The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
RosettaScripts: A Scripting Language Interface to the Rosetta Macromolecular Modeling Suite
Open accessAbstract
Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.
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
Leaver-Fay (2011) · doi:10.1016/b978-0-12-381270-4.00019-6 · Cited by 1,904
Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin
Fleishman, S. J., Whitehead, T. A., Ekiert, D. C., Dreyfus, C., Corn, J. E., Strauch, E.-M., Wilson, I. A., & Baker, D. (2011) · Science · doi:10.1126/science.1202617 · Cited by 585
Self-citation
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-citationRational HIV Immunogen Design to Target Specific Germline B Cell Receptors
Jardine, J., Julien, J.-P., Menis, S., Ota, T., Kalyuzhniy, O., McGuire, A., Sok, D., Huang, P.-S., MacPherson, S., Jones, M., et al. (2013) · Science · doi:10.1126/science.1234150 · Cited by 770
Self-citationAntidepressant drugs act by directly binding to TRKB neurotrophin receptors
Casarotto, P. C., Girych, M., Fred, S. M., Kovaleva, V., Moliner, R., Enkavi, G., Biojone, C., Cannarozzo, C., Sahu, M. P., Kaurinkoski, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.034 · Cited by 663
De novo design of potent and selective mimics of IL-2 and IL-15
Silva, D.-A., Yu, S., Ulge, U. Y., Spangler, J. B., Jude, K. M., Labão-Almeida, C., Ali, L. R., Quijano-Rubio, A., Ruterbusch, M., Leung, I., et al. (2019) · Nature · doi:10.1038/s41586-018-0830-7 · Cited by 531
Self-citationMassively parallel de novo protein design for targeted therapeutics
Chevalier, A., Silva, D.-A., Rocklin, G. J., Hicks, D. R., Vergara, R., Murapa, P., Bernard, S. M., Zhang, L., Lam, K.-H., Yao, G., et al. (2017) · Nature · doi:10.1038/nature23912 · Cited by 453
Self-citationSingle-sequence protein structure prediction using language models from deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Rochereau, C., Church, G. M., Sorger, P. K., & AlQuraishi, M. (2021) · doi:10.1101/2021.08.02.454840 · Cited by 455
“This refinement comprises hybrid optimization of side chains using five invocations of energy minimization in torsional space followed by a single step of quasi-Newton all-atom minimization in Cartesian space (using the FastRelax protocol of RosettaScripts 49 ).”
MethodSingle-sequence protein structure prediction using a language model and deep learning
Chowdhury, R., Bouatta, N., Biswas, S., Floristean, C., Kharkar, A., Roy, K., Rochereau, C., Ahdritz, G., Zhang, J., Church, G. M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01432-w · Cited by 455
Surrogate Wnt agonists that phenocopy canonical Wnt and β-catenin signalling
Janda, C. Y., Dang, L. T., You, C., Chang, J., de Lau, W., Zhong, Z. A., Yan, K. S., Marecic, O., Siepe, D., Li, X., et al. (2017) · Nature · doi:10.1038/nature22306 · Cited by 403
Self-citationHigh thermodynamic stability of parametrically designed helical bundles
Huang, P.-S., Oberdorfer, G., Xu, C., Pei, X. Y., Nannenga, B. L., Rogers, J. M., DiMaio, F., Gonen, T., Luisi, B., & Baker, D. (2014) · Science · doi:10.1126/science.1257481 · Cited by 277
Self-citationDe novo design of modular and tunable protein biosensors
Quijano-Rubio, A., Yeh, H.-W., Park, J., Lee, H., Langan, R. A., Boyken, S. E., Lajoie, M. J., Cao, L., Chow, C. M., Miranda, M. C., et al. (2021) · Nature · doi:10.1038/s41586-021-03258-z · Cited by 260
Self-citationCrystal Structures of Membrane Transporter MmpL3, an Anti-TB Drug Target
Zhang, B., Li, J., Yang, X., Wu, L., Zhang, J., Yang, Y., Zhao, Y., Zhang, L., Yang, X., Yang, X., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.003 · Cited by 263
RosettaAntibodyDesign (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
PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes
Zaidman, D., Prilusky, J., & London, N. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00589 · Cited by 224
Efficient flexible backbone protein-protein docking for challenging targets
Marze, N. A., Roy Burman, S. S., Sheffler, W., & Gray, J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty355 · Cited by 173
Principles for computational design of binding antibodies
Baran, D., Pszolla, M. G., Lapidoth, G. D., Norn, C., Dym, O., Unger, T., Albeck, S., Tyka, M. D., & Fleishman, S. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707171114 · Cited by 136
