The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Super-resolution biomolecular crystallography with low-resolution data
Open accessAbstract
X-ray diffraction plays a pivotal role in the understanding of biological systems by revealing atomic structures of proteins, nucleic acids and their complexes, with much recent interest in very large assemblies like the ribosome. As crystals of such large assemblies often diffract weakly (resolution worse than 4 Å), we need methods that work at such low resolution. In macromolecular assemblies, some of the components may be known at high resolution, whereas others are unknown: current refinement methods fail as they require a high-resolution starting structure for the entire complex. Determining the structure of such complexes, which are often of key biological importance, should be possible in principle as the number of independent diffraction intensities at a resolution better than 5 Å generally exceeds the number of degrees of freedom. Here we introduce a method that adds specific information from known homologous structures but allows global and local deformations of these homology models. Our approach uses the observation that local protein structure tends to be conserved as sequence and function evolve. Cross-validation with Rfree (the free R-factor) determines the optimum deformation and influence of the homology model. For test cases at 3.5–5 Å resolution with known structures at high resolution, our method gives significant improvements over conventional refinement in the model as monitored by coordinate accuracy, the definition of secondary structure and the quality of electron density maps. For re-refinements of a representative set of 19 low-resolution crystal structures from the Protein Data Bank, we find similar improvements. Thus, a structure derived from low-resolution diffraction data can have quality similar to a high-resolution structure. Our method is applicable to the study of weakly diffracting crystals using X-ray micro-diffraction as well as data from new X-ray light sources. Use of homology information is not restricted to X-ray crystallography and cryo-electron microscopy: as optical imaging advances to subnanometre resolution, it can use similar tools.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LGA: a method for finding 3D similarities in protein structures
Zemla, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg571 · Cited by 1,069
Structural definition of a conserved neutralization epitope on HIV-1 gp120
Zhou, T., Xu, L., Dey, B., Hessell, A. J., Van Ryk, D., Xiang, S.-H., Yang, X., Zhang, M.-Y., Zwick, M. B., Arthos, J., et al. (2007) · Nature · doi:10.1038/nature05580 · Cited by 770
Cited by
Crystal structure of the β2 adrenergic receptor–Gs protein complex
Rasmussen, S. G. F., DeVree, B. T., Zou, Y., Kruse, A. C., Chung, K. Y., Kobilka, T. S., Thian, F. S., Chae, P. S., Pardon, E., Calinski, D., et al. (2011) · Nature · doi:10.1038/nature10361 · Cited by 3,009
Protein structure prediction from sequence variation
Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622
Smart, O. S., Womack, T. O., Flensburg, C., Keller, P., Paciorek, W., Sharff, A., Vonrhein, C., & Bricogne, G. (2012) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444911056058 · Cited by 612
Outcome of the first electron microscopy validation task force meeting
Henderson, R., Sali, A., Baker, M. L., Carragher, B., Devkota, B., Downing, K. H., Egelman, E. H., Feng, Z., Frank, J., Grigorieff, N., et al. (2012) · Structure · doi:10.1016/j.str.2011.12.014 · Cited by 505
Self-citationArchitecture of the synaptotagmin–SNARE machinery for neuronal exocytosis
Zhou, Q., Lai, Y., Bacaj, T., Zhao, M., Lyubimov, A. Y., Uervirojnangkoorn, M., Zeldin, O. B., Brewster, A. S., Sauter, N. K., Cohen, A. E., et al. (2015) · Nature · doi:10.1038/nature14975 · Cited by 348
Metadata
- Authors
- Schröder, G. F., Levitt, M., & Brunger, A. T.
- Venue
- Nature
- Published
- 2010-04
- Volume
- 464
- Issue
- 7292
- Pages
- 1218-1222
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2020405262
- Cited by
- 271
- References
- 2
- FWCI
- 22.55
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Super-resolution biomolecular crystallography with low-resolution data. https://app.cochat.ai/papers/10.1038/nature08892
- The paper
- Schröder, G. F., Levitt, M., & Brunger, A. T. (2010). Super-resolution biomolecular crystallography with low-resolution data. Nature. https://doi.org/10.1038/nature08892
Data sources
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.