The Phyre2 web portal for protein modelling, prediction and analysis
By Lawrence A. Kelley, Stefans Mezulis, C. Yates, Mark N. Wass, Michael J.E. Sternberg. 2015
9,084 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
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In Genetics
- Yang Zhang
- Robin Pearce
- Jinbo Xu
- Sheng Wang
- Chris Tyler-Smith
- Karthik Anantharaman
- Michael J.E. Sternberg
- Torsten Schwede
In Molecular Biology
- Yang Zhang
- Jinbo Xu
- Sheng Wang
- Robin Pearce
- Torsten Schwede
- Chengxin Zhang
- Björn Wallner
- Arne Elofsson
Citations in the corpus
- Folding membrane proteins by deep transfer learning (2017, methodology): “However, the CAMEO participants include some well-known servers such as Robetta(Kim et al., 2004) and its test version Server19(Ovchinnikov et al., 2017), Phyre(Kelley et al., 2015), RaptorX(Källberg et al., 2012), Swiss-Model(Biasini et al., 2014) and HHpred(Remmert et al., 2012).”
- The Hcp proteins fused with diverse extended-toxin domains represent a novel pattern of antibacterial effectors in type VI secretion systems (2017, methodology): “The functional prediction was performed using HHpred52 and Phyre2.”
- Cryo-EM structure of the SARS-CoV-2 3a ion channel in lipid nanodiscs (2020, background): “72 e- Å2 in the first set (1-2007) and second set (2008-6309) of movies respectively.”
- Systematic discovery of anti-phage defense systems in the microbial pan-genome (2018, methodology): “The domains within the gene members of each system were analyzed bioinformatically using the tools HHpred (52, 53), Phyre2 (41), PSI-BLAST (54) and NCBI’s Conserved Domain Database (CDD) (55).”
- COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information (2017, methodology): “In the absence of an experimentally solved structure, the user can use models generated by the online structure prediction tools, such as I-TASSER (33,34), QUARK (35), Rosetta (36), HHpred (37) or Phyre2 (38).”