Self-citationProtein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Players, F., Baker, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017228118 · Cited by 144
Self-citationIg-VAE: Generative Modeling of Protein Structure by Direct 3D Coordinate Generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2020) · doi:10.1101/2020.08.07.242347 · Cited by 141
Procko, E., Berguig, G. Y., Shen, B. W., Song, Y., Frayo, S., Convertine, A. J., Margineantu, D., Booth, G., Correia, B. E., Cheng, Y., et al. (2014) · Cell · doi:10.1016/j.cell.2014.04.034 · Cited by 127
Self-citationIg-VAE: Generative modeling of protein structure by direct 3D coordinate generation
Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141
Lapidoth, G. D., Baran, D., Pszolla, G. M., Norn, C., Alon, A., Tyka, M. D., & Fleishman, S. J. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24779 · Cited by 107
Diffusing protein binders to intrinsically disordered proteins
Liu, C., Wu, K., Choi, H., Han, H. L., Zhang, X., Watson, J. L., Ahn, G., Zhang, J. Z., Shijo, S., Good, L. L., et al. (2025) · Nature · doi:10.1038/s41586-025-09248-9 · Cited by 79
Self-citationCombinatorial assembly and design of enzymes
Lipsh-Sokolik, R., Khersonsky, O., Schröder, S. P., de Boer, C., Hoch, S.-Y., Davies, G. J., Overkleeft, H. S., & Fleishman, S. J. (2023) · Science · doi:10.1126/science.ade9434 · Cited by 78
Self-citationDillon, M., Yin, Y., Zhou, J., McCarty, L., Ellerman, D., Slaga, D., Junttila, T. T., Han, G., Sandoval, W., Ovacik, M. A., et al. (2017) · mAbs · doi:10.1080/19420862.2016.1267089 · Cited by 75
“Two main computational design strategies were implemented together with flexible backbone modeling in Rosetta (see Supplementary Materials).(26) Design 1 (Fig.”
MethodSelf-citationDeep learning–guided design of dynamic proteins
Guo, A. B., Akpinaroglu, D., Stephens, C. A., Grabe, M., Smith, C. A., Kelly, M. J. S., & Kortemme, T. (2025) · Science · doi:10.1126/science.adr7094 · Cited by 67
Designing Peptides on a Quantum Computer
Mulligan, V. K., Melo, H., Merritt, H. I., Slocum, S., Weitzner, B. D., Watkins, A. M., Renfrew, P. D., Pelissier, C., Arora, P. S., & Bonneau, R. (2019) · doi:10.1101/752485 · Cited by 67
Spatiotemporal dynamics of GEF-H1 activation controlled by microtubule- and Src-mediated pathways
Azoitei, M. L., Noh, J., Marston, D. J., Roudot, P., Marshall, C. B., Daugird, T. A., Lisanza, S. L., Sandí, M.-J., Ikura, M., Sondek, J., et al. (2019) · Journal of Cell Biology · doi:10.1083/jcb.201812073 · Cited by 61
Structure of the DOCK2−ELMO1 complex provides insights into regulation of the auto-inhibited state
Chang, L., Yang, J., Jo, C. H., Boland, A., Zhang, Z., McLaughlin, S. H., Abu-Thuraia, A., Killoran, R. C., Smith, M. J., Côté, J.-F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17271-9 · Cited by 59
Wei, K. Y., Moschidi, D., Bick, M. J., Nerli, S., McShan, A. C., Carter, L. P., Huang, P.-S., Fletcher, D. A., Sgourakis, N. G., Boyken, S. E., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914808117 · Cited by 57
Self-citationDesign of amyloidogenic peptide traps
Sahtoe, D. D., Andrzejewska, E. A., Han, H. L., Rennella, E., Schneider, M. M., Meisl, G., Ahlrichs, M., Decarreau, J., Nguyen, H., Kang, A., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01578-5 · Cited by 48
Self-citationImproving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs
Bender, B. J., Marlow, B., & Meiler, J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007597 · Cited by 44
Self-citationShifting redox reaction equilibria on demand using an orthogonal redox cofactor
Aspacio, D., Zhang, Y., Cui, Y., Luu, E., King, E., Black, W. B., Perea, S., Zhu, Q., Wu, Y., Luo, R., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01702-5 · Cited by 43
Liu, X., Taylor, R. D., Griffin, L., Coker, S.-F., Adams, R., Ceska, T., Shi, J., Lawson, A. D. G., & Baker, T. (2017) · Scientific Reports · doi:10.1038/srep41306 · Cited by 39
Interpreting the molecular mechanisms of disease variants in human transmembrane proteins
Tiemann, J. K. S., Zschach, H., Lindorff-Larsen, K., & Stein, A. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2022.12.031 · Cited by 35
Modifying immune responses to adeno-associated virus vectors by capsid engineering
Bentler, M., Hardet, R., Ertelt, M., Rudolf, D., Kaniowska, D., Schneider, A., Vondran, F. W. R., Schoeder, C. T., Delphin, M., Lucifora, J., et al. (2023) · Molecular Therapy - Methods & Clinical Development · doi:10.1016/j.omtm.2023.08.015 · Cited by 34
