The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Modeling and docking of antibody structures with Rosetta
Open accessAbstract
We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL–VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers.
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
UCSF Chimera—A visualization system for exploratory research and analysis
Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20084 · Cited by 45,828
The PyMOL Molecular Graphics System
DeLano, W. L. (2002) · Medical Entomology and Zoology · Cited by 18,147
ZDOCK: An initial‐stage protein‐docking algorithm
Chen, R., Li, L., & Weng, Z. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10389 · Cited by 1,393
Gray, J. J., Moughon, S., Wang, C., Schueler-Furman, O., Kuhlman, B., Rohl, C. A., & Baker, D. (2003) · Journal of Molecular Biology · doi:10.1016/s0022-2836(03)00670-3 · Cited by 1,127
Self-citationPIPER: An FFT‐based protein docking program with pairwise potentials
Kozakov, D., Brenke, R., Comeau, S. R., & Vajda, S. (2006) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21117 · Cited by 865
Toward High-Resolution de Novo Structure Prediction for Small Proteins
Bradley, P., Misura, K. M. S., & Baker, D. (2005) · Science · doi:10.1126/science.1113801 · Cited by 864
Lefranc, M.-P., Pommié, C., Ruiz, M., Giudicelli, V., Foulquier, E., Truong, L., Thouvenin-Contet, V., & Lefranc, G. (2003) · Developmental & Comparative Immunology · doi:10.1016/s0145-305x(02)00039-3 · Cited by 818
Lefranc, M.-P., Pommié, C., Kaas, Q., Duprat, E., Bosc, N., Guiraudou, D., Jean, C., Ruiz, M., Da Piédade, I., Rouard, M., et al. (2005) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2004.07.003 · Cited by 807
Standard conformations for the canonical structures of immunoglobulins 1 1Edited by I. A. Wilson
Al-Lazikani, B., Lesk, A. M., & Chothia, C. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.1354 · Cited by 706
The promise and challenge of high-throughput sequencing of the antibody repertoire
Georgiou, G., Ippolito, G. C., Beausang, J., Busse, C. E., Wardemann, H., & Quake, S. R. (2014) · Nature Biotechnology · doi:10.1038/nbt.2782 · Cited by 652 · cited 2×
“Next-generation sequencing techniques have enabled rapid determination of large numbers of antibody sequences 1 .”
Background
Cited by
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888
Self-citationRosettaAntibodyDesign (RAbD): a general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., & Dunbrack, R. L. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006112 · Cited by 227
Self-citationComputational approaches to therapeutic antibody design: established methods and emerging trends
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbz095 · Cited by 219
“Examples of such methods are for example Swarmdock [136], HADDOCK [132] and SnugDock [9].”
BackgroundMethodInfluentialAntibody structure prediction using interpretable deep learning
Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022) · Patterns · doi:10.1016/j.patter.2021.100406 · Cited by 216
Self-citationRationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta
Bai, N., Miller, S. A., Andrianov, G. V., Yates, M., Kirubakaran, P., & Karanicolas, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01451 · Cited by 157
Metadata
- Authors
- Weitzner, B. D., Jeliazkov, J. R., Lyskov, S., Marze, N., Kuroda, D., Frick, R., Adolf-Bryfogle, J., Biswas, N., Dunbrack, R. L., & Gray, J. J.
- Venue
- Nature Protocols
- Published
- 2017-01-26
- Volume
- 12
- Issue
- 2
- Pages
- 401-416
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W2580666741
- Cited by
- 264
- References
- 58
- FWCI
- 10.00
- 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: Modeling and docking of antibody structures with Rosetta. https://app.cochat.ai/papers/10.1038/nprot.2016.180
- The paper
- Weitzner, B. D., Jeliazkov, J. R., Lyskov, S., Marze, N., Kuroda, D., Frick, R., Adolf-Bryfogle, J., Biswas, N., Dunbrack, R. L., & Gray, J. J. (2017). Modeling and docking of antibody structures with Rosetta. Nature Protocols. https://doi.org/10.1038/nprot.2016.180
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.
