PT  - JOURNAL ARTICLE
AU  - Michael Bernhofer
AU  - Christian Dallago
AU  - Tim Karl
AU  - Venkata Satagopam
AU  - Michael Heinzinger
AU  - Maria Littmann
AU  - Tobias Olenyi
AU  - Jiajun Qiu
AU  - Konstantin Schütze
AU  - Guy Yachdav
AU  - Haim Ashkenazy
AU  - Nir Ben-Tal
AU  - Yana Bromberg
AU  - Tatyana Goldberg
AU  - Laszlo Kajan
AU  - Sean O’Donoghue
AU  - Chris Sander
AU  - Andrea Schafferhans
AU  - Avner Schlessinger
AU  - Gerrit Vriend
AU  - Milot Mirdita
AU  - Piotr Gawron
AU  - Wei Gu
AU  - Yohan Jarosz
AU  - Christophe Trefois
AU  - Martin Steinegger
AU  - Reinhard Schneider
AU  - Burkhard Rost
TI  - PredictProtein – Predicting Protein Structure and Function for 29 Years
AID  - 10.1101/2021.02.23.432527
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.23.432527
4099  - http://biorxiv.org/content/early/2021/02/24/2021.02.23.432527.short
4100  - http://biorxiv.org/content/early/2021/02/24/2021.02.23.432527.full
AB  - 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; 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.Availability Freely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlabCompeting Interest StatementThe authors have declared no competing interest.