The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
The trRosetta server for fast and accurate protein structure prediction
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
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
The I-TASSER Suite: protein structure and function prediction
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015) · Nature Methods · doi:10.1038/nmeth.3213 · Cited by 5,596
Self-citationAccurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Self-citationI-TASSER server for protein 3D structure prediction
Zhang, Y. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-40 · Cited by 5,124
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288
How significant is a protein structure similarity with TM-score = 0.5?
Xu, J., & Zhang, Y. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq066 · Cited by 1,142
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
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931
De novo protein design by deep network hallucination
Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D. (2020) · doi:10.1101/2020.07.22.211482 · Cited by 629
Self-citationSARS-CoV-2 Disrupts Splicing, Translation, and Protein Trafficking to Suppress Host Defenses
Banerjee, A. K., Blanco, M. R., Bruce, E. A., Honson, D. D., Chen, L. M., Chow, A., Bhat, P., Ollikainen, N., Quinodoz, S. A., Loney, C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.004 · Cited by 583
Protein structure determination using metagenome sequence data
Ovchinnikov, S., Park, H., Varghese, N., Huang, P.-S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H., Kyrpides, N. C., & Baker, D. (2017) · Science · doi:10.1126/science.aah4043 · Cited by 520
Self-citation‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures
Callaway, E. (2020) · Nature · doi:10.1038/d41586-020-03348-4 · Cited by 499 · cited 1×
“The work of AlphaFold2 is regarded as one of the milestones in protein structure prediction and is anticipated to ‘ change everything ’ 10 .”
BackgroundGreener, J. G., Kandathil, S. M., & Jones, D. T. (2019) · Nature Communications · doi:10.1038/s41467-019-11994-0 · Cited by 178
Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25407 · Cited by 177
Cited by
Single-sequence protein structure prediction using supervised transformer protein language models
Wang, W., Peng, Z., & Yang, J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00373-3 · Cited by 150
Self-citationFang, X., Wang, F., Liu, L., He, J., Lin, D., Xiang, Y., Zhu, K., Zhang, X., Wu, H., Li, H., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00721-6 · Cited by 132
De novo gene synthesis by an antiviral reverse transcriptase
Tang, S., Conte, V., Zhang, D. J., Žedaveinytė, R., Lampe, G. D., Wiegand, T., Tang, L. C., Wang, M., Walker, M. W. G., George, J. T., et al. (2024) · Science · doi:10.1126/science.adq0876 · Cited by 58
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
- Du, Z., Su, H., Wang, W., Ye, L., Wei, H., Peng, Z., Anishchenko, I., Baker, D., & Yang, J.
- Venue
- Nature Protocols
- Published
- 2021-11-10
- Volume
- 16
- Issue
- 12
- Pages
- 5634-5651
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W3212854871
- Cited by
- 715
- References
- 14
- FWCI
- 29.38
- Type
- journal article
- Language
- en
- Open access
- closed
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.
