The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Serverification of Molecular Modeling Applications: The Rosetta Online Server That Includes Everyone (ROSIE)
Open accessAbstract
The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.
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Cites
Protein backbone angle restraints from searching a database for chemical shift and sequence homology
Cornilescu, G., Delaglio, F., & Bax, A. (1999) · Journal of Biomolecular NMR · doi:10.1023/a:1008392405740 · Cited by 2,957
Leaver-Fay (2011) · doi:10.1016/b978-0-12-381270-4.00019-6 · Cited by 1,904
A Specificity Map for the PDZ Domain Family
Tonikian, R., Zhang, Y., Sazinsky, S. L., Currell, B., Yeh, J.-H., Reva, B., Held, H. A., Appleton, B. A., Evangelista, M., Wu, Y., et al. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060239 · Cited by 476
RosettaAntibody: antibody variable region homology modeling server
Sircar, A., Kim, E. T., & Gray, J. J. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp387 · Cited by 161
Self-citationAlmagro, J. C., Beavers, M. P., Hernandez‐Guzman, F., Maier, J., Shaulsky, J., Butenhof, K., Labute, P., Thorsteinson, N., Kelly, K., Teplyakov, A., et al. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.23130 · Cited by 131
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Self-citationAI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319
Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review
(2023) · doi:10.1109/access.2023.3285596 · Cited by 205
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
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Savinov, A., Swanson, S., Keating, A. E., & Li, G.-W. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322412122 · Cited by 18
Turzo, S. M. B. A., Seffernick, J. T., Lyskov, S., & Lindert, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad308 · Cited by 13
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Madabushi, S., Chow, K. M., Song, E. S., Goswami, A., Hersh, L. B., & Rodgers, D. W. (2023) · PLOS ONE · doi:10.1371/journal.pone.0287086 · Cited by 9
Hayashi, R., Kamata, K., Gerdol, M., Fujii, Y., Hayashi, T., Onoda, Y., Kobayashi, N., Furushima, S., Ishiwata, R., Ohkawa, M., et al. (2024) · Marine Drugs · doi:10.3390/md22090400 · Cited by 7
Demeu, L. M. K., Soares, R. J., Miranda, J. S., Pacheco-Lugo, L. A., Oliveira, K. G., Cortez Plaza, C. A., Billiald, P., Ferreira de Moura, J., Yoshida, N., Alvarenga, L. M., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0223773 · Cited by 7
Molecular Display of the Animal Meta-Venome for Discovery of Novel Therapeutic Peptides
Hsiao, M.-H., Miao, Y., Liu, Z., Schütze, K., Limjunyawong, N., Chien, D. C.-C., Monteiro, W. D., Chu, L.-S., Morgenlander, W., Jayaraman, S., et al. (2025) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100901 · Cited by 5
Self-citationTjo, H., Jiang, V., Calvo, A., Joseph, J. A., & Conway, J. M. (2026) · Applied and Environmental Microbiology · doi:10.1128/aem.01284-25 · Cited by 5
Jaiswal, A., Roy, R., Tamrakar, A., Singh, A. K., Kar, P., & Kodgire, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-46448-7 · Cited by 3
Yang, Y., Qiao, L., Jiang, Y., Wang, Z., Zhang, D., Buratto, D., Huang, L., & Zhou, R. (2026) · eLife · doi:10.7554/elife.107785 · Cited by 2
Yang, Y., Qiao, L., Jiang, Y., Wang, Z., Zhang, D., Buratto, D., Huang, L., & Zhou, R. (2026) · eLife · doi:10.7554/elife.107785.4 · Cited by 1
Metadata
- Authors
- Lyskov, S., Chou, F.-C., Conchúir, S. Ó., Der, B. S., Drew, K., Kuroda, D., Xu, J., Weitzner, B. D., Renfrew, P. D., Sripakdeevong, P., Borgo, B., Havranek, J. J., Kuhlman, B., Kortemme, T., Bonneau, R., Gray, J. J., & Das, R.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-05-22
- Volume
- 8
- Issue
- 5
- Pages
- e63906
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2158412800
- Cited by
- 431
- References
- 5
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- gold
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