The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The PSIPRED Protein Analysis Workbench: 20 years on
Open accessAbstract
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 years. The main focus of our recent website upgrade work has been the acceleration of analyses in the face of increasing protein sequence database size. We additionally discuss any new software, the new hardware infrastructure, our webservices and web site. Lastly we survey updates to some of the key predictive algorithms available through our website.
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
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The Phyre2 web portal for protein modeling, prediction and analysis
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. E. (2015) · Nature Protocols · doi:10.1038/nprot.2015.053 · Cited by 9,652
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Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288
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Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
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FFPred 3: feature-based function prediction for all Gene Ontology domains
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A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127
Self-citationProtGPT2 is a deep unsupervised language model for protein design
Ferruz, N., Schmidt, S., & Höcker, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32007-7 · Cited by 795
Design of protein-binding proteins from the target structure alone
Cao, L., Coventry, B., Goreshnik, I., Huang, B., Sheffler, W., Park, J. S., Jude, K. M., Marković, I., Kadam, R. U., Verschueren, K. H. G., et al. (2022) · Nature · doi:10.1038/s41586-022-04654-9 · Cited by 577
Protein sequence design with a learned potential
Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171
Metadata
- Authors
- Buchan, D. W. A., & Jones, D. T.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-04-26
- Volume
- 47
- Issue
- W1
- Pages
- W402-W407
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2941112903
- Cited by
- 1,351
- References
- 7
- FWCI
- 52.71
- Type
- journal article
- Language
- en
- Open access
- gold
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