The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
ModeRNA: a tool for comparative modeling of RNA 3D structure
Open accessAbstract
RNA is a large group of functionally important biomacromolecules. In striking analogy to proteins, the function of RNA depends on its structure and dynamics, which in turn is encoded in the linear sequence. However, while there are numerous methods for computational prediction of protein three-dimensional (3D) structure from sequence, with comparative modeling being the most reliable approach, there are very few such methods for RNA. Here, we present ModeRNA, a software tool for comparative modeling of RNA 3D structures. As an input, ModeRNA requires a 3D structure of a template RNA molecule, and a sequence alignment between the target to be modeled and the template. It must be emphasized that a good alignment is required for successful modeling, and for large and complex RNA molecules the development of a good alignment usually requires manual adjustments of the input data based on previous expertise of the respective RNA family. ModeRNA can model post-transcriptional modifications, a functionally important feature analogous to post-translational modifications in proteins. ModeRNA can also model DNA structures or use them as templates. It is equipped with many functions for merging fragments of different nucleic acid structures into a single model and analyzing their geometry. Windows and UNIX implementations of ModeRNA with comprehensive documentation and a tutorial are freely available.
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Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080
Gilbert, W. (1986) · Nature · doi:10.1038/319618a0 · Cited by 2,721
The relation between the divergence of sequence and structure in proteins.
Chothia, C., & Lesk, A. M. (1986) · The EMBO Journal · doi:10.1002/j.1460-2075.1986.tb04288.x · Cited by 2,458
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Xue, L., Lenz, S., Zimmermann-Kogadeeva, M., Tegunov, D., Cramer, P., Bork, P., Rappsilber, J., & Mahamid, J. (2022) · Nature · doi:10.1038/s41586-022-05255-2 · Cited by 184
Kagaya, Y., Zhang, Z., Ibtehaz, N., Wang, X., Nakamura, T., Punuru, P. D., & Kihara, D. (2025) · Nature Communications · doi:10.1038/s41467-025-56261-7 · Cited by 70
Has AlphaFold 3 achieved success for RNA?
Bernard, C., Postic, G., Ghannay, S., & Tahi, F. (2025) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798325000592 · Cited by 61
State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction
Bernard, C., Postic, G., Ghannay, S., & Tahi, F. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae048 · Cited by 40
Metadata
- Authors
- Rother, M., Rother, K., Puton, T., & Bujnicki, J. M.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-02-07
- Volume
- 39
- Issue
- 10
- Pages
- 4007-4022
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W1995164506
- Cited by
- 273
- References
- 3
- FWCI
- 7.17
- Type
- journal article
- Language
- en
- Open access
- gold
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