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PredictProtein - Predicting Protein Structure and Function for 29 Years

Cited 109 time in Web of Science Cited 121 time in Scopus
Authors

Bernhofer, Michael; Dallago, Christian; Karl, Tim; Satagopam, Venkata; Heinzinger, Michael; Littmann, Maria; Olenyi, Tobias; Qiu, Jiajun; Schuetze, Konstantin; Yachdav, Guy; Ashkenazy, Haim; Ben-Tal, Nir; Bromberg, Yana; Goldberg, Tatyana; Kajan, Laszlo; O'Donoghue, Sean; Sander, Chris; Schafferhans, Andrea; Schlessinger, Avner; Vriend, Gerrit; Mirdita, Milot; Gawron, Piotr; Gu, Wei; Jarosz, Yohan; Trefois, Christophe; Steinegger, Martin; Schneider, Reinhard; Rost, Burkhard

Issue Date
2021-07
Publisher
Oxford University Press
Citation
Nucleic Acids Research, Vol.49 No.W1, pp.W535-W540
Abstract
Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.
ISSN
0305-1048
URI
https://hdl.handle.net/10371/202559
DOI
https://doi.org/10.1093/nar/gkab354
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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