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The Phyre2 web portal for protein modeling, prediction and analysis
Open accessAbstract
Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
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“With the exception of the Unknown class, high confidence structural templates were identified for the vast majority of proteins for at least part of the sequence (Supplementary Fig. 2 and Supplementary Data File 3).”
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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
A method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194
The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling
Arnold, K., Bordoli, L., Kopp, J., & Schwede, T. F. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti770 · Cited by 7,031
I-TASSER: a unified platform for automated protein structure and function prediction
Roy, A., Küçükural, A., & Zhang, Y. (2010) · Nature Protocols · doi:10.1038/nprot.2010.5 · Cited by 6,212
Jones, D. T. (1999) · Journal of Molecular Biology · doi:10.1006/jmbi.1999.3091 · Cited by 5,522
Protein structure prediction on the Web: a case study using the Phyre server
Kelley, L. A., & Sternberg, M. J. E. (2009) · Nature Protocols · doi:10.1038/nprot.2009.2 · Cited by 4,154
Self-citationMolProbity: all-atom contacts and structure validation for proteins and nucleic acids
Davis, I., Leaver‐Fay, A., Chen, V. B., Block, J. N., Kapral, G. J., Wang, X., Murray, L. W., Arendall, W. B., Snoeyink, J. S., Richardson, J. S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm216 · Cited by 3,918
Protein homology detection by HMM–HMM comparison
Söding, J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti125 · Cited by 2,459
SIFT web server: predicting effects of amino acid substitutions on proteins
Sim, N.-L., Kumar, P. N., Hu, J., Henikoff, S., Schneider, G., & Ng, P. C. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks539 · Cited by 2,419
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288
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Zhang, R. Q., Liu, Y., Zhang, Y., Feng, D., Hou, S., Guo, W., Niu, K., Jiang, Y., Han, L., Sindhu, L., et al. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-09928-3 · Cited by 58
Banerjee, A., Santra, D., & Maiti, S. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02435-4 · Cited by 58
Overcoming Fungal Echinocandin Resistance through Inhibition of the Non-essential Stress Kinase Yck2
Caplan, T., Lorente-Macías, Á., Stogios, P. J., Evdokimova, E., Hyde, S., Wellington, M. A., Liston, S., Iyer, K. R., Puumala, E., Shekhar-Guturja, T., et al. (2020) · Cell Chemical Biology · doi:10.1016/j.chembiol.2019.12.008 · Cited by 57
Uppuluri, P., Lin, L., Alqarihi, A., Luo, G., Youssef, E. G., Alkhazraji, S., Yount, N. Y., Ibrahim, B. A., Bolaris, M. A., Edwards, J. E., et al. (2018) · PLOS Pathogens · doi:10.1371/journal.ppat.1007056 · Cited by 57
Unicellular origin of the animal microRNA machinery
Bråte, J., Neumann, R. S., Fromm, B., Haraldsen, A. A. B., Tarver, J. E., Suga, H., Donoghue, P. C. J., Peterson, K. J., Ruiz-Trillo, I., Grini, P. E., et al. (2018) · Current Biology · doi:10.1016/j.cub.2018.08.018 · Cited by 53
Barbarin-Bocahu, I., & Graille, M. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798322002157 · Cited by 52
Molina-Obando, S., Vargas-Fique, J. F., Henning, M., Gür, B., Schladt, T. M., Akhtar, J., Berger, T. K., & Silies, M. (2019) · eLife · doi:10.7554/elife.49373 · Cited by 49
Cheng, W., Doering, J. A., LaLone, C., & Ng, C. (2021) · Toxicological Sciences · doi:10.1093/toxsci/kfab004 · Cited by 49
Computational models in the service of X‐ray and cryo‐ electron microscopy structure determination
Kryshtafovych, A., Moult, J., Albrecht, R., Chang, G. A., Chao, K., Fraser, A., Greenfield, J., Hartmann, M. D., Herzberg, O., Josts, I., et al. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26223 · Cited by 45
Ramos-Benítez, M. J., Ruiz-Jiménez, C., Aguayo, V., & Espino, A. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-05735-w · Cited by 41
