The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy
Open accessAbstract
Protein-protein and protein-DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein-protein and protein-DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10-20 min for a docking run. Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein-protein and protein-DNA benchmarks and performed better than template-based modeling on the three protein-RNA benchmarks when the top 10 predictions were considered. The performance of HDOCK became better when more predictions were considered. Combining the results of HDOCK and template-based modeling by ranking first of the template-based model further improved the predictive power of the server. The HDOCK web server is available at http://hdock.phys.hust.edu.cn/.
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
Improved tools for biological sequence comparison.
Pearson, W. R., & Lipman, D. J. (1988) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.85.8.2444 · Cited by 11,227
Comparative Protein Structure Modeling of Genes and Genomes
Martí-Renom, M. A., Stuart, A. C., Fiser, A., Sánchez, R., Melo, F., & Šali, A. (2000) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.29.1.291 · Cited by 3,169
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
CAPRI: A Critical Assessment of PRedicted Interactions
Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J. E., Vajda, S., Vakser, I., & Wodak, S. J. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10381 · Cited by 703
Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition
Lensink, M. F., Velankar, S., & Wodak, S. J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25215 · Cited by 222
Cited by
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA
Baek, M., McHugh, R., Anishchenko, I., Jiang, H., Baker, D., & DiMaio, F. (2024) · Nature Methods · doi:10.1038/s41592-023-02086-5 · Cited by 372
Chow, H.-M., Shi, M., Cheng, A., Gao, Y., Chen, G., Song, X., So, R. W. L., Zhang, J., & Herrup, K. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0505-1 · Cited by 171
Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., & Ma, J. (2022) · doi:10.1101/2022.07.10.499510 · Cited by 151
AlphaFold2-aware protein–DNA binding site prediction using graph transformer
Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab564 · Cited by 137
Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling
Drake, Z. C., Seffernick, J. T., & Lindert, S. (2022) · Nature Communications · doi:10.1038/s41467-022-35593-8 · Cited by 85
Metadata
- Authors
- Yan, Y., Zhang, D., Zhou, P., Li, B., & Huang, S.-Y.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-17
- Volume
- 45
- Issue
- W1
- Pages
- W365-W373
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2615136515
- Cited by
- 1,458
- References
- 5
- FWCI
- 15.43
- Type
- journal article
- Language
- en
- Open access
- gold
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: HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. https://app.cochat.ai/papers/10.1093/nar/gkx407
- The paper
- Yan, Y., Zhang, D., Zhou, P., Li, B., & Huang, S.-Y. (2017). HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic Acids Research. https://doi.org/10.1093/nar/gkx407
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.