The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Improvement of molecular-replacement models with Sculptor
Open accessAbstract
In molecular replacement, the quality of models can be improved by transferring information contained in sequence alignment to the template structure. A family of algorithms has been developed that make use of the sequence-similarity score calculated from residue-substitution scores smoothed over nearby residues to delete or downweight parts of the model that are unreliable. These algorithms have been implemented in the program Sculptor, together with well established methods that are in common use for model improvement. An analysis of the new algorithms has been performed by studying the effect of algorithm parameters on the quality of models. Benchmarking against existing techniques shows that models from Sculptor compare favourably, especially if the alignment is unreliable. Carrying out multiple trials using alternative models created from the same structure but using different algorithm parameters can significantly improve the success rate.
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Cited by
Liebschner, D., Afonine, P. V., Baker, M. L., Bunkóczi, G., Chen, V. B., Croll, T. I., Hintze, B., Hung, L.-W., Jain, S., McCoy, A. J., et al. (2019) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798319011471 · Cited by 7,953
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The Phenix software for automated determination of macromolecular structures
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Metadata
- Authors
- Bunkóczi, G., & Read, R. J.
- Venue
- Acta Crystallographica Section D Biological Crystallography
- Published
- 2011-03-18
- Volume
- 67
- Issue
- 4
- Pages
- 303-312
- ISSN
- 0907-4449 (ISSN-L)
- OpenAlex
- W2094646744
- Cited by
- 209
- References
- 3
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