Antibody affinity maturation by computational design
Kuroda, D., & Tsumoto, K. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8648-4_2 · Cited by 26
Disease-associated patterns of acetylation stabilize tau fibril formation
Li, L., Nguyen, B. A., Mullapudi, V., Li, Y., Saelices, L., & Joachimiak, L. A. (2023) · Structure · doi:10.1016/j.str.2023.05.020 · Cited by 25
Assessing and enhancing foldability in designed proteins
Listov, D., Lipsh‐Sokolik, R., Rosset, S., Yang, C., Correia, B. E., & Fleishman, S. J. (2022) · Protein Science · doi:10.1002/pro.4400 · Cited by 24
Self-citationMulti-modal cryo-EM reveals trimers of protein A10 to form the palisade layer in poxvirus cores
Datler, J., Hansen, J. M., Thader, A., Schlögl, A., Bauer, L. W., Hodirnau, V.-V., & Schur, F. K. M. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01201-6 · Cited by 23
Bond-centric modular design of protein assemblies
Wang, S., Favor, A., Kibler, R. D., Lubner, J. M., Borst, A. J., Coudray, N., Redler, R. L., Chiang, H. T., Sheffler, W., Hsia, Y., et al. (2025) · Nature Materials · doi:10.1038/s41563-025-02297-5 · Cited by 22
Self-citationA general computational design strategy for stabilizing viral class I fusion proteins
Gonzalez, K. J., Huang, J., Criado, M. F., Banerjee, A., Tompkins, S. M., Mousa, J. J., & Strauch, E.-M. (2024) · Nature Communications · doi:10.1038/s41467-024-45480-z · Cited by 20
Self-citationJatzlau, J., Burdzinski, W., Trumpp, M., Obendorf, L., Roßmann, K., Ravn, K., Hyvönen, M., Bottanelli, F., Broichhagen, J., & Knaus, P. (2023) · Communications Biology · doi:10.1038/s42003-022-04388-4 · Cited by 20
Flexible backbone assembly and refinement of symmetrical homomeric complexes
Roy Burman, S. S., Yovanno, R. A., & Gray, J. J. (2019) · Structure · doi:10.1016/j.str.2019.03.014 · Cited by 18
Ertelt, M., Moretti, R., Meiler, J., & Schoeder, C. T. (2025) · Science Advances · doi:10.1126/sciadv.adr7338 · Cited by 15
Self-citationCummins, M. C., Jacobs, T. M., Teets, F. D., DiMaio, F., Tripathy, A., & Kuhlman, B. (2022) · Protein Science · doi:10.1002/pro.4368 · Cited by 15
Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.018 · Cited by 14
Modeling membrane geometries implicitly in Rosetta
Woods, H., Leman, J. K., & Meiler, J. (2024) · Protein Science · doi:10.1002/pro.4908 · Cited by 9
Self-citationFerraz, M. V. F., Neto, J. C. S., Lins, R. D., & Teixeira, E. S. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05644e · Cited by 9
Deep learning guided design of dynamic proteins
Guo, A. B., Akpinaroglu, D., Kelly, M. J. S., & Kortemme, T. (2024) · doi:10.1101/2024.07.17.603962 · Cited by 9
Sampling of Structure and Sequence Space of Small Protein Folds
Linsky, T., Noble, K., Tobin, A., Crow, R., Carter, L., Urbauer, J., Baker, D., & Strauch, E. (2021) · doi:10.1101/2021.03.10.434454
Self-citationMapping targetable sites on the human surfaceome for the design of novel binders
Balbi, P. E. M., Sadek, A., Marchand, A., Yu, T.-Y., Damjanovic, J., Georgeon, S., Schmidt, J., Fulle, S., Yang, C., Khakzad, H., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2506269123 · Cited by 6
Advancing membrane-associated protein docking with improved sampling and scoring in Rosetta
Samnata, R., Harmalkar, A., Prathima, P., & Gray, J. J. (2024) · doi:10.1101/2024.07.09.602802 · Cited by 5
Evolutionary Engineering a Larger Porin Using a Loop-to-Hairpin Mechanism
Dhar, R., Bowman, A. M., Hatungimana, B., & SG Slusky, J. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168292 · Cited by 3
Combinatorial assembly and design of enzymes
Lipsh-Sokolik, R., Khersonsky, O., Schröder, S. P., de Boer, C., Hoch, S.-Y., Davies, G. J., Overkleeft, H. S., & Fleishman, S. J. (2022) · doi:10.1101/2022.09.17.508230 · Cited by 2
Self-citation
Metadata
- Authors
- Fleishman, S. J., Leaver-Fay, A., Corn, J. E., Strauch, E.-M., Khare, S. D., Koga, N., Ashworth, J., Murphy, P., Richter, F., Lemmon, G., Meiler, J., & Baker, D.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-06-24
- Volume
- 6
- Issue
- 6
- Pages
- e20161
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2084493621
- Cited by
- 657
- References
- 2
- FWCI
- 6.47
- 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
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.