The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf
Open accessAbstract
The ConSurf web‐sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per‐site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand‐binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user‐friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model‐based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re‐implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin‐1, the main protein of membrane invaginations called caveolae.
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Cites
MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
Katoh, K., & Standley, D. M. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst010 · Cited by 46,209
UniProt: the Universal Protein Knowledgebase in 2023
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1052 · Cited by 7,210
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 1×
“For queries from the AlphaFold database (Tunyasuvunakool et al. 2021) the scholar can provide the UniProt ID of the query in the box below “ Is there a known structure ” (Figure 1a).”
Method‘The entire protein universe’: AI predicts shape of nearly every known protein
Callaway, E. (2022) · Nature · doi:10.1038/d41586-022-02083-2 · Cited by 113 · cited 1×
“In this respect, it is noteworthy that AlphaFold structures are now available for most UniProt proteins (Callaway 2022).”
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Metadata
- Authors
- Yariv, B., Yariv, E., Kessel, A., Masrati, G., Chorin, A. B., Martz, E., Mayrose, I., Pupko, T., & Ben‐Tal, N.
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2023-02-21
- Volume
- 32
- Issue
- 3
- Pages
- e4582-e4582
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- OpenAlex
- W4318614730
- Cited by
- 428
- References
- 6
- FWCI
- 31.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Yariv, B., Yariv, E., Kessel, A., Masrati, G., Chorin, A. B., Martz, E., Mayrose, I., Pupko, T., & Ben‐Tal, N. (2023). Using evolutionary data to make sense of macromolecules with a “face‐lifted” ConSurf. Protein Science. https://doi.org/10.1002/pro.4582
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