered biomolecules.…”

Section: Introductionmentioning

confidence: 99%

“…The more recent FARFAR2 approach has been used to generate ensembles of short ssRNA polymers . Chojnowski et al developed a method to model 3D structures of short RNA polymers featuring base-paired strands as well as unpaired strands involved in loops by assembling RNA fragments from the PDB with the option to include experimental restraints …”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Assembly of Single-Stranded RNA

Pietrek,

Stelzl,

Hummer

2024

J. Chem. Theory Comput.

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Single-stranded RNA (ssRNA) plays a major role in the flow of genetic information−most notably, in the form of messenger RNA (mRNA)− and in the regulation of biological processes. The highly dynamic nature of chains of unpaired nucleobases challenges structural characterizations of ssRNA by experiments or molecular dynamics (MD) simulations alike. Here, we use hierarchical chain growth (HCG) to construct ensembles of ssRNA chains. HCG assembles the structures of protein and nucleic acid chains from fragment libraries created by MD simulations. Applied to homo-and heteropolymeric ssRNAs of different lengths, we find that HCG produces structural ensembles that overall are in good agreement with diverse experiments, including nuclear magnetic resonance (NMR), small-angle X-ray scattering (SAXS), and single-molecule Forster resonance energy transfer (FRET). The agreement can be further improved by ensemble refinement using Bayesian inference of ensembles (BioEn). HCG can also be used to assemble RNA structures that combine base-paired and base-unpaired regions, as illustrated for the 5′ untranslated region (UTR) of SARS-CoV-2 RNA.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Assembly of Single-Stranded RNA

Pietrek,

Stelzl,

Hummer

2024

J. Chem. Theory Comput.

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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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Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data

Thiel,

Bussi,

Poblete

et al. 2024

Nucleic Acids Research

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The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.

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RNA 3D structure modeling by fragment assembly with small-angle X-ray scattering restraints

Cited by 4 publications

References 29 publications

Hierarchical Assembly of Single-Stranded RNA

Hierarchical Assembly of Single-Stranded RNA

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

Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data

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