VeriNA3d: an R package for nucleic acids data mining

Gallego, Diego; Darré, Leonardo; Dans, Pablo D.; Orozco, Modesto

doi:10.1093/bioinformatics/btz553

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…obtaining an dataset of 174 protein-DNA complexes that in this work defines the protein-DNA interactome. The interactions involved in the structures of this dataset have been further studied using the R package VeriNA3D [54] in Supplementary Fig. S11.…”

Section: Dnamentioning

confidence: 99%

How B-DNA Dynamics Decipher Sequence-Selective Protein Recognition

Battistini

Buitrago

Gallego

et al. 2019

Journal of Molecular Biology

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The rules governing sequence-specific DNA-protein recognition are under a longstanding debate regarding the prevalence of base versus shape readout mechanisms to explain sequence specificity and of the conformational selection versus induced fit binding paradigms to explain binding-related conformational changes in DNA. Using a combination of atomistic simulations on a subset of representative sequences and mesoscopic simulations at the protein-DNA interactome level, we demonstrate the prevalence of the shape readout model in determining sequence-specificity and of the

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Section: Dnamentioning

confidence: 99%

How B-DNA Dynamics Decipher Sequence-Selective Protein Recognition

Battistini

Buitrago

Gallego

et al. 2019

Journal of Molecular Biology

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“…We explored here the conformational space of the “experimental” RNA backbone using our package veriNA3d (11). The large size of the database allowed to make subsets of the data and explore relationships that could have not been addressed before, such as the effect of proteins on the RNA backbone, or intrinsic differences due to the source of experimental structures.…”

Section: Discussionmentioning

confidence: 99%

“…The calculation of the distance between points in the η–θ map and the scoring of η–θ clusters were carried out as described in Wadley’s (7). RMSD, εRMSD (15), torsions and distances were computed in R using our package veriNA3d (11). RMSD calculations included backbone atoms from C4’ i-1 to C4’ i+1 or C1’ i-1 to C1’ i+1 in the case of η’–θ’.…”

Section: Methodsmentioning

confidence: 99%

“…This allows us to compare isolated RNA molecules vs. those bound to proteins, finding any potential bias related to the experimental sources of 3D structures, or to start adding sequence context to major conformers. Accordingly, in this work we used an updated version of our veriNA3d R (11) package to revisit the 3 pairs of pseudo-torsional angles (ηθ, η'-θ', and η''-θ'') as they emerge from non-redundant experimental structures available to date. We found that η-θ were the best pseudo-torsions to describe RNA conformational space.…”

Section: Introductionmentioning

confidence: 99%

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The Pseudo-Torsional Space of RNA

Grille

Gallego

Darré

et al. 2022

Preprint

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The characterization of the conformational landscape of the RNA backbone is rather complex due to the ability of RNA to assume a big variety of conformations. These backbone conformations can be depicted by pseudo-torsional angles linking RNA backbone atoms, from which Ramachandran-like plots can be built. We explored here different definitions of these pseudo-torsional angles, finding that the most accurate ones are the traditional η (eta) and θ (theta) angles, which represent the relative position of RNA backbone atoms P and C4'. We explore the distribution of η-θ in known experimental structures, comparing the pseudo-torsional space generated with structures determined exclusively by one experimental technique. We found that the complete picture only appears when combining data from different sources. The maps provide a quite comprehensive representation of the RNA accessible space, which can be used in RNA-structural prediction. Finally, our results highlight that protein interactions leads to significant changes in the population of the η-θ space, pointing towards the role of induced-fit mechanisms in protein-RNA recognition.

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“…DSSR has been extensively tested using all nucleic-acidcontaining structures in the PDB and is continuously developed following user feedback. Over the years, DSSR has been widely cited in scientific literature (11)(12)(13)(14)(15)(16)(17)(18) and adopted into many other structural bioinformatics resources (19)(20)(21)(22)(23)(24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

Faculty Opinions recommendation of DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL.

Vicens

2020

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature

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Sophisticated analysis and simplified visualization are crucial for understanding complicated structures of biomacromolecules. DSSR (Dissecting the Spatial Structure of RNA) is an integrated computational tool that has streamlined the analysis and annotation of 3D nucleic acid structures. The program creates schematic block representations in diverse styles that can be seamlessly integrated into PyMOL and complement its other popular visualization options. In addition to portraying individual base blocks, DSSR can draw Watson-Crick pairs as long blocks and highlight the minor-groove edges. Notably, DSSR can dramatically simplify the depiction of G-quadruplexes by automatically detecting Gtetrads and treating them as large square blocks. The DSSR-enabled innovative schematics with PyMOL are aesthetically pleasing and highly informative: the base identity, pairing geometry, stacking interactions, double-helical stems, and G-quadruplexes are immediately obvious. These features can be accessed via four interfaces: the command-line interface, the DSSR plugin for PyMOL, the web application, and the web application programming interface. The supplemental PDF serves as a practical guide, with complete and reproducible examples. Thus, even beginners or occasional users can get started quickly, especially via the web application at http://skmatic.x3dna.org.

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VeriNA3d: an R package for nucleic acids data mining

Cited by 6 publications

References 17 publications

How B-DNA Dynamics Decipher Sequence-Selective Protein Recognition

How B-DNA Dynamics Decipher Sequence-Selective Protein Recognition

The Pseudo-Torsional Space of RNA

Faculty Opinions recommendation of DSSR-enabled innovative schematics of 3D nucleic acid structures with PyMOL.

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