The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Namdinator – automatic molecular dynamics flexible fitting of structural models into cryo-EM and crystallography experimental maps
Open accessPublished version of a preprint
Abstract
Model building into experimental maps is a key element of structural biology, but can be both time consuming and error prone for low-resolution maps. Here we present Namdinator , an easy-to-use tool that enables the user to run a molecular dynamics flexible fitting simulation followed by real-space refinement in an automated manner through a pipeline system. Namdinator will modify an atomic model to fit within cryo-EM or crystallography density maps, and can be used advantageously for both the initial fitting of models, and for a geometrical optimization step to correct outliers, clashes and other model problems. We have benchmarked Namdinator against 39 deposited cryo-EM models and maps, and observe model improvements in 34 of these cases (87%). Clashes between atoms were reduced, and the model-to-map fit and overall model geometry were improved, in several cases substantially. We show that Namdinator is able to model large-scale conformational changes compared to the starting model. Namdinator is a fast and easy tool for structural model builders at all skill levels. Namdinator is available as a web service (https://namdinator.au.dk), or it can be run locally as a command-line tool.
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
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
PHENIX : a comprehensive Python-based system for macromolecular structure solution
Adams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L.-W., Kapral, G. J., Grosse-Kunstleve, R. W., et al. (2010) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444909052925 · Cited by 23,716
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532
Kühlbrandt, W. (2014) · Science · doi:10.1126/science.1251652 · Cited by 1,325
Research works printed but not linked: 20
- Afonine, 2013, Comput. Crystallogr. Newsl., 4, 43
- Afonine, 2018, Acta Cryst. D, 74, 814, 10.1107/S2059798318009324 · DOI · Paper page
- Blees, 2017, Nature, 551, 525, 10.1038/nature24627 · DOI · Paper page
- Chacón, 2002, J. Mol. Biol., 317, 375, 10.1006/jmbi.2002.5438 · DOI · Paper page
- Farnung, 2017, Nature, 550, 539, 10.1038/nature24046 · DOI · Paper page
- Frank, J. (2006). Three-Dimensional Electron Microscopy of Macromolecular Assemblies: Visualization of Biological Molecules in Their Native State. Oxford University Press.
- Huang, 2013, J. Comput. Chem., 34, 2135, 10.1002/jcc.23354 · DOI · Paper page
- Lou, 2006, J. Phys. Chem. B, 110, 12796, 10.1021/jp061976m · DOI
- Matthies, 2016, Cell, 164, 747, 10.1016/j.cell.2015.12.055 · DOI
- Müller, 1996, Structure, 4, 147, 10.1016/S0969-2126(96)00018-4 · DOI · Paper page
- Müller, 1992, J. Mol. Biol., 224, 159, 10.1016/0022-2836(92)90582-5 · DOI · Paper page
- Pettersen, 2004, J. Comput. Chem., 25, 1605, 10.1002/jcc.20084 · DOI · Paper page
- Rose, A. S., Bradley, A. R., Valasatava, Y., Duarte, J. M., Prlić, A. & Rose, P. W. (2016). Proceedings, 21st International Conference on Web3D Technology, pp. 185-186. New York: ACM.
- Stone, 2014, Faraday Discuss., 169, 265, 10.1039/C4FD00005F · DOI
- Subramaniam, 2019, IUCrJ, 6, 1, 10.1107/S2052252519000277 · DOI
- Tanner, 2011, J. Chem. Theory Comput., 7, 3635, 10.1021/ct200563j · DOI
- Trabuco, 2008, Structure, 16, 673, 10.1016/j.str.2008.03.005 · DOI · Paper page
- Trabuco, 2009, Methods, 49, 174, 10.1016/j.ymeth.2009.04.005 · DOI · Paper page
- Vashisth, 2012, Structure, 20, 1453, 10.1016/j.str.2012.08.007 · DOI
- Wlodawer, 2017, Structure, 25, 1589, 10.1016/j.str.2017.07.012 · DOI
Cited by
Wrobel, A. G., Benton, D. J., Xu, P., Roustan, C., Martin, S. R., Rosenthal, P. B., Skehel, J. J., & Gamblin, S. J. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-0468-7 · Cited by 544
SARM1 is a metabolic sensor activated by an increased NMN/NAD+ ratio to trigger axon degeneration
Figley, M. D., Gu, W., Nanson, J. D., Shi, Y., Sasaki, Y., Cunnea, K., Malde, A. K., Jia, X., Luo, Z., Saikot, F. K., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.02.009 · Cited by 324
A thermostable, closed SARS-CoV-2 spike protein trimer
Xiong, X., Qu, K., Ciazynska, K. A., Hosmillo, M., Carter, A. P., Ebrahimi, S., Ke, Z., Scheres, S. H. W., Bergamaschi, L., Grice, G. L., et al. (2020) · Nature Structural & Molecular Biology · doi:10.1038/s41594-020-0478-5 · Cited by 313
Visualizing translation dynamics at atomic detail inside a bacterial cell
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
Cryo-EM structure of the human cardiac myosin filament
Dutta, D., Nguyen, V., Campbell, K. S., Padrón, R., & Craig, R. (2023) · Nature · doi:10.1038/s41586-023-06691-4 · Cited by 164
Metadata
- Authors
- Kidmose, R. T., Juhl, J., Nissen, P., Boesen, T., Karlsen, J. L., & Pedersen, B. P.
- Venue
- IUCrJ
- Published
- 2019-06-27
- Volume
- 6
- Issue
- 4
- Pages
- 526-531
- ISSN
- 2052-2525 (ISSN-L)
- OpenAlex
- W2953798760
- Cited by
- 385
- References
- 5
- FWCI
- 16.65
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0/legalcode (vor)
- creativecommons.org/licenses/by/4.0/legalcode (tdm)
- Funders
- H2020 European Research Council (637372)
- Natur og Univers, Det Frie Forskningsråd (DFF-4002-00052)
- Aarhus Institute of Advanced Studies, Aarhus Universitet
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
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.
