DNATCO v5.0: integrated web platform for 3D nucleic acid structure analysis
Jazyk angličtina Země Anglie, Velká Británie Médium print
Typ dokumentu časopisecké články
Grantová podpora
RVO 86652036
Czech Academy of Sciences
PubMed
41495910
PubMed Central
PMC12774647
DOI
10.1093/nar/gkaf1491
PII: 8415847
Knihovny.cz E-zdroje
- MeSH
- DNA * chemie MeSH
- internet MeSH
- konformace nukleové kyseliny * MeSH
- molekulární modely MeSH
- párování bází MeSH
- RNA * chemie MeSH
- software * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- DNA * MeSH
- RNA * MeSH
As the number and complexity of RNA and DNA structures continue to expand, there is a growing need for robust yet accessible tools that support their accurate interpretation, validation, and refinement. We present DNATCO v5.0 (dnatco.datmos.org), an interactive web application for comprehensive structural analysis of nucleic acids. DNATCO integrates the NtC dinucleotide conformational classes and the CANA structural alphabet to provide an intuitive, geometrically complete description of local backbone and base orientations, complemented by interactive visualization of base pairing. The platform performs quantitative validation of conformational similarity and covalent bond lengths and angles, using newly established nucleic-acid valence-geometry standards. Quantitative validation encompasses the confal score and scattergrams mapping the fit between experimental electron density and geometry similarity to the closest NtC class. All outputs are downloadable. Integrated diagnostic tools help users identify unusual or problematic regions, explore alternative conformations, and generate torsion-restraint files for downstream. DNATCO v5.0 is implemented entirely client-side via WebAssembly, ensuring fast performance and preserving data privacy, and supports both PDB and user-provided structural models. By combining a rigorous geometric framework with an approachable interface, DNATCO enables both non-experts and specialists to evaluate nucleic-acid structures with greater confidence and to improve models in ways that support accurate biological interpretation.
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