Barski, M. S., Minnell, J. J., & Maertens, G. N. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01877 · Cited by 37
Bacterial secretion chaperones: the mycobacterial type VII case
Phan, T. H., & Houben, E. N. G. (2018) · FEMS Microbiology Letters · doi:10.1093/femsle/fny197 · Cited by 37
Touchette, M. H., Van Vlack, E. R., Bai, L., Kim, J., Cognetta, A. B., Previti, M. L., Backus, K. M., Martin, D. W., Cravatt, B. F., & Seeliger, J. C. (2017) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.6b00179 · Cited by 34
Structural analysis of haemoglobin binding by HpuA from the Neisseriaceae family
Wong, C. T., Xu, Y., Gupta, A., Garnett, J. A., Matthews, S. J., & Hare, S. A. (2015) · Nature Communications · doi:10.1038/ncomms10172 · Cited by 30
Wachnowsky, C., Wesley, N. A., Fidai, I., & Cowan, J. A. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.01.021 · Cited by 25
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Pfeiffenberger, E., & Bates, P. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0202652 · Cited by 21
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Tokmina-Lukaszewska, M., Patterson, A., Berry, L., Scott, L., Balasubramanian, N., & Bothner, B. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.01397 · Cited by 15
Cytosine base editing in cyanobacteria by repressing archaic Type IV uracil‐DNA glycosylase
Lee, M., Heo, Y. B., & Woo, H. M. (2023) · The Plant Journal · doi:10.1111/tpj.16074 · Cited by 15
Gut microbiota drives structural variation of exogenous probiotics to enhance colonization
Jiang, S., Huang, S., Zhang, Z., Ma, W., Han, Z., Song, Y., Huo, D., Cui, W., & Zhang, J. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2503371 · Cited by 15
“77 The experimental design diagram was created using BioRender (https://www.biorender.com/).”
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Shapson-Coe, A., Valeiras, B., Wall, C., & Rada, C. (2019) · PLOS ONE · doi:10.1371/journal.pone.0213553 · Cited by 13
Beutler, M., Harnischfeger, J., Weber, M. H. W., Hahnel, S. R., Quack, T., Blohm, A., Ueberall, M. E., Timm, T., Lochnit, G., Rennar, G. A., et al. (2023) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2023.115179 · Cited by 10
Structural Insights into the Dimeric Form of Bacillus subtilis RNase Y Using NMR and AlphaFold
Morellet, N., Hardouin, P., Assrir, N., van Heijenoort, C., & Golinelli-Pimpaneau, B. (2022) · Biomolecules · doi:10.3390/biom12121798 · Cited by 7
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Metadata
- Authors
- Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. E.
- Affiliations
- Imperial College London (GB)
- University College London (GB)
- University of Kent (GB)
- Corresponding author
- Kelley, Lawrence A
- Venue
- Nature Protocols
- Published
- 2015-05-07
- Volume
- 10
- Issue
- 6
- Pages
- 845-858
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- PMC
- PMC5298202
- OpenAlex
- W1803102843
- Cited by
- 9,652
- References
- 36
- Citations by year
- 2026: 250 · 2025: 512 · 2024: 778 · 2023: 902 · 2022: 1173 · 2021: 1401 · 2020: 1278 · 2019: 1062 · 2018: 905 · 2017: 734 · 2016: 529 · 2015: 116
- FWCI
- 331.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (publishedVersion)
- PubMed Central (acceptedVersion)
- Kent Academic Repository (University of Kent) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Funders
- Medical Research Council (G1000390-1/1)
- Biotechnology and Biological Sciences Research Council (BB/F020481/1, BB/J019240/1, BB/M011526/1, BB/G003912/1, BB/G022569/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- Phyre2, protein structure prediction, remote homology detection, protein modeling, ligand binding site prediction, amino acid variants, non-synonymous SNPs, protein function prediction, protein domains, web server, model quality assessment
- MeSH terms
- Models, Molecular, Protein Conformation, Software, Computational Biology, Internet
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