The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Raptorx: Exploiting structure information for protein alignment by statistical inference
Open accessAbstract
This work presents RaptorX, a statistical method for template‐based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single‐template threading, alignment quality prediction, and multiple‐template threading. This work summarizes the methods used by RaptorX and presents its CASP9 result analysis, aiming to identify major bottlenecks with RaptorX and template‐based modeling and hopefully directions for further study. Our results show that template structural information helps a lot with both single‐template and multiple‐template protein threading especially when closely‐related templates are unavailable, and there is still large room for improvement in both alignment and template selection. The RaptorX web server is available at http://raptorx.uchicago.edu. Proteins 2011; © 2011 Wiley‐Liss, Inc.
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Cited by
Haas, J., Barbato, A., Behringer, D., Studer, G., Roth, S., Bertoni, M., Mostaguir, K., Gumienny, R., & Schwede, T. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25431 · Cited by 168
“The top 3 servers participating both in CAMEO and CASP12 ranked by average lDDT are in identical order Robetta (Baker)[36] in the lead closely followed by RaptorX (Xu)[37–39] and IntFOLD4-TS (McGuffin)[40].”
MethodFang, 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
Biophysical prediction of protein–peptide interactions and signaling networks using machine learning
Cunningham, J. M., Rogers, J. R., Koytiger, G., Sorger, P. K., & AlQuraishi, M. (2020) · Nature Methods · doi:10.1038/s41592-019-0687-1 · Cited by 108
Deep embedding and alignment of protein sequences
Llinares-López, F., Berthet, Q., Blondel, M., Teboul, O., & Vert, J.-P. (2023) · Nature Methods · doi:10.1038/s41592-022-01700-2 · Cited by 58
Deep learning methods for protein function prediction
Boadu, F., Lee, A., & Cheng, J. (2025) · PROTEOMICS · doi:10.1002/pmic.202300471 · Cited by 53
Metadata
- Authors
- Peng, J., & Xu, J.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 2011-01
- Volume
- 79
- Issue
- S10
- Pages
- 161-171
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2152811165
- Cited by
- 361
- References
- 40
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- green
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